A transient molecular network model is built to describe the non- linear viscoelasticity of polymers by considering the effect of entanglement loss and regeneration on the relaxation of molecular strands. It is an ext...A transient molecular network model is built to describe the non- linear viscoelasticity of polymers by considering the effect of entanglement loss and regeneration on the relaxation of molecular strands. It is an extension of previous network theories. The experimental data on three thermoplastic polymers (ABS, PVC and PA6) obtained under various loading conditions are used to test the model. Agreement between the theoretical and experimental curves shows that the suggested model can describe successfully the relaxation behavior of the thermoplastic polymers under different loading rates by using relatively few relaxation modes. Thus the mi- cromechanism responsible for strain-rate dependence of relaxation process and the origin of nonlinear viscoelasticity may be disclosed.展开更多
基于台架采集数据,采用外部输入非线性自回归(nonlinear autoregressive model with exogenous input,NARX)神经网络建立了具备瞬态特性的柴油机排气温度计算模型作为虚拟传感器,并采用并发式训练方法对模型进行训练。将结果与前馈神经...基于台架采集数据,采用外部输入非线性自回归(nonlinear autoregressive model with exogenous input,NARX)神经网络建立了具备瞬态特性的柴油机排气温度计算模型作为虚拟传感器,并采用并发式训练方法对模型进行训练。将结果与前馈神经网络、长短期记忆网络(long short term memory,LSTM)神经网络及量产发动机的排温传感器采集结果进行对比。经验证,稳态工况下,两种神经网络均能达到较高精度;欧洲瞬态循环(European transient cycle,ETC)工况下,NARX神经网络计算温度的最大偏差为6.6℃,量产发动机排温传感器测得温度最大偏差为45.9℃。NARX神经网络所需的计算时间约为现有电控单元排温模型的2.5倍。展开更多
电力系统电磁暂态仿真中,常用异步电动机模型来代表动态负荷模型。然而,由于异步电动机数量众多和电机-电网接口方法的局限性,传统异步电动机模型很难兼顾仿真效率和数值稳定性。针对这一问题,该文提出一种用于电力系统电磁暂态仿真的...电力系统电磁暂态仿真中,常用异步电动机模型来代表动态负荷模型。然而,由于异步电动机数量众多和电机-电网接口方法的局限性,传统异步电动机模型很难兼顾仿真效率和数值稳定性。针对这一问题,该文提出一种用于电力系统电磁暂态仿真的异步电动机负荷解耦(induction motor load decoupling,IMLD)模型。该模型结合异步电动机等效电路和LC传输线时延,构造出具有天然时延的电机-电网解耦接口,从而将异步电动机与外部电网解耦,异步电动机的迭代求解过程无需与外部电网同步求解。根据负荷节点给定的潮流有功和无功功率,通过求解等效电路方程并配置IMLD模型参数,使仿真功率结果与潮流计算给定负荷节点功率相匹配。测试算例结果表明,所提IMLD模型可有效减少网络方程迭代次数,同时具备较高准确性和仿真效率,且具备良好的数值稳定性。展开更多
基金The project supported by the National Natural Science Foundation of China and Doctorial Fund
文摘A transient molecular network model is built to describe the non- linear viscoelasticity of polymers by considering the effect of entanglement loss and regeneration on the relaxation of molecular strands. It is an extension of previous network theories. The experimental data on three thermoplastic polymers (ABS, PVC and PA6) obtained under various loading conditions are used to test the model. Agreement between the theoretical and experimental curves shows that the suggested model can describe successfully the relaxation behavior of the thermoplastic polymers under different loading rates by using relatively few relaxation modes. Thus the mi- cromechanism responsible for strain-rate dependence of relaxation process and the origin of nonlinear viscoelasticity may be disclosed.
文摘基于台架采集数据,采用外部输入非线性自回归(nonlinear autoregressive model with exogenous input,NARX)神经网络建立了具备瞬态特性的柴油机排气温度计算模型作为虚拟传感器,并采用并发式训练方法对模型进行训练。将结果与前馈神经网络、长短期记忆网络(long short term memory,LSTM)神经网络及量产发动机的排温传感器采集结果进行对比。经验证,稳态工况下,两种神经网络均能达到较高精度;欧洲瞬态循环(European transient cycle,ETC)工况下,NARX神经网络计算温度的最大偏差为6.6℃,量产发动机排温传感器测得温度最大偏差为45.9℃。NARX神经网络所需的计算时间约为现有电控单元排温模型的2.5倍。
文摘电力系统电磁暂态仿真中,常用异步电动机模型来代表动态负荷模型。然而,由于异步电动机数量众多和电机-电网接口方法的局限性,传统异步电动机模型很难兼顾仿真效率和数值稳定性。针对这一问题,该文提出一种用于电力系统电磁暂态仿真的异步电动机负荷解耦(induction motor load decoupling,IMLD)模型。该模型结合异步电动机等效电路和LC传输线时延,构造出具有天然时延的电机-电网解耦接口,从而将异步电动机与外部电网解耦,异步电动机的迭代求解过程无需与外部电网同步求解。根据负荷节点给定的潮流有功和无功功率,通过求解等效电路方程并配置IMLD模型参数,使仿真功率结果与潮流计算给定负荷节点功率相匹配。测试算例结果表明,所提IMLD模型可有效减少网络方程迭代次数,同时具备较高准确性和仿真效率,且具备良好的数值稳定性。
