A Multiscale Reliability-Based Design Optimization Method for Carbon-Fiber-Reinforced Composite Drive Shafts

Carbon fiber composites,characterized by their high specific strength and low weight,are becoming increasingly crucial in automotive lightweighting.However,current research primarily emphasizes layer count and orientation,often neglecting the potential of microstructural design,constraints in the layup process,and performance reliability.This study,therefore,introduces a multiscale reliability-based design optimization method for carbon fiber-reinforced plastic(CFRP)drive shafts.Initially,parametric modeling of the microscale cell was performed,and its elastic performance parameters were predicted using two homogenization methods,examining the impact of fluctuations in microscale cell parameters on composite material performance.A finite element model of the CFRP drive shaft was then constructed,achieving parameter transfer between microscale and macroscale through Python programming.This enabled an investigation into the influence of both micro and macro design parameters on the CFRP drive shaft’s performance.The Multi-Objective Particle Swarm Optimization(MOPSO)algorithm was enhanced for particle generation and updating strategies,facilitating the resolution of multi-objective reliability optimization problems,including composite material layup process constraints.Case studies demonstrated that this approach leads to over 30%weight reduction in CFRP drive shafts compared to metallic counterparts while satisfying reliability requirements and offering insights for the lightweight design of other vehicle components.

Optimal Design of the Modular Joint Drive Train for Enhancing Cobot Load Capacity and Dynamic Performance

Automation advancements prompts the extensive integration of collaborative robot(cobot)across a range of industries.Compared to the commonly used design approach of increasing the payload-to-weight ratio of cobot to enhance load capacity,equal attention should be paid to the dynamic response characteristics of cobot during the design process to make the cobot more flexible.In this paper,a new method for designing the drive train parameters of cobot is proposed.Firstly,based on the analysis of factors influencing the load capacity and dynamic response characteristics,design criteria for both aspects are established for cobot with all optimization design criteria normalized within the design domain.Secondly,with the cobot in the horizontal pose,the motor design scheme is discretized and it takes the joint motor diameter and gearbox speed ratio as optimization design variables.Finally,all the discrete values of the optimization objectives are obtained through the enumeration method and the Pareto front is used to select the optimal solution through multi-objective optimization.Base on the cobot design method proposed in this paper,a six-axis cobot is designed and compared with the commercial cobot.The result shows that the load capacity of the designed cobot in this paper reaches 8.4 kg,surpassing the 5 kg load capacity commercial cobot which is used as a benchmark.The minimum resonance frequency of the joints is 42.70 Hz.

Research on the Mechanism of Multi-Sensor Fusion Configuration Based on the Optimal Principle of the Vehicle

In order to address the issue of sensor configuration redundancy in intelligent driving,this paper constructs a multi-objective optimization model that considers cost,coverage ability,and perception performance.And then,combining a specific set of parameters,the NSGA-II algorithm is used to solve the multi-objective model established in this paper,and a Pareto front containing 24 typical configuration schemes is extracted after considering empirical constraints.Finally,using the decision preference method proposed in this paper that combines subjective and objective factors,decision scores are calculated and ranked for various configuration schemes from both cost and performance preferences.The research results indicate that the multi-objective optimization model established in this paper can screen and optimize various configuration schemes from the optimal principle of the vehicle,and the optimized configuration schemes can be quantitatively ranked to obtain the decision results for the vehicle under different preference tendencies.

Long-term operation optimization of circulating cooling water systems under fouling conditions

Fouling caused by excess metal ions in hard water can negatively impact the performance of the circulating cooling water system(CCWS)by depositing ions on the heat exchanger's surface.Currently,the operation optimization of CCWS often prioritizes short-term flow velocity optimization for minimizing power consumption,without considering fouling.However,low flow velocity promotes fouling.Therefore,it's crucial to balance fouling and energy/water conservation for optimal CCWS long-term operation.This study proposes a mixed-integer nonlinear programming(MINLP)model to achieve this goal.The model considers fouling in the pipeline,dynamic concentration cycle,and variable frequency drive to optimize the synergy between heat transfer,pressure drop,and fouling.By optimizing the concentration cycle of the CCWS,water conservation and fouling control can be achieved.The model can obtain the optimal operating parameters for different operation intervals,including the number of pumps,frequency,and valve local resistance coefficient.Sensitivity experiments on cycle and environmental temperature reveal that as the cycle increases,the marginal benefits of energy/water conservation decrease.In periods with minimal impact on fouling rate,energy/water conservation can be achieved by increasing the cycle while maintaining a low fouling rate.Overall,the proposed model has significant energy/water saving effects and can comprehensively optimize the CCWS through its incorporation of fouling and cycle optimization.

A Loss-model-based Efficiency Optimization Control Method for Induction Traction System of High-speed Train under Emergency Self-propelled Mode

Increasing attention has been paid to the efficiency improvement of the induction traction system of high-speed trains due to the high demand for energy saving. In emergency self-propelled mode, however, the dc-link voltage and the traction power of the motor are significantly reduced, resulting in decreased traction efficiency due to the low load and low speed operations. Aiming to tackle this problem, a novel efficiency improved control method is introduced to the emergency mode of high-speed train traction system in this paper. In the proposed method, a total loss model of induction motor considering the behaviors of both iron and copper loss is established. An improved iterative algorithm with decreased computational burden is then introduced, resulting in a fast solving of the optimal flux reference for loss minimization at each control period. In addition, considering the parameter variation problem due to the low load and low speed operations, a parameter estimation method is integrated to improve the controller's robustness. The effectiveness of the proposed method on efficiency improvement at low voltage and low load conditions is demonstrated by simulated and experimental results.

Estimation of Vehicle Speed Based on Wheel Speeds from ASR System in Four-Wheel Drive Vehicles

Three major methods currently in the use of determining vehicle speed based on wheel speeds, the minimum wheel speed, minimum wheel speed corrected by slope method and the Kalman filter method, are analyzed, with merits and defects of each approach stated. Through simulations, the Kalman filter method based on minimum wheel speed shows improved accuracy, in addition to better adaptivity to vehicle reference speed. It also can be used to acceleration ship regulation （ASR） in part-time four-wheel drive vehicles.

Track Tension Analysis of Four-Wheel Drive Tracked Vehicles

The distribution of track tension on track link is complex when the tracked vehicles run at a high speed.A multi-drive track link structure,which changes the traditional induction wheel into the driving wheel was proposed.The mathematical model of the system was established and the distribution of track tension was studied.The combined simulation model of RecurDyn and Simulink of the structure with multi-drive track was established.The simulation results show that our proposed structure has more uniform tension distribution than traditional structures,especially under the high speed condition.The maximum tension can be reduced by 28 kN-36 kN and the transmission efficiency can be improved by10%-16% under high speed condition with this new structure.

Optimization and Performance Comparison of Hairpin?Winding PMSM for Electric Vehicles Under Drive Cycle

Aiming at the problem of large AC copper loss caused by skin effects and proximity effects,and low efficiency at high speed of the hairpin-winding permanent magnet synchronous motor(PMSM)for electric vehicles(EVs),this paper firstly established the electromagnetic analytical model of the hairpin winding to calculate AC resistance.And the finite element model(FEM)of the hairpin-winding driving motor is established to calculate the AC characteristic of the hairpin winding at different speeds and temperatures.Then,combining modified particle swarm optimization(MPSO)and FEM,a 60 k W hairpin-winding PMSM is optimized under driving cycle conditions,and the electromagnetic performance and heat dissipation performance are compared with that of the traditional strand-winding motor.Finally,a prototype is made and an experimental platform is built to test the efficiency Map and temperature rise of the hairpin-winding motor over the whole speed range and verify the accuracy of the proposed optimization design method.The results show that the hairpin-winding PMSM not only has higher slot filling rate,high?efficiency range and power density,but also has better heat dissipation performance,which is suitable for application in the field of electric vehicles.

Robust Multiobjective and Multidisciplinary Design Optimization of Electrical Drive Systems

Design and optimization of electrical drive systems often involve simultaneous consideration of multiple objectives that usually contradict to each other and multiple disciplines that normally coupled to each other.This paper aims to present efficient system-level multiobjective optimization methods for the multidisciplinary design optimization of electrical drive systems.From the perspective of quality control,deterministic and robust approaches will be investigated for the development of the optimization models for the proposed methods.Meanwhile,two approximation methods,Kriging model and Taylor expansion are employed to decrease the computation/simulation cost.To illustrate the advantages of the proposed methods,a drive system with a permanent magnet synchronous motor driven by a field oriented control system is investigated.Deterministic and robust Pareto optimal solutions are presented and compared in terms of several steady-state and dynamic performances(like average torque and speed overshoot)of the drive system.The robust multiobjective optimization method can produce optimal Pareto solutions with high manufacturing quality for the drive system.

OPTIMAL TRACK SEEKING CONTROL OF DUAL-STAGE ACTUATOR FOR HIGH DENSITY HARD DISK DRIVES

Based on generalized the variation method, by introducing Hamilton function and Lagrange multiplier, this paper proposed a linear quadratic optimal control strategy for an incomplete controllable system with fixed terminal state and time. Applying the proposed optimal control to the simple two-input dual-stage actuator magnetic head positioning system with three degrees-of-freedom, the simulation results show that the system has no residual vibration at the terminal position and time, which can reduce the total access time during head positioning process. To verify the validation of the optimal control strategy of three degrees-of-freedom spring-mass models in actual magnetic head positioning of hard disk drives, a finite element model of an actual magnetic head positioning system is presented. Substituting the optimal control force from simple three degrees-of-freedom spring-mass models into the finite element model, the simulation results show that the magnetic head also has no residual vibration at the end of track-to-track travel. That is to say, the linear quadratic optimal control technique based on simple two-input dual- stage actuator system with three degrees-of-freedom proposed in this paper is of high reliability for the industrial application of an actual magnetic head positioning system.

A New Energy Optimal Control Scheme for a Separately Excited DC Motor Based Incremental Motion Drive

This paper considers minimization of resistive and frictional power dissipation in a separately excited DC motor based incremental motion drive （IMD）. The drive is required to displace a given, fixed load through a definite angle in specified time, with minimum energy dissipation in the motor windings and minimum frictional losses. Accordingly, an energy optimal （EO） control strategy is proposed in which the motor is first accelerated to track a specific speed profile for a pre-determined optimal time period. Thereafter, both armature and field power supplies are disconnected, and the motor decelerates and comes to a halt at the desired displacement point in the desired total displacement time. The optimal time period for the initial acceleration phase is computed so that the motor stores just enough energy to decelerate to the final position at the specified displacement time. The parameters, such as the moment of inertia and coefficient of friction, which depend on the load and other external conditions, have been obtained using system identification method. Comparison with earlier control techniques is included. The results show that the proposed EO control strategy results in significant reduction of energy losses compared to the existing ones.

Design and Optimization of Comb Drive Accelerator for High Frequency Oscillation

Comb-drive devises have been widely applied in many fields. However, the application of high frequency sensor, such as rocket chamber, still remains problems. In this work, a finite element code was used for the design, optimization and visualization of a comb drive accelerator. In the simulation results, the post-optimization design has high performance in high frequency oscillation operating environment. The optimization is based on the ideal eigenfrequency and Nelder-Mead method. The 3-D working conditions are realized by testing and comparing the time and frequency domain of pre-optimized and optimized design whose frequency ranges from 2000 Hz to 5000 Hz. Finally, the electric potential and capacitance in comb drive are visualized, which shows the better electric signals and displacements.

Modeling and Control of Parallel Hybrid Electric Vehicle Using Sea-Lion Optimization

This paper develops a parallel hybrid electric vehicle(PHEV)propor-tional integral controller with driving cycle.To improve fuel efficiency and reduce hazardous emissions in hybrid electric vehicles(HEVs)combine an electric motor(EM),a battery and an internal combustion engine(ICE).The electric motor assists the engine when accelerating,driving longer highways or climbing hills.This enables the use of a smaller,more efficient engine.It also makes use of the concept of regenerative braking to maximize energy efficiency.In a Hybrid Electric Vehicle(HEV),energy dissipated while braking is utilized to charge the battery.The proportional integral controller was used in this paper to analyze engine,motor performance and the New European Driving Cycle(NEDC)was used in the vehicle driving test using Matlab/Simulink.The proportional integral controllers were designed to track the desired vehicle speed and manage the vehi-cle’s energyflow.The Sea Lion Optimization(SLnO)methods were created to reduce fuel consumption in a parallel hybrid electric vehicle and the results were obtained for the New European Driving Cycle.

系统观念视域下思想政治教育高质量发展的动力优化

新时代新征程要推进思想政治教育高质量发展,持久有效的动力至关重要。运用系统观念对思想政治教育高质量发展的动力结构进行科学审视,找准思想政治教育发展与国家战略、个人需求、自身变革的矛盾切入点。通过对动力主体、动力目标、动力传输、动力评估进行科学系统整体分析,厘清推动思想政治教育高质量发展的动力运行机理。从内生、外源、共生三重维度,激活内源生产力、增强外驱牵引力、整合协同推动力,化“压力”“阻力”为强劲“动力”,实现思想政治教育高质量发展系统动力的整体涌现。

基于优化地理探测器的生态环境质量驱动力分析

[目的]克服生态环境质量(EQI)演化驱动因子在离散化过程中的随机性和主观性,优化传统地理探测器。[方法]利用相等间隔法、分位数法、自然断点法、几何间隔法和标准差法5种离散方法,对重庆市22项EQI演化驱动因子进行离散化,并结合地理探测器确定各因子的合理离散法与分类数,从而探究各驱动因子的驱动力大小和各驱动因子间交互模式。[结果](1)2005—2020年重庆市EQI处于I、Ⅲ和Ⅳ级的区县数量占比减少0.211,处于Ⅱ和V级的区县数量占比上升0.211,总体呈下降趋势,在空间上呈由西北向东南走向的梯度分布特征。(2)研究中各离散方法适用性排序为:自然断点法>几何间隔法>分位数法>相等间隔法>标准差法。不同因子适用的离散方法各异,应根据因子数据特征择优选取离散方法。(3)识别出影响重庆市EQI演化的关键驱动因子4个(0.37~0.49),主要驱动因子13个(0.14~0.33)、次要驱动因子4个(0.05~0.13)和其他因子1个(0.04),各驱动因子间交互作用均为双重增强或非线性增强。[结论]合理的离散化可以一定程度上克服连续数据离散化过程中的随机性和主观性,从而优化重庆市EQI演化驱动力探测结果。

基于模型预测的直驱式波浪发电机机侧最优功率控制技术

直驱式波浪发电系统具有结构简单、成本低、效率高等优点,得到了国内外学者的广泛关注。该文以直驱式波浪发电系统为研究对象,分析并建立了直驱式波浪发电装置的水动力数学模型和永磁直线电机的动态数学模型。推导了基于模型预测的功率控制策略用于直驱式波浪发电系统。在功率控制策略中从功率捕获策略和功率跟踪控制策略展开研究以实现将波浪能转换为电能并汇入直流母线。重点围绕功率捕获策略开展基于模型预测控制技术研究,分别从控制策略、预测区间和成本函数三个角度分析了基于模型预测的最优功率捕获策略。建立了仿真模型并进行仿真分析,结果表明,采用基于模型预测的最优功率捕获策略可有效提高不规则波下发电机从波浪发电系统捕获能量。此外,采用以直流母线功率汇入最大为目标的成本函数可以有效地提高发电机输入直流侧能量,具有重要的实用价值。最后通过实验室模拟平台进行了实验测试。

基于数据驱动的范数最优迭代学习控制

在系统模型确定的前提下,传统的范数最优迭代学习控制(NOILC)可以有效提高伺服系统的跟踪精度。但是在实际控制过程中,系统模型参数往往是变化的,从而导致控制器性能的下降。为此,提出了一种基于数据驱动的范数最优迭代学习控制方法。以系统的输入输出为依据,建立系统估计模型的代价函数,对代价函数进行偏微分处理,得到一种基于数据驱动的非参数模型辨识方法,最后将此模型辨识方法和NOILC相结合。实验结果表明:针对时变系统,此控制方法的跟踪误差为NOILC(Norm optimal iterative learning control,NOILC)的57.1%,并且相比NOILC提前5次收敛。因此,提出的方法能有效改善时变系统的跟踪性能。

考虑驾驶风格和舒适性的电动汽车制动能量回收策略

现有的制动能量回收策略未充分考虑驾驶风格对前、后轴制动力矩分配的影响,降低了能量回收率,增加了制动减速度。为此,提出了一种考虑驾驶风格和制动舒适性的再生能量回收策略,对不同驾驶风格的驾驶员采用不同的权重系数去权衡制动能量回收效率与制动舒适性。设计模糊规则控制器对驾驶员舒适性权重进行分配,建立包含舒适性和制动能量回收效率的多目标优化模型。最后结合考虑电池、电机特性和紧急工况等影响因素确定制动力矩分配策略。仿真结果表明,与其他控制策略相比,所提出的控制策略使驾驶员处于舒适与轻微不适的占比分别增加3.4%和1.58%,并消除严重不适,电池SOC值损失0.3183%。

考虑速度模式的纯电动公交进出站生态驾驶策略

新能源汽车的大力推广给节能减排带来新机遇的同时,也突显出由于动力系统与传统燃油车存在差异,驾驶人延用传统燃油车的操作方式与驾驶习惯引起电动汽车能耗高的问题。为增加进站能量回收,减少出站能量消耗,本文考虑实际进出站速度模式建立进出站生态驾驶策略。首先,采集纯电动快速公交自然驾驶数据,分析其与燃油公交车的能量消耗差异性。其次,剖析实际进出站速度模式,并基于此分别建立5种考虑速度模式的进站减速策略和出站加速策略,对比能源消耗率分别确定进站和出站生态驾驶策略。之后,在基于速度模式生态驾驶策略的基础上,建立基于快速基因非支配排序的遗传算法(NSGA-Ⅱ)的生态驾驶策略。最后,利用3种驾驶风格下的实际进出站数据验证两种生态驾驶策略的节能效益。结果发现,基于速度模式和NSGA-Ⅱ的生态驾驶策略对耗能型、一般型和节能型这3种驾驶风格的节能率分别为17.04%/23.58%、14.76%/21.48%和5.78%/13.21%,提出的策略对于耗能型驾驶风格的节能率最高,其次为一般型,对节能型驾驶风格的节能率最低。基于NSGA-Ⅱ的策略比基于速度模式的策略节能7.89%。

2000—2020年三江源地区景观生态风险评价及驱动因素

三江源地区是重要的源头汇水区,其生态安全至关重要。探究三江源地区景观生态风险的时空变化及其驱动因素,对促进亚洲东南部的生态安全、水资源开发利用具有重要的意义。以三江源地区为研究区,基于5期土地利用数据,构建评价模型以分析其景观生态风险的时空分异特征和变化趋势,进而采用参数最优地理探测器对比分析其全局及局部区域景观生态风险空间分异的驱动因素。结果表明:(1)2000—2020年研究区景观生态风险在空间上呈显著正相关,以中风险区和中低风险区为主,高风险区较少。(2)2000—2020年研究区景观生态风险水平有所改善,研究区低风险和高风险分布向中低风险区、中风险区和中高风险区转化;澜沧江源区景观生态风险水平略高于长江源区和黄河源区。(3)三江源地区各土地类型的景观面积规模和景观完整性应是该区景观生态风险变化的关键因素,其景观生态风险空间分异是多种驱动因素共同作用的结果,其中归一化差分植被指数(NDVI)、高程、坡度为主要驱动因子;地势不同的地区,其因素的驱动作用会有较大差异。政府应严格保护与监测雪山冰川,采取适当措施抑制草地荒漠化,防范生态风险反弹,以保持生态系统的完整性,促使其生态功能的持续提升。