Large-scale physically accurate modelling of real proton exchange membrane fuel cell with deep learning

Nature Communications, Год журнала: 2023, Номер 14(1)

Опубликована: Фев. 14, 2023

Abstract Proton exchange membrane fuel cells, consuming hydrogen and oxygen to generate clean electricity water, suffer acute liquid water challenges. Accurate modelling is inherently challenging due the multi-phase, multi-component, reactive dynamics within multi-scale, multi-layered porous media. In addition, currently inadequate imaging capabilities are limiting simulations small areas (<1 mm 2 ) or simplified architectures. Herein, an advancement in achieved using X-ray micro-computed tomography, deep learned super-resolution, multi-label segmentation, direct multi-phase simulation. The resulting image most resolved domain (16 with 700 nm voxel resolution) largest flow simulation of a cell. This generalisable approach unveils multi-scale clustering transport mechanisms over large dry flooded gas diffusion layer fields, paving way for next generation proton cells optimised structures wettabilities.

Язык: Английский

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A review of micro-resolved crystochemical and mechanical probes for sustainable cement-based material studies

npj Materials Sustainability, Год журнала: 2025, Номер 3(1)

Опубликована: Янв. 7, 2025

Exploring sustainable alternative constituents is a key pathway to carbon-neutralization of concrete, but often limited insufficient understandings how they interact with conventional concrete components at microscale. In this paper we reviewed the most cutting-edge microprobes used for such purposes, from both laboratory setup synchrotron radiation-based techniques. We also provided practical guidelines on sample preparation and result analysis, which could benefit researchers who plan adopt these methods

Язык: Английский

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Multiscale Characterization of Wettability in Porous Media

Transport in Porous Media, Год журнала: 2021, Номер 140(1), С. 215 - 240

Опубликована: Май 26, 2021

Язык: Английский

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U-Net model for multi-component digital rock modeling of shales based on CT and QEMSCAN images

Journal of Petroleum Science and Engineering, Год журнала: 2022, Номер 216, С. 110734 - 110734

Опубликована: Июнь 13, 2022

Язык: Английский

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Benchmarking conventional and machine learning segmentation techniques for digital rock physics analysis of fractured rocks

Environmental Earth Sciences, Год журнала: 2022, Номер 81(3)

Опубликована: Янв. 25, 2022

Abstract Image segmentation remains the most critical step in Digital Rock Physics (DRP) workflows, affecting analysis of physical rock properties. Conventional techniques struggle with numerous image artifacts and user bias, which lead to considerable uncertainty. This study evaluates advantages using random forest (RF) algorithm for fractured rocks. The quality is discussed compared two conventional processing methods (thresholding-based watershed algorithm) an encoder–decoder network form convolutional neural networks (CNNs). segmented images RF method were used as ground truth CNN training. samples are acquired by X-ray computed tomography scanning (XCT). skeletonized 3D calculated, providing information about mean mechanical aperture roughness. porosity, permeability, flow fields, preferred paths analyzed DRP approach. Moreover, breakthrough curves obtained from tracer injection experiments evaluate each method. results show that overestimate fracture aperture. Both machine learning approaches promising handle all complexities without any prior CT-image filtering. However, implementation has superior inherent over CNN. resource-saving (e.g., quickly trained), does not need extensive training dataset, can provide uncertainty a measure evaluating quality. variation properties highlights importance choosing appropriate

Язык: Английский

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Multi-scale reconstruction of porous media from low-resolution core images using conditional generative adversarial networks

Journal of Natural Gas Science and Engineering, Год журнала: 2022, Номер 99, С. 104411 - 104411

Опубликована: Янв. 6, 2022

Язык: Английский

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Advances in the application of deep learning methods to digital rock technology

ADVANCES IN GEO-ENERGY RESEARCH, Год журнала: 2023, Номер 8(1), С. 5 - 18

Опубликована: Фев. 2, 2023

Digital rock technology is becoming essential in reservoir engineering and petrophysics. Three-dimensional digital reconstruction, image resolution enhancement, segmentation, parameters prediction are all crucial steps enabling the overall analysis of rocks to overcome shortcomings limitations traditional methods. Artificial intelligence technology, which has started play a significant role many different fields, may provide new direction for development technology. This work presents systematic review deep learning methods that being applied tasks within analysis, including reconstruction rocks, high-resolution acquisition, grayscale parameter prediction. The results these applications prove state-of-the-art can help advance approach scientific knowledge field rocks. also discusses future research developments on application Cited as: Li, X., B., Liu, F., T., Nie, X. Advances Geo-Energy Research, 2023, 8(1): 5-18. https://doi.org/10.46690/ager.2023.04.02

Язык: Английский

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Recent Advances in Multiscale Digital Rock Reconstruction, Flow Simulation, and Experiments during Shale Gas Production

Energy & Fuels, Год журнала: 2023, Номер 37(4), С. 2475 - 2497

Опубликована: Янв. 25, 2023

The complex and multiscale nature of shale gas transport imposes new challenges to the already well-developed techniques for conventional reservoirs, especially digital core analysis. Multiscale complicated pore systems distinctive properties limit most reconstruction methods not applicable. High-precision imaging experiments play a key role in characterization structures mineral components. While exhilarating breakthroughs physical experimental hybrid superposition have made significant achievements rock reconstruction, rapidly evolving deep learning also present promising option. Benefiting from techniques, pore-scale flow can be directly simulated based on or indirectly modeled using network model. It is precise realistic investigate at scale considering desorption, surface diffusion, slippage nanopores. In this paper, we reviewed recent advances off-mentioned processes presented hand research field.

Язык: Английский

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Application of automated mineralogy in petroleum geology and development and CO2 sequestration: A review

Marine and Petroleum Geology, Год журнала: 2023, Номер 151, С. 106206 - 106206

Опубликована: Фев. 28, 2023

Язык: Английский

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Using X-ray computed tomography and pore-scale numerical modeling to study the role of heterogeneous rock surface wettability on hydrogen-brine two-phase flow in underground hydrogen storage

Fuel, Год журнала: 2024, Номер 366, С. 131414 - 131414

Опубликована: Март 7, 2024

Язык: Английский

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