Fire Modeling Tools

1.5.1 Algebraic Models

Algebraic models may be standalone equations found in literature or may be contained within spreadsheets (such as the NRC’s FDT^s), and can help give a general understanding of one of the fire environment phenomena. These equations are typically closed-form algebraic

expressions, many of which were developed as correlations from empirical data. In some cases, they may take the form of a first-order ordinary differential equation, and, when used properly, can provide an estimate of fire variables, such as; HGL temperature, heat flux from flames or the HGL, smoke production rate, depth of the HGL, and the actuation time for detectors.

Algebraic models are helpful because they require minimal computational time and a limited number of input variables. When applying the results of the algebraic models, users need to be aware that the development of most equations involved approximations to simplify the analysis.

Algebraic models are useful primarily as screening tools (i.e., to provide a rough approximation for an analysis, perhaps as a check of an aspect of the results of the computer-based models), and are also applicable when only one phenomenon can be treated in isolation: for instance, plume or ceiling jet correlations are not applicable if there is a significant HGL unless they are modified to account for this effect.

1.5.2 Zone Models

A zone model, such as the Consolidated Fire Growth and Smoke Transport Model (CFAST) or MAGIC, calculates fire environment variables using control volumes, or zones, of a space. The zones correspond to a cooler lower layer and a HGL, as depicted in Figure 1-2. The

fundamental idea behind a zone model is that each zone is well-mixed and that all fire environment variables (temperature, smoke concentration, etc.) are therefore uniform throughout the zone. Conditions in each zone are calculated by applying conservation

equations and the ideal gas law. The variables in each zone change as a function of time and rely on the initial conditions specified by the user. There is a well-defined boundary separating the two zones, though this boundary may move up or down throughout the simulation.

INTRODUCTION

Figure 1-2. A two-zone enclosure fire with an HGL above and a cool lower layer below.

Zone models are most applicable in situations involving simple geometries or where spatial resolution within a compartment is not important. The preparation of input for a zone model, the computation time, and the amount of output data generated are slightly more extensive than a simple algebraic model; however, the overall computational time cost is still low.

Zone models can easily analyze conditions resulting from fires involving single compartments or compartments with adjacent spaces, and are often used to compute the HGL temperature, HGL composition, and target heat fluxes. They are also capable of modeling some effects of natural and mechanical ventilation in both horizontal and vertical directions. Some zone models allow the user to select a thermal plume model, which may assist in better characterization of a known fire scenario, while others use an axisymmetric smoke plume. Other features of a zone model may include a user-specified single zone or multiple fire plumes.

Simulations of spaces with complex ceilings or numerous compartments can be challenging with a zone model. Because zone models specify uniform conditions in the HGL and lower layer, results cannot be distinguished between locations at different distances from the fire. Due to the zone approach, smoke transport time lags are not considered in the simulation, which is an acceptable approximation in relatively small spaces but may lead to significant error in large-volume spaces or spaces with large aspect ratios.

Smoke production, fire plume dynamics, ceiling jet characteristics, heat transfer, and ventilation flows are all algebraic models embedded within zone models. Other parameters that can be calculated with a zone model include thermal behavior, detection response, and suppression response. The output of a zone model is typically simple to understand and is generally presented through an automatic user interface.

HGL

Mass outflow

Mass inflow Plume mass

flow

INTRODUCTION Most zone models have default values that must be recognized and adjusted as necessary to obtain an accurate solution. The model user must understand and justify the relevance of the default values used in any application. Fire model users are expected to assess the

appropriateness of default values provided in the fire models and make changes or adjust values as necessary. User manuals and technical references for each zone model outline such values and may provide recommended ranges for the parameters.

1.5.3 CFD Models

A computational fluid dynamics (CFD) model is often useful when trying to determine fire variables at a specific location or when there are geometric features that are expected to play a significant role in the results beyond what can be calculated in a zone model approximation. A typical CFD model consists of a preprocessor, a solver, and a postprocessor. CFD models can provide a detailed analysis in both simple and complex geometries.

CFD models essentially apply a series of conservation and state equations across multiple cell boundaries in a space. The number of cell boundaries depends on the mesh size, which breaks the geometry into three-dimensional subvolumes called cells. Solutions to the conservation equations of mass, momentum, and energy are updated as a function of time within each numerical grid cell, with the solutions in all cells collectively describing the fire environment within the geometry at the cell resolution.

The number of grid cells defines the type of mesh. A fine mesh is made up of numerous grid cells. Since the equations are applied at each cell’s boundaries, a more detailed distribution of fire parameters is characterized. A coarse mesh is made up of fewer grid cells and can result in less accurate results. The type of mesh and number of grid cells should be based on the geometry and the desired results. If a more detailed simulation is needed, then a finer mesh should be used. Be aware that a finer mesh significantly increases the computational running time of the model as well as the quantity of output data.

CFD models have much better spatial fidelity than zone models, being able to distinguish conditions in one part of the space from another. Because of the appreciable amount of time and effort required to apply CFD models as compared to zone models or algebraic models, CFD models are generally applied when:

 Spatial resolution is important, relative to either the locations of fuel packages or targets.

 Large compartments relative to the fire size are involved.

 Compartments have complex geometries, flow connections, or numerous obstructions in the upper part of the compartment.

 Large numbers of compartments are within the area of interest and the presence of each compartment is expected to affect the fire environment in the area of interest.

An example of a CFD simulation of a fire experiment is shown in Figure 1-3. The purpose of the calculation was to simulate an experiment that was part of the validation study described in NUREG-1824 (EPRI 1011999). In the experiment, a pan fire was placed in a relatively small

INTRODUCTION

compartment, and temperatures and heat fluxes were measured at various locations. The CFD simulation is able to describe the changing behavior of the fire as it interacts with its

surroundings.

Figure 1-3. A Smokeview visualization of a CFD model of a compartment fire experiment.

While CFD models provide a detailed analysis of a space, they are costly to create, simulate, and maintain. The input files created in the preprocessing stage require a significant effort to create. The user must understand the code syntax and the implications and approximations embedded in the model. A firm understanding of fire dynamics is important in providing input data that is relevant to the application. Most CFD models have default values that must be recognized and adjusted as necessary to obtain an accurate solution. The model user must understand and justify the relevance of the default values used in any application. Fire model users are expected to assess the appropriateness of default values provided in the fire models and make changes or adjust values as necessary. User manuals and technical references for each CFD model outline such values and may provide recommended ranges for the

parameters.

Depending on the complexity of the scenario and the computer’s computational power, the solver within the model can take anywhere from a few hours to weeks to complete all the calculations. This time cost depends on the measured parameters, the size of the geometry, and the mesh size of the calculations. Outputs of CFD models are visualized through a

post-INTRODUCTION processing program. The CFD model developed at NIST, Fire Dynamics Simulator (FDS), employs the program “Smokeview” to represent distributions of temperature, mass, heat flux, burning rate, etc. throughout the geometry. These parameters can be described through point locations, isocontours, or vector diagrams. Output data may also be stored in a comma-separated value file format that can be read by a standard spreadsheet program.

1.5.4 Fire Model Verification and Validation (V&V)

The use of fire models requires a good understanding of their limitations and predictive capabilities. For example, NFPA 805 states that fire models shall only be applied within the limitations of that fire model (section 2.4.1.2.2). ASTM E 1355, Standard Guide for Evaluating the Predictive Capability of Deterministic Fire Models, provides definitions of the terms model verification and model validation.

Model Verification is the process of determining that the implementation of a calculation method accurately represents the developer’s conceptual description of the calculation method and the solution to the calculation method. The fundamental strategy of verification of computational models is the identification and quantification of error in the computational model and its solution.

Model Validation is the process of determining the degree to which a calculation method is an accurate representation of the real world from the perspective of the intended uses of the calculation method. The fundamental strategy of validation is the identification and

quantification of error and uncertainty in the conceptual and computational models with respect to intended uses.

As noted in Section 1.1, NRC/RES and EPRI conducted a collaborative project for V&V of five fire models. The results of this project were documented in NUREG-1824 (EPRI 1011999), Verification and Validation of Selected Fire Models for Nuclear Power Plant Applications.