{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T02:02:38Z","timestamp":1782439358667,"version":"3.54.5"},"reference-count":32,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T00:00:00Z","timestamp":1736208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>Packed columns are commonly used in post-combustion processes to capture CO<jats:sub>2<\/jats:sub> emissions by providing enhanced contact area between a CO<jats:sub>2<\/jats:sub>-laden gas and CO<jats:sub>2<\/jats:sub>-absorbing solvent. To study and optimize solvent-based post-combustion carbon capture systems (CCSs), computational fluid dynamics (CFD) can be used to model the liquid\u2013gas countercurrent flow hydrodynamics in these columns and derive key determinants of CO<jats:sub>2<\/jats:sub>-capture efficiency. However, the large design space of these systems hinders the application of CFD for design optimization due to its high computational cost. In contrast, data-driven modeling approaches can produce fast surrogates to study large-scale physics problems. We build our surrogates using MeshGraphNets (MGN), a graph neural network framework that efficiently learns and produces mesh-based simulations. We apply MGN to a random packed column modeled with over 160K graph nodes and a design space consisting of three key input parameters: solvent surface tension, inlet velocity, and contact angle. Our models can adapt to a wide range of these parameters and accurately predict the complex interactions within the system at rates over 1700 times faster than CFD, affirming its practicality in downstream design optimization tasks. This underscores the robustness and versatility of MGN in modeling complex fluid dynamics for large-scale CCS analyses.<\/jats:p>","DOI":"10.3389\/frai.2024.1441985","type":"journal-article","created":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T06:55:22Z","timestamp":1736232922000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Accelerating computational fluid dynamics simulation of post-combustion carbon capture modeling with MeshGraphNets"],"prefix":"10.3389","volume":"7","author":[{"given":"Bo","family":"Lei","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yucheng","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jose","family":"Cadena","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amar","family":"Saini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yeping","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Bao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhijie","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brenda","family":"Ng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Phan","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,1,7]]},"reference":[{"key":"B1","article-title":"Physical design using differentiable learned simulators","author":"Allen","year":"2022","journal-title":"arXiv preprint arXiv:2202.00728"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i11.21511","article-title":"\u201cLatent space simulation for carbon capture design optimization,\u201d","author":"Bartoldson","year":"2022","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"B3","article-title":"Scientific computing algorithms to learn enhanced scalable surrogates for mesh physics","author":"Bartoldson","year":"2023","journal-title":"arXiv preprint arXiv:2304.00338"},{"key":"B4","article-title":"Relational inductive biases, deep learning, and graph networks","author":"Battaglia","year":"2018","journal-title":"arXiv preprint arXiv:1806.01261"},{"key":"B5","first-page":"2402","article-title":"\u201cCombining differentiable pde solvers and graph neural networks for fluid flow prediction,\u201d","volume-title":"International Conference on Machine Learning","author":"Belbute-Peres","year":"2020"},{"key":"B6","article-title":"Message passing neural pde solvers","author":"Brandstetter","year":"2022","journal-title":"arXiv preprint arXiv:2202.03376"},{"key":"B7","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1146\/annurev-fluid-010719-060214","article-title":"Machine learning for fluid mechanics","volume":"52","author":"Brunton","year":"2020","journal-title":"Annu. 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