1 DRAFT – ISABE CONFERENCE TECHNICAL PAPER

ISABE-2015-20264

UNIFORM CRYSTAL TEMPERATURE SENSOR (UCTS) APPLICATION TO VALIDATION, VERIFICATION AND TECHNICAL COMPARISON PROCESSES

Atul Sheth GE Aviation

Lynn, MA, USA

Anastasia Thomas LG Tech-Link, LLC Gilbert, AZ, USA

Abstract Whenever a new measurement technique or analytical tool is introduced within an organization, some form of internal approval is typically required. This evaluation step should enable practicing engineers to know whether the technology is of the right quality for a given application and decision makers to know how much confidence to invest in a particular measurement result. Proper assessment could have cost, efficiency and safety implications. Currently there are no industry guidelines and criteria agreed for this kind of procedure when it comes to the tools used in engine design and development [1, 2]. Furthermore, there is no uniform understanding of the basic vocabulary to support communication between interested parties internally or with participating vendors. At the same time, the drive in turbine design for greater efficiency, durability and reduced emissions is pushing materials and sensors to their limits. Improvements in design prediction validation under increasingly challenging real engine conditions will be critical to meeting the industry’s requirements [3]. As a result, along side advancements in computational techniques, there is good reason to desire a more formalized effort for testing and evaluating existing and newly developed sensors to give practicing engineers guidance on qualified sensors and their optimized application. The authors would like to share the ideas they developed and the experiences acquired on this topic in the process of introducing Uniform Crystal Temperature Sensor (UCTS) technology as a new way to measure maximum metal temperature of critical engine parts in the harsh turbine environment. Supporting examples include details of experimental setup and comparisons of analytical and empirical results. Nomenclature DBA Double Bragg Angle EDM Electrical Discharge Machining k recovery factor NIST National Institute of Standards and Technology T temperature t time

UCTS Uniform Crystal Temperature Sensor σ standard deviation θ error Subscripts i individual measurements syst systematic Introduction The on-going advancements in both numerical modeling and accessible computing power provide design engineers with increasingly powerful tools to attempt to model physical reality. Initially, the growing role of computational techniques in engineering design prompted much speculation on the future need for physical testing. Although in some areas, such as external aerodynamics, CFD advances have led to a reduction in test requirements the same did not happen for the complicated internal engine aero-thermodynamics [4]. The semi-empirical nature of these models with dependencies on turbine specifics and the many uncertainties related to the complexity of the real engine environment as well as the practical reality of manufacturing tolerances, make design validation testing a necessity [5, 6]. Further, with the innovations in fuels, manufacturing, materials and cooling techniques the problem complexity and the number of unknowns are not decreasing. In line with the industry’s recognition of the limitations of modeling such a complex system, there has been a growing focus in the literature and at conferences on the activities of verification and validation [6, 7, 8]. Much of the work currently takes place from the computational perspective and the focus is on thinking about the method of comparison between the results obtained through numerical modeling with those obtained through experiment and the choice of validation metric [9]. There has also been more emphasis on the topic of accuracy and uncertainty in application to both the numerical tools and the sensors used in testing [10]. However, while uncertainty and the processes used for verification of numerical models are discussed in great detail, experimental tools and techniques are given less careful attention both in the literature and in practice. Often, tools such as thermocouples that are commonly used in the industry are

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taken for granted, and the data may be presented without comment on details such as wire size, bead insulation, quality of thermal contact, etc. Comparable treatment of analytical and experimental data, including evaluation of random error for the sensors used, acknowledgement of all potential sources of error applicable in a particular test scenario and setup of the experiment to optimize application with respect to these factors, would enhance validation discussions and increase the value of the comparisons. Sensor Assessment Methods The authors found it productive to categorize sensor evaluation procedures into three levels: Validation, Verification and Technical Comparison. The core of all three is the direct comparison between two or more sets of data. The loose application of the terms validation, verification, and sometimes even calibration can lead to uncertainty in results interpretation and lower the confidence of decision makers in the information presented. The authors’ believe that today’s engineering practices in turbo-machinery would benefit from clarifying the terms themselves and properly applying the associated methodologies in a uniform way throughout the industry. The three levels of testing are distinct in terms of the required engineering support during preparation and execution, the costs, and, most important, the achievable result. Simplest and least costly would be a technical comparison, which provides a general confirmation of tool functionality and a “sanity” check on the results. Either the lab or the engine environment is acceptable for conducting such an experiment and the evaluation of the new tool as “good enough” is based on producing results within the “ball park” of the data obtained in the same environment using existing and comfortably accepted tools. Typically no formal criteria are applied and engineering intuition is the guide. Verification is the check of the claim (specifications) as declared by the new tool’s developer. It requires more elaborate evaluation of the new technique, which often includes the assessment of accuracy and range. This testing assumes the elimination of all systematic error as it is not a characteristic of the tool itself and, therefore, this testing must be carried out in a controlled lab environment. The standard for comparison should be a high accuracy NIST or similar traceable tool co-located with the new one being evaluated. This setup is commonly referred to as “blind testing”. Finally, validation is the highest level of comparison, which should be performed in realistic working conditions with strong engineering support ensuring a scientifically fair and meaningful assessment of the tool with very good accuracy. This procedure is used while new analytical modeling results are assessed. It requires a validation run of the software compared with the results of a validation engine test and presumes that both are based on the same physical reality in terms of geometrical characteristics and boundary conditions.

This is the most difficult level to achieve and needs serious engineering investment. The authors will offer their detailed recommendations on all three methods of comparison. The recently introduced UCTS technology, due to the fact that it is based on a unique principle of operation and not intuitively understood by users, lends itself well to being an illustrative case study of the above-described procedures.

Uniform Crystal Temperature Sensor (UCTS) The UCTS is a micro sized, max metal temperature sensor characterized by its uniform shape, dimensions and crystallographic orientation. Key technical characteristics are summarized in Table 1.

Table 1. UCTS CHARACTERISTICS

The principle of operation is both rugged and simple. It is based on the annihilation of specially induced defects (Frenkel Pairs) in the silicon carbide structure with exposure to heat over time. The changes accumulated in the crystal lattice are directly related to temperature and to a lesser degree to time. In general terms this could be expressed as follows:

DBA = A+B×T +C × log(t) (1)

• DBA is the Double Bragg Angle, which is the lattice characteristic measured by X-ray diffractometry

• A, B, and C are constants • T is temperature • t is time

A series of such curves were obtained with thousands of well-controlled experiments using a NIST traceable standard to create a Calibration Nomogram, pictured in Figure 1.

Figure 1. CALIBRATION NOMOGRAM (illustration only)

Temperature)Range 150)–)1430)°C)300)–)2640)°F

Accuracy σ)=)±)3.3)°C)σ)=)±)6)°F

Dimensions 0.20)x)0.20)x)0.38)mm)0.008)x)0.008)x)0.015)in.

Lead)/)Connectors Not)required

Survivability >)95%

Shelf)Life Unlimited

Supported)Test)Profile(s) Varied

RadiaYon)/)Chemical)Hazard None

Data)ReducYon)ProducYvity! 100)sensors)per)day)

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Max temperature experienced at the location of the sensor over the course of testing is determined using the Calibration Nomogram, precisely observed changes in the lattice structure using X-ray diffractometry, and a normalized time history of testing, which is converted to equivalent time at max [11, 12]. Typical application involves installing the UCTS at the bottom of a small cavity (0.75mm depth and diameter) in the test article, ensuring good thermal contact and covering it with high temperature ceramic adhesive. The sensor is completely embedded in the metal wall of a part and therefore isolated from the harshness and complexity of the external gas flow. Operation is based on thermal conduction, which is the simplest mechanism of heat transfer phenomenon. After testing, the sensor must be recovered and analyzed. Crystal temperature sensor technology has been used successfully for thorough thermal mapping on critical engine parts [13, 14, 15]. Because of its physical characteristics and principle of operation it has been particularly useful in hard to reach areas, rotating parts and parts with thin walls and complex geometry. Although proven through more than 7 years of real engine testing experience, UCTS does not have the same level of industry recognition as the more traditionally used techniques such as thermocouples or thermal paint. As a result, each organization that decides to use the UCTS technology in its engine test campaigns first goes through a process of verification or ‘blind testing’. Further, because the tool is less well known, different groups within each organization, revisit these results and the understanding of UCTS capabilities that accompany it, to support adoption. Proper evaluation at the outset has been used to facilitate effective exploitation of the technology internally as well as provide valuable data in support of model validation activity. Accuracy The terms ‘accuracy’ and ‘uncertainty’ are often used in reference to sensor technology, but not consistently defined. This can obfuscate comparisons and data interpretation efforts. The authors found it valuable to clarify whether a statement of uncertainty pertains to random and/or systematic error along with its confidence level. Temperature measurement result interpretation for any sensor can be generally expressed as:

𝑇 = (𝑇! + 𝜃!"!#) ± 𝜃!"#$%& (2)

Where the outcome (T) is the result of the individual measurement (Ti) plus the sum of all potential systematic errors and the margin of random error applicable to the measurement technique for a specified confidence level (σ). Typically, the uncertainty statement given by a vendor is the random error for the particular technology. In an experimental setting, particularly in the real engine environment, ignoring

potential systematic errors can have significant impact. In the case of a thermocouple (T/C) for example, published tolerances may be between ±0.1% and ±0.75% [16] depending on type (S, N, K, etc.). However, this does not take into account systematic errors that may be introduced due to installation and other test related factors. Even in the case of an S-type platinum thermocouple, for example, the error can rise with heat load to a potential ±10% [12] depending on the thermal contact achieved in the installation package. In the case of UCTS, the value of random error depends on variation in SiC crystal quality, variance in irradiation parameters and accuracy of X-ray diffractometry. Statistical analysis of thousands of available experimental data points helped to derive a random error of ±3.3°C for one standard deviation (σ). For a given batch of sensors and X-ray diffractometer these numbers will stay unchanged in application to a single measurement. The only way to lower random error in this situation is to perform more that one identical measurement (i >1) in which case we could achieve:

σ i =±3.3!C

i (3)

The UCTS measurement would thus be represented by the arithmetic average of “i” individual results. The potential sources of systematic errors for UCTS in the best-case scenario can be identified and eliminated making:

𝜃!"!# = 0 (4)

If this is impossible, the temperature measurement result must be compensated for the combined value of systematic errors calculated on the basis of experimental or analytical studies. The main sources of systematic error potentially relevant to temperature measurement using UCTS in the real engine environment have been identified and evaluated by the developer. Published results provide guidance to practicing engineers for optimized application [12, 17, 18]. Results and Discussion Verification The goal of verification is to confirm manufacturer’s claims. In the case of UCTS, this is a confirmation of the sensor’s physical characteristics and functionality of measuring max temperature with stated accuracy across time and temperature ranges. Assessment of accuracy is focused on that of the tool itself, the random error, and therefore requires care to eliminate sources of systematic error in the evaluation procedures as well as the use of a high quality standard for the comparison. UCTS’ uniform size and shape is easily observed and measured with the aid of a microscope. Completing installation steps into a metal sample is used to assess ease of application and the quality of thermal contact normally achievable. The result can

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be checked for accurate orientation in the cavity, thermal contact with the bottom of the cavity and the absence of any air pockets in the cement by filing down the sample to expose a cross sectional view (Figure 2).

Figure 2. SIDE CUT OF UCTS INSTALLATION SITE

“Blind testing” to verify the sensor’s ability to capture max temperature experienced at a location during the test with the stated accuracy (random error σ = ±3.3 °C) requires conducting an experiment in a well controlled lab environment using a NIST (or similar) traceable standard. In the case described here a tube furnace was used with a capacity of up to 1300°C. The experimental probe (Figure 3) consisted of a high temperature alloy tablet (~ 5mm diameter and 2mm thick) housing a NIST traceable S-type special thermocouple (±0.1% accuracy) on one side and three UCTS on the other. The thermocouple and UCTS were embedded in the tablet and secured with thermo-cement, which also shielded from the direct influence of radiation. UCTS cavities (0.75mm depth and diameter) were created using EDM and each UCTS was installed at the bottom of the cavity using standard installation methodology.

Figure 3. UCTS / S-TYPE THERMOCOUPLE PROBE

The thermocouple probe with attached tablet was then connected to a high accuracy Data Logger and installed into the lab furnace (Figure 4). The furnace was preprogrammed to follow a desired test profile and closely monitored throughout that time.

Figure 4. TUBE FURNACE, PROBE AND DATA LOGGER The graph (Figure 5) shows the time diagram used for the test.

Figure 5. NORMALIZED TIME DIAGRAM FOR LAB TEST

After test completion, the UCTS were removed and analyzed by LG Tech-Link. The results showed good agreement between the UCTS and the chosen standard (Table 2).

Table 2. LAB TEST RESULTS

On separate occasions such tests were repeated in different lab settings to confirm accuracy across a range of temperatures and times at max, each time showing good agreement between UCTS measurement results and the selected standard. For example, tests using the carefully controlled lab setup, as described, were repeated for a matrix of conditions (time and temperature). The results are graphed (see Figure 5) to demonstrate that they all stayed within ± 2 σ range when compared to the S-type special platinum T/C used as the standard.

Figure 5. VERIFICATION TEST RESULTS ACROSS A TEMPERATURE RANGE OF INTEREST

Further, some users conducted tests to examine performance under transient conditions. In the case shown here, accuracy was confirmed with the same furnace setup. The time diagram and the magnified portion in the vicinity of the peak are shown in Figure 6. The results were 5°C apart which is within ± 2 σ.

Figure 6. VERIFICATION TEST FOR CYCLICAL PROFILE

Super&Alloy& Thermo.Cement&

UCTS&

Tube%Furnace%

Thermocouple%Data%Logger%

Data%Logger%Record%

Deg.%C Δ%between%TC%and%UCTS%Measurement

Max.%TC%Temperature 1046

UCTS%Location%91 1047 91UCTS%Location%92 1048 92UCTS%Location%93 1045 +1

σ!=!±!3.3°C!!

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Tmax%measured%=%949%deg.C%Tmax%ucts% %=%954%deg.C%

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Technical Comparison If the more stringent requirements of a verification experiment are not met, the evaluation should be classed as a technical comparison and expectations adjusted accordingly. In these cases, some systematic error is introduced and the increased uncertainty should be acknowledged when reviewing results. Variance is driven by the experimental setup. The appropriateness of the comparison is dependent upon the user’s requirements and other factors like budget, resource availability and time. Various organizations have performed technical comparisons between UCTS Technology and other commonly used methods:

§ UCTS vs. T/C in lab oven conditions § UCTS vs. Temp-plugs § UCTS vs. Thermal Paint § UCTS vs. T/C + Slip Ring § UCTS vs. T/C + Telemetry

The objective was to confirm general functionality and “ball park” accuracy (“sanity check”). Greater uncertainty was expected due to the decision to limit cost and engineering support that would be required to attain a higher standard. This was particularly evident when comparisons were carried out in a real engine environment. One such T/C – UCTS comparison was conducted using two uncooled instrumentation rakes. These were positioned in the flow path after the turbine. Each one carried, in addition to standard instrumentation, 2 UCTS and 2 embedded metal k-type Thermocouples (T/C) positioned on opposite sides of the rake body on two different radial locations (A and B), as shown in Figure 7.

Figure 7. EXPERIMENTAL SETUP The T/C measurements and UCTS measurements were different on both sides of the same radial location on the same rake (Figure 8).

Figure 8. TC and UCTS MEASUREMENT RESULTS In spite of the fact that the rake is uncooled, the aerodynamics and consequently the heat-transfer situation on both sides of the rake is different. ΔT will depend on the angle of attack of the incoming flow [19]. Based on available data and typical engine assumptions, the level of magnitude of potential ΔT in °C for recovery factors (k), is shown in Figure 9.

Figure 9. DYNAMIC ΔT VS MACH NUMBER

Recommended improvements for this comparison test is to place the T/C and UCTS on the same side of the rake in close proximity to each other and to use a higher quality T/C, ideally platinum, in order to reduce the influence of systematic errors. Validation According to [3] one of the three biggest players in the future of heat transfer is design prediction validation. In contrast to the earlier described testing, validation experiments are conducted in the real engine environment to generate high quality results for comparison with modeling data. Verification experiment outcomes and careful evaluations of potential systematic errors for the measurement techniques to be used are important input to the setup of the experiment and the interpretation of the results. These enable a more comprehensive characterization of the uncertainty to be expected and can be used to selectively impose controls on the many variables inherent to this complex setting. In the case of tests involving UCTS, it is recommended to take into account the most influential heat transfer factors such as wall thickness, TBC thickness and film hole flow characteristics [18] when selecting the test articles and preparing the instrumentation layout. Analytical modeling to assess the potential influence of factors like cavity size and location are also encouraged. In turn, the validation modeling used for comparison should be performed with the blade geometry, boundary conditions, and

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all other model input parameters prescribed on the basis of the validation experiment. It should provide as many constraints as possible, requiring few, if any, assumptions on the part of the modeler. This could be accomplished in an iterative fashion such that the results of experimental activity are fed back into the model (Figure 10).

Figure 10. PROPOSED VALIDATION APPROACH Compared to other methodologies, validation is still in its early stages, but attempts are being made in the industry to make it more common practice. Application of the above recommendations requires time and resource, but even the initial attempts have provided valuable results. Therefore, while the implementation of a validation approach remains somewhat inconsistent, it is clear that the increased demands of gas turbine technologies will require the refinement of these processes. Conclusions And Recommendations Safety requires comfortable margins, while performance requires getting as close as possible to the ‘red line’. A good mix of all the “possible” and “probable” is needed to obtain an optimized balance of the two requirements. Computational advances such as the application of a stochastic approach to take into account real life variances of factors like manufacturing tolerances and inlet temperature distribution provide improved insight into the problem [20, 21]. However, model validation remains a critical activity and robust, high accuracy instrumentation is required to meet the raised bar for resolution, fidelity and accuracy of development testing.

Accuracy in these discussions should include both the random and potential systematic errors for a measurement technique to give designers the data to interpret experimental results and to make meaningful comparisons. Consistently and carefully

defined evaluation methodologies to confirm tool capability and accuracy, to verify experimental results or to support model validation would be useful to designers and support the objective of optimizing the balance of turbine efficiency and durability.

The experiences of verifying UCTS capabilities with OEMs, conducting physical and analytical experiments to evaluate potential sources of systematic error affecting UCTS application, and analyzing results of technical comparisons in real engine conditions has provided valuable data that supports model validation activity in real engine development. Applying this approach to evaluate other existing and upcoming temperature measurement techniques and to support comparisons between analytical and experimental data would be of value to practicing engineers.

REFERENCES [1] R. L. Anderson, D. N. Fry, J. A. McEvers, Advanced Turbine

Systems Sensors and Controls Needs Assessment Study Final Report, February 1997

[2] B. Stange, Presentation on Standards for Turbine Engine Test Cell Instrumentation, www.piwg.org, June 2012

[3] Dr. R.S. Bunker, Turbine Heat Transfer and Cooling: An Overview, GT2013-94174, June 2013

[4] The ASCAC Subcommittee on Exascale Computing, Summary Report of the Advanced Scientific Computing Advisory Committee (ASCAC), US Department of Energy, Fall 2010

[5] R. S. Bunker, The Effect of Manufacturing Tolerances on Gas Turbine Cooling, Journal of Turbomachinnery, 2009, vol. 131, pp. 41018-41018-11

[6] C.W. Moeckel, D.L. Darmofal, T.R. Kingston, R.J.G. Norton, Toleranced Design of Cooled Turbine Blades through Probabilistic Thermal Analysis of Manufacturing Variability, Proceedings of ASME Turbo Expo GT2007-28009, June 2007

[7] AIAA, Guide for the Verification and Validation of Computational Fluid Dynamics Simulations, American Institute of Aeronautics and Astronautics, AIAA-G-077-1998

[8] Dr. C. J. Freitas, ASME Verification and Validation Standards Committee and V&V Development Initiatives, ASME Verification and Validation Symposium, May 2014

[9] B. H. Thaker, S. W. Doebling, F. M. Hemez, M. C. Anderson, J. E. Pepin, E. A. Rodriquez, Concepts of Model Verification and Validation, LA-14167-MS, October 2004

[10] W. L. Oberkampf, M. F. Barone, Measures of Agreement Between Computation and Experiment: Validation Metrics, Journal of Computational Physics 217, 2006.

[11] L. Ginzbursky, UCTS: How it Works, http://www.lgtechlink.com, 2010

[12] J. Devoe,, L. Ginzbursky, D. Romanov, Optimization of Temperature Measurement Technique Used in High Heat Flux Environment, Proceedings of ASME Turbo Expo GT2011-45269, June 2011

[13] M. Annerfeldt, S. Shukin, M. Bjorkman, A. Karlsson, A. Jonsson, and E. Svistounova, GTX 100 Turbine Section Measurement Using a Temperature Sensitive Crystal Technique. A Comparison with 3D Thermal and Aerodynamic Analysis, PowerGen Europe, Barcelona, 2004

[14] S. Shukin, M. Annerfeldt, M. Bjorkman, Siemens SGT-800 Industrial Gas Turbine Enhanced to 47MW. Design Modifications

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and Operation Experience, ASME Turbo Expo Power for Land, Sea, and Air, GT2008-5008, 2008

[15] L. Wang, M. Bahador, S. Bruneflod, M. Annerfeldt, M. Bjorkman, I. Hultmark, Siemens Sgt-800 Industrial Gas Turbine Enhanced To 50 Mw:Turbine Design Modifications, Validation And Operation Experience, ASME Turbo Expo GT2013-95462, 2013

[16] P. R. N. Childs, Practical Temperature Measurement. Butterworth-Heinemann, Oxford, UK, 2001

[17] J. Devoe,, L. Ginzbursky, S. Odom, Uniform Crystal Temperature Sensor Accuracy under Transient Conditions, Proceedings of ASME Turbo Expo GT2012-68197, June 2012

[18] J. Devoe,, L. Ginzbursky, J. Brown, The Challenges of Uniform Crystal Temperature Application in Turbomachinery, Proceedings of ASME Turbo Expo GT2013-95909, June 2013

[19] B, Lakshminarayana, Fluid Dynamics and Heat Transfer of Turbomachinery, New York: John Wiley & Sons, 1996

[20] F. Montomoli, A. D’Ammaro, S. Uchida, Uncertainty Quantification and Conjugate Heat Transfer: A Stochastic Analysis, Proceedings of ASME Turbo Expo GT2012-68203, June 2012

[21] M. Carnevale, F. Montomoli, A. D’Ammaro, S. Salvadori and F. Martelli, Uncertainty Quantification: A Stochastic Method for Heat Transfer Prediction Using LES, Journal of Turbomachinery, 2013