Doklady Physics最新文献

The recent rise of machine learning in computer engineering, serving purposes such as prediction, image processing, classification, and clustering, has extended into various scientific fields. Recently, neural networks have been developed to be integrated with Reynolds-averaged Navier–Stokes (RANS) modeling to improve the prediction of turbulent flow. In this study, a multilayer perceptron network (MLP) and a cascade MLP network will be trained to predict the turbulent eddy viscosity (({nu _t})) and the Reynolds stress (({R_{ij}})). To predict the eddy viscosity, input variables are selected from high-fidelity data at . First, they are grouped based on feature importance to determine input categories for the MLP network, and then the performance of each group is evaluated. All studied groups demonstrated acceptable performance; among them, the model closest to the direct numerical simulation (DNS) solution was identified. In the spectrum of input variables, the wall distance emerged as the paramount parameter, boasting the highest feature importance value among the variables considered. To assess the accuracy of the neural network, the prediction of turbulent viscosity is carried out at . The output of the MLP network and the strain-rate tensor are considered as the input of the cascade MLP network to investigate the effect of these inputs on Reynolds stress prediction. The cascade MLP and MLP networks demonstrated acceptable accuracy in predicting Reynolds stress and eddy viscosity, with their results indicating a strong correlation between the outputs of the two neural networks.

In the contemporary study, the dynamics of the nanofluid thin film is investigated by considering the viscous dissipation and chemical reaction effects. Additionally, the surface is assumed to have a nonlinear slip rather than the conventional no-slip conditions. This helps in better flow and heat transfer characteristics. This nonlinear velocity slip at the boundary is modelled using the idea proposed by Thompson and Troian. Also, the presence of viscous dissipation in the energy equation, depicts the loss of energy due to the internal friction. Hence, the viscous dissipation turns out to be a critical factor in determining the thermal properties of the nanofluid thin film. The chemical reactions take place within the system because of the presence of nanoparticles, that in turn will have a significant impact on the mass transfer characteristics of the thin film nanofluid. The incorporation of the similarity transformation helps in converting the partial differential equations (PDEs) that govern the fluid flow into a system of nonlinear ordinary differential equations (ODEs). This resulting system is then solved using the BVP package in python whose accuracy is assessed through residual analysis. By this error analysis, convergence of residues was confirmed. Thus validating the method and the results obtained. The outcomes of the study are interpreted through the graphs which highlighted the intensification of heat transfer for the increase in the Eckert number while the magnetic field confirmed its flow controlling feature. Also, the streamlines and contours were plotted to understand and visulaise the flow, all these contours showed the significance of the presence of nonlinear velocity slip at the boundary.

Over the past few decades, optical sensors based on surface plasmon resonance (SPR) in attenuated total reflection (ATR) configurations have emerged as powerful tools for label-free biochemical sensing. SPR high sensitivity to refractive index variations enables precise monitoring of molecular interactions. Here, we propose a novel SPR-based sensor that employs Weyl semimetal (WSM) to excite low-loss surface plasmon polaritons (SPPs) in the mid-infrared regime, optimized for skin cancer detection. The designed structure supports a sharp Fano resonance (FR) localized at the WSM and sensing medium interface. This FR manifests as an asymmetric lineshape with an ultra-narrow width, significantly enhancing sensitivity to refractive index changes in the analyte. Numerical simulations demonstrate that the SPP mode resonance angle exhibits exceptional sensitivity (S = 113.50°/RIU) and an ultra-high figure of merit (FOM = 22 700 RIU^–1) to the dielectric environment. This performance makes the sensor ideal for detecting subtle biomolecular shifts associated with early-stage skin cancer biomarkers. We also systematically investigate the influence of the WSM layer Fermi energy and node separation length on sensor performance. These parameters critically govern SPP dispersion and loss characteristics, enabling spectral tunability across the mid-IR range.

The accrual of interface trap charges (ITC) at the margin of InAs/SiO₂ and HfO₂ interfaces considerably influences the performance and reliability of tunnel field effect transistor (TFET) devices, emphasising the need to address this issue. This work provides a thorough analysis of how interface trap charges (ITCs) affect the suggested device, the nonuniform InAs-channel dual metal hetero-oxide double-gate TFET (InAs-NUC-TFET), in terms of critical performance metrics like DC characteristics and analog/RF electrical performance. Both positive and negative ITCs were analysed to determine how they affected the device. The results demonstrate that variations in ITC concentrations have little impact on the device’s performance metrics, emphasising that InAs-NUC-TFETs are more resilient to ITCs compared to conventional semiconductor devices, which frequently suffer significant performance due to ITCs. For trap charges with minimum values of –1 × 10¹² and maximum values of 1 × 10¹², the deviations in ON-current, OFF-current, and their ratio are 26, 18, and 10%, respectively. In addition to demonstrating resistance to trap charges, the device has a high current ratio (4.32 × 10¹³) and an ON-current in milli-amperes. It is positioned as a possible contender for future electronics, especially for low power and high frequency applications, due to its remarkable performance under various situations and resistance to ITCs.

The prospect of La_0.8Ca_0.2Mn_1–xCo_xO₃ (0 ≤ x ≤ 0.3) perovskite oxides as magnetocaloric effect (MCE) based magnetic refrigerant has been explored. A phenomenological model has been employed to investigate various magnetocaloric properties, including magnetic entropy change (∆S), heat capacity change (∆C), full width at half band width (δT_FWHM), adiabatic temperature change (ΔT_ad) and relative cooling power (RCP) through the study of temperature dependence of magnetization. Using this model, the magnetocaloric property values are predicted by calculating the magnetization data under varying external magnetic fields. Since magnetization reduces abruptly near T_C a significant change in magnetic entropy change, heat capacity change and adiabatic temperature change occur. We observe as the doping concentration increases an enhancement in the predicted values of magnetocaloric parameters occur with the increasing magnetic field possibly due to the switching of the magnetic phase transition (MPT) in the material from first to second order. Thus, the results highlight the compounds as strong contenders for cooling applications across a broad range around room temperature. Moreover, the analysis affirms the extent of validity of the phenomenological model.

The generally accepted hypothesis is that the Popigai diamond deposit is of impact origin. A theoretical study using methods of gas-dynamic numerical modeling of processes corresponding to a high-speed impact of a massive chondrite asteroid with the Earth’s surface in the area of the Popigai River basin has been conducted. The collision of an asteroid made of ordinary chondrite with velocities of 20–25 km/s with a multilayer barrier to simulate the structure of the Earth’s soil in the considered area has been modeled. A layer of natural carbon is located in the near-surface zone. Calculations using a parallel three-dimensional implementation of the finite-size particle-in-cell method using wide-range multiphase models of the equation of state (EOS) of chondrite, quartz, and carbon have been carried out. In the study, the thermodynamic parameters of the process of impact compression of asteroid materials and soil realized at the stage of compression and cratering have been obtained.

Cartilage tissue engineering holds significant potential for regenerative medicine. In this study, we developed porous scaffolds composed of chitosan and graphene oxide (GO/CS) to enhance the properties and performance of scaffolds used for cartilage tissue regeneration. Findings of this study demonstrated that the porosity and swelling properties of GO/CS scaffolds can be altered by altering the ratio of GO to CS. More particularly, the swelling ratio of GO/CS scaffolds falls within the range of 23.20 to 27.38. As the concentration of GO increases from 0 to 0.1, 0.2, and 0.3%, the mechanical and physical characteristics have been significantly enhanced. The nanocomposite scaffolds facilitated the rapid growth of human articular chondrocytes (HAC) and contributed to an increase in the percentage of GO. This was particularly evident following 14 days of cultivation. For a 21-day in vitro culture period, a study on the morphology of HAC demonstrated a more spherical cell on the scaffolds.