Operating data-driven inverse design optimization for product usage personalization with an application to wheel loaders

2021 ◽  
Vol 23 ◽  
pp. 100212
Author(s):  
Wei Zhang ◽  
Shaojie Wang ◽  
Liang Hou ◽  
Roger J. Jiao
Keyword(s):  
Design Optimization ◽  
Inverse Design ◽  
Data Driven ◽  
Product Usage ◽  
Operating Data ◽  
Wheel Loaders

scholarly journals Inverse design of glass structure with deep graph neural networks

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Qi Wang ◽  
Longfei Zhang
Keyword(s):  
Neural Networks ◽  
Monte Carlo ◽  
Configuration Space ◽  
Glass Structure ◽  
Stable State ◽  
Specific Property ◽  
Inverse Design ◽  
Data Driven ◽  
Common Belief ◽  
Graph Neural Networks

AbstractDirectly manipulating the atomic structure to achieve a specific property is a long pursuit in the field of materials. However, hindered by the disordered, non-prototypical glass structure and the complex interplay between structure and property, such inverse design is dauntingly hard for glasses. Here, combining two cutting-edge techniques, graph neural networks and swap Monte Carlo, we develop a data-driven, property-oriented inverse design route that managed to improve the plastic resistance of Cu-Zr metallic glasses in a controllable way. Swap Monte Carlo, as a sampler, effectively explores the glass landscape, and graph neural networks, with high regression accuracy in predicting the plastic resistance, serves as a decider to guide the search in configuration space. Via an unconventional strengthening mechanism, a geometrically ultra-stable yet energetically meta-stable state is unraveled, contrary to the common belief that the higher the energy, the lower the plastic resistance. This demonstrates a vast configuration space that can be easily overlooked by conventional atomistic simulations. The data-driven techniques, structural search methods and optimization algorithms consolidate to form a toolbox, paving a new way to the design of glassy materials.

Design Optimization of Cryogenic Pump Inducer Considering Suction Performance and Cavitation Instability

2011 ◽  
Author(s):  
Hiroyoshi Watanabe ◽  
Hiroshi Tsukamoto
Keyword(s):  
Pressure Distribution ◽  
Design Optimization ◽  
Surface Pressure ◽  
Design Approach ◽  
Inverse Design ◽  
Blade Surface ◽  
Free Vortex ◽  
Surface Pressure Distribution ◽  
Cavitation Instability ◽  
Suction Performance

This paper presents the result of design optimization for three-bladed pump inducer using a three-dimensional (3-D) inverse design approach, Computational Fluid Dynamics (CFD) and DoE (Design of Experiments) taking suction performance and cavitation instability into consideration. The parameters to control streamwise blade loading distribution and spanwise work (free vortex or non-free vortex) for inducer were chosen as design optimization variables for the inverse design approach. Cavitating and non-cavitating performances for inducers designed using the design variables arranged in the DoE table were analyzed by steady CFD. Objective functions for non-cavitating operating conditions were the head and efficiency of inducers at a design flow (Qd), 80% Qd and 120% Qd. The volume of the inducer cavity region with a void ratio above 50% was selected as the objective function for inducer suction performance. In order to evaluate cavitation instability by steady CFD, the dispersion of the blade surface pressure distribution on each blade was selected as the evaluation parameter. This dispersion of the blade surface pressure distribution was caused by non-uniformity in the cavitation length that was developed on each inducer blade and increased when the cavitation number was reduced. The effective design parameters on suction performance and cavitation instability were confirmed by sensitivity analysis during the design optimization process. Inducers with specific characteristics (stable, unstable) designed using the effective parameters were evaluated through experiments.

An experience-independent inverse design optimization method of compressor cascade airfoil

2019 ◽  
Vol 233 (4) ◽  
pp. 431-442 ◽  
Author(s):  
Yujie Zhu ◽  
Yaping Ju ◽  
Chuhua Zhang
Keyword(s):  
Design Optimization ◽  
Design Method ◽  
Three Dimensional ◽  
Optimal Solution ◽  
Leading Edge ◽  
Optimization Method ◽  
Inverse Design ◽  
Compressor Cascade ◽  
Suction Surface ◽  
Inverse Design Method

Most of the inverse design methods of turbomachinery experience the shortcoming where the target aerodynamic parameters need to be manually specified depending on the designers’ experience and insight, making the design result aleatory and even deviated from the real optimal solution. To tackle this problem, an experience-independent inverse design optimization method is proposed and applied to the redesign of a compressor cascade airfoil in this study. The experience-independent inverse design optimization method can automatically obtain the target pressure distribution along the cascade airfoil through the genetic algorithm, rather than through the manual specification approach. The shape of cascade airfoil is then solved by the adjoint method. The effectiveness of the experience-independent inverse design optimization method is demonstrated by two inverse design cases of the compressor cascade airfoil, i.e. the inverse design of only the suction surface and the inverse design of both the suction and pressure surfaces. The results show that the proposed inverse design method is capable of significantly improving the aerodynamic performance of the compressor cascade. At the examined flow condition, a thin airfoil profile is beneficial to flow accelerations near the leading edge and flow separation avoidance near the trailing edge. The proposed inverse design method is quite generic and can be extended to the three-dimensional inverse design of advanced compressor blades.

Aerodynamic inverse design optimization for turbine cascades based on control theory

2012 ◽  
Vol 56 (2) ◽  
pp. 308-323 ◽  
Author(s):  
ZhenPing Feng ◽  
HaiTao Li ◽  
LiMing Song ◽  
YingChen Li
Keyword(s):  
Control Theory ◽  
Design Optimization ◽  
Inverse Design ◽  
Turbine Cascades

scholarly journals Data-Driven Reinforcement-Learning-Based Automatic Bucket-Filling for Wheel Loaders

2021 ◽  
Vol 11 (19) ◽  
pp. 9191
Author(s):  
Jianfei Huang ◽  
Dewen Kong ◽  
Guangzong Gao ◽  
Xinchun Cheng ◽  
Jinshi Chen
Keyword(s):  
Reinforcement Learning ◽  
Statistical Model ◽  
Physical Model ◽  
Working Environment ◽  
Data Driven ◽  
Automated Systems ◽  
Learning Capability ◽  
Q Learning ◽  
System A ◽  
Wheel Loaders

Automation of bucket-filling is of crucial significance to the fully automated systems for wheel loaders. Most previous works are based on a physical model, which cannot adapt to the changeable and complicated working environment. Thus, in this paper, a data-driven reinforcement-learning (RL)-based approach is proposed to achieve automatic bucket-filling. An automatic bucket-filling algorithm based on Q-learning is developed to enhance the adaptability of the autonomous scooping system. A nonlinear, non-parametric statistical model is also built to approximate the real working environment using the actual data obtained from tests. The statistical model is used for predicting the state of wheel loaders in the bucket-filling process. Then, the proposed algorithm is trained on the prediction model. Finally, the results of the training confirm that the proposed algorithm has good performance in adaptability, convergence, and fuel consumption in the absence of a physical model. The results also demonstrate the transfer learning capability of the proposed approach. The proposed method can be applied to different machine-pile environments.

scholarly journals A Simulation Data-Driven Design Approach for Rapid Product Optimization

2020 ◽  
Vol 20 (2) ◽  
Author(s):  
Yanli Shao ◽  
Huawei Zhu ◽  
Rui Wang ◽  
Ying Liu ◽  
Yusheng Liu
Keyword(s):  
Data Mining ◽  
Optimal Design ◽  
Prediction Model ◽  
Design Optimization ◽  
Dynamic Simulation ◽  
Design Approach ◽  
Data Driven ◽  
Design Solution ◽  
Simulation Data ◽  
Product Optimization

Abstract Traditional design optimization is an iterative process of design, simulation, and redesign, which requires extensive calculations and analysis. The designer needs to adjust and evaluate the design parameters manually and continually based on the simulation results until a satisfactory design is obtained. However, the expensive computational costs and large resource consumption of complex products hinder the wide application of simulation in industry. It is not an easy task to search the optimal design solution intelligently and efficiently. Therefore, a simulation data-driven design approach which combines dynamic simulation data mining and design optimization is proposed to achieve this purpose in this study. The dynamic simulation data mining algorithm—on-line sequential extreme learning machine with adaptive weights (WadaptiveOS-ELM)—is adopted to train the dynamic prediction model to effectively evaluate the merits of new design solutions in the optimization process. Meanwhile, the prediction model is updated incrementally by combining new “good” data set to reduce the modeling cost and improve the prediction accuracy. Furthermore, the improved heuristic optimization algorithm—adaptive and weighted center particle swarm optimization (AWCPSO)—is introduced to guide the design change direction intelligently to improve the search efficiency. In this way, the optimal design solution can be searched automatically with less actual simulation iterations and higher optimization efficiency, and thus supporting the rapid product optimization effectively. The experimental results demonstrate the feasibility and effectiveness of the proposed approach.

scholarly journals Advancing statistical learning and artificial intelligence in nanophotonics inverse design

Nanophotonics ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Qizhou Wang ◽  
Maksim Makarenko ◽  
Arturo Burguete Lopez ◽  
Fedor Getman ◽  
Andrea Fratalocchi
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Research Field ◽  
Inverse Design ◽  
Data Driven ◽  
Science And Engineering ◽  
Swarm Optimization ◽  
Hybrid Techniques ◽  
High Level ◽  
Sub Wavelength

Abstract Nanophotonics inverse design is a rapidly expanding research field whose goal is to focus users on defining complex, high-level optical functionalities while leveraging machines to search for the required material and geometry configurations in sub-wavelength structures. The journey of inverse design begins with traditional optimization tools such as topology optimization and heuristics methods, including simulated annealing, swarm optimization, and genetic algorithms. Recently, the blossoming of deep learning in various areas of data-driven science and engineering has begun to permeate nanophotonics inverse design intensely. This review discusses state-of-the-art optimizations methods, deep learning, and more recent hybrid techniques, analyzing the advantages, challenges, and perspectives of inverse design both as a science and an engineering.

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