为探究不同吸波特性材料之间的组合规律,以在更宽频段范围内达到最佳雷达波吸收效果,选用两种不同吸波剂浸渍的纸蜂窝结构型吸波材料板(分别命名为FW10和FW20),以石英纤维板为外侧蒙皮,制备出一种具有较好雷达波吸收能力的复合板件,该...为探究不同吸波特性材料之间的组合规律,以在更宽频段范围内达到最佳雷达波吸收效果,选用两种不同吸波剂浸渍的纸蜂窝结构型吸波材料板(分别命名为FW10和FW20),以石英纤维板为外侧蒙皮,制备出一种具有较好雷达波吸收能力的复合板件,该板件可与常规壁板结合使用。采用时域有限差分法计算不同材料厚度组合下的材料雷达散射截面(RCS)值和反射率,并采用正交设计法对最优厚度组合进行进一步的探究,结果表明:复合结构的吸波效果与各层材料组合及其厚度分布有关,所得最优复合板件厚度组合方案从外向内依次为1 mm石英纤维板,15 mm FW10和15 mm FW20。最优方案复合板件在0~18 GHz频段范围内反射率低于-10 dB的吸收频段宽度为13.1 GHz,最大吸收峰可达-29.5 dB。最后进行实物测试,验证了仿真分析的有效性。展开更多
Structures with negative Poisson’s ratio(NPR) have been widely used in engineering application due to its unusual properties. In this paper, crashworthiness of a novel cylindrical auxetic structure under axial impact...Structures with negative Poisson’s ratio(NPR) have been widely used in engineering application due to its unusual properties. In this paper, crashworthiness of a novel cylindrical auxetic structure under axial impact loading is investigated by the numerical methods. The software LS-DYNA is adopted to analyze the effects of the geometry parameters on the force, energy absorption(EA) and specific energy absorption(SEA). It is found that an overlarge number of layers and cells will make NPR structure extend outward from the mid;NPR structures with small number of layers and cells will make some layers of structures rotate in final crushing state. If the thickness of the long-inclined bean(L-beam) is larger than that of short-inclined beam(S-beam), there will be a smoother transition from the lower to a higher value in crushing force. Conversely, the force will maintain a relatively steady value, which determined by the sum of the thickness of L-and S-beams. In addition, there is also a critical inner circle radius which distinguishes different deformation modes. Once the critical inner circle radius is smaller than the critical value,NPR structures tend to deform unsteadily.展开更多
Ecological cruising control methods of vehicles have been extensively studied to further cut down energy consumption by optimizing vehicles’speed profiles.However,most controllers cannot be put into practical applica...Ecological cruising control methods of vehicles have been extensively studied to further cut down energy consumption by optimizing vehicles’speed profiles.However,most controllers cannot be put into practical application because of future terrain data requirements and excessive computational demand.In this paper,an eco-cruising strategy with real-time capability utilizing deep reinforcement learning is proposed for electric vehicles(EVs)propelled by in-wheel motors.The deep deterministic policy gradient algorithm is leveraged to continuously regulate the motor torque in response to road elevation changes.By comparing the proposed strategy to the energy economy benchmark optimized with dynamic programming(DP),and traditional constant speed(CS)strategy,its learning ability,optimality,and generalization performance are verified.The simulation results show that without a priori knowledge about the future trip,the proposed strategy provides 3.8%energy saving compared with the CS strategy.It also yields a smaller gap than the globally optimal solution of DP.By testing on other driving cycles,the trained strategy reveals good generalization performance and impressive computational efficiency(about 2 ms per simulation step),making it practical and implementable.Additionally,the model-free characteristic of the proposed strategy makes it applicable for EVs with different powertrain topologies.展开更多
文摘为探究不同吸波特性材料之间的组合规律,以在更宽频段范围内达到最佳雷达波吸收效果,选用两种不同吸波剂浸渍的纸蜂窝结构型吸波材料板(分别命名为FW10和FW20),以石英纤维板为外侧蒙皮,制备出一种具有较好雷达波吸收能力的复合板件,该板件可与常规壁板结合使用。采用时域有限差分法计算不同材料厚度组合下的材料雷达散射截面(RCS)值和反射率,并采用正交设计法对最优厚度组合进行进一步的探究,结果表明:复合结构的吸波效果与各层材料组合及其厚度分布有关,所得最优复合板件厚度组合方案从外向内依次为1 mm石英纤维板,15 mm FW10和15 mm FW20。最优方案复合板件在0~18 GHz频段范围内反射率低于-10 dB的吸收频段宽度为13.1 GHz,最大吸收峰可达-29.5 dB。最后进行实物测试,验证了仿真分析的有效性。
基金supported by China Scholarship Council(Grant No.201606840046),which sponsored one of the authors as a visiting scholar for two years at the University of Michigan,Ann Arbor,Michigan,USAsupported by the National Natural Science Foundation of China(Grant No.51675281)the National Key Research and Development Program of China(Grant No.2017YFC0803904)
文摘Structures with negative Poisson’s ratio(NPR) have been widely used in engineering application due to its unusual properties. In this paper, crashworthiness of a novel cylindrical auxetic structure under axial impact loading is investigated by the numerical methods. The software LS-DYNA is adopted to analyze the effects of the geometry parameters on the force, energy absorption(EA) and specific energy absorption(SEA). It is found that an overlarge number of layers and cells will make NPR structure extend outward from the mid;NPR structures with small number of layers and cells will make some layers of structures rotate in final crushing state. If the thickness of the long-inclined bean(L-beam) is larger than that of short-inclined beam(S-beam), there will be a smoother transition from the lower to a higher value in crushing force. Conversely, the force will maintain a relatively steady value, which determined by the sum of the thickness of L-and S-beams. In addition, there is also a critical inner circle radius which distinguishes different deformation modes. Once the critical inner circle radius is smaller than the critical value,NPR structures tend to deform unsteadily.
基金supported by the Graduate Student Innovation Project of Jiangsu Province,China(Grant No.KYCX20_0258)。
文摘Ecological cruising control methods of vehicles have been extensively studied to further cut down energy consumption by optimizing vehicles’speed profiles.However,most controllers cannot be put into practical application because of future terrain data requirements and excessive computational demand.In this paper,an eco-cruising strategy with real-time capability utilizing deep reinforcement learning is proposed for electric vehicles(EVs)propelled by in-wheel motors.The deep deterministic policy gradient algorithm is leveraged to continuously regulate the motor torque in response to road elevation changes.By comparing the proposed strategy to the energy economy benchmark optimized with dynamic programming(DP),and traditional constant speed(CS)strategy,its learning ability,optimality,and generalization performance are verified.The simulation results show that without a priori knowledge about the future trip,the proposed strategy provides 3.8%energy saving compared with the CS strategy.It also yields a smaller gap than the globally optimal solution of DP.By testing on other driving cycles,the trained strategy reveals good generalization performance and impressive computational efficiency(about 2 ms per simulation step),making it practical and implementable.Additionally,the model-free characteristic of the proposed strategy makes it applicable for EVs with different powertrain topologies.
