Search Results (186)

To facilitate the local recycling of coal mine waste gas and investigate multi-component gas adsorption under high pressure conditions, this study develops a coal nanopore model using molecular dynamics (MD) and grand canonical Monte Carlo (GCMC) methods and simulates the adsorption behavior of coal mine waste gas components (CO₂, H₂O, N₂) under varying pressure levels and gas molar ratios at 353.15 K. We evaluated the adsorption capacity and selectivity for both single-component and multi-component gases, quantifying adsorption interactions through adsorption heat, interaction energy, and energy distribution. The simulation results revealed that the contribution of the three gases to the total adsorption amount followed the order: H₂O > CO₂ > N₂. The selective adsorption coefficient of a gas exhibits an inverse correlation with its molar volume ratio. Isothermal heat adsorption of gases in coal was positive, decreasing sharply with increasing pressure before leveling off. Electrostatic interactions dominated CO₂ and H₂O adsorption, while van der Waals forces governed N₂ adsorption. As the gas mixture complexity increased, the overlap of energy distribution curves pronounced, highlighting competitive adsorption behavior. These findings offer a theoretical foundation for optimizing coal mine waste gas treatment and CO₂ sequestration technologies. Full article

Gas turbines in land-based microgrids and shipboard-isolated power grids frequently face operational challenges, such as the startup and shutdown of high-power equipment and sudden load fluctuations, which significantly impact their performance. To examine the dynamic behavior of gas turbines under transitional operating conditions, a three-dimensional computational fluid dynamic simulation is employed to create a model of the gas turbine rotor, incorporating thermal inertia, which is then analyzed in conjunction with three-dimensional finite element methods. The governing equations of the flow field are discretized, providing results for the flow and temperature fields throughout the entire flow path. A hybrid approach, combining temperature differences and heat flux density, is applied to set the thermal boundary conditions for the walls, with the turbine’s operational state determined based on the direction of heat transfer. Additionally, mesh division techniques and turbulence models are selected based on the geometric dimensions and operating conditions of the compressor and turbine. The simulation results reveal that thermal inertia induces a shift in the dynamic characteristics of the rotor components. Under the same heat transfer conditions, variations in rotational speed have a minimal impact on the shift in the characteristic curve. The working fluid temperature inside the compressor components is lower, with a smaller temperature difference from the wall, resulting in less intense heat transfer compared to the turbine components. Overall, heat transfer accounts for only about 0.1% of the total enthalpy at the inlet. When heat exchange occurs between the working fluid and the walls, around 6–15% of the exchanged heat is converted into changes in technical work, with this percentage increasing as the temperature difference rises. Full article

This paper focuses on the use of advanced optimization design strategies to improve the performance and service life of engine pistons, with emphasis on enhancing their stiffness, strength, and dynamic characteristics. As a core component of the engine, the structural design and optimization of the piston are of great significance to its efficiency and reliability. First, a three-dimensional (3D) model of the piston was constructed and imported into ANSYS Workbench for finite element modeling and high-quality meshing. Based on the empirical formula, the actual working environment temperature and heat transfer coefficient of the piston were accurately determined and used as boundary conditions for thermomechanical coupling analysis to accurately simulate the thermal and deformation state under complex working conditions. Dynamic characteristic analysis was used to obtain the displacement–frequency curve, providing key data support for predicting resonance behavior, evaluating structural strength, and optimizing the design. In the optimization stage, five geometric dimensions are selected as design variables. The deformation, mass, temperature, and the first to third natural frequencies are considered as optimization goals. The response surface model is constructed by means of the design of the experiments method, and the fitted model is evaluated in detail. The results show that the models are all significant. The adequacy of the model fitting is verified by the “Residuals vs. Run” plot, and potential data problems are identified. The “Predicted vs. Actual” plot is used to evaluate the fitting accuracy and prediction ability of the model for the experimental data, avoiding over-fitting or under-fitting problems, and guiding the optimization direction. Subsequently, the sensitivity analysis was carried out to reveal the variables that have a significant impact on the objective function, and in-depth analysis was conducted in combination with the response surface. The multi-objective genetic algorithm (MOGA), screening, and response surface methodology (RSM) were, respectively, used to comprehensively optimize the objective function. Through experiments and analysis, the optimal solution of the MOGA algorithm was selected for implementation. After optimization, the piston mass and deformation remained relatively stable, and the working temperature dropped from 312.75 °C to 308.07 °C, which is conducive to extending the component life and improving the thermal efficiency. The first to third natural frequencies increased from 1651.60 Hz to 1671.80 Hz, 1656.70 Hz to 1665.70 Hz, and 1752.90 Hz to 1776.50 Hz, respectively, significantly enhancing the dynamic stability and vibration resistance. This study integrates sensitivity analysis, response surface models, and genetic algorithms to solve multi-objective optimization problems, successfully improving piston performance. Full article

Experimental and theoretical design on thermal and structural properties of Cu-Zn-Al alloys are established. As such, from an experimental point of view, differential thermal analysis has been performed with the help of a DSC Netzsch STA 449 F1 Jupiter calorimeter with high levels of sensitivity, and the structural analysis has been accomplished through X-ray diffraction and SEM analysis. An unusual specific property for a metallic material has been discovered, which is known as “rubber-type behavior”, a characteristic determined by micro-structural changes. From the theoretical point of view, the thermal transfer in Cu-Zn-Al is presented by assimilating this alloy, both structurally and functionally, with a multifractal, situation in which the order–disorder transitions assimilated with thermal “dynamics” of Cu-Zn-Al, are mimed through transitions from non-multifractal to multifractal curves. In such a context, the thermal expansion velocity contains both the propagation speed of the phase transformation (be it a direct one: austenitic–martensitic transformation, or an indirect one: martensitic–austenitic transformation) and the thermal diffusion speed. Then, through self-modulations of the thermal field, the Cu-Zn-Al alloy will self-structure in channel-type or cellular-type thermal patterns, which can be linked to obtained experimental data. Consequently, since the thermal conductivity becomes a function of the observation scale, and heat transfer is modified to reflect the multifractal, non-differentiable paths in the material, it leads to anomalous diffusion and complex thermal behaviors. Full article

Computational fluid dynamics (CFD) can be used to analyze the airflow patterns within a naturally ventilated cattle barn in detail, taking into account the influence of the animals. Typically, animals are modelled either as solid obstacles or as a porous block representing the entire animal-occupied zone (AOZ). In the latter approach, extensive pre-simulations are required to determine the appropriate resistance parameters. This study developed a cow model that captures the general influence of the animals, is easy to implement without the need for extensive pre-simulations, and can be applied to various barn types and herd sizes. It is based on a porous block for a single animal. The anisotropic parameters for pressure drop and heat flux were derived from a simplified 3D cow model under different wind speeds, flow directions, cow positions, and ambient temperatures. These parameters were then incorporated into the newly developed porous cow model using regression curves. A comparison between the solid and porous modelling approaches in a randomly selected AOZ showed good agreement in terms of pressure drop and downstream temperature distribution. Full article

Natural rubber is a widely used biological polymer material because of its excellent comprehensive performance. Nevertheless, the performance of domestic natural rubber cannot meet the requirements for high-end products such as aviation tires, which has become a constraint on the innovation and upgrading of high-end manufacturing enterprises and the enhancement of global competitiveness in China. To solve the bottleneck problem of natural rubber processing technology, this study systematically analyzed the effects of different varieties of fresh latex ratios on the processing and dynamic properties of bio-coagulated natural rubber. By mixing PR107 and Reyan72059 fresh latex with Reyan73397 fresh latex according to proportion, the fresh latex was coagulated by enzyme-assisted microbials, and the effects of the fresh latex ratio on physical and chemical indexes, molecular weight distribution, vulcanization characteristics, processing properties, cross-link density and physical and mechanical properties of the natural rubber were analyzed. The results showed that the aging resistance of natural rubber coagulated with enzyme-assisted microbial decreased, and the aging resistance of natural rubber increased with the increase in the mixing ratio of PR107 and Reyan72059 fresh latex. The proportion of high molecular weight of the natural rubber coagulated with the enzyme-assisted microbial increased, and the fresh latex mixing had little effect on the molecular weight distribution curve. Under the carbon black formulation, the CRI of the enzyme-assisted microbial coagulated natural rubber compound was relatively larger. Under the same strain conditions, the H-3 compound (PR107:Reyan72059:Reyan73397 = 1:1:3) had the best viscoelasticity and the least internal resistance of rubber molecules. In addition, the cross-link density, tensile strength, elongation at break, and tear strength of H-3 vulcanized rubber were the largest, improved by 23.08%, 5.32%, 12.45% and 3.70% compared with the same H-2 vulcanized rubber. In addition, the heat generation performance was reduced by 11.86%, and the wear resistance improved. Full article

We present a compact and cost-effective real-time PCR system designed for point-of-care testing (POCT), utilizing a PCB-based disposable chip and an open-platform CMOS camera. The system integrates precise thermal cycling with software-synchronized fluorescence detection and provides real-time analysis through a dedicated user interface. To minimize cost and complexity, a polycarbonate reaction chamber was integrated with a PCB-based heater and thermistor. A slanted LED illumination setup and an open-platform USB camera were employed for fluorescence imaging. Signal alignment was enhanced using device-specific region-of-interest (ROI) tracking based on copper pad corner detection. Thermal cycling performance achieved a heating rate of 8.0 °C/s and a cooling rate of −9.3 °C/s, with steady-state accuracy within ±0.1 °C. Fluorescence images exhibited high dynamic range without saturation, and the 3σ-based ROI correction method improved signal reliability. System performance was validated using Chlamydia trachomatis DNA standard (10³ copies), yielding consistent amplification curves with a Ct standard deviation below 0.3 cycles. These results demonstrate that the proposed system enables rapid, accurate, and reproducible nucleic acid detection, making it a strong candidate for field-deployable molecular diagnostics. Full article

Sadi Carnot’s seminal work laid the foundation for exploring the effects of thermodynamics across diverse domains of physics, stretching from quantum to cosmological scales. Here, we build on the principles of the original Carnot heat engine, and apply it in the context of a particular toy model black brane. This theoretical construct of an effectively two-dimensional, stable, and stationary gravitational object in four-dimensional spacetime derives from a hypothetical flat planet collapsed under the influence of gravity. By constructing a thermodynamic cycle involving three such black branes, we explore the possibility of energy extraction or mining, driven by the temperature gradients and gravitational potential differences characteristic of curved spacetime. Analytic solutions obtainable within this toy model illuminate key aspects of black hole thermodynamics in general, particularly for spacetimes that are not asymptotically flat. Central to these findings is the relation between gravitationally induced temperature ratios and entropy changes, which collectively offer a novel perspective on obtainable energy transfer processes around gravitational structures. This analysis highlights potential implications for understanding energy dynamics in gravitational systems in general, including for black hole evaporation and experimentally implemented black hole analogues. The presented findings not only emphasise the universality of the thermodynamic principles first uncovered by Carnot, but also suggest future research directions in gravitational thermodynamics. Full article

Optimizing building equipment control is crucial for enhancing energy efficiency. This article presents a predictive control applied to a commercial building heated by a hydronic system, comparing its performance to a traditional heating curve-based strategy. The approach is developed and validated using TRNSYS18 modeling, which allows for comparison of the control methods under the same weather boundary conditions. The proposed strategy balances energy consumption and indoor thermal comfort. It aims to optimize the control of the secondary heating circuit’s water setpoint temperature, so it is not the boiler supply water temperature that is optimized, but rather the temperature of the water that feeds the radiators. Limited data poses challenges for capturing system dynamics, addressed through a black-box approach combining two machine learning models: an artificial neural network predicts indoor temperature, while a support vector machine estimates gas consumption. Incorporating weather forecasts, occupancy scenarios, and comfort requirements, a genetic algorithm identifies optimal hourly setpoints. This work demonstrates the possibility of creating sufficiently accurate models for this type of application using limited data. It offers a simplified and efficient optimization approach to heat control in such buildings. The case study results show energy savings up to 30% compared to a traditional control method. Full article

As data centers expand, immersion cooling systems are gaining attention for thermal management of memory devices. To enable widespread adoption, it is essential to evaluate the impact of coolants on the reliability of memory packages. In this study, high-voltage direct current (DC) stress tests were conducted on commercial dynamic random access memory (DRAM) packages in both single-phase coolant and air environments to analyze heat generation and electrical characteristics. A DC voltage ranging from 2.5 to 3.1 V, which is higher than the regular operating voltage of 1.2 V, was applied. Temperature changes were measured using an infrared camera in the air, and a contact-based thermometer in the coolant. The leakage current was also evaluated through I-V curve analysis. Heat generation and changes in leakage currents were not significant in either environment until the applied voltage stress exceeded approximately twice the standard voltage (2.5–2.8 V). However, the package’s degradation accelerated when the applied voltages exceeded 3.0 V, demonstrating a nonlinear increase in temperature and leakage current. Full article

An accurate analysis of the dynamic process of ice melting in an optical fiber composite overhead ground wire (OPGW) is of great reference significance for the selection of an ice melting current and the formulation of an ice melting strategy. Existing analytical models for the dynamic process of DC ice melting in an OPGW ignore the gap convective heat transfer after the formation of the air gap between the ground wire and the ice layer, and lack the study of the dynamic process of the phase transition of the ice layer. To this end, a finite element model of the DC ice melting process of OPGW was established by introducing the mushy zone constant to consider the influence of the convective heat transfer in the gap, and at the same time, the apparent heat capacity method was used to simulate the changes of the physical property parameters of the melted ice layer. The dynamic process of the ice layer phase transition and OPGW temperature rise during ice melting are calculated, and the effects of the half-width of phase transition interval dT and the mushy zone constant A_m on the DC ice melting process are summarized and analyzed. The accuracy of the OPGW DC ice melting model is verified by conducting DC ice melting experiments. The results show that during the ice melting process, the gap convection heat transfer mainly affects the temperature distribution of the air gap between the ice layer and the OPGW as well as the location of the phase transition interface, and the width of the air gap at the same height below the OPGW increases by about 3 mm after considering the gap convection; the half-width of phase transition interval, dT, mainly affects the location of the phase transition interface and the temperature rise of the modeled heat source, OPGW, while the mushy zone constant, A_m, mainly affects the temperature distribution in the mushy zone, the air gap region. The elliptical phase transition cross-section formed by the OPGW DC ice melting experiment is consistent with the shape of the ice melting simulation model results, and the measured temperature rise curves of the OPGW during DC ice melting are in good agreement with the simulation results, with a maximum difference of about 3.5 K in temperature and 10 min in ice melting time, but the overall trend is consistent, all showing as increasing first and then decreasing. Full article

A total of 108 maximum Kolmogorov entropy (S_K) values, calculated by means of chaos theory, are obtained from 108 time series (TSs) (each consisting of 28,463 hourly data points). The total TSs are divided into 54 urban meteorological (temperature (T), relative humidity (RH) and wind speed magnitude (WS)) and 54 pollutants (PM₁₀, PM_2.5 and CO). The measurement locations (6) are located at different heights and the data recording was carried out in three periods, 2010–2013, 2017–2020 and 2019–2022, which determines a total of 3,074,004 data points. For each location, the sum of the maximum entropies of urban meteorology and the sum of maximum entropies of pollutants, S_{K, MV} and S_{K, P}, are calculated and plotted against h, generating six different curves for each of the three data-recording periods. The tangent of each figure is determined and multiplied by the average temperature value of each location according to the period, obtaining, in a first approximation, the magnitude of the entropic forces associated with urban meteorology (F_{K, MV}) and pollutants (F_{K, P}), respectively. It is verified that all the time series have a fractal dimension, and that the fractal dimension of the pollutants shows growth towards the most recent period. The entropic dynamics of pollutants is more dominant with respect to the dynamics of urban meteorology. It is found that this greater influence favors subdiffusion processes (α < 1), which is consistent with a geographic basin with lower atmospheric resilience. By applying a heavy-tailed probability density analysis, it is shown that atmospheric pollution states are more likely, generating an extreme environment that favors the growth of respiratory diseases and low relative humidity, makes heat islands more stable over time, and strengthens heat waves. Full article

In view of the frequency regulation (FR) policy in Northeast China, a two-stage real-time rolling optimization model for power plants participating in FR ancillary services is established. The optimization object of the first stage is to maximize the overall profitability of the power plant and to obtain FR performance sub-indicators (K₁, K₂, K₃) and the electric power curve of combined heat and power (CHP) units with energy storage. The second stage of the model performs load distribution with the objective of minimizing operating cost, subject to the constraint of electric and heat power balance for CHP units and energy storage. Meanwhile, rolling optimization combined with dynamic correction is used to ensure the accuracy of the two-stage FR optimization model by updating the operating status of the CHP units and energy storage and reducing the prediction errors of the FR commands. The above models have been validated by actual case studies of a CHP plant in Northeast China. They can ensure the economic and sustainable operation of CHP units and energy storage, enabling the CHP plant to benefit in the FR ancillary services market. The models offer a useful reference for CHP enterprises in terms of FR. Full article

Photovoltaic (PV) wall panels are an integral part of Building-Integrated Photovoltaics (BIPV) and have great potential for development. However, inadequate heat dissipation can reduce power generation efficiency. To reduce the temperature of photovoltaic wall panels and improve the photovoltaic conversion efficiency, this paper constructs a computational fluid dynamics (CFD) numerical model of ventilated photovoltaic wall panels and verifies it, then simulates and analyzes the effects of three cavity structure forms on the thermal performance of photovoltaic wall panels and optimizes the dimensional parameters of the curved-ribbed cavity structure. The average surface temperatures of flat-plate, rectangular-ribbed, and arc-ribbed cavity structure PV wall panels were 59.42 °C, 57.56 °C, and 55.39 °C, respectively, under natural ventilation conditions. Among them, the arc-ribbed cavity structure PV wall panels have the best heat dissipation effect. Further studies have shown that the curvature, rib height, width, and spacing of the curved ribs significantly affect the heat dissipation performance of the photovoltaic panels. Compared to the flat-plate cavity structure, the parameter-optimized curved-rib cavity structure significantly reduces the average surface temperature of PV panels. As solar radiation intensity increases, the optimized structure’s heat dissipation effect strengthens, achieving a 6 °C temperature reduction at 1000 W/m² solar radiation. Full article

The air separation process is an important industrial process for the production of high-purity nitrogen and oxygen, representing the level of technological development in a country’s chemical industry. It has high energy consumption but very low energy utilization efficiency. In the overall environment of increasingly scarce global energy, the application of internal heat coupling technology in the air separation process can effectively reduce energy consumption. However, due to the low-temperature characteristics, ultra-high purity characteristics, and the nature of multi-component systems of the heat-integrated air separation column (HIASC), its modeling process and dynamic characteristic analysis are complex. To solve the disadvantages of overly complex mechanistic models and insufficient accuracy of traditional simplified models, a concentration distribution curve description method based on the mass transfer mechanism is proposed, and combined with the traditional wave theory, a nonlinear wave model of the HIASC is established. Based on this model, static and dynamic analyses were carried out, and the research results prove that the newly established nonlinear wave model maintains high accuracy while simplifying the model complexity. It can not only accurately track the concentration changes of key products but also fully reflect various typical nonlinear characteristics of the system. Compared to the mechanism model, the wave model can reduce the running time by approximately 20%, thereby improving operational efficiency. This method explains various characteristics of the system from a perspective different from that of the mechanistic model. Full article