Fire and Smoke Spread Modeling to Support Damage Control Assessment and Decision Making in Shipboard Environments

Transcription

1 Javier Trelles, Ph.D., Craig L. Beyler, Ph.D., Jason E. Floyd, Ph.D., Joseph L. Scheffey, P.E. (Hughes Associates, Inc.), and Kim W. Yee (Naval Surface Warfare Center) Fire and Smoke Spread Modeling to Support Damage Control Assessment and Decision Making in Shipboard Environments ABSTRACT Predictive modeling of the spread of fire, smoke, radiological, chemical, and biological agents is needed to reduce the manning requirements of future ships. Physics-based models for fire hazards do exist but they are not integrated with the ship s sensors and they are not designed to work as faster-than-real-time advisory tools. Current generation damage decision and assessment system uses the Fire and Smoke Spread Simulator (FSSIM), a physics-based network model, to pre-compute fire & smoke scenarios for the system s heuristic fire and smoke federate. The next generation will incorporate predictive federates. FSSIMShip is being developed as the real-time, deterministic fire and smoke spread model. The objective is to test and demonstrate a successful prototype capable of sensor driven predictions of shipboard conditions operating at or faster than real time. The results will forecast fire & smoke conditions in order to support the damage control decision making process. The effort involves removing inefficiencies in the source code and underlying algorithms as well as incorporating multi-processor ready solvers into FSSIM. A sensor-driven element is being added to the model, allowing for continual verification and adjustment of the fire and smoke spread prediction outputs. Use of the model in a multiprocessor shipboard computing environment is envisioned. As such, the algorithms are being updated for parallel processing. This paper provides an update on this effort. INTRODUCTION In an effort to reduce life-cycle cost, the United States Navy (USN) currently has a reduced manning initiative with the goal of cutting assigned tasks by means of automation and computer-based decisions. A key to the success of this initiative is to determine the right balance of automated control systems capable of supplementing manning for both normal and damage control operations. During casualty operations, manning can be reduced if the ship s automation is capable of determining the extent of current damage, the likely progression of the damage over time, and recommend and/or take action to mitigate the damage quickly and efficiently. Since damage can progress rapidly, automation for assessing damage must occur fast enough that mitigation can have a positive outcome. Hence on future naval ships, crew members will need to concentrate on their assigned tasks or specialties such as piloting the ship, telemetry, and weapons command. In a casualty event, crew members skilled in firefighting will be in limited supply as compared to current CG-47 or DDG-51 class ships. The crew will be complimented by an advisory system that accurately provides situational awareness and decision aids about damage control operations. The present effort supports a small business innovation and research (SBIR) topic to develop a shipboard computational titled Model to Support Damage Control Assessment and Decision-Making in Shipboard Environments. Its goal is to reduce the amount of time required to process damage control information and estimate casualty spread in order to support automated damage control systems in real time. Currently, the Fire and Smoke Spread Simulator (FSSIM) is the physics-based network model used to pre-compute scenarios in the heuristic 1

2 fire and smoke federate of the damage decision and assessment system (DDAS). FSSIM (Floyd et al. 2004) presently lacks real-time performance and sensor interaction. The next generation of this predictive software, FSSIMShip, will be developed to provide shipboard sensor driven, faster than real time simulations. The software will be integrated with the ship s sensor and alarm networks. This will ground the advisory system with the latest situational updates. In its new incarnation the software will also have similarities with the Sensor Driven Fire Model, a fire fighter tool proposed by the National Institute of Standards and Technology (NIST) (Davis, Cleary, Donnelly, & Hellerman, 2003; Davis & Forney, 2001) but yet to be realized in practice. Speed and the ability to accurately model large, complexly interconnected spaces equipped with ventilation, sensors, and suppression systems are the performance requirements for the model. Processing requirements, however, should also be kept to a minimum. While the processing power planned for future naval ships is considerable, there are many computational needs to be supported for both damage control functions and normal ship operations functions. The prudent approach, and the one embraced in this effort, is to minimize the computational footprint of advisory systems such as FSSIMShip. FSSIMShip s capability can be envisioned by a weapons-induced damage scenario onboard a ship. Immediately after a weapon hit there will be a period of automatic reconfiguration of electronic and hydraulic systems in an attempt to route system capabilities around damaged portions of the ship. These automated actions will be largely predetermined based doctrine and may or may not represent the optimal response. Following this initial response, further damage control (DC) activities may be relatively time consuming. First, information is collected by the damage detection and control systems. Then the information is analyzed. Finally, potential responses are determined and compared to optimize the response. Any manned response will involve the times to communicate the task, complete dressing out, acquire equipment, transit to the desired location, and gain access. Any response involving the reconfiguration of systems will require sufficient time to perform the reconfiguration and to monitor the results. At the point in time when either a manned response is on scene and ready to commence or a successful system reconfiguration has occurred, the action that was taken needs to still be valid. If the recommended action is negated by further fire and smoke spread, then some fraction of the time and effort expended on the action will have been wasted. The ability to look ahead far enough to avoid recommending an unsuitable response will greatly improve the overall effectiveness of the DC effort and minimize the consumption of ship personnel and equipment resources. The prediction from FSSIMShip will complete rapidly enough to allow the damage control assistant (DCA) to formulate a response before the prognostic time window expires. The goal of FSSIMShip is a 30 minute prediction horizon in under 20 seconds. Based on current sensor data and crew reports, the onboard software will locate actively burning compartments. Those spaces that are untenable would be identified so that response crews would thus avoid these areas. For areas where sensor data is lost/unavailable, the software will fill in the information gap. In the remaining spaces, the software will coordinate with the functional sensors. All logged crew reports would be imported by the model. The continuously updating 30-min projection gives the DCA a sense of not just the current situation but also what the near term consequences of the involved spaces will be. FSSIMShip will not only predict fire and smoke spread but will be able to adjust actions based on effects of deployed automatic fire protection systems. For example, on future naval ships fire suppression systems consist primarily of water mist systems. The quasi-equilibrium evaporation model of (Back, Beyler, & Hanssen, 2000) is used to rapidly predict the anticipated temperature drop following water mist activation. The model uses the provided 2

3 compartment temperature, estimated fire size (based on prediction or sensor data), and the compartment ventilation based on heating, ventilation, and air conditioning (HVAC) system settings and portal status to predict the effectiveness of the water mist system. Hence the DCA will acquire a sense of what compartments will need a manned response. The goal planned for FSSIMship is to provide the Navy with a real-time fire and smoke engine to replace the pre-computated fire and smoke database in support of a damage decision tool termed Damage Decision & Assessment (DDA). Overview of the FSSIMShip Network Model FSSIMShip is a network fire model written to simulate the spread of fire and smoke in a naval vessel. However, there is nothing in the model to preclude its use for other types of compartmented structures such as a building. In FSSIMShip each compartment in a structure is represented as a single node with surfaces (e.g., bulkheads, decks, and overheads) and vent openings (e.g., doors, hatches, etc.) represented as node connections. FSSIMShip encompasses the following capabilities: 1D flow model including friction losses and temperature-dependent specific heat. 1D multiple-layer, temperature-dependent heat transfer. N-surface, gray-gas radiation heat transfer, including radiation streaming through openings. Bidirectional flow through horizontal (hatches) and vertical (doors) flow connections. Combustion product species tracking. Oxygen and fuel limited combustion. Multiple fires and fire spread via compartment-to-compartment heat transfer. HVAC systems including ducts, dampers, chillers, and fans with forward and reverse flow losses and multiple fan models. Fire detection via heat, smoke, and fire detection Fire spread by compartment-specific criteria. Fire suppression via sprinklers, water mist, gaseous agents, aerosol agents, and foam. Fire spread prevention via boundary cooling. Binary control structures used to link operation of equipment to sensors or times. Faster-than-real-time execution speed. Both coordinated and complimentary interaction with shipboard sensors and/or fire alarm system. PHYSICS The core physics of FSSIMShip is little changed from that of FSSIM. These are briefly reviewed in the ensuing subsections. Compartment Flows FSSIMShip solves lumped, time-dependent conservation equations for mass, momentum and energy. Energy and mass are conserved explicitly, whereas momentum is conserved implicitly. Energy and mass conservation use a control-volume approach, where the control volume is either a single compartment or a ventilation system node. Momentum is implicitly solved for at vent connections or in ducts. The set of equations used is taken from MELCOR (Gauntt et al. 2000) as implemented in FSSIM (Floyd et al. 2004). This set of equations is solved numerically using ordinary differential equations (ODE) and differentialalgebraic equations (DAE) solvers. Combustion Upon activation, FSSIMShip will set up fires according to inputs from sensors and from the DCA. These fires will spread according to local conditions. Ignition of additional fires is determined at the beginning of each time step. Each compartment can have a use-type designation, which denotes a fuel loading and fuel classification. Separate temperature ignition criteria are specified for surfaces, temperature of incoming vent flows, and 3

4 compartment temperature (Back et al. 2003). Overhead surfaces have different ignition temperatures from other surfaces. Pyrolysis is based on a composite function that 2 includes t growth, continuous burn according to local conditions, and polynomial or exponential decay. Growth is limited by specification of a maximum pyrolysis rate. The calculated pyrolysis rate can be reduced by various mechanisms. If the fire is being suppressed by an agent, the pyrolysis will be reduced by a suppression factor. If the fire has become oxygen limited, then the pyrolysis rate is determined by a linear function of temperature. The maximum pyrolysis rate after oxygen-limiting conditions are reached is set at the point where the fire became oxygen limited; the compartment temperature for that point is also stored. The oxygen-limited pyrolysis rate is then calculated by the ratio of the current temperature to the stored temperature not to exceed the maximum rate. The actual heat release rate is adjusted to use the calculated amount of available oxygen. Species are generated based on the yields for each fuel being burned. These yields represent the mass of combustion products formed for a unit mass of fuel burned. \Heat Transfer Convection heat transfer is calculated at each surface for each time step. Two sets of convection heat transfer correlations are used. The first is used for spaces where either no fire exists or the volume of the space is small in comparison to the anticipated fire size. The second set of correlations applies a plume and ceiling jet model to improve computation of heat transfer to the overhead. If a surface has been specified as transparent to radiation, no convection heat transfer computation is performed. Radiation heat transfer is computed on a compartment-by-compartment basis using the beginning of time step surface temperatures, compartment temperature, compartment gas composition, and compartment heat release rate. The heat transfer is calculated using a modified gray-gas, n -surface net radiation method (Forney 1991). Conduction heat transfer is computed from a one-dimensional (1D) heat equation discretized with central differences (Strauss 1992) to arrive at a set of ODEs for each wall. Each 1D partition/bulkhead is divided into N -1 cells or nodes with N boundaries. Boundary conditions are obtained from the convection and radiation calculations. Each wall can have multiple layers of materials and temperature-dependent specific heats and conductivities. SENSITIVITY AND UNCERTAINTY The remaining sections detail new features in FSSIMShip. Because the DCA is using the data from FSSIMShip to address an ongoing situation, it is desirable to give the DCA an assessment of the accuracy of FSSIMShip predictions. Hence automatic estimation of sensitivity and uncertainty are part of every FSSIMship prediction cycle. Two variables are currently singled out for this treatment: the temperature, T, and the spread distance, x. Hence, if Ä x is the uncertainty in the spread, the prediction can be written as x Ä x. (1) Figure 1. Some standard distributions associated with measurements. 4

5 Formally, the squares of the uncertainties are the variances of the predictions. Each datum has a probability distribution. In Figure 1 the uncertainty is shown as the standard deviation of the symmetric distribution. Skew distributions are possible as well. For the present analysis, the top hat distribution is assumed. As Figure 1 shows, it is symmetric about its center and all values within the standard deviation bounds are equally probable. If the uncertainty in a set of parameters is taken as a given, a formal methodology can be followed to determined how this is manifested in variables calculated from this set (ASTM 1998). The compartment temperature plus the spatially discretized heat transfer equations in FSSIMShip form a system of differential algebraic equations (DAEs). The uncertainty is quantified by solving the DAE plus sensitivity system for each compartment and each computational cell. The uncertainty in the boundary conditions appear as extra algebraic terms. The sensitivity coefficients are then used to quantify the uncertainty in the temperature prediction in each compartment. The solvers DASPAK 3.0, CVODES, and IDAS (Hindmarsh 2000; Hindmarsh 2005; Li & Petzold 1999; Li & Petzold 2000; Petzold et al. 2006) have the requisite numerical machinery (such as the evaluation of the partial derivatives) built into them to solve these types of sensitivity problems. This formal procedure works fine for the temperature but not for the extent of the affected areas because spread is determined by the satisfaction of certain criteria. For example, when a compartment exceeds an ignition temperature for a certain amount of time it is designated as containing a fire. For the extent of the affected area, use is made of the characteristic distance (Trelles & Pagni 1997), x c 1 Q p g max 2 / 5, (2) that are currently burning, is the ratio of the specific heats, p is the ambient pressure, and g is the acceleration of gravity. The distance uncertainty as a function of the heat release rate is / 5 Ä Q Q max Ä x 3 / 5 p g max or, in terms of relative uncertainty,, (3) Ä x 2 Ä Q 2 Ä x Ä Q. xc 5 Q (4) max 5 Along the border of the involved compartments, distance is measured from the origin, x 0, to the edge, x. The uncertainty in the spread is therefore Ä x x x0 Ä x. (5) The characteristic length in Eq. 2 formally applies to an unconstrained plume. In a ship, the plume and fire would be affected by many factors such as passageway geometry and ventilation. However, since the plume is fundamental even in a compartment, it is a rational starting point. (Hamins & McGrattan 2007) give the example of the flame height based on the Heskestad expression. Even though flame height is a distance, because of its dependency on heat release rate and pool fire diameter, the asymptotic relative uncertainty of 1 the flame height scales as Ä L Ä Q. This 2 is greater than the 2/5 coefficient in Eq. 4. In general, the complications associated with potentially multiple fires that have flames impinging on the rocking overhead of a ship indicate increased uncertainty. For these reasons it is believed that the spread uncertainty in Eq. 5, which was based on Eqs. 2 and 4, is a lower bound. where x c is the characteristic length scale, Q max is the highest heat release rate from all the fires 5

6 INVERSE METHODS A key part of the damage control software for future naval ships is the integration of sensor data and crew reports into modeling tools and damage decision aids such as FSSIMShip. On the most basic level, FSSIMShip accepts sensor data as a means of model initialization. Another opportunity afforded by the sensor and crew inputs is the extrapolation of these reports to determine the heat release rate (HRR) of a fire. The meteorological, geophysical, and engineering disciplines have developed a variety of inverse methods in order to obtain model inputs from sparse readings (Backus 1971; Menke 1989). The robustness of inverse methods was improved with the advent of regularization techniques (Tikhanov & Arsenin 1977). In the realm of heat transfer, previous studies (Özisik & Orlande 2000) have shown how to arrive at heat fluxes and heat sources from temperature measurements made in laboratory settings. These efforts benefited from the ability to put as many sensors as would be feasibly desired within the laboratory sample. The same can be said for the early sensor-driven fire model (Davis & Forney 2001) and other fire protection engineering investigations of reconstructing fire physics from sensor measurements (Kramer et al 2003; Padakannaya, Richards, & Plumb 1994; Richards, Munk, & Plumb 1997; Richards et al. 1997). This may not be an option with future naval ships. The algorithms developed for FSSIMShip use nonlinear parameter estimation to analyze sensor inputs from local and nearby compartments in order to estimate the heat release rate (HRR). Although inverse methods (Beck & Arnold 1977; Tikhanov & Arsenin 1977) have been widely explored, they tend to be computationally expensive. This computational load needs to be minimized for FSSIMShip. Since FSSIMShip will be constantly updating itself, previous sensor results will also be utilized for this HRR calculation. Inverse methods will only be possible for compartments with working sensors that provide numerical readings. They can only be used from the start of the incident up to the present time. Once FSSIMShip goes beyond the present time inverse methods are not possible due to the lack of sensor input. The next two subsections detail inverse methods that can be used to improve the prediction of the heat release rate associated with a spreading set of fires. To recap, in FSSIMShip measurements are used to set conditions in compartments from the time of the incident to the present. This sensor data is utilized in the inverse methods to improve estimates for key parameters. During this incident to present period, simulations are still used to determine conditions in compartments which (1) do not have sensors, (2) have damaged sensors, (3) are not reporting sensor readings for whatever reason, or (4) have sensors that only provide an alarm of some type, i.e., no data. From the present to the future FSSIMShip only simulates conditions with, wherever possible, the sensors providing initial conditions and improved estimates for key model parameters. Compartments lacking this information use the last set of calculated conditions. Global Inverse Methods The classic inverse method is global in scope. This means that it takes data from throughout the platform in order to arrive at its estimates. The general approach is now outlined. Assume that the heat release rate can be modeled as N P t q C (6) f j 1 j j C j t are where j are the parameters and functions (typically polynomials of some type), both with N P elements. For example, fire growth is typically described as Here 2 (7) q f t N P = 1, 1 =, and C 1 t = 2 t. The goal is to use sensor data to arrive at better 6

7 estimates of 1. Even if the simple model of Eq. 7 is adopted, having a better handle on fire growth can significantly improve model predictions. A quadratic objective function, S, is defined to indicate the level of agreement between measured and predicted temperatures. In order to minimize the difference, the gradient of S with respect to the parameters is taken and the result is set to zero. This is analogous to taking the derivative of a scalar function to determine its extrema. The resulting equations form a system of nonlinear equations. In order to solve it, the gradient equations are fed to a nonlinear equations solver such as KINSOL (Collier at al. 2006). KINSOL in turn calls the appropriate subroutines in FSSIMShip to obtain the sensitivity Jacobian. KINSOL will work to satisfy an error tolerance (if possible). These basic types of algorithms tend to suffer from difficulties with numerical stability. As an example of why this is the case, remember the relationship between the flame height and the heat release rate uncertainties given above, Ä L 0.5 Ä Q. For the inverse problem the heat release rate would be determined from the flame height. For this case then Ä Q 2 Ä L. This is typical of inverse problems: measurement uncertainties become amplified. For numerical methods, the truncation of real numbers to the n -byte rational numbers that are available on digital computers can introduce enough error into the problem to result in an unstable algorithm. Regularization techniques add terms to the definition of the objective function in order to improve the robustness of the overall inverse method. Local Inverse Methods For a ship section modeled as a lumped mass of compartments, techniques such as deconvolution (Casey & Walnut 1994) can be used to obtain the physically meaningful variables from all of the sensor readings obtained at different sampling rates. This approach has the advantage that it is local in scope. It makes use of sensor data at different sampling rates. The method works formally for linear problems so the current set of equations would have to be appropriately linearized. Much of this work has already been done in FSSIM, though. The challenge for FSSIMShip is to arrive at suitable convolution integrals for the bulk of the equations that describe compartment temperature. The applicability of this method as a substitute for the other inverse methods, which are global in scope, will then be assessed. Start Monitor Monitor Sensors DCA Inputs Register Event? Yes Register Event Start Time t S Forecast No Yes Forecast All Clear? No Record Current Time t C Set Cycle Final Time t F = t C + 30 min Review Predict Report Output to Consoles Figure 2. Flowchart showing the basic software architecture of FSSIMShip. OVERALL SOLUTION SCHEME FSSIMShip is configured to continuously march out to a specified time interval beyond the present. The main loop calls the forcast loop. (Refer to Figure 2.) This loop has two sections. The first part is for events from the beginning of the emergency to the present time. The information processed in this loop will consist of new sensor data, new calculations, and 7

8 previously stored readings and calculations. The second part of the time marching loop will go from the present into the future. The state determined in the first part of the time marching loop will be used to initialize the deterministic models. The solution of the governing equations is realized by the use of DAE solvers. An outer loop is set up to march through time. (See Figure 3.) An inner loop is then initiated for node states at the current time. The solvers are called for the compartment conditions. They are then called to solve the heat transfer equations. If the exit code indicates a successful return then this inner loop runs for only one count. Otherwise measures are taken according to the exit status flag. Review Gather Inputs from All Sensors Determine Compartment Fire Statuses Inverse Methods Set HRR Parameters Predict Predict Set t = t C + t Determine Reduced Computational Domain Solve in Reduced Computational Domain t > t F? Yes Report Figure 3. Flowchart showing the sections of the FSSIMShip software architecture that interact with sensors and predict the impact of current fires. REAL-TIME COMPUTATION No Two techniques will be used to improve run time performance. Currently FSSIMShip simulates the entire ship. In general this will not be necessary for supporting damage control. Regions of the ship remote from the weapon hit should not see a significant fire and smoke spread impact. The time for fire and smoke to spread to these regions will provide time for damage control activities to halt the spread of secondary damage to these regions. Autonomous suppression systems in remote regions should be functional (or regain functionality prior to spread). The candidate approaches include strict limiting of the geometric extent being computed, a multi-region approach where computational fidelity varies with remoteness of the region (e.g., a hybrid heuristics-physics model), and multi-block approach where remote spaces and their associated ventilation are combined. No Predict Set t = t C + t Determine Reduced Computational Domain Solve in Reduced Computational Domain t > t F? Yes Report Set Openings / Closings Identify New / Extinguished Fires Regenerate Computational Domain Update Solver RHS Function Initialize Solver Call Solver Any Error Flags? No Continue Yes Corrective Measures Figure 4. Flowchart showing the section of the FSSIMShip code that solves the governing equations. The dashed rectangles on the right diagram are expansions of the corresponding left diagram actions. The bulk of the computation time within FSSIM lies in matrix-based solvers. Incorporating more efficient solvers and code structures (Drummond & Marques 2005) in FSSIMShip will result in faster turn-around. FSSIM is currently 8

9 structured as a single processor software tool. Many portions of the code are easily amenable to high performance (multiple-processor) computing (Pacheco 1997). FSSIMShip uses the Message Passing Interface (MPI) (Pacheco 1997; Gropp, Lusk, & Skjellum 1999; Snir 1998). This allows distributing the calculation on a set of computers/processors in the data center. MPI-capable solvers, such as those available with Argonne National Laboratory s Portable, Extensible Toolkit for Scientific Computation (PETSc) (Balay et al. 1997; Balay et al. 2004), will be employed. The goal is to perform as many implicit solves in parallel as possible. The appropriate number of processors needed to achieve faster than real time goals will be explored. Refer to Figure 4 for the envisioned software architecture. SENSORS AND DETECTORS For state-of-the-art ships of the future, advisory software developers will have input on the number and types of sensors to be installed. This will allow FSSIMShip to work cooperatively with sensors. However, older generation naval vessels are typically equipped with a fire alarm system which has a limited set of sensors. There is no guarantee that the readings from the sensors will be available at a ship console. More likely than not the fire alarm system provides just that: an alarm and an annunciated location and no further information. For these cases, FSSIMShip will compliment sensors, filling in the gap afforded by the alarm interface with simulation data on the conditions in affected areas. Refer to Figure 4 for the software architecture that shows how sensor data will be incorporated into FSSIMShip. Once sensor algorithms are defined, there remain significant implementation issues to optimize their performance with FSSIMShip. Whenever possible FSSIMShip will be interfaced with the sensor network. Typical interfaces are TCP/IP and MODBUS. Since FSSIMShip needs to address the problem of incomplete sensor readings, some damage assessment (Fritzen, Jennewein, & Kiefer 1998) is necessary within FSSIMShip. In addition, there are challenges to be addressed in identifying and implementing event type data into the simulation. These may include events like creation of an opening via damage or crew action, as well as fire mitigation via crew action. To aid in limiting the range of events that needs to be considered, it is envisioned that high level knowledge will be provided to the model from the DCA or other systems. This is expected to include clarifications such as fire due to hostile weapons (for known types) and other information about the incident that is developed via the various systems and crew observations. DATABASE Because FSSIMShip is an autonomous system, there will be no need to create an input file as was the case with FSSIM. However, FSSIMShip will need access to a database or a set of databases that contain crucial information such as ship information and physical constants. It remains to be determined whether this information will be contained within the compiled program or externally. It is currently expected to be a combination thereof: material properties can be contained within FSSIMShip but ship specifics will be contained in data files. The following is a list of important information to be contained in the databases. Connections based on the ship layout; Compartment volumes; Wall areas; Resistance coefficients for standard objects such as doors, hatches, ducts, etc; Thermal properties for common materials such as steel and insulation; Suppression systems data such as activation temperatures and response time indices; and Detector data such as threshold values and response lags; Sensor locations. Smart product model development and implementation could be leveraged to provide this ship specific data. 9

10 CONCLUSIONS The basic framework for shipboard FSS established FSSIM as an incident modeling and investigation tool. FSSIM is currently being transitioned to the real-time, onboard damage control tool known as FSSIMShip. To accomplish this, the following changes are in the process of being made to FSSIM: Parallel computation; Estimation of uncertainty and sensitivity; Creation of ship- and physics-specific databases; Integration with shipboard sensors and/or fire alarm system; and Use of sensor data to improve key model inputs such as the heat release rate. Approaches to accomplish this transition have been detailed above. They will be developed and refined as the project moves forward. REFERENCES ASTM, Standard Guide for Evaluating the Predictive Capability of Deterministic Fire Models, E 1355, New York: American National Standards Institute, Back, G., Beyler, C., and Hanssen, R., A Quasi-Steady-State Model for Predicting Fire Suppression in Spaces Protected by Water Mist Systems, Fire Safety Journal, 35, pp , Back, G., E. Mack, M. Peatross, J. Scheffey, D. White, F. Williams, J. Farley, and D. Satterfield, A Methodology for Predicting Fire and Smoke Spread Following a Weapon Hit, NRL Ltr Rpt Ser 6180/0014, Naval Research Laboratory, Washington, DC, Backus, G.E., Inference from Inadequate and Inaccurate Data, in Reid, W. H., (ed.), Mathematical Problems in the Geophysical Sciences, Providence, RI, American Mathematical Society, pp , Balay, S., Gropp, W.D., McInnes, L.C., & Smith, B.F., Efficient Management of Parallelism in Object Oriented Numerical Software Libraries, in Arge, E., Bruaset, A.M. and Langtangen, H.P. (eds.), Modern Software Tools in Scientific Computing, Boston, MA: Birkhäuser, pp , Balay, S., Buschelman, K., Eijkhout, V., Gropp, W.D., Kaushik, D., Knepley, M.G., McInnes, L.C., Smith, B.F., and Zhang, H., PETSc Users Manual, Argonne National Laboratory, ANL-95/11 - Revision 2.2.0, Beck, J.V. and Arnold, K.J., Parameter Estimation in Engineering and Science, New York: Wiley, Casey, S., and Walnut, D., Systems of Convolution Equations, Deconvolution, Shannon Sampling, and the Wavelet and Gabor Transforms, SIAM Review, 36:4, pp , Collier, A.M., Hindmarsh, A.C., Serban, R., & Woodward, C.S., User Documentation for KINSOL v2.5.0, UCRL-SM , Livermore, CA: Lawrence Livermore National Laboratory, November 6, Davis, W. D., and Forney, G. P., A Sensor- Driven Fire Model, Version 1.1, National Institute of Standards and Technology, Gaithersburg, MD, NISTIR 6705; January Drummond, L.A. and Marques, O.A., An Overview of the Advanced CompuTational Software (ACTS) Collection, ACM Transactions on Mathematical Software, 31:3, pp , Floyd, J., S. Hunt, F. Williams, and P. Tatem, Fire + Smoke Simulator Version 1 (FSSIM) Theory Manual, NRL, Washington, DC, Forney, G., Computing Radiative Heat Transfer Occurring in a Zone Fire Model, NISTIR 4709, National Institute of Standards and Technology, Gaithersburg, MD, Fritzen, C.-P., Jennewein, D., & Kiefer, T., "Damage Detection Based on Model Updating 10

11 Methods," Mechanical Systems and Signal Processing, 12:1, January 1998, pp Gauntt, R., R. Cole, C. Erickson, R. Gido, R. Gasser, S. Rodriguez, M. Young, MELCOR Computer Code Manuals: Reference Manuals Version 1.8.5, NUREG/CR-6119, Volume 2, Rev. 2, U.S. Nuclear Regulatory Commission, Washington, DC, Gropp, W., Lusk, E., and Skjellum, A., Using MPI: Portable Parallel Programming with the Message-Passing Interface, 2nd Edition, Cambridge, MA, MIT Press, Hamins, A., and McGrattan, K. B., Verification and Validation of Selected Fire Models for Nuclear Power Plant Applications, Volume 2: Experimental Uncertainty, U.S. Nuclear Regulatory Commission, Office of Nuclear Regulatory Research (RES), Rockville, MD, and Electric Power Research Institute (EPRI), Palo Alto, CA. NUREG-1824 and EPRI , May Hindmarsh, A.C., The PVODE and IDA Algorithms, LLNL Technical Report UCRL- ID , December Hindmarsh, A.C., Brown, P.N., Grant, K.E., Lee, S.L., Serban, R., Shumaker, D.E., and Woodward, C.S., SUNDIALS: Suite of Nonlinear and Differential/Algebraic Equation Solvers, ACM Transactions on Mathematical Software, 31:3, pp , Li, S., and Petzold, L., Design of New DASPK for Sensitivity Analysis, Technical Report, Dept. of Computer Science, Technical Report UCSB, University of California at Santa Barbara, Li, S., and Petzold, L., Software and Algorithms for Sensitivity Analysis of Large- Scale Differential-Algebraic Systems, Journal of Computational and Applied Mathematics, 125, pp , Liley, P. E., Reid, R. C., and Buck, E. (1984), Physical and Chemical Data, in R.H. Perry, D.W. Green, & J.O. Maloney (eds.), Perry s Chemical Engineers Handbook, 6 th ed., New York, McGraw Hill, pp Menke, W., Geophysical Data Analysis: Discrete Inverse Theory, San Diego, CA, Academic Press, Özisik, M.N., & Orlande, H.R.B., Inverse Heat Transfer, New York: Taylor & Francis, Pacheco, P., Parallel Programming with MPI, San Francisco, CA, Morgan Kaufmann Publishers, Inc., Padakannaya, K., Richards, R.F., and Plumb, O.A., Inverse Radiation Solution for Fire Detection, NISTIR 5499, September 1994, Gaithersburg, MD, National Institute of Standards and Technology, Petzold, L., Li, S., Cao, Y., and Serban, R., Sensitivity Analysis of Differential-Algebraic Equations and Partial Differential Equations, to appear, Computers and Chemical Engineering, Richards, R.F., Munk, B.N., and Plumb, O.A., Fire Detection, Location and Heat Release Rate Through Inverse Problem Solution. Part 1. Theory, Fire Safety Journal, 28:4, pp , Richards, R.F., Ribail, R.T., Bakkom, A.W., and Plumb, O.A., Fire Detection, Location and Heat Release Rate Through Inverse Problem Solution. Part 2, Experiment, Fire Safety Journal, 28:4, pp , Snir, M., et al., MPI: The Complete Reference, Cambridge, MA, MIT Press, Strauss, W., Partial Differential Equations: An Introduction, John Wiley & Sons, New York, NY, Tikhanov, A.N., and Arsenin, V.Y., Solution of Ill-Posed Problems, Washington, DC, Winston & Sons, Trelles, J., and Pagni, P.J., Fire-Induced Winds in the 20 October 1991 Oakland Hills Fire, Fire 11

12 Safety Science: Proceedings of the Fifth International Symposium, March 3 7, 1997, Melbourne, Victoria, Australia, Elsevier, London, 1997, pp AKNOWLEDGMENTS Scott Hill of HAI assisted with the collection of sensor data and with the preparation of the software design description. Javier Trelles, Ph.D., is a senior engineer at Hughes Associates. He has written models for a variety of situations including blood carboxyhemoglobin, thermal decomposition of materials, spreading of fuel spills, and the dispersion of smoke under the influence of various weather conditions. He has conducted numerous simulation studies. Dr. Trelles is the developer of many CFD animation and visualization programs. Baltimore, MD. He received a BS in fire protection engineering at the University of Maryland and is a Fellow in the Society of Fire Protection Engineers. He has over 25 years experience in managing and directing full-scale fire tests. He has directed numerous manned firefighting and damage control tests for the U.S. Navy. Kim W. Yee, is the USN DDG 1000 Damage Control Automation Lead and the author of this SBIR topic. He has over 20 years of experience working with the Navy s Surface Combatants HM&E Controls Systems. In his role as DDG1000 DC Automation Controls Lead, he is working with industry to develop a Damage Decision & Assessment (DDA) capability that addresses Hull stability, Hull stress and fire, smoke, flooding, Chemicial/Biological spread. Craig L. Beyler, Ph.D., is the technical director at Hughes Associates. He has extensive expertise in fire dynamics/fire modeling and has been the primary editor for the Fire Dynamics Section of the SFPE Handbook of Fire Protection Engineering. He has been involved in model development and use for analysis of U.S. Navy ships for over 15 years. He is currently the chairman of the International Association for Fire Safety Science and the winner of the Guise Medal, SFPE s highest technical award. Jason E. Floyd, Ph.D., is a senior engineer at Hughes Associates. Dr. Floyd s R&D/design experience includes simulation tools development, numerical modeling, and design of large-scale experiments and experimental apparatus. He is the lead developer of FSSIM. Dr. Floyd has performed CFD simulations of various types and is currently a member of the FDS development team. Joseph L. Scheffey, P.E., is the Director of Fire Protection RDT&E at Hughes Associates, Inc. 12