Ignition modelling Are our approaches aligned?

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1 Ignition modelling Are our approaches aligned? Lars Rogstadkjernet Gexcon

2 Outline Experience from 3 rd party reviews Areas of diverging approach Dispersion and ignition modelling

3 3 rd party reviews 3 rd party reviews of 5-6 prob analyses ala Z-013 GexCon perspective: as provider of the tool we like user to be successful Industry perspective: inconsistency does not benefit to anyone Accuracy vs consistency

4 Norsok Z-13 Annex F Detailed guidance 1.Model representation 2.Leak rates and transients 3.Leak directions 4.Winds conditions 5.Time dependent ignition model Widely adopted

5 ERA work tasks Assumption and input Geometry modeling Ventilation analysis Dispersion study Explosion study Risk calculation Reporting

6 ERA work tasks Assumption and input Geometry modeling Ventilation analysis Dispersion study Explosion study Risk calculation Reporting

Geometry modelling Early design stage: incomplete models

(significantly) Congestion estimated based on experience

congestion quantification Congestion characterized as

7 Geometry modelling Early design stage: incomplete models Anticipated congestion modelling basis seem to vary (significantly) Congestion estimated based on experience from previous projects Not consistent measure for congestion quantification Congestion characterized as packing density: Meter of pipe/volume = OK Meter of box/volume???? Databases predictions relying on box length counts have fundamental flaws! Considerable spread in practice x3 x5 x9

8 Practicalities ACM A model that look reasonable can be assessed by the engineering party. Is this possible with extensive use of randomized congestion? How can correctness be challenged? Is this a reasonable congestion level around a compressor?

9 Practicalities ACM Does it matter where the missing congestion are located? Should much of this be neatly arranged here? How much does it matter?

10 ERA work tasks Assumption and input Geometry modeling Ventilation analysis Dispersion study Explosion study Risk calculation Reporting

11 Dispersion modelling in ERA The normal approach: 1. Run X number of dispersion simulations 2. Derive cloud size and ignition probability 3. Not all scenarios can be simulated hence many are «extrapolated» X vary enormously! Consequently the degree of «extrapolation» varies greatly Little consistency with efforts on explosion simulations

Basic logic of TDIIM /OLF ignition models Ignition is more likely when the cloud covers a large area When explosive atmosphere persist for a long time, ignition would increase

12 Basic logic of TDIIM /OLF ignition models Ignition is more likely when the cloud covers a large area When explosive atmosphere persist for a long time, ignition would increase Likelihood of ignition should be high at first time exposure and then drop Gas alarm/activation of ignition control measures should have an effects All relate directly to gas dispersion

13 And it s complex. 6 kg/s 96 kg/s 24 kg/s 3 kg/s 1,5 kg/s Ignition probability drops as time passes

14 Case example 1 2 releases with same: Release rate Leak profile Release location Wind speed Wind direction Wind Only difference is leak direction

15 Case example 1

16 Case example 1 3,000 2,500 2,000 1,500 1, Cloudsize vs time

17 Case example 1 TDIIM ignition probability as function of time Sum ignition probability A: 3.65% Sum ignition probability B: 0.49%

18 Case example m m 3

19 Case example 1 Scenario Ignition probability Pressure A 3.65% 0.40 B 0.49% 0.02 Can simplify or average? Can you get by with only doing one? Sensible approach seem to include both scenarios

20 Case example 1 What about the other scenarios? Scenario Ignition probability Pressure A 3.65% 0.40 B 0.49% 0.02 C D E An average rate specific ignition probability?

21 Case example 2 ERA for FLNG Generic TDIIM approach Ignition predicted from 1. Leak = source term 2. Quantity released 3. Ventilation = sink term 4. Module volume Ign prob calculated for each rate and wind condition

22 Case example 2 FLNG - buoyant and non buoyant gas Two identical release scenarios same vessel, same wind, same leak rate, same location Only difference is composition: A = Propane (refrigerant) B = Natural gas (LNG)

23 Case example 2: 8m/s wind

24 Cloud size [m3] Case example 2 Refrigerant and natural gas produce almost identical cloud sizes Ignition probability will be almost the same Wind trumps buoyancy effects 1,600 1,400 1,200 1, Flammable cloud vs time C3-8m/s C1-8m/s Time [s] OK

25 Wind = 1m/s

26 Cloud size [m3] Case example 2 Flammable cloud vs time 6,000 5,000 4,000 3,000 2,000 1,000 0 C3-1m/s C1-1m/s Time [s]

27 Ignition probability Ignition probability Case example 2 2.0E-05 Ignition probability: C3,1m/s wind 2.0E-05 Ignition probability: C1,1m/s wind 1.5E-05 Continuos Intermitent Sum 1.5E-05 Continuos Intermitent 1.0E E-05 Sum 5.0E E E Time [s] 0.0E Time [s] Sum ignition probability A: Sum ignition probability B: 1.67% 0.77%

28 Case example 2 Propane cloud: 4800m3: 2.4 barg Natural gas cloud: 2100m3 0.20barg

29 Case example 2 Scenario Ignition probability Pressure A 1.65% 2.40 B 0.77% 0.20 Not OK Because buoyancy trumps wind at low wind speed

30 Design changes Process deck (yellow) changed from grating to plating Reduce volume where gas can accumulate

31 Design change

32 Design change Design change affect non buoyant releases but not buoyant!

33 Summary Explosion risk is governed by many parameters that affect each other in a non linear manner We ve seen: 1. Leak orientation 2. How wind affect compositions differently 3. Effect of geometry vary with composition And there are more Scenari o Ign P Pressur e A 3.65% 0.40 B 0.49% 0.02 C AAA Is it possible to simplify dispersion and ignition modelling without loosing accuracy? Are we confident these simplifications predict the right answer? AAB AAC

34 Summary Industry practice for dispersion «Clever methods» and black box predictions (untraceable) Different approaches by different consultants Simplifications in dispersion and ignition modelling 1. The reason for diverging load prediction? 2. The impact may be substantial can affect design recommendations So: Scale back on black boxes and «clever» simplifications? CPU cost are not a valid argument Explicit ignition modelling from CFD results are easily available Increased transparency and trust in results A more explicit standard?

35 Does anyone benefit from this inconsistency?

36 Questions?

CFD Based Approach for VCE Risk Assessment

CFD Based Approach for VCE Risk Assessment 2009 MKOPSC International Symposium Anna Qiao, Steven Zhang, Asmund Huser DNV Energy Objective Determine the maximum design load at a specified frequency Provide