atw 2017-07

atw Vol. 62 (2017) | Issue 7 ı July
Uncertainty Analysis for Parameters
of CFAST in the Main Control Room Fire
Scenario
Wanhong Wang, Yun Guo and Changhong Peng
1 Introduction A fire accident is one of the most serious threats to safety in nuclear power plants. Analyzing
potential internal fire risk is part of important tasks in nuclear safety analysis. It is shown that internal fire accident
frequency in plant power plants is higher than we thought it was. More importantly, without appropriate emergency
processing, fire accidents in nuclear power plants could cause great economic losses. According to M&M Insurance Consulting
Company in the United States, losses caused by plant internal fire accidents account for 90 % of total losses. [1]
The main control room is the control
center and the core of operations and
emergency accident processing in the
nuclear power plant. If a fire accident
happens here, it will threaten the
safety of the units and possibly lead to
reactor core damage. Therefore, Fire
probability safety analysis in the main
control room has become a significant
part of fire analysis.
Fire modeling can be used within
the framework of the fire PRA to
characterize the risk associated
with fire scenarios, where CFAST,
developed by BFRL, is one of the most
widely-used software.
CFAST is a typical two-zone fire
model which is able to figure out
fire-induced environmental conditions
as a function of time for all kinds
of fire scenarios. The reason why
CFAST is a zone model is that it
subdivides each compartment into
two zones in order to numerically
cope with differential equations,
together with the ideal gas law and
the equation of heat conduction into
the walls. It attempts to simulate
environmental conditions generated
by a user-prescribed fire. Therefore, it
is widely used as a tool for fire
probabilistic safety analysis in nuclear
power plants. It is capable of predicting
temperature and distribution of
smoke and fire gases in compartments
where a user-prescribed fire exist. [2]
The fire model, CFAST, consists
of environment conditions, thermal
properties, compartments, wall
vents, ceiling/floor vents, mechanical
ventilation, fires, targets, detection/
suppression, surface connections
and output. Input appropriate and
accurate parameters and then it
auto matically analyzes time-evolving
distributions of smoke and gaseous
combustion production as well as
layer temperature throughout compartments
during a user-prescribed
fire scenarios.
Uncertainty is considered as one of
the challenges that need to be treated
carefully when carrying out fire
consequence analysis using fire simulation
models. This is due to the fact
that some of the input parameters that
are considered when conducting fire
modeling are of stochastic nature.
Random input parameters are not
easily determine and may be subjected
to uncertainty. Example of such
parameters are fire location, fire size
and fire growth parameter. [3]
The main task of uncertainty
analysis is to evaluate the influence
of inputs on the outputs of a
model. Among uncertainty analysis
approaches, the Monte Carlo simulation-based
sampling approach is
probably the most widely used. [4]
Typically, fire models are run using a
discrete set of input parameters that
describe a single, specific fire scenario.
However, for some Fire PRA applications,
it may be necessary to consider
the range of consequences due to the
variability that can result from that
specific fire scenario within a particular
compartment. If the key input
parameters can be expressed in the
form statistical distributions, then the
model output quantities may also be
expressed as distributions. In this
way, it is possible to determine the
probability of exceeding a critical
temperature, heat flux, or some other
critical value. [5]
Monte Carlo simulation method is
actually a computerized mathema tical
technique, which makes it possible for
experts to count for risk in qualitative
and quantitative analysis and decision
making. In Monte Carlo, a large
number of sample is randomly chosen
from the input space and mapped
through the system into the target
distribution. Although Monte Carlo as
a technique is almost sixty years old,
its use in fire scenario simulations has
been prohibitively expensive and
complex. With the rapid development
of modern computers, the simulation
has changed and tools described
here have already been applied to
engineering problems.
Monte Carlo mothed does fire risk
analysis by building models of possible
calculation results by adjusting values
for inherently uncertain factors. It is
achieved by calculating results
over and over, each time using different
random values from probability
density distributions. Depending
upon numbers of uncertainties and
ranges specified for them, Monte
Carlo method could involve thousands
of recalculations before it is
completely finished. Consequently,
Monte Carlo simulation method
produces probability density distributions
of possible outcome values,
which is a much more realistic way to
describe uncertainty in variables of a
risk analysis.
2 Methodology
2.1 Monte Carlo Method
What probability distribution functions
of output parameters look like
and what output values and standard
deviation are can be calculated by the
Monte Carlo method, along with
mathematical models and computer
software. It could be completely
finished by a near infinite sampling.
Moreover, Monte Carlo method is
based on mathematical function,
y = y(x) = f(x), whose input parameters,
[x 1 , x 2 ,…, x j ], are uncertain
values and have their probability
density distributions, D 1 , D 2 ,…, D j .
Do N times of Monte Carlo random
sampling according to probability density
distributions of input parameters,
and we can figure out probability
density distributions of output parameters,
which can be used to calculate
values and standard deviations. Maybe
probability density distributions of
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Operation and New Build
Uncertainty Analysis for Parameters of CFAST in the Main Control Room Fire Scenario ı Wanhong Wang, Yun Guo and Changhong Peng