International Journal of Thermophysics最新文献

The dispersion stability and thermophysical properties of ternary composite nanofluids (NF) containing graphene oxide (GO), aluminum oxide (Al₂O₃), and silica (SiO₂) at different concentrations and ratios, with water-based ethylene glycol and 1,2-butanediol added as antifreeze agents, were investigated. First, the ternary nanofluid was prepared using a two-step method, and the prepared nanoparticles were characterized using field emission scanning electron microscopy (SEM), X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FTIR). The influence of surfactant addition on the stability of the nanofluid was analyzed. The density and thermal conductivity (TC) of NF were measured at different volume concentrations (0.2–0.6 vol%) and temperature ranges (298.15–333.15 K and 293.15–363.15 K, respectively). As the GO concentration increased, the proportion of thermal conductivity improvement showed a stepwise increase with concentration changes. When the GO concentration reached 80 % in the three types of nanoparticles, the thermal conductivity of NF at 363.15 K was 58 % higher than that of the base fluid. For the two different base fluids, under the same ratio, concentration, and temperature conditions, the base fluid containing 1,2-butanediol showed a more significant effect in enhancing thermal conductivity.

Urban photovoltaic (PV) generation forecasting is crucial for energy efficiency and grid stability. This study proposes a two-stage machine learning (ML) framework that extends the concept of a hurdle model by utilizing climatic and temporal variables and quantitatively analyzes variable contributions through SHAP-based interpretation. The proposed framework integrates a probabilistic classifier that identifies PV generation status in the first stage with a nonlinear conditional regression model in the second stage, thereby forming an adaptive boundary between generation and non-generation intervals. A total of 320 model combinations were evaluated in four urban buildings and under five seasonal conditions. The results showed that the two-stage conditional regression model exhibited superior generalization performance compared to a single-stage regression model. Specifically, the multi-layer perceptron (MLP)-based conditional regression model recorded a test R² value of 0.7 or above (R² ≥ 0.7) under most conditions. Also, in SHAP analysis, direct solar irradiance (DSI), diffuse solar irradiance (DiffSI), hour of the day (Hour), visibility (Vis), and relative humidity (RH) were derived as common key drivers of fluctuations in PV generation, and the cumulative contribution of these five drivers was more than 80%. Furthermore, the interpretive consistency and transferability of SHAP results were quantitatively verified using Kendall’s τ and Top-k overlap indicators across buildings and seasons, demonstrating the robustness and reproducibility of the proposed framework. These results demonstrate that solar irradiance (i.e., DSI and DIffSI) and temporal patterns (i.e., Hour) are key drivers of urban PV generation fluctuations, offering practical application values in terms of real-time operation optimization, policy design, and scalability to diverse regions and buildings.

This work explores how molecules interact in binary and ternary aqueous mixes of choline salicylate with sodium sulphate (Na₂SO₄) and sodium bicarbonate (NaHCO₃) using a combined thermodynamic, volumetric, and acoustic approach. Density and sound speed were measured at 288.15–318.15 K and choline salicylate concentrations of 0.0590, 0.0688, and 0.0885 mol∙kg⁻¹. Based on these measurements, values such as apparent and partial molar volumes, isentropic compressibility, apparent specific volume, thermal expansion, partial molar expansibilities, and transfer volumes were determined. The analysis includes derivative thermodynamic properties and evaluates relative association and relaxation strength to assess molecular aggregation and structural dynamics. The co-sphere overlaps model and McMillan–Mayer theory reveals how choline salicylate and inorganic salts modulate interactions between solute and solvent molecules, influencing the structure-making or disrupting behaviour of the system. These findings provide new insights into solvation dynamics and molecular association in aqueous media, with implications for the design of advanced solvent systems and a deeper understanding of solution chemistry.

The adoption of phase change materials (PCMs) is hindered by the complex characterization of their thermophysical properties, as most studies still address only a narrow range of properties. In this work, temperature-dependent measurements deliver accurate data and enable functional modeling essential for advancing practical applications. Thermophysical characterization of three organic PCMs was conducted, including two commercial paraffins (RT28HC and RT26) and industrially processed pork fat. The comprehensive experimental analysis (temperature-dependent characterization) was provided using differential scanning calorimetry (DSC), the transient hot wire (THW) method, density measurements, and rheological analysis. It was determined that paraffins RT26 and RT28HC have high values of latent heat, amounting to 198.1 kJ kg⁻¹ and 215.8 kJ kg⁻¹ for the endothermic process, respectively. Paraffin RT26 was observed to have two melting peaks, at 19.6 °C and 28.8 °C, while RT28HC showed one at 29.6 °C. The results also revealed that both paraffins in liquid state have almost identical values of thermal conductivity and diffusivity, while in solid state these values differ. Results obtained with DSC and THW deviate significantly from the manufacturer’s datasheet with discrepancies ranging from 10% to 44%. Pork fat showed lower values of latent heat, but slightly higher thermal conductivity and diffusivity. The melting peak of pork fat was measured at 34.8 °C. All three materials were found to behave like Newtonian fluids, with pork fat having the highest viscosity of 73.2 mPas at 20 °C. Specific heat capacity was also calculated for all samples, with the highest value of 2.559 kJ kg⁻¹ K⁻¹ determined for RT28HC at 60 °C. It was also discovered that the THW apparatus is able to detect the onset of liquid-to-solid transition in paraffins and pork fat. The key research outcomes of this work are useful for numerical modeling since reliable dataset of thermophysical properties is provided herein, and which is ultimately needed for accurate numerical modeling of PCM-based thermal energy storage (TES) systems.

This study investigates the thermophysical properties of mono and hybrid nanofluids based on TiO₂ nanoparticles dispersed in bidistilled water (DDW), with the addition of CuO, ZnO, and Al₂O₃ at a total volume concentration of 4 %vol in a 1:1 ratio. Nanofluids were synthesized using a two-step method with ultrasonic dispersion and surfactant stabilization (SDBS, 1:0.1 ratio). Thermal conductivity was measured using the transient hot wire method, while kinematic viscosity was assessed across a temperature range of 293 K to 333 K. Results showed that all nanofluids exhibited improved thermal conductivity and higher viscosity compared to pure DDW. Among them, the TiO₂–CuO/DDW hybrid demonstrated the best overall performance, with a thermal conductivity increase of up to 14 % and the lowest relative increase in viscosity. In contrast, TiO₂–Al₂O₃/DDW showed the highest viscosity increase (up to 140 % at 293 K) and the lowest conductivity enhancement. Additionally, the experimental thermal conductivity data were compared with theoretical models, revealing that the Maxwell model consistently showed the closest agreement, with minimal deviations across all nanofluids (e.g., MAPE: 1.1 % for TiO₂ and 1.2 % for TiO₂–ZnO). In terms of viscosity modeling, the Maïga model provided the most accurate predictions in most cases, particularly for TiO₂–CuO (MAPE: 4.3 %), while the Pak-Cho model significantly overestimated viscosity in hybrid nanofluids, with errors exceeding 100 %. These findings suggest that CuO and ZnO nanoparticles are more effective than Al₂O₃ in improving heat transfer while minimizing flow resistance, making it better suited for practical thermal applications.

The enthalpy increment of pure lithium of natural isotopic composition has been measured and heat capacity has been determined using high-temperature drop calorimetry method over a temperature range 421 K–1126 K covering solid and liquid states. Based on the measurement results, temperature dependences of the studied properties were derived and the heat of fusion was determined. Good agreement between our data and the results from the literature has been obtained. It is shown that the most probable behavior of the heat capacity of liquid lithium is relatively rapid decrease in the temperature range from fusion to ~ 700 K and gradual flattening at higher temperatures.

The current research examines the thermophysical behavior of systems involving propyl methanoate (PM) and a homologous series of 1-alkanol from 1-hexanol to 1-decanol within the temperature interval 293.15–323.15 K. The primary objective was to elucidate the intermolecular forces and the extent of non-ideality in these systems. The results demonstrate that across all binary systems, the excess molar volume maintained positive values over the entire concentration range. Moreover, these positive deviations in volume become more pronounced with both increasing temperature and elongation of the alcohol’s carbon chain. A consistent negative trend in viscosity deviations was observed for every system, with the deviation magnitude rising as the carbon chain extended. To interpret the volumetric behavior more comprehensively, the PC-SAFT model was employed to model liquid densities. The calculated densities exhibited excellent agreement with the corresponding experimental values across the studied mixtures. Among all investigated mixtures, the propyl methanoate with 1-decanol pair showed the maximum density deviation of 0.81% between experiment and model. The close match between calculated and experimental data attests to the strength of the PC-SAFT formulation in modeling non-ideal interactions in binary mixtures.

This study evaluates the performance of three reference equations of state (EoS), AGA8-DC92, GERG-2008, and SGERG-88, in predicting the density of regasified liquefied natural gas (RLNG) mixtures. A synthetic nine-component RLNG mixture was gravimetrically prepared. High-precision density measurements were obtained using a single-sinker magnetic suspension densimeter over a temperature range of (250 to 350) K and pressures up to 20 MPa. The experimental data were compared with EoS predictions to evaluate their accuracy. AGA8-DC92 and GERG-2008 showed excellent agreement with the experimental data, with deviations within their stated uncertainty. In contrast, SGERG-88 exhibited significantly larger deviations for this RLNG mixture, particularly at low temperatures of (250 to 260) K, where discrepancies reached up to 3 %. Even at 300 K, deviations larger than 0.4 % were observed at high pressures, within the model’s uncertainty, but notably higher than those of the other two EoSs. The analysis was extended to three conventional 11-component natural gas mixtures (labeled G420 NG, G431 NG, and G432 NG), previously studied by our group using the same methodology. While SGERG-88 showed reduced accuracy for the RLNG mixture, it performed reasonably well for these three mixtures, despite two of them have a very similar composition to the RLNG. This discrepancy is attributed to the lower CO₂ and N₂ content typical in RLNG mixtures, demonstrating the sensitivity of EoS performance to minor differences in composition. These findings highlight the importance of selecting appropriate EoS models for accurate density prediction in RLNG applications.

The present work introduces a modification to the expanded fluid-based viscosity correlation (originally proposed by Yarranton and Satyro in 2009 for hydrocarbons) in order to obtain improved representations of the dynamic viscosity of several representative modern ionic fluids: pure ionic liquids (ILs) and deep eutectic solvents (DESs). The strong non-linearity introduced by the two-nested exponential form in the original Yarranton–Satyro correlation has been presently simplified by expressing the argument of the outer exponential as a linear combination of inverse powers of the reduced temperature and a logarithmic term involving the compressed state density in a vacuum ρ_s⁰, the fluid density ρ, and the pressure. The resulting modified Yarranton–Satyro (MYS) correlation thus contains two key thermodynamic potentials (ρ_s⁰ and ρ) which in turn were estimated via the use of two simple cubic equations of state of the van der Waals type: Soave–Redlich–Kwong or Peng–Robinson. The present MYS approach was successfully verified during the correlation and prediction of experimental dynamic viscosities of 3 families of imidazolium-based ILs ([C_Xmim][BF₄], [C_Xmim][PF₆], and [C_Xmim][Tf₂N]), one pyridinium-based IL ([b3mpy][BF₄]), one pyrrolidinium-based IL ([P14][Tf₂N]), one ammonium-based IL ([N1114][Tf₂N]), and four ILs having non-fluorinated anions ([dmim][MeSO₄], [bmim][EtSO₄], [bmim][Ac], and [b3mpy][dca]) over a temperature range varying from 273.15 K to 438.15 K and at pressures from 1 to 3000 bar. We also considered three archetypal choline chloride-based DESs for model validation: Reline, Ethaline, and Glyceline within a temperature range varying from 293.15 K to 373.15 K and at pressures from 1 to 1000 bar.

Thin coatings have emerged as a critical component of advanced nuclear fuels. Located in the path of heat dissipation, a thorough investigation of coating thermal conductivity is imperative. The spatial-domain thermoreflectance (SDTR) technique is ideally suited for characterizing their thermal conductivity due to its high spatial resolution. However, applying SDTR to cross-sectional samples is complicated by their asymmetric geometry and layered structure, which preclude analytical heat transfer solutions and introduce significant uncertainty in the data analysis. Here, we employ finite element modeling, validated by experiments, to quantify the size effects that govern SDTR measurements on cross-sectional coating samples. We determine the minimal coating dimensions required for accurate, artifact-free measurement and reveal how these dimensions are influenced by the thermal property mismatch between the coating, substrate, and transducer film. We further quantify the measurement error induced by off-center laser positioning. Through systematic investigation of progressively complex scenarios, analytical expressions for rapid determination of minimum dimensions free from boundary artifacts are derived. Our framework establishes practical guidelines for accurate thermal characterization of coatings by SDTR for energy materials research.