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2021 年度 実施状況報告書

Systematic Machine leARning Training for PORous mEdia characterizationS (SMART PORES)

研究課題

研究課題/領域番号 21K13999
研究機関東京工業大学

研究代表者

パトモノアジ アニンディティヨ  東京工業大学, 工学院, JSPS特別研究員 (20899265)

研究期間 (年度) 2021-04-01 – 2023-03-31
キーワードPorous media / Machine Learning / Pore throat network / 3D printer / Microtomography
研究実績の概要

Throughout this first year of research work, i have developed a method to incorporate 3D printer into this research work.
First, by using the 3D printer, I have successfully fabricated a custom 2D micromodel. This fabrication model is low cost and very useful for rapid prototyping. This result was published in an academic paper this year.
In addition, I have started 3D printing various granular particle that will be used for 3D porous media characterization. By using this artificially 3D printed granular particle, various porous media with various characteristics can be fabricated and further studies by using micro-tomography and image processing.

現在までの達成度 (区分)
現在までの達成度 (区分)

1: 当初の計画以上に進展している

理由

By incorporating a 3D printer, I can generate any particles that is required for the porous media characterization. Therefore, it will drastically increase the flexibility and open broader possibility to the scope of this research work because we can investigate various porous media in various applications.
In addition, comparison between these 3D printed particles and the naturally available particles can be performed to elucidate the difference between the simple and controlled particles to the more complicated shape of natural particles. As a result, the porous media characterization model for the artificially fabricated granular particles and naturally occurring granular particles can be made and thus compare to elucidate the differences.

今後の研究の推進方策

I will utilize the various generated particles to generate packed porous media. By using micro-tomography and image processing, I will derive various parameter, such as porosity, pore characteristics, and throat characteristics. By using generating the pore-throat networks, pore throat network simulations can be performed, and other parameters, such as permeability, can also be obtained. Afterward, I will develop the model for porous media characterization by using statistical and mathematical approach. Eventually, I will incorporate the machine learning by using the statistical data and also porous media image for the layer of deep learning.

次年度使用額が生じた理由

In this financial year, I have cancelled this funding because my recent affiliation is not eligible to receive this funding.

  • 研究成果

    (1件)

すべて 2022

すべて 雑誌論文 (1件)

  • [雑誌論文] Stereolithography 3D Printer for Micromodel Fabrications with Comprehensive Accuracy Evaluation by Using Microtomography2022

    • 著者名/発表者名
      Patmonoaji Anindityo、Mahardika Mohammmad Azis、Nasir Muhammad、She Yun、Wang Weicen、Muflikhun Muhammad Akhsin、Suekane Tetsuya
    • 雑誌名

      Geosciences

      巻: 12 ページ: 183~183

    • DOI

      10.3390/geosciences12050183

URL: 

公開日: 2022-12-28  

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