Dispersion Modelling for Explosion. Risk Analysis

Transcription

1 Dispersion Modelling for Explosion Tim Jones MMI Engineering The Brew House Wilderspool Park Greenalls Ave Warrington WA4 6HL +44 (0) (0) Risk Analysis

2 Motivation Three main objectives of the work; 1. Validate use of CFD for dispersion modelling. 2. Validate the use of the Frozen Cloud concept for dispersion modelling. 3. Look at future methods

3 Experimental Summary Experimental Configuration Configuration A Configuration C

4 Experimental Summary Experimental Monitor Points Ground Floor Side Elevation Mezzanine Floor

5 Experimental Summary Example Results

6 Test Number Rel. Loc. Rel. Dir. Mass Flow Rate (kg/s) Wind Speed (m/s) Wind Dir. A25 R1 West A22 R1 West A24 R1 West A31 R1 East A31 R1 East A06 R1 East A21 R1 East A30 R1 East A20 R1 East A05 R1 East A19 R1 East A34 R1 V.Up A18 R1 V.Up A33 R1 V.Up A13 R1 V.Up A32 R1 V.Up A12 R1 V.Up A17 R1 V.Up A16 R1 V.Up A11 R1 V.Up A14 R2 Down A15 R2 Down A29 R2 West A01 R2 West A28 R2 West A02 R2 West A03 R2 West A09 R2 South A08 R2 South A07 R2 South A27 R3 South A26 R3 South Experimental Summary List of Experiments Test Number Rel. Loc. Rel. Dir. Mass Flow Rate (kg/s) Wind Speed (m/s) Wind Dir. C07 R1 East C06 R1 East C05 R1 East C10 R1 V.Up C15 R1 V.Up C11 R2 V.Up C13 R2 South C09 R2 South C12 R2 South C08 R2 South C14 R2 South C03 R2 West C02 R2 West C04 R2 West C16 R2 East C18 R3 East C17 R3 East C19 R3 South

7 CFD Validation Dispersion versus Experimental Data The following parameters were compared: Volume above the Lower Flammable Limit (ALFL) Shape of the cloud Location of the cloud

8 CFD Validation Dispersion versus Experimental Data Experimental CFD

9 Dispersion versus Experimental Data Four experiments have been simulated. Configuration A CFD ALFL Cloud Size (m 3 ) Exp. ALFL Cloud Size (m 3 ) A A A A A A Generally a good match with experimental data All CFD cloud sizes are larger than experimental data. A31 and A30 give the most significant over prediction.

10 Dispersion versus Experimental Data Release Dir. and Location Configuration A Wind Dir. A02 Experimental CFD Wind Dir. Release Location Experimental A15 CFD

11 CFD Validation Conclusions Cases investigated for Configuration C showed a similar trend to those presented for A. In general Good match and conservative Some discrepancy but could be based on experimental information

12 Frozen Cloud Validation Frozen Cloud Concept Not practical to consider all scenarios in CFD. Frozen Cloud concept offers an alternative. Used as a basis to interpolate between CFD results. Mix Factor = m 1 V 2 m2 V 1 FLACS manual indicates Mix Factor should be between 0.5 and 2 Based on literature it is not clear on the origins of this method hence the need for validation. m 1 m 2 Mass flow rate for scenario 1 and 2 V 1 V 2 Ventilation rate for scenario 1 and 2

13 Configuration A Frozen Cloud Results Case 1 Release Direction Wind Direction

14 Configuration A Frozen Cloud Results Case 1 Release Direction Wind Direction

15 Configuration A Frozen Cloud Results Case 3 Release Direction Wind Direction

16 Configuration A Frozen Cloud Results Case 3

17 Configuration A Frozen Cloud Results Case 5 Wind Direction Vertical Release

18 Configuration A Frozen Cloud Results Case 5 Wind Direction Vertical Release

19 Configuration A Frozen Cloud Results Case 6 Release Direction Wind Direction

20 Configuration A Frozen Cloud Results Case 6 Release Direction Wind Direction

21 Frozen Cloud Validation Frozen Cloud Conclusions Frozen Cloud Validation Results are physical Large degree of variance Case 5 shows trend for mix factor of 1 giving better predictions but in some instances mix factors outside 0.5 to 2 perform better. Should verify method for a particular case using CFD

22 Gaussian Emulation What next? Collaborating with Risk and Uncertainty Group at Liverpool University Objective of the work is to develop an emulator for different types of release. Develop emulator based on range of mass flow rates and ventilated velocities within modules to limit the number of CFD runs required.

23 Ventilated Velocity (m/s) Gaussian Emulation What next??? Mass Flow Rate (kg/s)

24 Gaussian Emulation What next? Initially tried to use existing CFD result to build emulator. Unsuccessful Had to define the boundaries of the input space. Mass flow rates of 0 to 200kg/s Ventilated velocity of 0 to 6.5m/s Define training runs and validation runs Analysis conducted on Wellhead Platform (not experimental test rig)

25 Cloud Size (m 3 ) Gaussian Emulation Initially 20 training runs What next?

26 Gaussian Emulation What next? Initially 20 training runs and 10 validation runs

27 Gaussian Emulation What next? Initially 20 training runs and 10 validation runs Poor Performance in this region Training Run Validation Run

28 Gaussian Emulation Increase the number of training runs What next? 20 Training Runs 30 Training Runs 40 Training Runs 50 Training Runs

29 Gaussian Emulation Increase the number of training runs What next? 20 Training Runs 30 Training Runs 40 Training Runs 50 Training Runs

30 Gaussian Emulation Increase the number of training runs What next? 20 Training Runs 30 Training Runs 40 Training Runs 50 Training Runs

31 Initial conclusions Gaussian Emulation What next? Obvious that the more training runs you do the better the model gets. You only know what you know Model doesn t like extrapolating (like all models). Do we need to accurately model across the whole input space?

32 Gaussian Emulation What next? Risk Acceptance Criteria (RAC) Not as concerned with accuracy in these regions.

33 Next steps Gaussian Emulation Incorporate knowledge of previous analysis Extend input space to avoid extrapolation What next? Initially look at limited number of data points using lattice hypercube Then focus on key areas with targeted training runs Over time with more simulations and more data model will be able to learn.

34 Conclusions CFD Validation Good match and conservative Frozen Cloud Validation Results are physical Large degree of variance Should verify method for a particular case using CFD Gaussian Emulation offers an alternative to Frozen Cloud

35 Any Questions?

36 Additional slides to address potential questions.

37 Sensitivity Study Ventilation versus Experimental Data Initially a number of sensitivity studies were conducted. The following variables were varied Control volumes size Domain size This was done to check that a grid independent solution was being obtained and that the domain boundaries are not affecting the simulation results. Sensitivity Control Volume Size Domain Size Study times the module length in all directions times the module length in all directions times the module length in all directions time platform length downwind and 2 times platform length upwind and crosswind 2. 1 Based on FLACS best practice guidance 2 Based on guidance in ISO15138

38 Sensitivity Study Ventilation versus Experimental Data The sensitivity study was conducted for experiment A20. The results for the different cases are shown below. It can be seen that there is little difference between the different cases but the 0.25m case gives a significant increase in computational time. Based on the results of the sensitivity the 0.5m grid was used for future cases.

39 Configuration A Ventilation versus Experimental Data The results are plotted on a logarithmic scale showing the following: Target i.e. CFD is equal to experimental; +/- a factor of two; Error bars for the experimental results based on the accuracy of the anemometers.

40 Configuration B Ventilation versus Experimental Data

41 Configuration C Ventilation versus Experimental Data For all configurations the results show there is a good match between the CFD and experimental results and for all the scenarios the calculated values Date: lie01 within January the error 2011 bars.

42 Geometric Resolution Ventilation versus Experimental Data Ventilation results are used for 3 main purposes Calculation of ACPH values to verify that 12ACPH is met 95% of the time; To feed into wind chill calculations; To select representative wind speeds and directions for dispersion analysis. It is well know that geometry has a significant impact on overpressures in explosion modelling. A further sensitivity was conducted to investigate the impact of geometric resolution for ventilation modelling to understand if analysis were to be conducted at FEED whether it would be necessary to include anticipated congestion.

43 Geometric Resolution Ventilation versus Experimental Data Full Model Reduced Model All geometry <6 was removed from the model.

44 Geometric Resolution Ventilation versus Experimental Data The reduced model results in ~30% increase in the ACPH due to the level of blockage being reduced. Therefore it is necessary to include not just the large scale geometry if using CFD to calculate ventilation rates and geometry is not just important for explosion modelling.