.png){kind=icon}
返回
Performance-based design of 2D gas diffusion layer microstructure using denoising diffusion probabilistic model
.png){kind=icon}
PRE
AI
DOI：10.1080/15376494.2023.2286629.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper proposes a performance-based microstructure design methodology specifically tailored for gas diffusion layers (GDLs). The generative machine learning denoising diffusion probabilistic model (DDPM) is trained and used for performance-based microstructure design. This study demonstrates significant progress in performance-based design by incorporating DDPM into the microstructure design process, offering a promising approach for GDL microstructure design. This methodology provides the advantage of generating 2D microstructures with desired permeability and volume fractions. Moreover, it is not limited to a specific performance criterion, making it adaptable to target other metrics. To train the DDPM, a 2D GDL microstructure dataset is constructed using the Lattice Boltzmann Method (LBM) for permeability estimation. Subsequently, we employ the DDPM with U-net architecture, which leverages positional encoding to learn the microstructure's volume fraction and permeability effectively. The input label pair of permeability and volume fraction is generated considering the inherent relationship between these two parameters to ensure the generation of meaningful microstructures. This relationship is supposed to ensure that the resulting microstructures align with realistic and physically meaningful characteristics. The simulated performance results obtained from the generated microstructures using the proposed methodology demonstrate a strong consistency with the targeted performance objectives.
Keyword:
Material characterization and reconstruction
microstructure
gas diffusion layer
denoising diffusion probabilistic model
generative machine learning model
期刊
IF:
0
论文数:
4.9K
被引数:
1.4W
机构
引用论文
Sensorless Control of Z Source Inverter fed BLDC Motor Drive by FOC - DTC Hybrid Control Strategy Using Fuzzy Logic Controller采用模糊逻辑控制器的foc-dtc混合控制策略的Z源逆变器馈电BLDC电机驱动的无传感器控制
PREAI
分享收藏Advances and challenges in deep generative models for de novo molecule generation从头生成分子的深度生成模型的进展和挑战
PREAI
分享收藏Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical systems在生成对抗网络中加强统计约束以建模混沌动力系统
OAAI
OAAI分享收藏分享收藏Computational microstructure characterization and reconstruction: Review of the state-of-the-art techniques计算微观结构表征和重建: 最新技术的回顾OAAI
OAAI分享收藏Optimisation of real-time quantitative RT-PCR for the evaluation of non-viral mediated gene transfer to the airways.png)
Gene Therapy
Gene TherapyIF0
PREAI
分享收藏
查看更多内容