Sensitivity analysis and modeling of ternary hybrid nanofluid flow in rotating annulus using a hybrid physics-informed neural network
Researchers have developed a hybrid physics-informed neural network (PINN) and artificial neural network (ANN) model to analyze ternary hybrid nanofluid flow in rotating machinery. The study demonstrates high-accuracy predictions for heat transfer rates, offering a solution to convergence issues found in traditional numerical methods.
Why it matters
This advancement in computational fluid dynamics provides more efficient modeling tools for critical engineering sectors like aerospace and nuclear energy.
Scientific Reports ( 2026 ) Cite this article
We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.
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