Optimal Designing of Tidal/Ocean Power Generator by Machine Learning Architecture

2023 Fiscal Year Final Research Report

• Project/Area Number: 21K14081
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 19010:Fluid engineering-related
• Research Institution: Hakodate National College of Technology
• Principal Investigator: Fujiwara Ryo 函館工業高等専門学校, 生産システム工学科, 准教授 (70791375)
• Project Period (FY): 2021-04-01 – 2024-03-31
• Keywords: 深層学習 / 変分オートエンコーダ / 海洋発電機

Outline of Final Research Achievements

The objective of this research project is to estimate the optimal design values of a generator that utilizes the tidal currents and ocean currents of the Tsugaru Straits by learning big data with an architecture that combines machine learning methods such as deep learning in order to speed up multivariate analysis in generator design and to obtain general-purpose results. Using a deep neural network and a variational autoencoder, we were able to predict the current velocity field from the design values and boundary conditions and establish the basis for an architecture to predict the performance of marine generators. This architecture can be applied to other fluid machinery design problems.

Free Research Field

機械学習

Academic Significance and Societal Importance of the Research Achievements

本研究課題で開発したアーキテクチャを応用すれば他の流体機械の設計問題にも応用可能である．機械学習アーキテクチャによる解法は同様の問題にも適用できることが他の問題解決手法と大きく異なる特徴である．