The thermal deformation behavior of FV520B stainless steel is investigated.Isothermal compression tests were conducted at temperatures ranging from 600 to 900℃ and strain rates from 0.001 to 10 s^(−1).The true stress...The thermal deformation behavior of FV520B stainless steel is investigated.Isothermal compression tests were conducted at temperatures ranging from 600 to 900℃ and strain rates from 0.001 to 10 s^(−1).The true stress–strain curves were corrected for friction and temperature due to the drum shape and adiabatic heating.The comparison shows that there is a large difference between the stress before and after the correction,which proves that the correction is necessary.Five constitutive models were developed:the original Arrhenius model,the strain correction Arrhenius model,a new modified Arrhenius model,the back propagation neural network model(BPNN)and the dandelion optimization BPNN model(DO-BPNN).The DO-BPNN model showed the highest prediction accuracy though it was more computationally intensive than the other models.The new modified Arrhenius model performed a better predictive capacity than the strain correction version,while it showed a negligible increase in the number of parameters and computational time.Although artificial neural network-based models exhibit superior accuracy compared to the Arrhenius models,their application in finite element simulations still faces notable challenges.展开更多
To optimize the operating efficiency and extend the lifespan of the multistack fuel cell hybrid system(MFCHS),this paper proposes a two-layer multiobjective optimal energy management strategy that considers the degrad...To optimize the operating efficiency and extend the lifespan of the multistack fuel cell hybrid system(MFCHS),this paper proposes a two-layer multiobjective optimal energy management strategy that considers the degradation of the fuel cell and the battery.Regarding the issues that power fluctuations damage the fuel cells'lifespan and high-current charging and discharging lead to battery capacity decay,the first layer of the strategy adopts locally weighted scatterplot smoothing(LOWESS)to smooth the output power of the fuel cells and prevent the battery from operating under high-current conditions.The second layer considers the uneven degree of degradation among the fuel cells and employs the dandelion optimizer(DO)algorithm to solve the objective function with an aging adaptive factor,optimizing the efficiency and lifespan.Meanwhile,the DO algorithm is enhanced by tent chaotic mapping and differential variation to improve the convergence speed and accuracy.Compared with the equivalent hydrogen consumption minimization strategy(ECMS)and the equal distribution strategy,the proposed strategy improves the average operating efficiency of the fuel cells,effectively reduces the degradation of the fuel cells and the capacity degradation of the battery,and maintains the performance consistency among the fuel cells.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.52275373)the National Natural Science Foundation of China(Grant No.52105397)the Open Foundation of National Key Laboratory of Metal Forming Technology and Heavy Equipment(Grant No.S2308100.W08).
文摘The thermal deformation behavior of FV520B stainless steel is investigated.Isothermal compression tests were conducted at temperatures ranging from 600 to 900℃ and strain rates from 0.001 to 10 s^(−1).The true stress–strain curves were corrected for friction and temperature due to the drum shape and adiabatic heating.The comparison shows that there is a large difference between the stress before and after the correction,which proves that the correction is necessary.Five constitutive models were developed:the original Arrhenius model,the strain correction Arrhenius model,a new modified Arrhenius model,the back propagation neural network model(BPNN)and the dandelion optimization BPNN model(DO-BPNN).The DO-BPNN model showed the highest prediction accuracy though it was more computationally intensive than the other models.The new modified Arrhenius model performed a better predictive capacity than the strain correction version,while it showed a negligible increase in the number of parameters and computational time.Although artificial neural network-based models exhibit superior accuracy compared to the Arrhenius models,their application in finite element simulations still faces notable challenges.
基金supported by the Postgraduate Research and Practice Innovation Program of Jiangsu Province(Grant No.SJCX24_0161)the National Natural Science Foundation of China(Grant Nos.61374153 and 61403199).
文摘To optimize the operating efficiency and extend the lifespan of the multistack fuel cell hybrid system(MFCHS),this paper proposes a two-layer multiobjective optimal energy management strategy that considers the degradation of the fuel cell and the battery.Regarding the issues that power fluctuations damage the fuel cells'lifespan and high-current charging and discharging lead to battery capacity decay,the first layer of the strategy adopts locally weighted scatterplot smoothing(LOWESS)to smooth the output power of the fuel cells and prevent the battery from operating under high-current conditions.The second layer considers the uneven degree of degradation among the fuel cells and employs the dandelion optimizer(DO)algorithm to solve the objective function with an aging adaptive factor,optimizing the efficiency and lifespan.Meanwhile,the DO algorithm is enhanced by tent chaotic mapping and differential variation to improve the convergence speed and accuracy.Compared with the equivalent hydrogen consumption minimization strategy(ECMS)and the equal distribution strategy,the proposed strategy improves the average operating efficiency of the fuel cells,effectively reduces the degradation of the fuel cells and the capacity degradation of the battery,and maintains the performance consistency among the fuel cells.
