Prediction on pitting depth of 316 L instrument tube in salt-lake atmospheric environment
In order to improve the prediction accuracy of pitting depth by the 316 L instrument tube in the salt-lake atmos-pheric environment,the fractional order cumulative grey model(FGM(1,1,r))was improved by using the variable order av-erage weakening buffer operator,integrated background value and metabolism.Firstly,the optimization ability and convergence speed of slime mould algorithm(SMA)were improved by improving the Tent chaos mapping,Levy flight and interval adaptive reverse learning strategies.Then,the parameters r and p in FGM(1,1,r,p)were optimized by ISMA.Finally,the ISMA-FGM(1,1,r,p)prediction model of the pitting depth of instrument tube was constructed.The results show that the optimized new model has smaller error and higher fitting degree than the original model,and has better performance in predicting the pitting depth of instrument tube.The research results can provide a reference for the integrity evaluation and risk warning of instrument tube system.
salt-lake atmospheric environment316 L instrument tubepitting depthimproved slime mold algorithm(IS-MA)FGM(1,1,r)model
NETL
NSTL
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