Numerical investigation of conjugate heat transfer in a partitioned cavity using NiO nanofluid and firefly algorithm

Abstract

The fluid's heat transfer properties must be effectively utilized to create efficient heat exchangers. In various existing studies, the application of forced convection leads to pressure drops in conjugate heat transfer systems due to the nanoparticles used. To overcome this issue an efficient heat transfer enhancement using a new approach based on NiO with Cu clad metalcore in a partitioned cavity is introduced. NiO scoria nanofluid minimizes pressure drops, pumping powers enhances stability, saves energy, and allows for smooth heat transfer. Numerical simulation results show that when the concentration of NiO scoria is 4%, the thermal conductivity is at 46.024 W/mK. At a heat transfer coefficient of 175 W/m2K, the maximum heat flux is 39,000 W/m2. Then, to simulate the viscosity and thermal conductivity of nanofluids and also to reduce the number of iterations and the computational time K-Adam Hybrid Enhanced Attractive Automata Firefly algorithm has been used. The numerical solution of the ordinary differential equations associated with conjugate heat transfer is achieved using a Predictor–Corrector Method. The study’s novel integration of advanced algorithms and nanofluid composition offers practical applications in power generation, electronic cooling, and HVAC systems, demonstrating the method's relevance and the need.

Keywords

• Conjugate heat transfer
• forced convection
• NiO scoria
• Attractive Automata Firefly algorithm