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A Model-Order-Reduction Method for Large-Scale Topology Optimization Designs Based on Domain Decomposition and Artificial Neural Networks

HKUST
Overview

The invention propose and develop an ANN-based model-order-reduction method to greatly reduce the computational cost of the expensive numerical evaluation of the objective and constraints and thus to speed up the TO process for large-scale designs.

  • A Model-Order-Reduction Method for Large-Scale Topology Optimization Designs Based on Domain Decomposition and Artificial Neural Networks
Commercialisation opportunities
Technology licensing agreement / Industry collaboration
Problem addressed

The proposed method replaces the time-consuming full-scale simulation required in each design iteration with the coarse-scale simulation to gain efficiency. It uses an artificial neural network (ANN) to obtain the full-scale field of interest from the coarse-scale field and uses domain decomposition to expand the application scope of the method.

Innovation
  • A pretrained ANN model
  • ANN maps coarse field to fine field
  • Domain decomposition
Key impact
  • Drastically reduce the computational cost of large-scale design
  • For 2D structure design with scale of 16, 2 orders of magnitude reduction in computational time
Application
  • A large-range design for Aircraft component
  • Any software design tool using FEM calculation
The Hong Kong University of Science and Technology (HKUST)

The Hong Kong University of Science and Technology (HKUST) (https://hkust.edu.hk/) is a world-class research intensive university that focuses on science, technology and business as well as humanities and social science. HKUST offers an international campus, and a holistic and interdisciplinary pedagogy to nurture well-rounded graduates with global vision, a strong entrepreneurial spirit and innovative thinking. Over 80% of our research work were rated “Internationally excellent” or “world leading” in the Research Assessment Exercise 2020 of Hong Kong’s University Grants Committee. We were ranked 3rd in Times Higher Education’s Young University Rankings 2022, and our graduates were ranked 23rd worldwide and among the best from universities from Asia in Global University Employability Survey 2021.

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