Humaira Kanwal

This concept has numerous uses, including sophisticated thermal management, porous media conveyance, and industrial cooling systems. Boger hybrid nanofluids' (HNFs') magnetized Darcy–Forchheimer flow improves heat transmission in geothermal systems, packed‐bed reactors, filtration devices, and energy storage technologies. The Cattaneo–Christov flux model defines heat transport more accurately by accounting for thermal relaxation effects, whereas thermophoretic particle deposition is crucial in coating processes, aerosol technology, and nanoparticle (NP)‐based manufacturing. Furthermore, the use of artificial neural networks (ANNs) for precise enhancement of complex flow and thermal behaviors makes the model useful for smart engineering designs, electronic cooling, biomedical devices, and renewable energy applications. This study uses the Cattaneo–Christov heat and mass flux model and integrated numerical computing to evaluate the Marangoni convection (MC) flow of MHD Boger HNF across a sheet with thermophoretic particle deposition using the intelligent Levenberg–Marquardt (ILM) optimization algorithm and an ANN algorithm. Moreover, the algorithm's consistency and stability are guaranteed. Mapping thermal, velocity, and solutal profiles from input to output is another use for neural networking. These outcomes show how accurate ANN forecasts and optimizations may be. The data used by the ANN‐based LM optimization technique is divided into three categories: validation (15%), testing (15%), and training (70%). As the values of the thermal and concentration relaxation parameters rise, the thermal and concentration profiles decrease.

The chemical reactive flow of an MHD ternary hybrid nanofluid with LTNE effects and microorganisms is briefly examined in this study using the extended Xue, Yamada-Ota, and Hamilton Crosser models. Momentum is estimated with consideration for the Darcy Forchheimer porous medium effect. This model aims to assess the performance of three THNF (trihybrid nanofluid) models: YOM, Xue, and Hamilton-Crosser. The development of cutting-edge biotechnology applications like targeted medication delivery systems and bio-remediation techniques can benefit from the valuable insights that this research provides into how microorganisms behave in intricate fluid environments. Additionally, knowing how these microorganisms interact in non-equilibrium thermal environments in nanofluid systems can be used to design more effective heat transfer systems and optimize thermal management in a variety of industrial processes, ultimately leading to increased energy efficiency and sustainability. The system of ODEs derived from the leading PDEs is solved by the MATLAB solver bvp4c package using suitable similarity variables to produce the numerical solution. The impacts of the appropriate values on the relevant fields have been illustrated graphically. It is crucial to note that the YOM and Xue ternary hybrid nanofluid models have an even greater effect than the HCM. When the interphase heat transfer characteristic increases, the thermal profile of the liquid phase rises and that of the solid phase decreases.

The suggested artificial neural networks surrogate model for Ellis’s hybrid nanofluid with Dufour-Soret characteristics and Joule heating under local thermal non-equilibrium conditions has numerous applications in advanced energy and thermal systems. It can increase mass and heat transmission in electronic cooling systems, nuclear reactors, geothermal reservoirs, and microfluidic devices. Furthermore, it is beneficial for optimizing systems involving electrically conducting fluids, such as MHD power generation, solar thermal collectors, and medicinal heat treatments, where accurate prediction of complicated non-linear transport phenomena is required. To examine how an Ellis hybrid nanofluid’s Darcy-Forchheimer flow is affected by LTNE across a spinning disk. The key aim of this study is to provide a new mathematical intelligence approach of the AI-based intelligent Levenberg–Marquardt technique under the influence of artificial neural networks (ILMT-ANNs) for optimizing Soret-Dufour effects and Joule heating in conjunction with Magneto-Marangoni phenomena. For ILMT-ANNs, the numerical data-sheet is separated into 90.8% training, 4.1% testing, and 5.1% validation. The estimated solution is analyzed, and its evaluation with a numerical solution using bvp4c is described. Regression investigation, error histogram, and fitness curves based on mean squared error (MSE) are used to verify the effectiveness and consistency of ILMT-ANNs.

This work aims to investigate the effects of thermal radiation and LTNE on the chemical reactive flow of a ternary hybrid nanofluid over a sheet containing thermo-bioconvection and oxytactic microorganisms. The model, which use artificial neural networks (ANNs) to forecast and optimize viscosity, heat dissipation, and thermal conductivity, is ideal for sophisticated cooling systems, energy storage, and biomedical applications. The ANNs has been trained using the Levenberg-Marquardt technique. The effectiveness of the scheme is supported by a number of statistical measures, such as analysis of error histograms, regression index, and convergence analysis, which show a minimum level of the best performance value ( 6 . 01 × E − 8 to 1 . 77 × E − 7 ) for the comprehensive simulation of the proposed model. Its applications include nuclear reactors, solar energy harvesting, electronic cooling, and chemical processes that require precise heat regulation. The addition of oxytactic microorganisms enhances heat transfer dynamics, boosting system efficiency and sustainability. The numerical findings are shown as tables and graphs on a Bvp4c. The liquid phase thermal profile increases while the solid phase thermal profile decreases as the interphase heat transfer parameter values grow.

This work uses the Cattaneo-Christov heat flux model to examine the bioconvective flow of a hybrid nanofluid with deferred dust particles over a sheet. The possibility includes gyrotactic microbes, activation energy, and elastic deformation. This study’s main aim is to inspect the hybrid nanofluid thermal mobility with water as base fluid. The MnZnFe O 4 and CoF e 2 O 4 nanoparticles are suitable for thermal analysis. In the relevant flow equations, density of microorganisms, momentum, energy and mass, concentration are taken into consideration. The nonlinear PDEs are transformed into nonlinear ODEs from the rising system. The numerical answers are obtained using a shooting method based on the RKF-45th order. It may be used to improve cooling operations in energy systems and electrical devices where exact control over mass and heat transport is essential. Because microbe motion is crucial to the operation of microbial fuel cells, bioreactors, and bioengineering applications, the inclusion of bioconvection is particularly pertinent. Additionally, the model can be used to build sophisticated cooling mechanisms for high-performance systems, like automotive or aerospace applications, where fluid flow and heat flux must be effectively controlled under elastic deformation conditions. The fluid and liquid phase heat profiles decrease as the elastic deformation parameter values increase. The solid and liquid phase thermal profiles decrease with increasing levels of the elastic deformation parameter.

The aim of this endeavor is to apply artificial intelligence to provide original solutions for very complicated and challenging physical model. The results of chemical reaction involving Marangoni convection, gyrotactic microbes and thermo-bioconvection in water-based THNF (trihybrid nanofluid) flow across a sheet with heat source, thermal radiation, thermophoretic particle deposition is assessed in this work using a feed-forward neural network in conjunction with back-propagation Bayesian regularization optimization (FFNN-BPBRO). The suggested Bayesian-Regularized Neural Network model for assessing mass and heat transportation in ternary nanofluids including motile microorganisms can be used to improve thermal performance in biomedical systems, microfluidic devices, energy storage, and cooling technologies. It also offers important insights for wastewater treatment, bio-convective transport, and nanofluid-based industrial processes, where precise prediction and thermodynamic optimization are critical for increasing efficiency, stability, and sustainability. As the Peclet number increases, the microorganism profile decreases.

This paper investigates the effects of local thermal nonequilibrium and thermal radiation on the chemical reactive flow of a trihybrid nanofluid (THNF) around a rotating sphere in the presence of thermophoretic and electrophoretic particle deposition. The diamond, cobalt oxide ([ ], and titanium dioxide ([ ] nanoparticles dispersed in diathermic oil comprise the THNF flow model. Such a model would be extremely helpful to industries that require efficient heat transfer in rotating systems, such as those found in nuclear reactors, turbine blades and rotary heat exchangers. The unique mix of [ ], [ ] and diamond nanoparticles in diathermic oil enhances thermal conductivity and reactivity, boosting heat transfer stability and efficiency at high temperatures. Additionally, this model may promote applications in automotive and aerospace engineering, where thermal management is essential, by fostering developments in nanofluid-based cooling systems for electronics. The numerical solution of the simplified equations is obtained using a shooting technique and the bvp4c. Graphics are used to show the mathematical results. We look at how different limits affect their distinctive characteristics. The fluid and solid phases decrease when the porosity-modified conductivity ratio rises. The THNF rate of mass transfer values increase by 6.20% when the thermophoretic parameter value is increased from 0.1 to 0.6.

This study inspects the properties of Stephan blowing and thermal radiation on trihybrid nanofluid convective flow with thermophoretic and electrophoretic particle deposition using LTNECs (local thermal non-equilibrium conditions). A trihybrid nanofluid consists of Zr O 2 , Si O 2 , Mo S 2 , and silicone oil as the base liquid is used. The proposed approach can be used in a variety of engineering processes and sophisticated heat management systems. By employing trihybrid nanofluids with enhanced thermal conductivity, it can be used to increase heat transmission efficacy in electronic cooling devices, nuclear reactors, and solar energy systems. Furthermore, it is pertinent to the design of effective cooling systems in aerospace engineering and the optimization of chemical reactors due to the incorporation of Stephan blowing effects and LTNECs. The method has significant results for biomedical engineering as well, particularly in drug delivery systems and hyperthermia therapies where exact heat control is essential. The governing equations’ numerical results can be obtained by applying the analytical method bvp4c. The solid phase thermal distributions decrease as the interphase heat transfer parameter rises for both the classical and modified Hamilton-Crosser models, whereas the liquid phase thermal distribution and the solid and liquid rate of heat transmission increase.