In the scenario of multidisciplinary applications in thermal engineering, there remains a gap in applications of artificial neural network (ANN) to fluid flow problems for magnetohydrodynamic (MHD) permeable wedge in presence of Soret-diffusivity effects. Similarity transformations were used for obtaining non-linear ordinary differential equations and solved using MATLAB bvp4c routine. Augmenting skin friction value by 37.45% is observed for an incrementing wedge angle parameter while Nusselt number increased for suction parameter (Fw) less than 0.6. Nusselt number portrays a decreasing nature for increasing magnetic parameter and increasing for incrementing suction parameter. Velocity profile decreased with increase of magnetic parameter (M) while concentration profiles decreased for Fwand Prandtl number. The maximum absolute error for neural network back-propagation results were of the order 10-5.. With 70% points used for training, the mean squared error (MSE) for Nusselt number was 3.3175x10-10in validation phase and 2.4922x10-10in training phase with 69 epochs.
This article has tried to study the jet flow of the ternary nanofluid suspended with motile microorganisms across a porous medium under the influence of radiation energy. These conditions have been carefully considered to enhance the heating conductivity of the nanoparticles. The ternary nanofluids (rGo-CoFe2O4-TiO2/H2O) enhanced thermal conductivity due to (TiO2) and (rGo) improves its performance in heat exchange model and thermal energy storage solutions. The inclusion of magnetic nanoparticles (CoFe2O4) gives rise to magnetohydrodynamic (MHD) effects, allowing for control of flow and heat transfer through external magnetic fields. In this study, thermophoresis and Brownian motion have been observed using the modified Buongiorno's model. The numerical solutions of the governing equations for various parameters have been calculated by the "bvp4c" method in the MATLAB software. The obtained graph represents the results of axial velocity, temperature, concentration, and motile density profiles for the ternary nanofluid. The study observed that by increasing radiation, magnetic, and mixed convection parameters, the velocity of the fluid gets enhanced. The concentration of the fluid is majorly enhanced by the increment of the thermophoresis parameter. Meanwhile, the motile density increases with the fall of the bio-convection Schmidt number. In addition, tables of skin friction coefficient, Nusselt number, Sharewood number, and motile density number displayed significant results. This model can be utilized in rocket engines, medical sciences, water jets, atmospheric science, agriculture, Cooling, and heating systems.
The study examined the behavior of Casson nanofluid flow, heat, and mass transfer around a wavy cylinder with convective Nield conditions. We analyzed the effects of several factors, including thermophoresis activation energy, and Brownian motion. We transformed the governing partial nonlinear system into system of ordinary differential equations by applying similarity variables. To derive numerical solutions, we employed the MATLAB bvp4c solver and presented our results using graphical representations. Our graphs illustrated the impact of different parameters, including the thermophoresis (Nt), magnetic parameter (M), Brownian motion parameter (Nb), Casson fluid parameter (beta), Lewis number (Le), Saddle/Nodal indicative parameter (c), and Chemical reaction parameter (sigma). We also computed Sherwood number and local Nusselt number, and skin friction coefficient along the x and y axes to obtain further understanding of the behavior of these emerging parameters. Increasing the Casson fluid parameter increases fluid velocity concentration while the temperature magnitudes tend to diminish. Improvement in Brownian motion, Biot number, and thermophoresis parameters results in a more effective thermal gradient and concentration.
Hyperthermia has been attracting great attention, research resources and clinical translation efforts as a cancer treatment. Metallic nanoparticles can enhance heat deposition in tumors when subjected to external energy sources like lasers. However, challenges remain in accurately estimating state variables, such as the temperature and heat sources, during treatments. This study presents a combined Physics-Informed Neural Network (PINN) and particle filter approach for state estimation in a model, representing a sample of a nanofluid heated by a near-infrared diode laser. The PINN is trained to solve the heat transfer model and serve as the state evolution model in the particle filter. Synthetic and actual temperature measurements from heating experiments involving a nanofluid of palladium-cerium oxide nanoparticles are used in the solution of the state estimation problem. Verification tests show that the particle filter can robustly estimate states with 850 particles in few seconds of computational time, due to the efficient PINN predictions. Overall, the combined PINN - particle filter approach demonstrates potential for solving state estimation problems in complex engineering systems, such as in cancer thermotherapy.
