Machine learning intelligent based hydromagnetic thermal transport under Soret and Dufour effects in convergent/divergent channels: a hybrid evolutionary numerical algorithm.

In this research, we analyze the complex dynamics of hydro-magnetic flow and heat transport under Sorent and Dofour effects within wedge-shaped converging and diverging channels emphasizing its critical role in conventional system design, high-performance thermal equipment. We utilized artificial neural networks (ANNs) to investigation the dynamics of the problem. Our study centers on unraveling the intricacies of energy transport and entropy production arising from the pressure-driven flow of a non-Newtonian fluid within both convergent and divergent channel. The weights of ANN based fitness function ranging from - 10 to 10. To optimize the weights and biases of artificial neural networks (ANNs), employ a hybridization of advanced evolutionary optimization algorithms, specifically the artificial bee colony (ABC) optimization integrated with neural network algorithms (NNA). This approach allows us to identify and fine-tune the optimal weights within the neural network, enabling accurate prediction. We compare our results against the established different analytical and numerical methods to assess the effectiveness of our approach. The methodology undergoes a rigorous evaluation, encompassing multiple independent runs to ensure the robustness and reliability of our findings. Additionally, we conduct a comprehensive analysis that includes metrics such as mean squared error, minimum values, maximum values, average values, and standard deviation over these multiple independent runs. The minimum fitness function value is 1.32 × 10-8 computed across these multiple runs. The absolute error, between the HAM and machine learning approach addressed ranging from 3.55 × 10-7 to 1.90 × 10-8. This multifaceted evaluation ensures a thorough understanding of the performance and variability of our proposed approach, ultimately contributing to our understanding of entropy management in non-uniform channel flows, with valuable implications for diverse engineering applications.

Suppression of wetting transition on evaporative fakir droplets by using slippery superhydrophobic surfaces with low depinning force.

This study experimentally investigated the evaporation and wetting transition behavior of fakir droplets on five different microstructured surfaces. Diamond-like carbon was introduced as the substrate, and the influence of varying the width, height, and pitch of the micropillars was assessed. The experimental results showed that the interfacial properties of the surfaces change the evaporation behavior and the starting point of the wetting transition. An important result of this study is the demonstration of a slippery superhydrophobic surface with low depinning force that suppresses the transition from the Cassie-Baxter state to the Wenzel state for microdroplets less than 0.37 mm in diameter, without employing large pillar height or multiscale roughness. By selecting an appropriate pillar pitch and employing tapered micropillars with small pillar widths, the solid-liquid contact at the three-phase contact line was reduced and low depinning forces were obtained. The underlying mechanism by which slippery superhydrophobic surfaces suppress wetting transitions is also discussed. The accuracy of the theoretical models for predicting the critical transition parameters was assessed, and a numerical model was developed in the surface evolver to compute the penetration of the droplet bottom meniscus within the micropillars.