Validation of Modeling Tools for Detection Design in High Air Flow Environments

Transcription

1 Validation of Modeling Tools for Detection Design in High Air Flow Environments Final Phase II Report: Part 2 Prepared by: Dr. Jason Floyd Haavard Boehmer Dr. Daniel T. Gottuk Hughes Associates, Inc. Baltimore, MD August 2014 Fire Protection Research Foundation THE FIRE PROTECTION RESEARCH FOUNDATION ONE BATTERYMARCH PARK QUINCY, MASSACHUSETTS, U.S.A Foundation@NFPA.org WEB:

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3 FOREWORD Information-technology and telecommunications (IT/telecom) facilities provide critical services in today s world. From a risk standpoint, the indirect impact of fire loss due to business interruption and loss of critical operations, sometimes geographically very distant from the IT/telecom facility itself, can far outweigh the direct property loss. In the past few years, there have been dramatic changes in the equipment housed in these facilities, which have placed increased demands on HVAC systems. As a result, airflow containment solutions are being introduced to increase energy efficiency. From a fire safety design perspective, the use of airflow containment creates a high airflow environment that dilutes the smoke, which poses challenges for providing adequate detection, and affects the dispersion of suppression agents. Fire protection requirements for IT/telecom facilities are directly addressed by NFPA 75, Protection of Information Technology Equipment, and NFPA 76, Fire Protection of Telecommunications Facilities. Installation of detection systems are covered by NFPA 72, National Fire Alarm and Signaling Code, which is referenced by both NFPA 75 and NFPA 76. Annex Section B.4.5 of NFPA 72, National Fire Alarm and Signaling Code, states, There currently are no quantitative methods for estimating either smoke dilution or airflow effects on locating smoke detectors. Although tools exist to model fire development, detection time, and suppression agent dispersion, they have not been validated for this application. Accordingly, the Fire Protection Research Foundation initiated this project with an overall goal to validate a CFD model that can be used for providing reliable analysis of detection performance in IT/telecom facilities and to provide guidance to the NFPA 75 and 76 Technical Committees on detection in IT/telecom facilities. A Phase 1 project reviewed the candidate models and identified the knowledge gaps on this topic. Phase 2 of the project involved filling in these knowledge gaps by characterizing fire sources, characterizing detector response, full scale model validation, and development of guidance for the Technical Committees using the validated model. This report contains a summary of the work done on the full scale model verification and validation (Task 4 of the project). The Research Foundation expresses gratitude to the report authors Daniel Gottuk, Jason Floyd, and Joshua Dinaburg, who are with Hughes Associates, Inc located in Baltimore, Maryland. The Research Foundation appreciates the guidance provided by the Project Technical Panelists, the funding provided by the project sponsors, and all others that contributed to this research effort. Special thanks to FM Global who donated all resources associated with conducting the full scale tests. A separate FM Global report contains the data and results from this testing. Thanks also go to the National Institute of Standards and Technology for helping support the validation effort and Verizon Wireless for donation of equipment for full scale tests. The content, opinions and conclusions contained in this report are solely those of the authors. Page iii

4 About the Fire Protection Research Foundation The Fire Protection Research Foundation plans, manages, and communicates research on a broad range of fire safety issues in collaboration with scientists and laboratories around the world. The Foundation is an affiliate of NFPA. About the National Fire Protection Association (NFPA) NFPA is a worldwide leader in fire, electrical, building, and life safety. The mission of the international nonprofit organization founded in 1896 is to reduce the worldwide burden of fire and other hazards on the quality of life by providing and advocating consensus codes and standards, research, training, and education. NFPA develops more than 300 codes and standards to minimize the possibility and effects of fire and other hazards. All NFPA codes and standards can be viewed at no cost at Keywords: IT facilities, telecom facilities, data centers, air containment, high airflow, detection design, detection installation, incipient fires Page iv

5 PROJECT TECHNICAL PANEL Thomas Fabian, Underwriters Laboratories Steve Dryden, Poole Fire Protection Inc. Ken Dungan, Risk Technologies, LLC Jay Ierardi, AKF Group LLC Kishor Khankari, PhD, Khankari Consulting Chris Marrion, Marrion Fire & Risk Consulting Randy McCarver, Telcordia Kevin McGrattan, NIST Dan O Connor, Aon Fire Protection David Quirk, DLB Associates Ralph Transue, RJA Tom Wysocki, Guardian Services, Inc Lee Richardson, NFPA Staff Liaison Jonathan Hart, NFPA Staff Liaison Page v

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7 PROJECT SPONSORS Prateep Chatterjee Ben Ditch FM Global Sergey Dorofeev Sai Thumuluru Yi Wang Chris Wieczorek Honeywell/System Sensor SimplexGrinnell UTC / Kidde-Fenwal, Inc Verizon Wireless Scott Lang Rodger Reiswig Jonathan Ingram Joseph Senecal Bob Yurcik Page vii

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9 Validation of Modeling Tools for Detection Design in High Air flow Environments: Phase II Prepared for Amanda Kimball National Fire Protection Research Foundation 1 Batterymarch Park Quincy, MA Prepared by Dr. Jason Floyd Haavard Boehmer Dr. Daniel T. Gottuk Hughes Associates, Inc Commerce Dr., Suite 817 Baltimore, MD July 31, 2014 FIRE SCIENCE & ENGINEERING

10 Validation of Modeling Tools for Detection Design 1DTG PAGE ii TABLE OF CONTENTS 1. BACKGROUND OBJECTIVE FULL SCALE MOCK FACILITY Mockup Description Testing Summary Air Flow Testing Smoke Source Test Summary FDS MODEL OF FACILITY Perforated Tiles/Screens Flow Obstructions Exhaust Flow Rate Smoke Sources Propylene Source Circuit Board Source Polyethylene Foam Source AIRFLOW TEST VALIDATION Pressure Measurements Velocity Measurements Assessment of Measured Velocity Error ACH Fan Test ACH Test Grid Study Conclusions of Velocity Comparison Full Scale Smoke Source Tests Uncertainty Smoke Source Aspirated Laser Detector Response Curve Uncertainty Total Uncertainty Notes on FM Data and FDS Results Aspirated Laser (FM Prototype) Results Propylene Simulation Results Circuit Board Fire Polyethylene Foam Fire... 38

11 Validation of Modeling Tools for Detection Design 1DTG PAGE iii 6.7. Cable Fire SUMMARY REFERENCES APPENDIX A: FDS Predicted Detection Response A.1. Propylene, Subfloor, 78 ACH A.2. Propylene, Subfloor, 265 ACH A.3. Propylene, Hot Aisle, 78 ACH A.4. Propylene, Hot Aisle, 265 ACH A.5. Circuit Board, 78 ACH A.6. Circuit Board, 265 ACH A.7. Polyethylene Foam, 78 ACH A.8. Polyethylene Foam, Subfloor, 265 ACH A.9. Cable Bundle, 78 ACH A.10. Cable Bundle, 265 ACH... 67

12 A-Validation of Modeling Tools for Detection Design: 1DTG PAGE 1 1. BACKGROUND Modern information-technology and telecommunications (IT/telecom) facilities create challenges for the design of fire protection systems and for the development of code guidance for those systems. The high flow rates (upwards of 100 ACH or more) needed to provide adequate cooling impact the performance of detection and suppression systems. This impact has not generally been quantified for those higher flow rates by experiment, and current modeling tools, while having the ability, have not been validated for the specific application of detection performance in high air flow rate conditions. Phase I work [1] conducted as part of this effort identified a number of computer models capable of modeling these facilities. The task also identified four critical knowledge gaps limiting the use of those models for the response of detection devices in high airflow IT/Telecom environments. A phase II effort was developed to address three of the four gaps: specification of appropriate smoke sources, evaluation of detector response for those sources in high airflow environments, and model validation with large-scale, integral test data. The complete Phase II effort of this project consists of several tasks, including: Task 1 Identification of representative fire sources Task 2 Characterization of smoke and heat output from representative fire sources Task 3 Characterization of the response of smoke detection devices Task 4 Validation of Task 2 and 3 data with full scale testing and modeling Task 5 Modeling of common IT/Telecom facility designs This report presents model verification and validation (V&V) portion of Task 4 in which tests were conducted in a full scale mockup using the smoke sources developed in Tasks 2 and 3 [1]. 2. OBJECTIVE The primary objectives of the work described in this report were to: 1. Develop a computer model simulation identical to the full scale mock facility and simulate fire detection testing. 2. Compare the results from Task 4 and provide a detailed assessment of the evaluation of the model. The evaluation assesses whether the CFD model s prediction in the following areas is within experimental accuracy: air velocity, temperature, air flow direction and dynamics (including dilution of smoke), smoke concentration, and detector activation. Successful validation efforts should provide an 80% confidence level or better (test data and CFD modeling results should be within +/ 20% accuracy of one another) for each critical model parameter. Even if a model is considered validated, all limitations of the model should be described in the report. 3. FULL SCALE MOCK FACILITY 3.1. Mockup Description A full scale mockup was constructed at the FM Global research campus and used in a short test program that utilized the smoke sources developed and characterized in Tasks 2 and 3. Full details of the facility and the test program can be found in the FM Global test report [2]. The mockup was 16 ft wide by 24 ft long by 16 ft high. An external view of the room is shown in Figure 1, Figure 2 shows a plan view of the data floor, and Figure 3 shows a plan view of the ceiling plenum. The room contained a 3 ft subfloor and a 3 ft ceiling plenum. Air enters through the east side of the subfloor, rises through a pair of 2 ft x 17.5 ft cold aisles on the north and south sides of the room (orange areas in Figure 2), moves through a row of server cabinets next to each cold aisle (cabinet outlines seen next to cold aisle in Figure 2), rises through a series of ceiling grates into the plenum (blue in Figure 3), and then exits

13 Validation of Modeling Tools for Detection Design 1DTG PAGE 2 the west wall of the ceiling plenum via a duct. The cold aisle uses a 54 % open area grated panel manufactured by Maxcess Aluminum Floors. The ceiling uses a McMaster-Carr TP T Bar perforated filter grille panel with a 56 % open area. Figure 1 External view of east face of full scale mockup Figure 2 Floor plan of room

14 Validation of Modeling Tools for Detection Design 1DTG PAGE 3 Figure 3 Plan view of ceiling plenum The server cabinets are six feet tall and have 56 % open perforated metal doors on the front and back sides (see Figure 4). A 36 % open wire mesh (40 mesh 0.01 in diameter screen) was placed one inch from the hot side doors of each cabinet to add additional pressure drop through the cabinets. Figure 4 Photograph of hot aisle

15 Validation of Modeling Tools for Detection Design 1DTG PAGE 4 The test room was instrumented at the ceiling of the subfloor, the ceiling of the server area, and the ceiling of the plenum space. Each area had three locations instrumented with a bi-flow probe, a thermocouple, a FAAST detector, a VIEW detector, and a True Alarm detector. The three locations can be seen in Figure 3 (the northeast corner, center, and southwest corner). Additional instrumentation consisted of differential pressure measurements made between the ambient and the subfloor, cold aisle, hot aisle, and ceiling plenum and an array of pitot tubes located in the exhaust duct prior to the exhaust fan. To prevent smoke being discharged from the room from being entrained into the supply air the subfloor was extend from the room at an angle to the outside of the high bay area where the mock up was located. This is shown in Figure 5 below. This extension was added after flow testing was performed Testing Summary Air Flow Testing Figure 5 Subfloor extension to ensure clean supply air Two sets of tests were performed to measure air flows within the test mock up. Air flows were measured using a sonic anemometer capable of simultaneous measurement of all three components of velocity. One set of tests was performed with the fan at 100 % speed (265 ACH) and the second set with the fan at 37 % speed (78 ACH). A total of 55 measurements were made at 100 % speed, and 51 measurements were made at 37 % speed. Recent research suggests that sonic anemometers can have intrinsic velocity errors on the order of 10 % depending upon the wind direction angle with respect to the orientation of the anemometer due to shadowing and other effects of the support armature [2] Smoke Source Test Summary A total of ten tests were performed using smoke sources characterized in phases 2 and 3. The eight tests were: 1. Propylene fire in the subfloor with 37 % (78 ACH) fan speed 2. Propylene fire in the hot aisle with 37 % (78 ACH) fan speed

16 Validation of Modeling Tools for Detection Design 1DTG PAGE 5 3. Propylene fire in the subfloor with 100 % (265 ACH) fan speed 4. Propylene fire in the hot aisle with 100 % (265 ACH) fan speed 5. Circuit board source in a cabinet with 37 % (78 ACH) fan speed 6. Circuit board source in a cabinet with 100 % (265 ACH) fan speed 7. Polyethylene foam blocks in the cold aisle with 37 % (78 ACH) fan speed 8. Polyethylene foam blocks in the cold aisle with 100 % (265 ACH) fan speed 9. Two cable bundles in the subfloor with 37 % (78 ACH) fan speed. 10. Two cable bundles in the subfloor with 100 % (265 ACH) fan speed. Tests for the propylene, cable, and circuit board sources followed the procedure used in the source characterization effort (although two rather than one cable bundle was used). The polyethylene foam block source was modified from the original source procedures. An initial test following the source characterization test setup did not result in any detection; therefore, a larger source was required. Figure 6 shows the original source on the left, which was a single 2 in. x 16 in. x 6 in. block of foam with two 1 in. holes where alcohol was placed for ignition. On the right side of Figure 6 is the as tested source which stacked two pieces of foam together and used a second stack adjacent to the first. Figure 6 Comparison of initial specification to actual source for the polyethylene foam blocks 4. FDS MODEL OF FACILITY FDS version 6 [4-8] was used to model the full-scale mockup tests. To avoid potential model biases due to boundary conditions, the full exhaust duct and supply air extension were included in the model. The model was constructed with a uniform grid resolution of 3 inches. Given the high airflow rates, the relatively short test durations, and the relatively small heat sources being used, room surfaces were modeled as cold wall boundaries. The FDS model included the supply air plenum; the room (subfloor, main floor, and ceiling plenum); all perforated materials, cabinets, cable trays, and screens; and the exhaust duct including the reduction from the room outlet to the fan duct, the flow straightener, and the wire mesh filter. Two versions of the model were created: one with and one without the supply air plenum. The model without the supply air plenum used four computational grids: one for the room, one for a small region outside the supply inlet, and two for the exhaust duct. A total of 480,000 grid cells were used. The model with the supply air plenum used five computational grids: one for the room, two for the supply air plenum, and two for the exhaust duct. A total of 610,000 grid cells were used. The following three sections discuss modeling of the perforated tiles and screens, the obstructions modeled, specification of the exhaust flow rate, and the implementation of the smoke sources.

17 Validation of Modeling Tools for Detection Design 1DTG PAGE Perforated Tiles/Screens A new feature in FDS allows for the use of Lagrangian particles to model the pressure drop across a wire mesh screen. This feature was added to benefit user s modeling fires in residential structures where screened windows and doors are commonplace. This feature uses a modified version of a general porous media model to account for the pressure drop across a screen. The pressure drop is given as dp µ Y = u + ρ dx K K u 2 ; K = Y = 0.043φ φ 1.6 Eq. (1) where dp is the pressure drop, dx is the dimension normal to the screen (the screen thickness), ρ is the density, u is the normal velocity to the screen, µ is the viscosity, and φ is the screen porosity (free area/total area). Inputs to the model are the screen wire diameter and the screen porosity. For the wire mesh screen placed in the server cabinets and before the exhaust fan, the model used the actual physical parameters of the screen material. The floor tiles, ceiling tiles, and cabinet doors required modification of the approach. These items were not wire mesh screens; however, they were a thin porous media that imposed a pressure drop to the flow. For these items a simple test case was created. The test case was a 2 ft x 2 ft cross-section channel with a 3 in. grid (same as used in the model of the mockup). A screen was defined across the channel and given the porosity of the floor tile, ceiling tile, or cabinet door as appropriate. Using the volumetric flow for the 37 % (78 ACH) and 100 % (265 ACH) fan speeds and the total area of each of the components, an average velocity across each can be determined. For example at 100 % fan speed, the 27,136 cfm output of the fan will result in a 388 fpm velocity on average through each of the two 2 ft x 17.5 ft cold aisles. The test case was run for both fan speeds for the cold aisle tiles, the hot aisle tiles, and the cabinet doors. The input for wire diameter (dx) component and fan speed and the screen diameter was adjusted to obtain the correct pressure drop. The pressure drop as a function of flow speed for the ceiling tiles and floor tiles was taken from the manufacturers datasheets. The pressure drop for the cabinets was assumed to be the same as the ceiling tiles. This assumption was based on the similar porosity and hole size. The result of this exercise was two sets of screen drag inputs for the two fan speeds which were then used in the FDS simulations Flow Obstructions To avoid any potential errors resulting from limitations of flow vectors for thin obstructions, all walls, floors, ceilings, and other objects in the room were specified as being a minimum of one grid cell thick. All substantial structures in the room were included in the model including: the cabinets, the floor and ceiling tiles, the cable trays above each row of cabinets, the studs sitting in the opening for the supply and exhaust from the room, and the exhaust duct including the flow straightener (which was implemented as an array of thin obstructions). Figure 7 shows the FDS model with and without the supply air plenum. Figure 8 shows the same figures with the Lagrangian particles imposing the screen drag made visible.

18 Validation of Modeling Tools for Detection Design 1DTG PAGE 7 Figure 7 FDS Geometry model with (right) and without (left) supply air plenum 4.3. Exhaust Flow Rate Figure 8 FDS geometry models showing particle planes for screens The exhaust duct is shown below in Figure 9. Flow measurements were taken using five pitot tubes in a cross section near the midpoint of the straight duct section. The pitot tubes were arranged to measure the diagonal and one major axis of the duct cross section. FM determine the total exhaust flow rate by fitting a square duct velocity profile to the measured data. Figure 10 shows the reflected probes and the integration areas used. This process resulted in total flow rates of 78 and 265 ACH respectively for the 37 % and 100 % fan speeds. The remainder of this report will identify the tests using ACH.

19 Validation of Modeling Tools for Detection Design 1DTG PAGE 8 Figure 9 Exhaust duct and flow measurement locations Figure 10 FM Global determined velocity profiles for 37 % fan speed (left) and 100 % fan speed (right) Pitot probe data was provided for the ten smoke source tests but not the airflow tests. For each test, the average pitot probe data was compared to the average data from Figure 10. This provides an estimate on the error in the total flow rate. These errors for all tests are shown in Table 1 below. As can be seen, the average flow rate error over all tests is 2 % with the maximum error of 3.9 %. These are similar values to the RMS error observed in the average pitot probe data. Therefore, the overall average flow rate was used in the modeling. The lack of the supply air plenum during the flow tests means that there was likely a slightly larger flow for the air flow tests since the fan did not have to overcome the additional pressure losses due to the supply air plenum.

20 Validation of Modeling Tools for Detection Design 1DTG PAGE 9 Table 1 Deviation from average values for all smoke tests Fuel Location 78 ACH 264 ACH (cfm) (cfm) Propane Subfloor 0.2 % -1.2 % Propane Hot Aisle -3.9 % 0.2 % Cable Subfloor 3.6 % -1.6 % Circuit Board Cabinet 3.2 % 2.5 % Polyethylene Cold Aisle -2.7 % -1.4 % 4.4. Smoke Sources Propylene Source The propylene source is the simplest of the smoke sources to model. Propylene is a predefined species within FDS, the flow of propylene was measured (and remained constant) during the test, and its combustion will have a fixed soot yield. A burner obstruction was placed at the fire location, and the propylene fire was defined as a simple burner in FDS using the species propylene with and the FM measured fuel flow rate (9.4 slpm) and a 9.5 % soot yield (determined in the Task 2/3 study) Circuit Board Source The circuit board source was modeled solely as a mass flow of smoke. This source did not involve a flaming fire and the heat output of the heater used is small (maximum of 2 kw) in comparison to the total airflow. In the characterization tests, two circuit boards were placed in a vertical orientation with the heater at the bottom between the boards as shown on the left in Figure 11. Air was then drawn through the channel between the boards by the Hughes detection characterization test apparatus. In the full scale tests, the boards were placed in a horizontal orientation in the server cabinet with the heater on the cold aisle side of the boards as show in the right of Figure 11 (view inside of cabinet looking down with the cold aisle at the top). Post-test examination of the circuit boards during the Task 2/3 testing showed a small amount of vertical spread away from the heater. This spread may have been aided by a buoyancy effect in addition to the airflow. The horizontal orientation used in the FM tests would not have allowed for any buoyancy effect. This may have impacted the smoke production rate; however, any impact was anticipated to most likely be later in the test as the initial decomposition would have occurred in the region in contact with the heater where the highest heat transfer was. Figure 11 Task 2 and 3 orientation (left) vs. cabinet test orientation (right)

21 Validation of Modeling Tools for Detection Design 1DTG PAGE 10 Two smoke source curves were developed in the Task 2/3 characterization. The curves were for smoke source air flow rates of 100 fpm and 500 fpm. Flow within the cabinets was not measured; however, it can be estimated. The flow area of the two rows of cabinets is a factor of three larger than the cold aisle. Therefore, for the two fan speeds, the average flow in the cabinets would have been on the order of 40 and 130 fpm. Given those flow rates, the 100 fpm circuit board smoke source curve was used in the FDS model for both fan speeds Polyethylene Foam Source As discussed in Section 3.2.2, the polyethylene foam source used during testing was not the same as the source used during characterization. During the characterization effort it was observed that the foam fire spread outward from the alcohol ignition points, melted the foam into a pool that burned in the aluminum pan, and then decayed as the fuel was consumed. This burning behavior was applied to the new source. The spread and growth outward from the alcohol ignition point was assumed to be the same. The decay phase of the pool burning out was assumed to be the same. Since the pan area was the same, it was assumed that the burning duration would increase to account for the extra foam mass. This was applied to both the smoke production and the heat release rate. The result of this for smoke production is shown in Figure 12. Heat release was processed in a similar manner; however, the total heat release rate was reduced by 30 % to just the convective portion (since surface heating is not the significant issue, the computational expense of radiation was omitted). In the model, the polyethylene blocks were defined as two obstructions with the dimensions of the source used in the test (e.g. each was 16 in. x 6 in. x 4 in.). The heat release and smoke production curves were defined as fixed mass flux and heat flux surfaces to the sides and top of the obstructions. Smoke Production Rate (mg/s) Time (min) Original Modified 5. AIRFLOW TEST VALIDATION Figure 12 Original and modified smoke production curves Two sets of tests were performed to characterize airflows at the 78 ACH and 265 ACH flow rates. A 3D sonic anemometer was placed at various locations in the test room to measure the velocity. Moving the anemometer required opening the room door to allow a member of the test staff inside the room to move the probe. After each breach of the room boundary, a short period was allowed to elapse before collecting data at the new location. The coordinate system for the airflow tests is shown in Figure 13.

22 Validation of Modeling Tools for Detection Design 1DTG PAGE 11 The x-axis origin is centered on the hot aisle and spans the short width of the room with positive-x towards the viewing window. The y-axis origin is centered on the hot aisle and spans the long width of the room with positive-y towards the doorway. The z-axis has its origin on the floor (either the test building floor for the subfloor point or the data center floor for all other points) with positive-z towards the ceiling. The u, v, and w components of velocity correspond to the x, y, and z axes Pressure Measurements Figure 13 Coordinate system for flow measurements Differential pressure was measured between the subfloor and the cold aisle and between the hot aisle and the ceiling plenum. Pressure drop is a function of the square of the flow rate. Given the estimated flow rate uncertainty of 5 % plus an assumed 8 % or 1 % error in the pressure measurement respectively for the 78 ACH and 265 ACH flow rates, results in respective two sigma expanded error estimates of 19 % and 10 % for the differential pressure. A comparison of predicted and measured pressure drops is shown in Table 2. For the 78 ACH test, FDS has prediction errors of 11 % and 18 %. For the 265 ACH tests, FDS has prediction errors for the two locations of 11 % and 6 %. Table 2 Predicted and measured differential pressure Test Subfloor to Cold Aisle (Pa) Hot Aisle to Ceiling Plenum (Pa) Test FDS Test FDS 78 ACH ACH Velocity Measurements Assessment of Measured Velocity Error From the point of view of modeling the FM datacenter mockup, the measurement of velocity using the sonic anemometer is prone to three known errors: the anemometer, the total fan flow rate, and placement of the anemometer. The sonic anemometer error is primarily that the size of the anemometer can slightly change the flow being measured. The manufacturer estimates this error as 6 % of the measurement. The pitot probe error is estimated by the manufacturer as 5 %. Pitot error

23 Validation of Modeling Tools for Detection Design 1DTG PAGE 12 represents an uncertainty in the fan flow rate used as an input to FDS. For small error in fan flow one would anticipate a near-linear relationship between fan flow and the velocity at any point in the datacenter. Placement of the anemometer is also a source of error. The FDS measurement will be precisely located, this is not the case with the anemometer which will have some error in its position. With a small position error, one would expect on average for there to be relatively small errors in velocity. During the flow mapping, a number of repeat measurements were made in various locations within the FM mockup. By determining the average error for each repeated measurement, an estimate can be made of the positioning error. Over the two fan speeds there were 16 combinations of fan speed and location with repeat measurements; some of which had more than two measurements made. Combining all these errors results in a positioning error of approximately m/s in total velocity. Figure 14 shows all the repeat measurements plotted with their RMS values as error bars. The dotted lines in the plot represent the error associated with propagating the positioning error (fixed at m/s) with the uncertainty in the anemometer and pitot tube measurement errors (percentage of the total velocity). As can be seen all but a few points lie inside the error bars. The RMS values for most points are relatively large in comparison to the total velocity. This is an indication that, in much of the mockup, the flow field is highly turbulent. 2.5 Repeat velocity (m/s) Measured velocity (m/s) Figure 14 Repeat flow measurements. Dotted lines are the measurement error and errors bars the point-by-point RMS values ACH Fan Test Figure 15 and Figure 16 show time-averaged vector plots of the FDS predicted flow field for the 265 ACH fan test. 78 ACH results are similar. Qualitatively, the predicted flow field is as expected. In the cold aisle, the mostly horizontal subfloor flow to the back of the room turns into near vertical flow in the cold aisle enclosure. There is a recirculation regions at the ends of the cold aisle enclosure. In the hot aisle, the flow is that of two equal, opposing jets that collide. In the absence of an enclosure, the expected flow from equal, horizontally opposed jets is an equal, vertically radial outflow. The presence of the room and the ceiling plenum openings will obviously modify this. However, it is still expected that there will be a downward flow region near the floor with most of the hot aisle having a predominantly upward flow towards the ceiling where the flow is extracted. Additionally, since the cabinets are towards the rear wall of the room and 25 % of the open ceiling area is towards the front of the room, there was

24 Validation of Modeling Tools for Detection Design 1DTG PAGE 13 an expectation of a net flow towards the front of the room in the hot aisle. This can also be observed in the figure. Flows in the ceiling plenum and subfloor are also as expected with a strong flow in the direction towards the exhaust vent in the ceiling plenum and away from the inlet in the subfloor. Figure 15 FDS results for 265 ACH test for cold aisle (left) and hot aisle (right) Figure 16 FDS results for 265 ACH test for ceiling plenum (top), hot aisle (left), subfloor (right) The measurement locations and measured velocity components for the 265 ACH test are shown in Table 3. A few observations are made on the measurements. Anemometer orientation was not provided to be able to assess which flow component might have shadowing errors.

25 Validation of Modeling Tools for Detection Design 1DTG PAGE 14 The overall extent of the anemometer armature (0.25 to in. tubing) are approximately 1.5 ft x 1.5 ft x 0.6 ft. As the cold aisle width is 2 ft, some perturbation of the local flow is expected but it is unclear what that extent is. Measurements were made in only one cold aisle. There is likely some asymmetry in the building construction; however, this cannot be easily evaluated with measurements only in one cold aisle. All measurement locations along the centerline of the hot aisle show negative w-velocities (vertical) including measurements made near the tops of the cabinets. Ultimately, flow from the cabinets must turn vertically (positive-w) to exhaust into the ceiling plenum. In a perfectly symmetric facility, one would expect equal but opposite horizontal flows from the cabinets to meet along the centerline. At some elevation from the floor to near the mid-height of the cabinets, flows would be expected to turn down to the floor and turn back up near the room door. Above that point flows would be expected to be vertical on average. The fact that the measured velocities do not show this could indicate a sign error from the armature orientation or some unknown asymmetry. Measurements were made at three elevations along the centerline of the hot aisle. In a perfectly symmetric facility, zero average flow would be expected along the centerline. At each height there is a slight bias in flow to negative u-velocities, this could be an indication of a slight asymmetry. Two of the measurements in the hot aisle near the cabinets were repeated on the opposite side of the room centerline. These are in bold italic in the table. The two locations differ in u-velocity by -8 % and +28 %. V-velocity and w-velocity values differ by larger amounts; however, the velocities measured are relatively small. One measurement in the hot aisle near the cabinets was repeated in the same location. This is shaded. There is a 30 % difference in the u-velocity for a relatively large value (approximately 200 fpm). One measurement along the ceiling were repeated in the same location. The data are shown shaded. Note not all repeat measurements shown in Figure 14 were provided as u, v, w component velocities.

26 Validation of Modeling Tools for Detection Design 1DTG PAGE 15 Table 3 Measurement locations, velocity components, and velocity RMS for 265 ACH test x (in) y (in) z (in) u (fpm) v (fpm) w (fpm) u rms (fpm) v rms (fpm) w rms (fpm) Subfloor Cold Aisle Hot Aisle Next to Server Cabinets

27 Validation of Modeling Tools for Detection Design 1DTG PAGE 16 x (in) y (in) z (in) u (fpm) v (fpm) w (fpm) u rms (fpm) v rms (fpm) w rms (fpm) Hot Aisle Centerline Ceiling Ceiling Plenum Figure 17 through Figure 22 show scatter plots for the subfloor, hot aisle centerline, hot aisle near cabinets, cold aisle, and ceiling measurement locations compared to the model predicted velocities. The following observations are made: The predominant flow direction in regions with strong flows is correctly predicted. This includes the v-velocity for the subfloor, the u-velocity near the cabinets, and the w-velocity at the floor of the cold aisle and the w-velocity at the two subfloor points beneath the cold aisle openings. The ceiling and subfloor w-velocity is predicted well with the exception of one ceiling point where FDS is greatly over predicting the velocity. This is due to the 3 in. grid. The measurement location is at the edge of a perforated ceiling tile. The actual tiles have a 1 in. border that is not perforated. The FDS grid resolution is not fine enough to resolve the border; therefore, in the FDS simulation the measurement point lies beneath the opening rather than beneath the solid border. The room centerline w-velocity results show that FDS predicts positive flows at many points as expected given that flows exhaust through the ceiling. This suggests a sign error in the measured data or possibly an indication of asymmetry in the room. Figure 23 shows two

28 Validation of Modeling Tools for Detection Design 1DTG PAGE 17 horizontal slices of vertical velocity across the bottom third and upper third of the cabinets. The color bar has been set to accentuate the positive vs. negative vertical velocity. The FDS prediction shows what is expected from physical observation. There are regions of downward flow along the center near the floor; however, near the tops of the cabinets the flow is upwards. If the signs are reversed, then the majority of the vertical components would lie within the estimated uncertainty. Areas without a strong flow direction, for example the hot aisle centerline, show a larger degree of scatter; however, the scatter is generally within the measurement uncertainty. The cold aisle shows a good match for the u-velocity and w-velocity. There is somewhat more scatter for the v-velocity. Figure 24 shows FDS predicted velocity vectors in the cold aisle and the vector resulting from the y and z components of the measured value (note these are not scaled to overall speed). FDS shows a regions of recirculation at the upper corners of the enclosure that do not appear to be seen in the data. The v-velocity in the ceiling plenum is slightly over predicted by FDS (more negative). Most points lie within the uncertainty but a small number show higher velocities towards the outlet. The u-velocity and w-velocities are well predicted. Predicted u-velocity (m/s) Measured u-velocity (m/s) Predicted v-velocity (m/s) Measured v-velocity (m/s) Predicted w-velocity (m/s) Measured w-velocity (m/s) Figure 17 Scatterplots for 265 ACH subfloor measurement locations Predicted u-velocity (m/s) Predicted v-velocity (m/s) Predicted w-velocity (m/s) Measured u-velocity (m/s) Measured v-velocity (m/s) Measured w-velocity (m/s) Figure 18 Scatterplots for 265 ACH cold aisle measurement locations

29 Validation of Modeling Tools for Detection Design 1DTG PAGE Predicted u-velocity (m/s) Predicted v-velocity (m/s) Predicted w-velocity (m/s) Measured u-velocity (m/s) Measured v-velocity (m/s) Measured w-velocity (m/s) Figure 19 Scatterplots for 265 ACH hot aisle near cabinet measurement locations Predicted u-velocity (m/s) Predicted v-velocity (m/s) Predicted w-velocity (m/s) Measured u-velocity (m/s) Measured v-velocity (m/s) Measured w-velocity (m/s) Figure 20 Scatterplots for 265 ACH hot aisle centerline measurement locations Predicted u-velocity (m/s) Measured u-velocity (m/s) Predicted v-velocity (m/s) Measured v-velocity (m/s) Predicted w-velocity (m/s) Measured w-velocity (m/s) Figure 21 Scatterplots for 265 ACH ceiling measurement locations

30 Validation of Modeling Tools for Detection Design 1DTG PAGE 19 Predicted u-velocity (m/s) Measured u-velocity (m/s) Predicted v-velocity (m/s) Measured v-velocity (m/s) Predicted w-velocity (m/s) Measured w-velocity (m/s) Figure 22 Scatterplots for 265 ACH ceiling plenum measurement locations Figure ACH vertical flow in the hot aisle at two elevations

31 Validation of Modeling Tools for Detection Design 1DTG PAGE 20 Figure ACH flow vectors in the cold aisle Figure 25 shows scatterplots for each velocity component over all measurement locations. Figure 26 shows scatterplots of total velocity for all measurement locations and all components over all locations. Predicted u-velocity (m/s) Measured u-velocity (m/s) Predicted v-velocity (m/s) Measured v-velocity (m/s) Predicted w-velocity (m/s) Measured w-velocity (m/s) Figure 25 Scatterplots for all 265 ACH measurement locations Predicted velocity (m/s) Measured velocity (m/s) Predicted velocity (m/s) Measured velocity (m/s) Figure 26 Scatterplots for 265 ACH total velocity (left) and components for all measurement locations (right)

32 Validation of Modeling Tools for Detection Design 1DTG PAGE ACH Test Table 4 below shows the measurement locations and velocity components for the 78 ACH test. The following observations are made on the data in the table: Two repeat measurements were made for the ceiling location only (shaded in table). While the flows were low, they vary significantly both in magnitude and direction from -300 % to 280 %. Based upon the pitot probe data, if there are no significant changes in the overall flow pattern, a factor of 3.1 difference would be expected. In regions with strong directional flow (v-velocity in the subfloor near the inlet and w-velocity in the cold aisle at the floor) an average factor of 3.4 is seen. Other flow components and other flow locations show a wide range of variation with factors from -6 to 45 suggesting that away from the flow restrictions of the grates, that there has been some shift in the flow field. The same general observations on the 265 ACH measurements apply to the 78 ACH measurements. Figure 27 through Figure 34 show scatterplots for the 78 ACH measurements Predicted u-velocity (m/s) Predicted v-velocity (m/s) Predicted w-velocity (m/s) Measured u-velocity (m/s) Measured v-velocity (m/s) Measured w-velocity (m/s) Figure 27 Scatterplots for 78 ACH subfloor measurement locations Predicted u-velocity (m/s) Predicted v-velocity (m/s) Predicted w-velocity (m/s) Measured u-velocity (m/s) Measured v-velocity (m/s) Measured w-velocity (m/s) Figure 28 Scatterplots for 78 ACH cold aisle measurement locations

33 Validation of Modeling Tools for Detection Design 1DTG PAGE 22 Table 4 Measurement locations, velocity components, and velocity RMS for 78 ACH test x (in) y (in) z (in) u (fpm) v (fpm) w (fpm) u rms (fpm) v rms (fpm) w rms (fpm) Subfloor Cold Aisle Hot Aisle Next to Server Cabinets Hot Aisle Centerline

34 Validation of Modeling Tools for Detection Design 1DTG PAGE 23 x (in) y (in) z (in) u (fpm) v (fpm) w (fpm) u rms (fpm) v rms (fpm) w rms (fpm) Ceiling Ceiling Plenum Predicted u-velocity (m/s) Predicted v-velocity (m/s) Predicted w-velocity (m/s) Measured u-velocity (m/s) Measured v-velocity (m/s) Measured w-velocity (m/s) Figure 29 Scatterplots for 78 ACH hot aisle near cabinet measurement locations

35 Validation of Modeling Tools for Detection Design 1DTG PAGE Predicted u-velocity (m/s) Predicted v-velocity (m/s) Predicted w-velocity (m/s) Measured u-velocity (m/s) Measured v-velocity (m/s) Measured w-velocity (m/s) Figure 30 Scatterplots for 78 ACH hot aisle centerline measurement locations Predicted u-velocity (m/s) Predicted v-velocity (m/s) Predicted w-velocity (m/s) Measured u-velocity (m/s) Measured v-velocity (m/s) Measured w-velocity (m/s) Figure 31 Scatterplots for 78 ACH ceiling measurement locations Predicted u-velocity (m/s) Predicted v-velocity (m/s) Predicted w-velocity (m/s) Measured u-velocity (m/s) Measured v-velocity (m/s) Measured w-velocity (m/s) Figure 32 Scatterplots for 78 ACH ceiling plenum measurement locations

36 Validation of Modeling Tools for Detection Design 1DTG PAGE 25 Predicted u-velocity (m/s) Measured u-velocity (m/s) Predicted v-velocity (m/s) Measured v-velocity (m/s) Predicted w-velocity (m/s) Measured w-velocity (m/s) Figure 33 Scatterplots for all 78 ACH measurement locations Predicted velocity (m/s) Measured velocity (m/s) Predicted velocity (m/s) Measured velocity (m/s) Figure 34 Scatterplots for 78 ACH total velocity (left) and components for all measurement locations (right) Grid Study The 78 ACH simulation was also run with a grid resolution of 2 inches and 4 inches. Results are shown in Figure 35 below for all three grid resolutions. There are slight differences in results with the 2 inch grid having the best performance and the 4 inch grid having the worst. However, these differences are only a couple of percentage points in overall performance.

37 Validation of Modeling Tools for Detection Design 1DTG PAGE Predicted velocity (m/s) Predicted velocity (m/s) Measured velocity (m/s) Measured velocity (m/s) Predicted velocity (m/s) Predicted velocity (m/s) Measured velocity (m/s) Measured velocity (m/s) Predicted velocity (m/s) Predicted velocity (m/s) Measured velocity (m/s) Measured velocity (m/s) Figure 35 Scatterplots for 78 ACH total velocity (left) and components for all measurement locations (right) for 2 inch (top), 3 inch (middle) and 4 inch (bottom) grid resolutions Conclusions of Velocity Comparison In locations with a predominant flow direction, FDS correctly predicts the overall flow (v-velocity and w- velocity in subfloor, floor of cold aisle, u-velocity for cabinets, v-velocity and w-velocity for ceiling). In other locations FDS does correctly predict the overall trends seen in the measured data with the exception of the w-velocity in the center of the hot aisle. For other velocity components a large degree of scatter is seen; however, repeat velocity measurements also show a large degree of scatter in the non-dominant components. Additionally, as observed, the measured data for the hot aisle centerline shows unexpected flow patterns in the form of negative flow everywhere in the upper center of the hot aisle. Overall FDS predicts 80 % of the total velocity measurements and 80 % of the individual velocity components within the experimental uncertainty.

38 Validation of Modeling Tools for Detection Design 1DTG PAGE Full Scale Smoke Source Tests All ten of the full scale smoke source tests conducted by FM were simulated using FDS. FDS predictions of velocity and smoke density at each detector location was post processed using the detection correlations developed in Task 2/3. As noted in the Task 2/3 report, the correlations are unique to the specific detectors and specific smoke sources tested. They cannot be presumed applicable to other smoke sources or smoke detectors (even ones using similar technology or different models from the same manufacturer). However, correlations could be developed for any given specific detector or smoke source by following the approach documented in the task 2/3 report. Plots of all FDS predicted detection responses for the VIEW, TrueAlarm, and FAAST detectors can be found in Appendix A Uncertainty Smoke Source Propylene The propylene smoke source represents the source with the least soot production uncertainty. Determining the actual soot production rate during the test is accomplished simply by applying a soot yield to the fuel mass flow rate as discussed in Section Both of these parameters have error associated with them. Based on manufacturer data for digital mass flow meters, there is an expectation of a 10 % error in the propylene flow rate at the low flow rates being used. Additionally, if it is presumed a similar error exists in the mass flow rate used to derive the 9.5 % soot yield reported by Tewarson, then there is a total error in the soot production rate of 15 % Circuit Board Figure 27 in the Task 2/3 report shows the derived smoke production curve for each circuit board test along with the average smoke production curve. As seen, there is a considerable variance in the test data. The time to peak production ranges from 4 to 5 minutes, the peak production rate varies from 0.9 to 4.4 mg of smoke/s, and the length of the peak production period varies from 1 to 5 minutes. With an average peak production of 3.4 mg/s the error in smoke production is 50 % in the assumed production curve Polyethylene Foam Figure 40 in the Task 2/3 report shows the derived smoke production curve for each polyethylene foam test along with the average smoke production curve. As seen, there is considerable variance in the test data in terms of the time to peak smoke production and the duration of smoke production; however, the peak values are all fairly similar. Since the data analysis in Section 6.6 uses the period of peak production, the production rate is not a significant source of uncertainty based on test measurements (estimated as 2 %). However, the burning rate used in the FDS model was based on observations made during the characterization testing. Observations during the FM testing indicate that the polyethylene foam did not melt into a pool during burning (this may be due to the aluminum pan not encompassing as large a fraction of the side height of the blocks). It is likely, therefore, that the FM peak burning rate and potentially the soot yield varied from the characterization test in Task 2/3. However, it is not known what that variance is Cables Figure 30 in the Task 2/3 report shows the derived smoke production curve for each cable fire test along with the average smoke production curve. If the data for 0 fpm (closest tested to speed to the

39 Validation of Modeling Tools for Detection Design 1DTG PAGE 28 velocities in the subfloor location) and 600 C is examined, it can be seen that there is a fairly constant smoke production. The error in the average smoke production rate is estimated as 8 % Aspirated Laser FM Global used an aspirated laser extinction measurement at three locations in the test facility. Smoke densities are calculated using the extinction measurement and an assumed extinction coefficient. FM Global provides a curve of the error for the extinction measurement based on voltage drift. Error in the extinction coefficient for the propylene and other smoke sources is estimated at 10 % based upon data in the FM test report. From a modeling perspective there are additional components of error related to the total airflow in the facility and to the input for the smoke production. The only two sources to have a measurable extinction were the propylene fires and the cable smoke source. These smoke sources have similar errors in their smoke production rate. Accounting for the airflow and smoke sources as components of the extinction measurement results in the total expanded error shown in Figure 36. The figure indicates that at very low smoke densities the error is dominated by that of the measurement device, but as smoke density increase and the accuracy of the device becomes high the error represents the combined error of the smoke source, extinction coefficient, and total airflow. Propylene Cable Error (%) Smoke Density (mg/m 3 ) Figure 36 Error for the aspirated laser extinction measurement Detector Response Curve Uncertainty Propylene The propylene smoke source has one of the larger datasets of the four smoke sources tested in Task 2/3. Examining Table 6 in the Task 2/3 report, it was observed that there are a number of table entries which are essentially replicate tests. For example, the third entry for Test 3 at 349 fpm with a 0.7 micron filter and smoke density of 16.3 mg/m 3 is essentially identical to Test 4 at 350 fpm with a 0.7 micron filter and smoke density 15.5 mg/m 3. Tests with the same airflow rate, filter size, and similar smoke concentrations were used to assess the uncertainty in the response curve. For each pair of identical tests the relative difference in smoke detection response was computed for each detector. It was assumed the response scaled linearly to smoke concentration and one of each pair was scaled to represent the same smoke density. Thirteen test points met the comparison criteria. The average response difference over these tests were: 22 % for the VIEW detector (1.1 % to 54 %), 16 % for the FAAST detector (0.3 % to 35 %), and 19 % for the True Alarm detector (4.8% to 52 %).

40 Validation of Modeling Tools for Detection Design 1DTG PAGE Circuit Board Following a similar approach to the propylene tests, nine sets of data pairs were identified in the circuit board smoke detection response tests. The average response difference over these tests were: 56 % for the VIEW detector (0.0 % to 110 %), 65 % for the FAAST detector (5.1 % to 110 %), and 48 % for the True Alarm detector (48 % to 96 %). These are approximately a factor of three higher than the propylene tests Polyethylene Foam Following a similar approach to the propylene tests, seven sets of data pairs were identified in the polyethylene foam smoke detection response tests. The average response difference over these tests were: 32 % for the VIEW detector (5.6 % to 81 %), 39 % for the FAAST detector (16 % to 80 %), and 20 % for the True Alarm detector (0.3 % to 48 %). The FAAST response difference is a factor of two higher than the propylene, but the TrueAlarm and VIEW detectors have a similar response difference Cables Following a similar approach to the propylene tests, fifteen sets of data pairs were identified in the polyethylene foam smoke detection response tests. The average response difference over these tests were: 51 % for the VIEW detector (7.3 % to 140 %), 43 % for the FAAST detector (5 % to 140 %), and 44 % for the True Alarm detector (3.9 % to 120 %). The FAAST response difference is a factor of two higher than the propylene, but the TrueAlarm and VIEW detectors have a similar response difference Total Uncertainty Propagation of errors was used to combine the smoke source uncertainty with the estimated detection response curve uncertainty. These uncertainties at 95 % confidence are provided in the table headers for each test in Section Notes on FM Data and FDS Results Comparisons of FDS results to the measured data were made by comparing the average FDS response to average measured response over the steady state portion of each test. The steady portion was identified by examination of the data. Data provided by FM for the VIEW detector consisted only of the specific alarm threshold the detector was at. Therefore, as a function of time the data is either no measurement, 0.02, 0.03, 0.1, 0.5, 1, 1.5, or 2 %/ft. Therefore, if the VIEW detector was seeing 0.2 %/ft, the response data provided would only indicate 0.1 %. This adds an additional level of uncertainty to the VIEW data. For example, if the view had 50 pts at 0.1 %/ft and 10 pts at 0.5 %/ft over the period being averaged, the average result would be reported as 0.17 %/ft. Since 0.1 %/ft means > 0.1 %/ft and less than 0.5 %/ft, the actual average value could be higher than 0.25 %/ft. The detection correlations developed in Task 2/3 do not provide zero response for zero soot. A slight positive or negative value can result depending on the local flow speed. Additionally the correlations can result in values exceeding the saturation of the detector. The correlation formulas were implemented with a floor at 0 %/ft and a ceiling at the saturation level for the specific detector. Under some flow conditions the correlations can result in a slight positive response with no soot present. Therefore, the FDS data in the sections that follow is presented in two columns where FDS is the average FDS response and FDSz is the response for the local flow at zero soot. In the discussions that follow a correct prediction is considered a prediction where the FDS results fall close to or within the error bounds of the measured data. A correct result is also considered a result where both show a near zero response. The error bounds include correlation errors, source production errors, and total fan flow rate errors.

41 Validation of Modeling Tools for Detection Design 1DTG PAGE 30 Detector locations have been previously shown in Figure 2 and Figure Aspirated Laser (FM Prototype) Results Table 5 shows the predicted and measured aspirated laser measurements for each of the measurement locations for the FM prototype aspirated laser measurements. For the propylene fires, both the FDS and the test data were averaged over the steady-state period that developed shortly after the start of the test. For the cable tests, the data were averaged over a window representing the width at one-half of the peak extinction. Errors for the measured data are per Figure 36. Table 5 Predicted and measured aspirated laser (FM prototype) measurements Subfloor Ceiling Ceiling Plenum Test FDS (mg/m 3 ) Data (mg/m 3 ) FDS (mg/m 3 ) Data (mg/m 3 ) FDS (mg/m 3 ) Data (mg/m 3 ) Prop-SF ± ± ± 1.7 Prop-SF ± ± ± 0.43 Prop-HA-78 BDL ± ± 1.1 Prop-HA-265 BDL ± ± 0.41 Cable-SF ± BDL ± 1.8 Cable-SF ± BDL ± 0.55 *BDL = below detectable limit If the correct smoke source production rate has been specified, it is expected that the ceiling plenum data should be well predicted. The ceiling plenum aspiration location is near the outlet and flows will have undergone a maximum amount of mixing at that point. For the propylene cases we see a slight over prediction for all the tests. Figure 37 shows the time averaged mass fraction of soot in the ceiling plenum for the propylene burner in the hot aisle at 265 ACH with the aspiration location circled. Since the burner is not centered in the room (white circle in bottom right of the image), the flow into the ceiling is not symmetric. Based on the burner mass flow, soot yield, and fan flow rate, the average soot density is expected to be 2.3 mg/m 3. It is unclear if this over prediction is a result of not predicting enough mixing in the ceiling plenum or if there are other errors related to the assumption of the source. Subfloor values for the propylene test are well predicted. The cable fire tests are significant under predicted. However, ceiling plenum results (discussed below) suggest the smoke source term used was too small. Predictions on the ceiling are mixed for the propylene source with the high airflow tests predicted well and the low airflow tests not well predicted.

42 Validation of Modeling Tools for Detection Design 1DTG PAGE 31 Figure 37 Soot mass fraction in the ceiling plenum for the 265 ACH, hot aisle, propylene fire (white circle - subfloor fire location, purple circle - plenum detector location) For the cable fires, FDS correctly predicts the ceiling plenum location for the 265 ACH simulation (low but just within the large error bars) but greatly under predicts it for the 78 ACH simulation. Based upon the task 2/3 report, the soot production rate for two cable bundles with a 600 C heater is 2.4 mg/s or an average soot density of 0.6 mg/m 3. Figure 38 shows the FDS predicted soot mass fraction in the ceiling plenum for this test. The source is location in the subfloor is shown with the white circle and the aspiration location is shown with the purple circle. The FM measurement of 6 mg/m 3 for this test implies that there was significantly more soot production than was assumed based on the task 2/3 report. Figure 38 Soot mass fraction in the ceiling plenum for the 78 ACH, subfloor, cable source (white circle - subfloor cable location, purple circle - plenum detector location) A grid study was conducted using the 78 ACH propylene fire in the subfloor test. The simulation was also run using 2 inch and 4 inch grids with the results shown below in Table 6. For all three grid resolutions the subfloor and ceiling plenum locations are predicted within the experimental uncertainty and the ceiling location is not. There is little change in the ceiling plenum measurement over the three grid resolutions suggesting that some of the error at this location is due to errors in the smoke source assumptions.

43 Validation of Modeling Tools for Detection Design 1DTG PAGE 32 Table 6 Predicted and measured aspirated laser (FM prototype measurements for varying grid size for the 78 ACH propylene, subfloor case Subfloor Ceiling Ceiling Plenum Test FDS (mg/m 3 ) Data (mg/m 3 ) FDS (mg/m 3 ) Data (mg/m 3 ) FDS (mg/m 3 ) Data (mg/m 3 ) 2 inch inch ± ± ± inch Propylene Simulation Results Table 7 shows the FDS predicted and the FM measured detection response for each of the nine detector locations for Test 1 (Propylene fire in subfloor at 78 ACH). This fire was located near the west subfloor detector location and downstream of the other two subfloor locations. Cells shaded green indicate locations where the FDS prediction is within the measured error. Cells shaded blue indicate locations where both FDS and the data show no response. Table 7 Predicted and measured detection response for Test 1 (Propylene fire in subfloor at 78 ACH) Detector Location Detection Response (%/ft) Height Position VIEW (54 % exp. error) TrueAlarm (49 % exp. error) FAAST (45 % exp. error) FDSz FDS FM FDSz FDS FM FDSz FDS FM West Subfloor Center East West Ceiling Center East West Ceiling Center Plenum East The following observations are noted: In the subfloor, FDS correctly predicts the response of the west location for the TrueAlarm and FAAST detectors. FDS is under predicting the FAAST response at the center location. FDS correctly predicts the lack of response for the VIEW detector. An examination of the raw FDS data shows intermittent soot present at very low concentrations, but not enough for the FAAST correlation to provide a response. On the ceiling, FDS correctly predicts the TrueAlarm and VIEW responses. The exception is the VIEW center location where the data shows no response at the center location even though the other two locations both have a response. FDS is overpredicting the FAAST response at the other locations. It is unclear if this is an issue with the response correlation or the FDS soot prediction as the subfloor response at the west FAAST is correct and both the subfloor and ceiling predictions of the laser measurement are correct.

44 Validation of Modeling Tools for Detection Design 1DTG PAGE 33 In the ceiling plenum, FDS correctly predicts the TrueAlarm and VIEW response (except for the west location), but, as with the ceiling, over predicts the FAAST response. In general based upon the response predictions, FDS appears to be predicting more soot entering the ceiling plenum in the southwest corner than actually occurred. The FAAST detector response is generally over predicted by FDS. It is noted that much of the detector characterization was done at higher levels of response than occurred in this test. For example only 10 % of the TrueAlarm Task 2/3 data is at response levels below 0.7 %/ft while half of the full scale test data is below 0.7 %/ft. The same holds for the other detector types. This was recognized during development of the correlations and extra weight was given to low response levels when performing the fits. However, the smaller amount of data plus the uncertainty of that data (more variation in detection response during Task 2/3 at the low level) may have resulted in some additional uncertainty in the detection correlation for low levels as compared to higher levels of response. A grid study was done using Test 1. The simulation was performed at the additional grid resolutions of 2 inches and 4 inches. The results are shown in Table 8 below. As seen there is a nearly identical overall performance in the predictions. Table 8 Grid study results, predicted and measured detection response, for Test 1 (Propylene fire in subfloor at 78 ACH) Detector Location Detection Response (%/ft) VIEW TrueAlarm FAAST (54 % exp. error) (49 % exp. error) (45 % exp. error) Height Position FDS FDS FDS FM FM FM 2 in 3 in 4 in 2 in 3 in 4 in 2 in 3 in 4 in West Subfloor Center East West Ceiling Center East West Ceiling Center Plenum East Table 9 shows the FDS predicted and the FM measured detection response for each of the nine detector locations for Test 2 (Propylene fire in subfloor at 265 ACH). This fire was located near the west subfloor detector location and downstream of the other two subfloor locations.

45 Validation of Modeling Tools for Detection Design 1DTG PAGE 34 Table 9 Predicted and measured detection response for Test 2 (Propylene fire in subfloor at 265 ACH) Detector Location Detection Response (%/ft) Height Position VIEW (54 % exp. error) TrueAlarm (49 % exp. error) FAAST (45 % exp. error) FDSz FDS FM FDSz FDS FM FDSz FDS FM West Subfloor Center East West Ceiling Center East West Ceiling Center Plenum East The following observations are noted: FDS correctly predicts the response in the subfloor. FDS correctly predicts the VIEW response at the ceiling and the ceiling plenum locations except for the east ceiling plenum (recalling that VIEW output is limited to thresholds). FDS is over predicting the TrueAlarm response at the ceiling center and east location; however, both the data and FDS show a very low response. The TrueAlarm is correctly predicted in the ceiling plenum. FDS over predicts the FAAST response on the ceiling (the data shows no response in two locations and FDS shows a very small response) and the ceiling plenum (one location is correctly predicted). Since the laser soot concentration was well predicted in the ceiling plenum, this suggests the issue may lie in the response correlation. In this test, almost all detection response was at very low levels. Table 10 shows the FDS predicted and the FM measured detection response for each of the nine detector locations for Test 3 (Propylene fire in hot aisle at 78 ACH). This fire was located near the west end of the test compartment.

46 Validation of Modeling Tools for Detection Design 1DTG PAGE 35 Table 10 Predicted and measured detection response for Test 3 (Propylene fire in hot aisle at 78 ACH) Detector Location Detection Response (%/ft) Height Position VIEW (54 % exp. error) TrueAlarm (49 % exp. error) FAAST (45 % exp. error) FDSz FDS FM FDSz FDS FM FDSz FDS FM West Subfloor Center East West Ceiling Center East West Ceiling Center Plenum East The following observations are noted: FDS correctly predicts no response in the subfloor. FDS correctly predicts the VIEW response on the ceiling for the west and center locations and in the ceiling plenum. The ceiling East is slightly over predicted; however, the measured TrueAlarm response indicates that some soot is present in that general location. The TrueAlarm response is predicted correctly in half of the ceiling and ceiling plenum locations. In the areas where it is not well predicted, FDS is over predicting the response. The response are; however, at the same order of magnitude (near 0.1 %/ft at the ceiling East and near 1 %/ft at the ceiling plenum center). FDS is over predicting the FAAST response in most locations echoing the results of the previous tests. Table 11 shows the FDS predicted and the FM measured detection response for each of the nine detector locations for Test 4 (Propylene fire in hot aisle at 265 ACH). This fire was located near the west end of the test compartment.

47 Validation of Modeling Tools for Detection Design 1DTG PAGE 36 Table 11 Predicted and measured detection response for Test 4 (Propylene fire in hot aisle at 265 ACH) Detector Location Detection Response (%/ft) Height Position VIEW (54 % exp. error) TrueAlarm (49 % exp. error) FAAST (45 % exp. error) FDSz FDS FM FDSz FDS FM FDSz FDS FM West Subfloor Center East West Ceiling Center East West Ceiling Center Plenum East The following observations are noted: FDS correctly predicts no response in the subfloor. The VIEW shows no response on the ceiling; however, both the other detectors do show a response. The ceiling West location is very close to the fire and it is surprising that no response is seen there when both the other detectors show clear responses. The FAAST response predictions by FDS are mixed with an almost equal split between over and under prediction. In all cases the errors are large. All measured responses are very low levels in comparison to the data used for development of the response correlations in Task 2/ Circuit Board Fire The Task 2/3 testing showed that there was a considerable variance in the smoke production from test to test. However, with the exception of one test, the tests had similar peak smoke production rates. To compare the measured vs. the predicted data, each data set was averaged over the peak production. In the case of the predictions, this was done over the time period given in the smoke source characterization. For the measured data, the detection response was examined and a period of time identified when the detection response appeared to plateau for the most responsive device. Table 12 shows the FDS predicted and the FM measured detection response for each of the nine detector locations for Test 5 (Circuit board in a cabinet at 78 ACH). This fire was located mid-height in a cabinet at the center of the south row of cabinets.

48 Validation of Modeling Tools for Detection Design 1DTG PAGE 37 Table 12 Predicted and measured detection response for Test 5 (Circuit board in cabinet at 78 ACH) Detector Location Detection Response (%/ft) Height Position VIEW (148 %) TrueAlarm (137 %) FAAST (169 %) FDSz FDS FM FDSz FDS FM FDSz FDS FM0 West Subfloor Center East West Ceiling Center East West Ceiling Center Plenum East The following observations are noted: Very few detectors showed any significant response during the test. FDS correctly predicts no response in the subfloor FDS predicts a small response at the ceiling center location which is not seen in the measured data. FDS predicts most of ceiling plenum response; however, it is noted that the measurement errors are large and essentially any non-zero prediction would be correct. Since the ceiling plenum center and west locations are well predicted but the ceiling plenum east is not, this suggests that some of the ceiling error may be a shift in the smoke plume location between the FDS and test. Table 13 shows the FDS predicted and the FM measured detection response for each of the nine detector locations for Test 6 (Circuit board in a cabinet at 265 ACH). This fire was located mid-height in a cabinet at the center of the south row of cabinets.

49 Validation of Modeling Tools for Detection Design 1DTG PAGE 38 Table 13 Predicted and measured detection response for Test 6 (Circuit board in cabinet at 265 ACH) Detector Location Detection Response (%/ft) Height Position VIEW (148 %) TrueAlarm (137 %) FAAST (169 %) FDSz FDS FM FDSz FDS FM FDSz FDS FM West Subfloor Center East West Ceiling Center East West Ceiling Center Plenum East The following observations are noted: Very few detectors showed any significant response during the test. FDS correctly predicts no response in the subfloor FDS correctly predicts a small response at the ceiling center location. FDS does not show any response at the other ceiling locations. The test data shows very small levels of response at the threshold of detectability. FDS predicts no significant response in the ceiling plenum whereas the data does show response in the center and east. It is noted that the responses for all three detectors in the plenum are fairly similar (with the 265 ACH slightly higher) even though the airflow rate has increased by a factor of three. This suggests that soot production was larger in the test than modeled) Polyethylene Foam Fire Table 14 shows the FDS predicted and the FM measured detection response for each of the nine detector locations for Test 7 (polyethylene foam in the cold aisle at 78 ACH). This fire was located midheight in a cabinet at the center of the south row of cabinets.

50 Validation of Modeling Tools for Detection Design 1DTG PAGE 39 Table 14 Predicted and measured detection response for Test 7 (Foam fire in cold aisle at 78 ACH) Detector Location Detection Response (%/ft) Height Position VIEW (63 % exp. error) TrueAlarm (41 % exp. error) FAAST (78 % exp. error) FDSz FDS FM FDSz FDS FM FDSz FDS FM West Subfloor Center East West Ceiling Center East West Ceiling Center Plenum East The following observations are noted: Very few detectors showed any significant response during the test. FDS correctly predicts no response in the subfloor. FDS shows strong responses at all locations on the ceiling and in the ceiling plenum. The test data shows relatively low or no response. This suggests the assumed smoke source production rate is too large. Relative responses are similar. The locations with the largest measured response during the test are the locations where FDS predicts the largest response. This indicates that the smoke movement is likely being predicted well even though the magnitude is incorrect. Table 15 shows the FDS predicted and the FM measured detection response for each of the nine detector locations for Test 8 (polyethylene foam in the cold aisle at 265 ACH). This fire was located mid-height in a cabinet at the center of the south row of cabinets.

51 Validation of Modeling Tools for Detection Design 1DTG PAGE 40 Table 15 Predicted and measured detection response for Test 8 (foam fire in cold aisle at 265 ACH) Detector Location Detection Response (%/ft) Height Position VIEW (63 % exp. error) TrueAlarm (41 % exp. error) FAAST (78 % exp. error) FDSz FDS FM FDSz FDS FM FDSz FDS FM West Subfloor Center East West Ceiling Center East West Ceiling Center Plenum East The same observations hold for the 265 ACH test as for the 78 ACH test Cable Fire Table 16 shows the FDS predicted and the FM measured detection response for each of the nine detector locations for Test 9 (cable source in the subfloor at 78 ACH). Table 16 Predicted and measured detection response for Test 9 (Cable fire in subfloor at 78 ACH) Detector Location Detection Response (%/ft) Height Position VIEW (113 % exp. error) TrueAlarm (90 % exp. error) FAAST (86% exp. error) FDSz FDS FM FDSz FDS FM FDSz FDS FM West Subfloor Center East West Ceiling Center N/A East West Ceiling Center Plenum East The following observations are noted: Locations with very strong response are well predicted with the exception of the subfloor west location. In the subfloor FDS is not predicting much soot being mixed into the detector locations (green dots) as seen in Figure 39.

52 Validation of Modeling Tools for Detection Design 1DTG PAGE 41 In most locations FDS is under predicting the detection response. This was also seen in the laser data in Table 5. This suggests the cable source was larger in the FM full scale facility than it was in the reduced scale testing during Task 2/3. Figure 39 Soot movement for the 78 ACH cable source Table 17 shows the FDS predicted and the FM measured detection response for each of the nine detector locations for Test 10 (cable source in the subfloor at 265 ACH). Table 17 Predicted and measured detection response for Test 10 (Cable fire in subfloor at 265 ACH) Detector Location Detection Response (%/ft) Height Position VIEW (113 % exp. error) TrueAlarm (90 % exp. error) FAAST (86% exp. error) FDSz FDS FM FDSz FDS FM FDSz FDS FM West Subfloor Center East West Ceiling Center N/A East West Ceiling Center Plenum East