ASTM E1355-23

Standard Guide for Evaluating the Predictive Capability of Deterministic Fire Models

SIGNIFICANCE AND USE
5.1 The process of model evaluation is critical to establishing both the acceptable uses and limitations of fire models. It is not possible to evaluate a model in total; instead, this guide is intended to provide a methodology for evaluating the predictive capabilities for a specific use. Validation for one application or scenario does not imply validation for different scenarios. Several alternatives are provided for performing the evaluation process including: comparison of predictions against standard fire tests, full-scale fire experiments, field experience, published literature, or previously evaluated models.  
5.2 The use of fire models currently extends beyond the fire research laboratory and into the engineering, fire service and legal communities. Sufficient evaluation of fire models is necessary to ensure that those using the models can judge the adequacy of the scientific and technical basis for the models, select models appropriate for a desired use, and understand the level of confidence which can be placed on the results predicted by the models. Adequate evaluation will help prevent the unintentional misuse of fire models.  
5.3 This guide is intended to be used in conjunction with other guides under development by Committee E05. It is intended for use by:  
5.3.1 Model Developers—To document the usefulness of a particular calculation method perhaps for specific applications. Part of model development includes identification of precision and limits of applicability, and independent testing.  
5.3.2 Model Users—To assure themselves that they are using an appropriate model for an application and that it provides adequate accuracy.  
5.3.3 Developers of Model Performance Codes—To be sure that they are incorporating valid calculation procedures into codes.  
5.3.4 Approving Officials—To ensure that the results of calculations using mathematical models stating conformance to this guide, cited in a submission, show clearly that the model is used withi...
SCOPE
1.1 This guide provides a methodology for evaluating the predictive capabilities of a fire model for a specific use. The intent is to cover the whole range of deterministic numerical models which might be used in evaluating the effects of fires in and on structures.  
1.2 The methodology is presented in terms of four areas of evaluation:  
1.2.1 Defining the model and scenarios for which the evaluation is to be conducted,  
1.2.2 Verifying the appropriateness of the theoretical basis and assumptions used in the model,  
1.2.3 Verifying the mathematical and numerical robustness of the model, and  
1.2.4 Quantifying the uncertainty and accuracy of the model results in predicting of the course of events in similar fire scenarios.  
1.3 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety, health, and environmental practices and determine the applicability of regulatory limitations prior to use.  
1.4 This fire standard cannot be used to provide quantitative measures.  
1.5 This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.

General Information

• Status: Published
• Publication Date: 30-Jun-2023
• ICS: 13.220.01 - Protection against fire in general
• Technical Committee: E05 - Fire Standards
• Drafting Committee: E05.33 - Fire Safety Engineering

Relations

• Refers: ASTM E176-24 - Standard Terminology of Fire Standards
• Effective Date: 01-Jan-2024
• Refers: ASTM E1591-20 - Standard Guide for Obtaining Data for Fire Growth Models
• Effective Date: 01-Apr-2020
• Refers: ASTM E176-18a - Standard Terminology of Fire Standards
• Effective Date: 15-Dec-2018
• Refers: ASTM E176-18 - Standard Terminology of Fire Standards
• Effective Date: 01-Mar-2018
• Refers: ASTM E176-15a - Standard Terminology of Fire Standards
• Effective Date: 01-Aug-2015
• Refers: ASTM E176-15ae1 - Standard Terminology of Fire Standards
• Effective Date: 01-Aug-2015
• Refers: ASTM E176-15 - Standard Terminology of Fire Standards
• Effective Date: 01-Feb-2015
• Refers: ASTM E176-14c - Standard Terminology of Fire Standards
• Effective Date: 01-Oct-2014
• Refers: ASTM E176-14b - Standard Terminology of Fire Standards
• Effective Date: 15-Aug-2014
• Refers: ASTM E176-14a - Standard Terminology of Fire Standards
• Effective Date: 01-Aug-2014
• Refers: ASTM E176-14 - Standard Terminology of Fire Standards
• Effective Date: 01-Jul-2014
• Refers: ASTM E603-13 - Standard Guide for Room Fire Experiments
• Effective Date: 01-Aug-2013
• Refers: ASTM E176-13 - Standard Terminology of Fire Standards
• Effective Date: 01-Apr-2013
• Refers: ASTM E1591-13 - Standard Guide for Obtaining Data for Fire Growth Models
• Effective Date: 01-Apr-2013
• Refers: ASTM E176-12b - Standard Terminology of Fire Standards
• Effective Date: 15-Dec-2012

Overview

ASTM E1355-23 - Standard Guide for Evaluating the Predictive Capability of Deterministic Fire Models provides a comprehensive methodology for assessing the predictive capabilities of deterministic numerical fire models used in fire safety engineering. This guide, developed by ASTM International, covers the evaluation process specifically for fire modeling applications, helping users determine the adequacy, limitations, and reliability of fire models employed to simulate fire scenarios in and around structures.

Effective model evaluation is essential for model developers, users, code developers, officials, and educators. By applying this guide, stakeholders can select suitable models for targeted uses, understand the strengths and weaknesses of each model, and minimize the risk of improper application in fire safety analysis and engineering.

Key Topics

• Model Definition & Scenario Selection

  • Clearly define the fire model under evaluation, specifying key features and applications.
  • Outline scenarios and phenomena of interest, detailing predicted quantities and required accuracy.

• Verification of Theoretical Basis

  • Assess the appropriateness of the underlying science, including physics and chemistry.
  • Review theoretical assumptions and the completeness of supporting documentation.

• Mathematical and Numerical Robustness

  • Check that computer implementations accurately represent model theory.
  • Evaluate the robustness of algorithms, equation solutions, and handling of numerical errors.
  • Perform analytical tests, code checking, and numerical validation.

• Quantifying Uncertainty and Accuracy

  • Analyze the sensitivity of model outputs to uncertainties in input parameters.
  • Use sensitivity analysis, including methods such as Latin Hypercube Sampling or local/global techniques.
  • Assess experimental and completeness uncertainties, ensuring robust comparison with real fire scenarios.

• Model Evaluation Process

  • Use a tiered approach: blind calculation (limited input details), specified calculation (provided details), and open calculation (full disclosure including reference results).
  • Compare model predictions against standard tests, full-scale experiments, published literature, or previously evaluated models.

• Documentation and Reporting

  • Ensure thorough documentation of the model, scenario definitions, theoretical basis, algorithm implementation, data sources, and known limitations.
  • Provide clear user guidance, including input/output requirements and model restrictions.

Applications

ASTM E1355-23 is a valuable resource across multiple sectors involved in fire safety:

• Fire Safety Engineering: Assists professionals in validating fire simulation software used for building design, risk assessment, and fire protection planning.
• Model Developers: Provides a systematic process for developers to document, test, and benchmark new and existing fire models for transparency and reliability.
• Regulatory Approval: Useful for officials and code developers requiring documented evidence that fire models conform to established evaluation criteria.
• Research and Education: Enables educators and researchers to demonstrate and teach best practices in fire modeling and model validation.
• Legal and Forensic Analysis: Supports the robust evaluation of models used as evidence or in expert analysis during investigations or legal proceedings.

Related Standards

Several ASTM and international standards are closely related and provide additional guidance:

• ASTM E176: Terminology of Fire Standards – foundational definitions for fire science.
• ASTM E603: Guide for Room Fire Experiments – procedures for experimental fire data collection.
• ASTM E1591: Guide for Obtaining Data for Fire Growth Models – benchmarking input data for modeling.
• ISO/IEC Guide 98: Uncertainty of Measurement – frameworks for addressing measurement uncertainty.
• ISO 13943: Fire Safety Vocabulary – lexicon supporting clarity in fire safety discussions.
• ISO 16730: Fire Safety Engineering – Assessment, verification, and validation of calculation methods.

By following ASTM E1355-23, stakeholders can enhance the reliability, transparency, and acceptance of deterministic fire models, ensuring safer built environments through rigorous and consistent model evaluation practices.

Standards Content (Sample)

This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the
Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.
Designation: E1355 − 23 An American National Standard
Standard Guide for
Evaluating the Predictive Capability of Deterministic Fire
Models
This standard is issued under the fixed designation E1355; the number immediately following the designation indicates the year of
original adoption or, in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval. A
superscript epsilon (´) indicates an editorial change since the last revision or reapproval.
1. Scope E603 Guide for Room Fire Experiments
E1591 Guide for Obtaining Data for Fire Growth Models
1.1 This guide provides a methodology for evaluating the
2.2 International Standards Organization Standards:
predictive capabilities of a fire model for a specific use. The
ISO/IEC Guide 98 (2008) Uncertainty of measurement –
intent is to cover the whole range of deterministic numerical
Part 3: Guide to the expression of uncertainty in measure-
models which might be used in evaluating the effects of fires in
ment
and on structures.
ISO 13943 (2008) Fire safety – Vocabulary
1.2 The methodology is presented in terms of four areas of
ISO 16730 (2008) Fire safety engineering – Assessment,
evaluation:
verification and validation of calculation methods
1.2.1 Defining the model and scenarios for which the
evaluation is to be conducted,
3. Terminology
1.2.2 Verifying the appropriateness of the theoretical basis
3.1 Definitions: For definitions of terms used in this guide
and assumptions used in the model,
and associated with fire issues, refer to terminology contained
1.2.3 Verifying the mathematical and numerical robustness
in Terminology E176 and ISO 13943. In case of conflict, the
of the model, and
definitions given in Terminology E176 shall prevail.
1.2.4 Quantifying the uncertainty and accuracy of the model
3.2 Definitions of Terms Specific to This Standard:
results in predicting of the course of events in similar fire
3.2.1 model evaluation—the process of quantifying the
scenarios.
accuracy of chosen results from a model when applied for a
1.3 This standard does not purport to address all of the
specific use.
safety concerns, if any, associated with its use. It is the
3.2.2 model validation—the process of determining the
responsibility of the user of this standard to establish appro-
degree to which a calculation method is an accurate represen-
priate safety, health, and environmental practices and deter-
tation of the real world from the perspective of the intended
mine the applicability of regulatory limitations prior to use.
uses of the calculation method.
1.4 This fire standard cannot be used to provide quantitative
3.2.2.1 Discussion—The fundamental strategy of validation
measures.
is the identification and quantification of error and uncertainty
1.5 This international standard was developed in accor-
in the conceptual and computational models with respect to
dance with internationally recognized principles on standard-
intended uses.
ization established in the Decision on Principles for the
Development of International Standards, Guides and Recom- 3.2.3 model verification—the process of determining that
mendations issued by the World Trade Organization Technical the implementation of a calculation method accurately repre-
Barriers to Trade (TBT) Committee. sents the developer’s conceptual description of the calculation
method and the solution to the calculation method.
2. Referenced Documents
3.2.3.1 Discussion—The fundamental strategy of verifica-
2.1 ASTM Standards:
tion of computational models is the identification and quanti-
E176 Terminology of Fire Standards
fication of error in the computational model and its solution.
3.2.4 The precision of a model refers to the deterministic
This guide is under the jurisdiction of ASTM Committee E05 on Fire Standards
capability of a model and its repeatability.
and is the direct responsibility of Subcommittee E05.33 on Fire Safety Engineering.
Current edition approved July 1, 2023. Published August 2023. Originally 3.2.5 The accuracy refers to how well the model replicates
approved in 1990. Last previous edition approved in 2018 as E1355 – 12 (2018).
the evolution of an actual fire.
DOI: 10.1520/E1355-23.
For referenced ASTM standards, visit the ASTM website, www.astm.org, or
contact ASTM Customer Service at [e-mail]. For Annual Book of ASTM
Standards volume information, refer to the standard’s Document Summary page on Available from American National Standards Institute, 11 West 42nd Street,
the ASTM website. 13th Floor, New York, NY 10036.
Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United States
E1355 − 23
4. Summary of Guide 5.3.5 Educators—To demonstrate the application and ac-
ceptability of calculation methods being taught.
4.1 A recommended process for evaluating the predictive
5.4 This guide is not meant to describe an acceptance testing
capability of fire models is described. This process includes a
procedure.
brief description of the model and the scenarios for which
evaluation is sought. Then, methodologies for conducting an
5.5 The emphasis of this guide is numerical models of fire
analysis to quantify the sensitivity of model predictions to
evolution.
various uncertain factors are presented, and several alternatives
5.5.1 The precision of a model refers to the deterministic
for evaluating the accuracy of the predictions of the model are
capability of a model and its repeatability.
provided. Historically, numerical accuracy has been concerned
5.5.2 The accuracy of a model refers to how well the model
with time step size and errors. A more complete evaluation
replicates the evolution of an actual fire.
must include spatial discretization. Finally, guidance is given
6. General Methodology
concerning the relevant documentation required to summarize
the evaluation process.
6.1 The methodology is presented in terms of four areas of
evaluation:
5. Significance and Use
6.1.1 Defining the model and scenarios for which the
evaluation is to be conducted,
5.1 The process of model evaluation is critical to establish-
6.1.2 Assessing the appropriateness of the theoretical basis
ing both the acceptable uses and limitations of fire models. It is
and assumptions used in the model,
not possible to evaluate a model in total; instead, this guide is
6.1.3 Assessing the mathematical and numerical robustness
intended to provide a methodology for evaluating the predic-
of the model, and
tive capabilities for a specific use. Validation for one applica-
6.1.4 Quantifying the uncertainty and accuracy of the model
tion or scenario does not imply validation for different sce-
results in predicting the course of events in similar fire
narios. Several alternatives are provided for performing the
scenarios.
evaluation process including: comparison of predictions
6.1.5 This general methodology is also consistent with the
against standard fire tests, full-scale fire experiments, field
methodology presented in ISO 16730, Fire safety engineering
experience, published literature, or previously evaluated mod-
– Assessment, verification and validation of calculation
els.
methods, which is a potentially useful resource which can be
5.2 The use of fire models currently extends beyond the fire
used with ASTM E1355.
research laboratory and into the engineering, fire service and
6.2 Model and Scenario Documentation:
legal communities. Sufficient evaluation of fire models is
6.2.1 Model Documentation—Sufficient documentation of
necessary to ensure that those using the models can judge the
calculation models, including computer software, is absolutely
adequacy of the scientific and technical basis for the models,
necessary to assess the adequacy of the scientific and technical
select models appropriate for a desired use, and understand the
basis of the models, and the accuracy of computational
level of confidence which can be placed on the results
procedures. Also, adequate documentation will help prevent
predicted by the models. Adequate evaluation will help prevent
the unintentional misuse of fire models. Guidance on the
the unintentional misuse of fire models.
documentation of computer-based fire models is provided in
5.3 This guide is intended to be used in conjunction with Section 7.
other guides under development by Committee E05. It is 6.2.2 Scenario Documentation—Provide a complete de-
intended for use by: scription of the scenarios or phenomena of interest in the
evaluation to facilitate appropriate application of the model, to
5.3.1 Model Developers—To document the usefulness of a
aid in developing realistic inputs for the model, and to develop
particular calculation method perhaps for specific applications.
criteria for judging the results of the evaluation. Details
Part of model development includes identification of precision
applicable to evaluation of the predictive capability of fire
and limits of applicability, and independent testing.
models are provided in 7.2.
5.3.2 Model Users—To assure themselves that they are
using an appropriate model for an application and that it 6.3 Theoretical Basis and Assumptions in the Model—An
provides adequate accuracy. independent review of the underlying physics and chemistry
inherent in a model ensures appropriate application of submod-
5.3.3 Developers of Model Performance Codes—To be sure
els which have been combined to produce the overall model.
that they are incorporating valid calculation procedures into
Details applicable to evaluation of the predictive capability of
codes.
fire models are provided in Section 8.
5.3.4 Approving Offıcials—To ensure that the results of
calculations using mathematical models stating conformance to 6.4 Mathematical and Numerical Robustness—The com-
this guide, cited in a submission, show clearly that the model puter implementation of the model should be checked to ensure
is used within its applicable limits and has an acceptable level such implementation matches the stated documentation. De-
of accuracy. tails applicable to evaluation of the predictive capability of fire
E1355 − 23
models are provided in Section 9. Along with 6.3, this phenomena it is designed to simulate. However, completeness
constitutes verification of the model. uncertainty is addressed indirectly by the same process used to
address the model uncertainty.
6.5 Quantifying the Uncertainty and Accuracy of the
Model—The uncertainty of the result of a model calculation
7. Model and Scenario Definition
consists of three components. The following description of
these components is based in part on pertinent sections of 7.1 Model Documentation—Provides details of the model
NUREG-1934.
evaluated in sufficient detail such that the user of the evaluation
6.5.1 Parameter Uncertainty—Input parameters are gener- could independently repeat the evaluation. The following
ally obtained from measurements in experiments or estimated
information should be provided:
from generic reference data. In either case, the uncertainties of 7.1.1 Program Identification:
these input parameters are propagated through the calculation,
7.1.1.1 Provide the name of the program or model, a
and the resulting uncertainty in the model prediction is known
descriptive title, and any information necessary to define the
as the parameter uncertainty. For fire models that rely on
version uniquely.
numerical solutions of the model equations, a Monte Carlo
7.1.1.2 Define the basic processing tasks performed, and
method can be used to estimate the parameter uncertainty. This
describe the methods and procedures employed. A schematic
method estimates the uncertainty of the model output based on
display of the flow of the calculations is useful.
a large number of "trials". Each trial involves a random
7.1.1.3 Identify the computer(s) on which the program has
selection (or sample) of input parameter values, followed by
been executed successfully and any required peripherals,
the calculation of the corresponding model output. The sam-
including memory requirements and tapes.
pling process is guided by the statistical distributions of the
7.1.1.4 Identify the programming languages and versions in
input parameters (typically Gaussian), which determine the
use.
probability of selecting a particular value for each trial. The
7.1.1.5 Identify the software operating system and versions
fidelity of the Monte Carlo uncertainty estimate can be
in use, including library routines.
improved by increasing the number of trails. Consequently, the
7.1.1.6 Describe any relationships to other models.
required number of trials depends on the numerical tolerance of
7.1.1.7 Describe the history of the model’s development and
the uncertainty prediction that needs to be achieved. For a
the names and addresses of the individual(s) and organiza-
complex numerical fire model with a large number of input
tions(s) responsible.
parameters, using the Monte Carlo method to obtain a reason-
7.1.1.8 Provide instructions for obtaining more detailed
ably accurate estimate of parameter uncertainty is often too
information about the model from the individual(s) responsible
time-consuming and not practical, even after ignoring specific
for maintenance of the model.
input parameters identified through a sensitivity analysis as
7.1.2 References—List the publications and other reference
having a small or negligible effect on model output uncertainty.
materials directly related to the fire model or software.
Details of sensitivity analyses applicable to evaluation of the
7.1.3 Problem or Function Identification:
predictive capability of fire models are provided in Section 10.
7.1.3.1 Define the fire problem modeled or function per-
6.5.2 Model Uncertainty—The model equations are not an
formed by the program, for example, calculation of fire growth,
exact representation of the simulated physical phenomena. In
smoke spread, people movement, etc.
addition, the numerical solutions of model equations are
7.1.3.2 Describe the total fire problem environment. Gen-
approximate. Model uncertainty is estimated via the processes
eral block or flow diagrams may be included here.
of verification and validation (V&V). Verification is the pro-
7.1.3.3 Include any desirable background information, such
cess to determine that the implementation of a calculation
as feasibility studies or justification statements.
method accurately represents the developer’s conceptual de-
7.1.4 Theoretical Foundation:
scription of the calculation method and the solution to the
7.1.4.1 Describe the theoretical basis of the phenomenon
calculation method. Validation seeks to quantify the error
and the physical laws on which the model is based.
associated with the simplifying physical approximations, typi-
7.1.4.2 Present the governing equations and the mathemati-
cally through comparison of model predictions and full-scale
cal model employed.
experiments. NUREG-1824 Supplement 1 provides a detailed
7.1.4.3 Identify the major assumptions on which the fire
discussion of the V&V of various algebraic and numerical fire
model is based and any simplifying assumptions.
models that are used in support of risk-informed performance-
7.1.4.4 Provide results of any independent review of the
based fire protection of nuclear power plants in the United
theoretical basis of the model. This guide recommends a
States.
review by one or more recognized experts fully conversant
6.5.3 Completeness Uncertainty—This component refers to
with the chemistry and physics of fire phenomena but not
the fact that a model may not be a complete description of the
involved with the production of the model.
7.1.5 Mathematical Foundation:
7.1.5.1 Describe the mathematical techniques, procedures,
"Nuclear Power Plant Fire Modeling Analysis Guidelines (NPP FIRE MAG),"
NUREG-1934 (ML12314A165), U.S. Nuclear Regulatory Commission, Washing-
and computational algorithms employed to obtain numerical
ton DC, 2012.
solutions.
"Verification and Validation of Selected Fire Models for Nuclear Power Plant
7.1.5.2 Provide references to the algorithms and numerical
Applications," NUREG-1824 Supplement 1 (ML16309A011), U.S. Nuclear Regu-
latory Commission, Washington DC, 2016. techniques.
E1355 − 23
7.1.5.3 Present the mathematical equations in conventional 7.1.7.6 Provide both general and specific limitations of the
terminology and show how they are implemented in the code. fire model for specific applications.
7.1.5.4 Discuss the precision of the results obtained by 7.1.8 Input Data:
important algorithms and any known dependence on the
7.1.8.1 Describe the source of input information, for
particular computer facility.
example, handbooks, journals, research reports, standard tests,
7.1.5.5 For iterative solutions, discuss the use and interpre-
experiments, etc.
tation of convergence tests, and recommend a range of values
7.1.8.2 Provide the default values or the general conven-
for convergence criteria. For probabilistic solutions, discuss the
tions governing those values.
precision of the results having a statistical variance.
7.1.8.3 Identify the limits on input based on stability,
7.1.5.6 Identify the limitations of the model based on the
accuracy, and practicality, as well as their resulting limitations
algorithms and numerical techniques.
to output.
7.1.5.7 Provide results of any analyses that have been
7.1.8.4 When property values are defined within the
performed on the mathematical and numerical robustness of
program, list the properties and the assigned values.
the model. Analytical tests, code checking, and numerical tests
7.1.8.5 Identify the procedures that should be used or were
are among the analyses listed in this guide that are appropriate
used to obtain property and other input data.
for this purpose.
7.1.8.6 Provide information on the dominant variables in the
7.1.6 Program Description:
models.
7.1.6.1 Describe the program.
7.1.9 Output Information:
7.1.6.2 List any auxiliary programs or external data files
7.1.9.1 Describe the program output.
required for utilization of this program.
7.1.9.2 Relate the edited output to input options.
7.1.6.3 Describe the function of each major option available
7.1.9.3 Relate the output to appropriate equations.
for solving various problems, pay special attention to the
7.1.9.4 Describe any normalization of results and list asso-
effects of combinations of options.
ciated dimensional units.
7.1.6.4 Describe alternate paths that may be dynamically
7.1.9.5 Identify any special forms of output, for example,
selected by the program from tests on calculated results.
graphics display and plots.
7.1.6.5 Describe the relationship between input and output
7.1.10 List of Variables:
items for programs that reformat information.
7.1.10.1 List the program and subprogram variables and
7.1.6.6 Describe the method and technical basis for deci-
parameters. The list should include their use and purpose
sions in programs that perform logical operations.
within the program, as well as in its inputs and results. Identify
7.1.6.7 Describe the basis for the operations that occur in
them as local or global variables; that is, do they apply within
the program.
the module, or are they common to two or more modules of the
7.1.6.8 Identify the source language(s).
system?
7.1.6.9 Include a flowchart showing the overall program
7.1.10.2 Define all meaningful symbols and arrays used in
structure and logic, and detailed flowcharts, where appropriate.
the routine. Refer to the mathematical or technical notations
The subprogram names should be included on these charts.
and terms used in the technical document. Provide units, where
7.1.6.10 Pinpoint any known areas of dependency on the
applicable. Describe the nominal and initial values of param-
local computer installation support facilities.
eters (for example, a computational zero, step sizes, and
7.1.6.11 Include a detailed narrative and graphical descrip-
convergence factors), along with their ranges. Discuss how
tion of the programming techniques used in writing the
they affect the computational process.
program, that is, calling sequence, overlay structure, test plan,
7.2 Scenarios for which the Model has been Evaluated—
common usage, etc.
Provides details on the range of parameters for which the
7.1.6.12 Provide a source listing, or make sure it is readily
evaluation has been conducted. Sufficient information should
available.
be included such that the user of the evaluation could indepen-
7.1.6.13 Use comments within the program. The liberal use
dently repeat the evalutation. At a minimum, the following
of comments is a key to understandable programs. An alterna-
information should be provided:
tive is a commentary keyed to the executable statements of the
7.2.1 A description of the scenarios or phenomena of
program.
interest,
7.1.7 Restrictions and Limitations:
7.2.2 A list of quantities predicted by the model for which
7.1.7.1 List hardware and software restrictions.
evaluation is sought, and
7.1.7.2 Provide data ranges and capacitities.
7.2.3 The degree of accuracy required for each quantity.
7.1.7.3 Describe the program behavior when restrictions are
violated, and describe recovery procedures.
8. Theoretical Basis for the Model
7.1.7.4 If accuracy characteristics are significant, describe
them in detail.
8.1 The theoretical basis of the model should be subjected to
7.1.7.5 Provide information and cautions on the degree and a peer review by one or more recognized experts fully
level of care to be taken in selecting input and running the conversant with the chemistry and physics of fire phenomena
model. but not involved with the production of the model. Publication
E1355 − 23
of the theoretical basis of the model in a peer-reviewed journal numerical difficulties. Such problems are called stiff. Some
article may be sufficient to fulfill this review. This review numerical methods have difficulty with stiff problems since
should include: they slavishly follow the rapid changes even when they are less
8.1.1 An assessment of the completeness of the documen- important than the general trend in the solution. Special
algorithms have been devised for solving stiff problems.
tation particularly with regard to the assumptions and approxi-
mations. 9.1.5 Numerical accuracy of predictive fire models has been
considered in the literature.
8.1.2 An assessment of whether there is sufficient scientific
evidence in the open scientific literature to justify the ap-
10. Model Sensitivity
proaches and assumptions being used.
8.1.3 An assessment of the accuracy and applicability of the 10.1 Fire growth models are typically based on a system of
ordinary differential equations of the form
empirical or reference data used for constants and default
values in the context of the model.
dz
5 f~z, p, τ! z~τ 5 0! 5 z (1)
8.1.4 The set of equations that is being solved; in cases for 0
dτ
which closure equations are needed (not included in 8.1.3) the
where:
assumption and implication of such choices.
z (z , z , . . ., z ) = the solution vector for the system of
1 2 m
equations (for example, mass,
9. Mathematical and Numerical Robustness
temperature, or volume)
9.1 Analyses which can be performed include:
p (p , p , . . ., p ) = a vector of input parameters (for
1 2 n
9.1.1 Analytical Tests—If the program is to be applied to a
example, room area, room height, heat
situation for which there is a known mathematical solution,
release rate), and
analytical testing is a powerful way of testing the correct
τ = time.
functioning of a model. However, there are relatively few
The solutions to these equations are, in general, not known
situations (especially for complex scenarios) for which analyti-
explicitly and must be determined numerically. To study the
cal solutions are known. Analytic tests for submodels should be
sensitivity of such a set of equations, the partial derivatives of
performed. For example, it is possible to provide a closed-form
an output z with respect to an input p (for j = 1, . . ., m and I
j i
solution for heat loss through a partition; the model should be
= 1, . . ., n) should be examined.
able to do this calculation.
10.2 A sensitivity analysis of a model is a study of how
9.1.2 Code Checking—The code can be verified on a struc-
changes in model parameters affect the results generated by the
tural basis preferably by a third party either totally manually or
model. Model predictions may be sensitive to uncertainties in
by using code checking programs to detect irregularities and
input data, to the level of rigor employed in modeling the
inconsistencies within the computer code. A process of code
relevant physics and chemistry, and to the accuracy of numeri-
checking can increase the level of confidence in the program’s
cal treatments. The purpose of conducting a sensitivity analysis
ability to process the data to the program correctly, but it
is to assess the extent to which uncertainty in model inputs is
cannot give any indication of the likely adequacy or accuracy
manifested to become uncertainty in the results of interest from
of the program in use.
the model. This information can be used to:
9.1.3 Numerical Tests—Mathematical models are usually
10.2.1 Determine the dominant variables in the models,
expressed in the form of differential or integral equations. The
10.2.2 Define the acceptable range of values for each input
models are in general very complex, and analytical solutions
variable,
are hard or even impossible to find. Numerical techniques are
10.2.3 Quantify the sensitivity of output variables to varia-
needed for finding approximate solutions. These numerical
tions in input data, and
techniques can be a source of error in the predicted results.
10.2.4 Inform and cauti
...

This document is not an ASTM standard and is intended only to provide the user of an ASTM standard an indication of what changes have been made to the previous version. Because
it may not be technically possible to adequately depict all changes accurately, ASTM recommends that users consult prior editions as appropriate. In all cases only the current version
of the standard as published by ASTM is to be considered the official document.
Designation: E1355 − 12 (Reapproved 2018) E1355 − 23 An American National Standard
Standard Guide for
Evaluating the Predictive Capability of Deterministic Fire
Models
This standard is issued under the fixed designation E1355; the number immediately following the designation indicates the year of
original adoption or, in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval. A
superscript epsilon (´) indicates an editorial change since the last revision or reapproval.
1. Scope
1.1 This guide provides a methodology for evaluating the predictive capabilities of a fire model for a specific use. The intent is
to cover the whole range of deterministic numerical models which might be used in evaluating the effects of fires in and on
structures.
1.2 The methodology is presented in terms of four areas of evaluation:
1.2.1 Defining the model and scenarios for which the evaluation is to be conducted,
1.2.2 Verifying the appropriateness of the theoretical basis and assumptions used in the model,
1.2.3 Verifying the mathematical and numerical robustness of the model, and
1.2.4 Quantifying the uncertainty and accuracy of the model results in predicting of the course of events in similar fire scenarios.
1.3 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility
of the user of this standard to establish appropriate safety, health, and environmental practices and determine the applicability of
regulatory limitations prior to use.
1.4 This fire standard cannot be used to provide quantitative measures.
1.5 This international standard was developed in accordance with internationally recognized principles on standardization
established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued
by the World Trade Organization Technical Barriers to Trade (TBT) Committee.
2. Referenced Documents
2.1 ASTM Standards:
E176 Terminology of Fire Standards
E603 Guide for Room Fire Experiments
E1591 Guide for Obtaining Data for Fire Growth Models
This guide is under the jurisdiction of ASTM Committee E05 on Fire Standards and is the direct responsibility of Subcommittee E05.33 on Fire Safety Engineering.
Current edition approved July 1, 2018July 1, 2023. Published August 2018August 2023. Originally approved in 1990. Last previous edition approved in 20122018 as
E1355 – 12.E1355 – 12 (2018). DOI: 10.1520/E1355-12R18.10.1520/E1355-23.
For referenced ASTM standards, visit the ASTM website, www.astm.org, or contact ASTM Customer Service at [e-mail]. For Annual Book of ASTM Standards
volume information, refer to the standard’s Document Summary page on the ASTM website.
Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United States
E1355 − 23
2.2 International Standards Organization Standards:
ISO/IEC Guide 98 (2008) Uncertainty of measurement – Part 3: Guide to the expression of uncertainty in measurement
ISO 13943 (2008) Fire safety – Vocabulary
ISO 16730 (2008) Fire safety engineering – Assessment, verification and validation of calculation methods
3. Terminology
3.1 Definitions: For definitions of terms used in this guide and associated with fire issues, refer to terminology contained in
Terminology E176 and ISO 13943. In case of conflict, the definitions given in Terminology E176 shall prevail.
3.2 Definitions of Terms Specific to This Standard:
3.2.1 model evaluation—the process of quantifying the accuracy of chosen results from a model when applied for a specific use.
3.2.2 model validation—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.
3.2.2.1 Discussion—
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.
3.2.3 model verification—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.
3.2.3.1 Discussion—
The fundamental strategy of verification of computational models is the identification and quantification of error in the
computational model and its solution.
3.2.4 The precision of a model refers to the deterministic capability of a model and its repeatability.
3.2.5 The accuracy refers to how well the model replicates the evolution of an actual fire.
4. Summary of Guide
4.1 A recommended process for evaluating the predictive capability of fire models is described. This process includes a brief
description of the model and the scenarios for which evaluation is sought. Then, methodologies for conducting an analysis to
quantify the sensitivity of model predictions to various uncertain factors are presented, and several alternatives for evaluating the
accuracy of the predictions of the model are provided. Historically, numerical accuracy has been concerned with time step size and
errors. A more complete evaluation must include spatial discretization. Finally, guidance is given concerning the relevant
documentation required to summarize the evaluation process.
5. Significance and Use
5.1 The process of model evaluation is critical to establishing both the acceptable uses and limitations of fire models. It is not
possible to evaluate a model in total; instead, this guide is intended to provide a methodology for evaluating the predictive
capabilities for a specific use. Validation for one application or scenario does not imply validation for different scenarios. Several
alternatives are provided for performing the evaluation process including: comparison of predictions against standard fire tests,
full-scale fire experiments, field experience, published literature, or previously evaluated models.
5.2 The use of fire models currently extends beyond the fire research laboratory and into the engineering, fire service and legal
communities. Sufficient evaluation of fire models is necessary to ensure that those using the models can judge the adequacy of the
scientific and technical basis for the models, select models appropriate for a desired use, and understand the level of confidence
which can be placed on the results predicted by the models. Adequate evaluation will help prevent the unintentional misuse of fire
models.
5.3 This guide is intended to be used in conjunction with other guides under development by Committee E05. It is intended for
use by:
Available from American National Standards Institute, 11 West 42nd Street, 13th Floor, New York, NY 10036.
E1355 − 23
5.3.1 Model Developers—To document the usefulness of a particular calculation method perhaps for specific applications. Part of
model development includes identification of precision and limits of applicability, and independent testing.
5.3.2 Model Users—To assure themselves that they are using an appropriate model for an application and that it provides adequate
accuracy.
5.3.3 Developers of Model Performance Codes—To be sure that they are incorporating valid calculation procedures into codes.
5.3.4 Approving Offıcials—To ensure that the results of calculations using mathematical models stating conformance to this guide,
cited in a submission, show clearly that the model is used within its applicable limits and has an acceptable level of accuracy.
5.3.5 Educators—To demonstrate the application and acceptability of calculation methods being taught.
5.4 This guide is not meant to describe an acceptance testing procedure.
5.5 The emphasis of this guide is numerical models of fire evolution.
5.5.1 The precision of a model refers to the deterministic capability of a model and its repeatability.
5.5.2 The accuracy of a model refers to how well the model replicates the evolution of an actual fire.
6. General Methodology
6.1 The methodology is presented in terms of four areas of evaluation:
6.1.1 Defining the model and scenarios for which the evaluation is to be conducted,
6.1.2 Assessing the appropriateness of the theoretical basis and assumptions used in the model,
6.1.3 Assessing the mathematical and numerical robustness of the model, and
6.1.4 Quantifying the uncertainty and accuracy of the model results in predicting the course of events in similar fire scenarios.
6.1.5 This general methodology is also consistent with the methodology presented in ISO 16730, Fire safety engineering –
Assessment, verification and validation of calculation methods, which is a potentially useful resource which can be used with
ASTM E1355.
6.2 Model and Scenario Documentation:
6.2.1 Model Documentation—Sufficient documentation of calculation models, including computer software, is absolutely
necessary to assess the adequacy of the scientific and technical basis of the models, and the accuracy of computational procedures.
Also, adequate documentation will help prevent the unintentional misuse of fire models. Guidance on the documentation of
computer-based fire models is provided in Section 7.
6.2.2 Scenario Documentation—Provide a complete description of the scenarios or phenomena of interest in the evaluation to
facilitate appropriate application of the model, to aid in developing realistic inputs for the model, and to develop criteria for judging
the results of the evaluation. Details applicable to evaluation of the predictive capability of fire models are provided in 7.2.
6.3 Theoretical Basis and Assumptions in the Model—An independent review of the underlying physics and chemistry inherent
in a model ensures appropriate application of submodels which have been combined to produce the overall model. Details
applicable to evaluation of the predictive capability of fire models are provided in Section 8.
6.4 Mathematical and Numerical Robustness—The computer implementation of the model should be checked to ensure such
implementation matches the stated documentation. Details applicable to evaluation of the predictive capability of fire models are
provided in Section 9. Along with 6.3, this constitutes verification of the model.
E1355 − 23
6.5 Quantifying the Uncertainty and Accuracy of the Model: Model—The uncertainty of the result of a model calculation consists
of three components. The following description of these components is based in part on pertinent sections of NUREG-1934.
6.5.1 ModelParameter Uncertainty—Even deterministic models rely on inputs often based on experimental measurements,
empirical correlations, or estimates made by engineering judgment. Uncertainties Input parameters are generally obtained from
measurements in experiments or estimated from generic reference data. In either case, the uncertainties of these input parameters
are propagated through the calculation, and the resulting uncertainty in the model inputs can lead to correspondingprediction is
known as the uncertainties inparameter uncertainty. the model outputs. Sensitivity analysis is used to quantify these uncertainties
in the model outputs based upon known or estimated uncertainties in model inputs. Guidance for obtaining input data for fire
models is provided by GuideFor fire models that rely on numerical solutions of the model equations, a Monte Carlo method can
be used to estimate the parameter uncertainty. This method estimates the uncertainty of the model output based on a large number
of "trials". Each trial involves a random selection (or sample) of input parameter values, followed by the calculation of the
corresponding model output. The sampling process is guided by the statistical distributions of the input parameters (typically
Gaussian), which determine the probability of selecting a particular value for each trial. The fidelity of the Monte Carlo uncertainty
estimate can be improved by increasing the number of trails. Consequently, the required number of trials depends on the numerical
tolerance of the uncertainty prediction that needs to be achieved. For a complex numerical fire model with a large number of input
parameters, using the Monte Carlo method to obtain a reasonably accurate estimate of parameter uncertainty is often too
time-consuming E1591. and not practical, even after ignoring specific input parameters identified through a sensitivity analysis as
having a small or negligible effect on model output uncertainty. Details of sensitivity analysisanalyses applicable to evaluation of
the predictive capability of fire models are provided in Section 10.
6.5.2 Experimental Uncertainty—In general, the result of measurement is only the result of an approximation or estimate of the
specific quantity subject to measurement, and thus the result is complete only when accompanied by a quantitative statement of
uncertainty. Guidance for conducting full-scale compartment tests is provided by Guide E603. Guidance for determining the
uncertainty in measurements is provided in the ISO Guide to the Expression of Uncertainty in Measurement.
6.5.2 Model Evaluation—Uncertainty—Obtaining accurate estimates of fire behavior using predictive fire models involves
insuring correct model inputs appropriate to the scenarios to be modeled, correct selection of a model appropriate to the scenarios
to be modeled, correct calculations by the model chosen, and correct interpretation of the results of the model calculation.
Evaluation of a specific scenario with different levels of knowledge of the expected results The model equations are not an exact
representation of the simulated physical phenomena. In addition, the numerical solutions of model equations are approximate.
Model uncertainty is estimated via the processes of verification and validation (V&V). Verification is the process to determine that
the implementation of a calculation method accurately represents the developer’s conceptual description of the calculation
addresses these multiple sources of potential error. Details applicable to evaluation method and the solution to the calculation
method. Validation seeks to quantify the error associated with the simplifying physical approximations, typically through
comparison of model predictions and full-scale experiments. NUREG-1824 Supplement 1 provides a detailed discussion of the
predictive capability of fire models are provided in SectionV&V of various algebraic and numerical fire models that are used in
support 11.of risk-informed performance-based fire protection of nuclear power plants in the United States.
6.5.3 Completeness Uncertainty—This component refers to the fact that a model may not be a complete description of the
phenomena it is designed to simulate. However, completeness uncertainty is addressed indirectly by the same process used to
address the model uncertainty.
7. Model and Scenario Definition
7.1 Model Documentation—Provides details of the model evaluated in sufficient detail such that the user of the evaluation could
independently repeat the evaluation. The following information should be provided:
7.1.1 Program Identification:
7.1.1.1 Provide the name of the program or model, a descriptive title, and any information necessary to define the version uniquely.
"Nuclear Power Plant Fire Modeling Analysis Guidelines (NPP FIRE MAG)," NUREG-1934 (ML12314A165), U.S. Nuclear Regulatory Commission, Washington DC,
2012.
"Verification and Validation of Selected Fire Models for Nuclear Power Plant Applications," NUREG-1824 Supplement 1 (ML16309A011), U.S. Nuclear Regulatory
Commission, Washington DC, 2016.
E1355 − 23
7.1.1.2 Define the basic processing tasks performed, and describe the methods and procedures employed. A schematic display of
the flow of the calculations is useful.
7.1.1.3 Identify the computer(s) on which the program has been executed successfully and any required peripherals, including
memory requirements and tapes.
7.1.1.4 Identify the programming languages and versions in use.
7.1.1.5 Identify the software operating system and versions in use, including library routines.
7.1.1.6 Describe any relationships to other models.
7.1.1.7 Describe the history of the model’s development and the names and addresses of the individual(s) and organizations(s)
responsible.
7.1.1.8 Provide instructions for obtaining more detailed information about the model from the individual(s) responsible for
maintenance of the model.
7.1.2 References—List the publications and other reference materials directly related to the fire model or software.
7.1.3 Problem or Function Identification:
7.1.3.1 Define the fire problem modeled or function performed by the program, for example, calculation of fire growth, smoke
spread, people movement, etc.
7.1.3.2 Describe the total fire problem environment. General block or flow diagrams may be included here.
7.1.3.3 Include any desirable background information, such as feasibility studies or justification statements.
7.1.4 Theoretical Foundation:
7.1.4.1 Describe the theoretical basis of the phenomenon and the physical laws on which the model is based.
7.1.4.2 Present the governing equations and the mathematical model employed.
7.1.4.3 Identify the major assumptions on which the fire model is based and any simplifying assumptions.
7.1.4.4 Provide results of any independent review of the theoretical basis of the model. This guide recommends a review by one
or more recognized experts fully conversant with the chemistry and physics of fire phenomena but not involved with the production
of the model.
7.1.5 Mathematical Foundation:
7.1.5.1 Describe the mathematical techniques, procedures, and computational algorithms employed to obtain numerical solutions.
7.1.5.2 Provide references to the algorithms and numerical techniques.
7.1.5.3 Present the mathematical equations in conventional terminology and show how they are implemented in the code.
7.1.5.4 Discuss the precision of the results obtained by important algorithms and any known dependence on the particular
computer facility.
7.1.5.5 For iterative solutions, discuss the use and interpretation of convergence tests, and recommend a range of values for
convergence criteria. For probabilistic solutions, discuss the precision of the results having a statistical variance.
7.1.5.6 Identify the limitations of the model based on the algorithms and numerical techniques.
E1355 − 23
7.1.5.7 Provide results of any analyses that have been performed on the mathematical and numerical robustness of the model.
Analytical tests, code checking, and numerical tests are among the analyses listed in this guide that are appropriate for this purpose.
7.1.6 Program Description:
7.1.6.1 Describe the program.
7.1.6.2 List any auxiliary programs or external data files required for utilization of this program.
7.1.6.3 Describe the function of each major option available for solving various problems, pay special attention to the effects of
combinations of options.
7.1.6.4 Describe alternate paths that may be dynamically selected by the program from tests on calculated results.
7.1.6.5 Describe the relationship between input and output items for programs that reformat information.
7.1.6.6 Describe the method and technical basis for decisions in programs that perform logical operations.
7.1.6.7 Describe the basis for the operations that occur in the program.
7.1.6.8 Identify the source language(s).
7.1.6.9 Include a flowchart showing the overall program structure and logic, and detailed flowcharts, where appropriate. The
subprogram names should be included on these charts.
7.1.6.10 Pinpoint any known areas of dependency on the local computer installation support facilities.
7.1.6.11 Include a detailed narrative and graphical description of the programming techniques used in writing the program, that
is, calling sequence, overlay structure, test plan, common usage, etc.
7.1.6.12 Provide a source listing, or make sure it is readily available.
7.1.6.13 Use comments within the program. The liberal use of comments is a key to understandable programs. An alternative is
a commentary keyed to the executable statements of the program.
7.1.7 Restrictions and Limitations:
7.1.7.1 List hardware and software restrictions.
7.1.7.2 Provide data ranges and capacitities.
7.1.7.3 Describe the program behavior when restrictions are violated, and describe recovery procedures.
7.1.7.4 If accuracy characteristics are significant, describe them in detail.
7.1.7.5 Provide information and cautions on the degree and level of care to be taken in selecting input and running the model.
7.1.7.6 Provide both general and specific limitations of the fire model for specific applications.
7.1.8 Input Data:
7.1.8.1 Describe the source of input information, for example, handbooks, journals, research reports, standard tests, experiments,
etc.
7.1.8.2 Provide the default values or the general conventions governing those values.
7.1.8.3 Identify the limits on input based on stability, accuracy, and practicality, as well as their resulting limitations to output.
E1355 − 23
7.1.8.4 When property values are defined within the program, list the properties and the assigned values.
7.1.8.5 Identify the procedures that should be used or were used to obtain property and other input data.
7.1.8.6 Provide information on the dominant variables in the models.
7.1.9 Output Information:
7.1.9.1 Describe the program output.
7.1.9.2 Relate the edited output to input options.
7.1.9.3 Relate the output to appropriate equations.
7.1.9.4 Describe any normalization of results and list associated dimensional units.
7.1.9.5 Identify any special forms of output, for example, graphics display and plots.
7.1.10 List of Variables:
7.1.10.1 List the program and subprogram variables and parameters. The list should include their use and purpose within the
program, as well as in its inputs and results. Identify them as local or global variables; that is, do they apply within the module,
or are they common to two or more modules of the system?
7.1.10.2 Define all meaningful symbols and arrays used in the routine. Refer to the mathematical or technical notations and terms
used in the technical document. Provide units, where applicable. Describe the nominal and initial values of parameters (for
example, a computational zero, step sizes, and convergence factors), along with their ranges. Discuss how they affect the
computational process.
7.2 Scenarios for which the Model has been Evaluated—Provides details on the range of parameters for which the evaluation has
been conducted. Sufficient information should be included such that the user of the evaluation could independently repeat the
evalutation. At a minimum, the following information should be provided:
7.2.1 A description of the scenarios or phenomena of interest,
7.2.2 A list of quantities predicted by the model for which evaluation is sought, and
7.2.3 The degree of accuracy required for each quantity.
8. Theoretical Basis for the Model
8.1 The theoretical basis of the model should be subjected to a peer review by one or more recognized experts fully conversant
with the chemistry and physics of fire phenomena but not involved with the production of the model. Publication of the theoretical
basis of the model in a peer-reviewed journal article may be sufficient to fulfill this review. This review should include:
8.1.1 An assessment of the completeness of the documentation particularly with regard to the assumptions and approximations.
8.1.2 An assessment of whether there is sufficient scientific evidence in the open scientific literature to justify the approaches and
assumptions being used.
8.1.3 An assessment of the accuracy and applicability of the empirical or reference data used for constants and default values in
the context of the model.
8.1.4 The set of equations that is being solved; in cases for which closure equations are needed (not included in 8.1.3) the
assumption and implication of such choices.
E1355 − 23
9. Mathematical and Numerical Robustness
9.1 Analyses which can be performed include:
9.1.1 Analytical Tests—If the program is to be applied to a situation for which there is a known mathematical solution, analytical
testing is a powerful way of testing the correct functioning of a model. However, there are relatively few situations (especially for
complex scenarios) for which analytical solutions are known. Analytic tests for submodels should be performed. For example, it
is possible to provide a closed-form solution for heat loss through a partition; the model should be able to do this calculation.
9.1.2 Code Checking—The code can be verified on a structural basis preferably by a third party either totally manually or by using
code checking programs to detect irregularities and inconsistencies within the computer code. A process of code checking can
increase the level of confidence in the program’s ability to process the data to the program correctly, but it cannot give any
indication of the likely adequacy or accuracy of the program in use.
9.1.3 Numerical Tests—Mathematical models are usually expressed in the form of differential or integral equations. The models
are in general very complex, and analytical solutions are hard or even impossible to find. Numerical techniques are needed for
finding approximate solutions. These numerical techniques can be a source of error in the predicted results. Numerical tests include
an investigation of the magnitude of the residuals from the solution of the system of equations employed in the model as an
indicator of numerical accuracy and of the reduction in residuals as an indicator of numerical convergence. Algebraic equations
should be subject to error tests (uncertainty), ordinary differential equations to time step errors, and partial differential equations
to grid discretization analysis. This would include check of residual error of the solution, the stability of output variables, a global
check on conservation of appropriate quantities, the effect of boundary conditions, and that there is grid and time step convergence.
Finally, it is necessary to check that the requirements for consistency and stability are met.
9.1.4 Many fire problems involve the interaction of different physical processes, such as the chemical or thermal processes and
the mechanical response. Time scales associated with the processes may be substantially
...