Application of Deep Learning to Predict Cavitation Flow in Centrifugal Pump

Li, Gaoyang; He, Jiachao; Ding, Xuhui; Zhu, Yonghong; Zhu, Wenkun; Qin, Caiyan; Zhang, Xuelan; Liu, Siwei; Sun, Haiyi; Wang, Yu; Yang, Bin; Guo, Yuting

doi:10.2139/ssrn.4182265

Cited by 1 publication

(2 citation statements)

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“…The flexibility, attribute scalability, and network compatibility of point cloud datasets distinguished our deep learning algorithm as the potential to model cross-scale phenomena in some of the components of PEMFCs (e.g., gas diffusion layer and proton-exchange membrane). The feasibility has been demonstrated by our work on the use of the same network structure to accomplish the prediction of the internal flow field of the patient-personalized multiscale cardiovascular systems, including the coarse aorta and the fine coronary capillaries, the internal flow field of an aneurysm containing a porous media stent model, and the internal multiphase flow and cavitation fields of a centrifugal pump . Compared to these tasks, prior macroscopic components of the PEMFCs did not exhibit an increased complexity of their own geometry and internal physical fields.…”

Section: Resultsmentioning

confidence: 99%

“…The feasibility has been demonstrated by our work on the use of the same network structure to accomplish the prediction of the internal flow field of the patient-personalized multiscale cardiovascular systems, including the coarse aorta and the fine coronary capillaries, 18 the internal flow field of an aneurysm containing a porous media stent model, 19 and the internal multiphase flow and cavitation fields of a centrifugal pump. 49 Compared to these tasks, prior macroscopic components of the PEMFCs did not exhibit an increased complexity of their own geometry and internal physical fields. Therefore, reasonable speculations could be given that our deep learning had the potential to be able to model macroscopic and microscopic physical fields in different components of PEMFCs.…”

Section: Universal Demonstration Of the Proposed Deepmentioning

confidence: 99%

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Deep Learning to Reveal the Distribution and Diffusion of Water Molecules in Fuel Cell Catalyst Layers

Zhu

Guo

et al. 2023

ACS Appl. Mater. Interfaces

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Water management in the catalyst layers (CLs) of proton-exchange membrane fuel cells is crucial for its commercialization and popularization. However, the high experimental or computational cost in obtaining water distribution and diffusion remains a bottleneck in the existing experimental methods and simulation algorithms, and further mechanistic exploration at the nanoscale is necessary. Herein, we integrate, for the first time, molecular dynamics simulation with our customized analysis framework based on a multiattribute point cloud dataset and an advanced deep learning network. This was achieved through our workflow that generates simulated transport data of water molecules in the CLs as the training and test dataset. Deep learning framework models the multibody solid–liquid system of CLs on a molecular scale and completes the mapping from the Pt/C substrate structure and Nafion aggregates to the density distribution and diffusion coefficient of water molecules. The prediction results are comprehensively analyzed and error evaluated, which reveals the highly anisotropic interaction landscape between 50,000 pairs of interacting nanoparticles and explains the structure and water transport property relationship in the hydrated Nafion film on the molecular scale. Compared to the conventional methods, the proposed deep learning framework shows computational cost efficiency, accuracy, and good visual display. Further, it has a generality potential to model macro- and microscopic mass transport in different components of fuel cells. Our framework is expected to make real-time predictions of the distribution and diffusion of water molecules in CLs as well as establish statistical significance in the structural optimization and design of CLs and other components of fuel cells.

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Section: Resultsmentioning

confidence: 99%