Thin Film Thermoelectrics September 2011 Today and …s3.electronics-cooling.com/issues/ECM_September2011.pdf · Clemens J.M. Lasance Calculation Corner 4 A Useful Catalog of Calculation

September 2011

www.electronics-cooling.com

Real-Time Data Center Cooling Analysis

Thin Film Thermoelectrics Today and Tomorrow

Strategies For Using Thermal Calculation Methods

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electronics-cooling.com ElectronicsCooling 1

ContentsEditorial 2ThoughtsSomePeopleMayEnjoy,OthersMayNot

Clemens J.M. Lasance

CalculationCorner 4AUsefulCatalogofCalculationCornerArticles

Robert E. Simons

TechnicalBrief 8ReducingEnergyCostbyFanSelectionandOptimization

Norman Smith, Ametek Rotron

ThermalFactsandFairyTales 10DoesYourCorrelationHaveanImposedSlope?

Clemens J.M. Lasance

FeatureArticlesReal-TimeDataCenterCoolingAnalysis 14Jim VanGilder, APC by Schneider Electric

StrategiesforUsingThermalCalculationMethods 20James Petroski, Rambus Inc.; and

Cathy Biber, Biber Thermal Design, Ltd.

ThinFilmThermoelectricsTodayandTomorrow 24Burkhard Habbe and Dr. Joachim Nurnus, Micropelt GmbH

IndexofAdvertisers 32

Page 18

Page 16

Page 28

2 ElectronicsCooling

Editorial Board

Associate Technical EditorsBruce Guenin, Ph.D.

Principal Hardware EngineerOracle

[email protected]

Clemens Lasance, IrPrincipal Scientist - Retired

Consultant at [email protected]

Robert SimonsSenior Technical Staff Member - Retired

[email protected]

Jim Wilson, Ph.D., P.E. Engineering Fellow Raytheon Company [email protected]

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T his time I found it difficult to find a suitable subject for my editorial. My origi-nal idea was to write about heat spreading, again. I found some interesting facts pointing at problems of interpretation for dual layers when using the often-used

heat spreading equations while writing a white paper on basic thermal management for LED applications for this year’s APEX/IPC conference (devoted mainly to printed circuit boards). On second thought, I realized that the subject was far more suited for the Thermal Facts and Fairy Tales column, the problem being that I was on the verge of finishing a column on the subject of the danger of imposing a slope on certain Nu-correlation graphs, the one that features in this issue. I am afraid that you have to wait another half year on my comments on dual-layer heat spreading.

So, what to do, I wondered? It is the privilege of an editor writing an editorial to discuss any subject that comes to mind, as long as it is (remotely) linked to the theme of the journal. My Thermal Facts and Fairy Tales column is, as already indicated above, devoted again to the danger of using correlations for thermal management design purposes. I keep wondering why so many people stick to their use. In my opinion, the main reason is that correlations on the one hand fulfill the need to reduce the significant complexity of the real thermal world to something that is manageable, while on the other hand correlations seem to be based on science because so many handbooks devote so many chapters to their derivation and use in practice (“... for engineering convenience ...”).

Fortunately, my feelings on the abuse of correlations are shared by two famous Stanford scholars: Bill Kays and Bob Moffat, who more or less gave up on dimensionless correlations about 40 years ago. Let me quote Bob Moffat: “The habit of assembling “dimensionless groups” and trusting the Buckingham Pi theorem was very important in the early years of the development of physics and engineering, but it has become so ingrained in our teaching that the dangers of blind obedience (the religious aspect) has been forgotten. When “irrelevant parameters” are included in the parameter list before the groups are formed, what comes out are correlations that have some elements that simply conceal the interactions among irrelevant parameters. Sometimes simpler is better.”

Moffat’s using the phrase “religious aspect” reminded me of the following. I once coined the abundant use of correlations “correligion,” not without a reason, because it has some similarity to certain aspects that all religions share. Holy books, priests and believers, preaching the pros but being blind to the cons. People who don’t share their beliefs are considered unreliable, at least. I realize that America is a very religious country, but I was really shocked by the following: the 2007 Gallup poll asked Americans whether they would vote for “a generally well-qualified” presidential candidate nominated by their party with each of the following characteristics: Jewish, Catholic, Mormon, an atheist, a woman, black, Hispanic, homosexual, 72 years of age, and someone married for the third time. The result? Atheists closed the row: 45%. A Mormon president would not raise objections by 72%.

What happened since the days of Jefferson and Adams, who wrote around 1800: “As the Government of the United States of America is not, by any sense, founded on the Christian religion; as it has in itself no character of enmity against the laws, religion, or tranquility, of Musselmen; and as the said States never have entered into any war or act of hostility against any Mehomitan nation, it is declared by the parties that no pretext arising from religious opinions shall ever produce an interruption of the harmony existing between the two countries.” This concerned a treaty with Tripoli, the capital of Libya! Indeed Mr. Dylan, times they are a-changin’.

What is the lesson to be learned, be it for the claims of any religion or the claims in heat transfer textbooks when we don’t talk fairy tales, but real life? As Timothy Leary once put it: “Think for yourself, Question authority”. l

Editorial

ITEM™

Thoughts Some People May Enjoy, Others May Not

Clemens J.M. Lasance Editor-in-Chief, September 2011 Issue


mailto:[email protected]










4 ElectronicsCooling September 2011

“Estimating Natural Convection Heat Transfer for Arrays of Vertical Parallel Flat Plates”, Robert Simons, February 2002.www.electronics-cooling.com/2002/02/estimating-natural-convection-heat-transfer-for-arrays-of-vertical-parallel-flat-plates/

“Calculations for Thermal Interface Materials”, Bruce Guenin, August 2003.www.electronics-cooling.com/2003/08/calculations-for-thermal-interface-materials/

“The 45 Heat Spreading Angle — An Urban Legend?”, Bruce Guenin, November 2003.www.electronics-cooling.com/2003/11/the-45-heat-spreading-angle-an-urban-legend/

“A Simple Thermal Resistance Model — Isoflux Versus Isothermal”, Robert Simons, February 2006.www.electronics-cooling.com/2006/02/a-simple-thermal-resistance-model-isoflux-versus-isothermal/

“Comparing Heat Transfer Rates of Liquid Coolants Using the Mouromtseff Number”, Robert Simons, May 2006.www.electronics-cooling.com/2006/05/comparing-heat-transfer-rates-of-liquid-coolants-using-the-mouromtseff-number/

Thermal Spreading FormulaS and CalCulaTionS

“The 45 Heat Spreading Angle — An Urban Legend?”, Bruce Guenin, November 2003.www.electronics-cooling.com/2003/11/the-45-heat-spreading-angle-an-urban-legend/

“Simple Formulas for Estimating Thermal Spreading Resistance”, Robert Simons, May 2004.www.electronics-cooling.com/2004/05/simple-formulas-for-estimating-thermal-spreading-resistance/

“Heat Spreading Calculations Using Thermal Circuit Elements”, Bruce Guenin, August 2008.www.electronics-cooling.com/2008/08/heat-spreading-calculations-using-thermal-circuit-elements/

heaT Sink analySiS and perFormanCe

“Estimating Parallel Plate-Fin Heat Sink Thermal Resistance”, Robert Simons, February 2003.www.electronics-cooling.com/2003/02/estimating-parallel-plate-fin-heat-sink-thermal-resistance/

“Estimating Parallel Plate-Fin Heat Sink Pressure Drop”, Robert Simons, May 2003.www.electronics-cooling.com/2003/05/estimating-parallel-plate-fin-heat-sink-pressure-drop/

The first issue of ElectronicsCooling magazine was pub-lished in June 1995 with Kaveh Azar as Editor-in-Chief. In 1997 Bruce Guenin joined ElectronicsCooling as an

Associate Editor. In the September issue, he published the first Calculation Corner article, which provided a simple description of “One-Dimensional Heat Flow” and occupied one-half page. I was invited to join ElectronicsCooling as an Associate Technical Editor in January 2001. In August of that year I began to share responsibility for the Calculation Corner articles with Bruce. Together we have authored a total of 47 Calculation Corner articles.

All of these Calculation Corner articles are still available on the ElectronicsCooling website (www.electronics-cooling.com) by entering the appropriate keyword(s). As an aid to the reader, fol-lowing is a listing of all the Calculation Corner articles, organized chronologically in topic areas. These topic areas include:

1) Heat Transfer Fundamentals; 2) Thermal Spreading Formulas; 3) Heat Sink Analysis and Performance; 4) Package Component Analysis and Performance; and 5) System Cooling Analysis, Applications and Trade-Offs.Each listing provides the article title, author and date of publica-

tion. Beneath each listing is the Internet address of the webpage on which the article may be found. As a further aid to the reader, in the electronic version of this article on the ElectronicsCooling website, the Internet address beneath each article is a link, which if clicked on, will take the reader directly to the article.

heaT TranSFer FundamenTalS

“One-Dimensional Heat Flow”, Bruce Guenin, September 1997.www.electronics-cooling.com/1997/09/one-dimensional-heat-flow/

“Convection and Radiation”, Bruce Guenin, January 1998.www.electronics-cooling.com/1998/01/convection-and-radiation/

“Convection and Radiation Loss From a Fin”, Bruce Guenin, January 1999.www.electronics-cooling.com/1999/01/convection-and-radiation-loss-from-a-fin/

“Don’t Underestimate Radiation in Electronics Cooling”, Bruce Guenin, February 2001.www.electronics-cooling.com/2001/02/dont-underestimate-radiation-in-electronic-cooling/

“Simplified Formula for Estimating Natural Convection Heat Transfer Coefficient on a Flat Plate”, Robert Simons, August 2001.www.electronics-cooling.com/2001/08/simplified-formula-for-estimating-natural-convection-heat-transfer-coefficient-on-a-flat-plate/

a useful Catalog of Calculation Corner articles

Robert E. Simons Associate Technical Editor

calculation corner


“Estimating the Effect of Flow Bypass on Parallel Plate-Fin Heat Sink Performance”, Robert Simons, February 2004.www.electronics-cooling.com/2004/02/estimating-the-effect-of-flow-bypass-on-parallel-plate-fin-heat-sink-performance/

“Using an Equivalent Heat Transfer Coefficient to Model Fins on a Fin”, Robert Simons, May 2005.www.electronics-cooling.com/2005/05/using-an-equivalent-heat-transfer-coefficient-to-model-fins-on-a-fin/

“Estimating Thermal Resistance for Fin-to-Fin Thermal Couplers”, Robert Simons, February 2008.www.electronics-cooling.com/2008/02/estimating-thermal-resistance-for-fin-to-fin-thermal-couplers/

“A Simple Method to Estimate Boiling Heat Sink Performance”, Robert Simons, February 2009.www.electronics-cooling.com/2009/02/a-simple-method-to-estimate-boiling-heat-sink-performance/

“Thermal Interactions Between High-Power Packages and Heat Sinks, Part 1”, Bruce Guenin, December 2010.www.electronics-cooling.com/2010/12/calculation-corner-thermal-interactions-between-high-power-packages-and-heat-sinks-part-1/

“Thermal Interactions Between High-Power Packages and Heat Sinks, Part 2”, Bruce Guenin, March 2011.www.electronics-cooling.com/2011/03/calculation-corner-thermal-interactions-between-high-power-packages-and-heat-sinks-part-2/

pa C k a g e a n d C o m p o n e n T a n a ly S i S a n d perFormanCe

“Conduction Heat Transfer in a Printed Circuit Board”, Bruce Guenin, May 1998.www.electronics-cooling.com/1998/05/conduction-heat-transfer-in-a-printed-circuit-board/

“Convection and Radiation Heat Loss From a Printed Circuit Board”, Bruce Guenin, September 1998.www.electronics-cooling.com/1998/09/convection-and-radiation-heat-loss-from-a-printed-circuit-board/

“Determining the Junction Temperature in a Plastic Semiconductor Package, Part I”, Bruce Guenin, May 1999.www.electronics-cooling.com/1999/05/determining-the-junction-temperature-in-a-plastic-semiconductor-package-part-1/

“Determining the Junction Temperature in a Plastic Semiconductor Package, Part II”, Bruce Guenin, September 1999.www.electronics-cooling.com/1999/09/determining-the-junction-temperature-in-a-plastic-semiconductor-package-part-ii/

“Determining the Junction Temperature in a Plastic Semiconductor Package, Part III: The Use of the Junction-to-Board Thermal Characterization Parameter”, Bruce Guenin, May 2000.www.electronics-cooling.com/2000/05/determining-the-junction-temperature-in-a-semiconductor-package-part-iii-the-use-of-the-junction-to-board-thermal-characterization-parameter/

“Determining the Junction Temperature in a Plastic Semiconductor Package, Part IV: Localized Heat Generation on the Die”, Bruce Guenin, September 2000.www.electronics-cooling.com/2000/09/determining-the-junction-temperature-in-a-semiconductor-package-part-iv-localized-heat-generation-on-the-die/

“Characterizing a Package on a Populated Printed Circuit Board”, Bruce Guenin, May 2001.www.electronics-cooling.com/?s=characterizing+a+package+on+a+populated+printed+circuit+board&x=36&y=12

“Simplified Transient Model for IC Packages”, Bruce Guenin, August 2002.www.electronics-cooling.com/2002/08/simplified-transient-model-for-ic-packages/

“Thermal Calculations for Multi-Chip Modules”, Bruce Guenin, November 2002.www.electronics-cooling.com/2002/11/thermal-calculations-for-multi-chip-modules/

“Thermal Vias — A Packaging Engineer’s Best Friend”, Bruce Guenin, August 2004.www.electronics-cooling.com/2004/08/thermal-vias-a-packaging-engineers-best-friend/

“Entrance Effects for Heat Flow Into a Multi-Layer Printed Circuit Board”, Bruce Guenin, November 2004.www.electronics-cooling.com/2004/11/entrance-effects-for-heat-flow-into-a-multi-layer-printed-circuit-board/

“A Funny Thing Happened on the Way to the Heat Sink”, Bruce Guenin, August 2005.www.electronics-cooling.com/2005/08/a-funny-thing-happened-on-the-way-to-the-heatsink/

“So Many Chips, So Little Time: Device Temperature Prediction in Multi-Chip Packages”, Bruce Guenin, August 2006.www.electronics-cooling.com/2006/08/so-many-chips-so-little-time-device-temperature-prediction-in-multi-chip-packages/

“Toward a Thermal Figure of Merit for Multi-Chip Packages,” Bruce Guenin, November 2006.www.electronics-cooling.com/2006/11/toward-a-thermal-figure-of-merit-for-multi-chip-packages/

“Thermal Strain in Semiconductor Packages, Part I”, Bruce Guenin, August 2007.www.electronics-cooling.com/2007/08/thermal-strain-in-semiconductor-packages-part-i/

“Thermal Strain in Semiconductor Packages, Part II”, Bruce Guenin, November 2007.www.electronics-cooling.com/2007/11/thermal-strain-in-semiconductor-packages-part-ii/

“Power Map Calculations Using Image Sources and Superposition”, Bruce Guenin, November 2008.www.electronics-cooling.com/2008/11/power-map-calculations-using-image-sources-and-superposition/

“Using a Matrix Inverse Method to Solve a Thermal Resistance Network”, Robert Simons, May 2009.www.electronics-cooling.com/2009/05/using-a-matrix-inverse-method-to-solve-a-thermal-resistance-network/

“A Spreadsheet Based Matrix Solution for a Thermal Resistance Network: Part 1”, Ross Wilcoxon, September 2010.www.electronics-cooling.com/2010/09/calculation-corner-a-spreadsheet-based-matrix-solution-for-a-thermal-resistance-network-part-1/

calculation corner

electronics-cooling.com ElectronicsCooling7

“Using Vendor Data to Estimate Thermoelectric Module Cooling Performance in an Application Environment”, Robert Simons, July 2010.www.electronics-cooling.com/2010/07/using-vendor-data-to-estimate-thermoelectric-module-cooling-performance-in-an-application-environment/

SyStem Cooling AnAlySiS, AppliCAtionS And trAde-offS

“Estimating Temperatures in a Water-to-Air Hybrid Cooling System”, Robert Simons, May 2002.www.electronics-cooling.com/2002/05/estimating-temperatures-in-a-water-to-air-hybrid-cooling-system/

“Estimating Temperatures in an Air-Cooled Closed Box Electronics Enclosure”, Robert Simons, February 2005.www.electronics-cooling.com/2005/02/estimating-temperatures-in-an-air-cooled-closed-box-electronics-enclosure/

“Using a Simple Air Recirculation Model to Explore Computer Rack Cooling”, Robert Simons, February 2007.www.electronics-cooling.com/2007/02/using-a-simple-air-recirculation-model-to-explore-computer-rack-cooling/

“Estimating the Effect of Intercoolers for Computer Rack Cooling”, Robert Simons, May 2007.www.electronics-cooling.com/2007/05/estimating-the-effect-of-intercoolers-for-computer-rack-cooling/

“Estimating Dew Point Temperature for Water Cooling Applications”, Robert Simons, May 2008.www.electronics-cooling.com/2008/05/estimating-dew-point-temperature-for-water-cooling-applications/

“Measuring Heat Load to Water in a Rear Door Heat Exchanger Application,” Robert Simons, June 2011.www.electronics-cooling.com/2011/06/calculation-corner-measuring-heat-load-to-water-in-a-rear-door-heat-exchanger-application/

It is hoped that you will find these listings helpful in finding earlier Calculation Corner articles which may be of interest to you. If you have any ideas for specific topics you would like to see covered in future articles, please send Bruce ([email protected]) or myself ([email protected]) a note. Also, you may note that the September 2010 Calculation Corner article was written by a guest author, Ross Wilcoxon. If you, too, have an idea for an article you would like to submit as a guest author, for possible publication in the Calculation Corner, please contact us. l


In addition to Calculation Corner columns, find other technical articles in the Issue Archive by logging onto

more on the Web


As energy costs continue to increase, there is an increased awareness of energy usage and a greater emphasis on methods to reduce the energy consumed by electronic

equipment. The portion of electronic equipment power de-voted to cooling can be significant. For example, the servers in a typical data center can require up to two times the useful computing power for cooling. The biggest of these data cen-ters contain 400,000 servers and consume 250 megawatts of power [1]. It has been estimated that 20% of the total power supplied for a high end server is consumed by cooling fans [2]. This “non-value added” power required for fans has been a target for engineers attempting to reduce energy consumed by electronic equipment. Improving overall efficiency of the cooling fan has other cascading benefits for additional energy savings. With lower fan power consumption, there is lower current demanded from the equipment DC power supply and for all the power conversion equipment required between the fan and utility feed. Finally, a more efficient fan will require a smaller, less powerful, lower torque motor and lower current electronic drive saving both weight and space.

There are a number of methods of improving fan efficiency including speed control, optimized motor design, optimized electronic brushless motor drive design, and optimized sys-tem design. These are fairly well understood and have been deployed in modern forced convection cooling solutions. What is often not accounted for, even among sophisticated designs, is the optimization of the cooling fan for a specific flow pressure operating point. This Technical Brief will deal with the significance of fan selection and aerodynamic design optimization.

There is a surprising amount of power savings available by

Reducing Energy Cost by Fan Selection and Optimization Norman SmithAmetek Rotron, Woodstock, New York USA

optimizing the fan design around a particular flow pressure point. Many of the fan manufacturers’ data sheets provide a flow pressure air performance curve but only provide power data during free delivery air flow conditions (zero back pres-sure). This is an unrealistic operating point within electronic equipment since there is always some restriction to air flow and resulting back pressure. Every cooling application pro-duces a characteristic air resistance curve which intersects the fan air performance curve as shown on Figure 1. There is a trend in the electronics industry for applications to have greater air resistance as density of electronics within the enclosure increases. This is represented by the air resis-tance curve in Figure 1 with higher pressure for any given air flow value. When an electronic cooling fan application has a relatively high air resistance, choosing a fan that has been optimized for free delivery can result in unnecessary wasted power. Every fan has a peak aerodynamic efficiency point somewhere on its flow-pressure curve and there can be significant energy savings by ensuring the actual operating point of the application matches the peak efficiency point of the fan. In Figure 1 the flow pressure curve developed by fan “A” is one such design optimized for high flow but the fan performance of the flow pressure curve for fan “B” is opti-mized for the actual high resistance operating point. Many of today’s standard, commercially available fans produced in high volume are not easily modified without significant investment. These fans are often selected by engineers since they produce a flow-pressure curve that passes through the desired fan performance operating point — but often not at the peak efficiency possible.

A typical example of an actual fan application is shown below where a customized design significantly reduced power consumption when compared to the standard production catalog fan. Both fan designs delivered the same air perfor-mance with the same space claim, but fan power consumption was significantly different, in fact, cut in half!

ExAmplE One of the most commonly used cooling fans in the industry is the 120mm tube axial fan. This fan has generally been opti-mized for low pressure applications with very little air resis-tance. An optimized version was developed for an airborne vapor cycle liquid chiller application where the density of heat exchanger fins requires the fan to operate in the high pres-sure portion of its flow-pressure curve. The flow and pressure performance point is: 38.6 mm H2O 23.6 l/s (50 CFM, 1.45 in H2O). The optimized fan required a new propeller design with more blades, lower pitch blades and a higher operating speed. Table 1 provides of comparison of these features along

Figure 1. Air resistance curves for two applications and two possible fan designs.

technical brief

electronics-cooling.com ElectronicsCooling9

Table 1. Measured performance comparison (two designs delivering the same pressure and air flow).

Original Production 120 mm Tube Axial Fan

Redesigned Propeller 120 mm Tube Axial Fan

Operating point:23.6 l/s sec (50 CFM),

36.8 mm H2O (1.45 in H2O)

23.6 l/s sec (50 CFM), 36.8 mm H2O (1.45 in H2O)

Number of blades 5 11

Pitch of blades/Blade depth

Hub: 38°/ 24.13 mm (0.95”)

Tip: 30°/ 24.13 mm (0.95”)

Hub: 28°/ 8.13 mm (0.32”)

Tip: 18°/ 10.67 mm (0.42”)

Power at operating point 57 W 26 W

Speed (RPM) 6700 RPM 7700 RPM

with test results showing input power cut in half when com-pared to the standard production fan operating at the same high pressure point. The basic motor design, electronic drive and venturi remained the same. The Computational Fluid Dynamics (CFD) simulation output showing velocity vectors for the two designs operating at the relatively high pressure, low flow point is provided in Figure 2. Note the airflow stall region in the existing production design just under the blade. Overall sound power was reduced by 2 dB even though the new design is operating with a significantly higher speed. In addition, an annoying pure tone was eliminated.

COnClusiOnWe have provided a convincing example of opportunities to save power by optimizing the fan aerodynamics for specific application operating points. For the application shown, the original catalog production fan selected by the customer achieved its desired flow pressure operating point. The prob-lem was that the fan was not performing very efficiently. The technology to develop more efficient fans is in place. The precision of the latest CFD software, when used by a skilled

engineer, has been shown to replicate actual test data within a few percentage points, greatly reducing engineering time and cost for custom designs. The barrier to more widespread fan design optimization is primarily an economic one due to the increased cost of a non-standard customized de-sign produced in lower volume. However, the greater initial purchase price may be offset by the lifetime energy savings of the product.

Looking to the future, the increased life cycle cost of energy and social awareness of

energy production by-products is surely going to move the threshold of justification in favor of high efficiency cooling fans [3].

ReFeRenCes[1] Hardy, Q., “Switchcraft,” Forbes, pp. 69 -73, September 29, 2008.[2] “The Green Grid Opportunity- Decreasing Datacenter and other IT Energy

Usage Patterns”, WP#2” February 16, 2007.[3] Smith, N., “High Efficiency Electronic Cooling Fans”, in Proceedings of 25th

Semi-Therm Symposium, 2009. l

Figure 2. CFD results showing stall at high pressure and redesign.

1.5” x 1.5” Heat sinkDimensions: Footprint 39mm x 39 mm or 1.5” x 1.5”Material: Copper std/Aluminum optOptimal Pump Pressure: Coolant liquid, 4-8psiMax Pump Pressure: Coolant liquid, 60psiMinimum flow: Liquid 0.05gpmThermal resistance average: 0.04°C/W

2.0” x 2.0” Heat sink and cold plateDimensions: Footprint 53mm x 53 mm or 2.0” x 2.0”Material: Copper standard/Aluminum optionalOptimal Pump Pressure: Coolant liquid, 4-8psiMax Pump Pressure: Coolant liquid, 60psiMinimum flow: Liquid 0.05gpmThermal resistance average: 0.04°C/W

2.5” x 2.5” Heat sink and cold plateDimensions: Footprint 63mm x 63 mm or 2.5” x 2.5”Material: Copper std/Aluminum optOptimal Pump Pressure: Coolant liquid, 4-8psiMax Pump Pressure: Coolant liquid, 60psiMinimum flow: Liquid 0.05gpmThermal resistance average: 0.05°C/W

Heat Transfer Research & Development, Ltd. (HTRD)1010 W. Lonnquist Blvd., Mt. Prospect, Illinois 60056www.htrdltd.com Voice : 1-847-577-5967Fax: 1-847-577-4229 e-mail: [email protected]

For Manufacturers of Thermal Heads for Thermo-Cycling Operations - Thermoelectric Modules

For Manufacturers of Servers – High Performance Heat Sinks/Cold Plates Designed by Cool Technology Solutions, Inc. (US Patent Pending, Application US 61506093)

Heat Transfer Ad.indd 1 7/22/2011 2:26:42 PM


Re but h vs. v.”I would like to mention two other flaws of publishing

correlations: loss of original data and danger of imposing a slope. The first flaw is easily understood. Read any article in ASME journals and try to get hold of the original test data. You can’t. Additionally, because all data points are first made dimensionless and then put on double-log paper, details are lost. Following Bob Moffat: “Years of poorly controlled and inadequately described experiments have filled the literature with data that appear to be ‘comparable’ but are not, with the consequence that the average designer seems to accept the fact that there will be always ± 20-50% scatter in heat transfer data.” In general, you can be confident that the original data did not show this large scatter.

The second flaw is less obvious. In mathematics, everyone strives towards separation of variables because doing so simplifies the solution. Not so in heat transfer. This becomes immediately obvious when looking at the following general Nu-Ra equation describing general heat transfer for natural convection, Nu = C.RaN:

n

p

kTLcg

CkL

Tq

∆

=∆ ν

βρ 3

Nu = 0.65 Ra 0.2 ( 2 )

q = 0.7 L 0.4 ΔT 1.2 ( 1 )

(3) (3)

where ΔT, L and k are common to both sides of the equation. This confounding of parameters has led to a curious obser-vation, originally formulated by Rowe [3] and discussed by Wilkie in two papers [4,5]. The results presented by Rowe are very illustrative. He used data points from random number tables for the parameters that are common to both sides of a set of dimensionless equations, and plotted the ‘results’ on log-log paper. After ignoring a few rogue points, combina-tions such as Nu-Re, Nu-Gr and several others clearly showed a correlation where obviously there can be none. Wilkie reinterpreted Rowe’s data as follows. If Equation (3) is plot-ted on log-log paper, a straight line is obtained. He showed that by varying L a slope of 1/3 is imposed. It is interesting to note that this slope is close to the often-quoted values in the literature for turbulent heat transfer. In his second paper, Wilkie extended the analysis to the situation where the three common variables L, ΔT and k are varied simultaneously, which could be the case in natural convection studies, and presented some convincing arguments that some often-used correlations (especially for turbulent natural convection heat transfer) should be regarded with caution. If the observed slope from an experiment is not significantly different from the imposed slope (as determined by proper statistical means), no conclusion can be drawn about the correlation between the

There must be an ideal worldA sort of mathematicians’ paradiseWhere everything happensAs it does in textbooks —BertrandRussell

This is the third column devoted to the sense and nonsense of correlations. My earlier comments [1] regarding Rus-sell’s quote are maybe worth repeating: “Generations of

mechanical engineers have been educated to use the correla-tions that fill the textbooks Russell is mentioning because: ‘For convenience of engineering applications, correlation equations are developed over the entire range of dimensions and for any Pr-number.’ Unfortunately, for a thermal designer, real-life is more like hell than paradise, and hence it is high time to ad-dress the question, ‘How convenient are these correlations in real-life?’ When it comes to thermal management of electronic systems, the answer is that correlations are not only useless but also dangerous because their use by non-experts gives a false sense of safety.” Maybe “useless” is a bit exaggerated. As one reviewer put it: “Correlations can be used to provide a basic sense of how a system might react under some simple geometries and equivalent inputs and can potentially yield important information of general behavior.” The problem is that in practice simple geometries are not the rule, but the exception.

The paper goes on: “Suppose some alien wants to study the heat transfer behaviour of a flat heat sink cooled by air. He, she or it never had the opportunity to read standard textbooks on heat transfer. The alien varies the parameters of inter-est, for example the power q and the length L, measures the temperature rise, and comes up with the following equation: q = 0.7 L 0.4 ΔT1.2 (1) providing exactly the information needed: the dependency of the temperature rise on the power and the heat sink di-mensions.” The alien would be very surprised if someone told him to present the results in the following way, because it should be “more convenient for engineering applications”: Nu = 0.65 Ra0.2 (2) The author fully agrees with the comments made by Botterill [2] some 20 years ago: “Give preference for the most simple form of representation instead of the much more ambitious generalisations.” The same way of working Bill Kays and Bob Moffat at Stanford proposed already 40 years ago (personal communication): “Simpler is better, hence don’t plot Nu vs.

Does Your Correlation Have an Imposed Slope?Clemens J.M. Lasance Associate Technical Editor

thermal facts and fairy tales


Figure 1. Nu-Ra correlation for horizontal cylinders (McAdams).

D v h1.E+00 1 51.E+00 2 51.E+00 3 91.E+01 1 51.E+01 2 21.E+01 3 71.E+02 1 91.E+02 2 71.E+02 3 71.E+03 1 31.E+03 2 41.E+03 3 81.E+04 1 51.E+04 2 61.E+04 3 41.E+05 1 81.E+05 2 31.E+05 3 21.E+06 1 51.E+06 2 91.E+06 3 21.E+07 1 11.E+07 2 21.E+07 3 91.E+08 1 71.E+08 2 51.E+08 3 2

Re-Nu correlationh=random, mean =5

1.E+00

1.E+02

1.E+04

1.E+06

1.E+08

1.E+10

1.E+00 1.E+02 1.E+04 1.E+06 1.E+08 1.E+10

Re

Nu

(Fg2)

Figure 2. Nu-Re for random h.

Figure 3. h as a function of D, with v as parameter.

variables that are common to both sides. As a consequence, randomly chosen values of the heat transfer coefficient could produce an apparent correlation with an index given by the imposed slope, or, alternatively, a slope of magnitude different from the imposed slope could remain undetected. Especially when a large data set is being used, for example when one of the common variables has been varied over a wide range, the danger of an imposed slope becomes relevant. This is clearly the case for the famous correlation for natural convection from horizontal cylinders spanning more than 13 decades in Gr presented by McAdams [6] almost 60 years ago. Note that the upper-half slope is 1/3, exactly the exponent for turbulent heat transfer, but also of the imposed slope.

One of Wilkie’s recommendations is to vary only those param-eters that are not common to both sides. One of my recommen-dations is to refrain from common-variable formulae altogether.

The following simple example illustrates the problem raised by Rowe and Wilkie in a convincing way. Suppose we want to study turbulent pipe flow in a strange world where all physical properties happen to be 1. We set the dissipation to 1 W, vary the diameter over quite a big range, the velocity at three levels and measure the temperature rise. The ratio q/ΔT provides us

with the heat transfer coefficient h. For reasons yet unknown in this strange world to be explored, whatever we choose for D and v, h varies between 1 and 10 in a random way. Let us now plot Nu=h∙D/k against Re=v∙D/v, with k and v set to 1.

I hope the reader understands that the essential point here is not the value for Nu and Re (can be scaled of course to suit any realistic world) but the fact that D varies over a large range (but not as large as in the McAdams example cited above). From the graph a clear correlation between Nu and Re becomes apparent, namely Nu=C.ReN, with C=1.67 and n=0.97. In other words, the graph suggests that h is about proportional to v, which is clearly not the case. Figure 3 shows a ‘traditional’ plot of h against D with v as parameter from which it is immediately obvious that there is no correlation at all.

ConCluSIonSIt should be noted that correlations have proven their value over the last century in a wide range of applications, some of which are not only of academic interest. However, when focus-ing on electronics cooling, we face a different situation. There is one major reason why correlations are not recommended: the inherently complex geometries. Correlations make sense only when three conditions are fulfilled [1]: similarity, congruency and the boundary layer approximation. In most practical cases these conditions are not met, and if so, the number of required dimensionless groups becomes intractable. The bottom line is: geometrically and physically complex phenomena cannot be described by simple equations.

This column focuses on a peculiarity of correlations: a non-physical slope can be imposed when attempting to develop relations using common-variable formulae leading to wrong conclusions. A simple example convincingly demonstrates this phenomenon.

RefeRenCeS[1] Lasance C., “Sense and Nonsense of Heat Transfer Correlations Applied to

Electronics Cooling,” Proc. 5th Eurosime, Berlin, 2005.[2] Botterill J., “No Substitute for Experimentation - Despite the Fallibility of

Experimenters,” Exp. and Thermal Fluid Science, Vol. 3, pp. 463-466, 1990.[3] Rowe P., “The Correlation of Engineering Data,” The Chem. Eng., No. 166,

March, pp. CE 69-CE76, 1963.[4] Wilkie D., “The Correlation of Engineering Data Reconsidered,” Int. J. Heat

& Fluid Flow, Vol. 6, pp. 99-103, 1985.[5] Wilkie D., “Some Doubtful Natural Convection Correlations,” Proc. 9th Int.

Heat Transfer Conf., Jerusalem, pp. 555-560, 1990.[6] McAdams W., Heat Transmission, 3d Edition, McGraw-Hill, p. 176, 1954. l

thermal facts and fairy tales

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The question to be addressed in this article is: Do you really need a traditional CFD package to design or manage your data center under all circumstances?

Are there any practical alternatives on the cooling-analysis spectrum between it and spreadsheet calculations by your in-house cooling guru? What are the consequences of unre-liable input data for important geometric details and server power and airflow (see sidebar, "Server Data Are Lacking," on Page 15)?

Recently, alternatives to full CFD have been discussed in the technical literature [1-8], some of which even provide CFD-like 3D velocity, pressure, and temperature predictions. Such tools can be used directly or incorporated within a more comprehensive data center design and management software package [9].

In the initial planning stages for a new facility or modifica-tion, what is most needed is a dynamic go/no-go indication of cooling performance as the design takes shape. For example, racks and perforated tiles can be re-colored instantly based on cooling performance and airflow rate, respectively, as you move objects and change settings. Although the attractive colored planes and airflow vectors familiar from CFD tools provide real engineering value, simple empirical predictions can be used to provide a quick estimate of rack cooling perfor-mance. For example, estimating a single average rack inlet or cooler return temperature for all such objects provides greater value-per-effort than predicting the temperature at all points in the data center. In Figure 1, racks are colored based on a metric termed "capture index" (see sidebar, "Capture Index," on Page 16), which is a measure of the quality with which air is delivered to rack inlets and captured from rack exhausts. The rack-level-empirical model approach works particularly well when equipment is arranged in geometrically regular groupings surrounding a hot or cold aisle, extreme examples of which are cold and hot aisle containment systems.

Several empirical rack-level cooling prediction techniques have been developed, for example [1-3], and the simplest tech-niques fit algebraic models to the airflow physics as determined by simple CFD analyses. With this approach, thousands of CFD simulations of simpler building-block units of data cen-ters are analyzed covering the range of input parameters in the tool, then the models are “tuned” to provide the best fit

Jim VanGilder has been in the cooling and airflow simulation field for 14 years starting with electronics-

cooling applications at Flomerics (now Mentor Graphics). Presently, he directs the cooling-prediction R&D efforts

for APC by Schneider Electric’s data center design and management software products. He has a master’s degree

in mechanical engineering from Duke University and is a registered professional engineer. VanGilder has authored

or co-authored over 25 published papers and holds several patents related to data center cooling. He is also chair of

the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) Technical Committee

4.10, Indoor Environmental Modeling.

Real-Time Data Center Cooling AnalysisJim VanGilder APC by Schneider Electric, Billerica, Massachusetts, USA


V a n G i l d e r

to the CFD data. In other words, the accuracy of a CFD solu-tion is still leveraged but the time and complexity associated with running models is moved “off line”. The resulting models are robust and simple enough to run every time the model is changed. Using the rack-level algorithms discussed above, one can predict capture index and, by extension, average rack inlet temperatures, average cooler return temperatures, and cooler loads.

While rack-level empirical models can form the key “cool-ing calculation engine” of data center design and management tools, many users would also like to “see” airflow vectors, tem-peratures, and pressures – just like CFD. For this purpose, and also for the very practical purpose of predicting perforated tile airflow rates, a Potential Flow Model (PFM) can be employed. The term “potential flow” applies to an irrotational velocity field. When the flow can also be assumed to be incompress-ible, velocities can be determined from Poisson’s Equation. For general room-airflow predictions (e.g., above the raised floor in the data center white space), pressures are never explicitly required; however, they are needed when modeling pressure-dependent flow boundary conditions such as perforated floor tiles. Finally, once airflow patterns are known, temperatures

Figure 1. Racks color-coded by capture index.

Figure 2. PFM-CFD capture index prediction comparisons for eight data center layouts.

Sample server data relative to typical assumptions.

Although accurate server power and airflow data are obviously necessary for realistic data center simulations, such data are,

for the most part, unavailable or impracticable to obtain. Even the best-available data from ven-dor web sites, shows that power and airflow vary greatly with subtle configuration differences for a given server model. Further, with most modern servers, airflow varies based on inlet temperature and other operational states; this type of detail is often impractical to include in a simulation — even if supported by the modeling tool — as it requires an even more detailed knowledge of the server’s operational modes and may create stability prob-lems for a steady-state airflow simulation.

For a “greenfield” design, data center designers and operators typically make an estimate of power at each planned rack location. For a cutting-edge existing facility, with power metering and man-agement software, accurate, in situ power can be determined; however, while some servers now report fan speed, there is no general way to relate this to airflow rate without additional informa-tion. In either case, server airflow is almost always assumed to be in direct proportion to power and often assumed in the range indicated in the figure (compiled from indicated server vendor website data). Based on the current server data shown in the figure, it is clear that this assumption is far from ideal. More-or-less corresponding servers from different vendors and even identical model servers, with different configurations, exhibit substantially different airflow per unit power. Technical Com-mittee 9.9 of ASHRAE has developed standardized server data sheets and the US Department of En-ergy’s Energy Star program has started reporting limited airflow data for servers; however, to date, data for most servers are unavailable.

SeRveR DATA ARe lACking


r e a l -T i m e d aTa C e n T e r C o o l i n G a n a ly s i s

may be determined from the energy equation. Compared to the formidable set of Navier-Stokes equations

solved by traditional CFD, the potential flow model is, indeed, straightforward. Solutions are well behaved and convergence is achieved quickly and predictably. By using an efficient un-structured grid and optimized solution-control parameters, even full 3D room-level solutions can be obtained in several seconds on common computing hardware.

What do we have to give up for this tremendous improve-ment in solution time and robustness? Since rotationality — neglected in PFM — is often present in highly-viscous regions, potential flow solutions lack the ability to capture strong jet-like flow features, turbulence, and recirculation zones. Further, since the flow field is determined indepen-dently from the momentum and energy equations, buoyancy is neglected. Is this a reasonable assumption? It would not be reasonable for predicting airflow in general buildings where buoyancy may be a primary flow driver, but, in data centers, airflow patterns are generally dominated by the large amounts of airflow driven by racks and coolers. A recent study [10] concluded that airflow is generally dominated by momentum when the typical temperature rise across a rack is on the order of 10°C but when that figure rises to 20°C or more buoyancy becomes important.

Accuracy requirements must be tied to overall objectives. For data center design and management purposes, it is often enough to generally predict the correct bulk movement of airflow around racks and coolers which will, at least, reveal which areas are sufficiently cooled, inadequately cooled, or marginal. A high level of airflow-prediction accuracy may be wasted in most practical scenarios where the input data is either poorly estimated or impractical to include in the model.

Considering the issue of accuracy more quantitatively, several recent studies [4-8] have assessed the use of PFM for data center cooling prediction. A study of 8 data center layouts, based on actual modern facilities [7], produced the compari-sons of the capture index of PFM to CFD shown above (Figure 2). For example, layout A represents a standard best-practices raised-floor facility utilizing alternating cold and hot aisles with the Computer Room Air Conditioners (CRACs) aligned

Figure 3. 2D plenum example.

Physical meaning of cold and hot-aisle capture index.

The cold and hot-aisle capture index (CI) cooling performance metrics are based on the airflow streams associated with the

supply of cool air to and the removal of hot air from a rack respectively [11]. The cold-aisle CI is the fraction of rack inlet air which originates from local cooling resources (for example, perforated floor tiles or coolers). The hot-aisle CI is the frac-tion of air exhausted by a rack which is captured by local extracts (for example, local coolers or return vents) and is generally used to assess the cooling performance in architectures where row-based coolers or local return vents are employed. Values of 100% and 0% represent the extremes of “good” and “bad” cooling-performance for both types of CI. The physical meaning of the cold and hot-aisle capture indexes is illustrated in the figure. The center-top rack in the left-hand figure draws 84% of its airflow directly from the perforated tiles and its cold-aisle capture index is, therefore, 84%. In the right-hand figure, only 66% of the lower-right rack’s exhaust is captured by the two row-based coolers shown in blue and its hot-aisle capture index is, therefore, 66%. Although it is possible for rack inlet temperatures to be sufficiently cool even when CIs are “bad”, “good” CIs further ensure that airflow patterns are managed in a controlled and scalable way. The capture index characterizes the quality with which the most important airflow patterns are managed and therefore provides value above and beyond temperature and temperature-based metrics. Capture index is computed automatically in commercially-available data center design and management [9] and CFD [12] software.

CApTuRe inDex


R e a l -T i m e D aTa C e n T e R C o o l i n g a n a ly s i s

Figure 4. 3D room example.

with the hot aisles. In this case, over 90% of PFM predictions are better than 93% accurate and 10% of the predictions are better than 98% accurate. For every layout, predictions for over half the racks are better than 80% accurate. Average inlet temperature predictions are of similar accuracy to capture index, however, as might be expected, maximum rack inlet temperatures, which are local in nature, are not predicted very accurately by PFM.

Of course, if server and other input data can be accurately captured, traditional CFD will, indeed, yield better predic-tions. Further, commercial CFD packages typically provide a rich set of tools for modeling such things as equipment with pressure or flow-dependent control, unique architectural fea-tures of the building, and transient processes; all of which may be beyond the scope of design and management tools which utilize PFM. Additionally, CFD provides particularly better accuracy in cases where there is strong coupling between the room and under-floor plenum, such as when large-open-area perforated tiles are used.

Potential Flow Model exaMPleFirst consider a plenum airflow application where the primary goal is to estimate perforated tile airflow rates. In this case, a depth-averaged 2D model is sufficient and calculations run instantaneously on any modern computer [6]. In other words, drag in a new cooling unit or move a perforated tile and the flow vectors and perforated tile flow rates (Figure 3) are updated instantly as new objects are added or settings are modified. This is a much different user experience than that of CFD which typically involves a lot of time waiting for results and subsequently comparing two or more static solutions.

While 2D plenum calculations run faster than full 3D room calculations, the plenum application actually involves extra steps. Since perforated tile airflows are a primary output of the analysis, they are unknown at the beginning of the analysis and must be related back to pressure differences across the tiles based on tile performance data. Consequently, plenum pressures need to be computed and coupled to the airflow solution necessitating additional iteration.

Once the perforated tile airflow rates are determined, they can be used as boundary conditions for the 3D PFM solution

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for the above-the-raised-floor portion of the facility. This cal-culation is not quite real time but runs in just a few seconds on common computers, even for very large rooms. Velocity vector and temperature results (Figure 4) look like those from traditional CFD and provide the same ability to quickly locate problem spots and understand the underlying causes.

SuMMaryData center design and management software utilizing the methods discussed here can be used to quickly optimize a data center design with much less time, cost, and specialized train-ing than required by CFD. Further, at least one such package [9] provides the ability to export models to an XML neutral file format or directly to the native file format of a data center and buildings modeling CFD package [12]. This allows the user to leverage the advantages of both PFM and CFD tools.

Returning to our initial question: do you really need the ac-curacy of a full CFD package? Or are simple empirical models and PFM “good enough”? Given the poor quality of and limited time available for collecting the required input data and the huge improvements in speed and usability as compared to CFD, these new tools represent a very attractive alternative for many data center design and management applications.

reFerenceS[1] VanGilder, J., and Shrivastava, S.K., 2006, “Real-Time Prediction of Rack-

Cooling Performance”, ASHRAE Transactions, Vol. 112, Part 2, pp. 151-162.[2] VanGilder J., Zhang, X., and Shrivastava, S.K., 2007, “Partially Decoupled

Aisle Method for Estimating Rack-Cooling Performance in Near-Real Time”,

Proceedings of InterPACK’07, International Electronic Packaging Technical Conference and Exhibition, July, Vancouver, Canada.

[3] Shrivastava, S.K., VanGilder, J. and Sammakia, B.G., 2007, “Use of Artificial Neural Network in Data Center Cooling Prediction”, Proceedings of InterPACK’07, International Electronic Packaging Technical Conference and Exhibition, July, Vancouver, Canada.

[4] Lopez, V. and Hamann, H., 2010, “Measurement-Based Modeling for Data Centers”, Proceedings of ITHERM, June 2-5, Las Vegas, NV.

[5] Hamann, H., Lopez, V., and Stepanchuk, A., 2010, “Thermal Zones for More Efficient Data Center Energy Management”, Proceedings of ITHERM, June 2-5, Las Vegas, NV.

[6] VanGilder, J., Sheffer, Z., Zhang, X., and Healey, C., 2011, “Potential Flow Model for Predicting Perforated Tile Airflow in Data Centers”, ASHRAE Transactions, Vol. 117, Part 2.

[7] Healey, C., VanGilder, J., Sheffer, Z., and Zhang, X., 2011, “Potential-Flow Modeling for Data Center Applications”, Proceedings of InterPACK, July 6-8, Portland, OR.

[8] Toulouse, M., Doljac, G., Carey, V., and Bash, C., 2009, “Exploration of A Potential-Flow-Based Compact Model of Air-Flow Transport in Data Centers”, Proceedings of IMECE, November 13-19, Lake Buena Vista, FL.

[9] StruXureWare for Data Centers. http://www.apc.com/site/software.[10] Demetriou, D.W. and Khalifa, H.E., 2011, "Evaluation of a Data Center

Recirculation Non-Uniformity Metric Using Computational Fluid Dynamics", Proceedings of InterPACK, July 6-8, Portland, OR.

[11] VanGilder, J. and Shrivastava, S.K., 2007, “Capture Index: An Airflow-Based Rack Cooling Performance Metric,”ASHRAE Transactions, Vol. 113, Part 1, pp. 126-136.

[12] FLOVENT by Mentor Graphics, http://www.mentor.com/products/mechanical/products/flovent. l


T hermal analysis tools available to engineers and scientists offer a wide variety of methods to solve problems. A cursory review of the past decade’s issues

of ElectronicsCooling magazine can show methods ranging from analytical techniques (such as hand calculations) to spreadsheets to full numerical/computational solutions such as CFD (Computational Fluid Dynamics) and FEA (Finite Element Analysis).

Although articles have discussed important basics such as how to use a particular method, and what to do to ensure sound results, the present authors note the issues of when or where to use any particular method have not been discussed as thoroughly. A few articles in the literature have partially addressed this (see [1]-[2]). Often thermal engineers do use a method appropriate to the problem, but as a group default to a method or technique that is comfortable and familiar. A better solution method may be available but not considered because of this bias. Due to this normal characteristic of human behavior, it is time for a careful examination of the methods available today, and for strategically using different methods (the when or where of the method, which means understanding the underlying why one method may be best).

ClassifiCation of ProblemThe best place to begin deciding what type (or multiple types) of solution method may be preferred is to classify the problem to be solved. The two classification methods used for this ar-ticle will be examining the geometry definition of the problem and the goals of the thermal model.

This means understanding two things clearly: what type of information is available at the problem definition; and the end goals of the solution. Examining these two areas will tend to point one in a specific direction. They also will point out the possibility of other solution methods and the advantages they may contain over one’s typical method.

Geometry definition is a statement about how much detail about the geometry is known at the time the problem is to be solved. Anyone who has been involved in a variety and signifi-cant number of projects has seen a wide range in this category. Often thermal analysts have been brought in late to solve an issue after the entire project is nearly complete; in these cases the geometry is well defined and often thoroughly detailed

James Petroski is a principal engineer (thermal) at Rambus in the thermal management of electronics

with a special focus on solid-state lighting (LEDs) and owner of Design by Analysis consulting. Petroski

has been involved in thermal, shock and vibration management of electronics systems for DoD, NASA and

commercial applications. He received his bachelor’s degree in Engineering Science and Mechanics from

Georgia Institute of Technology and a master's degree in Engineering Mechanics from Cleveland State University.

He has authored several papers related to LED and electronics packaging and has 18 patents pertaining to

solid-state lighting. Petroski is a member of the ASME K-16 Subcommittee on Heat Transfer in Electronics.

strategies for Using thermal Calculation methodsJames Petroski, Rambus Inc., Brecksville, Ohio, USA

Cathy Biber, Biber Thermal Design, Ltd., Portland, Oregon, USA

Dr. Cathy Biber is a consultant helping engineers design in thermal solutions using a practical analytical approach. She enjoys being part of the creative design

process to develop a thermal architecture that satisfies the many design requirements: industrial design, safety,

EMI, acoustic noise, manufacturability, service, and so on. Biber’s clients include national and international

companies large and small, in a wide range of business areas, including solar power, displays, lighting,

microprocessor-based systems, and medical and analytical instruments. She received her engineering

degrees from MIT. She has taught seminars on electronics cooling and related subjects throughout the U.S. and in

Europe. She also holds several patents.


P e t r o s k i , B i B e r

in explicit CAD models with complete bills of materials. One could label these as “fully defined” geometry definitions.

Sometimes thermal issues are addressed early in a design process, and in these cases very little may be explicitly defined. The analyst may know there will be some number of PCBs, and that the overall product dimensions will be about x by y by z, and that the enclosure will have certain features typical of the product class, but perhaps little is known beyond that. The thermal dissipation may even be only roughly known and may have a wide possible range owing to product features be-ing not well defined. Such a situation could be described as a “nebulous” geometry definition, or architecture phase.

Certainly some combination of the “fully defined” and “nebulous” cases exist, and often this is found in many design situations. Perhaps the PCB is an existing item and will be reused in a new product, so it is well defined while the re-maining design around it is mostly unknown. This condition will be labeled as the “partially defined” geometry definition.

All three of these conditions encompass the full range of geometry definitions one may find when beginning a ther-mal analysis, and as the authors explain, it is beneficial to consider the starting point when making a choice of tool for the analysis.

The second problem classification revolves around the goals of the thermal model. These may change over time. The ther-mal model goal typically falls into one of two broad categories.

The first goal category could be called the multiple-scenario or design trade-off study. In this situation, there may be several types of solutions that could be used to solve the thermal prob-lem. Each type of solution may be quite different from other ones in geometry, material or type of cooling system. Changes to design strategies or thermal paths may be significant among the options. Adopting a particular solution in one design may

involve a trade-off with other desirable features from another.For example, one could examine a product that is a chassis

with internal electronics. Perhaps the design could be cooled by natural convection; this requires a certain surface area and possible ventilation openings. Care would be required to place heat sinks, components, etc., in the appropriate places for cooling. Touch temperatures may also need to be considered. The same product could also be cooled by forced convection. In this case, fan placement, air inlet and outlet sizes, and noise requirements all must be evaluated. The installation of the product in its environment also factors into the product solu-tion; blowing hot exhaust air onto an end user would not be suitable for most products. Other variables besides these can also be important, but this example shows that some thermal modeling goals may have numerous starting points. Ultimately this type of problem requires evaluating many geometries and finding the thermal performance of each one.

A second goal category could be called fully specified. In this case, there are few types of solutions to examine, but they are detailed designs. This is common in later project phases when a particular solution has been chosen. While chassis designs and layouts may be close to final form, some smaller details may be undecided, such as type and placements of thermal interfaces, heat sink fin spacing, effect of gap pads, or rearrangement of PCB hot components. Once a product final design is completed, a final thermal analysis is often performed as part of the product launch verification, and would also fit this category.

solUtion methods for Problem ClassesWith these classifications and goals in mind, one can then examine the solution methods available and see that there are some reasonable fits between the problem and type of solution method. This is an important step. One mistake people tend to make is to use and re-use methods they are most familiar with rather than what may be most suitable. This leads to forcing a method or tool to solve the problem. While this will still lead to solutions, it is not necessarily the best manner to go about this process. From this viewpoint, the authors propose the following matches of methods to the problem classes previously described:

Numerical analysis with systems of fundamental equations for nebulous geometry or multiple scenarios

When geometry is largely unknown, or multiple scenarios must be evaluated, it is most efficient to keep factors set to a variable or numerical value to allow for faster changes. For example, the convective surface area of an enclosure could be represented by number (say .25 m2), or it could be explicitly modeled in 3D CAD. If one wished to change this area to 0.3 m2, this is an easy change if it is just a variable in an equation; the change becomes much harder if the 3D model must be changed (and this becomes cumbersome if several variables have a number of values to be evaluated).

To use this method, systems of fundamental, simultaneous equations are written and solved. In the authors’ experience this is tractable if the number of equations and unknowns is under 25 or so; beyond that may be difficult to set up and solve effectively, depending on the solution algorithm. The equa-

Figure 1. Sample fundamental equation network [5].

Given

30 °C Tshell 70 °C Suitable range for outer shell temperature; this is the avg temp of the shell

Qrad σ f em Aext Tshell4

Ta4

=

Qnc_ext hnc Aext Tshell Ta =

Qnc_int Qnc1 Qnc2=

Qnc1 hnc Aint_shell Tshell Ta =

Qnc2 hnc Aint_horiz Thoriz2Ta

2

=

QTh Qrad Qnc_ext Qnc_int=

Qrad kgraf

Agraf

Lgraf Thoriz Tshell( ) Qnc1 Qnc_ext=

hnc L1

kf1.05

cp μf

kf

gr β ρf2

L13

Tshell Ta

μf2

.215

=

Qnc_int Coeff Aint_airflowTout Ta

Tout Ta

Ht

X

Tout

Ta

1

0.5

=

Author: J Petroski 3 of 5

this is the avg temp of the shell


S t r at e g i e S f o r U S i n g t h e r m a l C a l C U l at i o n m e t h o d S

1kW dissipation with good correlation to product tests. The network equations can also be implemented in spread-

sheet form if needed, see [4]. There are many powerful pro-gramming tools available in spreadsheets far beyond simply coding formulas using cell references with row and column indicators. The biggest advantage of spreadsheet analysis is that the software is available on nearly every computer as part of an office software suite, requiring no additional purchase or installation. This feature facilitates sharing and discussion with team members who are not thermal specialists. A dis-advantage of solving the network equations in a spreadsheet is that the solution equations must be coded afresh if there are changes in number of nodes, or in the way they are linked together. Also, since everything is done manually, debugging and assessing the suitability of the network are entirely up to the user. Another disadvantage to spreadsheets is that non-linearity in the equations (for example, properties that depend on temperature) can be tricky to handle, although there are ways to include these effects that are beyond the scope of this article.

Numerical analysis with discretization for nebulous geometry or multiple scenarios

Solving the same type of problem as the previous case is possible with a resistance network that would discretize the model into more regions. It also may allow for easier solutions with other programming methods; some non-linear areas of a problem may be translated into linear forms without much loss of fidelity. For example, a flat plate with a small heat source may

tions are forms of the three basic heat transfer equations for conduction, convection and radiation familiar to the reader:

kA l(T1 - T2)Q=hA(T1 - T2)Q=Afε(T1

4 - T24)

A key point here is that the heat flow path must be visual-ized enough to write the equations correctly. The visualization exercise is an extremely powerful thought and discussion tool. The solved equations serve to quantify the relative heat flow paths, identifying trouble spots and opportunities for improvement. Of course, the accuracy of the results depends heavily on the accuracy of the thermal network. Sometimes several versions of the network are needed to arrive at a suit-able representation — just as several versions of experimental or computational models are needed to achieve confidence in the model. Also, sometimes the accuracy of the solution is less important than the ability to quantify relative effects in order to make a good design decision. For the design decision, the ability of the network to capture the effect of a design variation is key.

To solve the system of equations, software that is essen-tially high level programming (e.g., commercial codes such as MATLAB or Mathcad, or network solvers such as the SPICE codes for solutions) is well suited to the analytical method. Figure 1 shows a sample problem set up using Mathcad. Author Petroski has used this method for small electronic devices dissipating a few watts to much larger electronic cabinets of

Q=

http://www.conceptgroupinc.com


P e t r o s k i , B i B e r

contain spreading resistances; such a problem may be handled by discretizing the region into smaller regions with the ap-propriate conduction equations (see [3] for such equations). Such a problem could allow for multiple spreading scenarios to be examined without using a single equation for spreading and possibly violating its assumptions. Viewed another way, perhaps a design may or may not incorporate spreading as a significant contribution to the heat transfer. A discretized region that can incorporate spreading, or not, by using the conduction equations may simplify solving the model for a variety of materials and geometries.

For this method, solutions can be found with many of the same network solution techniques described in the previous section. Another class of solvers known as finite difference solvers can be used and thermal versions of such programs (SINDA is a well-known example) provide the advantage of thermal resistance elements for each type of heat transfer, along with debugging and solution assessment.

Hybrid solutions for partially known geometry and some trade-off studies

When some of a problem’s geometry is well-defined, a combination of a fully discretized solution and some ability to handle unknown geometry is useful. For example, one may have a PCB or electronics module whose design is complete, but whose surrounding chassis or enclosure design is not de-fined. The two previous methods work well for the unknown area of the design where single or small numbers of nodes represent major sections of it. A well-defined numerical model can be used for the known module.

These two types of models then need to be combined for a solution. With the proper heat transfer equations one can connect these two parts. Perhaps the simplest manner to model this is to use the finite difference modeler. A discretized model is feasible for the well-defined portion of the model, and simple thermal resistance elements make up the connections to the undefined areas and the undefined geometry as well. As an example, see Figure 2. A portion of the model is well discretized and defined, while the rest of the model is com-posed of few elements. Model creation, with the solutions for different scenarios, is a straightforward process. The flexibility this approach provides for the unknown geometry is useful to find a final design that should meet the system temperature requirements, while providing good fidelity for the known geometry thermal profile.

Automated numerical solutions for fully defined geometry and fully specified goals

The final case is where the geometry of the problem is fully defined. This occurs near the end of the development when full CAD-based geometry is complete. At this point, a fully automated numerical solution is feasible with a complete discretized grid or mesh and appropriate boundary condi-tions, material properties, thermal loads, etc., applied. Any of the commercial or academic codes from the finite element method, computational fluid dynamics or the finite difference method can be used for this solution type. For this solution, the goal is often a final or near-final analysis of the problem and iterations for geometry changes or different scenarios are usually few or none. The greatest detailed solution is found with this method, but at the cost of knowing the final design and often is the longest solution time.

ConClUsionsThere are several methods to solve any thermal analysis, but given the different levels of geometry definition one can face, and the different types of goals for the end analysis, it is best to choose a solution appropriate for the class of the problem. Different solution methods have different advantages, and one should choose a method best compatible with the end goal(s). Ideally one should choose a solution method that provides the best efficiency for the type of problem at hand. This will as-sist in avoiding approaches that resemble the proverb, “If you only own a hammer, everything looks like a nail.” CFD is a fine problem solver, but if one is looking to evaluate multiple scenarios and many geometric conditions, a situation where dozens of analyses may result in weeks passing before every-thing is evaluated where another modeling approach would complete the task in hours or a few days.

Another drawback of using CFD directly is that funda-mental limitations to the problem aren’t flagged, whereas the thought process required by model construction forces identification of the limiting factors. Thus, evaluating the basics first is also important — one could do a lot of model-ing, only to find that the constraints are infeasible. This leaves the engineer doing a great deal of work with no result or poor results, and may be likened to using a hammer to pound in a screw, to alter the proverb. Simpler methods often identify important features and point to the solutions without resort-ing to extensive modeling.

referenCes[1] Luiten, G.A., “Cooling of a Flat TV Monitor”, ElectronicsCooling, Vol. 9

No. 2, May 2003.[2] Luiten, G.A., "The Better Box Model”, ElectronicsCooling, Vol. 15 No. 3,

August 2009.[3] Lasance, C., “How to Estimate Heat Spreading Effects in Practice”, Journal of

Electronics Packaging, 031004, Vol. 132, September 2010.[4] Wilcoxon, R., “Calculation Corner: A Spreadsheet Based Matrix Solution for

a Thermal Resistance Network, Part 1”, ElectronicsCooling, Vol. 16 No. 3, September 2010.

[5] Equation system developed and solved in Mathcad software by PTC.[6] Model produced in Sauna MS software by Thermal Solutions.[7] Belady, C., and Minichiello, A., “Effective Thermal Design for Electronic

Systems”, ElectronicsCooling, Vol. 9 No. 2, May 2003. l

Figure 2. Sample hybrid network in a finite difference modeler [6].


Thermoelectric effects were discovered around 1830 by French and German scientists. Thomas Seebeck observed that a magnetic field was produced by a

closed loop of two different metals with each junction having a different temperature. Soon after, Danish scientist Hans-Christian Oersted found the magnetic field to be caused by an electric current, so he assigned to Seebeck’s effect the term "thermoelectricity," paving the way for solid state power generation.1 Some years later, in 1834, Jean Peltier discovered that between the junction points of the said loop of different metals a temperature difference is established when an elec-tric power source forces current through the loop. Seebeck’s discovery made its way to the market mostly in the form of thermocouples and thermopiles used as temperature sensors and security switches for gas cookers. More significant power generation applications have left the planet, traveling to outer space where they provide power to deep space satellites, such as Explorer. During the past 50 years, commercial use has been made mostly from Peltier’s thermoelectric cooling effect in applications ranging from small refrigerator devices for camping and outdoor activities, cooled car seats and tin can holders to thermal management of tiny laser diodes used in fiberoptic telecom and datacom backhaul networks.

While all metals exhibit thermoelectric effects to some de-gree, semiconductors generally perform much better because they can be doped with additional charge carriers. Bismuth Telluride (Bi2Te3) is a semiconductor compound material that is grown in two flavors of large mono-crystals, one p-doped the other n-doped. P- and n-doped Bismuth Telluride elements have proven to be most efficient across the temperature range common in the human environment, up to 150°C approxi-mately. The thermoelectric efficiency is a material property and referred to as the "Figure of Merit" ZT; it is basically derived from the voltage generated per temperature gradient and the ratio between thermal and electrical conductivity [1] as ZT = (S2 · σ/κ)·T, where σ is the electrical conductivity, S is Seebeck Coefficient (given in µV/K) and κ is thermal conductivity. T is the arithmetic average of the hot and cold temperatures at the ends of the thermocouple, (Th+Tc)/2.

The maximum value of ZT is found around 1 for most thermoelectrically active materials actually used, due to physical effects at the level of the crystalline lattice of those materials. Since the electrical conductivity of a good thermo-

Burkhard Habbe is Vice President Business Development at Micropelt, a thin film thermoelectric chip maker located in Freiburg, Germany. He holds a degree in

Mechanical Engineering from Munich’s Technical University. Since Burkhard started his career as an

automation engineer he has held various senior positions in sales, marketing and business development, each

related to cutting edge technologies such as robotics, videoconferencing (founding a nation-wide rental service),

hybrid microelectronics and machine vision. Since 2007 Burkhard promotes Micropelt’s thin film thermoelectrics,

targeting applications like energy harvesting, low-power micro-coolers, rapid thermal control, and thermal and

calorimetric sensing applications.

Thin Film Thermoelectrics Today and Tomorrow

Burkhard Habbe and Dr. Joachim Nurnus Micropelt GmbH, Freiburg, Germany

1. It should be realized that the Seebeck effect is not a junction effect, it occurs in a single material over which a temperature difference is established.

Dr. Joachim Nurnus received his Dr.rer.nat. degree (PhD) from Albert-Ludwigs-Universität Freiburg, Germany, in

2001. In 1997 he started research at the Fraunhofer IPM Institute in Freiburg, focusing on the fields of IV–VI

and V–VI based semiconductors for use in thermoelectric and optoelectronic low profile structures. In 2006

Nurnus became a co-founder of Micropelt GmbH. As a member of the management team he heads technology

development and project management. He has more than 50 publications in conference proceedings, journals as

well as books related to thermoelectrics, and holds multiple patents.


T h i n F i l m T h e r m o e l e c T r i c s T o d ay a n d T o m o r r o w

electric material should range somewhere between a metal and an insulator, it is not much of a surprise that almost all high performance thermoelectric bulk materials are semi-conductors. ZT shows a material characterizing temperature dependency with a peak in a fairly narrow temperature range, found at levels between 100 and more than 1000 K (Figure 1). In order to obtain a higher ZT value for more efficient power generation, a high Seebeck coefficient S is required. This calls for a low thermal conductivity of the thermoelectric materi-

als. Accordingly, the math for coolers demands that effective thermoelectric materials need to have a high Seebeck coef-ficient S, i.e. a high electrical conductivity and a small thermal conductivity at the same time. This, however, conflicts with the Wiedemann-Franz law [2] linking a higher electrical conductivity proportionally to a higher thermal conductivity.

As established production methods for thermoelectric semiconductor materials do not offer any means to affect the material properties which influence electrons and phonons

Figure 1. Temperature dependent dimensionless figures of Merit ZT for various semiconductor materials. In a rough approximation the band gap Egap determines the optimum operating temperature: the larger the band gap the higher the maximum material efficiency on the absolute temperature scale.

Figure 2. Bulk thermoelectric material is cut in cubic legs (pellets) and sandwiched between two ceramic plates plated with conductive structures to establish an electric serial circuit. P and N legs are assembled mostly manually in a strict alternating sequence.

Temperature (K)


h a b b e , n u r n u s

Figure 3. Process flow for cross-plane TE chip devices. Steps 1-4 are applied separately to P- and N-type wafers.

as carriers of charge and heat, emerging thin film deposi-tion technologies have been considered potential candidates for breaking into those structural dimensions. In the early 1990s fundamental research had identified low-dimensional structures [3] and their "configurable" thermal and electrical conduction properties as the long-sought handle on ZT – also see [4] for an overview of approaches.

NaNostructured thermoelectric materials To overcome the ZT = 1 limit of homogeneous bulk materi-als, multiple nanostructured thin film materials have been investigated, because these materials offer design access at the dimensional level of both charge and heat carrier transport. An optimization of the thermoelectric figure of merit was considered possible using e.g. InPbTe or SiGe in a Multiple Quantum Well (MQW) structure, which was supposed to establish suitable barriers with ideally indefinite band gap and negligible electrical conductivity [5]. In bulk TE materi-als, ZT can only be optimized by varying the charge carrier concentration. MQW structures offer one more parameter for ZT optimization: the thickness of the quantum well. Theory predicted an increase of the figure of merit by more than a fac-tor of 5. Soon these predictions were confirmed; compared to bulk materials (ZT of 0.2 at 400 K) two-dimensional quantum wells generated ZT values of up to 2 at 400 K. One problem remained, though. The entire thermoelectric structure neces-sarily had to include barrier layers whose poor thermoelectric properties fully contributed — negatively — to the overall ZT. As a result, the effective three-dimensional ZT values were found even smaller than those of homogeneous thin film materials. The initial model calculations also showed that one-dimensional structures, so called quantum wires, would allow for an even larger increase of ZT. However, such quantum wires would need to be embedded into matrix ma-terials with a specific orientation to form mechanically stable thermoelectric layers. Like two-dimensional quantum wells, said matrix materials necessarily have poor thermoelectric properties. Consequently, materials with an effectively higher three-dimensional ZT have not yet been developed.

Current research trends include the combination of two good thermoelectric nanomaterials in layers, each with pe-riodic structures. Interference effects of such layers promise to reduce thermal conductivity without inhibiting electrical conductivity. Another approach tries to leverage attractive thermoelectric properties of p-type Half-Heusler nano-sized cubes [6]. Reportedly, the good properties of individual Half-Heuslers initially give a 90% rise to ZT; however, this increase does not survive thermal treatment of the thermoelectric layers on their way to a functional device.

As of today, obviously, none of the activities which focus on substantial improvements of the thermoelectric Figure of Merit have resulted in a scalable industrial process and production technology which is indispensable to cater for large thermo-electric generators which would cover surfaces such as exhaust pipes, solar collectors and process equipment dissipating streams of waste heat — on a level that sensibly contributes to system efficiencies of those engines, thermal and solar power generation units, and many industrial processes. Hence, material choice is still limited to bulk materials and raw materials, and to what today’s process technology can do with them.



Thin Film ThermoelecTric Devices Ongoing miniaturization of electronics, largely condensed and increasingly three-dimensional circuitry calls for miniatur-ization of TE coolers, while the Seebeck law as such demands as many as possible thermocouples per area to generate high thermovoltages from small thermogenerators. Commercially available thermoelectric bulk devices for use around room temperature are produced by sawing wafer-like disks of ther-moelectric materials into small cubes, referred to as "legs." Ar-rays of alternating p- and n-legs are sandwiched and soldered between pre-metalized Al203 substrates (Figure 2). Neither dicing TE legs from brittle TE disks nor prevalent manual

and semi-automated assembly processes lend themselves well towards miniaturization. The potentials of nanostructured thin film materials are open exclusively to thin film based processes which can provide the means for incorporating the distinct nano-structuring approaches discussed above and make them applicable to both small chip-sized devices for micro energy harvesting and large area heat recovery systems. Two general approaches for thin film TE device production have been established: in-plane legs and cross-plane legs. In-plane structures can produce high numbers of thermocouples, hence high voltages, but thermal resistance and consequently effective gradients across thin layers are low [7]. The electri-cal resistance of long aspect ratio TE structures, in addition, tends to be in the kilo Ohms, which in summary yields low microwatt power levels when integrated to chip scale [8]. This limitation may be overcome, though, by large area concepts.

A cross-plane concept has been developed initially at the Freiburg, Germany-based Fraunhofer IPM. Established and matured process flows and production approaches obtain com-mercially viable thermoelectric thin film devices using a MEMS-like processing approach applying 4" and 6" wafers. While the same Bismuth Telluride-based compound semiconductor materials are being used, a proprietary thin film deposition process provides a viable platform to using nanostructured, high performance materials, once they become available. Wafers are processed by first depositing and patterning metal conductive structures (Figure 3.1), followed by an overgrowth with up to 36 microns of thermoelectric materials (Figure 3.2) covered by solder metals (Figure 3.3). Downstream the thermoelectric film

Figure 4. Flip chip bonding eventually yields an electrical series of numerous thermocouples per TE device.

http://www.schlegelemi.com






is structured to form the entire array of p- or n- thermoelectric legs using photo masking and etching processes (Figure 3.4). Separate processes are required each for p- and n-type wafers. Flip chip bond-ing and bulk soldering of corresponding

p-and n-type parts results in individual parts or arrays of functional device as shown in Figure 3.5. So-called tiles, con-sisting of arrays of devices, are separated into individual devices by another dicing process. In a bond process mating seg-

Figure 5. Scalable feature sizes range from 30 to 600µm in plane, controlled by a simple set of photomasks which allows for mixing different patterns across one wafer. The thin film layer thickness has been realized up to 36 µm.

ments from p- and n-doped wafers are bonded to form one or multiple functional devices (Figure 4).

Structure geometries range from 30 µm to 600 µm feature sizes, as shown in Figure 5, providing a flexible platform for thermoelectric thin film devices match-ing the differing requirements requested by Seebeck generators and sensors or Peltier coolers respectively. Overall de-vice geometries have been realized from footprints of 0.5 mm2 up to 25mm2.

Figure 6 illustrates the size ratio of the smallest produced thin film TE cooler, featuring a cold side area well below 1 mm2, versus a legacy thermoelectric Peltier cooler: the thin film cooler eas-ily fits on a single leg of the legacy bulk TEC. Across its effective leg height of as little as 36 µm the sputtered thermoelec-tric structure can establish an effective temperature difference of more than 60 K down from a hot side of 85°C. The same gradient across a 0.5 mm high TE leg would, theoretically, result in a ΔT of 850K. Theoretically, as 80% of the temperature difference of a legacy TEC is achieved along less than 5% of its re-spective height.

Typical generator design

500 µm

Typical cooler design

500 µm



Micro-scale leg height and thermal mass of thin film coolers provide benefits to many applications. The thermal resistance of thin film TE devices is very low, leading to high cooling power densities of up to 100W/cm2 (650 W/sq in) — over 10 times more than conventional bulk TE devices. Such high heat flux densities — in concert with small associated thermal masses — allow for improved dynamics of thermal control, applicable to sub-square millimeters of footprint. Temperature levels of small amounts of material can be "switched" rather than being ramped up and down. With no thermal load at-

tached, temperature change rates of up to 180°C per second are achieved. Thermal loads with 100 times the thermal capacity of the driving thin film cooler can be controlled at heating and cooling rates in the range of 10-20°C/s. This level of performance qualifies micro coolers as an ideal 'engine' for fast and accurate temperature control of sensors, thermal management of small devices and distinct hot spots, and for thermal cycling of genetic material samples down to chip level.

Following the imminent needs for further reduction of size, power consumption and cost of thermoelectric cooling systems a variation of a TEC was developed to optimally match the larger system scope of the TEC including its associated drive circuit. Small power TECs suffer from low electrical resistances below 1 Ohm. This leads to low power regulator efficiencies of the driver circuit which often has to supply well over 1 A of current to this low resistance load. Large amounts of additional waste heat are being produced in such high cur-rent cooling systems, characterized by low overall coefficients of performance (COP).

The inherent structural scaling flexibility of thermoelectric thin films supports optimized matching of the TEC’s electri-cal resistance to low current drive components, letting them operate at a high level of electrical efficiency. A photomask set is needed to produce the respective TEC micro structure with size and number of thermocouples that creates a resis-tance of about 30 Ohms. Required structural dimensions are determined by numerical simulation based on the well-known properties of the sputtered thin film material. The resulting TEC device (Figure 7) consumes only 200 mA for pumping 600 mW of heat. The drive voltage of around 3 Volt is read-ily available in most electronic systems which require some amount of thermal management on a square millimeter scale. The designed impedance match yields an electrical efficiency of the low cost small footprint drive circuit near 90%. The total power consumption for of the entire TEC/driver combo is about 50% of a comparable legacy design. Since the device’s maximum delta T is limited to little over 30 K, applications are likely found in VCSEL (Vertical Cavity Surface Emitting Laser: solid state lasers with beam emission perpendicular to its surface, typically low power) and small edge emitter laser systems which only require limited temperature offsets but both highly dynamic and precise control enabled by the low thermal mass of a silicon based micro TEC.

While thermoelectric nano-structured materials still re-quire considerable development efforts to reach commercial viability, venture capital is presently funding the world’s first volume fab for thermoelectric thin film products in the German city of Halle/Saale. The upcoming additional device capacity of 5 to 10 million micro coolers and thermogenerators will likely not have much effect on the established markets of bulk thermoelectrics and their well-established and proven ap-plication fields. Assembly automation of bulk TE will continue to drive cost down and likely also the minimum size of TE legs. Choosing between bulk and thin film is getting more difficult at the overlap of both technologies. Expectations in thin film TE are focusing primarily on Seebeck devices which provide micro- and milliwatts of power to autonomous wireless sen-sors and other micro systems incorporated in very compact thermal paths of high power density. Strong growth expecta-

Figure 6. The smallest micro TE coolers produced fit on a single TE leg of a legacy bulk TEC.



tions in this market are driven by largely increased pressure on reduction of energy consumption, status information based asset management and expanding building automation. Micro TECs will benefit from this volume driver through lower cost and quality levels common to chip production. Less defen-sive applications of micro TECs in particular are expected to emerge big time in life sciences where disposable narrow band genetic testing is seen at every doctor’s desk helping to identify specific diseases faster and help matching medication to a patient on an individual basis. Why less defensive? A laser system can always be specified or improved to work without

Figure 7. MPC-D403 high resistance micro TEC for low current/high voltage operation can be controlled by cheap audio components. Device mounted on a TO header for laser applications.

a cooler — saving megawatts across all installed devices. A one-off genetic test worth the energy of a light bulb running for an hour, however, can save a life — and in many cases it can’t run at the desirable dynamic level without a micro TEC for thermal cycling.

reFerences[1] Sootsman, J., Chung, D., Kanatzidis M., Angew. Chem. Int. Ed. 2009, 48,

8616-8639.[2] Lasance, C., "How Thermal Conductivity Relates to Electrical Conductivity,"

ElectronicsCooling, May 2000.[3] Hicks, L.D., Dresselhaus, M.S., "Effect of Quantum-weH Structures on the

Thermoelectric Figure of Merit," Phys. Rev. B 47(19). 12727-12731 (1993).[4] Lasance, C., Simons, R.E., "Advances in High-Performance Cooling for

Electronics, ElectronicsCooling, November 2005.[5] Numus, J., Böttner, H., Lambrecht, A., Chapter 48 in: Thermoelectrics

Handbook: Macro to Nano: Nanoscale Thermoelectrics, ISBN 0-8493-2264-2.[6] Xiao Yan, Giri Joshi, Weishu Liu, Yucheng Lan, Hui Wang, Sangyeop Lee, J.

W. Simonson, S. J. Poon, T. M. Tritt, Gang Chen, and Z. F. Ren in Nano Lett, 2011, 11 (2), pp 556–560.

[7] Leonov, V., Van Hoof, C., Vullers, R., "Thermoelectric and Hybrid Generators in Wearable Devices and Clothes," bsn, pp. 195-200, 2009; Sixth International Workshop on Wearable and Implantable Body Sensor Networks, 2009.

[8] Leonov, V., Van Hoof, C., Vullers, R., "Thermoelectric and Hybrid Generators in Wearable Devices and Clothes," Proceedings of the 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks. l

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The Bergquist Company .......................................................................................................................................................... Inside Front Cover

Cofan USA ............................................................................................................................................................................................................... 26

Concept Group ........................................................................................................................................................................................................ 22

Deep-Cool .............................................................................................................................................................................................................. 31

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Electronic Components and Technology Conference (ECTC) .....................................................................................................................19

EIC Solutions, Inc. ................................................................................................................................................................................................... 7

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Heat Transfer Research & Development, Ltd. ....................................................................................................................................................9

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Thin Film Thermoelectrics September 2011 Today and …s3.electronics-cooling.com/issues/ECM_September2011.pdf · Clemens J.M. Lasance Calculation Corner 4 A Useful Catalog of Calculation