Neural Network Surrogate Models MHD Nanofluid Flow Between Porous Disks
Researchers trained Levenberg–Marquardt neural networks on bvp4c solutions to rapidly predict coupled MHD nanofluid transport between porous disks.
Researchers trained Levenberg–Marquardt neural networks on bvp4c solutions to rapidly predict coupled MHD nanofluid transport between porous disks.
A computational study found climate-dependent double-skin façade designs that reduced operational CO₂ emissions by 13–23% in Tashkent and 8–20% in ...
Researchers trained a Kolmogorov-Arnold neural network on numerical simulations of Boger nanofluid flow over a Riga surface, achieving coefficients of ...
Laboratory irradiation of gold nanorod suspensions showed that temperature rise varied approximately linearly with laser power and increased with particle ...
Experimental tests in Tunisia show that copper fins, a transparent glass lid and precise pot positioning can raise panel solar ...
A new thermodynamic simulation study shows that LPG delivers higher efficiency and lower power consumption in a domestic refrigerator, while ...
Researchers in Iraq developed an XGBoost-based AI framework that predicts and classifies flow maldistribution in 3D-printed water-flow distributors with over ...
A new conserved-variable wellbore flow model quantifies how CO2 compressibility delays pressure transmission during fracturing and identifies pump rate as ...
A new computational study shows that the magnetic field induced by a flowing Cu–TiO2/water hybrid nanofluid inside a parabolic solar ...
Researchers combined topology-optimized housings, chimney-effect airflow, heat pipes, and micro-grooved surfaces to cut deep-sea LED fish-attracting lamp temperatures by 6.73 ...
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© 2026 Scienmag - Science Magazine