Basic Example 1. (2 Symmetric Tanks)
In this example, 2 Symmetric Tanks are considered. Because of the symmetry of the model, it is expected that resulted scenarios become compatible with each other and results also become symmetric.
In this example data are recorded by recorder Object not with Objs_recorder. Also the code completely can be downloaded from here in the Jupyter Notebook format.
Import required packages
In this example only OpenSRANE package is enough and there is no need to any other Python packages. So:
import opensrane as opr #Enter Token opr.Token.userpass('username', 'password')
Initialize the model and define reorder
#Wipe the model to clear any created object if it is created before opr.wipe() #Define the recorder opr.Recorders.recorder(tag=1, filename='Recorder_ex1', fileAppend=False, recordfield='DamageLevel',) #Clear Warning File content opr.Misc.warningClear()
Define Hazard Curve and DateTime object and wind Data and Site data
#define Hazard Curve PGA=[1.4, 1.29984,1.27091,1.24865,1.22194,1.20191,1.17297,1.13959,1.11065,1.08172, 1.05501,1.03275,0.994913,0.961526,0.92814,0.899205,0.872496,0.843561,0.816852, 0.790143,0.761208,0.732273,0.696661,0.667727,0.636566,0.609857,0.589825,0.569793, 0.549762,0.525278,0.503021,0.48744,0.469634,0.451828,0.431797,0.409539,0.38283, 0.356121,0.338315,0.307154,0.280445,0.260413,0.23593,0.211447,0.17806,0.158029, 0.142448,0.126868,0.113514,0.0979332,0.0845787,0.0712242,0.0578696,0.0534181, 0.0489666,0.0445151,0.0356121,0.0356121,0.0311606, 0] PGA=[p*9.81 for p in PGA] Prob=[0, 0.000000446937,0.000000529832,0.000000607087,0.000000695608,0.000000824624,0.000000944864, 0.00000115888,0.00000137382,0.00000157414,0.0000019307,0.00000206667,0.00000262252,0.00000321654, 0.00000381312,0.00000452035,0.00000535876,0.00000657255,0.00000779158,0.00000955643,0.0000113289, 0.0000134301,0.0000176322,0.000021626,0.0000265244,0.0000325323,0.0000372759,0.0000441896,0.000050633, 0.0000621017,0.0000711569,0.0000843545,0.0000966544,0.000110748,0.000131288,0.000155639,0.0001975, 0.000259294,0.000318026,0.000403563,0.000548171,0.000744597,0.00104642,0.00152148,0.00253478,0.00356225, 0.00467682,0.00657255,0.00892769,0.0109499,0.0143759,0.0209024,0.0274425,0.0325323,0.0399011,0.048939, 0.060024,0.0687762,0.0843545, 0.9999] opr.Hazard.Earthquake(1,'PGA',PGA,Prob) #Create Hazard Object with tag=1 that is 0th Object #Define date time distribution opr.DateAndTime.DateTime(1,Day_Night_Ratio=2) #Define a simple windRose windobj=opr.WindData.WindRose(1) windobj.WindDayClassList=['F','D','D'] windobj.WindNightClassList=['F','D','D'] windobj.AlphaCOEFlist=[0.6,0.25,0] windobj.DayWindSpeedList=[[1,5],[5,9],[9]] windobj.NightWindSpeedList=[[1,5],[5,9],[9]] windobj.DayWindFreqMatrix=[[0.5,0.5,0], [0.5,0.5,0], [0.5,0.5,0], [0.5,0.5,0], ] windobj.NightWindFreqMatrix=windobj.DayWindFreqMatrix #Define Site Condition and Geometry SiteTAg=1 opr.Sites.Site(SiteTAg, Temperature=25+273, Pressure=1*10**5, XSiteBoundary=[0,100,100,0], YSiteBoundary=[0,0,100,100], g=9.81)
Define Materials and Fragilities and Probits
Butene considered as tank content. A new value considered for the specific heat of combustion of this material, so the considered value of defined object modified after the definition. Only, pool fire event considered for this model, so only a probit for considering the vulnerability under radiation of tanks defined.
#Define Substances opr.Substance.DataBank.Butene(1) #Use DataBank to Load Material opr.Substance.ObjManager[1].Specific_Heat_of_Combustion=45.334*10**6 #Define Fragilities opr.Fragilities.Fragility(tag=1,Distribution_Type='lognormal',modename='EBF',mean=0.8,StdDev=0.8) opr.Fragilities.Fragility(tag=2,Distribution_Type='lognormal',modename='GDF',mean=1.18,StdDev=0.61) #Define Probits Radiation=3 opr.Fragilities.Probit(tag=Radiation, Distribution_Type='normal', K1=1.0, K2=-6.5,Scale_Factor=1500)
Define Outflow, Dispersion and physical effect models
Two different outflow model considered. Also, for all considered outflow models just one dispersion model defined. And fire point source model defined for physical events.
#Define Outflow Models tag=1 opr.OutFlowModel.TankHole(tag, Hole_Diameter=0.05, Hole_Height_FromBot=0, delta_t=500, Cd=1) opr.OutFlowModel.SimultaniousLiquid(2) #Define Dispersion Spread Models and their connections to the materials and outflows opr.DispersionSpreadModels.LiquidSpread(tag=1, MatTags=[1], OutFlowModelTags=[1,2],MinDisThickness=0.005,) #Define Physical Effect models opr.PhysicalEffect.fire_point_source(tag=1, minf=0.055, k=1.5)
Define connectors to connect models to each other
DS_LOC:
Using DS_LOC for damages caused under Fragility tag 1 the outflow model with tag 2 will be consider as the outflow model. Also, for damages caused under Fragility tag 2 the outflow model with tag 1 will be consider as the outflow model.
Out_Physic:
For any unit that have material with tag 1 and Outflow with tag 1 the physical effect with tag 1 will be considered. Also, For any unit that have material with tag 1 and Outflow with tag 2 the physical effect with tag 1 will be considered again.
Pb_LOC:
Finally for any units that damaged under because probit tag equal to 3 (Radiation was equal to 3) outflow model with tags 1 or 2 will be consider for them with equal probability. The probability of of seleccting outflowmodel 1 or 2 is equal because the defined weight for them is similar (LOCProbabilityList=[1,1]).
#Define the DS_LOC for each Fragility opr.Connectors.DS_LOC(1,FragilityTag=1,OutFlowModelTagList=[2],LOCProbabilityList=[1]) opr.Connectors.DS_LOC(2,FragilityTag=2,OutFlowModelTagList=[1],LOCProbabilityList=[1]) #Define OutFlow-Physical Effect connection opr.Connectors.Out_Physic(tag=3,OutFlowTag=1, MaterialsTagList=[1],PhysicalEffectTagList=[1],PhysProbabilityList=[1]) opr.Connectors.Out_Physic(tag=4,OutFlowTag=2, MaterialsTagList=[1],PhysicalEffectTagList=[1],PhysProbabilityList=[1]) #Define Probit - LOC loss of containment Connectors opr.Connectors.Pb_LOC(tag=5, ProbitTag=Radiation, OutFlowModelTagList=[1,2], LOCProbabilityList=[1,1])
Define Safety dike and plant units
Plant units defined and its properties according defined models tag specified for them. The Fragility tag and vulnerability probit and their location and material and internal pressure and temprature and …
#Define Dike Object opr.Safety.Dike(1,2,30**2) #Define Plant Units opr.PlantUnits.ONGStorage(tag=1, SiteTag=1, DikeTag=1, SubstanceTag=1, FragilityTagNumbers=[1,2], Horizontal_localPosition=0, Vertical_localPosition=0, Surface_Roughness=0.0001, Pressure=1.1*10**5, Temperature=25+273, SubstanceVolumeRatio=0.8, Diameter=10, Height=10, GroundTemperature=25+273, radiation_probit_tag=Radiation,) opr.PlantUnits.ONGStorage(tag=2, SiteTag=1, DikeTag=1, SubstanceTag=1, FragilityTagNumbers=[1,2], Horizontal_localPosition=40, Vertical_localPosition=0, Surface_Roughness=0.0001, Pressure=1.1*10**5, Temperature=25+273, SubstanceVolumeRatio=0.8, Diameter=10, Height=10, GroundTemperature=25+273, radiation_probit_tag=Radiation,)
Define Analysis
By finishing the modeling, Using analysis command the number or analysis and type of analysis specified for model. In this model it is considered to do analysis for 10000 times. MultiAnalysis type considered for analysis and this type implement multiple analysis using only one cpu.
#Analysis opr.Analyze.ScenarioAnalyze.MultiAnalysis(AnalysisNumber=10_000)
Post Processing
By finishing the analysis, using the PostProcess subpackage the probability of damage scenarios for each plant unit and for both are calculated. As it is seen in the above results, as we expected, the resulted scenarios are also symmetric. Obviously by increasing the number of the analysis, the probability of scenario (0)-[1] become closer to (0)-[2].
#Post Process Results=opr.PostProcess.RecorderPP(['Recorder_ex1']) DM0Scen=Results.Damagelevel_Scenario_Dict()[0] ScenProb=Results.ScenariosProbability() print('\n\n','Recorder Scenarios in Damage level 0 =',DM0Scen,'\n') for Scenario in DM0Scen: print(f'Probability of Scenario {Scenario} is equal: {ScenProb[Scenario]}')
Example by: Bijan Sayyafzadeh