冷热电联供(combined cooling,heating and power,CCHP)系统是分布式能源系统发展的主流趋势,针对CCHP系统的能量调度问题,提出了储电、储热相结合的复合储能技术;为实现CCHP系统的运行优化控制,建立了CCHP系统拓扑架构、系统模型、多...冷热电联供(combined cooling,heating and power,CCHP)系统是分布式能源系统发展的主流趋势,针对CCHP系统的能量调度问题,提出了储电、储热相结合的复合储能技术;为实现CCHP系统的运行优化控制,建立了CCHP系统拓扑架构、系统模型、多目标函数及约束条件,采用线性加权和法将多目标函数转化为单目标函数,利用遗传算法进行优化求解,并与不含复合储能的CCHP系统进行对比分析。结果表明:将复合储能引入CCHP系统,能有效降低系统运行成本和一次能源消耗量,提高系统节能率和削峰填谷能力,为CCHP系统的优化运行策略提供了较好的参考方法。展开更多
In this work,the nickel-based powder metallurgy superalloy FGH95 was selected as experimental material,and the experimental parameters in multiple overlap laser shock processing(LSP)treatment were selected based on or...In this work,the nickel-based powder metallurgy superalloy FGH95 was selected as experimental material,and the experimental parameters in multiple overlap laser shock processing(LSP)treatment were selected based on orthogonal experimental design.The experimental data of residual stress and microhardness were measured in the same depth.The residual stress and microhardness laws were investigated and analyzed.Artificial neural network(ANN)with four layers(4-N-(N-1)-2)was applied to predict the residual stress and microhardness of FGH95 subjected to multiple overlap LSP.The experimental data were divided as training-testing sets in pairs.Laser energy,overlap rate,shocked times and depth were set as inputs,while residual stress and microhardness were set as outputs.The prediction performances with different network configuration of developed ANN models were compared and analyzed.The developed ANN model with network configuration of 4-7-6-2 showed the best predict performance.The predicted values showed a good agreement with the experimental values.In addition,the correlation coefficients among all the parameters and the effect of LSP parameters on materials response were studied.It can be concluded that ANN is a useful method to predict residual stress and microhardness of material subjected to LSP when with limited experimental data.展开更多
文摘冷热电联供(combined cooling,heating and power,CCHP)系统是分布式能源系统发展的主流趋势,针对CCHP系统的能量调度问题,提出了储电、储热相结合的复合储能技术;为实现CCHP系统的运行优化控制,建立了CCHP系统拓扑架构、系统模型、多目标函数及约束条件,采用线性加权和法将多目标函数转化为单目标函数,利用遗传算法进行优化求解,并与不含复合储能的CCHP系统进行对比分析。结果表明:将复合储能引入CCHP系统,能有效降低系统运行成本和一次能源消耗量,提高系统节能率和削峰填谷能力,为CCHP系统的优化运行策略提供了较好的参考方法。
基金Projects(51875558,51471176)supported by the National Natural Science Foundation of ChinaProject(2017YFB1302802)supported by the National Key R&D Program of China。
文摘In this work,the nickel-based powder metallurgy superalloy FGH95 was selected as experimental material,and the experimental parameters in multiple overlap laser shock processing(LSP)treatment were selected based on orthogonal experimental design.The experimental data of residual stress and microhardness were measured in the same depth.The residual stress and microhardness laws were investigated and analyzed.Artificial neural network(ANN)with four layers(4-N-(N-1)-2)was applied to predict the residual stress and microhardness of FGH95 subjected to multiple overlap LSP.The experimental data were divided as training-testing sets in pairs.Laser energy,overlap rate,shocked times and depth were set as inputs,while residual stress and microhardness were set as outputs.The prediction performances with different network configuration of developed ANN models were compared and analyzed.The developed ANN model with network configuration of 4-7-6-2 showed the best predict performance.The predicted values showed a good agreement with the experimental values.In addition,the correlation coefficients among all the parameters and the effect of LSP parameters on materials response were studied.It can be concluded that ANN is a useful method to predict residual stress and microhardness of material subjected to LSP when with limited experimental data.
