人工神经网络与失效评定图在压力容器安全评定中的应用

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XXX(),R6,,a,mm;c,mm;D,mm;FL;K,Nmm-3P2;Kp,KspsK,Nmm-3P2;KIc,Nmm-3P2;Kr,Ky=Krp+Krs;KrpK1p;KrsKs;Lr,Lr=pP;p,MPa;t,mm;p,NPmm2;s,NPmm2;y,NPmm2,,,[1],,,,,[2]1(FAD)(CEGB),,[3]3R63,1,,1R6,Lr1.0,,Lr1.0R6,10%,0821999XXX,,19623,,,730050(98251313),[4]400MPa112[5],BP,,,2BP[2],32(BP)3.122,(22,1),psta2cyKIc,,,,6pa,23,,R61DmmpMPapNmm-2sNmm-2tmmamm2cmmyNmm-2KcNmm-32R6LrKrFLLrKrFL1[6]22002.8571.0144441910024019070.301.010.690.331.020.642[6]FG43123000.6046.027640357046033800.201.000.220.171.080.423[6]PCrNiMo260240.00315.3085636094028000.381.180.810.391.180.624[6]20g202003.60144.813825.11032.523028210.630.521.350.600.511.365[6]20g202003.60196.6025.13.5280523028210.860.241.130.860.251.176[7]16MnR92001.57169.9182.283826100303.832040.571.190.480.571.200.587[7]16MnR92001.57244.8182.283815.5500303.832041.001.780.201.031.750.128[7]16MnR92001.57244.8182.28381575303.832040.821.000.710.831.030.689[8]22g9002266.0509048025029350.530.141.880.520.131.8310[8]SM6001253.708522.6230377.721600.140.263.540.150.243.4511[8]SM6001253.708517.1108377.727600.140.155.310.150.184.8312[8]SM60032147.008515.56529529780.500.321.750.480.301.8213[8]16MnR92002.1468.224929.333041525411.130.850.721.130.860.7114[8]16MnR92002.1468.224929.33841525411.130.630.781.130.680.7715[8]16MnR92002.1468.224929.32.5841525411.130.610.791.130.650.7916[8]16MnR92002.1290.783302.55014525410.710.411.270.710.391.2317[8]16MnR92002.1290.783302.513541525410.850.481.060.730.451.1318[8]16MnR92002.1266.583302.59041525410.660.401.360.670.401.2719[8]16MnR92002.1266.5833026041524510.650.341.410.660.351.3620[8]16MnR92002.1274.683302.55041525410.670.391.340.670.371.3021[8]16MnR92002.1302.9833026041525410.740.381.240.740.391.2022[8]16MnR92002.131583302.520041525410.790.441.140.810.471.051822652pR6PMPaLrKrFLPMPaLrKrFL149.00.21000.97601.000048.00.228800.933651.01000681.90.27000.96431.000082.90.321530.886411.0081016366.70.89560.49371.0033363.70.877460.478670.9978918359.50.89030.50471.0045342.50.847570.501420.9987621372.90.91100.44931.0030369.90.896160.466880.9991522358.00.89780.48781.0033330.00.848020.486220.998523aR6PmmLrKrFLPmmLrKrFL114.40.29780.96211.000014.20.327750.844341.00310612.00.57320.90000.998310.40.596530.809171.00060167.70.70650.82191.00427.60.712210.709591.00000187.40.65790.85601.00156.80.673860.700840.99653216.10.73840.79401.00415.60.745880.623120.99665224.30.80300.73030.99633.00.812990.499230.9888223,,,3.2,,(),16,36(3,),,3p4a5KIc28219996y3.3,16FLpa,78(3,),paFL,,7aFL8KIcFL44.1,R6,4.2,FLpa,4.3,,,,FAD4.41,..,1995,22(3):1761792,,..,1999,26(3):1761783.RPHPR623.,19884,,..,1995,22(2):1151205ToshioFukuda&TakanoriShibata.TheoryandApplicationsofNeuralNetworksforIndustralControlSystems.IEEETrans.I.E.,1992,39(6):4724876..:,19947CVDA21984.,1985,2(1):1178,,..:,1991(:1999206229)382265ceptinoptimumstructure,andtheoptimumdesignandmanufac2tureweremadefortheupanddownnozzlesinsphericalvesselsofsuper2highstrengthpressuresteelforspaceflight.Thepracticalcheckandtestshowthatthedesignreducedtheweightofvesselsandraisedexplosionpressureofvessels.KeyWords:FullStressDesign,PressureVessel,FiniteEle2mentMethodDevelopmentandapplicationsofspraying2dryingtech2niqueforpastymaterials.WangJiangwen,LiZhenfa,TianRui,ShiZhigang(InnerMongoliaPolytechnicUniversity,Huhehaote,InnerMongolia,China)(275)Thecommondryingmethodsofpasty,non2Newtonianfluidathomeandabroadwerepresented,andthecharacteristicsofchlortetracylinepastewith60%70%watercontentwereanal2ysed.Thedifficultiesoftransportationandatomizationduringdry2ingthematerialandtheimprovementswereproposed,andtheap2plicationexamplesofthetechniqueweregiven.KeyWords:Paste,Spraying2DryingDevelopmentof1.2m3multi2functionalstirringreac2tors.MaZhenjie(Economy&TradeCommitteeofDongyingDis2trict,Dongying,Shandong,China)(278)Thekeysofdesignandmanufactureof1.2m3multi2func2tionalstirringreactorswerepresented,thereactorshavegoodsuitabilityandstableperformanec,andareparticularlysuitableforlabsandsmall2scalechemicalplants.KeyWords:Reactor,Stirrer,Design,ManufactureApplicationsofartificialneuralnetworkandfailureas2sessmentdiagraminthesafetyestimationofpressureves2sels.YuShurong,LiErguo,JiaLi(GansuUniversityofTechnol2ogy,Lanzhou,Gansu,China)(280)Theartificialneuralnetworkandfailureassessmentdiagramwereusedtodothesafetyestimationofpressurevessels,thesam2pleassessmentresultisinagreementwithR6failurediagramas2sessmentresult,andthesensitiveanalysisofparameterswasmadefortheassessmentresults.Theanalysisresultwassatisfactory.Theartificialneuralnetworkprovidesanefficientmethodfortheintelligentsafetyassessmentofpressurevessels.KeyWords:PressureVessel,SafetyAssessment,FailureAssessmentDiagram,ArtificialNeuralNetworkResearchesonthestrengthoffractureandfatigueoftheheatexchangetubesincondensers.DuanWenjiang,QianZhenming(ChongqingUniversity,Chongqing,China)(284)Thecrackexpansionsandfatiguelifeoftheheatexchangetubesincondensersinhighpressuremethylaminepumpswerestudiedbyusingthefracturemechanicstheory,andthecriticalcracksizesandfatiguelifewerecalculated.Theresultsshowthattheservicelifeofthepumpscanbeextendedinn
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