Multi-objective Optimal Design of High Frequency Probe for Scanning Ion Conductance Microscopy

Scanning ion conductance microscopy（SICM） is an emerging non-destructive surface topography characterization apparatus with nanoscale resolution. However, the low regulating frequency of probe in most existing modulated current based SICM systems increases the system noise, and has difficulty in imaging sample surface with steep height changes. In order to enable SICM to have the capability of imaging surfaces with steep height changes, a novel probe that can be used in the modulated current based bopping mode is designed. The design relies on two piezoelectric ceramics with different travels to separate position adjustment and probe frequency regulation in the Z direction. To fiarther improve the resonant frequency of the probe, the material and the key dimensions for each component of the probe are optimized based on the multi-objective optimization method and the finite element analysis. The optimal design has a resonant frequency of above 10 kHz. To validate the rationality of the designed probe, microstructured grating samples are imaged using the homebuilt modulated current based SICM system. The experimental results indicate that the designed high frequency probe can effectively reduce the spike noise by 26% in the average number of spike noise. The proposed design provides a feasible solution for improving the imaging quality of the existing SICM systems which normally use ordinary probes with relatively low regulating frequency.

Optimization design of the hydro-pneumatic suspension system for high clearance self-propelled sprayer using improved MOPSO algorithm

Large high clearance self-propelled sprayers were widely used in field plant protection due to their high-efficiency operation capabilities.Influenced by the characteristics of field operations such as high power,heavy weight,high ground clearance,and fast operation speed,the comprehensive requirements for the ride comfort,handling stability and road friendliness of the sprayer were increasingly strong.At the present stage,the chassis structure of the high clearance selfpropelled sprayer that attaches great importance to the improvement of comprehensive performance still has the problems of severe bumps,weak handling performance and serious road damage in complex field environments.Therefore,this paper proposes an optimization design method for hydro-pneumatic suspension system of a high clearance self-propelled sprayer based on the improved MOPSO(Multi-Objective Particle Swarm Optimization)algorithm,covering the entire process of configuration design,parameter intelligent optimization,and system verification of the high clearance self-propelled sprayer chassis.Specifically,chassis structure of the hydro-pneumatic suspension suitable for the high clearance self-propelled sprayer was designed,and a design method combining the improved MOPSO algorithm based on time-varying fusion strategy and adaptive update with the parameter optimization of hydro-pneumatic suspension based on this algorithm was proposed,and finally the software simulation and bench performance verification were carried out.The results show that the optimized hydropneumatic suspension has excellent vibration reduction effect,and the body acceleration,suspension dynamic deflection and tire deflection were increased by 16.5%,9.9%and 0.9%respectively,compared with those before optimization.The comprehensive performance of the hydro-pneumatic suspension designed in this study is better than that of the traditional suspension.

Application for Optimization of New Soft Magnetic Material Motor by Taguchi Method

The application of new soft magnetic materials in permanent magnet motor can effectively reduce the loss of motor and improve the efficiency of motor. Taguchi method is a local multivariable and multi-objective optimization method widely used in various engineering problems, which can effectively improve the efficiency of engineering optimization. In this paper, based on a 25 kW, 1700 r/min three-phase permanent magnet motor, the relevant motor model is established in the finite element simulation software, and the relevant simulation analysis is carried out. Combined with Taguchi method optimization, the local optimal structure scheme is obtained. Through optimization, the motor can maintain high efficiency, reduce the cogging torque of the motor by 53.45%, reduce the torque ripple by 36.79%, and increase the torque generated by the permanent magnet per unit mass by 21.42%. Through this optimization, the overall performance of the motor has been significantly improved. The research content of this paper verifies the feasibility of the application of Taguchi method in the optimization of new soft magnetic material motor, provides a new idea for the optimization design of new soft magnetic material motor, and also provides a certain reference for the local multi-objective optimization of the electromagnetic structure of other similar motors.

Model Updating for High Speed Aircraft in Thermal Environment Using Adaptive Weighted-Sum Methods

Model updating for aircraft in a high temperature environment(HTE)is proposed based on the hierarchical method.With this method,the problem can be decomposed into temperature field updating and dynamic structural updating.In order to improve the estimation accuracy,the model updating problem is turned into a multi-objective optimization problem by constructing the objective function which combined with residues of modal frequency and effective modal mass.Then the metamodeling,support vector regression(SVR)is introduced to improve the optimization efficiency,and the solution can be determined by adaptive weighted-sum method(AWS).Finally,the proposed method is tested on a finite element(FE)model of a reentry vehicle model.The results show that the multi-objective model updating method in HTE can identify the input parameters of the temperature field and structure with good accuracy.

Numerical Optimization and Noise Analysis of High-Tip-SpeedWind Turbine

With lower turbulence and less rigorous restrictions on noise levels,offshore wind farms provide favourable conditions for the development of high-tip-speed wind turbines.In this study,the multi-objective optimization is presented for a 5MW wind turbine design and the effects of high tip speed on power output,cost and noise are analysed.In order to improve the convergence and efficiency of optimization,a novel type of gradient-based multi-objective evolutionary algorithm is proposed based on uniform decomposition and differential evolution.Optimization examples of the wind turbines indicate that the new algorithm can obtain uniformly distributed optimal solutions and this algorithm outperforms the conventional evolutionary algorithms in convergence and optimization efficiency.For the 5MW wind turbines designed,increasing the tip speed can greatly reduce the cost of energy(COE).When the tip speed increases from 80m/s to 100m/s,under the same annual energy production,the COE decreases by 3.2%in a class I wind farm and by 5.1%in a class III one,respectively,while the sound pressure level increases by a maximum of 4.4dB with the class III wind farm case.

基于熵和隶属度函数的高维多目标优化问题求解

为求解高维多目标优化问题,提出一种新的适应度分配策略,即模糊关联熵方法(FREM)。结合模糊信息熵理论和隶属度函数给出FREM,采用隶属度函数将Pareto解和理想解映射为模糊集,运用模糊信息熵理论处理Pareto解模糊集与理想解模糊集之间的内在关系,并进行适应度分配。以模糊关联熵系数引导群体智能算法进化。在DTLZ测试函数集上的实验结果表明,FREM能够解决高维多目标优化问题,避免子目标数量增加对算法的影响,并得到比随机权重法和NSGA-II更好的优化效果。

Optimizing the lattice design of a diffraction-limited storage ring with a rational combination of particle swarm and genetic algorithms

In the lattice design of a diffraction-limited storage ring（DLSR） consisting of compact multi-bend achromats（MBAs）, it is challenging to simultaneously achieve an ultralow emittance and a satisfactory nonlinear performance, due to extremely large nonlinearities and limited tuning ranges of the element parameters. Nevertheless, in this paper we show that the potential of a DLSR design can be explored with a successive and iterative implementation of the multi-objective particle swarm optimization（MOPSO） and multi-objective genetic algorithm（MOGA）. For the High Energy Photon Source, a planned kilometer-scale DLSR, optimizations indicate that it is feasible to attain a natural emittance of about 50 pm·rad, and simultaneously realize a sufficient ring acceptance for on-axis longitudinal injection, by using a hybrid MBA lattice. In particular, this study demonstrates that a rational combination of the MOPSO and MOGA is more effective than either of them alone, in approaching the true global optima of an explorative multi-objective problem with many optimizing variables and local optima.