FPRF Final Report Volume 3

7/29/2019 FPRF Final Report Volume 3

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Validation of a Smoke DetectionPerformance Prediction Methodology

Volume 3. Evaluation of Smoke Detector Performance

Prepared by:

James A. Milke and Frederick W. MowrerUniversity of Maryland

Pravinray Gandhi

Underwriters Laboratories Inc.

October 2008 Fire Protection Research Foundation


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FOREWORD

This report presents the results of a Foundation project whose goal was to develop avalidated engineering methodology to calculate and accurately predict the responsetime of spot-type and aspirated smoke detection systems exposed to incipient fires and

growing fires. The report, divided into four volumes, describes the test methods, testresults, computer simulations and analyses used for this project, which addresses thevalidation of a smoke detection performance prediction methodology.

The four volumes of this report include:

Volume 1, which addresses the characterization of the heat and smoke releaserates of eight incipient fire sources selected for this project;

Volume 2, which addresses the large-scale room fire tests conducted as part ofthis project;

Volume 3, which addresses evaluation of smoke detector performance in thelarge-scale room fire tests conducted as part of this project;

Volume 4, which addresses comparisons of current FDS smoke detectionprediction methodologies with actual smoke detector performance in the large-scale room fire tests.

The Research Foundation expresses gratitude to the project sponsors and technicalpanelists listed on the following page.

The content, opinions and conclusions contained in this report are solely those of theauthors.


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Validation of a Smoke Detection Performance PredictionMethodologyResearch Project

Technical Panel

Shane Clary, Bay Alarm Company

Kenneth Dungan, PLC Foundation

Jay Ierardi, R.W. Sullivan Engineering

Kevin McGrattan, National Institute of Standards and Technology

Dan Nichols, NYS Code Enforcement and Administration

Ali Rangwala, Worcester Polytechnic Institute

Joseph Su, National Research Council of Canada

Principal Sponsors

Honeywell Life Safety

National Electrical Manufacturers Association

Siemens Building Technologies, Inc.

SimplexGrinnell

Contributing Sponsors

Automatic Fire Alarm Association

Bosch Security Systems

Xtralis, Inc.


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Validation of a Smoke Detection Performance Prediction Methodology


Prepared for:

Kathleen AlmandFire Protection Research Foundation

1 Batterymarch ParkQuincy, MA 02169

Prepared by:


Pravinray Gandhi

Underwriters Laboratories, Inc.

October 10, 2008


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Validation of a Smoke Detection Performance Prediction Methodology Volume 3October 10, 2008 p. ii

Executive Summary

This report, divided into four volumes, describes the test methods, test results, computer

simulations and analyses used for this project, which addresses the validation of a smoke

detection performance prediction methodology. This project was conducted jointly by theUniversity of Maryland (UM) and Underwriters Laboratories, Inc., (UL) under the auspices of

the Fire Protection Research Foundation (FPRF). The financial and technical support of theFPRF, the project sponsors and the project technical panel are gratefully acknowledged.

The four volumes of this report include:

Volume 1, which addresses the characterization of the heat and smoke release rates ofeight incipient fire sources selected for this project;

Volume 2, which addresses the large-scale room fire tests conducted as part of this

project;

Volume 3, which addresses evaluation of smoke detector performance in the large-scaleroom fire tests conducted as part of this project;

Volume 4, which addresses comparisons of current FDS smoke detection predictionmethodologies with actual smoke detector performance in the large-scale room fire tests.

The overall objective of this project has been to evaluate the capabilities of the current release

version (5.1.0) of the Fire Dynamics Simulator (FDS) to predict smoke detector activation in

response to relatively low energy incipient fire sources. The project was subdivided into fourtasks, consistent with the four volumes included in this report.

The first task was to characterize the heat and smoke release rates of eight incipient fire sources

selected for this project. The incipient fire sources are described in Table E1; the fire sourcesinclude four flaming fire sources and four smoldering/pyrolyzing fire sources. The heat and

smoke release rates of these incipient fire sources were measured in the same IMO intermediate

scale calorimeter that UL used previously as part of its FPRF-sponsored smoke characterizationproject [Fabian, et al., 2007]. Three replicate tests were conducted for each of the eight incipient

fire sources to provide a measure of the repeatability of these tests. Volume 1 of this report

provides descriptions of the incipient fire source fuels and ignition sources, the fire test apparatusand instrumentation used for this task, and the results of these tests. Volume 1 also addresses

FDS simulations of these tests conducted in the IMO calorimeter as a means to evaluate the

predictive capabilities of the FDS model on a preliminary basis. These FDS simulations were

not originally planned, but have proven valuable in troubleshooting issues related to the

simulation of fires involving these incipient sources. They provide an indication of theuncertainty in simulating the fire source terms in FDS.

The second task was to perform large-scale room fire tests using the eight incipient fire sourcescharacterized in Task 1. The large-scale room fire tests were conducted in two rooms at the UL

facility in Northbrook, IL. The first set of large-scale tests was conducted under unventilated

conditions in the standard room used to test smoke detectors for the UL 217/268 standards; thisroom measures 10.8 m (36 ft.) long by 6.6 m (22 ft.) wide by 3.0 m (10 ft.) tall. Three replicate


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tests were conducted with each of the eight incipient fire sources, for a total of 24 unventilatedroom fire tests. The second set of large-scale tests was conducted in a 7.2 m (24 ft) long by 7.2

m (24 ft) wide by 3.0 m (10 ft) high room constructed specifically for this project to represent a

mechanically ventilated commercial space. This room was provided with mechanical injection

ventilation and a ceiling return air plenum to represent a typical commercial type of installation.Three replicate tests were conducted with each of the eight incipient fire sources at nominal

mechanical ventilation rates of 6 and 12 air changes per hour; two replicate tests were conductedwith each of the incipient fire sources under unventilated conditions in this room. Thus, 64 fire

tests were conducted in the ventilated room, for a total of 88 large-scale fire tests in the two

rooms. A matrix showing the designations of the 88 large-scale tests is provided in Table E2.

Table E1. Incipient fire sources

Fuel source Ignition source Fire type

Shredded office paper Small flame (50 W) Flaming

Flexible PU foam /microfiber fabric

Small flame (50 W) Flaming

Flexible PU foam /microfiber fabric

Hotplate Smoldering/pyrolysis

Ponderosa pine Hotplate Smoldering/pyrolysis

Cotton linen fabric Hotplate Smoldering/pyrolysis

PVC wire Electric overcurrent Smoldering/pyrolysis

Computer case Small flame (UL 94) Flaming

Printed circuit board Small flame (ATIS T1.319) Flaming

Table E2. Matrix of large-scale room fire test designations

Incipient fire source Unventilated

room

Ventilated room

6 ach 12 ach 0 ach

Shredded office paper 1, 2, 3 25, 26, 27 49, 50, 51 73, 74

Flaming PU foam /

microfiber fabric

4, 5, 6 28, 29, 30 52, 53, 54 75, 76

Smoldering PU foam /

microfiber fabric

7, 8, 9 31, 32, 33 55, 56, 57 77, 78

Ponderosa pine 10, 11, 12 34, 35, 36 58, 59, 60 79, 80

Cotton linen fabric 13, 14, 15 37, 38, 39 61, 62, 63 81, 82

PVC wire 16, 17, 18 40, 41, 42 64, 65, 66 83, 84

Computer case 19, 20, 21 43, 44, 45 67, 68, 69 85, 86

Printed circuit board 22, 23, 24 46, 47, 48 70, 71, 72 87, 88

ach = nominal mechanical injection ventilation rate in air changes per hour

The large-scale rooms were instrumented with a number of thermocouples, velocity probes andlight obscuration measurement devices to provide a basis for evaluating the current capability of

FDS to predict fire-induced conditions throughout a room in response to incipient fire sources.


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The rooms were both equipped with a number of spot-type commercial smoke detectors from

two manufacturers. The ventilated test room was also equipped with three aspirated smoke

detection systems from one manufacturer. The response of these different smoke detection

devices during these tests provides a basis for evaluating the current capability of FDS to predict

smoke detector activation in response to incipient fire sources. Volume 2 of this report describesthe details of the large-scale room fire tests and provides the instrumentation and detection data

from these 88 fire tests. More than 1,200 graphs have been developed to illustrate the results ofthese 88 tests; these graphs are too voluminous to print, so they are provided on electronic media

in Excel files associated with each test. This large-scale room fire test data set should prove

useful for future smoke transport and smoke detection validation exercises as well as for this one.

The third task was to evaluate smoke detector performance during the large-scale fire tests. For

this task, the response of the spot-type and aspirated smoke detectors during the fire tests was

evaluated and characterized. These results were then compared with methodologies available inthe fire safety literature for predicting the activation of smoke detectors. Volume 3 of this report

describes the details of these comparisons.

One objective of this project has been to develop the means, based on experimental data, toestimate the response of smoke detectors using the simulated results of the smoke conditions

computed by FDS. Smoke conditions estimated by FDS throughout the domain include

temperature, velocity and mass fraction of smoke (which can be related to light obscuration orvisibility). One of the relatively unique aspects of this study is an examination of the role of

ventilation conditions in identifying surrogate measures to predict smoke detector response.

Within the last 10 years, there have been five significant studies examining the response ofsmoke detectors. These studies, examined as part of this project, include:

Kemano by the National Research Council of Canada

Naval Research Laboratory and Hughes Associates tests for shipboard applications

Home Smoke Alarm Project by NIST

Smoke Characterization Project by Underwriters Laboratories for the Fire ProtectionResearch Foundation

Experiments program in this project.

These experimental programs include a sufficiently wide variety of spaces, fuels and ventilation

conditions to form a substantial basis for the development of robust, simple guidelines for

estimating smoke detector response. Unfortunately, the smoke detector responses appear to be

strongly dependent on the specific characteristics of the smoke and in some cases on the detectortechnology. Consequently, proposing a single set of guidelines for obscuration, temperature rise

and velocity which can apply to a wide range of applications is difficult, other than suggesting

guidelines which would be very conservative in some applications.

For flaming fires, the obscuration level in tests without forced ventilation ranged from 1.4 to 10.7

%/ft for ionization detectors and from 2.7 to 12.9 %/ft for photoelectric detectors. Given the


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noted range in the 80th percentile values of obscuration at the time of response, a guideline which

reasonably captures much of the data for smoke detectors of either type of technology is 8 %/ft.

In the case of flaming fires in ventilated rooms, the 80th

percentile values of the obscuration

levels differ appreciably for the two detection technologies. For flaming fires with ventilation,the 80

thpercentile values of the obscuration level for photoelectric smoke detectors were 4.3 to

4.9 %/ft. In contrast, the 80th

percentile values of the obscuration level for ionization smokedetectors were 8.0 to 10.3 %/ft, although it is noted that the 10.3% is based on only two tests

conducted at a forced ventilation rate of 12 ACH. As such, a possible guideline of obscuration

levels for photoelectric detectors could be 5 %/ft for ventilation rates ranging from 6 to 12 ACH.For ionization detectors, the 8 %/ft value appears to be an appropriate guideline considering only

the results from the tests with 6 ACH. With the limited number of tests conducted at 12 ACH

where ionization detectors responded, a guideline to estimate their response cannot be suggested.

For non-flaming fires without ventilation, the 80th percentile values of the obscuration levels

ranged from 4.4 to 18.5 %/ft for ionization smoke detectors and 1.6 to 12.1 %/ft for photoelectricsmoke detectors. The 80

thpercentile values of the obscuration levels for non-flaming fires with

ventilation were all less than 1 %/ft in this study and approximately 5 %/ft for ionizationdetectors in the NRL study. Given the limited data in this area, a recommendation for

establishing a guideline of only 1 %/ft is questionable, especially in light of the difference in

results obtained from experiments conducted as part of this study and the NRL study. Untilfurther data is obtained, a value in excess of 1 %/ft is recommended and should perhaps be as

large as 2.5 %/ft.

The temperature rise at the time of detection response for flaming fires with no forced ventilationis highly dependent on the detection technology. A temperature rise of approximately 5 K can

be suggested as a reasonable conservative guideline for ionization detectors, though should begreater than 5 K, e.g. 15 K given the measurements obtained in the NRL and NIST tests. Fornon-flaming fires and all fires with forced ventilation a temperature rise of approximately 3 K

appears to be a reasonable guideline to estimate smoke detector response of either technology.

Because the velocities associated with the forced ventilation provided in the test room were

appreciably greater than the ceiling jet velocity, a guideline based on velocity cannot be

recommended for such cases.

An appreciable variation of smoke conditions was noted at the time of response of smoke

detectors in all of the experimental programs reviewed. While guidelines of obscuration level or

temperature rise can be suggested, these are very approximate in nature and may involveappreciable errors. One reason for this error is the fact that light obscuration and temperature arenot related to the operating mechanisms of current smoke detector technologies, i.e. light

scattering and ionization. Volume 3 presents an outline of additional research which could be

used to better correlate light obscuration with light scattering measurements.

The fourth task of this project was to evaluate the capabilities of the current release version

(5.1.0) of the Fire Dynamics Simulator (FDS) to predict smoke detector activation in the two


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rooms described in Task 2 in response to the relatively low energy incipient fire sources

characterized in Task 1. As part of this task, FDS simulations were performed of the 32 different

room fire scenarios conducted as part of this project. The FDS simulated results were then

compared with the experimental results. Volume 4 of this report describes the details of these

simulations and comparisons.

The baseline FDS simulations of the room tests were performed with a uniform grid size of 10cm (4 in.). This resulted in a total number of 233,280 computational cells in both the

unventilated enclosure domain, which had dimensions of 10.8 m (108 cells) by 7.2 m (72 cells)

by 3.0 m (30 cells) high, as well as in the ventilated enclosure domain, which had dimensions of7.2 m (72 cells) by 7.2 m (cells) by 4.5 m (45 cells) high. On a single-processor PC, it took a

few hours to run the 5 to 10 minute simulations of the flaming fire sources to a few days to run

the 80 to 90 minute simulations of the smoldering fire sources at this resolution. Doubling the

grid resolution from 10 cm (4 in.) to 5 cm (2 in.) changes these run times from a few days to anumber of weeks and consequently would be unreasonable for most applications.

It is difficult to generalize about the comparisons of the FDS simulations of detector activation in

the room tests with the actual room test detection data because of the wide range of results. Insome cases, the simulated and actual smoke conditions at the detection stations were relatively

close to one another and within the experimental scatter, while in other cases, the simulated

smoke concentrations exceeded the measured smoke concentrations by relatively large margins.

There are at least three potentially significant sources of uncertainty associated with FDS

simulation of smoke detector performance in room fire scenarios:

Uncertainties in the initial and boundary conditions specified for a scenario, including

uncertainties in specification of the fire heat and smoke release rate histories and inspecification of the mechanical ventilation;

Uncertainties in the calculations performed by FDS to simulate heat and smoke transport;

Uncertainties in the empirical models FDS currently uses to calculate smoke detector

response and to predict smoke detector activation.

Quantitative uncertainty analysis has not been performed as part of this project, but qualitativelyit appears that the greatest uncertainties are associated with the first and third sources of

uncertainty identified here.

The eight incipient fire sources used for this project each exhibited a range of fire growth, heat

release and smoke release rates that limited the reproducibility of the bench-scale and large-scalefire tests. It is unreasonable to expect the simulation of these fire scenarios to be any better than

the scatter in the experiments being simulated.

It is suspected that the treatment of mechanical ventilation represents another source of

considerable uncertainty in the FDS simulations performed as part of this project. Realventilation grilles and resulting airflows are more complicated than the simulated grilles and

airflows in the ventilated enclosure. More work is needed to more fully explore this issue.


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Before this project was undertaken, the prediction of smoke production in FDS was based only

on a user-specified constant soot yield tied to the heat release rate of a fire. During this project,

at least three limitations with this approach to predicting smoke production were recognized:

Only a single fire source could be specified, which did not allow separate specification of

an ignition source and other fuels subsequently ignited;

The smoke release rate could not vary independently of the heat release rate, so productswith variable smoke yields could not be modeled properly;

Smoldering and pyrolyzing smoke sources that produce substantial quantities of smokebut little heat could not be modeled properly.

As a result of these limitations, the developers of the FDS model incorporated a new algorithmthat permits the user to specify smoke release independently of heat release. This new feature

was used to specify smoke release rates for this project.

The primary findings of this project can be summarized as follows:

The smoke release rates of eight different incipient fire sources, including four flamingsources, three smoldering sources and one overheated electrical wire, have beenmeasured under well-characterized conditions in replicate bench-scale tests conducted in

the IMO intermediate scale calorimeter at Underwriters Laboratories in Northbrook, IL.

The primary smoke signature of interest in this project was the obscuration of visiblelight. Additional data was gathered during the bench-scale tests, including particle countdensity, mean particle diameter, carbon monoxide production and carbon dioxide

production. This additional data may be of use in future investigations, but has not been

analyzed for this project.Smoke obscuration was measured in the exhaust duct of the IMO intermediate scalecalorimeter by projecting a white light beam across the diameter of the exhaust duct onto

a photocell and measuring the change in voltage at the photocell caused by smoke

particles in the light beam.

Smoke release rates are characterized in units of m2/s, where the smoke release rate is

calculated as the product of the smoke extinction coefficient, k (m-1), by the volumetric

flow rate in the exhaust duct, V (m3/s):

V

L

IIVkS o

)/ln(

The total smoke release (TSR) is characterized in units of m2 and is calculated as theintegral of the smoke release rate over the period of a test, i.e., the area under the smoke

release rate curve:


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t

dtSTSR

0

The mass of smoke released during a test is characterized in units of g s and is calculatedas the quotient of the total smoke release to the specific extinction coefficient, km, which

was assumed to have a constant value of 8.7 m2/gs:

m

sk

TSRm

The average smoke yield during a test is calculated as the quotient of the mass of smokereleased to the fuel mass loss during a test:

f

ss

m

my

When calculated in this way, the average smoke yields obtained for the eight incipientsources in the IMO apparatus are shown in Table E3 along with other data from the IMOtests. These data provide an indication of the variability in the replicate tests.

When this project started, smoke production was calculated in FDS only in terms of

constant smoke yield factors tied to the specified heat release rate through the mixture

fraction model used by FDS. During this project, it became apparent that smoke yieldsfor the eight incipient sources are not constant and that characterizing smoke production

in terms of a constant smoke yield factor would not produce accurate smoke production

or transport results in FDS for these incipient fire sources.During this project, the developers of FDS implemented a new method to specify smokeproduction independently of heat release. Called the species ID method, this methodwas used throughout this project to specify smoke production in FDS for both the IMO

test simulations and the room fire simulations.

The bench-scale tests conducted in the IMO apparatus were simulated in FDS as onemeans to validate the capabilities of FDS to model smoke production and transport. For

these FDS simulations, a uniform grid size of 2.5 cm was used. These simulations of the

IMO tests showed that the calculated smoke quantity transported past the smoke eye inthe exhaust duct was similar to the quantity of smoke released from the fuel package, as

shown in Table E4. The largest variation between output and input was 5.4%.

Differences in the peak obscuration values and the times to reach these peaks between theIMO physical tests and FDS simulations are shown in Table E5. The simulated peak

smoke release rate was within 17.3% of the specified peak smoke release rate for all fuelsexcept the PVC insulated wire. The FDS simulated time to peak obscuration lagged the

specified peak time by 4 to 33 seconds, with two exceptions. This lag time is most likely

related to the transport lag between smoke release at the fuel source and measurement atthe smoke eye in the exhaust duct. The IMO apparatus smoke test data was not corrected


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for transport lag; this suggests that the actual smoke release in the IMO tests occurred

earlier than represented in the smoke release rate curves for these tests.

For the FDS simulations of the IMO tests, one replicate test for each fire source was

selected for simulation and comparison with the measured data from that test. For the

FDS simulations of the room fire tests, the IMO test data was typically averaged for eachfire source and this average data was used as input to the FDS simulations. The expected

uncertainty in the FDS input data based on this approach has not yet been characterized.


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Table E3. Summary of data obtained from tests conducted in IMO apparatus

Sample Description Mode Peak HRR Peak SRR Smoke Yield Total SR Total HR

(kW) (m /s) (g/g) (m) (MJ)

Shredded Paper-1 Flaming 7.76 1.350 0.094 110.5 0.388



Shredded Paper Average 9.72 1.447 0.091 111.2 0.530

PU Foam/Microfiber-1 Flaming 8.54 0.432 0.094 74.2 1.896

PU Foam/Microfiber-2 Flaming 11.23 0.512 0.096 84.2 2.240

PU Foam/Microfiber-3 Flaming 9.79 0.513 0.095 80.6 1.974PU Foam/Microfiber Average 9.85 0.486 0.095 79.7 2.037

Circuit Board-1 Flaming 1.90 0.534 0.215 22.0 0.826



Circuit Board Average 2.30 0.537 0.252 22.3 0.957

Computer Case-1 Flaming 0.00 0.119 0.785 9.2 0.000



Computer Case Average 0.45 0.219 0.877 15.9 0.069

PU Foam/Microfiber-1 Smoldering N/A 0.066 0.085 39.9 N/A



PU Foam/Microfiber Average 0.059 0.082 39.9

Ponderosa Pine-1 Smoldering N/A 0.161 0.141 182.7 N/A



Ponderosa Pine Average 0.143 0.141 182.7

Cotton Linen Fabric-1 Smoldering N/A 0.084 0.254 43.1 N/A



Cotton Linen Fabric Average 0.096 0.221 38.0

PVC Insulated Wire-1 Smoldering N/A 0.072 0.237 2.1 N/A



PVC Insulated Wire Average 0.107 0.250 2.3


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Table E4. Variation in FDS modeling results of smoke measurement in IMO apparatus

Fuel Source Model Output to Input Model Output to Test

Shredded Office Paper -0.8% 4.8%

PU Foam with Micro-

fiber Fabric -5.4% -4.7%

Printed Circuit Board 0.1% -1.6%

Computer Case ABS

Plastic 4.3% 4.7%

PU Foam with Micro-

fiber Fabric -3.1% -1.5%

Ponderosa Pine -1.6% 1.9%Cotton Linen Fabric -0.9% 1.3%

PVC Insulated Wire -1.6% -5.2%

Flaming

Smoldering

Table E5. Peak obscuration values and times in the IMO physical tests and FDS simulations.

The 88 room fire tests conducted as part of this project provide a wealth of data on theconditions resulting from the eight incipient fire sources and the response of spot, beamand aspirated detection systems to these conditions in both unventilated and mechanically

ventilated enclosures. Only a fraction of this data has been analyzed in detail as part of

this project, but all the data acquired during this project has been summarized in tabular


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and chart form in Excel spreadsheet files and will be made available for future analysis.

More than 1,200 data charts have been generated to illustrate the data from these tests.

The responses of the two brands of photoelectric detectors used in this project were

generally consistent with each other, but the levels of smoke obscuration reported by

these detectors was not always consistent with the smoke obscuration levels measured atthe adjacent detection stations. This may be due to the different methods used to measure

smoke obscuration by the detectors, which use light reflection, and by the adjacentphotocell assemblies, which use light obscuration.

The levels of smoke obscuration reported by the spot detectors are based on correlationsdeveloped from testing in the UL smoke box using only a single smoke source, a

smoldering cotton wick. This correlation has not been demonstrated for the smokesources used in this project; this may account for at least some of the differences between

the smoke obscuration levels reported by the spot detectors and those measured by the

adjacent photocell assemblies.

Based on analysis of the smoke detector data from the room fire tests in this project, the

smoke obscuration at detection, represented in %/ft and based on the 80 th percentilevalues, are shown in Table E6 for the different ventilation conditions, fire conditions and

detector types.

Table E6. Smoke obscuration at detection in room tests based on 80th

percentile values.

12 ACH6 ACH

1?

Insuff. Data

5

Insuff. Data

Non-

flaming

Flaming

1?10Photoelectric

1?12Ionization

58Photoelectric

88Ionization

Ventilated

Unventilated

12 ACH6 ACH

1?

Insuff. Data

5

Insuff. Data

Non-

flaming

Flaming

1?10Photoelectric

1?12Ionization

58Photoelectric

88Ionization

Ventilated

Unventilated

Based on analysis of the smoke detector data from the room fire tests in this project, thetemperature rise at detection, represented in K and based on the 80th percentile values, are

shown in Table E7 for the different ventilation conditions, fire conditions and detector

types.

Table E7. Temperature rise at detection in room tests based on 80th

percentile values.


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12 ACH6 ACH

3

Insuff. Data

3

Insuff. Data

Non-

flaming

Flaming

33Photoelectric

33Ionization

35Photoelectric

35Ionization

Ventilated

Unventilated

12 ACH6 ACH

3

Insuff. Data

3

Insuff. Data

Non-

flaming

Flaming

33Photoelectric

33Ionization

35Photoelectric

35Ionization

Ventilated

Unventilated

Based on analysis of the smoke detector data from the room fire tests in this project,substantial errors are indicated in using simplistic guidelines of obscuration andtemperature rise based on 80th percentile values. The values reported in the previous

tables both overestimate and underestimate response times in specific tests.

These errors may be reduced through use of a dual parameter approach, e.g. obscurationand velocity in unventilated rooms:

o Flaming fires, photoelectric detectors: 5.5-9.5 %/ft and 0.14-0.33 m/so Non-flaming fires, photoelectric detectors: 1.5-2.5 %/ft and 0.03-0.07 m/s

The near-ceiling velocity of ventilation in the ventilated room tests with 6 and 12 ACHexceeds the velocity of the ceiling jet from the incipient fires in these tests.

The near-ceiling velocity field caused by the mechanical injection of air at 6 and 12 ACHhas not been experimentally characterized.

The responses of the aspirated systems in the 64 tests in the ventilated room have been

summarized in Excel spreadsheets, but have not yet been analyzed. The data from theaspirated systems has not yet been synchronized with the other experimental data due to

technical difficulties with the synchronization process.Baseline FDS simulations have been conducted for 32 different room fire scenariosinvolving the 8 incipient fire sources under 4 different conditions, including unventilatedtests conducted in the UL 217/268 standard smoke room, unventilated tests conducted in

the ventilated room constructed for this project, and ventilated tests conducted at 6 and 12

air changes per hour in this ventilated room. For the baseline FDS simulations, a 10 cmuniform grid was used, resulting in a total of 233,280 computational cells for both the

unventilated and ventilated enclosures. For the baseline FDS simulations, the specified

smoke release rate was based on measurements of smoke release rate in the IMO bench-

scale tests and was not corrected for transport lag.

Additional FDS simulations have been conducted for a few scenarios using the multi-

mesh feature of FDS to provide a higher level of resolution of 5 cm in the fire plume andceiling jet regions of the two enclosures, but these simulations have not yet been

compared with the experimental data or the baseline FDS simulations. These results andcomparisons will be reported separately.

Additional FDS simulations have also been conducted for the 16 mechanically ventilatedscenarios using a different description for the ceiling vents than in the baseline

calculations. These simulations use a uniform cell size of 10 cm, but they have not yet


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been compared with the experimental data or the baseline FDS simulations. These results

and comparisons will be reported separately.

The 32 FDS baseline simulations demonstrate a wide range of results in comparison with

the related room fire tests so it is difficult to generalize about the current capability of

FDS to predict smoke detector activation over the range of fuels and ventilationconditions evaluated in this project.

In many of the 32 baseline FDS simulations, the predicted maximum level of smokeobscuration is higher than the measured level of smoke obscuration in the related roomfire tests. This may be due to the relatively coarse resolution of 10 cm used for the

baseline FDS simulations. In these simulations, it appears that the dynamics of plume

entrainment is not fully captured, which would lead to higher concentrations of smoke inthe FDS simulations. Another factor that may contribute to the higher predicted smoke

obscuration levels is smoke deposition to room surfaces, which is not currently addressed

in FDS.

The levels of smoke obscuration measured by the photocell assemblies at the detection

stations during the mechanically ventilated tests were low in comparison with the levelsof smoke obscuration reported by the adjacent smoke detectors and in comparison with

the levels of smoke obscuration predicted by the associated FDS simulations. The reason

for this has not yet been determined.

The mechanically ventilated tests conducted at 6 and 12 ACH demonstrated conditionsdifferent from those observed in the unventilated tests. In particular, smoke did not

readily transport past the plane defined by the line between the two injection louvers at

the center of the room. Instead, the smoke tended to stack up on the fire side of thisplane, suggesting that the mechanical injection of air was acting as an air curtain.

Qualitatively, this was observed in both the room fire tests as well as in the baseline FDS

simulations. This also had the effect of delaying smoke detector response on the

downstream side of the injection louvers. The impact of mechanical ventilation on smokedetector response warrants further investigation.

Recommendations for further study include:

Develop the relationship between light scattering and light obscuration for fuels ofprimary interest (UL 217 fuels, PU foam, etc.) as a means to resolve the differences in

smoke obscuration levels reported by the smoke detectors and those measured by theadjacent photocell assemblies.

Perform additional FDS simulations at higher resolutions to evaluate the effects onpredicted smoke obscuration levels.

Perform additional mechanically ventilated room tests to characterize the velocity fieldcaused by the injection of air through representative air louvers.

Establish methods to more accurately simulate the injection of air through representativeair louvers in FDS.

Further investigate the impact of mechanical ventilation on smoke detector response.

In summary, this project has generated a wealth of new data on the fire-induced conditions in the

room of origin resulting from a range of different incipient fire sources under both unventilated


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Validation of a Smoke Detection Performance Prediction Methodology Volume 3October 10, 2008 p. xv

and mechanically ventilated conditions. It has also generated a wealth of data on the response of

both spot-type and aspirated smoke detection systems to these conditions. Thirty-two different

room fire scenarios were conducted in replicate in 88 large-scale tests and each scenario was

simulated using the current release version (5.1.0) of the Fire Dynamics Simulator to evaluate the

current capabilities of FDS to predict smoke detector response and activation. In light of thelarge number of room fire tests conducted and FDS simulations performed, it has not been

possible to perform a comprehensive analysis of the results. The data from these tests and FDSsimulations demonstrate a range of results that warrants further analysis.


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Validation of a Smoke Detection Performance Prediction Methodology Volume 3October 10, 2008 p. xvi

Validation of a Smoke Detection Performance Prediction Methodology


Prepared for:

Kathleen AlmandFire Protection Research Foundation

1 Batterymarch ParkQuincy, MA 02169

Prepared by:


Pravinray Gandhi

Underwriters Laboratories, Inc.

October 10, 2008


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Validation of a Smoke Detection Performance Prediction Methodology Volume 3October 10, 2008 p. xvii

CONTENTSPage

1.0 Introduction 1

1.1 Smoke Detector Principles 1

2. Modeling Response of Smoke Detectors 4

2.1 Threshold Guidelines, NFPA 72 4

2.2 Zone Models 5

2.3 FDS 5

3. Experimental Data of Smoke Detector Response 6

3.1 Kemano 7

3.2 NRL 8

3.3 Home Smoke Alarm Project 11

3.4 Smoke Characterization Project 17

3.5 Experimental program in this project 24

4. Discussion 34

4.1 Trends in detector response 34

4.2 Variation in obscuration levels at detector response 38

4.3 Velocity 39

4.4 Dual parameter guidelines 405. Summary 42

6. References 42

Appendix. Conditions at the time of response for Ventilated Room 45


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Validation of a Smoke Detection Performance Prediction Methodology Volume 3October 10, 2008 p. xviii

List of FiguresPage

Figure 1. Mean particle diameters for light obscuration of 0.5 %/ft 4

Figure 2. Cumulative distribution of optical density at the time of smoke detector response 8Figure 3. Cumulative distribution of optical density at the time of smoke detector response,

flaming fires 9Figure 4. Cumulative distribution of optical density at the time of smoke detector response,

non-flaming fires 10

Figure 5. Cumulative distribution of temperature rise at the time of smoke detector response,flaming fires 10

Figure 6. Cumulative distribution of velocity at detector response, flaming fires 11

Figure 7. Obscuration levels at detector response, flaming fires 12

Figure 8. Obscuration levels at detector response, non-flaming fires 13Figure 9. Temperature rise at detector response, flaming fires 13

Figure 10. Temperature rise at detector response, non-flaming fires 14Figure 11. Cumulative distribution of obscuration at detector response, flaming fires 15

Figure 12. Cumulative distribution of obscuration at detector response, non-flamingfires 15

Figure 13. Cumulative distribution of temperature rise at detector response, flaming

fires 16Figure 14. Cumulative distribution of temperature rise at detector response,


Figure 15. Obscuration levels at detector response, flaming fires 17

Figure 16. Obscuration levels at detector response, non-flaming fires 18Figure 17. Temperature rise at detector response, flaming fires 18

Figure 18. Temperature rise at detector response, non-flaming fires 19Figure 19. Velocity at detector response, flaming fires 19Figure 20. Velocity at detector response, non-flaming fires 20

Figure 21. Cumulative distribution of obscuration at detector response, flaming fires 21

Figure 22. Cumulative distribution of obscuration at detector response, non-flaming fires 21Figure 23. Cumulative distribution of temperature rise at detector response, flaming fires 22

Figure 24. Cumulative distribution of temperature rise at detector response,


Figure 25. Cumulative distribution of velocity at detector response, flaming fires 23Figure 26. Cumulative distribution of velocity at detector response, non-flaming fires 23

Figure 27. Light obscuration measurement in shredded paper test 24

Figure 28. Cumulative distribution of obscuration at detector response, non-ventilated room 26Figure 29. Cumulative distribution of temperature rise at detector response,

non-ventilated room 26

Figure 30. Obscuration levels at detector response, ventilated room 27

Figure 31. Temperature rise at detector response, ventilated room 27Figure 32. Cumulative distribution of obscuration at photoelectric detector response,

flaming fires, ventilated room 29


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Validation of a Smoke Detection Performance Prediction Methodology Volume 3October 10, 2008 p. xix

Figure 33. Cumulative distribution of temperature rise at photoelectric detector response,

flaming fires, ventilated room 30

Figure 34. Cumulative distribution of obscuration at photoelectric detector response,

non-flaming fires, ventilated room 30

Figure 35. Cumulative distribution of temperature rise at photoelectric detector response,non-flaming fires, ventilated room 31

Figure 36. Cumulative distribution of obscuration at ionization detector response,flaming fires, ventilated room 32

Figure 37. Cumulative distribution of temperature rise at ionization detector response,

flaming fires, ventilated room 32Figure 38. Cumulative distribution of obscuration at ionization detector response,

non-flaming fires, ventilated room 33

Figure 39. Cumulative distribution of temperature rise at ionization detector response,

non-flaming fires, ventilated room 33Figure 40. 80th percentile obscuration level at detector response 34

Figure 41. 80th

percentile obscuration level at detector response in recent experimentalprograms 35

Figure 42. Visibility to illuminated exit sign through smoke 36Figure 43. 80th percentile temperature rise at detector response 37

Figure 44. 80th

percentile obscuration level at detector response in recent experimental

programs 37Figure 45. Near ceiling velocities in ventilated room tests 40

Figure 46. Light obscuration and velocity at photoelectric detector response,

unventilated room 40

Figure 45. Near ceiling velocities in ventilated room tests 41Figure 46. Light obscuration and velocity at photoelectric detector response,

unventilated room 41


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Validation of a Smoke Detection Performance Prediction Methodology Volume 3October 10, 2008 p. xx

List of TablesPage

E1. Incipient fire sources iii

E2. Matrix of large-scale room fire test designations iiiE3. Summary of data obtained from tests conducted in IMO apparatus ix

E4. Variation in FDS modeling results of smoke measurement in IMO apparatus xE5. Peak obscuration values and times in the IMO physical tests and FDS simulations. x

E6. Smoke obscuration at detection in room tests based on 80th percentile values. xi

E7. Temperature rise at detection in room tests based on 80th percentile values. xi1. Obscuration levels for response of smoke detectors to flaming fires (%/ft) 4

2. Temperature rise for response of smoke detectors to flaming fires (K) 5

3. Detector response statistics for flaming and non-flaming fires,

Home Smoke Alarm Project 144. 80th percentile values of parameters at detector response, Home Smoke Alarm Project 16

5. Detector response statistics for flaming and non-flaming fires,Smoke Characterization Project 20

6. 80th

percentile values of parameters at detector response, Smoke Characterization Project 247. Photoelectric detector response statistics for flaming and non-flaming fires 25

8. 80th

percentile values of parameters at detector response 26

9. Detector response statistics for flaming and non-flaming fires, photoelectric detector SG 2810. Detector response statistics for flaming and non-flaming fires, photoelectric detector SS 28

11. Detector response statistics for flaming and non-flaming fires, ionization detector SG 29

12. 80th

percentile values of parameters at detector response, photoelectric detector SG 31

13. 80th

percentile values of parameters at detector response, ionization detector SG 33


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FPRF incipient fire project final report Volume 3 p. 1

1. Introduction

An objective of this project is to develop the means to estimate the response of smoke detectors

using the computed results of the smoke conditions by FDS. This objective is consistent with

the growing interest to develop engineering methods for estimating the response of fire

protection systems, either for applications in design or fire investigation.

Methods have been developed to estimate the response of flame, thermal and smoke detectors.

The method for estimating the response of thermal detectors has been included in NFPA 72 formany years and is based on an estimate of the temperature of the ceiling jet, originally developed

by Alpert [1972]. The response of flame detectors is based on an estimate of the radiation heat

transfer from a flame plume to a detector. A computational approach to provide this estimate hasbeen more recently included in NFPA 72 [2007]. In both cases, the sensitivity of the heat or

flame detectors to the respective signature needs to be determined via experiment.

1.1 Smoke Detector Principles

The response of traditional spot smoke detectors is dependent on the characteristics of the smoke

in the vicinity of the detector and the characteristics of the detector. Most of the current smokedetectors operate based on one of two types of detection technologies: photoelectric or

ionization. Contemporary, photoelectric smoke detectors respond based on the scattering of light

caused by smoke particles [Schifiliti, et al., 2002]. The response of an ionization smoke detector

is based on a change in the local ionization field within the detection chamber caused byintroduction of smoke particles within the chamber. Both types of alarms activate when a set

threshold is reached.

Two of the principal characteristics of the smoke affecting the response of the smoke detector

technologies include the distribution of particle sizes and concentration of smoke particles. The

role of the two characteristics depends on the sensing technology included in the detector.

Relationships between light scattering and current change within an ionization chamber relativeto the characteristics of smoke particles are provided in the literature [Schifiliti and Pucci, 1996].

While light scattering is dependent on the smoke particle characteristics (size distribution andconcentration), it is also affected by the wavelength of the transmitted light, refractive index of

the smoke and the angle between the light source and the light receiver. There are three regions

of light scattering behavior described in the literature for single, spherical particles. The three

regions depend on the ratio of the particle diameter (d) and wavelength of light, , as follows:

Rayleigh: d/ < 0.1Mie: 0.1 < d/ < 0.4

Bricard: d/ > 0.4

Considering the wavelength of light used in photoelectric detectors and the range of particle sizesproduced in fires, Mie theory can be applied to appreciate the effect of smoke particle

characteristics on photoelectric smoke detector responses. According to Mie theory, light


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scattering is linearly proportion to the number of smoke particles and the square of the diameter

of the particles:

2

ii dnLS (1)

Performance of the ionization detector is dependent on the attachment coefficient of air-molecule

ions to the soot particles, , where = 2 Ddm [Litton, et al., 2004] Thus, the MIC response isrelated to the product of particle count and diameter as shown in Eq. 2.

MIC dini (2)

The conditions in a ceiling jet or smoke layer determined via engineering methods generally do

not include smoke particle size and concentration. Instead, the methods include estimates ofinvolve light obscuration, temperature and velocity. As indicated by Schifilti, et al., this

provides an inherent difficulty in estimating the response of smoke detectors.

The ability of smoke particles to obscure light is described by Beers Law, expressed in terms ofthe optical density per unit path length. According to Beers Law, the optical density (i.e. light

obscuration) is linearly proportional to the concentration of smoke, as expressed in Eq. 3.

sCOD

(3)

Where OD is the optical density, is the path length, and Cs is the smoke concentration. The

smoke concentration, Cs, is proportional to the smoke number density as follows:

3iis dnC (4)

Where ni, and di are the number count (density) and particle diameter for a small range of

particle size i, referred to as a bin in the recent UL study [Fabian, et a., 2007]. Arelationship between optical density per path length and the smoke number density count at a

given time can be developed by combining Eq. 3 and Eq. 4:

3

ii dnD

(5)

Optical density and obscuration per foot can be related as expressed in Eq. 6:

DOBS 101100 (6)

Where OBS is obscuration per meter and D is the optical density (m-1

).

Unfortunately, a particular level of obscuration does not uniquely describe the characteristics of a

particular smoke. This issue was evident in the recent Smoke Characterization Project conducted


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by UL collected highly detailed information of the smoke particle size, concentration, light

obscuration, and other parameters relative to smokes produced by several different fuels [Fabian,etal., 2007]. The mean particle diameter measured for smoke from flaming and non-flaming

fires that produced a light obscuration of 0.5%/ft in the Smoke Characterization Project are

presented in Figure 1. For the series of experiments indicated in Figure 1, the mean particle

diameter producing the same level of light obscuration ranges from 0.08 to 0 0.22 microns. Assuch, even though the level of light obscuration is the same for these smokes, their detectability

by a light scattering detector or ionization detector would vary, given that these detection

technologies are dependent on the square and first power of the particle diameter, respectively.

Nonetheless, the purpose of this project is to identify relationships between smoke detector and

smoke parameters which are included within current numerical models. Thus, being that opticaldensity is computed by several numerical models, the relationship of smoke detector response to

light obscuration is sought in this project.

One of the relatively unique aspects of this study is an examination of the role of ventilation

conditions in identifying surrogate measures to predict smoke detector response. Most of theprevious studies in this area have been conducted in quiescent atmospheres.

Figure 1. Mean particle diameters for light obscuration of 0.5 %/ft [Fabian, et al., 2007]

2. Modeling Response of Smoke Detectors

2.1 Threshold Guidelines, NFPA 72

Annex B of NFPA 72 includes an engineering approach for estimating the response of smokedetectors to flaming fires. Three parameters identified in the Annex include temperature rise,

obscuration (or optical density) and velocity. Given that neither of the two detection

technologies respond to conditions represented by any of these three parameters, inherent errors

0.00

0.05

0.10

0.15

0.20

0.25

Douglasfir

Pond

erosapine

News

pape

r

Heptan

e/To

luen

e

Polys

tyrene

Coffe

emaker

PUfoam

PUfoam

inCotto

n/Po

ly

PUfoam

inCotto

n

PUfoam

inPoly

Nyloncarpet

Polyi

socyan

urate

Brea

d

Me

anParticleDiameter(micron)

Flaming

Non-flaming


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are to be expected when applying any of these parameters for estimating the response of smoke

detectors [Schifiliti and Pucci, 1996].

Heskestad and Delichatsios [1977] suggested values of the optical density that coincided with

smoke detector response based on their measurements. Their suggestions are incorporated into

the optical densities noted in Annex B of NFPA 72. The optical densities noted in NFPA 72 areconverted into obscuration levels using equation 4 and are included in Table 1.

As indicated in Table 1, the obscuration level associated with detector response varies by thedetection technology and fuel. The range in obscuration levels for smokes from the various fuels

varies by a factor of 11 for photoelectric detectors and 180 for ionization detectors.

Table 1. Obscuration levels for response of smoke detectors to flaming fires (%/ft)

Material Photoelectric Ionization

Wood Crib 3.39 1.14Cotton fabric 1.83 0.12

Polyurethane foam 10.9 10.9

PVC 20.6 20.6

Schifiliti and Pucci [1996] estimated the temperature rise necessary for detection to firesinvolving the fuels noted in Table 1 based on ratios of the optical density and temperature at

detector response determined by Heskestad and Delichatsios. The resulting temperature rises

suggested to estimate smoke detector response are included in Annex B of NFPA 72 and are

reproduced here in Table 2. A default temperature rise of 13 K, presumably applicable to any

fuel is sometimes suggested, though it is apparent that for fires involving wood cribs and cottonfabrics, such a value would provide an optimistic view of detector response. Geimans review of

previous experiment programs indicated that a significant proportion of ionization smokedetectors responded at temperature rises much less than those indicated in Table 2.

Table 2. Temperature rise for response of smoke detectors to flaming fires (K) (NFPA 72)

Material Photoelectric Ionization

Wood Crib 41.7 13.9

Cotton fabric 27.8 1.7

Polyurethane foam 7.2 7.2PVC 7.2 7.2

The critical velocity of the ceiling jet associated with the response of smoke detectors ranges

from 0.13-0.15 m/s for flaming fires [Borzovski, 1989][Geiman, 2003]. The critical velocity isindependent of the type of smoke detector.


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2.2 Zone Models

Some zone models include an algorithm to estimate the response of smoke detectors. CFAST is

one zone model that includes such an algorithm [Jones, et al., 2005]. The approach used in

CFAST is to treat a smoke detector as a thermal detector, where the sensitivity of the smoke

detector is expressed as a Response Time Index and the operating temperature is expressed as amagnitude of temperature rise above ambient. Guidance on the temperature rise to select is

currently available in Annex B of NFPA 72, though much of the purpose Geimans research was

to assess the appropriateness of the guidance included in NFPA 72. An elementary heat transfercalculation is conducted (treating the smoke detector as a thermal detector) to determine when

the smoke detector reaches the activation temperature.

2.3 FDS

The algorithm within Fire Dynamics Simulator (FDS) [McGrattan, et al., 2008] is based on the

mass fraction of smoke in the sensing chamber of the smoke detector and velocity within the

ceiling jet in the vicinity of the smoke detector. The mass fraction is related to the obscurationper unit length as expressed in equation (7).

lYOBS cexp1100 (7)

The FDS Technical Manual [McGrattan, 2007] suggests a value of 8700 m2/kg 1100 m

2/kg at a

wavelength of 633 nm for for most flaming fires.

The FDS model relates mass fraction of smoke in the sensing chamber of the detector to the

mass fraction of smoke in the ceiling jet outside the detector by one of two approaches. The

simpler of the two approaches is that described by Heskestad [1975]. Heskestads approach is

presented in the governing equation included as Eq. 8:

uL

tYtY

dt

dY cec (8)

where Ye and Ycare the mass fraction of smoke external to the detector and in the detectorssensing chamber, respectively, L is the characteristic length of the detector and u is the ceiling jet

velocity at the location of the smoke detector. The ceiling jet velocity and mass fraction external

to the detector are parameters included in the basic calculations performed by FDS. Thecharacteristic length of the detector needs to be determined via experimentation. The

characteristic length for the light scattering detectors determined in previous experimental efforts

range from 2.6 to 15 m [Schifiliti, et al., 2002].

An alternative approach to estimate the response time of smoke detectors included within FDS is

an approach suggested by Cleary [2000]. This approach requires a determination of four

parameters (rather than Heskestads one) to address the entrance lag issues. Roby, et al. [2007]suggest that the Cleary model is a better predictor of smoke detector response for cases where the

detector is exposed to ceiling jets with velocities less than 0.5 m/s.


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In Clearys approach, the entrance time is divided into two segments. The time segments are

associated with the time required for the smoke to pass through the exterior housing and thenbaffles in order to fill the sensing chamber. The two time segments are approximated using Eq.

9 and 10 [Cleary, et al., 1999].

e

ut ee (9)cut cc (10)

The s and s need to be empirically determined, with suggested values for these fourparameters for smoke detectors included in the FDS Users Guide [McGrattan, et al., 2008a].

With this approach, the governing equation for the change in the mass fraction of smoke in the

sensing chamber is presented as Eq. 11.

c

ceec

t

tYttY

dt

dY(11)

3. Experimental Data of Smoke Detector Response

Within the last 10 years, there have been five significant studies examining the response of

smoke detectors. These studies are:

Kemano [Su, at al., 2003]

Naval Research Laboratory (NRL) [Gottuk, et al., 1999][Rose-Pehrsson, et al.,2000][Wong, et al., 2000]

Home Smoke Alarm Project by NIST [Bukowski, et al., 2008]Smoke Characterization Project [Fabian, et al., 2007]

Experiments program in this project.

Observations and trends of the response of detectors included in each of the projects will be

discussed in their own section.

3.1 Kemano

The Kemano tests were conducted by the National Research Council of Canada (NRC). Thesetests were conducted in residences consisting of a one-story bungalow and a two-story single-

family home. The one-story bungalow had approximate internal dimensions of 7.7 x 12.2 x 2.4m. The internal dimensions for each story of the two-story single-family home were 6.9 x 8.9 x

2.4 m.

A total of 12 tests, were conducted where detectors responded. All of the test scenarios included

the fires starting as non-flaming fires and transitioning to flaming fires. In this section, particulartests are referred to as flaming or non-flaming fires based on the mode of combustion present

when detectors activated.


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Both battery-powered ionization and photoelectric smoke alarms were used in the tests.Measurements for the tests included optical density and temperature. The optical density meters

used a 940 nm, pulsed, near-IR LED light source with a photodiode, separated by a distance of

0.6 m. Temperature was measured with 26 AWG, Type K thermocouples.

The cumulative probability distribution for the optical density at the time or response of a smoke

detector is presented in Figure 2. Geiman, et al., observed that the optical density for flaming

fires was very similar for both ionization and photoelectric detectors. The optical densityassociated with the ionization smoke detector response for flaming fires at the 80 th percentile

level was approximately 0.11 m-1

, (7.4 %/ft) and 0.14 m-1

(8.4 %/ft) for photoelectric smoke

detectors. The 80th

percentile levels for the non-flaming fires were 0.19 m-1

(10.8 %/ft) for thephotoelectric detectors and 0.21 m-1 (13.7 %/ft) for the ionization detectors. The 80th percentile

levels for the two types of detectors were very similar for a particular type of fire.

Figure 2. Cumulative distribution of optical density at the time of smoke detector response

[Geiman, et al.,2006]

The most significant difference in the optical density at response was observed for the non-

flaming fires, at least for the lesser percentiles. The difference in optical density observed at the

time of photoelectric and ionization smoke detector response for 80 percent of the casesmoderated. For photoelectric detectors, the 80th percentile value of the optical density was

approximately 0.20 m-1

(13.1 %/ft) and for ionization detectors was approximately 0.21 m-1

(13.7

%/ft), only a 5% difference.

The temperature rise observed at the time of smoke detector response was in the range of 1-3 K

and was dismissed as being too small to be meaningful.


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3.2 NRL

The NRL tests were conducted by Hughes Associates, Inc. and NRL. Some of these tests were

conducted in a small compartment (6.1 x 3.6 x 3.0 m) while others were conducted in a medium-

size test compartment (5.9 x 8.1 x 3.0 m). Two ventilation scenarios were used in these tests,

one including no forced airflow and the other providing an airflow of 0.22 m

3

/s in the smallcompartment and 0.45 m3/s in the medium compartment which represented 12 air changes per

hour in the respective compartments.

Forty-one flaming and non-flaming fires were conducted as part of this test series. Two models

each of ionization and photoelectric smoke detectors were included in the Navy tests The optical

density was measured with an optical density meter, which utilized an 880 nm infrared (IR) lightemitting diode (LED) and receptor arrangement over a 1.0 m path length. Temperature was

measured with Inconel-sheathed, type K thermocouples. A sonic anemometer was used to

measure the gas velocity in two orthogonal, horizontal directions.

The cumulative probability distribution for the optical density at the time or response of a smokedetector is presented in Figure 3. The data included in Figure 3 is for tests with and without

ventilation. In these tests, the 80th

percentile of the ionization detector response is at a lesseroptical density (0.02 m-1 or 1.4 %/ft) than the photoelectric detector (0.02 m-1 or 2.8 %/ft). The

smoke characteristics at response were observed to be largely unaffected by the presence of

ventilation.

Figure 3. Cumulative distribution of optical density at the time of smoke detector response,

flaming fires [Geiman, et al., 2004]

The cumulative probability distribution of the optical density associated with ionization smoke

detector response for non-flaming fires is presented in Figure 4. The 80th

percentile of theoptical density in the ventilated tests was approximately 0.07 m-1 (4.8 %/ft) as compared to 0.19

Optical Density at Alarm (m-1

)

0.00 0.05 0.10 0.15 0.20 0.25 0.30CumulativePercentageofDet

ectorAlarms

20

50

80

IonPhoto


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m-1

(12.5 %/ft) for the unventilated tests. The difference in the optical density in the ventilated

and unventilated tests at the time of response for the photoelectric detector was similar to thatobserved for the ionization detector.

Figure 4. Cumulative distribution of optical density at the time of smoke detector response,

non-flaming fires [Geiman, et al., 2004]

The temperature rise for flaming fires at the time of response is presented in Figure 5. The 80th

percentile value is approximately 3 K for the ionization detectors and 16 K for the photoelectric

detectors. The temperature rise observed at the time of response for the non-flaming fires wasnegligible.

The mean ceiling jet velocity at the time of detector response for flaming fires was 0.13 m/s.

These experiments are part of the basis for the velocities indicated in NFPA 72. Geiman, et al.,

indicate that detectors did respond at velocities as small as 0.05 m/s, well below the criticalvelocity noted in NFPA 72, refuting the notion that the cited range of 0.13 to 0.15 m/s is aminimum threshold velocity below which detector response is not expected. The velocity at the

time of detector response for non-flaming fires was negligible in most tests.

Optical Density at Alarm (m-1)0.00 0.05 0.10 0.15 0.20 0.25 0.30

CumulativePercentageofDetectorAlarms

20

50

80

0 Air Changes / Hour

12 Air Changes / Hour


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Figure 5. Cumulative distribution of temperature rise at the time of smoke detector

response, flaming fires [Geiman, et al., 2006]

Figure 6. Cumulative distribution of velocity at detector response, flaming fires [Geiman,

et al., 2006]


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3.3 Home Smoke Alarm Project

The full-scale experimental program that was included as part of the Home Smoke Alarm Project

was conducted by the National Institute of Standards and Technology (NIST). These tests were

conducted in two spaces, a one-story manufactured home and a two story house. Details of the

spaces in which the tests were conducted are included in Bukowski, et al. [2008]. The overalldimensions of the manufactured home were 10.12 m x 4.17 m. The ceiling height inside the

home was sloped, ranging from 2.1 m at the exterior walls to 2.4 m in the center. Tests were

conducted in a bedroom, living room and kitchen. No forced ventilation was provided duringthese tests.

The footprint of the two-story home had dimensions of 6.19 m x 9.65 m. The ceiling height on

both levels is unspecified in the NIST report, though is on the order of 2.4 m. Again, no forced

ventilation was provided during the tests.

A total of 32 fire tests were conducted where detectors responded. The test scenarios includedboth non-flaming and flaming fires and involved a variety of fuel items. Both ionization and

photoelectric smoke alarms were used in the tests. For the purposes of the analysis included inthis document, only the response of the closest array of detectors to the fire was considered.

Measurements for the tests included optical density and temperature. The optical density meters

used a low-cost laser pointer consisting of Class II laser diode with awavelength range of 630 nm to 680 nm (the separation distance between the light source and

photocell is not specified in the NIST report). Temperature was measured with Type K

thermocouples.

The obscuration levels at which the ionization and photoelectric detectors responded is presented

in Figure 7 for flaming fires and Figure 8 for non-flaming fires. As is evident in Figure 7, the

obscuration level at the time of response varies appreciably. The obscuration level at the time ofresponse for the non-flaming fires indicated in Figure 8 is greater than that for the flaming tests

and is even more varied. The mean and standard deviation of the obscuration level for the

flaming and non-flaming fires is indicated in Table 3.


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0

2

4

6

8

10

12

14

0 5 10 15 20

Test #

Obscuration

(%/ft)

ion1

ion2

photo

Figure 7. Obscuration levels at detector response, flaming fires

The temperature rise at which the ionization and photoelectric detectors responded is presentedin Figure 9 for flaming fires and Figure 10 for non-flaming fires. The mean and standard

deviation of the temperature rise for the flaming and non-flaming fires is indicated in Table 3.

For many of the non-flaming tests, the temperature rise indicated is very small.

0

5

10

15

20

25

30

0 2 4 6 8 10 12

Test #

Obscuration(%/ft)

ion1

ion2

photo

Figure 8. Obscuration levels at detector response, non-flaming fires


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0

5

10

15

20

25

30

35

40

0 5 10 15 20

Test #

TemperatureR

ise(K)

ion1ion2

photo

Figure 9. Temperature rise at detector response, flaming fires

0

5

10

15

20

25

30

35

40

0 2 4 6 8 10 12

Test #

TemperatureRise(K)

ion1

ion2

photo

Figure 10. Temperature rise at detector response, non-flaming fires

Table 3. Detector response statistics for flaming and non-flaming fires, home smoke alarm

project

Ionization PhotoelectricParameter Flaming Non-flaming Flaming Non-flaming

Obscuration (%/ft)

Mean 3.04 14.1 5.73 8.13

Standard Deviation 3.78 8.24 3.08 8.83

Temperature Rise (K)

Mean 5.99 7.49 15.5 0.90



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Cumulative distributions of the obscuration level and temperature rise at the time of response areprovided in Figures 11 to 14. The 80th percentile values of the smoke conditions when the

detectors responded in the various tests are presented in Table 4. The 80th

percentile values

follow the general trends observed in previous studies. In flaming fires, ionization detectors

responded when the obscuration level and temperature rise were less than those associated withthe response of photoelectric detectors. In most tests in this experimental program this was

accomplished by the ionization detectors responding prior to the photoelectric detectors in the

flaming tests. Conversely, in the tests with the non-flaming fires, the photoelectric detectorsresponded when the obscuration level and temperature rise were less than those values when the

ionization detectors responded. Again, in most tests the ionization detectors responded after the

photoelectric detectors, and in some cases appeared to operate just as the temperature began toincrease significantly, perhaps as the fire was transitioning to flaming combustion.

0

20

40

60

80

100

0 2 4 6 8 10 12 14

Obscuration (%/ft)

CumulativePercentageofDetec

tor

Alarms

ion

photo

Figure 11. Cumulative distribution of obscuration at detector response, flaming fires


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0

20

40

60

80

100

0 5 10 15 20 25 30

Obscuration (%/ft)

CumulativePercentag

eofDetector

Alarms

ion

photo

Figure 12. Cumulative distribution of obscuration at detector response, non-flaming fires

0

20

40

60

80

100

0 5 10 15 20 25 30 35 40


Cumulative

PercentageofDetector

Alarms

ion

photo

Figure 13. Cumulative distribution of temperature rise at detector response, flaming fires


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0

20

40

60

80

100

0 5 10 15 20 25 30 35


CumulativePercentag

eofDetector

Alarms

ion

photo

Figure 14. Cumulative distribution of temperature rise at detector response, non-flaming

fires

Table 4. 80th

percentile values of parameters at detector response, Home Smoke Alarm

Project

Flaming Non-flaming

Parameter Ionization Photoelectric Ionization Photoelectric

Obscuration (%/ft) 5.55 8.06 18.5 12.1

Temperature Rise (K) 7.70 23.4 14 1.4

3.4 Smoke Characterization Project

The experiments in the Smoke Characterization Project were conducted by Underwriters

Laboratories (UL). These tests were conducted in ULs Fire Test Room with dimensions of 11.0

6.7 3.1 m. No airflow was provided during the tests.

Detectors responded in thirty-three tests to flaming and non-flaming fire sources. Ionization and

photoelectric smoke detectors were included in the tests. Obscuration measurements were takenusing the same white light source and photocell assembly used in standard UL 217 tests. The

lamp and photocell were spaced 1.52 m apart. Temperature was measured with Inconel-

sheathed, Type K thermocouples located 5.4 m from the fire source and 0.15 m below the

ceiling. A sonic anemometer 5.4 m from the fire source and 0.1 m below the ceiling was used tomeasure the gas velocity in the radial and orthogonal, horizontal directions.

The obscuration levels at which the ionization and photoelectric detectors responded is presentedin Figure 15 for flaming fires and Figure 16 for non-flaming fires. In this experimental program,

the range of obscuration levels at the time of response is comparable among both sets of tests,

ranging from less than 1 %/ft to almost 30 %/ft. The mean and standard deviation of the

obscuration level for the flaming and non-flaming fires is indicated in Table 5.


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0.0

5.0

10.0

15.0

20.0

25.0

30.0

0 5 10 15 20

Test #

OBS(%/ft)

Ionization

Photoelectric

Figure 15. Obscuration levels at detector response, flaming fires

0.0

5.0

10.0

15.0

20.0

25.0

30.0

0 5 10 15 20

Test #

OB

S(%/ft)

Ionization

Photoelectric

Figure 16. Obscuration levels at detector response, non-flaming fires

The temperature rise at which the ionization and photoelectric detectors responded is presentedin Figure 17 for flaming fires and Figure 18 for non-flaming fires. The mean and standard

deviation of the temperature rise for the flaming and non-flaming fires is indicated in Table 5.


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0

2

4

6

8

10

12

0 5 10 15 20

Test #

TemperatureRise(K)

Ionization

Photoelectric

Figure 17. Temperature rise at detector response, flaming fires

0.0

0.5

1.0

1.5

2.0

2.5

0 5 10 15 20

Test #

TemperatureRise(K)

Ionization

Photoelectric

Figure 18. Temperature rise at detector response, non-flaming fires

The velocity at which the ionization and photoelectric detectors responded is presented in Figure

19 for flaming fires and Figure 20 for non-flaming fires. In the case of the non-flaming fires, all

of the velocities are less than the critical velocity of 0.13 m/s noted in NFPA 72. The mean and

standard deviation of the velocity for the flaming and non-flaming fires is indicated in Table 5.


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0.00

0.05

0.10

0.15

0.20

0.25

0.30

0 5 10 15 20

Test #

Velocity(m/s

)

Ionization

Photoelectric

Figure 19. Velocity at detector response, flaming fires

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0 5 10 15 20

Test #

Velo

city(m/s)

Ionization

Photoelectric

Figure 20. Velocity at detector response, non-flaming fires


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Table 5. Detector response statistics for flaming and non-flaming fires, Smoke

Characterization Project

Ionization Photoelectric

Parameter Flaming Non-flaming Flaming Non-flaming

Obscuration (%/ft)Mean 5.80 4.52 4.74 7.60



Mean 2.24 1.15 4.30 1.22


Velocity (m/s)

Mean 0.17 0.08 0.18 0.06


Cumulative distributions of the obscuration level, temperature rise and velocity at the time ofdetector response are provided in Figures 21 to 26.

0

20

40

60

80

100

0 5 10 15 20 25 30

Obscuration (%/ft)

CumulativeP

ercentageofDetector

Alarms

Ionization

Photoelectric

Figure 21. Cumulative distribution of obscuration at detector response, flaming fires


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0

20

40

60

80

100

0 5 10 15 20 25 30

Obscuration (%/ft)

CumulativeProbabilit

yofDetector

Alarms

Ionization

Photoelectric

Figure 22. Cumulative distribution of obscuration at detector response, non-flaming fires

0

20

40

60

80

100

0 2 4 6 8 10 12


Cumula

tivePercentageofDetector

Alarms

Ionization

Photoelectric

Figure 23. Cumulative distribution of temperature rise at detector response, flaming fires


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0

20

40

60

80

100

0.0 0.5 1.0 1.5 2.0 2.5


CumulativeProbabilityofDetector

Alarms

Ionization

Photoelectric

Figure 24. Cumulative distribution of temperature rise at detector response, non-flaming

fires

The temperature rise at the time of response is less for the ionization detectors than the

photoelectric detectors for the flaming fires. For the non-flaming fires, the temperature rise is

less for the 70th

percentile level and greater values for the ionization detectors for the non-flaming fires. The velocities at the time of response of the ionization detectors are less than that

for the photoelectric detectors for flaming fires. The converse is true for the non-flaming fires.

0

20

40

60

80

100

0.00 0.05 0.10 0.15 0.20 0.25 0.30

Velocity (m/s)

CumulativePercentageofD

etector

Alarms

Ionization

Photoelectric

Figure 25. Cumulative distribution of velocity at detector response, flaming fires


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0

20

40

60

80

100

120

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14

Velocity (m/s)

CumulativeProbabilityofDetector

Alarms

Ionization

Photoelectric

Figure 26. Cumulative distribution of velocity at detector response, non-flaming fires

The 80th

percentile values of the smoke conditions when the detectors responded in the tests in

the Smoke Characterization Project are presented in Table 6. One of the interesting aspects ofthe results presented in Table 6 is the greater obscuration level noted at the 80 th percentile value

for the ionization detectors for flaming fires and for the photoelectric detectors for non-flaming

fires. This trend is contrary to that found in previous studies that have shown ionizationdetectors to be generally more sensitive to flaming fires and photoelectric detectors for non-

flaming fires. Consequently, the expectation was that the obscuration level at activation for

ionization detectors should be less than that for photoelectric detectors for flaming fires, and the

converse for non-flaming fires.

Table 6. 80th percentile values of parameters at detector response, Smoke CharacterizationProject

Flaming Non-flaming

Parameter Ionization Photoelectric Ionization Photoelectric

Obscuration (%/ft) 8.57 7.15 6.70 7.05

Temperature Rise (K) 3.15 5.65 1.25 1.55

Velocity (m/s) 0.20 0.22 0.11 0.09

The seemingly contrary results are attributed to the transient nature of some of the tests included

in this experimental program. The shredded paper tests are the more notable tests with a

significant transient nature. The obscuration measurements over the duration of one of the

shredded paper tests are presented in Figure 27. In this test, the obscuration increasessignificantly in the early portion of the test prior to the test transitioning to flaming combustion.

The ionization detector responds at 87 s when the obscuration is 18 %/ft, during the first peak in

obscuration indicated in the figure. The photoelectric detector responds at 134 s, when theobscuration is 4.3 %/ft, i.e. after the second peak in the obscuration indicated in the figure.


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0

5

10

15

20

25

0 60 120 180 240 300 360 420 480

Time (s)

Obscuration(%/ft)

Figure 27. Light obscuration measurement in shredded paper test

3.5 Experimental program in this project

The experimental protocol was described in Volume II. The characteristics of the environmentin the vicinity of the detectors at the time of response are described in terms of the obscuration

and temperature rise. Spot photoelectric smoke detectors from two manufacturers (SG and SS)

Documents

FPRF Final Report Volume 3