Control of double diffusive flow field of nanoenhanced phase change material via small satellite body around square heater in a lid-driven cavity with machine learning integration - Hussain, Shafqat; Öztop, Hakan Fehmi; Abidi, Awatef; Ertam, Fatih (Pergamon-Elsevier Science Ltd, 2026)
This study presents a comprehensive numerical investigation of double-diffusive natural convection in a lid-driven square cavity filled with a nanoencapsulated phase change material (NEPCM) suspension. The cavity incorporates a central heated and solutally enriched square obstacle alongside a small adiabatic circular satellite obstacle. The present paper also proposes a comprehensive mathematical framework for the application of machine learning algorithms to predict concentration and temperature fields in flows characterized by varying Richardson numbers. The governing equations, formulated under the assumptions of laminar, incompressible, steady-state, Newtonian flow with the Boussinesq approximation, are solved using a Galerkinbased finite element method, enabling accurate treatment of complex geometries and boundary conditions. The study elucidates the effects of the satellite obstacle's angular position, the Lewis number, the Reynolds number, and the magnetic field strength on the flow structure, temperature and concentration distributions, and overall transport characteristics. Quantitative analyses of dimensionless parameters, including the average Nusselt and Sherwood numbers as well as mean kinetic energy, provide insights into the interplay between convective and diffusive transport phenomena in NEPCM-laden fluids. The findings demonstrate that machine learning algorithms, particularly Random Forest, can attain nearly perfect predictive accuracy (R-2 > 0.9996 for both concentration and temperature fields across a range of Richardson number regimes. Furthermore, the location of the satellite obstacle can be used as a control parameter for regulating the calculated values inside the cavity. The results reveal critical interactions between obstacle placement and flow behavior, offering design guidance for enhanced thermal and solutal management in advanced energy and microfluidic applications.
Keywords: Lid-driven cavity; Nanoencapsulated phase change materials; (NEPCMs); Double-diffusive convection; Adiabatic obstacle; Galerkin finite element method; Machine learning