Predictive ANN analysis of hydrothermal and entropy behavior of corrugated heat exchanger featuring tetra-hybrid nanofluid

• Tetra-hybrid nanofluids (MWCNT, BN, ND, G) enhance heat transfer in sinusoidal wavy CFMCHE. • Factors like solid fraction, wave cycle number, inlet velocity, and nanoparticles mixing ratio are examined. • Nusselt number increases by 2.60 % at nanoparticle mixing ratio 1/8:1/8:2/8:4/8 compared to 0:1/3:1/3:1/3 and by 2.06 % versus an equal mixture of 1/4:1/4:1/4:1/4. • Peak efficiency is 71.2 %, with a hydraulic performance index of 601 × 10³ at an inlet velocity of 0.0038 m/s. • ANN predictions indicate strong reliability for Nusselt number and thermal efficiency in CFMCHE design. Compact energy systems need working fluids with exceptional heat transfer (HT) properties for effective thermal management. Though their behavior in complex microchannels is little studied, tetra-hybrid nanofluids of four nanoparticles show great potential. This research employs numerical analysis to investigate the thermo-hydraulic characteristics of a tetra-hybrid nanofluid comprising multi-walled carbon nanotubes (MWCNT), boron nitride (BN), nanodiamond (ND), and graphene (G) in a sinusoidal wavy crossflow microchannel heat exchanger (SWCFMCHE). The governing equations were solved using Galerkin’s weighted residual finite element method (FEM). Furthermore, an artificial neural network (ANN) model was developed to accurately predict key performance parameters such as Nusselt number ( Nu ), efficiency (ε), and performance index ( η ). The wavy microchannel heat exchanger’s heat transfer rate (HTR) was assessed by varying several important factors, such as the nanoparticle solid-volume fraction ( ϕ = 0.01–2 %), number of wave cycles (NWC = 0–3), inlet velocity ( u i = 0.0038–0.2479 m/s), and heat exchanger (HE) material (copper, aluminum, and stainless steel). For comparison, several heat transferring fluids, including water and water-based hybrid, ternary, and tetra-hybrid nanofluids, were also investigated. Results show that tetra-hybrid nanofluid offers the highest HTR among all tested fluids at equal concentration ratios, ϕ = 2 %, particle diameter ( dp ) = 1 nm, nanoparticles shape factor ( n ) = 16.1576, and NWC = 2. In case of tetra-hybrid nanofluid, the highest N u = 16.749 is achieved at u i = 0.2479 m/s, showing enhancements of 19 %, 13 %, 9 %, and 4 % compared to all other heat transferring fluids. The highest efficiency (71.2 %) and performance index (601 × 10³) are observed for the tetra hybrid nanofluid at u i = 0.0038 m/s. The ANN model accurately predicts thermo-hydraulic parameters. Overall, this integrated numerical and data-driven framework offers new insights into nanoparticle synergy and geometric enhancement, making a significant contribution to the design of efficient heat exchangers (HEs).