scholarly journals A Simulation-Based Multi-Objective Optimization Design Method for Pump-Driven Electro-Hydrostatic Actuators

Processes ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 274 ◽  
Author(s):  
Longxian Xue ◽  
Shuai Wu ◽  
Yuanzhi Xu ◽  
Dongli Ma
Keyword(s):  
Energy Consumption ◽  
Design Method ◽  
Dynamic Stiffness ◽  
Control Surface ◽  
Design Parameters ◽  
Design Phase ◽  
Multi Objective Optimization ◽  
Python Script ◽  
Multi Objective ◽  
Simulation Based

A pump-driven actuator, which usually called an electro-hydrostatic actuator (EHA), is widely used in aerospace and industrial applications. It is interesting to optimize both its static and dynamic performances, such as weight, energy consumption, rise time, and dynamic stiffness, in the design phase. It is difficult to decide the parameters, due to the high number of objectives to be taken into consideration simultaneously. This paper proposes a simulation-based multi-objective optimization (MOO) design method for EHA with AMESim and a python script The model of an EHA driving a flight control surface is carried out by AMESim. The python script generates design parameters by using an intelligent search method and transfers them to the AMESim model. Then, the script can run a simulation of the AMESim model with a pre-set motion and load scenario of the control surface. The python script can also obtain the results when the simulation is finished, which can then be used to evaluate performance as the objective of optimization. There are four objectives considered in the present study, which are weight, energy consumption, rise time, and dynamic stiffness. The weight is predicted by the scaling law, based on the design parameters. The performances of dynamic response energy efficiency and dynamic stiffness are obtained by the simulation model. A multi-objective particle swarm optimization (MOPSO) algorithm is applied to search for the parameter solutions at the Pareto-front of the desired objectives. The optimization results of an EHA, based on the proposed methodology, are demonstrated. The results are very useful for engineers, to help determine the design parameters of the actuator in the design phase. The proposed method and platform are valuable in system design and optimization.

A Parametric Optimization Approach of an Induction Heating System for Energy Consumption Reduction

2017 ◽  
Author(s):  
Paolo Cicconi ◽  
Anna Costanza Russo ◽  
Mariorosario Prist ◽  
Francesco Ferracuti ◽  
Michele Germani ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Induction Heating ◽  
Heating System ◽  
High Energy ◽  
Design Parameters ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Molding Process ◽  
Multi Objective ◽  
Induction Heating System

Nowadays, electromagnetic high-frequency induction is very used for different non-contact heating applications such as the molding process. Every molding process requires the preheating and the thermal maintenance of the molds, to enhance the filling phase and the quality of the final products. In this context, an induction heating system, mostly, is a customized equipment. The design and definition of an induction equipment depends on the target application. This technology is highly efficient and performant, however it provides a high-energy consumption. Therefore, optimization strategies are very suitable to reduce energy cost and consumption. The proposed paper aims to define a method to optimize the induction heating of a mold in terms of time, consumption, and achieved temperature. The proposed optimization method involves genetic algorithms to define the design parameters related to geometry and controller. A test case describes the design of an induction heating system for a polyurethane molding process, which is the soles foaming. This case study deals with the multi-objective optimization of parameters such as the geometrical dimensions, the inductor sizing, and the controller setting. The multi-objective optimization aims to reduce the energy consumption and to increase the wall temperature of the mold.

scholarly journals SOM-Based High-Dimensional Design Spaces Mapping for Multi-Objective Optimization

2020 ◽  
Vol 38 (3) ◽  
pp. 677-684
Author(s):  
Zebin Zhang ◽  
Pengfei Zhang ◽  
Ruizhen Li
Keyword(s):  
Design Method ◽  
Low Cost ◽  
Data Extraction ◽  
Load Ratio ◽  
Design Parameters ◽  
High Dimensional ◽  
Multi Objective Optimization ◽  
Pocket Depth ◽  
Sliding Bearing ◽  
Multi Objective

Multi-objective optimization can reveal the complex parameter-objective relationships in the high-dimensional design problems. However, the data-extraction and data-presentation of the high-dimensional complex nonlinear system suffers from the increasing dimensionality. Key features and data-distribution of high-dimensional design spaces:parameter and objective spaces could be obtained by using Self-Organizing Maps (SOM) method, which re-clusters the high-dimensional multi-attribute data existing on the Pareto front into several low-dimensional maps. Correlations among all the design variables can be drawn according the colorized topological structure of the maps. Under the constraints including geometric structure and operating parameters, a low-cost and high accurate Kriging surrogate model was established to optimize a hybrid sliding bearing based on the sequential design method. Correlations between 3 objectives:"friction-to-load" ratio, temperature rise, instability threshold speed and 4 design parameters were extracted by SOM. Optimal feature regions were captured and analyzed. Results show that, within the specific feasible design space, supply pressure, axial bearing land width have important impact on the selected objectives, whereas the other parameters such as deep pocket depth and shallow pocket angle have relatively limited impact. A series of corresponding design decisions and optimization results help to understand the mechanism of the hybrid sliding bearing system in a much more intuitive way.

scholarly journals Research on Blank Optimization Design Based on Low-Carbon and Low-Cost Blank Process Route Optimization Model

2021 ◽  
Vol 13 (4) ◽  
pp. 1929
Author(s):  
Yongmao Xiao ◽  
Wei Yan ◽  
Ruping Wang ◽  
Zhigang Jiang ◽  
Ying Liu
Keyword(s):  
Optimization Model ◽  
Optimization Design ◽  
Design Method ◽  
Low Cost ◽  
Route Optimization ◽  
Low Carbon ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Blank Design ◽  
Process Route

The optimization of blank design is the key to the implementation of a green innovation strategy. The process of blank design determines more than 80% of resource consumption and environmental emissions during the blank processing. Unfortunately, the traditional blank design method based on function and quality is not suitable for today’s sustainable development concept. In order to solve this problem, a research method of blank design optimization based on a low-carbon and low-cost process route optimization is proposed. Aiming at the processing characteristics of complex box type blank parts, the concept of the workstep element is proposed to represent the characteristics of machining parts, a low-carbon and low-cost multi-objective optimization model is established, and relevant constraints are set up. In addition, an intelligent generation algorithm of a working step chain is proposed, and combined with a particle swarm optimization algorithm to solve the optimization model. Finally, the feasibility and practicability of the method are verified by taking the processing of the blank of an emulsion box as an example. The data comparison shows that the comprehensive performance of the low-carbon and low-cost multi-objective optimization is the best, which meets the requirements of low-carbon processing, low-cost, and sustainable production.

Multi-objective optimization of squirrel cage fan for range hood based on Kriging model

2021 ◽  
pp. 095440622199586
Author(s):  
Qianhao Xiao ◽  
Jun Wang ◽  
Boyan Jiang ◽  
Weigang Yang ◽  
Xiaopei Yang
Keyword(s):  
Flow Rate ◽  
Optimization Design ◽  
Volume Flow Rate ◽  
Kriging Model ◽  
Volume Flow ◽  
Design Parameters ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Squirrel Cage

In view of the multi-objective optimization design of the squirrel cage fan for the range hood, a blade parameterization method based on the quadratic non-uniform B-spline (NUBS) determined by four control points was proposed to control the outlet angle, chord length and maximum camber of the blade. Morris-Mitchell criteria were used to obtain the optimal Latin hypercube sample based on the evolutionary operation, and different subsets of sample numbers were created to study the influence of sample numbers on the multi-objective optimization results. The Kriging model, which can accurately reflect the response relationship between design variables and optimization objectives, was established. The second-generation Non-dominated Sorting Genetic algorithm (NSGA-II) was used to optimize the volume flow rate at the best efficiency point (BEP) and the maximum volume flow rate point (MVP). The results show that the design parameters corresponding to the optimization results under different sample numbers are not the same, and the fluctuation range of the optimal design parameters is related to the influence of the design parameters on the optimization objectives. Compared with the prototype, the optimized impeller increases the radial velocity of the impeller outlet, reduces the flow loss in the volute, and increases the diffusion capacity, which improves the volume flow rate, and efficiency of the range hood system under multiple working conditions.

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