367_kolesnikov

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The Fifth International Symposium on Computational Wind Engineering (CWE2010)

Chapel Hill, North Carolina, USA May 23-27, 2010

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Exhaust dispersion modeling. A comparison of wind tunnel, CFD and

AERMOD modeling approaches.

Alexy Kolesnikov, Ph.D.CPP, Inc., 1415 Blue Spruce Drive, Fort Collins, CO 80524, [email protected]

ABSTRACT

Over the past 30 years Computational Fluid Dynamics (CFD) had been used to simulate increasinglymore complex fluid/gas flows and heat exchange processes for applications encountered in the

aerospace, automotive and nuclear industries, while wind tunnel testing has been maturely used to study

airflows around buildings within a framework of exhaust dispersion, cladding pressures, pedestrian windcomfort and similar studies. Aided by rapid advances in the two areas most pertinent to HVAC and wind

engineering applications namely, turbulence modeling and parallel computing technology, CFD has

been gaining acceptance as one of the design tools of choice for indoor airflow prediction,heating/cooling characterization as well as occupant safety and comfort optimization. While just a few

years ago, models consisting of 1-2 million cells represented a standard of “high-fidelity” CFD, today’scomputer resources allow for 20-30 million cell models to be routinely used for large scale simulations.

LES and RANS turbulence models have been extensively benchmarked for indoor and outdoor airflowprediction. Nevertheless, while inherent unsteadiness of outdoor applications requires a LES/DES

numerical approach to accurately represent the nature of the flow, the “physical” scale of the application

itself renders steady-state CFD simulations the only “commercially-feasible” solution at present.

The goal of this paper is to benchmark the use of steady-state RANS CFD simulations in providing

predictions of exhaust dispersion patterns and contaminant distributions for simple stack configurations.CFD simulations were conducted for a number of wind speeds and directions and the results were

compared to those generated in the wind tunnel as well as to the data obtained via AERMOD (EPA

approved steady-state plume model) system to ascertain the strengths and limitations of each approachand to outline a potential hybrid strategy for wind tunnel/CFD study framework development.

INTRODUCTION

CFD is the science of utilizing advanced computer modeling techniques to solve the Navier-Stokesequations governing fluid/gas flows (Baker, 1983). The Navier-Stokes system is derived by applying the

principles of conservation of mass, momentum, and energy to a control volume of fluid. The resultant

equations are extremely complex and possess no known analytical (exact) solution. Instead, theirapproximate computer-simulated solutions are considered, with additional assumptions related to

turbulence modeling and properties of the flow field being made based on the physics of the specific

process. The solution is obtained using discretization techniques applicable upon division of the initial,

continuous geometric domain into a finite number of non-overlapping discrete volumes that are referredto as computational mesh in technical literature. Consequently, numerical simulation results in velocity,

pressure, and temperature values calculated for each of the individual volumes and whose combination

provides a detailed airflow distribution inside the original geometry. Given the approximate nature of the numerical solution, the computational results need to be validated experimentally. The steady-state

conservation law system formulation written for the incompressible flow class characteristic of most

ventilation-type flows and incorporating the Bousinesq approximation is shown below (Baker, 1983,Emmerich, 1997).





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Conservation of mass

Conservation of momentum

Conservation of energy

where i, j = summation indices

gi = gravitational acceleration in xi directionH = volumetric heat source generation rate

P = instantaneous static pressure difference

t = timeui = instantaneous velocity component in xi direction

xi = Cartesian coordinates

β = volumetric coefficient of expansion

κ = thermal diffusivity

θ = instantaneous temperature difference

ρ = density

ν = kinematic viscosity

Most airflows of interest are turbulent and characterized by velocity fluctuations with a number of irregular turbulent eddies forming within the flow structure as opposed to laminar stratified flows.

Mathematically, the ability to accurately model and resolve these turbulent fluctuations within the

“bulk” airflow pattern during computer simulation ultimately determines the accuracy of the overallnumerical solution. Turbulence modeling and its applicability to indoor and outdoor airflow prediction

has been a subject of numerous research studies focused on benchmarking CFD-generated results

against available experimental data. Chen (1995, 1996), Kolesnikov (2006) studied application of

various Reynolds-Averaged approaches, namely Reynolds-Stress and k-ε turbulence models for indoorflow modeling. Both model types were shown to provide satisfactory results for the mean velocity

distributions, with Reynolds-Stress model better capturing secondary recirculation zones due to its

inherent anisotropic (directional non-uniform) turbulence modeling assumption. Application of LargeEddy Simulation modeling has been discussed among others by Zhang and Chen (2000) and Su et al.

(2001) and was shown to provide good agreement with experimental results. Unlike Reynolds

Averaging, which relies on ensemble-averaging in its mathematical formulation and calculates meancharacteristics of the flow, Large Eddy Simulation models divide the overall flow structure into large-

scale and small-scale motions (Su et al., 2001, Piomelli, 1999). The large scale motion is directly

calculated, while small-scale motion is modeled during the simulation. This approach can provide

instantaneous flow information as well as mean properties of the flow, but is inherently morecomputationally expensive, since significantly finer grids and time dependant calculations are required

to directly resolve physically meaningful smaller scale motions.

θ β ν ρ

i

i

j

j

i

j j

jii g x

u

x

u

x

P

x

uu

t

u −

∂∂+

∂∂

∂∂−=

∂∂+

∂∂ 1

0=∂

∂

i

i

x

u

H x x x

u

t j j j

j+

∂

∂

∂

∂=

∂

∂+

∂

∂ θ κ

θ θ





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Rapid progress being made in the area of computing technology and its parallel implementation coupled

with continuous developments in advanced modeling research ensures the ever expanding role CFD

modeling enjoys as a design tool of choice in both academic and industrial applications. Thus,simulations involving tens of millions of computational cells needed to adequately resolve the physics of

airflow phenomena in industrial scale applications are now routinely performed at leading CFD

industrial and research centers. As a result, high fidelity velocity, temperature and contaminantconcentration data obtained during numerical simulations is now available for use during design phase

implementations of real-life building projects. Towards this goal, Lin et al. (2005) investigated airborne

pathogen transport in aircraft cabins, Yang et al. (2000) used numeric analysis to optimize ventilationand predict air quality in indoor skating areas, while Baker et al. (2000, 2001) studied airflow patterns

within commercial aircraft cabins and industrial scale plant facilities. A comprehensive CFD study of

pedestrian wind environment prediction was conducted by Tominaga et. al. (2008), wall function

implementation recommendations pertinent for atmospheric boundary layer simulations were outlinedby Blocken et. al. (2007) and boundary conditions appropriate for computational wind engineering

studies were assessed by Richards (1993).

BUILDING WAKE DISPERSION MODELING

The goal of this paper is to address CFD modeling applicability to wake dispersion modeling bybenchmarking CFD predictions against wind tunnel generated results. Wake dispersion modeling aims

to predict pollutant dispersion propagation as it is released from a contaminant source. Sources include

utility plants, datacenters, a variety of chemical and pharmaceutical labs among many others and operate

under a variety of meteorological, operational, terrain and urban conditions. A variety of modelingtechniques are available including steady-state Gaussian plume models, such as AERMOD, atmospheric

boundary layer wind tunnels, that take advantage of dimensionless similarity relationships (Cermak

(1975)) derived from fundamental equations of motion and Computational Fluid Dynamics modelingthat aims to solve above mentioned equations directly.

Specifically, the paper focuses on the USEPA Bowline Point Field data base representing a full year of

data for a moderately buoyant source representative of electric utility plants. The CFD modeling resultsare compared to wind tunnel and AERMOD generated measurements.

CFD MODEL DESCRIPTION.

The Bowline Point data base represents a full year of data for a moderately buoyant source reflective of electric utility plants, which tests the models under a wide variety of meteorological conditions. This is

one of the data bases used to develop and evaluate AERMOD.

Bowline Point generating plant is located on the western shore of the Hudson River (Figure 1)

approximately 37.5 miles north of the Battery at the southern tip of Manhattan. The plant consists of two

existing units that burn either natural gas or oil to produce a combined output of approximately 1,139

MW. Unit 1 began operation in September 1972 and Unit 2 began operation in May 1974. SO 2 emissions were monitored over the course of a year.





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Figure 1: Bowline Point study area showing stack and receptor locations.

The field SO2 monitors included: Bowline Point (Receptor 1) and Boat Ramp (Receptor 3).

A three-dimensional CFD analysis is used herein to predict pollutant dispersion profiles. While transientLES/DES analysis is expected to produce more accurate results, the computational cost would be

prohibitive for everyday user implementation and a steady state RANS analysis is therefore

implemented within the framework of the study. A scaled CFD model was build to facilitate thesimulation. The model scale was selected identical to that of the wind tunnel investigations (1:500) to

allow for direct result comparison. The CFD and wind tunnel results were subsequently re-scaled for

field data comparison. Bowline Point wind tunnel model is shown in Figure 2 for reference purposes.CFD model mesh sizes ranged from 40 to 0.625 mm with refinement areas concentrated along the

surfaces for adequate boundary layer resolution. The resulting model size was 3.6 million cells.

Figure 2: Photograph of the Bowline Point Station model installed in the boundary layer wind tunnel.

The details of the wind tunnel data gathering and processing are presented by Petersen (2009).Computations were performed using a commercially available software package STAR-CD developed

by CD-adapco. CFD model geometry and discretization detail is presented in Figure 3.

Figure 3: CFD model detail.





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Steady-State distributions of velocity, pressure and tracer gas mass fractions were obtained by solving

the Reynolds-averaged Navier-Stokes equation system using the two-equation Low-Reynolds number k-

ε model with hybrid wall functions to model turbulence. Ideal gas law with a reference pressurecorresponding to that of the wind tunnel experiment was used to incorporate buoyancy effects and the

flow was assumed to be under isothermal conditions to accommodate wind tunnel experiment

conditions. Boundary conditions were specified using a log-log atmospheric boundary layer profile with

reference velocity set at 4m/s at a height of 1m and the exponent of 0.218. No-slip (zero velocity atstationary walls) boundary conditions were specified at the walls and the terrain with symmetry

boundary conditions at the domain ceiling completing the model specification. Numerical solution was

obtained using MARS high order spatial discretization to promote solution accuracy and monotonicity.The roughness elements present in the wind tunnel to accurately preserve the boundary layer approach

profile within the area of interest were simulated directly in the CFD model as shown in Figure 3. The

wind direction was selected at 300 degrees NW and tracer gas concentrations were monitored at alocation corresponding to that of the Boat Ramp and a receptor analogous to Bowline Point (identical

distance to the stacks, but located directly downwind of the building and the Boat Ramp receptor). It

was assumed that moving the Bowline Point receptor to be in line with the Boat Ramp receptor would

result in negligible divergence from the original location measurements obtained for the correspondingupwind direction and it significantly cuts down on computational time. CFD simulations were run at

stack exhaust rates calculated to represent the range of full scale approach wind velocities via

momentum similarity to ascertain maximum concentration dependence on the wind speeds.

BOWLINE POINT DATABASE COMPARISON

Characteristic tracer gas profiles for an equivalent approach wind speed of 19m/s are shown in Figure 4.

A tracer gas profile obtained during one of the wind tunnel runs is shown in Figure 5 for comparison.

Figure 4: CFD calculated tracer gas profiles (ppm). 19m/s equivalent wind approach speed.

Figure 5: Characteristic wind tunnel plume rise profile.





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Sensor 1

0

50

100

150

200

0 5 10 15 20

Wind spee d (m/s)

M a s s f r a c t i o n ( p p m )

CFD

WT

Sensor 3

0

20

40

60

80

100

120

0 5 10 15 20

Wind spee d (m/s)

M a s s f r a c t i o n ( p p m )

CFD

WT

Visually, for a given incident wind speed the wind tunnel tracer gas profile maintains a well definedplume rise pattern for a significantly longer distance, while the CFD predicted profile touches down to

the ground almost immediately behind the stack. The visual observations are confirmed by the

numerical results presented in Figure 6.

Figure 6: CFD (blue) and wind tunnel (pink) predicted tracer gas mass fractions vs. equivalent incident wind speed for the

Bowline Point receptor (Sensor 1) and Boat Ramp receptor (Sensor 3).

This can be directly attributed to a classical numerical diffusion inherent in Steady-State RANS solvers.In that, the physical variable gradients (temperature, velocity, tracer gas, etc.) are smeared due to the

numerical scheme implementation. Mathematically, discretization of the continuous Navier Stokes

equation system results in a discrete system which can be shown to converge to a “modified” equationsystem in the spatial discretization step limit. The difference between the original and modified systems

can be shown to contain terms responsible for numerical diffusion (and dispersion), thus manifesting

themselves in the obtained solution (Kolesnikov and Baker (2001)). Specifically, the modeled stack throw is shorter than the experimental value and the “wider” plume pattern comes into contact with the

ground upon traveling a shorter distance. Since the scale model approach wind speed remains the same

in all CFD and wind tunnel simulations (4m/s) with increased stack mass flow aimed at mimicking thecorresponding full scale approach wind speed decrease according to dimensionless momentum

calculations, the excess numerical diffusion quickly “drops” the plume close to the exhaust stack and the

higher mass flow rates result in higher monitor point concentrations. As can be seen from the graph data,

CFD simulations automatically under predict the approach wind speed expected to result in highestmonitor concentrations for both sensor locations compared to the wind tunnel observations. The error in

maximum concentration prediction is 25-30%. CFD models correctly resolve the increase in maximum

concentration with the increase in distance away from the stack. In that, sensor 1 (Bowline Point)evidences higher possible concentration as compared to sensor 3 (Boat Ramp). Both CFD and windtunnel results were subsequently corrected for the real scale buoyant plume rise and hourly

concentrations were predicted, decoupled in time, sorted and compared to the field observations (Figure

7). The details of that analysis are presented in Petersen (2009) and the CFD profiles are confirmed toover predict the experimental observations.





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Figure 7: Q-Q plot of the AERMOD results (green) compared to the wind tunnel (red and blue) and CFD predictions (pink

and black) for the Bowline Point receptor (Receptor 1) and Boat Ramp receptor (Receptor 3).

CONCLUSIONS

CFD results are shown to under predict incident wind speeds corresponding to maximum sensorconcentrations due to exhaust stack release by 30%. The expected maximum concentration increase with

distance away from the stack is correctly predicted. The simulation error is attributed to excess

numerical diffusion present in CFD simulations, resulting in premature plume touchdown to the groundand shorter stack throws. It is important to note, that this discrepancy can be addressed by mesh

refinement and employment of LES/DES algorithms, both of which would make corresponding

simulations more computationally demanding. With the present paper focusing on investigating

potential modeling issues associated with plume rise RANS based modeling approach, theabovementioned questions will form a basis for a future study.

REFERENCES

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