Data-driven machine learning for scale-up of bubbling and turbulent fluidized beds: flow hydrodynamics and reactor performance
Fluidized beds provide excellent solids mixing and high heat and mass transfer rates, making them widely used across various industries. However, conventional experimental and computational fluid dynamics (CFD) approaches are often time-consuming and computationally expensive. To address these chall...
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| Автори: | , , , , , , , |
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| Формат: | Artigo |
| Мова: | Inglês |
| Опубліковано: |
Taylor & Francis Group
2026-12-01
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| Серія: | Engineering Applications of Computational Fluid Mechanics |
| Предмети: | |
| Онлайн доступ: | https://www.tandfonline.com/doi/10.1080/19942060.2026.2670348 |
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