Predicting the thermal distribution in a convective wavy fin using a novel training physics-informed neural network method.

Fins are widely used in many industrial applications, including heat exchangers. They benefit from a relatively economical design cost, are lightweight, and are quite miniature. Thus, this study investigates the influence of a wavy fin structure subjected to convective effects with internal heat generation. The thermal distribution, considered a steady condition in one dimension, is described by a unique implementation of a physics-informed neural network (PINN) as part of machine-learning intelligent strategies for analyzing heat transfer in a convective wavy fin. This novel research explores the use of PINNs to examine the effect of the nonlinearity of temperature equation and boundary conditions by altering the hyperparameters of the architecture. The non-linear ordinary differential equation (ODE) involved with heat transfer is reduced into a dimensionless form utilizing the non-dimensional variables to simplify the problem. Furthermore, Runge-Kutta Fehlberg's fourth-fifth order (RKF-45) approach is implemented to evaluate the simplified equations numerically. To predict the wavy fin's heat transfer properties, an advanced neural network model is created without using a traditional data-driven approach, the ability to solve ODEs explicitly by incorporating a mean squared error-based loss function. The obtained results divulge that an increase in the thermal conductivity variable upsurges the thermal distribution. In contrast, a decrease in temperature profile is caused due to the augmentation in the convective-conductive variable values.

Two-phase simulation of entropy optimized mixed convection flow of two different shear-thinning nanomaterials in thermal and mass diffusion systems with Lorentz forces.

This research compares the momentum, thermal energy, mass diffusion and entropy generation of two shear thinning nanofluids in an angled micro-channel with mixed convection, nonlinear thermal radiation, temperature jump boundary condition and variable thermal conductivity effects. The [Formula: see text] approach was used to solve the Buongiorno nonlinear governing model. The effect of different parameters on the flow, energy, concentration, and entropy generating fields have been graphically illustrated and explained. The hyperbolic tangent nanoliquid has a better velocity than the Williamson nanofluid. The Williamson nanofluid has higher thermal energy and concentration than the hyperbolic tangent nanoliquid in the microchannel. The Grashof number, both thermal and solutal, increases the fluid flow rate throughout the flow system. The energy of the nanoliquid is reduced by the temperature jump condition, while the energy field of the nanoliquid is enhanced by the improving thermal conductivity value. The nanoliquids concentration rises as the Schmitt number rises. The irreversibility rate of the channel system is maximized by the variable thermal conductivity parameter.

Facile synthesis and characterization of multi-walled carbon nanotubes decorated with hydroxyapatite from cattle horns for adsorptive removal of fluoride.

Developing a new adsorbent for fluoride removal from cattle horn waste materials by a facile chemical method has shown great potential for fluoride removal. This paper reports the synthesis of multi-walled carbon nanotubes decorated with hydroxyapatite from cattle horns (MWCNT-CH) using a facile chemical method. Characterization studies using standard techniques showed that the composite is mesoporous with a rough morphology and contained MWCNTs uniformly encapsulated by the hydroxyapatite forming a crystalline MWCNT-CH composite. Optimization of fluoride adsorption by the as-synthesized composite using Response Surface Methodology (RSM) showed that a maximum fluoride removal efficiency of 80.21% can be attained at initial fluoride concentration = 10 mg/L, pH = 5.25, adsorbent dose = 0.5 g and a contact time of 78 min. ANOVA indicates contribution of the process variables in descending order as pH > contact time > adsorbent dose > initial fluoride concentration. Langmuir isotherm (R2 = 0.9991) best described the process, and the maximum adsorption capacity of fluoride onto the as-synthesized MWCNT-CH composite was 41.7 mg/g. Adsorption kinetics data were best fitted in the pseudo-second-order kinetic model (R2 = 0.9969), indicating chemisorption. The thermodynamic parameter ( Δ H = 13.95 J/mol and Δ S = 65.76 J/mol/K) showed that fluoride adsorption onto the MWCNT-CH composite was a spontaneous, endothermic, and entropy-driving process. Moreover, the adsorption mechanism involves ion exchange, electrostatic interaction, and hydrogen bonding. Fluoride was successfully desorbed (using 0.1 M NaOH) from the composite in four cycles, retaining fluoride removal efficiency in the fourth cycle of 57.3%.