International Journal of Thermophysics最新文献

Fluid flow analysis in nanofluids within the absorbing pipe of a flat plate solar collector has implications in enhancing the strength of solar thermal energy systems, increasing the productivity of water and space heating units, and increasing the use of renewable energy in the industrial and condominium heating processes. The current study examines the effects of the inclination angle and porosity factor on the natural flow convection of a water–CuO nanofluid using an inclined cylindrical tube within a Flat-Plate Solar Collector (FPSC) system with measures the consequences of tilting angle and porosity of the porous medium to quantify the physical attributes of heat transfer and velocity. The Lobatto IIIA computing method is adopted to compute the dynamics of velocity and temporal distributions for the governing FPSC system by modulating parameters of interest including the Prandtl number, skin friction, Nusselt number, curvature parameter, Grashof number, and Reynolds number. A Lobatto IIIA data-driven neurostructure is designed using Deep Autoregressive Exogenous Neural Networks (DARX-NNs) trained with the Backpropagated Levenberg–Marquardt (BLM) algorithm, i.e., DARX-NNs-BLM for predictive solutions of FPSC systems. The FPSC dynamical systems restoration using the DARX-NNs-BLM technique is visually interpreted using juxtaposing time-series analysis and absolute error progression curves. The simulated results reveal mean squared errors ranging from 10^–08 to 10^–10 with further endorsement using heterogeneous error analysis on the histogram, and regression analytics for the numerous tendencies of the FPSC model. The DARX-NNs-BLM framework accurately replicates the temporal evolution and probabilistic features of FPSC systems, rendering a firm grounding for prospective studies into neurocomputational science pursuits.

Densities, sound speeds, excess molar volumes, and excess isentropic compressibilities were measured for binary mixtures of bromobenzene with methanol, ethanol, propan-1-ol, butan-1-ol, and pentan-1-ol over (293.15–333.15) K at atmospheric pressure. This work presents a systematic thermophysical study of bromobenzene + alkan-1-ol (C₁-C₅) mixtures conducted over a previously unexplored temperature range and providing the first unified dataset for the complete bromobenzene + alkan-1-ol (C₁-C₅) series. Excess properties were correlated using Redlich–Kister polynomials, providing insight into molecular interactions and packing effects. The Jouyban-Acree model accurately reproduced densities, sound speeds, isobaric thermal expansivities, and isentropic compressibilities, with average absolute percentage deviations below 0.53%. Densities were further modeled with the Perturbed-Chain Statistical Associating Fluid Theory, yielding an overall deviation of 0.20%. Nomoto’s relation outperformed Schaaff’s collision factor theory in predicting sound speeds, with an overall deviation of 1.54%. These results demonstrate the effectiveness of empirical and theoretical models in describing thermophysical behavior in bromobenzene + alkan-1-ol mixtures of varying chain length.

Due to the global warming issue, next-generation refrigerants having very low global warming potential (GWP) have been interesting increasingly. In order to develop high-profile refrigerants having GWP < 10, several thermophysical quantities, for examples, density, heat capacity, viscosity, and thermal conductivity, etc., should be measured precisely. R1132a and CF₃I are substances with low-GWP that are being considered as potential alternative refrigerants. In this study, the density of their mixture was measured at various temperatures (-40 °C ~ 55 °C) and pressures (~ 70 bar) using a vibrating tube densimeter. The densities of R1132a and CF₃I as single refrigerants were measured within the same temperature and pressure range, and these measurements were used to obtain the calibration coefficients for the vibrating tube densimeter. The measurement results for R1132a and CF₃I as single refrigerants were compared with existing research and REFPROP results, with maximum deviations of 0.38% and 0.48%, respectively. Uncertainty evaluation was performed, and the maximum uncertainty was 0.6%. R1132a and CF₃I were mixed in mass ratios of 75:25, 50:50, and 25:75, and the densities of these mixtures were measured at various temperatures and pressures. These measurement results, being the first of their kind globally, could aid in the development of equations of state for future alternative refrigerants.

The present study compares different battery thermal management system (BTMS) designs for lithium-ion batteries using computational fluid dynamics (CFD) simulations, with battery packs modeled in SolidWorks considering realistic heat dissipation characteristics and flow behavior. Effective thermal regulation of lithium-ion batteries (LIBs) is essential to ensure the performance, reliability, and safety of electric vehicles (EVs). Radiator-based BTMS, which operates without a compressor, has recently gained prominence due to its lower energy consumption and efficient temperature control. LIB performance is highly sensitive to temperature-dependent parameters such as state of charge (SOC), self-discharge rate, and state of health (SOH), necessitating the maintenance of an optimal thermal window to enhance overall battery longevity. Three BTMS configurations were evaluated: Design 1, a basic flow arrangement; Design 2, an improved airflow design with enhanced vent placement; and Design 3, an optimized gradient cooling-channel configuration. All three designs successfully reduced the battery temperature from an initial 60 ℃, with Design 3 exhibiting the highest temperature drop of approximately 45.27 ℃ and maintaining a low spatial temperature variation (< 2 ℃) across the pack. Its balanced airflow distribution and improved thermal uniformity make it particularly suitable for high-ambient-temperature environments. To further evaluate predictive capabilities, machine-learning models including MLP-NN, XGBoost, SVR, Random Forest, Gradient Boosting, and a Stacked Hybrid model were developed. The Stacked Hybrid model achieved the highest accuracy with an R² of 0.96 and the lowest mean square error, while XGBoost and Gradient Boosting showed stable convergence. In contrast, SVR and Random Forest models underperformed due to their reduced sensitivity to nonlinear thermal variations.

Vapour–liquid equilibrium measurements are essential for developing equations of state, tuning binary interaction parameters, and improving the thermodynamic description of fluid mixtures. Conventionally, multiple apparatus are needed to quantify equilibrium phase behaviour and fluid properties, including composition, density, and phase fractions (quality). Using several different instruments to acquire the desired data can be laborious and increase measurement uncertainty. Microwave cavities are a promising technology capable of measuring these properties simultaneously in binary mixtures. Here, we apply recent advances in cavity design and signal processing to demonstrate measurements of phase volume fractions, compositions, and densities in mixtures of methane and propane at vapour–liquid equilibrium. Furthermore, we present an uncertainty analysis for phase composition and density measurements made with a single apparatus consisting of several microwave cavities and demonstrate levels of accuracy comparable to those obtained with conventional analytical techniques.

Hydrofluoroethers (HFEs) are considered promising replacements for the high global warming potential (GWP) per- and polyfluoroalkyl substances (PFAS) in refrigeration, heat transfer, and electronic cooling applications. However, the scarcity and inconsistency of available thermophysical property data have hindered their reliable implementation and modeling. This study reports a comprehensive experimental investigation of HFE-7300 (1,1,1,2,2,3,4,5,5,5-decafluoro-3-methoxy-4-(trifluoromethyl)pentane), including measurements of critical properties, density, vapor pressure, isobaric heat capacity, and speed of sound over wide temperature and pressure ranges. The experiments were complemented by ab initio calculations of ideal-gas thermodynamic properties, resulting in a consistent dataset suitable for model development. Correlations for vapor pressure, density, and heat capacities were derived and mutually validated for internal thermodynamic consistency. These results provide a robust foundation for developing a multiparameter equation of state for HFE-7300, while the performance of cubic, PC-SAFT, and LKP equations of state was also evaluated as an interim approach.

Microwave-to-thermal energy conversion in aluminum particle compacts can be controlled by modifying the phase and thickness of the native alumina passivation shell. In this study, the alumina shell surrounding aluminum nanoparticles was altered through thermal annealing in air at 400 °C for thermal treatment times of 30, 60, 90, and 120 min. Transmission electron microscopy (TEM) revealed shell growth from an average thickness of 2.76 nm to 9.63 nm with increasing annealing time. Thermal treatment also induced a transition from an initially amorphous shell to increasingly crystalline alumina, as confirmed by X-ray Diffraction (XRD). Compacts fabricated from the annealed powders were exposed to microwave radiation at 2.45 GHz for 5 min in an electromagnetic exposure chamber. Transient surface temperatures were recorded in situ using infrared thermograph. Compacts prepared from the as-received powders exhibited significantly higher steady-state temperatures than those made from annealed powders. The transition from an amorphous to a crystalline alumina shell resulted in an approximately 25 % reduction in steady-state temperature during microwave exposure. Microwave energy absorption is inferred from comparative thermal response under identical exposure conditions, rather than from direct measurements of the dielectric loss or absorbed microwave power. These results demonstrate that microwave heating of aluminum particle compacts is strongly influenced by the phase of the alumina shell and that shell crystallinity can be used to tailor material response to microwave energy.

This work investigates the ternary liquid system butan-1-ol + benzene + acetophenone, together with its corresponding binary mixtures. The system exhibits complex non-ideal behavior arising from pronounced differences in polarity, molecular size, shape, and intermolecular interactions among the components. To gain insight into these effects, thermophysical and thermodynamic properties were determined experimentally and analyzed across the full composition range. Density (ρ) and speed of sound (u) were measured at (293.15, 303.15, 313.15, 323.15, and 333.15) K and ambient pressure for the ternary system for the first time, with the same methodology applied to the binary subsystems. From these measurements, excess molar volumes (({V}_{m}^{E})) and excess isentropic compressibilities (({kappa }_{S}^{E})) were evaluated and correlated using Redlich–Kister polynomial for the binaries and the Cibulka equation for the ternary data. The Jouyban–Acree model accurately reproduced the composition and temperature dependence of the measured and derived properties using a compact set of parameters. The experimentally determined ternary excess properties were further compared with predictions based on symmetric (Kohler, Muggianu) and asymmetric (Hillert, Toop) binary-contribution models. Among these, the Hillert formulation yielded the most reliable agreement with experiment, particularly when acetophenone was treated as the asymmetric component, consistent with its distinct polarity and electronic structure compared to butan-1-ol and benzene. To substantiate the origin and extent of asymmetry, the Chou’s General Solution model was additionally applied. The Chou analysis independently identified acetophenone as the dominant asymmetric contributor, thereby confirming that the Hillert model provides the physically and mathematically most appropriate representation of the ternary system. This outcome reflects the fact that the Hillert model can be interpreted as a limiting case of the Chou formulation, explaining the strong internal consistency between the two approaches.

Geothermal fluids are highly variable in chemical composition and ion concentration. Parametrising density, as one of the most important fluid properties for geothermal reservoir development, is therefore challenging. The purpose of this paper is to test the hypothesis that saline geothermal fluids can well be characterised for density when only the dominating dissolved salts (i.e., NaCl, KCl, and CaCl₂) are taken into account, thus neglecting any minor fluid constituents. For the example of four geothermal sites with known chemical fluid composition and significant differences in total salt content (Groß Schönebeck and Insheim, Germany, Balmatt, Belgium, and Heemskerk, The Netherlands) synthetic aqueous solutions of the main salts without and with three different other salts (i.e., LiCl, SrCl₂, and MgCl₂) representing the minor fluid constituents were parametrised for density at atmospheric pressure and temperatures between 293 K and 353 K. Moreover, density was derived numerically using the PHREESCALE chemical code and evaluated against the analytical data. The results demonstrate that: (1) an effect of the ion type is evident with density increasing in the order of added LiCl, MgCl₂, and SrCl₂ at a given concentration. (2) The uncertainty in density when neglecting any minor fluid components is at most 2 % which indicates that fluid density can be well characterised when applying the database of the main salts only. (3) The match between analytical and numerical data is excellent with differences generally less than 1 % evidencing that PHREESCALE permits to reliably predict the density of complex and multicomponent geothermal fluids.

Molten salts play a key role in the development of advanced energy technologies, including molten salt reactors (MSRs). However, many of the chemical properties of multi-component salt systems relevant to MSRs, such as vapor pressure, remain insufficiently characterized. As a crucial first step toward studying the vapor pressure of multi-component salt systems, this work provides a critical review of vapor pressure measurements over the solid and liquid phases of pure lanthanide and actinide chlorides. This paper demonstrates appropriate nonlinear data regression methods and identifies areas for future research. Based on this review, and where reliable data are available, new vapor pressure correlations and their parameters were regressed using thoroughly vetted data to provide more reliable vapor pressure predictions for pure lanthanide and actinide chlorides as a function of temperature. Further, the analysis includes robust uncertainty quantification through both a thermodynamic analysis (where data permits) and a robust nonlinear statistical analysis that incorporates confidence bands and joint parameter confidence regions.