In this paper, three applications are presented to illustrate current techniques for flow calculation and visualization. The first two applications use a commercial computational fluid dynamics (CFD) code, FLUENT, performed on a Cray Y-MP. The results are animated with the aid of data visualization software, apE. The third application simulates a particulate deposition pattern using techniques inspired by developments in nonlinear dynamical systems. These computations were performed on personal computers

Brown, Susan T.

Crisafulli, Jeffrey J.

Saunders, James H.

Southern, Leslie A.

English

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N93"13386
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Applications of CFD and Visualization Techniques
James H. Saunders, Susan T. Brown, and Jeffrey J. Crisafulli
Battelle
Columbus, Ohio
Leslie A. Southern
Ohio Supercomputer Center
Columbus, Ohio
Introduction
Computational fluid dynamics (CFD) and data animation are powerful tools
for understanding and solving complex engineering problems. The large data sets
generated by time-dependent simulations can be dramatically illustrated with computer
animation, often readily revealing the physics of the flow field.
In this paper, three applications are presented to illustrate current
techniques for flow calculation and visualization. The first two applications use a
commercial CFD code, FLUENT, performed on a Cray Y-MP. The results are animated
with the aid of data visualization software, apE. The third application simulates a
particulate deposition pattern using techniques inspired by developments in nonlinear
dynamical systems. These computations were performed on personal computers.
Details of the simulations are presented elsewhere [refs. 1, 2, 3]. In this
paper, we focus on visualization of the data.
Air Flow Within Aj.r Conditioned Rooms
In the first application, we simulated the three-dimensional air flow in two
air conditioned rooms connected by a doorway, with the goal of understanding the
effects of blower fan on-time and return air vent placement on comfort level and air
exchange within the room. Although real house flows are usually more complex, this
simplified case represents the essential physics, and thus can be used to investigate
basic flow patterns.
Figure 1 shows the rooms, which were 3.0 by 2.4 by 4.3 m and 3.9 by 2.4
by 4.3 m with a single 0.9 by 2.3 m door and insulated outside walls. The outdoor
temperature was 90 F to simulate a hot summer day. The ceiling and floor were held
at temperatures of 90 F and 73 F, respectively. 260 cfm of cool air at 55 F entered
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the room through three inlet vents on the floor and exits through an outlet vent. The
outlet vent could be located either on the floor or high on a wall. We considered two
modes of fan operation: (1) running the fan only when the air conditioning was on, and
(2) running the fan continuously. The air conditioner cycle of 15 minutes had an on-
time of 6 minutes and an off-time of 9 minutes.
Figure 1. Animation of two air conditioned rooms
The room was modeled with 6061 nodes, using FLUENT, which solves the
time dependent mass, momentum, and energy equations using a finite volume method.
The temperature and velocity fields were then processed by apE to visualize the results.
Three-dimensional objects and scenes were rendered by apE, using a scanline Z buffer
approach to obtain photorealistic images that appropriately handled lighting, trans-
parency and shading [ref. 4]. Polygonal iso-valued surfaces were constructed from the
FLUENT data using a marching cubes algorithm [ref. 5]. For each timestep, three tem-
peratures (77 F, 75 F, and 73 F) were illustrated with red, yellow, and blue isosurfaces,
respectively.
The primary purposeof the visualizationeffort was t_ help characterize the
air exchangewithin the two rooms as a function of fan on-time and outlet vent loca-
tion. To visualizethe air exchange process,a set of "glyphs" was used to mark the
fluid. These masslessparticles, which track but do not interact with the flow, had two
different shapes: pyramid shapesfor existing room air and sphericalshapesfor the air
entering through the vents as shown in Figure 2. For the glyphs to track the flow
accurately, they had to interact with the velocity data generated by FLUENT. A special
facility was written to perform this function in apE. The glyphs were color coded to
indicate the local air temperature.
Figure 2. Visualization of the air exchange process: pyramid glyphs
represent existing room air, spheres represent entering air
With the outlet vent on the floor and the fan running either intermittently or
continuously, the isotherms are very flat, indicating poor mixing within the rooms. The
glyphs clearly showed that the air short-circuited from the inlet vents to the outlet; the
primary air flow, which was cool and dense, remained close to the floor and exited
through the outlet vent without appreciable mixing with the older air in the room.
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Placing the outlet high on the wall solved the poor a#-exchange problem.
The glyphs showed that the air flowed through a larger portion of the room volume as
it passed to the outlet vent.
Indoor Flpmmable Plumes from CNG Leaks
Buses are often stored and maintained in large transit facilities, which may
hold a large number of buses. A concern with natural gas-fueled buses is that a leak
could create a flammable atmosphere in the transit building. Knowledge of the size of
the plume for representative leaks is very important for developing future ventilation
standards.
We analyzed the dispersion of leakage plumes inside a typical transit
building that was 119 m by 108 m by 5.5 m high. During our simulation, the building
was fully occupied with parked buses and all doors were shut. The ventilation system
was on and operated at a rate of 5 air changes per hour.
Two leak scenarios were investigated:
1. A rapid leak corresponding to a ruptured fuel
manifold line connecting the CNG cylinders or the
failure of a pressure relief device.
2. Slow leaks from a poorly fitting fuel line connection.
The leakage rate was up to 2.0 g/s.
Because of symmetry, one-fourth of the room was modeled with FLUENT
using a grid of 12,O00 cells. The effect of using a coarse grid on a flow with a wide
range of geometrical length scales was assessed with some preliminary calculations.
We found that leaking gas that entered the region between buses was strongly driven
toward the ceiling by buoyancy forces. The details of the flow under or within buses
were not important in determining ,the overall evolution of the plume.
The flammable concentration was tracked in time using apE. Two iso-valued
surfaces were constructed; one represented the minimum flammable concentration,
and the other represented the maximum. Transparency property effects were used for
the maximum isosurfces, so the flammable region was clearly depicted. The extent of
the plume as a function of time was dramatically displayed with animation. Figure 3
shows a gray scale rendition of the fast-leakage-rate plume. The region between the
dark and light surfaces of the plume represents the volume of the building with a
flammable concentration of gas.
Particulate Deposition in Flow Systems
Particulate deposition and plugging in flow systems are important in a
variety of industrial applications. We have simulated deposition in high-velocity gas
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Figure 3. Rendition of fast-leakage rate indoor plume
flows where the flow is normal to a porous plate or collecting surfaces. Under these
conditions, the particles travel in essentially straight lines without lateral diffusion or
response to changes in the direction of the flow streamlines. Particles may collide with
any surface they encounter. Because of the high velocity and high particle loadings,
the deposit layer grows rapidly.
We modeled this process by tracking individual particles moving on a two-
dimensional lattice as they form the deposit layer. Rules based upon the microphysics
of the gas-particle-surface interactions determine whether a particle sticks to a deposit
site, misses it, or bounces off. The compute r algorithm displays the result on a high-
resolution monitor, so that the development of the deposit can be observed
continuously.
Our initial motivation for developing this technique came from studies on
diffusion limited aggregation [ref. 6] and later from studies on ballistic deposition
[ref. 7]. Near the end of our work we became aware of other work on similar
deposition models [ref. 8] with extensions shown in [ref. 9].
Figure 4 shows the results of simulating particles depositing on a porous
plate. Particles, which are assigned one or more pixels on the graphics screen, are
released at a random location above the deposit layer and are tracked as they move in
a straight line toward the deposit. Deposited particles are shown as colored pixels on
the screen. At each timestep, the algorithm examines pixels that are in and near the
immediate path of the particle. If a collision with a deposited particle is imminent, the
sticking probability is computed for that set of circumstances. If the collision will be a
frontal collision, the particle may either stick or bounce. If the collision involves the
sides or corners of the deposit, then the particle may stick or pass by.
Figure 4. Simulation of particles depositing on a porous plate
Because of the continuously updated graphic display, the effects of rule
changes can quickly be seen, allowing the researcher to evaluate the ramifications of
the assumptions and develop an understanding of the role of th_ microphysics on the
formation of the resulting structure.
Our work has shown that the resulting deposit structure is sensitive to the
form of the rules [ref. 5]. However, rules can be estimated from the detailed micro-
physics and future research should focus on extending this ability• For instance, in high
speed flows, lateral dendritic growth may be strongly limited by shear-induced breakage
of the dendrites• Rule selection should therefore be guided by careful comparisons of
the predicted morphology of the deposit structure with detailed experimental
measurements.
Conclusions
Visualization has been shown to be an important part of three engineering
research problems using hardware ranging from supercomputers to personal computers.
In the room ventilation example, the visualization revealed the impact of vent place-
ment on air mixing in the two rooms• The visualization of the flammable gas plume in
the transit building analysis gives the ventilation engineer a much clearer indication of
potential weaknesses in the ventilation scheme than would be possible with traditional
techniques. The graphical display of particle deposition gives the researcher a unique
perspective on the growth of particle beds and allows detailed investigations of the
particle microphysics in these processes• These visualization techniques have many
applications that dramatically increase the usefulness of scientific data•
References
apE is a trademark of The Ohio State University
FLUENT is a trademark of FLUENT, Inc.
• Murphy, M. J., S. T. Brown and D. B. Philips, "Extent of Indoor Flammable
Plumes Resulting from CNG Bus Fuel System Leaks," to be presented at the SAE
1992 Truck and Bus Meeting and Exposition, November 16-19, 1992.
• Brown, S. T., J. J. Crisafulli, D. B. Philips, L. A. Southern and D. F. Knight,
"Analysis of Vent Placement and Fan Operation for Air Conditioning in a
Partitioned Room Using Advanced Visualization Techniques," submitted for
presentation at ASHRAE Winter Meeting, Denver CO, 1993.
. Saunders, J. H., J. J. Crisafulli and G. H. Stickford, "Analysis of Particulate
Depositions and Plugging in Flow Systems," ASME Fluids Engineering
Conference, Los Angeles CA, June 21-24, 1992.
° Snyder, J. M. and A. H. Barr, "Ray Tracing Complex Models Containing Surface
Tessellations," Proceedings of SlGGRAPH 1987, Computer Graphics, Vol. 21,
No. 4, July 1987, pp. 119-128.
. Lorensen, W. Eo and H. E. Cline, "Marching Cubes: A High Resolution 3D Surface
Construction Algorithm," Proceedings of SIGGRAPH 1987, Computer Graphics,
Vol. 21, No. 4., July 1987, pp. 163-170.
6. Witten, T. A. and L. M. Sander, "Diffusion-Limited Aggregation, a Kinetic Critical
Phenomenon," Phys. Rev. Letters, Vol. 47, No. 19, 1981, pg. 1400-1403.
7. Meakin, P., P. Ramanlal, L. M. Sander and R. C. Ball, "Ballistic Deposition on
Surfaces," PhysicalRev. A, Vol. 34, No. 6, 1986, pp. 5091-5103.
8. Tassopoulos, M., J. A. O'Brien and D. E. Rosner, "Simulation of Microstructure/
Mechanisms," AIChEJ., Vol. 35, No. 6, 1989, pp. 967-980.
9. Tassopoulos, M. and D. E. Rosner, "Simulation of Vapor Diffusion in Anisotropic
Particulate Deposits," Chem. Eng. Sci., Vol. 47, No. 2., 1992, pp. 421-443.
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