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◆ Results in Surfaces and Interfaces2026-04-12· Artificial neural network

Role of hyper-heuristic optimization with deep neural network scheme to improve exergoeconomic evaluation trough solar collector in hybrid nanomaterial

Imran Abbasi, Amjad Ali Pasha, Muhammad Naeem, Mohamed H. Ahmed, Naveed Ahmed, Faisal J. Alzahrani, Mehboob Ali, Waqar Azeem Khan

原始摘要(英文原文)· Original abstract
Research on hybrid nanofluids has enhanced significantly in the recent years, and the results shows the ideal heat transfer fluids for engineering applications are hybrid nano-fluids because of their enriched thermo-physical properties. The current numerical computational examination aims to simulate the properties of hybrid nanofluids heat transportation on a vertical stretching/shrinking surface. Alumina ( ) and silver ( ) are the hybrid nanoparticles, while water is the base fluid. Thermal radiation, heat source/sink effects, and Joule heating all contribute to the transportation phenomena in Darcy-Forchheimer medium porous materials. Non-linear ODEs are obtained from governing PDEs by using suitable similarity transformation. MATLAB’s numerical technique LM-BPS is applied to estimate the integration of flow work. Analysis of the behaviors of a few physical flow limitations is done using graphical and numerical results. The values of parameters including M (Magnetic field), S (Suction parameter) and Pr (Prandtl number) are changed to obtain the results. The results shows that increasing values of and cause the velocity profile to decrease and increasing values of and cause the temperature profile to decrease.
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Role of hyper-heuristic optimization with deep neural network scheme to improve exergoeconomic evaluation trough solar collector in hybrid nanomaterial — 科研速览 Science Skim