Mechanics of Time-Dependent Materials最新文献

The primary objective of this study was to evaluate the use of available off-the-shelf finite element software like ABAQUS Standard™, ANSYS Workbench™, and Sandia National Laboratory Sierra Mechanics™ to model linear viscoelastic materials and compare their results to an analytically exact model. The study makes use of a standard beam under constant extension loading originally proposed by R.H. MacNeal and R.L. Harder in 1984 for testing the accuracy of finite element analysis tools. The results indicate that these finite element codes approximate the viscoelastic effects of the analytical formulation. When mesh and time step convergence studies were performed, the displacement results obtained diverged by (pm 6%) from the analytical solution for a 3000-hour analysis as stipulated by ASTM D2990 and by (pm 16%) for a 12-year analysis. The computed results show a continuous divergence between the computational and analytical solutions in time. A parametric study on the effect of Poisson’s ratio on the tip displacement was also considered. The parametric studies suggest that the finite element algorithms apply a constant Poisson’s ratio for viscoelastic case studies.

Integrating mechanical nanosensors with biological structures allows evaluating the mass, displacements, and forces in subcellular and cellular activities. On the other hand, studying bio-nanosensors is crucial for identifying biological, chemical, and physical structures. Therefore, the vibration analysis and critical damping behavior of Lipid/Graphene sandwich viscoelastic nanoplates must be studied. The current work investigates a bio-nanostructure referred to as sandwich viscoelastic nanoplates. The differential equations of bio-nanostructure embedded on the viscoelastic substrate have been derived based on the principle of Hamilton and solved numerically using a general differential quadrature method (GDQM) to predict the vibration behaviors of the bio-nanostructure. The differential quadrature method is utilized to extract the natural frequency and critical damping of the Lipid/ Graphene sandwich nanoplates with structural damping for the first time, and also examines the impact of the viscoelastic medium and the size effect (nonlocal parameter) on the vibration behavior of the bio-nanostructure. The findings of this study indicate that the frequencies of nanostructures decrease noticeably as the structural damping and the damping coefficients of the viscoelastic foundation increase. Moreover, by increasing the damping coefficient values of the viscoelastic foundation, the critical damping of Lipid/Graphene sandwich nanoplates (bifurcation curve) occurs at lower values of the nonlocal parameter. On the contrary, with the increase of structural damping, the critical damping of this bio-nanostructure occurs at higher nonlocal parameter values. These findings can be advantageous for the design and production of nanoscale equipment, including bio-nanosensors, resonators, and nano-devices, which require high precision and sensitivity.

The liquid film is mainly used in coating, cooling, lubrication, thermal, and mechanical engineering. The viscosity of a cross fluid is governed by its shear rate, which lies in the class of non-Newtonian fluids. Furthermore, this model correctly distinguishes the flow region into both high and low shear rates regions. The current study concentrates on the electromagnetohydrodynamic (EMHD) liquid-film flow of the cross nanofluid over an inclined disk for heat- and mass-transfer applications. The cross-nanofluid flow of the liquid film is considered time dependent and variable in thickness. The solution of the problem is obtained through the homotopy analysis method (HAM). The HAM results are then handled through the Least Mean-Square (LMS)-based Artificial Neural Network (ANN). The proposed (LMS-ANN) models are tested for dependability, capability, validity, and reliability through regression, error analysis, and histograms. The ANN outputs are drawn in figures and tables and are discussed. Epochs 218, 96, 297, 180, 213, 184, 173, and 155 marked the best performance for the fluid model. The various parameters reveal that cross nanofluids enhance heat-transfer efficiency by promoting convective heat transfer.

The paper is concerned with the impact of memory-dependent (MD) derivatives in the hygrothermal (HTE) three-phase-lag (3PL) hollow cylinder under thermal and moisture loading. We derive equations for temperature, moisture, displacement, and stress components. We solve these HTE field quantity equations using the variable separation method and Laplace transform. We then perform numerical calculations via Laplace transform inversion. We use Mathematica software to understand the hygrothermal behavior of fiber-reinforced 3PHL hollow cylinders. The model validity is assessed by comparing it to existing results. The analysis focuses on the effect of MD derivatives in the HTE 3PL model by examining their impact on heat and moisture field quantities in the presence of time delay parameters and singular kernel functions. This study further highlights the significant influence of employing various kernel functions on the behavior of the HTE hollow cylinder. The author believes that this research will help develop more robust and efficient methods for incorporating memory effects in mathematical models.

As a basic constituent of micro- and nanoelectromechanical systems, the analysis of the thermodynamic properties of rotating nanobeams is crucial for the safe operation of the systems. However, the classical continuum mechanics theory and Fourier’s law of heat conduction can no longer accurately predict the size-dependent effect in the elastic deformation of micro- and nanostructures and the thermal hysteresis effect in the heat transfer process of micro- and nanostructures, respectively. Therefore, in this paper, a new mathematical model based on the concept of memory derivatives is proposed to analyze the properties of viscoelastic rotating nanobeams surrounded by a magnetic field as well as excited by a heat source. The size-dependent effects of this rotating nanobeam are characterized using the nonlocal modified coupled stress theory, and the controlling equations are constructed in the context of generalized thermoelasticity taking into account the memory-dependent effects using the concepts of the Euler–Bernoulli beam theory, Maxwell electromagnetic equations, and fractional-order Kelvin–Voigt viscoelasticity model. The rotating nanobeam deflection, thermodynamic temperature, displacement, and bending moment are numerically solved using the Laplace transform and its inverse transform technique. The effects of time-delay factors, kernel functions, nonlocal parameters, and internal characteristic parameters of the material on the dimensionless field quantities of the rotating nanobeam are also investigated and characterized graphically.

A literature review shows that nanofluids are more effective for heat transfer than traditional fluids. However, our understanding of current methods to enhance heat transfer in nanofluids still needs to be completed, necessitating further research. This study explores the combined effects of magnetized surface and Maxwell–Sutterby–Casson nanofluid inside a stretchy sheet, taking into account the effects of Joule heating, variable thermal conductivity, and thermal radiation. The research examines activation energy, heat sources/sinks, bioconvection, and gyrotactic microbes, considering Brownian motion and thermophoresis effects. Using similarity functions, the boundary layer ODEs are created from PDEs. The shooting strategy is used to solve these altered equations numerically. A supervised Levenberg–Marquardt backpropagation algorithm and BVP5C built-in function of MATLAB are utilized to generate datasets for developing continuous neural network mappings. Analytical approaches like regression-based statistical and error histogram graphs are utilized to assess the precision of the existing method. The study provides graphical and numerical evaluations of the distributions of motile microorganisms, temperature, velocity, and concentration for various parameters when Casson parameters (beta )=(infty ) and (beta )=1.1. The findings indicate that the velocity profile rises with a higher magnetic parameter but falls with an increase in the magnetic parameter. The heat flux distribution improves when the thermophoresis and magnetic parameters are increased. On the other hand, when the Prandtl number and Brownian motion parameter increase, the energy profile falls. The spread of motile microorganisms decreases as the Peclet and bioconvection Lewis numbers rise. On the other hand, when the Prandtl number and Brownian motion parameter increase, the energy profile falls. The spread of motile microorganisms decreases as the Peclet and bioconvection Lewis numbers rise. Table: 1 compares Artificial Neural Networks (ANN) results and numerical results driven in the present study.

In this paper, we examine the flow of a convective Maxwell fluid through a channel with a sensor surface placed between two parallel plates, for applications in cooling electronic devices, microfluidics, environmental monitoring, and the oil and gas industries. The flow is squeezed from one side, and the channel surface is instrumented with a microcantilever sensor. The heat- and mass-transfer equations are formulated using the Cattaneo–Christov model, to incorporate heat absorption and a chemical reaction. Boundary-layer approximations are considered, and similarity transforms convert the partial differential equations into linear ordinary differential equations, which are solved numerically. The effects of various parameters on velocity, concentration, and temperature gradients are analyzed. Results show that the velocity of the Maxwell fluid decreases with higher thermal and solutal Grashof numbers and the Maxwell fluid parameter. The thermal-relaxation parameter and heat-absorption coefficient contribute to a reduced temperature distribution. The concentration decreases with variations in the solutal relaxation coefficient and reaction parameter. Physical quantities, such as skin friction, decline due to the Maxwell fluid parameter. A comparison with previously published results is also included.

This study investigates the effect of Friction Stir Processing (FSP) on the creep response in air at 550 and 600 °C of commercially pure titanium (Ti-Grade 2). FSP resulted in an inhomogeneous microstructure, which generally exhibited lower minimum creep rates compared to the base unmodified metal. Oxygen diffusion in the superficial layer of the creep samples caused a local marked increment of the hardness, a phenomenon already observed when testing the base metal. A constitutive model, which was initially developed to describe the effect of initial hardness and oxygen diffusion during the test in the unmodified grade 2 Ti, was significantly improved and implemented. The model provided an excellent description of the minimum creep rate dependence on the applied stress and temperature for unmodified and friction-stir processed materials without needing any data fitting of the creep results. In addition, the proposed model also suggests the reason for the differences in the shape of the creep curves observed when comparing short and long experiments.

Numerical simulation in conjunction with artificial neural networks (ANN) has shown to be an advanced method for simulating and modeling intricate fluid dynamics problems. However, to guarantee that the model can precisely forecast transport phenomena, ANN modeling is necessary yet difficult. This study examines the symmetry of blood-based MHD squeezing nanofluid ((Au sim Blood)), bi-hybrid ((Au + Fe_{3}O_{4} sim Blood)), and tri-hybrid nanofluid ((Au + Fe_{3}O_{4} + MWCNTs sim Blood)) flow between parallel plates using a comprehensive numerical simulation and artificial neural network (ANN) analysis. The heat transfer mechanism is investigated employing the heat source/sink, thermal radiation, suction/injection, the magnetic field, and porous media. The governing partial differential equations are solved numerically with an improved finite difference approach the Keller-box technique after being modified by similarity transformations. In order to effectively predict fluid flow characteristics, this study proposes a novel approach that combines a multilayer ANN with the Levenberg–Marquardt algorithm (LMA). A strong magnetic field reduces fluid flow at the contact due to Lorentz effects, resulting in lower radial velocity as (M) increases. In comparison to injection, the rising values of the suction parameter raise the temperature by giving the velocity of the fluid layer and eliminating isolated boundary layers. Increased permeability at the bottom plate results in higher flow resistance and reduced velocity profiles toward the upper plate. The proposed ANN approach provides fast convergence and reduced processing costs without the need for linearization. This research offers valuable insights into the performance gains made possible by tri-hybrid nanofluids, such as increased thermal conductivity, paving the way for advancements in biological applications like cancer treatment, blood pumping, and targeted drug delivery.