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A Semi-Analytical and Artificial Neural Network Study Considering Christov-Cattaneo Flux Model Effects on Nimonic 80A-AA7072/HFE-7100 Based Hybrid Nanoliquid

Saba LiaqatDepartment of Computer Engineering, Biruni University, 34010, Istanbul, TurkeyDurdana Rustamova FarkhadHead of Mechanics and Mathematics Department of the Western Caspian University, Baku, AZ1072, AzerbaijanFarkhod RakhmonovNational University of UzbekistanMunawar AbbasDepartment of Mathematics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, 602105, Tamil Nadu, IndiaAli RazaInstitute of Chemical Sciences, Gomal University, Dera Ismail khan, Khyber Pakhtunkhwa, 29220, PakistanAmr AlalawiDepartment of Mathematical Sciences, College of Engineering, University of Business and Technology, Jeddah, 21361, Saudi ArabiaHakim AL GarallehDepartment of Mathematical Sciences, College of Engineering, University of Business and Technology, Jeddah, 21361, Saudi Arabia

2026en

Ochiq kirish: yoʻq

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Annotatsiya

This model is valuable in engineering applications that need accurate forecasting and management of non-Fourier heat transport in sophisticated cooling and thermal management systems. The semi-analytical and Machine Learning-based study of Nimonic 80A-AA7072/HFE-7100 hybrid nanoliquid with Christov-Cattaneo flux effects has applications in high-temperature heat exchangers, aerospace and gas turbine cooling components, electronic and microchannel cooling systems, and energy storage devices. By capturing thermal relaxation effects and maximizing thermal performance, the model aids in the design of efficient, low-energy-loss systems for advanced manufacturing, nuclear and renewable energy technologies, and small thermal devices. Using an intelligent back-propagation neural network with Levenberg Marquardt strategy (IBNN-LMS), the current work aims to assess the significance of Christov-Cattaneo flux model on Nimonic 80A-AA7072/HFE-7100 based hybrid nanoliquid. The simplified equations are evaluated implementing the Homotopy Analysis technique. The impacts of significant variables on microbiological, solutal, thermal, and flow profiles are evaluated using graphs. This study covers the intelligent computing-based neural network of IBNN-LMS, which has been developed using datasets and then trained, tested, and verified to produce approximation results.

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Identifikatorlar

DOI: 10.1166/jon.2026.2347

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