Python-based deep learning for optimizing thermal performance of Prandtl-Eyring hybrid nanofluids in solar systems

Python-based deep learning for optimizing thermal performance of Prandtl-Eyring hybrid nanofluids in solar systems

Improved heat transfer efficiency is greatly needed for the advancement of sustainable solar energy systems. Hybrid nanofluids could present strong options for maximizing thermal performance due to improved thermophysical properties. This study presents a Python-based hybrid framework integrating th...

Full description

Saved in:
Bibliographic Details
Main Authors: Nidhal Ben Khedher, Bouthaina Dammak, Zahoor Shah, Hamza Iqbal, Maryam Jawaid, Hafedh Mahmoud Zayani, Mohamed Medani
Format: Article
Language:English
Published: Elsevier 2025-09-01
Series:Case Studies in Thermal Engineering
Subjects:
Hybrid nanofluids
Artificial intelligence
Deep learning
Neural networks
Solar energy applications
Magnetohydrodynamics
Online Access:http://www.sciencedirect.com/science/article/pii/S2214157X25009773
Tags: Add Tag
No Tags, Be the first to tag this record!
Login for hold and recall information

Internet

http://www.sciencedirect.com/science/article/pii/S2214157X25009773

Similar Items