Design IPMSM Structures for Enlarging High-Efficiency Operation Area Using Automatic Design System with New Algorithm

2020 ◽  
Author(s):  
Keita Yamauchi ◽  
Masayuki Sanada ◽  
Shigeo Morimoto ◽  
Yukinori Inoue
Keyword(s):  
High Efficiency ◽  
Design System ◽  
Automatic Design

scholarly journals Optimal Falling Track Design for Twice detonating Fuze of Double event Fuel air Explosive with High Speed

2020 ◽  
Vol 70 (4) ◽  
pp. 366-373
Author(s):  
Congliang Ye ◽  
Qi Zhang
Keyword(s):  
High Speed ◽  
High Efficiency ◽  
Single Point ◽  
Design System ◽  
Motion Trajectory ◽  
Novel Approach ◽  
Scale Experiment ◽  
Set Up ◽  
Point Center ◽  
Double Event

To prevent the initiation failure caused by the uncontrolled fuze and improve the weapon reliability in the high-speed double-event fuel-air explosive (DEFAE) application, it is necessary to study the TDF motion trajectory and set up a twice-detonating fuze (TDF) design system. Hence, a novel approach of realising the fixed single-point center initiation by TDF within the fuel air cloud is proposed. Accordingly, a computational model for the TDF motion state with the nonlinear mechanics analysis is built due to the expensive and difficult full-scale experiment. Moreover, the TDF guidance design system is programmed using MATLAB with the equations of mechanical equilibrium. In addition, by this system, influences of various input parameters on the TDF motion trajectory are studied in detail singly. Conclusively, the result of a certain TDF example indicates that this paper provides an economical idea for the TDF design, and the developed graphical user interface of high-efficiency for the weapon designers to facilitate the high-speed DEFAE missile development.

scholarly journals Automatic Design System with Generative Adversarial Network and Convolutional Neural Network for Optimization Design of Interior Permanent Magnet Synchronous Motor

2021 ◽  
Author(s):  
Yuki Shimizu ◽  
Shigeo Morimoto ◽  
Masayuki Sanada ◽  
Yukinori Inoue
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Convolutional Neural Network ◽  
Permanent Magnet ◽  
Optimization Design ◽  
Design System ◽  
Automatic Design ◽  
Wide Range ◽  
Interior Permanent Magnet ◽  
Long Time

The optimal design of interior permanent magnet synchronous motors requires a long time because finite element analysis (FEA) is performed repeatedly. To solve this problem, many researchers have used artificial intelligence to construct a prediction model that can replace FEA. However, because the training data are generated by FEA, it takes a very long time to obtain a sufficient amount of data, making it impossible to train a large-scale prediction model. Here, we propose a method for generating a large amount of data from a small number of FEA results using machine learning. An automatic design system with a deep generative model and a convolutional neural network is then constructed. With its sufficient data, the proposed system can handle three topologies and three motor parameters in a wide range of current vector regions. The proposed system was applied to multi-objective optimization design, with the optimization completed in 13-15 seconds.

scholarly journals Automatic Design System with Generative Adversarial Network and Convolutional Neural Network for Optimization Design of Interior Permanent Magnet Synchronous Motor

2021 ◽  
Author(s):  
Yuki Shimizu ◽  
Shigeo Morimoto ◽  
Masayuki Sanada ◽  
Yukinori Inoue
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Convolutional Neural Network ◽  
Permanent Magnet ◽  
Optimization Design ◽  
Design System ◽  
Automatic Design ◽  
Wide Range ◽  
Interior Permanent Magnet ◽  
Long Time

The optimal design of interior permanent magnet synchronous motors requires a long time because finite element analysis (FEA) is performed repeatedly. To solve this problem, many researchers have used artificial intelligence to construct a prediction model that can replace FEA. However, because the training data are generated by FEA, it takes a very long time to obtain a sufficient amount of data, making it impossible to train a large-scale prediction model. Here, we propose a method for generating a large amount of data from a small number of FEA results using machine learning. An automatic design system with a deep generative model and a convolutional neural network is then constructed. With its sufficient data, the proposed system can handle three topologies and three motor parameters in a wide range of current vector regions. The proposed system was applied to multi-objective optimization design, with the optimization completed in 13-15 seconds.

Star tracker lens mechanical automatic design system on parametric modeling

2021 ◽  
Vol 0 (0) ◽  
pp. 1-11
Author(s):  
ZHU Jun-qing ◽  
◽  
◽  
SHA Wei ◽  
FANG Chao ◽  
...  
Keyword(s):  
Parametric Modeling ◽  
Design System ◽  
Star Tracker ◽  
Automatic Design

3D Parametric Unfolding Method for Sheet-Metal Part

2014 ◽  
Vol 488-489 ◽  
pp. 79-82
Author(s):  
Bo Sun ◽  
Long Chen
Keyword(s):  
Sheet Metal ◽  
3D Modeling ◽  
Mathematic Model ◽  
Design System ◽  
Automatic Design ◽  
Sheet Metal Part ◽  
Metal Part ◽  
3D Environment ◽  
Unfolding Method

The unfolding is the first step for the manufacturing of the sheet-metal part, which plays a major role for the accuracy and quality of the final product. Unfortunately, the inefficiency of the traditional drawing-based method made the process boring and sometime confusing. The CAD method made benefit for the designer. By means of the 3D modeling kernel and the mathematic model of unfolding process, the automatic design system of sheet-metal part was developed, in which the models are parametric and in 3D environment.

An integrated automatic design system for complex DSP algorithms

1990 ◽  
Vol 1 (4) ◽  
pp. 265-278
Author(s):  
J. van Meerbergen ◽  
J. Huisken ◽  
P. Lippens ◽  
O. McArdle ◽  
R. Segers ◽  
...  
Keyword(s):  
Design System ◽  
Automatic Design

Design of a High-Performance Axial Compressor for Utility Gas Turbine

1992 ◽  
Vol 114 (2) ◽  
pp. 277-286 ◽  
Author(s):  
A. Sehra ◽  
J. Bettner ◽  
A. Cohn
Keyword(s):  
Gas Turbine ◽  
High Performance ◽  
High Efficiency ◽  
Guide Vane ◽  
Design Flow ◽  
Design Parameters ◽  
Design System ◽  
Inlet Guide Vane ◽  
Variable Geometry ◽  
Compressor Design

An aerodynamic design study to configure a high-efficiency industrial-size gas turbine compressor is presented. This study was conducted using an advanced aircraft engine compressor design system. Starting with an initial configuration based on conventional design practice, compressor design parameters were progressively optimized. To improve the efficiency potential of this design further, several advanced design concepts (such as stator ends bends and velocity controlled airfoils) were introduced. The projected poly tropic efficiency of the final advanced concept compressor design having 19 axial stages was estimated at 92.8 percent, which is 2 to 3 percent higher than the current high-efficiency aircraft turbine engine compressors. The influence of variable geometry on the flow and efficiency (at design speed) was also investigated. Operation at 77 percent design flow with inlet guide vanes and front five variable stators is predicted to increase the compressor efficiency by 6 points as compared to conventional designs having only the inlet guide vane as variable geometry.

Probabilistic Fracture Analysis of Circumferentially Cracked Pipes

2014 ◽  
Author(s):  
V. R. Bhimanadam ◽  
F. J. Blom
Keyword(s):  
High Efficiency ◽  
Probabilistic Method ◽  
Fracture Analysis ◽  
Probability Of Failure ◽  
304 Stainless Steel ◽  
Design System ◽  
J Integral ◽  
Mechanics Model ◽  
Moment Load ◽  
Leak Before Break

In the framework of the R6 development programme on leak before break NRG developped a probabilistic fracture-mechanics model for analyzing circumferential through-walled cracked pipe made of Type 304 stainless steel, subjected to bending loads. An elastic-plastic analysis has been carried out using ANSYS for estimating J-integral as a function of moment load. The validity of the J-integral based on the ANSYS calculations was evaluated by comparison with LBB.ENG2 method and Rahman’s calculations. Probabilistic fracture analysis has been carried out using ANSYS Probabilistic Design System (PDS) module to find out the failure probability of a pipe as a function of applied moment. These results have been compared with LBB.ENG2 probabilistic calculations, which has been developed using MATLAB. To achieve high efficiency, accuracy and robustness to design structural component with a low probability of failure, Advanced sampling methods (ADIS) have been used for probabilistic calculations. These ADIS results have been compared with Monte Carlo probabilistic results. The probabilistic method is subsequently extended to a Leak Before Break case (LBB). It is demonstrated that the probability of failure reduces when more probabilistic data for input parameters is added instead of using deterministic safety factors.

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