ArticleScientific reports2025
Machine learning investigation of marangoni convection in hybrid nanofluids with Darcy-Forchheimer.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning-based prediction models for severeFrontiers in public health · 2026Pooled it
- Supervised machine learning computing paradigm of energy activation for magnetic nanofluid flow via porous surface with nonlinear variant viscosity.Discover nano · 2026Article
- Task-specific compensatory joint control strategies in adolescent idiopathic scoliosis during dynamic balance tasks.Scientific reports · 2026Article
- Prediction of pulmonary tuberculosis case trends among older adults in Chongqing based on time series models.Frontiers in public health · 2026Article
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Authors and funding
3 authors.
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Abstract
This research utilizes machine learning to investigate Marangoni convection in a hybrid nanofluid [Formula: see text] within a Darcy-Forchheimer porous framework. We conduct both qualitative and quantitative assessments of heat transfer, mass transfer, and viscous dissipation irreversibility during the flow. Numerical results are obtained using a Python finite difference algorithm, after which MATLAB is employed for AI-based analysis. Additionally, the Levenberg-Marquardt neural network algorithm is trained and utilized. Our findings show that fluid velocity diminishes as the inverse Darcy parameter, Marangoni ratio, and Forchheimer parameter increase. Moreover, the temperature rises with the Eckert number and Prandtl ratio. As concentration increases, activation energy and Schmidt parameter also grow. Mean Square Error for the results reaches up to 10
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