An improved Reduced-Order Model(ROM)is proposed based on a flow-solution preprocessing operation and a fast sampling strategy to efficiently and accurately predict ionized hypersonic flows.This ROM is generated in low...An improved Reduced-Order Model(ROM)is proposed based on a flow-solution preprocessing operation and a fast sampling strategy to efficiently and accurately predict ionized hypersonic flows.This ROM is generated in low-dimensional space by performing the Proper Orthogonal Decomposition(POD)on snapshots and is coupled with the Radial Basis Function(RBF)to achieve fast prediction speed.However,due to the disparate scales in the ionized flow field,the conventional ROM usually generates spurious negative errors.Here,this issue is addressed by performing flow-solution preprocessing in logarithmic space to improve the conventional ROM.Then,extra orthogonal polynomials are introduced in the RBF interpolation to achieve additional improvement of the prediction accuracy.In addition,to construct high-efficiency snapshots,a trajectory-constrained adaptive sampling strategy based on convex hull optimization is developed.To evaluate the performance of the proposed fast prediction method,two hypersonic vehicles with classic configurations,i.e.a wave-rider and a reentry capsule,are used to validate the proposed method.Both two cases show that the proposed fast prediction method has high accuracy near the vehicle surface and the free-stream region where the flow field is smooth.Compared with the conventional ROM prediction,the prediction results are significantly improved by the proposed method around the discontinuities,e.g.the shock wave and the ionized layer.As a result,the proposed fast prediction method reduces the error of the conventional ROM by at least 45%,with a speedup of approximately 2.0×105compared to the Computational Fluid Dynamic(CFD)simulations.These test cases demonstrate that the method developed here is efficient and accurate for predicting ionized hypersonic flows.展开更多
为解决网联汽车由于驾驶员误差存在导致的速度轨迹偏移问题,本文提出一种实时的考虑驾驶员误差的网联混合车队生态驾驶策略。首先通过实车试验采集不同驾驶员的驾驶员误差数据,建立基于马尔可夫链的驾驶员误差模型,用于预测未来一段时...为解决网联汽车由于驾驶员误差存在导致的速度轨迹偏移问题,本文提出一种实时的考虑驾驶员误差的网联混合车队生态驾驶策略。首先通过实车试验采集不同驾驶员的驾驶员误差数据,建立基于马尔可夫链的驾驶员误差模型,用于预测未来一段时间的驾驶员误差。然后以最小化整个车队的燃油消耗为优化目标,将车队速度轨迹优化问题描述为一个最优控制问题,采用快速随机模型预测控制(fast stochastic model predictive control,FSMPC)算法求解车队中网联汽车的最优速度轨迹。仿真和智能网联微缩车试验结果表明,相比于传统的基于快速模型预测控制(fast model predictive control,FMPC)的生态驾驶策略,本文所提出的生态驾驶策略能够有效减小车辆的速度轨迹偏移,并降低整个车队的燃油消耗,且满足实时性要求。展开更多
针对三相LCL并网逆三电平中点钳位(Neutral Point Clamped,NPC)逆变器中有限集模型预测控制(Finite Control Set-Model Predictive Control,FCS-MPC)计算量大和控制速度慢导致NPC逆变器性能变差的问题,设计了一种快速模型预测控制方法...针对三相LCL并网逆三电平中点钳位(Neutral Point Clamped,NPC)逆变器中有限集模型预测控制(Finite Control Set-Model Predictive Control,FCS-MPC)计算量大和控制速度慢导致NPC逆变器性能变差的问题,设计了一种快速模型预测控制方法。通过改变快速模型预测控制中参考电流电角度控制逆变器输出电流的相位,实现入网电流与电网同步;通过在冗余短矢量中选择合适的短矢量,实现直流侧中点电压平衡,减少计算量;通过缩小计算扇区,使快速模型预测控制仅在当前最优输出电压矢量附近进行寻优计算,进一步减少计算量。在MATLAB/SIMULINK中搭建了三相LCL并网NPC逆变器仿真模型,仿真结果验证了上述控制方法的快速性和可行性,运算速度提高了18.31%,入网电流总谐波失真(Total Harmonic Distortion,THD)值减少了2.38%。展开更多
A model that rapidly predicts the density components of raw coal is described.It is based on a threegrade fast float/sink test.The recent comprehensive monthly floating and sinking data are used for comparison.The pre...A model that rapidly predicts the density components of raw coal is described.It is based on a threegrade fast float/sink test.The recent comprehensive monthly floating and sinking data are used for comparison.The predicted data are used to draw washability curves and to provide a rapid evaluation of the effect from heavy medium induced separation.Thirty-one production shifts worth of fast float/sink data and the corresponding quick ash data are used to verify the model.The results show a small error with an arithmetic average of 0.53 and an absolute average error of 1.50.This indicates that this model has high precision.The theoretical yield from the washability curves is 76.47% for the monthly comprehensive data and 81.31% using the model data.This is for a desired cleaned coal ash of 9%.The relative error between these two is 6.33%,which is small and indicates that the predicted data can be used to rapidly evaluate the separation effect of gravity separation equipment.展开更多
In order to reach a compromise between fast response control and torques matching control in double turboshaft engines,research on nonlinear model predictive control for turboshaft engines based on double engines torq...In order to reach a compromise between fast response control and torques matching control in double turboshaft engines,research on nonlinear model predictive control for turboshaft engines based on double engines torques matching is conducted.Meanwhile,a Nonlinear Model Predictive Control(NMPC)method is proposed,which combines the control index of the power turbine speed with torques matching of double engines creatively.In addition to the control index,the difference of output torques between each engine is also incorporated in the objective function as a penalty term to ensure constant speed control and short torques matching time.Simulation results demonstrate that relative to unilateral torques matching,the settling time of the bidirectional matching method can be reduced by nearly 30.8%.Nevertheless,compared with the bidirectional torques matching method under the cascade PID controller,the NMPC method can decrease the overshoot of the power turbine speed by 65%and reduce the matching time by 15.5%synchronously.Besides fast response control of turboshaft engines,fast torques matching control of double engines is accomplished as well.展开更多
针对有限控制集模型预测控制方法在多电平多相逆变器中预测模型和目标函数在线计算量大的不足,提出一种快速有限控制集模型预测控制方法。该方法根据参考矢量的空间位置,让远离参考矢量的电压矢量不参与预测模型在线计算和目标函数在线...针对有限控制集模型预测控制方法在多电平多相逆变器中预测模型和目标函数在线计算量大的不足,提出一种快速有限控制集模型预测控制方法。该方法根据参考矢量的空间位置,让远离参考矢量的电压矢量不参与预测模型在线计算和目标函数在线评估。对于三电平三相逆变器,快速有限控制集模型预测控制方法使参与计算的电压矢量由27个减少到12个,大大提高计算效率。最后,建立起5 k W二极管钳位型三电平三相逆变器实验平台。对于传统有限控制集模型预测控制和快速有限控制集模型预测控制进行对比稳态和动态实验。实验结果表明:所提出快速有限控制集模型预测控制方法使系统具有良好的静、动态性能。展开更多
基金supported by the National Natural Science Foundation of China(Nos.11902271 and 91952203)the Fundamental Research Funds for the Central Universities of China(No.G2019KY05102)111 project on“Aircraft Complex Flows and the Control”of China(No.B17037)。
文摘An improved Reduced-Order Model(ROM)is proposed based on a flow-solution preprocessing operation and a fast sampling strategy to efficiently and accurately predict ionized hypersonic flows.This ROM is generated in low-dimensional space by performing the Proper Orthogonal Decomposition(POD)on snapshots and is coupled with the Radial Basis Function(RBF)to achieve fast prediction speed.However,due to the disparate scales in the ionized flow field,the conventional ROM usually generates spurious negative errors.Here,this issue is addressed by performing flow-solution preprocessing in logarithmic space to improve the conventional ROM.Then,extra orthogonal polynomials are introduced in the RBF interpolation to achieve additional improvement of the prediction accuracy.In addition,to construct high-efficiency snapshots,a trajectory-constrained adaptive sampling strategy based on convex hull optimization is developed.To evaluate the performance of the proposed fast prediction method,two hypersonic vehicles with classic configurations,i.e.a wave-rider and a reentry capsule,are used to validate the proposed method.Both two cases show that the proposed fast prediction method has high accuracy near the vehicle surface and the free-stream region where the flow field is smooth.Compared with the conventional ROM prediction,the prediction results are significantly improved by the proposed method around the discontinuities,e.g.the shock wave and the ionized layer.As a result,the proposed fast prediction method reduces the error of the conventional ROM by at least 45%,with a speedup of approximately 2.0×105compared to the Computational Fluid Dynamic(CFD)simulations.These test cases demonstrate that the method developed here is efficient and accurate for predicting ionized hypersonic flows.
文摘为解决网联汽车由于驾驶员误差存在导致的速度轨迹偏移问题,本文提出一种实时的考虑驾驶员误差的网联混合车队生态驾驶策略。首先通过实车试验采集不同驾驶员的驾驶员误差数据,建立基于马尔可夫链的驾驶员误差模型,用于预测未来一段时间的驾驶员误差。然后以最小化整个车队的燃油消耗为优化目标,将车队速度轨迹优化问题描述为一个最优控制问题,采用快速随机模型预测控制(fast stochastic model predictive control,FSMPC)算法求解车队中网联汽车的最优速度轨迹。仿真和智能网联微缩车试验结果表明,相比于传统的基于快速模型预测控制(fast model predictive control,FMPC)的生态驾驶策略,本文所提出的生态驾驶策略能够有效减小车辆的速度轨迹偏移,并降低整个车队的燃油消耗,且满足实时性要求。
文摘针对三相LCL并网逆三电平中点钳位(Neutral Point Clamped,NPC)逆变器中有限集模型预测控制(Finite Control Set-Model Predictive Control,FCS-MPC)计算量大和控制速度慢导致NPC逆变器性能变差的问题,设计了一种快速模型预测控制方法。通过改变快速模型预测控制中参考电流电角度控制逆变器输出电流的相位,实现入网电流与电网同步;通过在冗余短矢量中选择合适的短矢量,实现直流侧中点电压平衡,减少计算量;通过缩小计算扇区,使快速模型预测控制仅在当前最优输出电压矢量附近进行寻优计算,进一步减少计算量。在MATLAB/SIMULINK中搭建了三相LCL并网NPC逆变器仿真模型,仿真结果验证了上述控制方法的快速性和可行性,运算速度提高了18.31%,入网电流总谐波失真(Total Harmonic Distortion,THD)值减少了2.38%。
基金National Natural Science Foundation of China (No. 51174202)Doctoral Fund of Ministry of Education of China (No. 20100095110013)
文摘A model that rapidly predicts the density components of raw coal is described.It is based on a threegrade fast float/sink test.The recent comprehensive monthly floating and sinking data are used for comparison.The predicted data are used to draw washability curves and to provide a rapid evaluation of the effect from heavy medium induced separation.Thirty-one production shifts worth of fast float/sink data and the corresponding quick ash data are used to verify the model.The results show a small error with an arithmetic average of 0.53 and an absolute average error of 1.50.This indicates that this model has high precision.The theoretical yield from the washability curves is 76.47% for the monthly comprehensive data and 81.31% using the model data.This is for a desired cleaned coal ash of 9%.The relative error between these two is 6.33%,which is small and indicates that the predicted data can be used to rapidly evaluate the separation effect of gravity separation equipment.
基金co-supported by the National Natural Science Foundation of China(No.51576096)Qing Lan and 333 Project and Research Funds for Central Universities(No.NF2018003).
文摘In order to reach a compromise between fast response control and torques matching control in double turboshaft engines,research on nonlinear model predictive control for turboshaft engines based on double engines torques matching is conducted.Meanwhile,a Nonlinear Model Predictive Control(NMPC)method is proposed,which combines the control index of the power turbine speed with torques matching of double engines creatively.In addition to the control index,the difference of output torques between each engine is also incorporated in the objective function as a penalty term to ensure constant speed control and short torques matching time.Simulation results demonstrate that relative to unilateral torques matching,the settling time of the bidirectional matching method can be reduced by nearly 30.8%.Nevertheless,compared with the bidirectional torques matching method under the cascade PID controller,the NMPC method can decrease the overshoot of the power turbine speed by 65%and reduce the matching time by 15.5%synchronously.Besides fast response control of turboshaft engines,fast torques matching control of double engines is accomplished as well.
文摘针对有限控制集模型预测控制方法在多电平多相逆变器中预测模型和目标函数在线计算量大的不足,提出一种快速有限控制集模型预测控制方法。该方法根据参考矢量的空间位置,让远离参考矢量的电压矢量不参与预测模型在线计算和目标函数在线评估。对于三电平三相逆变器,快速有限控制集模型预测控制方法使参与计算的电压矢量由27个减少到12个,大大提高计算效率。最后,建立起5 k W二极管钳位型三电平三相逆变器实验平台。对于传统有限控制集模型预测控制和快速有限控制集模型预测控制进行对比稳态和动态实验。实验结果表明:所提出快速有限控制集模型预测控制方法使系统具有良好的静、动态性能。
