AI-RNN approach for radiative dissipative bioconvective flow of eyring-powell hybrid and ternary nanofluids

In this study, we present an artificial intelligence-based approach recurrent neural network (AI-RNN) to investigate bioconvective MHD flow of Eyring-Powell ternary nanofluids (EP-TNFM) under Radiative–Dissipative Effects. We use blood as a base fluid, which incorporates silver, copper, and aluminum nanoparticles. Two fluid arrangements were employed, specifically one fluid being a hybrid nanofluid ( Ag+Cu+blood ) and the second, a ternary hybrid nanofluid ( Ag+Cu+Al+blood ). The governing nonlinear ordinary differential equations were developed using similarity transformations and were solved using boundary value analysis approaches. The datasets composed of converged numerical solutions over a series of parametric values, generated from above numerical model, were used for training and validation of AI-RNN, and testing. The procedure presented strong ability to converge quickly, predictive reliability, and provided evidence that both the training, validation, and testing loss are practically identical, R² values at least 0.98 regression results in terms of the training loss. Gradient magnitude and weight distribution indicated that the models were stable. The parametric results showed that for skin friction, Le decreased and α increased, whereas for Nusselt number, Le is decreased, and Pr is increased. Velocity and thermal boundary layer were heavily influenced by α, M, λ, and Pr . The higher Sc and Le numbers suppressed mass and microorganism transport. In terms of comparisons, ternary nanofluids had smaller velocities and reduced bioconvection than hybrid nanofluids due to the larger viscosity and density. Thus, results demonstrate that AI-RNN is a novel and reliable alternative to traditional solvers for hybrid nanofluids, with potential applications in biomedical engineering, as well as thermal management and microfluidic applications. Further work could expand on this work in several ways, by including other losses such as MAE and RMSE, using different architectures like LSTMs and PINNs, and by investigating the application of the approach to more complex non-Newtonian models and actual biomedical flows.