在研究潮流能桨叶和实验测试系统的基础上,设计了2 k W潮流能捕获桨叶综合实验测试系统,主要由潮流模拟装置、潮流能捕获装置、传动装置、测控装置组成。利用该实验系统可模拟2 m/s以下的潮流运动,测试不同流场下桨叶的捕能效率。设计...在研究潮流能桨叶和实验测试系统的基础上,设计了2 k W潮流能捕获桨叶综合实验测试系统,主要由潮流模拟装置、潮流能捕获装置、传动装置、测控装置组成。利用该实验系统可模拟2 m/s以下的潮流运动,测试不同流场下桨叶的捕能效率。设计并制作了不同设计流速的两种桨叶实物,进行测试比较,同时对测试数据进行分析,得到桨叶捕能性能的综合评判。实验测试系统运行稳定,工作可靠,控制方便。展开更多
In order to deal with modeling problem of a pressure balance system with time-delay, nonlinear, time-varying and uncertain characteristics, an intelligent modeling procedure is proposed, which is based on artificial n...In order to deal with modeling problem of a pressure balance system with time-delay, nonlinear, time-varying and uncertain characteristics, an intelligent modeling procedure is proposed, which is based on artificial neural network(ANN) and input-output data of the system during shield tunneling and can overcome the precision problem in mechanistic modeling(MM) approach. The computational results show that the training algorithm with Gauss-Newton optimization has fast convergent speed. The experimental investigation indicates that, compared with mechanistic modeling approach, intelligent modeling procedure can obviously increase the precision in both soil pressure fitting and forecasting period. The effectiveness and accuracy of proposed intelligent modeling procedure are verified in laboratory tests.展开更多
文摘在研究潮流能桨叶和实验测试系统的基础上,设计了2 k W潮流能捕获桨叶综合实验测试系统,主要由潮流模拟装置、潮流能捕获装置、传动装置、测控装置组成。利用该实验系统可模拟2 m/s以下的潮流运动,测试不同流场下桨叶的捕能效率。设计并制作了不同设计流速的两种桨叶实物,进行测试比较,同时对测试数据进行分析,得到桨叶捕能性能的综合评判。实验测试系统运行稳定,工作可靠,控制方便。
基金Project(2013CB035402) supported by the National Basic Research Program of ChinaProjects(51105048,51209028) supported by the National Natural Science Foundation of China
文摘In order to deal with modeling problem of a pressure balance system with time-delay, nonlinear, time-varying and uncertain characteristics, an intelligent modeling procedure is proposed, which is based on artificial neural network(ANN) and input-output data of the system during shield tunneling and can overcome the precision problem in mechanistic modeling(MM) approach. The computational results show that the training algorithm with Gauss-Newton optimization has fast convergent speed. The experimental investigation indicates that, compared with mechanistic modeling approach, intelligent modeling procedure can obviously increase the precision in both soil pressure fitting and forecasting period. The effectiveness and accuracy of proposed intelligent modeling procedure are verified in laboratory tests.
