International Journal of Numerical Methods for Heat & Fluid Flow最新文献

Purpose

This study aims to examine the plane wave reflection problem in micropolar orthotropic magneto-thermoelastic half space, considering the influence of impedance as a boundary in a nonlocal elasticity.

Design/methodology/approach

This study presents the novel formulation of governing partial differential equations for micropolar orthotropic medium with impact of nonlocal thermo-elasticity under magnetic field.

Findings

This study provides the numerical results validation for a particular numerical data and expression for the amplitude ratios of reflected waves and identifies the existence of four different waves, namely, quasi longitudinal displacement qCLD-wave, quasi thermal wave qCT-wave, quasi transverse displacement qCTD-wave and quasi-transverse micro-rotational qCTM-wave. The study derives the velocity equation giving the speed and phase velocity of these waves. The study also shows that the small-scale size effect gives significant impact on phase velocity.

Research limitations/implications

The graphical analysis examines the variation of speeds and coefficients of attenuation of these waves due to frequency, magnetic field and nonlocal parameters. Also, significant conclusions on the variation of reflection coefficient against nonlocal parameter, frequency, impedance parameter and angle of incidence are provided graphically.

Practical implications

The creation of more effective micropolar orthotropic anisotropic materials which are very useful in the daily life and their applications in earth science are greatly impacted by the findings of this study.

Originality/value

The authors of the submitted document initiated and produced it collectively, with equal contributions from all members.

Purpose

This study aims to investigate numerically the impact of the three-dimensional convective nanoliquid flow on a rotating frame embedded in the non-Darcy porous medium in the presence of activation energy. The cross-diffusion effects, i.e. Soret and Dufour effects, and heat generation are included in the study. The convective heating condition is applied on the bounding surface.

Design/methodology/approach

The control model consisted of a system of partial differential equations (PDE) with boundary constraints. Using suitable similarity transformation, the PDE transformed into an ordinary differential equation and solved numerically by the Runge–Kutta–Fehlberg method. The obtained results of velocity, temperature and solute concentration characteristics plotted to show the impact of the pertinent parameters. The heat and mass transfer rate and skin friction are also calculated.

Findings

It is found that both Biot numbers enhance the heat and mass distribution inside the boundary layer region. The temperature increases by increasing the Dufour number, while concentration decreases by increasing the Dufour number. The heat transfer is increased up to 8.1% in the presence of activation energy parameter (E). But, mass transfer rate declines up to 16.6% in the presence of E.

Practical implications

The applications of combined Dufour and Soret effects are in separation of isotopes in mixture of gases, oil reservoirs and binary alloys solidification. The nanofluid with porous medium can be used in chemical engineering, heat exchangers and nuclear reactor.

Social implications

This study is mainly useful for thermal sciences and chemical engineering.

Originality/value

The uniqueness in this research is the study of the impact of activation energy and cross-diffusion on rotating nanoliquid flow with heat generation and convective heating condition. The obtained results are unique and valuable, and it can be used in various fields of science and technology.

Purpose

This study aims to investigate magnetohydrodynamic (MHD) conjugate pure mixed convection considering interior heat production and resistive heating inside a square closed/open cavity featuring a rotating cylinder for aiding (clockwise) and opposing (counterclockwise) flow configurations. Moreover, the impacts of altering cylinder size and conductivity on the system’s overall performance to determine optimum conditions are examined in this investigation.

Design/methodology/approach

The closed chamber is differentially heated by keeping high and low temperatures at the vertical boundaries. In contrast, the open cavity has a heated left wall and an open right boundary. The Galerkin finite element method is used to solve the Navier–Stokes and the thermal energy equations, which construct the present study’s mathematical framework. Numerical simulations are conducted for the specified ranges of several controlling parameters: Reynolds (31.62 ≤ Re ≤ 1000), Grashof (10³ ≤ Gr ≤ 10⁶) and Hartmann numbers (0 ≤ Ha ≤ 31.62), and volumetric heat generation coefficient (Δ = 0, 3).

Findings

When Gr, Re and Ha simultaneously increase, the average Nusselt number along the warmed boundary rises accordingly. Conversely, interior heat production lowers heat transmission within the computational domain, which is also monitored regarding mean fluid temperature, overall entropy production and thermal performance criterion. Finally, the open cavity confirms better thermal performance than the closed cavity.

Originality/value

Comprehending the impacts of the magnetic field, Joule heating, internal heat generation and enclosed or open boundary on pure MHD combined free-forced convective flow offers valuable understandings of temperature fluctuations, velocity propagations, heat transport and irretrievable energy loss in numerous engineering applications.

Purpose

Cilia serves numerous biological functions in the human body. Malfunctioning of nonmotile or motile cilia will have different kinds of consequences for human health. More specifically, the directed and rhythmic beat of motile cilia facilitates the unidirectional flow of fluids that are crucial in both homeostasis and the development of ciliated tissues. In cilia-dependent hydrodynamic flows, tapering geometries look a lot like the structure of biological pathways and vessels, like airways and lymphatic vessels. In this paper, the Carreau fluid model through the cilia-assisted tapered channel (asymmetric) under the influence of induced magnetic field and convective heat transfer is investigated.

Design/methodology/approach

Lubrication theory is a key player in the mathematical formulation of momentum, magnetic field and energy equations. The formulated nonlinear and coupled differential equations are solved with the aid of the homotopy perturbation method (HPM). The graphical results are illustrated with the help of the computational software “Mathematica.”

Findings

The impact of diverse emerging physical parameters on velocity, induced magnetic field, pressure rise, current density and temperature profiles is presented graphically. It is observed that the cilia length parameter supported the velocity and current density profiles, while the Hartman number and Weissenberg number were opposed. A promising effect of emerging parameters on streamlines is also perceived.

Originality/value

The study provides novel aspects of cilia-driven induced magnetohydrodynamics flow of Carreau fluid under the influence of induced magnetic field and convective heat transfer through the asymmetric tapered channel.

Purpose

To improve the thermal performance of base fluid, nanoparticles of three types are dispersed in the base fluid. A novel theory of non-Fourier heat transfer is used for design and development of models. The thermal performance of sample fluids is compared to determine which types of combination of nanoparticles are the best for an optimized enhancement in thermal performance of fluids. This article aims to: (i) investigate the impact of nanoparticles on thermal performance; and (ii) implement the Galerkin finite element method (GFEM) to thermal problems.

Design/methodology/approach

The mathematical models are developed using novel non-Fourier heat flux theory, conservation laws of computational fluid dynamics (CFD) and no-slip thermal boundary conditions. The models are approximated using thermal boundary layer approximations, and transformed models are solved numerically using GFEM. A grid-sensitivity test is performed. The accuracy, correction and stability of solutions is ensured. The numerical method adopted for the calculations is validated with published data. Quantities of engineering interest, i.e. wall shear stress, wall mass flow rate and wall heat flux, are calculated and examined versus emerging rheological parameters and thermal relaxation time.

Findings

The thermal relaxation time measures the ability of a fluid to restore its original thermal state, called thermal equilibrium and therefore, simulations have shown that the thermal relaxation time associated with a mono nanofluid has the most substantial effect on the temperature of fluid, whereas a ternary nanofluid has the smallest thermal relaxation time. A ternary nanofluid has a wider thermal boundary thickness in comparison with base and di- and mono nanofluids. The wall heat flux (in the case of the ternary nanofluids) has the most significant value compared with the wall shear stresses for the mono and hybrid nanofluids. The wall heat and mass fluxes have the highest values for the case of non-Fourier heat and mass diffusion compared to the case of Fourier heat and mass transfer.

Originality/value

An extensive literature review reveals that no study has considered thermal and concentration memory effects on transport mechanisms in fluids of cross-rheological liquid using novel theory of heat and mass [presented by Cattaneo (Cattaneo, 1958) and Christov (Christov, 2009)] so far. Moreover, the finite element method for coupled and nonlinear CFD problems has not been implemented so far. To the best of the authors’ knowledge for the first time, the dynamics of wall heat flow rate and mass flow rate under simultaneous effects of thermal and solute relaxation times, Ohmic dissipation and first-order chemical reactions are studied.

Purpose

The performance of solid fins inside a differentially heated cubical cavity is numerically studied in this paper. The main purpose of the study is to make an optimization to reach the maximum heat transfer in the enclosure having the solid fins with studied parameters.

Design/methodology/approach

The considered domain of interest is a differentially heated cube having heat-conducting solid fins placed on the heated wall while an opposite wall is a cooled one. Other walls are adiabatic. Governing equations describing natural convection in the fluid filled cube and heat conduction in solid fins have been written using non-dimensional variables such velocity and vorticity taking into account the Boussinesq approximation for the buoyancy force and ideal solid/fluid interfaces between solid fins and fluid. The formulated equations with appropriate initial and boundary conditions have been solved by the finite difference method of the second of accuracy. The developed in-house computational code has been validated using the mesh sensitivity analysis and numerical data of other authors. Analysis has been performed in a wide range of key parameters such as Rayleigh number (Ra = 10⁴–10⁶), non-dimensional fins length (l = 0.2–0.8), non-dimensional location of fins (d = 0.2–0.6) and number of fins (n = 1–3).

Findings

From numerical methods point of view the used non-primitive variables allows to perform numerical simulation of convective heat transfer in three-dimensional (3D) regions with two advantages, namely, excluding difficulties that can be found using vector potential functions and reducing the computational time compared to primitive variables and SIMPLE-like algorithms. From a physical point of view, it has been shown that using solid fins can intensify the heat transfer performance compared to cavities without any fins. Fins located close to the bottom wall of the cavity have a better heat transfer rate than those placed close to the upper cavity surface. At high Rayleigh numbers, increasing the fins length beyond 0.6 leads to a reduction of the average Nusselt number, and one solid fin can be used to intensify the heat transfer.

Originality/value

The present numerical study is based on hybrid approach for numerical analysis of convective heat transfer using velocity and vorticity that has some mentioned advantages. Obtained results allow intensifying the heat transfer using solid fins in 3D chambers with appropriate location and length.

Purpose

This study aims to explore the evolving field of physics-informed neural networks (PINNs) through an analysis of 996 records retrieved from the Web of Science (WoS) database from 2019 to 2022.

Design/methodology/approach

WoS database was analyzed for PINNs using an inhouse python code. The author’s collaborations, most contributing institutes, countries and journals were identified. The trends and application categories were also analyzed.

Findings

The papers were classified into seven key domains: Fluid Dynamics and computational fluid dynamics (CFD); Mechanics and Material Science; Electromagnetism and Wave Propagation; Biomedical Engineering and Biophysics; Quantum Mechanics and Physics; Renewable Energy and Power Systems; and Astrophysics and Cosmology. Fluid Dynamics and CFD emerged as the primary focus, accounting for 69.3% of total publications and witnessing exponential growth from 22 papers in 2019 to 366 in 2022. Mechanics and Material Science followed, with an impressive growth trajectory from 3 to 65 papers within the same period. The study also underscored the rising interest in PINNs across diverse fields such as Biomedical Engineering and Biophysics, and Renewable Energy and Power Systems. Furthermore, the focus of the most active countries within each application category was examined, revealing, for instance, the USA’s significant contribution to Fluid Dynamics and CFD with 319 papers and to Mechanics and Material Science with 66 papers.

Originality/value

This analysis illuminates the rapidly expanding role of PINNs in tackling complex scientific problems and highlights its potential for future research across diverse domains.

Purpose

Enzymes play a pivotal role in orchestrating essential biochemical processes and influencing various cellular activities in tissue. This paper aims to provide the process of enzyme diffusion within the tissue matrix and enhance the nano system performance by means of the effectiveness of enzymatic functions. The diffusion phenomena are also documented, providing chemical insights into the complex processes governing enzyme movement.

Design/methodology/approach

A computational analysis is used to develop and simulate an optimal control model using numerical algorithms, systematically regulating enzyme concentrations within the tissue scaffold.

Findings

The accompanying videographic footages offer detailed insights into the dynamic complexity of the system, enriching the reader’s understanding. This comprehensive exploration not only contributes valuable knowledge to the field but also advances computational analysis in tissue engineering and biomimetic systems. The work is linked to biomolecular structures and dynamics, offering a detailed understanding of how these elements influence enzymatic functions, ultimately bridging the gap between theoretical insights and practical implications.

Originality/value

A computational predictive model for nanozyme that describes the reaction diffusion dynamics process with enzyme catalysts is yet not available in existing literature.

Purpose

This paper aims to present a comprehensive analysis of conjugate heat transfer phenomena occurring within the developing region of square ducts under both isothermal and isoflux boundary conditions. The study involves a rigorous numerical investigation, using advanced computational methods to simulate the complex heat exchange interactions between solid structures and surrounding fluid flows. The results of this analysis provide valuable insights into the heat transfer characteristics of such systems and contribute to a deeper understanding of fluid–thermal interactions in duct flows.

Design/methodology/approach

The manuscript outlines a detailed numerical methodology, combining computational fluid dynamics and finite element analysis, to accurately model the conjugate heat transfer process. This approach ensures both the thermal behaviour of the solid walls and the fluid flow dynamics are well captured.

Findings

The results presented in the manuscript reveal significant variations in heat transfer characteristics for isothermal and isoflux boundary conditions. These findings have implications for optimizing heat exchangers and enhancing thermal performance in various engineering applications.

Practical implications

The insights gained from this study have the potential to influence the design and optimization of heat exchange systems, contributing to advancements in energy efficiency and engineering practices.

Originality/value

The research introduces a novel approach to study conjugate heat transfer in square ducts, particularly focusing on the developing region. This unique perspective offers fresh insights into heat transfer mechanisms that were previously not thoroughly explored.

Purpose

This study aims to use deep neural networks (DNNs) to learn the conduction heat transfer physics and estimate temperature distribution images in a physical domain without using any physical model or mathematical governing equation.

Design/methodology/approach

Two novel DNNs capable of learning the conduction heat transfer physics were defined. The first DNN (U-Net autoencoder residual network [UARN]) was designed to extract local and global features simultaneously. In the second DNN, a conditional generative adversarial network (CGAN) was used to enhance the accuracy of UARN, which is referred to as CGUARN. Then, novel loss functions, introduced based on outlier errors, were used to train the DNNs.

Findings

A UARN neural network could learn the physics of heat transfer. Within a few epochs, it reached mean and outlier errors that other DNNs could never reach after many epochs. The composite outlier-mean error as a loss function showed excellent performance in training DNNs for physical images. A UARN could excellently capture local and global features of conduction heat transfer, whereas the composite error could accurately guide DNN to extract high-level information by estimating temperature distribution images.

Originality/value

This study offers a unique approach to estimating physical information, moving from traditional mathematical and physical models to machine learning approaches. Developing novel DNNs and loss functions has shown promising results, opening up new avenues in heat transfer physics and potentially other fields.