为验证某款燃料电池商用车动力系统选型的合理性,以该款车型动力系统的关键参数为基础,在Matlab仿真平台上搭建包含复合能量源(包含燃料电池和蓄电池包)、电机系统(包含驱动电机和电机控制器)及车辆传动系统(包含主减速器和车轮)的模型。采用基于动态规划算法的全局优化能量管理策略,设定空载、半载和满载3种载重模式,以欧洲典型行驶工况EUDC_LOW 为仿真工况,分析车辆在不同载重模式下的动力经济性。仿真结果显示,在3种载重模式下,车辆配置的复合能量源均能满足车辆在指定行驶工况下的功率要求,但随着车辆载重的增大,蓄电池包终端SOC逐渐偏离初始值,考虑到商用车需要在大载重的模式下工作较长时间,较低的SOC不利于车辆保持最佳的动力性。针对该车型的验证结果表明,在后续的研究和实车验证中,应考虑配置功率更大的车载燃料电池。

With growing interest in Laser Additive Manufacturing (LAM) of High-entropy alloys (HEAs) during most recent years, the design of compositional elements and process strategies are primary methods to overcome undesirable microstructures and defects. Here we propose a new approach, a novel real-time Laser Shocking of Melt Pool (LSMP), to obtain melt pool modifications for yielding HEAs with desired characteristics. LSMP utilizes a pulsed laser shocking a liquid melt pool caused by a continuous wave laser, enabling non-destructive and real-time modulations for high-performance HEAs. The numerical simulation reveals the convection mechanism of the melt pool in the LSMP process, and the intervention of the pulsed laser promotes melt pool flow type to convert the Marangoni effect into a multi-convective ring, which accelerates melt pool flow and inhibits columnar crystal growth. Experimental results show the evolution law of the microstructure in the LSMP process. The microstructure of CrFeCoNi HEAs undergoes a Columnar-Equiaxed Transition (CET), and higher hardness is obtained. Laser shock is demonstrated to be an effective in-situ modulative tool for controlled additive manufacturing.

在电子封装领域中,所用的无铅钎料主要是二元或者三元Sn基共晶或近共晶合金,其基体相为富Sn相。为得到富Sn相的力学性能及应力-应变关系,由纳米压痕试验测试获得了富Sn相的弹性模量与硬度,并得到载荷-位移曲线。采用有限元反演分析的方法确定了富Sn相的特征应力和特征应变,并由量纲函数确定应变强化指数。将特征应力和特征应变强化指数等参数代入幂强化模型中,计算得到富Sn相的屈服强度为31.51 MPa,并最终确定富Sn相的应力-应变关系函数表达式。

近年来，数字孪生受到了广泛关注，数字孪生也正在变得越来越复杂。目前的数字孪生案例大多集中在一个特定的场景上，面对多层次多场景的工作环境，甚至模型的交互与耦合，仍缺乏构建复杂数字孪生的方法。本文提出了一种标准化的基于模型分割和组装的复杂数字孪生模型的建模方法。首先，将数字孪生的复杂模型按照4C架构中的层次(Composition)、场景(Context) 、组件(Component)和代码(Code)划分为若干简单模型。层次和场景使数字孪生专注于特定尺度和场景中的有效元素。组件和代码用于开发简单数字孪生模型。其次，通过信息融合、多尺度关联、多场景交互，将数字孪生的简单模型组装成复杂模型。本体模型构建了不同数字孪生中实体的完整信息库。知识图谱在不同尺度的数字孪生之间架起了关系的桥梁。场景迭代实现行为交互和计算结果精度的提高。本文提供了一种可实现的方法来构建复杂的数字孪生模型，并支持组件和代码的复用促进数字孪生的快速开发。

‘The spectral leakage (SL) from windowing and the picket fence effect (PEF) from discretization’ have been among the standard contents in textbooks for many decades. The SL and PEF would cause the distortions in amplitude, frequency, and phase of signals, which have always been of concern, and attempts have been made to solve them. This paper proposes two novel decomposition theorems that can totally eliminate the SL and PEF, they could broaden the knowledge of signal processing. First, two generalized eigenvalue equations are constructed for multifrequency discrete real signals and complex signals. The two decomposition theorems are then proved. On these bases, exact decomposition methods for real and complex signals are proposed. For a noise-free multifrequency real signal with m sinusoidal components, the frequency, amplitude, and phase of each component can be exactly calculated by using just 4m–1 discrete values and its second-order derivatives. For a multifrequency complex signal, only 2m–1 discrete values and its first-order derivatives are needed. The numerical experiments show that the proposed methods have very high resolution, and the sampling rate does not necessarily obey the Nyquist sampling theorem. With noisy signals, the proposed methods have extraordinary accuracy.