Fire Dynamics Simulation and Analysis

This assignment delves into the complexities of fire dynamics simulation using computational fluid dynamics (CFD). It explores key concepts such as radiative heat transfer, finite volume methods for convective transport, narrow-band absorption coefficient models, and rectilinear mesh analysis. The document further examines the role of parallel processing in these simulations and compares two crucial fire safety evaluation metrics: Available Safe Egress Time (ASET) and Required Safe Egress Time (RSET). Finally, it highlights the broader applications of CFD in various fields, including healthcare.

Computational Fluid Dynamics 1
COMPUTATIONAL FLUID DYNAMICS
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Computational Fluid Dynamics 2
Computational Fluid Dynamics
Introduction
Computational fluid dynamics is the qualitative and quantifiable extrapolation of the flow of
fluids through mathematical models like partial differential equations, numerical methods such
as discretization and solution techniques and software tools such as solvers. It involves the use of
algorithms and numerical methods to implement accurately simulate the flow of fluids. CFDs are
very useful in evaluating numerical experiments in fluid flow (Chung 2010). This field of
science over the years has become more exciting because of its extensive applications in day to
day lives of humans. CFDs have various applications ranging from medicine, biology, defense,
transportation, environment, energy among others. The Wright brothers who discovered the
airplane used fluid dynamics to build a flying plane.
The main aim of CFDs is to substitute the continuous domain with the discrete domain by the
use of a grid. In the constant domain, evaluation of the rate of flow is at set points in the stream.
On the other hand in the discrete domain, the variables of flow are reviewed at grid points.
Computational Fluid Dynamics Work Flow
The workflow in computational fluid dynamics consists of three steps. These steps
include preprocessing, processing and post-processing. Preprocessing defines the geometry to
term the domain of concern. This domain is then separated into mesh segments before specifying
the boundary conditions. During processing, the engineer analyzes the grids and performs the
required simulation with the aim of solving the problem. A computer software like CFL3D aids
in the solution of the task. Post processing, on the other hand, encompasses the extraction of the
preferred properties of flows like the drag, lift, and thrust obtained from the flow field.

Computational Fluid Dynamics 3
Applications of CFDs
 CFDs can be used in simulating the flow over a car as well as in the analysis of the
connections between rotors and the body of the aircrafts.
 CFDs can also be applied in the analysis of the distribution of temperatures within a
mixing manifold.
 They are also used in biomedical engineering to monitor the circulatory and respiratory
systems. The flow simulations are however prone to numerous errors, and for that reason,
a lot of engineering proficiency is required to achieve valid results.
 CFDs are also applied in film coating and thermoforming in applications requiring
material processing.
 They also find essential application in ventilation of buildings for temperature control.
 CFDs are also used in heat transfer applications for packaging of electronic components.
How CFDs Work
The evaluation of CFDs starts with the designing of a mathematical model representing
the physical problem and note must be taken to conserve momentum and energy along the area
of interest. Properties of fluid are then empirically modeled, and appropriate initial boundary
conditions are provided. By application of discretization, approximations of the equations that
govern fluid mechanics in the area of interest are developed. The differential equations that
govern fluid mechanics are usually algebraic. The algebraic equations must be elucidated
numerically for the variables of the flow field (Drysdale 2011). On the other hand, the system
equations are simultaneously solved to obtain the necessary solutions. After getting the solutions,
they are processed to achieve the expected quantities of torque, drag and pressure loss among
others.

Computational Fluid Dynamics 4
The obtained domain is then discretized to form the predetermined set of control cells.
The general equations of conservation of mass are also discretized to form algebraic equations
and all equations evaluated in render flow field. After discretization is done, a grid is designed
and created, taking into account the perfect resolution required for the grid in each of the domain
regions. One must consider the number of cells needed.
Orthodox Computational Fluid Dynamics
In orthodox CFD, the development of algorithms requires one to approximate the
partial derivatives with respect to the available space variables. In doing so, the generation of
systems of nonlinear ordinary differential equations takes place in the process of the method of
lines (Ferziger and Peric 2012). The approximation, therefore, has a critical impact on the on the
final CFD stability such that if the estimate is not right, solutions obtained of the ordinary
differential equations may be increasing exponentially even in the case that the previous partial
differential equations containing bounded initial and boundary conditions hold a meaningful
solution
Mathematical Model
The CFD mathematical model encompasses a set of partial differential equations which
could be dependent on both the time variables and space variables. Mathematical models in
computational fluid dynamics that cause significant physical solutions are known as well-posed
problems (Karniadakis and Sherwin 2013). A well-posed problem will exhibit the following
three properties;
 An existing solution
 The existing solution is distinctive

Computational Fluid Dynamics 5
 The present answer is always dependent on the auxiliary records
From the third property above it is evident that an alteration in the auxiliary data causes
an equal alteration in the solution. To characterize the significance of the above three features,
one uses the parabolic equation as shown below.
∂ q
∂ t + a ∂ q
∂ x - 1
Pe
∂ ² q
∂ x ² = 0 where “a” is a constant representing the component of
convectional velocity while 1
Pe represents the coefficient of diffusion. The parabolic equation
above can be used to model the temperature distribution within a fluid flowing at a velocity that
is constant along a straight pipe.
Orthodox computational fluid dynamics uses the theory of steady solution
monotonicity. This theory explains the constant flow rates of fluids within a tube. In most cases,
the flow of fluids is always steady unless the container has bends which might cause variations in
the speed of flow of molecules of the same liquid (Le Maître and Knio 2010). This difference
creates turbulence, therefore, leading to unsteady movement of the fluid under scrutiny. An
ordinary CFD simulation has the following steps;
 A geometric CAD model aids in approximating the physical system geometry. As close
as the model geometry symbolizes the actual design, the results increasingly become
more accurate.
 A numerical grid is created within the geometric model by first identifying the physical
phenomena of flow that is expected.
 Choice of models and modeling parameters and the definition of boundary conditions in
the domain.

Computational Fluid Dynamics 6
 Evaluation of the values of the variables through discretization which produces various
algebraic equations which are then evaluated using an iterative method in Numerical
Mathematics.
 Analysis of an adequately converged solution. In this step, the answer is evaluated to
find out whether it has united fully.
 After the calculation of the converging solutions, the results are exemplified in either the
form of numerical values or in the way of pictures.
 Finally, the solutions are verified and validated for accuracy.
Specific Computational Fluid Dynamics
Particular CFD is applied in the analysis and evaluation of specific theories of fluid
flow, for example, the computational fluid dynamics model of a patient’s left atrium. It is used to
analyze the mechanism of hemodynamic and the role they play in arterial fibrillation condition
(McGrattan et al., 2013). This hemodynamic tool aims to improve the stratification of stroke risk
and the optimization of therapy.
The method used in this analysis is known as Dynamic CT. in Dynamic CT the patient
is imaged in continuous AF after which the doctor develops the 3D model of the human’s
anatomy. This development of the 3D model is done by applying an explicitly designed
algorithm established on image histogram and augmented with curvature motion and
morphological operators. To conduct CFD simulation in the LA, an arbitrary Lagrangian Euler
method of the Naiver-Stokes equation is used in AF condition and the sinus rhythm.
Computational fluid dynamics can also be used in the comprehension of the alterations
caused by LVAD outflow tract placement that deviates from the usual flow direction in the

Computational Fluid Dynamics 7
ascending aorta. The study is done with the aim of comprehending the hemodynamic effects
LVAD outflow. The simulation involving CFD is performed for patients with LVAD and a
reconstruction of geometry done from cardiac CTA imaging (Taylor et al., 2013, p. 2235). The
specific flow information of the patient is retrieved from echocardiogram that is performed
during the cardiac CTA process. CFD is widely applied in the aerospace industries as well as
power generation, automobile, process engineering, petrochemicals, oil and gas industries among
others.
Practical Advantages of CFD
 CFDs anticipate performance way before implementation in the system.
 Modelling using computational fluid dynamics saves the costs and time and also
provides accurate results.
 They can predict the type of changes required in the layout of the design to tremendously
improve the performance of a system.
 CFD models provide accurate and comprehensive information regarding heating, air
conditioning, and ventilation.
 They are instrumental in the development of HVAC technology which in most cases
demands exhaustive information about fluid flow within an engaged zone..
Practically CFD is used commonly used in multidimensional ways to lower the costs of
HVAC designs. The areas of application include the design of fume hoods, smoking lounges, the
management of fire and smoke, ventilation in swimming pools, external building flows, a model
of industrial ventilation, enclosed vehicular facilities and simulations in clean rooms among
others. One essential type of CFD modeling is the fire dynamic simulator.

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Fire Dynamic Simulator
Fire dynamic simulator is a CFD model type of fluid flow run by fire. This software is
used to numerically solve the Naiver-Stokes equation suitable for a low-speed kind of flow that
puts prominence on smoke and the transfer of heat from fires. The output from the FDS and
CFAST simulation is displayed using a visualization program known as smoke-view (Tu et al.,
2012). This software has an evacuation simulation module called FDS+Evac that is used to
simulate the locomotion of people in situations demanding evacuation. The main aim of fire
dynamic simulator is to solve real-time problems involving fire in fire protection engineering.
FDS is used in construction to design the phenomena below;
 Heat transport at low speeds and fire combustion products
 Heat transfer between gases and solid surfaces using radiation and convection.
 Activation of smoke detector, heat detector, and sprinkler
 Growth of fire and spreading of flames
 Pyrolysis
 Water suppression and sprinkler sprays
Features of FDS
i. Radiation transport.
Radiation transport is necessary for the evaluation of radiative heat transfer. Solving the
gray gas’ equation of radiation transport helps in this analysis. To address the equation one
applies the finite volume methods for convective transport. The limited volume needs around 20
percent of the total time allocated for the CPU for the evaluation, using roughly 100 distinct

Computational Fluid Dynamics 9
angles. To compute the absorption coefficients of the mixture of gas and smoke, narrow-band
model is applied.
ii. Geometry
Fire dynamic simulator estimates the equations that govern rectilinear mesh. The
analysis is used to reconstruct geometric histograms for evaluation.
iii. The parallel processing
Calculations that involve fire dynamic simulations can be done on more than one
computer at the same time by the use of message passing interface.
iv. Combustion Model
In most cases, fire dynamic simulators apply a single step, a chemical reaction that
makes use of air, fuel, and products whereby products and fuel are overtly computed.
Comparison between ASET and RSET
ASET stands for available safe egress time which is evaluated through simulation of the
worst case fire design using a zone model computer program. During ASET analysis, the
engineer checks the time required to get to unsustainable conditions. It aids in the review of fire
safety. RSET, on the other hand, stands for required safe egress time which is assessed by
simulation of the evacuation of people from a room by making assumptions about how people
may react to situations like an outbreak of fire. Computers that are extremely powerful are
helpful in this type of simulation.
Conclusion

Computational Fluid Dynamics 10
Computational fluid dynamics is essential in the modern day analysis of a lot of life
occurrences. It also finds importance in health where CFD can be used to monitor heart
conditions and flow of blood in the veins and arteries. Once an analysis has been done and the
situation identified, technology can be used to administer the appropriate medication.
References

Computational Fluid Dynamics 11
Chung, T.J., 2010. Computational fluid dynamics. Cambridge university press.
Drysdale, D., 2011. An introduction to fire dynamics. John Wiley & Sons.
Ferziger, J.H., and Peric, M., 2012. Computational methods for fluid dynamics. Springer Science
& Business Media.
Karniadakis, G. and Sherwin, S., 2013. Spectral/hp element methods for computational fluid
dynamics. Oxford University Press.
Le Maître, O. and Knio, O.M., 2010. Spectral methods for uncertainty quantification: with
applications to computational fluid dynamics. Springer Science & Business Media.
McGrattan, Kevin, Simo Hostikka, Randall McDermott, Jason Floyd, Craig Weinschenk, and
Kristopher Overholt. "Fire dynamics simulator user’s guide." NIST special publication1019
(2013): 6thEdition.
Taylor, C.A., Fonte, T.A. and Min, J.K., 2013. Computational fluid dynamics applied to cardiac
computed tomography for noninvasive quantification of fractional flow reserve. Journal of the
American College of Cardiology, 61(22), pp.2233-2241.
Tu, J., Yeoh, G.H. and Liu, C., 2012. Computational fluid dynamics: a practical approach.
Butterworth-Heinemann.