Models, Monitoring and CFD Simulation: Putting CFD in Perspective

Transcription

1 Models, Monitoring and CFD Simulation: Putting CFD in Perspective Charles Feigley Department of Environmental Health Sciences Arnold School of Public Health 800 Sumter Street, Rm 311 University of South Carolina Columbia, SC Phone Fax

2 1. It works and I can use it without thinking about it 2. It doesn t work and I can reject it without thinking about it

3 1. It works and I can use it without thinking about it 1.5. It works really well sometimes and I do need to think about it 2. It doesn t work and I can reject it without thinking about it

4 Topics Intended applications affect validation requirements Issues in generating validation data Need to characterize boundary conditions

5 Certainly every experiment has some uncertainty associated with the results, and certainly it makes no sense to expect or even look for computational comparisons finer than this experimental uncertainty. -- Roache Verification and Validation in Computational Science and Engineering

6 The rigor of validation of CFD should also consider the intended application of the results.

7 Range of Exposure Estimate Uses In occupational hygiene, applications requiring the greatest accuracy include determining compliance with an exposure standard or OEL Use as the basis for setting standards and OELs

8 Range of Exposure Estimate Uses Applications with lesser accuracy requirements include: Setting priorities for exposure assessment and control program -- for example, control banding Assigning workers to exposure categories for occupational epidemiology association between exposure and a particular outcome

9 Example: Exposure Estimation Applications Bias = E avg estimate E average actual Worker protection If estimate is biased, + bias is more acceptable because it provides a margin of protection

10 Exposure Estimation Applications Occupational epidemiology Random errors lead to random misclassification of exposures More likely to accept a false null hypothesis Want unbiased estimates with minimal uncertainty

11 Exposure Estimation Applications Setting Occupational Exposure Limits Estimating threshold of exposure for a particular undesirable outcome Or For non-threshold outcomes, estimating exposure at which risk is acceptable Bias leads to a higher or lower OEL than necessary

12 Issues in Validation Data Collection May require extensive collection of concentration data Large number of points in a room (10 2 ) How to collect data at multiple points Single instrument -- measurements taken sequential at each of the points Multiple instruments measurements taken simultaneously at all points Number replicate experiments at each set of experimental conditions

13 Issues in Validation Data Collection Conditions in space may change over time, eg. Temperatures Air flow rates, source and exhaust characteristics Processes/activities Time of year, time of day, day of week Experiment control and characterize conditions Field accept and characterize

14 Issues in Validation Data Analysis Compare mean vs compare point-by-point Means relevant when people are moving about a space Point-by-point comparison is very rigorous, but does not adequate account for spatial correlation of CFD and measure values Contour plots allow a qualitative appraisal of agreement Account for error in the measured concentrations

15 Example Room Configuration & Position of Sampling Points Z Y air inlet A-1 A-2 B-1 B-2 C m (H) A-3 B-3 C m(L) source pedestal C m (W) outlet

16 Preliminary Tests in Validation Data Collection Check for infiltration of air and adsorption and absorption of propylene real time monitoring with room stirred but no intentional air exchange Air and propylene mass balances Compare inlet and outlet mass flow rates Performed experiments to correct for the effect of humidity and linearize the signal of the photoionization detector

17 Calculating Required Sampling Time Adapted approach described by Luoma and Batterman (2000) Accounts for variability and autocorrelation For each flowrate we intended to use, we calculated the time required to achieve precisions of 5%, 10% and 20% of the mean. Desired precision = half-width of 95% CI of the mean concentration

18 Example 1: Determine how long to sample Room Configuration & Position of Sampling Points Z Y air inlet A-1 A-2 B-1 B-2 C m (H) A-3 B-3 C m(L) source pedestal C m (W) outlet

19 How long do you need to sample? Variations at two sampling points B-1 and C-2 (0.8 m 3 /min) 150 Concentration vs. Time C-2 B-1 Concentration (ppm) Time (sec)

20 Even when you have done all of the above instability happens Reading for 5 hours at point C-1 (Q= 0.8 m3/min) Constant emission rate and exhaust concentration

21 Example 2: Boundary Conditions

22 Air Inlet Velocity Measurement x x x x x x x x x x x x x x x x x x x x x x x x

23 Inlet Velocity Re = VELOCITY (m/sec) DISTANCE (LENGTH, M)

24 Contaminant Concentration Re = 500 MEASURED (Re = 500) SIMULATION UNIFORM INLET VELOCITY SIMULATION PROFILED INLET VELOCITY TOP A PPM 6.5E E E E E E E E E E E E E E E E E E E E E E E E E+02 E I B F J VERTICAL BOTTOM MIDDLE C G K D H L

25 Re = VELOCITY (m/sec) DISTANCE (LENGTH, M) Re = INLET2 INLET3 INLET4 VELOCITY (m/sec) DISTANCE (LENGTH, M)

26 Contaminant Concentration Re = 5000 MEASURED (Re = 5000) SIMULATION UNIFORM INLET VELOCITY SIMULATION PROFILED INLET VELOCITY TOP A PPM 1.2E E E E E E E E E E E E E E E E E E E E E E E E E+01 E I MIDDLE B F J BOTTOM C G K VERTICAL D H L

27 Source Boundary Condition

28 Emission Simulated as Pure Isoamyl Acetate (IAA) ρ ρ IAA air = 4.5 Side Top

29 Emission Simulated as Dilute Isoamyl Acetate (IAA) in Air ρ IAA+ air ρ air 1.0 Side Top

30 Summary Validation must consider both the quality of the measured data and intended use of the CFD results Experiments to generate validation data must be well planned and validated themselves by preliminary tests and replication Comparison of CFD with measurements must account for uncertainty in measured data, as well as the intended use

31 Summary Small differences in the boundary condition can give rise to large differences between the CFD concentration field and the measured concentration field

32 References E. Lee and C.E. Feigley. An Investigation of Air Inlet Velocity in Simulating the Dispersion of Indoor Contaminants via Computational Fluid Dynamics. Annals of Occupational Hygiene. 46: (2002). J.S.Bennett, C.E. Feigley, and J. Khan. Comparison of Emission Models with Computational Fluid Dynamic Simulation and a Proposed Improved Model. Amer. Industrial Hygiene Assoc. J. 64: (2003). C.E. Feigley, J.S. Bennett, J. Khan, and E. Lee. Performance of Deterministic Exposure Assessment Models for Various Contaminant Source, Air Inlet, and Exhaust Locations. Amer. Industrial Hygiene Assoc. J. 63: (2002).

33 References Roache, PJ, Verification and Validation in Computational Science and Engineering, Hermosa Publishers. Albuquerque, NM. Luoma, M and SA Batterman Autocorrelation and Variability of Indoor Air Quality Measurements. Am. Ind. Hyg. Assoc. J. 61: