Analytical techniques are used in this article to examine the forced flow and heat transfer in a non-Newtonian Reiner-Rivlin fluid between two infinitely revolving disks in a magnetic field. The partial differential equations (PDEs) emphasizing flow and heat transfer were transformed into a nonlinear ordinary differential equations (ODEs) system using the Von Karman similarity transformation. The system of nonlinear ODEs with boundary conditions was solved using the Homotopy Perturbation Method (HPM) and Akbari-Ganji's Method (AGM). The study assessed the effects of physical factors on temperature profiles and the radial, transverse, and axial velocity components, including the visco-inelastic parameter, Reynolds number, Prandtl number, forced parameter, and magnetic field. The Python programming language was used for symbolic and numeric calculations of velocity components and temperature profiles. The accuracy of the results obtained through the analytical approach validates the applicability and efficiency of the proposed approach for studying the forced flow and heat transfer in non-Newtonian fluids between rotating disks. 

Thermal analysis of Non-Newtonian visco-inelastic fluid MHD flow between rotating disks

Abstract In the present article we consider the physical model of two-dimensional Casson hybrid nanofluids flow, which is magnetized and thermally radiative, laminar, incompressible inside the channel. Flow equations have been modelled for two dimensional axial and radial velocity components $$u$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>u</mml:mi> </mml:math> along $$x{\text{-axis}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>x</mml:mi> <mml:mtext>-axis</mml:mtext> </mml:mrow> </mml:math> and $$v$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>v</mml:mi> </mml:math> along the $$y{\text{-axis}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>y</mml:mi> <mml:mtext>-axis</mml:mtext> </mml:mrow> </mml:math> . There exists temperature $$\left({T}_{1}&gt;{T}_{2}\right)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mfenced> <mml:msub> <mml:mi>T</mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:mo>&gt;</mml:mo> <mml:msub> <mml:mi>T</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:mfenced> </mml:math> which is constant for upper and lower walls. The Casson nanofluids model with nano type particles includes heat transfer effect between two stretched and shrinking walls of the channel was constructed. The continuity, momentum and energy equations are modelled in cartesian coordinates system. The finite element technique is used to evaluate numerical solutions for velocity, temperature, Skin friction and Nusselt number. It is evident that the hybrid Casson nanofluids exhibit opposite behaviors in the stretching and shrinking cases near the upper and lower walls of the channel. It is also observed that in the stretching case, increasing the values of the Casson parameter leads to a rise in both shear stress and heat transfer rate for both plates of the channel. However, the results contradict this trend in the shrinking case. Understanding the thermal characteristics of magnetized hybrid fluids can be applied to the design of advanced cooling systems in engineering applications, biomedical fluid dynamic, in energy system this study can be applied to improve the efficiency of energy systems where fluid flow and heat transfer play crucial roles. Further use of nanofluids suggests a connection to nanotechnology, and the study may have implications for the development of advanced nanomaterial-based heat transfer fluids. 

Thermal characteristics of magnetized hybrid Casson nanofluid flow in a converging–diverging channel with radiative heat transfer: a computational analysis

We Consider the physical model of two-dimensional hybrid nanofluids flow, which is time dependent, laminar, incompressible inside the two co-axial stretchable disks. The length of upper and lower disks is2s(t). The two axial and radial velocity components (u,w) depends upper the radial of disks r and normal of direction z. There exists temperature T1>T2 which is constant for upper and lower disks. The continuity, momentum and energy equations are modeled in cylindrical coordinates system. The finite element technique is used to evaluate numerical solutions. It is observed that the velocity near upper and lower disks decreases by increasing the values of magnetic parameter. Increased the values of shape factorNs of (Nanoparticles) then the thermal conductivity (HDnf) decreased. The shear stress and heat transfer rate decreased by influence α,ϕ1,ϕ2 but an opposite reaction observed when increase the values of M,Re andNs. 

Thermodynamic study of hybrid nanofluid to explore synergistic effects of multiple ferromagnetic nanoparticles in co-axial disks for magnetized fluid

Water scarcity is a critical global issue, necessitating efficient water purification and desalination methods. Membrane separation methods are environmentally friendly and consume less energy, making them more economical compared to other desalination and purification methods. This survey explores the application of artificial intelligence (AI) to predict membrane behaviour in water purification and desalination processes. Various AI platforms, including machine learning (ML) and artificial neural networks (ANNs), were utilised to model water flux, predict fouling behaviour, simulate micropollutant dynamics and optimise operational parameters. Specifically, models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs) and support vector machines (SVMs) have demonstrated superior predictive capabilities in these applications. This review studies recent advancements, emphasising the superior predictive capabilities of AI models compared to traditional methods. Key findings include the development of AI models for various membrane separation techniques and the integration of AI concepts such as ML and ANNs to simulate membrane fouling, water flux and micropollutant behaviour, aiming to enhance wastewater treatment and optimise treatment and desalination processes. In conclusion, this review summarised the applications of AI in predicting the behaviour of membranes as well as their strengths, weaknesses and future directions of AI in membranes for water purification and desalination processes. 

Utilising Artificial Intelligence to Predict Membrane Behaviour in Water Purification and Desalination

In this study, using knowledge management and response surface methodology (RSM), modeling and prediction of viscosity of oil-based hybrid nanofluid (HNF) with Multi-walled carbon nanotube (MWCNT) and Al2O3 nanoparticles in temperature conditions of 25-50 oC and solid volume fraction of SVF=0.0625-1% have been addressed. To find the most accurate model for predicting viscosity, Cubic, Quartic, and Fifth models were examined in terms of various model accuracy evaluation parameters. Based on the obtained results, the Fifth model has the most accuracy in terms of coefficient of determination and coefficient of variance with values of 0.9997 and 1.10, respectively. Based on the correlation relationship presented to predict viscosity due to the dependence of viscosity on shear rate, the behavior of HNF is non-Newtonian. The graphs of temperature in terms of SVF for viscosity in all three models show that viscosity decreases with increasing temperature and viscosity increases with increasing SVF. For hot lubrication conditions, the best condition (255.28 mPa.sec) is at 35 oC and SVF is 0.161%. Also, knowledge management is considered as a safe and optimal method to increase efficiency, productivity and effectiveness in the field of nanofluids (NFs), which brings high added value to this sector. 

Applying Knowledge Management in Optimal Modeling of Viscosity of Nanofluids by response surface methodology for Use in Automobiles Engine

These days, heat transfer plays a significant role in the fields of engineering and energy, particularly in the biological sciences. Ordinary fluid is inadequate to transfer heat in an efficient manner, therefore, several models were considered for the betterment of heat transfer. One of the most prominent models is a single-phase nanofluid model. The present study is devoted to solving the problem of micropolar fluid with a single-phase model in a channel numerically. The governing partial differential equations (PDEs) are converted into nonlinear ordinary differential equations (ODEs) by introducing similarity transformation and then solved numerically by the finite difference method. Response surface methodology (RSM) together with sensitivity analysis are implemented for the optimization analysis. The study reveals that sensitivity of the skin friction coefficient (Cfx) to the Reynolds number (R) and magnetic parameter (M) is positive (directly proportional) and negative (inversely proportional) for the micropolar parameter.

/pdf/optimization-of-mhd-flow-of-radiative-micropolar-nanofluid-20b27z4u.pdf

Optimization of MHD Flow of Radiative Micropolar Nanofluid in a Channel by RSM: Sensitivity Analysis

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Optimization of MHD Flow of Radiative Micropolar Nanofluid in a Channel by RSM: Sensitivity Analysis