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Uncertainty analysis of Phast's atmospheric dispersion model for two industrial use cases

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An uncertainty analysis of the leak source term and the outdooer dispersion model of Phast version 6.7. Continous releases of two toxic materials (ammonia, nitric oxide) and two flammable materials (methane, propane). Research undertaken by the Toulouse Chemical Engineering Laboratory and the FonCSI, presented at the 2013 Loss Prevention conference in Firenze.

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Uncertainty analysis of Phast's atmospheric dispersion model for two industrial use cases

  1. 1. Uncertainty analysis of Phast’s atmospheric dispersion model for two industrial use cases Nishant Pandya, Nadine Gabas & Eric Marsden Loss Prevention 2013, Firenze
  2. 2. Context Postdoctoral work of Nishant Pandya • Toulouse Chemical Engineering laboratory (CNRS) • Foundation for an Industrial Safety Culture • industrial partners including DNV Software Simulation of atmospheric dispersion of gas releases • complex physical phenomena • often dimensioning scenarios for land-use planning Phast is widely used to analyze these release scenarios • modeling involves a large number of variables and parameters • variables and parameters affected by uncertainty Political pressure to improve characterization of uncertainty in modelling results • new French legislation on land-use planning around Seveso-type installations 2 / 18 Loss Prevention 2013
  3. 3. Study objectives Uncertainty analysis of Phast version 6.7 • leak source term • outdoor dispersion model Analyze 10–30 minute continuous releases of four materials • two toxic (ammonia, nitric oxide) • two flammable (methane, propane) Examine the impact of representative variations of physical variables & internal model parameters • uncertainty propagation Two different use-cases: • accident-investigation • risk-prevention 3 / 18 Loss Prevention 2013
  4. 4. Uncertainty analysis choice of submodel Phast numerical resolution parameters input variables sub-model parameters uncertainty analysis sensitivity analysis [Pandya et al 2011] outputs ik p v ∏ ∂ = Max number of iterations, of timesteps, etc. Jet model, wind speed profile, etc. Weather: - wind speed - stability class - atmospheric temp. Source term: - orifice diameter - release angle - roughness Jet dilution params, passive transition params (rupas), etc. 4 / 18 Loss Prevention 2013
  5. 5. Uncertainty analysis Study the effect on model outputs of variability or uncertainty affecting model inputs 5 / 18 Loss Prevention 2013
  6. 6. Uncertainty analysis Study the effect on model outputs of variability or uncertainty affecting model inputs Histograms concentration at 1000 m 5 / 18 Loss Prevention 2013
  7. 7. Uncertainty analysis Study the effect on model outputs of variability or uncertainty affecting model inputs Histograms Quantified using the coefficient of variation • CV = σ µ concentration at 1000 m CV = 10%CV = 125% 5 / 18 Loss Prevention 2013
  8. 8. Analysis strategy Compare output variability for two industrial use-cases: accident-investigation: • user models a historical accident, for which he has some (uncertain) information on release conditions & weather conditions • wishes to assess the level of confidence given these “irreducible” input uncertainties • input uncertainties: defined with help from expert Phast users risk-prevention: • risk assessment for regulatory purposes / process design • modeling guidelines impose stereotypical assumptions on release conditions to increase homogeneity of risk assessments across a regulatory domain • assess confidence in model outputs given uncertainty on internal Phast parameters • input uncertainties: gaussian distribution with ±10% variability around default value 6 / 18 Loss Prevention 2013
  9. 9. Analysis strategy Compare output variability for two industrial use-cases: accident-investigation: • user models a historical accident, for which he has some (uncertain) information on release conditions & weather conditions • wishes to assess the level of confidence given these “irreducible” input uncertainties • input uncertainties: defined with help from expert Phast users risk-prevention: • risk assessment for regulatory purposes / process design • modeling guidelines impose stereotypical assumptions on release conditions to increase homogeneity of risk assessments across a regulatory domain • assess confidence in model outputs given uncertainty on internal Phast parameters • input uncertainties: gaussian distribution with ±10% variability around default value 6 / 18 Loss Prevention 2013
  10. 10. Analysis strategy Compare output variability for two industrial use-cases: accident-investigation: • user models a historical accident, for which he has some (uncertain) information on release conditions & weather conditions • wishes to assess the level of confidence given these “irreducible” input uncertainties • input uncertainties: defined with help from expert Phast users risk-prevention: • risk assessment for regulatory purposes / process design • modeling guidelines impose stereotypical assumptions on release conditions to increase homogeneity of risk assessments across a regulatory domain • assess confidence in model outputs given uncertainty on internal Phast parameters • input uncertainties: gaussian distribution with ±10% variability around default value releaseconditions uncertainty modeluncertainty 6 / 18 Loss Prevention 2013
  11. 11. Analysis strategy Compare output variability for two industrial use-cases: accident-investigation: • user models a historical accident, for which he has some (uncertain) information on release conditions & weather conditions • wishes to assess the level of confidence given these “irreducible” input uncertainties • input uncertainties: defined with help from expert Phast users risk-prevention: • risk assessment for regulatory purposes / process design • modeling guidelines impose stereotypical assumptions on release conditions to increase homogeneity of risk assessments across a regulatory domain • assess confidence in model outputs given uncertainty on internal Phast parameters • input uncertainties: gaussian distribution with ±10% variability around default value releaseconditions uncertainty modeluncertainty 6 / 18 Loss Prevention 2013
  12. 12. Method 1 Select products and storage conditions 2 Select relevant Phast parameters and their distributions 3 Decide on relevant outputs 4 Execute Phast multiple times and analyze distribution of outputs NO NH3 methane propane 7 / 18 Loss Prevention 2013
  13. 13. Method 1 Select products and storage conditions 2 Select relevant Phast parameters and their distributions 3 Decide on relevant outputs 4 Execute Phast multiple times and analyze distribution of outputs Done with help from expert Phast users, to be representative of industrial use cases bounds allowed by Phast 7 / 18 Loss Prevention 2013
  14. 14. Method 1 Select products and storage conditions 2 Select relevant Phast parameters and their distributions 3 Decide on relevant outputs 4 Execute Phast multiple times and analyze distribution of outputs 12 C500: concentration at 500 m C1k: concentration at 1 km C2k: concentration at 2 km Downwind distance (m) 7 / 18 Loss Prevention 2013
  15. 15. Method 1 Select products and storage conditions 2 Select relevant Phast parameters and their distributions 3 Decide on relevant outputs 4 Execute Phast multiple times and analyze distribution of outputs 7 / 18 Loss Prevention 2013
  16. 16. Method 1 Select products and storage conditions 2 Select relevant Phast parameters and their distributions 3 Decide on relevant outputs 4 Execute Phast multiple times and analyze distribution of outputs No comparison with experimental results More than a million Phast executions over duration of project! 7 / 18 Loss Prevention 2013
  17. 17. Scenario tree: risk-prevention use-case Fine-grained scenario-based approach facilitates interpretation of results 4 “bifurcation parameters”: release duration, release rate, weather conditions, release angle → 16 scenarios θ 0° 10 min. duration 30 min. duration continuous release low release rate high release rate θ 90° neutral weather stable weather θ 0° θ 90° Sc 3 Sc 4 θ 0° θ 90° Sc 5 θ 0° θ 90° Sc 6 Sc 7 Sc 8 θ 0° θ 90° Sc 9 θ 0° θ 90° Sc 10 Sc 11 Sc 12 θ 0° θ 90° Sc 13 θ 0° θ 90° Sc 14 Sc 15 Sc 16Sc 1 Sc 2 high release ratelow release rate neutral weather neutral weather neutral weather stable weather stable weather stable weather “Low” and “high” release rates selected to be product-appropriate 8 / 18 Loss Prevention 2013
  18. 18. Parameters: risk-prevention use-case Parameter Default value Distribution µ(mean) σ(std dev.) α1 (jet entrainment parameter) 0.17 normal 0.17 0.0085 α2 (cross-wind entrainment parameter) 0.35 normal 0.35 0.0175 CDa (drag coefficient of plume in air) 0 exponential λ = 69.2 γ (dense cloud side entrainment parameter) 0 exponential λ = 34.6 CE (cross-wind spreading parameter) 1.15 normal 1.15 0.0575 epas (near-field passive entrainment parameter) 1 normal 1 0.05 ru pas (max cloud/ambient velocity parameter) 0.1 normal 0.1 0.005 rro pas (max cloud/ambient density parameter) 0.015 normal 0.015 0.00075 rE pas (max non-passive entrainment fract°param.) 0.3 normal 0.3 0.015 Ri* pas (max Richardson number) 15 normal 15 0.75 rtr pas (distance for phasing in passive entrainment) 2 normal 2 0.1 Ri (Richardson number for lift-off criterion) -20 normal -20 1 rquasi (quasi-instantaneous parameter) 0.8 normal 0.8 0.04 Ripool (Richardson for passive transition above pool) 0.015 normal 0.015 0.00075 Entpool (pool vaporisation entrainment parameter) 1.5 normal 1.5 0.075 tav tox (s) (averaging time for toxic release) - uniform For 10 min release: [540 – 660] For 30 min release: [1620 – 1980] 9 / 18 Loss Prevention 2013
  19. 19. Scenario tree: accident-investigation use-case Examine influence of uncertainty in “physical” parameters of scenario Similar to risk-prevention, but bifurcation parameters also uncertain duration: [5-15] min duration: [25-35] min continuous release orifice diameter [20-60] mm orifice diameter [160-200] mm θ [0-30]° θ [60-90]° orifice diameter [20-60] mm orifice diameter [160-200] mm neutral weather stable weather Sc 103 Sc 104 Sc 105 Sc 106 Sc 107 Sc 108 Sc 109 Sc 110 Sc 111 Sc 112 Sc 113 Sc 114 Sc 115 Sc 116Sc 101 Sc 102 neutral weather neutral weather neutral weather stable weather stable weather stable weather θ [0-30]° θ [0-30]° θ [0-30]°θ [60-90]° θ [60-90]° θ [60-90]° θ [0-30]° θ [0-30]° θ [0-30]°θ [0-30]°θ [60-90]° θ [60-90]° θ [60-90]° θ [60-90]° 10 / 18 Loss Prevention 2013
  20. 20. Parameters: accident-investigation use-case Parameter Nomenclature / Unit Distribution Range of variation Tst Storage temperature / K triangular NH3: [263.15 – 283.15] centered at 273.15 K NO, CH4, C3H8: [273.15-293.15] centered at 283.1K Lh Liquid height / m uniform [12.75 - 17.25] Ta Atmospheric temperature / K triangular [282.65 - 287.65] centered at 285.15 K Pa Atmospheric pressure / Pa uniform [0.99·10 5 - 1.035·10 5 ] Ha Relative atmospheric humidity / - triangular [0.55 - 0.85] centered at 0.7 DO Orifice diameter / m triangular Value 1: [0.02 - 0.06] centered at 0.04 Value 2: [0.16 - 0.20] centered at 0.18 Durmax Maximum release duration / s uniform Value 1: [300 - 900] Value 2: [1500 - 2100] angle Release angle / degree uniform Value 1: [0 - 30] Value 2: [60 - 90] SC Stability Class / - discrete Neutral: [10 % C/D, 80 % D, 10 % E] Stable: [10 % E, 80 % F, 10 % G] ua Wind speed / m·s-1 uniform Neutral: [4 - 6] Stable: [1.5 - 3] Sflux Solar radiation flux / W·m-2 triangular Neutral: [250 - 1000] centered at 500 Stable: [0 - 500] centered at 250 ZR Release height above ground/ m uniform [1 - 10] Z0 Surface roughness length / m triangular [0.5 - 1.5] centered at 1 m 11 / 18 Loss Prevention 2013
  21. 21. Other modeling assumptions Continuous discharges from a storage tank (“leak” module of Phast) Cloud is assumed to progress in an open field (no impingement) Study downwind concentrations: • from 50 m to 200 m for flammable releases • from 500 m to 2 km for toxic releases Reference height for outputs: • 1.5 m for toxic releases • center of cloud for flammable releases Core averaging time set to averaging time for all simulations 12 / 18 Loss Prevention 2013
  22. 22. Three types of results 1 Scenario-specific uncertainty information for decision-makers 2 Comparing uncertainty for two industrial use-cases 3 Identify release conditions which lead to the highest level of uncertainty 13 / 18 Loss Prevention 2013
  23. 23. Use-case uncertainty comparisonmeanCV(%) NO-RP NH3-RP NO-AI NH3-AI CH4-RP C3H8-RP CH4-AI C3H8-AI C200 C100 C50C500 C1k C2k 14 / 18 Loss Prevention 2013
  24. 24. Use-case uncertainty comparisonmeanCV(%) NO-RP NH3-RP NO-AI NH3-AI CH4-RP C3H8-RP CH4-AI C3H8-AI C200 C100 C50C500 C1k C2k As expected, level of uncertainty always higher for “accident-investigation” than for “risk-prevention” use-cases 14 / 18 Loss Prevention 2013
  25. 25. Release conditions leading to highest uncertainty Risk-prevention use-case 15 / 18 Loss Prevention 2013
  26. 26. Release conditions leading to highest uncertainty Risk-prevention use-case Highest CV for vertical NO releases in stable weather conditions, with high release rates 15 / 18 Loss Prevention 2013
  27. 27. Conclusions 16 / 18 Loss Prevention 2013
  28. 28. Conclusions For the 4 materials studied, model uncertainty is significantly lower than uncertainty resulting from variation in source term and weather conditions We have identified the release conditions which lead to the highest level of model uncertainty (material-dependent) Quantitative information on level of uncertainty in consequence estimations: • helps risk analysts understand the degree of confidence they can place in modeling results • when comparing risk reduction measures, tells whether investment ranking is robust, given modeling uncertainties • when modeling results inform land-use planning, provides information which can help arbitrate between different strategies All modeling results presented to decision-makers should ideally include information on level of uncertainty 17 / 18 Loss Prevention 2013
  29. 29. Thanks for your attention! Follow the FonCSI on Twitter: @TheFonCSI This presentation is distributed under the terms of the Creative Commons Attribution – ShareAlike licence. 18 / 18 Loss Prevention 2013

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