International Journal of Thermophysics最新文献

This work presents experimental data on the density and viscosity of mixtures composed of nonanal and 1-alkanols (ranging from 1-propanol to 1-heptanol) at 0.1 MPa and within the temperature range of 293.15 K to 323.15 K. The resulting properties, including excess molar volume and viscosity deviation, were fitted using the Redlich–Kister equation. Moreover, the PC-SAFT equation of state successfully reproduced the experimental density results. The study also revealed that the intermolecular interactions between nonanal and the 1-alkanols can vary in strength (strong or weak).

An original experimental investigation was conducted to characterize the thermophysical behavior of various nanocolloids. These suspensions utilized a binary mixture of PEG 200 and PEG 400 as the base fluid, with dispersed nanoparticles of copper (Cu), silver (Ag), alumina (Al₂O₃), and magnesium oxide (MgO). Comprehensive measurements were performed to ascertain their thermal conductivity, dynamic viscosity, density, and specific heat capacity, facilitating a comparative analysis of their performance attributes. All thermophysical properties were acquired across a temperature range of up to 333.15 K. Viscosity was additionally scrutinized over a broad range of shear rates, up to 264 s⁻¹, and the presence of hysteresis effects was assessed through cyclic thermal loading. Results indicate a marginal increase in viscosity, approximately 10 %, upon nanoparticle integration. Notably, the observed viscosity hysteresis of the nanocolloids closely mirrored that of the PEG mixture. Regarding thermal transport properties, specific heat exhibited enhancements up to 10 %, and thermal conductivity up to 11.1 %, contingent upon the specific nanoparticle material. The study further provides novel correlations and discusses their agreement with existing theoretical and empirical models.

A deep understanding of thermophysical properties is crucial for accurately predicting the behavior of molecules under extreme conditions. In this work, we present a comprehensive methodology grounded in statistical mechanics, which integrates quantum, semiclassical, and classical formulations of the partition function for diatomic species. As a case study, this methodology was applied to homonuclear alkali-metal dimers through the fitting of high-level ab initio calculations to the Extended Hartree–Fock Approximate Correlation Energy. A total of 154 potential energy curves were considered, with explicit consideration of low-lying electronic states. This approach enables accurate modeling of both low and high-temperature regimes for ({textrm{Li}}_{2}), (textrm{Na}_{2}), (textrm{K}_{2}), (textrm{Rb}_{2}), (textrm{Cs}_{2}) , and (textrm{Fr}_{2}). Our results reveal that neglecting excited electronic states leads to significant deviations in key properties, particularly heat capacity and enthalpy at elevated temperatures. Systematic trends along the alkali-metal series are observed. The methodology demonstrates agreement with experimental data and underscores the limitations of classical approaches, where the quantized nature of molecular eigenvalue becomes non-negligible. This framework provides a robust and generalizable tool for reliable prediction of thermodynamic properties in molecular systems, the results emphasize the fundamental role of electronic structure in determining thermodynamic properties, and they can be directly extended to improve high-temperature models in chemical kinetics, plasma physics, and materials science.

This study presents stochastic numerical computing paradigm of the Maxwell hybrid nanofluid (MHNF) with magnetohydrodynamic (MHD) effects using deep learning formation of artificial intelligence by exploiting layered recurrent neural networks backpropagated with Levenberg–Marquardt (LRNNs-LM) scheme. The intention of the present work is to offer better insight in to the dynamics of nanofluid by applying LRNNs-LM to produce numerical solution of the MHNF models, that is initially expressed with PDEs, and then transmuted into nonlinear ordinary ODEs using similarity transformations. The synthetic dataset for the MHNF model is numerically created for LRNNs-LM technique using Adams solver for varied physical quantities such as the magnetic parameter, radiation parameter, Prandtl number, and Eckert number. The designed deep neuro-structures of LRNNs-LM technique are implemented on the generated synthetic data to minimize the error and get the approximate solutions for several scenarios of MHNF system. The effectiveness of LRNNs-LM algorithm is verified through learning curves on mean square error, transition state index, fitness plots, error histogram, and regression analysis, intended for computational fluid dynamics of Maxwell hybrid nanofluid.

Excessive water in natural gas can lead to pipeline blockages or equipment corrosion, and thus accurate measurements of the water content of natural gas are essential. Water content analyzers based on tunable diode laser absorption spectroscopy (TDLAS) can accurately measure the water content of natural gas. In the present study, a calibration system comprising a humid methane generator, using a permeation tube, and a chilled mirror hygrometer was developed for TDLAS analyzers. The generator can produce humid gas mixture of methane and nitrogen with a frost point ranging from –65 ℃ to –29 ℃ at the pressure of 101.325 kPa and an arbitrarily adjustable volume fraction of methane. This calibration system was used to calibrate two TDLAS analyzers from different manufacturers. When the volume fraction of methane (on a dry basis) was 100% and the frost point of humid gas was between –65 ℃ and –29 ℃, the indication errors of the frost points were between –0.41 ℃ and –0.03 ℃ for one analyzer and between –2.24 ℃ and –0.75 ℃ for the other analyzer. The indication errors of the frost point were fitted using a quadratic function. In the frost point range of –65 to –29 ℃, the typical uncertainty of the frost point indication error was 0.38 ℃ (U, k = 2), corresponding to a water content uncertainty of 0.3 to 16.4 ppmV. This calibration system can be used for factory calibration, pre-installation calibration, and periodic calibration of TDLAS water content analyzers of natural gas, ensuring the accuracy, reliability, and equivalent consistency of water content results.

The additive law of logarithmic viscosity of molten alloys is widely established. However, the logarithmic viscosity in the molten Al–Cu system deviates significantly from the additive law. In this system, the molar volume deviates significantly and negatively at intermediate compositions. Al–Ti system has the same characteristics; however, there are few reports on the viscosity of molten Al–Ti alloys. In this study, the viscosities of molten Al–0, 10, 20, 30, 40, 55 mol% Ti alloys were measured using the oscillating crucible method. The measured viscosities of all alloys exhibited good consistency in the heating and cooling processes, and the logarithmic viscosities showed good Arrhenius-type linearity. The viscosity increased significantly with increasing Ti concentration. The logarithmic viscosity of the alloys increased drastically with increasing Ti concentration, deviating considerably from the additive law in a similar manner to Al–Cu melts; notably, the degree of deviation was far greater. The activation energy exhibited a composition dependence similar to that of the logarithmic viscosity. However, the maximum value was observed on the Al-rich side. The activation energy in the Al–Ti system was significantly higher than that in the Al–Cu system, indicating that the attractive force between the Al and Ti atoms was significantly stronger than that between Al and Cu.

Magnetorheological fluids (MRFs) are a class of smart materials with adjustable rheological properties under external magnetic fields, making them highly suitable for applications such as MRF clutches. In recent years, increasing demand for precise torque control and rapid response has driven extensive research on the modeling, simulation, and optimization of these systems. This review provides a comprehensive overview of MRF clutches, covering their fundamental working principles, design considerations, and investigation approaches. The rheological behavior of MR fluids is examined through both macroscopic and microscopic models, emphasizing their non-Newtonian characteristics. Additionally, structural properties and key parameters influencing MR fluid performance are discussed. The study categorizes MRF simulation and analysis methods into two main approaches: continuum methods, which model the bulk behavior of the fluid using constitutive equations, and discrete methods, which track individual particles to capture microstructural dynamics. Experimental methods and computational fluid dynamics (CFD) simulations are reviewed to evaluate their effectiveness in predicting MRF behavior and improving clutch performance. Finally, the study summarizes key findings and highlights future research directions to enhance MRF clutch development and its diverse applications in engineering systems.

Graphene nanoplatelets (GNP) are emerging as promising nanomaterials in nanofluid technology due to their exceptional intrinsic thermal properties. The hybrid combination of GNP with Multi-walled carbon nanotubes (MWCNTs) and carbon nanofibers (CNFs) can demonstrate a synergistic effect, impact stability, and effective thermal behavior, which is yet to be investigated. Therefore, this research explores the thermal profile, i.e., thermal conductivity, specific heat capacity, and density of GNP, GNP + MWCNTs, and GNP + CNF-based hybrid Nanofluids. A two-step method is employed to formulate three sets of nanofluid mass concentrations, ranging from 0% to 2.0%, with an optimized concentration of non-ionic Span85 surfactant. The dynamic stability is analyzed using viscosity profiles over time at three different temperatures, exhibiting excellent stability at high temperatures. Experimental thermal conductivity analysis of nanofluids reveals a direct relationship with increasing temperature and nanofluid concentration, with maximum enhancements of 182.19%, 175.34%, and 169.86% for GNP, GNP + MWCNTS, and GNP + CNF nanofluids, respectively, at a 2.0% weight concentration. Specific heat capacity (SHC) increases with temperature but decreases with higher concentrations, with decrements of 37.06%, 29.3%, and 22.1% observed at 2.0% concentration for GNP, GNP + MWCNT, and GNP + CNF nanofluids, respectively. While density increases with mass concentration, the enhancement remains negligible. The synergistic effects in thermal conductivity favor GNP nanofluids over hybrid systems, yet hybrid nanofluids exhibit superior SHC and density. Multivariable correlations are developed from experimental data, demonstrating an excellent prediction of thermal properties. The findings highlight the potential of GNP and its hybrid nanofluids for improving energy efficiency in thermal management systems.

The Al–28%Cu–6%Si (mass%) eutectic alloy represents a possible high-temperature phase change material (PCM) for latent heat thermal energy storage (LHTES). In this paper, its microstructural characteristics and thermal properties were examined in the as-cast and annealed conditions using scanning electron microscopy (SEM), energy-dispersive spectroscopy (EDS), differential scanning calorimetry (DSC), and light flash method. The microstructure of the studied alloy consists of (Al) solid solution phase, θ-Al₂Cu intermetallic phase, and (Si) phase. The annealing at 450°C for 50 h led to significant changes in the morphology of the θ-Al₂Cu and (Si) eutectic phases. The temperature dependences of thermal diffusivity and thermal conductivity were investigated within the temperature range from 25 to 400 °C. It was found that the thermal diffusivity and thermal conductivity of the annealed alloy are considerably higher than that of the as-cast alloy at temperatures lower than 300 °C. With increasing temperature, due to changes in the microstructure of the as-cast alloy, these differences decrease and finally diminish at 400 °C. The measured eutectic temperature is 522.3 °C and latent heat of melting is 358.3 Jg^-1. The findings suggest that the Al–Cu–Si eutectic alloy shows good potential for use in phase change energy storage technologies.

Soil thermal conductivity is a fundamental thermophysical property that characterizes the soil’s ability to conduct heat. It plays a critical role in applications such as geothermal energy development and thermal energy storage. However, existing prediction models for soil thermal conductivity often suffer from complex functional forms and difficulties in obtaining the required input parameters. To address these challenges, this investigation developed an empirical prediction model based on the relationship between soil saturation and thermal conductivity. The model’s performance was evaluated using the coefficient of determination (R²) and root mean square error (RMSE) as statistical metrics. The proposed model was compared with three theoretical models and two existing empirical models using both published datasets and laboratory measurements. Results showed that predicting the thermal conductivity of sandy soils is more challenging for classical model. Among the three empirical models evaluated, the new model consistently achieved R² values greater than 0.85 and RMSE values below 0.20 W·m⁻¹·k⁻¹ across all three datasets. This suggests that the new model offers lower predictive uncertainty and better adaptability to different soil types, providing a new approach for estimating soil thermal conductivity. It should be noted, however, that the new model was developed based on data from unfrozen mineral soils under room temperature conditions. In practical applications involving other soil types such as organic-rich, frozen, or contaminated soils, alternative predictive models may be more appropriate.