Journal of Thermal Analysis and Calorimetry最新文献

A theoretical framework to investigate three-dimensional Williamson fluid flow over a bidirectional extended flat horizontal surface is proposed in this dissertation. Artificial intelligence and machine learning fields have seen tremendous growth in prominence along with the rapid advancement of related technology. This work trains a machine learning model based on artificial neural networks to handle the mathematical formulation incorporating heat source and Hall effects using the Levenberg–Marquardt approach. Additionally, the impact of activation energy on fluid concentration is incorporated into the analysis. Cattaneo-Christov double diffusion models are used to model heat transfer combined with the effects of thermal radiation. The solutions, serving as reference datasets for various scenarios, have been generated numerically using the BVP4C approach. Artificial neural networks are utilized for training, testing, and validating these numerical computations using a 70:15:15 ratio. The predictive model accuracy is evaluated using various statistical metrics, including linear regression, histograms, fitting analysis, and mean squared error evaluations, with the least error ranging between 10⁻³ and 10⁻⁴, based on individual error analysis of four parameters. The findings show that temperature rises with the M parameter, whereas velocity declines by increasing the M parameter. Concentration rises with increasing activation energy parameter and falls with decreasing Sc. The results show that artificial neural networks can provide a successful replacement for forecasts for the future, and the fluid flow structure simulated here may result in better industrial designs.

This paper investigates the ferrofluid flow within a square cavity considering the effects of viscosity. An elliptical obstacle with a high temperature is placed in the center of the cavity, and the vertical walls are cooled and covered with a conductive layer of varying thickness. Electrical current-carrying wires alongside the cooled walls generate the Kelvin force in the ferrofluid. Variables studied include the Ra and Ha, varying magnetic fields (MF), the thickness and thermal conductivity of the conductive wall, and the aspect ratio (AR). The equations are solved using the finite element method, and entropy (EnY) data and Nu are studied using the response surface method. Statistical analysis revealed that the AR significantly impacts the variations in the Ha and MNF. Results indicated that increasing the Ha decreases the generated EnY and the ({Nu}_{text{m}}) in the cavity, whereas increasing the strength of the varying MF increases both the generated EnY and the ({Nu}_{text{m}}). An increase in the AR also leads to increased EnY production and ({Nu}_{text{m}}). The maximum and minimum Nu were observed at conductive wall thicknesses of 0.05 and 0.1, respectively, with a difference of 88.6%. Increasing the wall thickness reduces thermal EnY by up to 91%, fluid EnY by 82.3%, and total EnY by 90.7% compared to their maximum values. Increasing the Ra from 1000 to 1,000,000 results in a 296, 2355, and 65.8% increase in the ({Nu}_{text{m}}), fluid EnY, and total EnY, respectively, while reducing thermal EnY by 19.6% and Be by 88.8%.

The thermal conductivity is one of the key thermal property's parameters in the design, modeling, and simulation of lithium-ion battery thermal management systems. Accurate measurement of thermal conductivity allows for a deep understanding of the heat transfer behavior inside lithium-ion batteries, providing essential insights for optimizing battery design, enhancing energy density, and improving safety. In this study, the surface temperature variation data of lithium-ion batteries were obtained by externally heating the batteries using a constant pressure source in an accelerating rate calorimeter enhanced system (ARC). Based on the Fourier one-dimensional heat conduction model, the average specific heat capacity and vertical thermal conductivity of the lithium-ion batteries were calculated. Additionally, the Bayesian optimization algorithm was employed to significantly reduce the number of iterations and rapidly invert the in-plane thermal conductivity of the batteries. The accuracy of the thermal conductivity measurement results was verified by comparing the consistency between experimental and simulation data. The results indicate that the transient deviation between experimental and simulation data at each temperature measurement point does not exceed 0.2 °C, demonstrating the high accuracy of the proposed method. Furthermore, the thermal conductivity of the lithium-ion battery was measured using the Hot Disk method for comparative validation. The results show that the maximum transient deviation of the Hot Disk data is 0.4 °C, indicating that compared to the Hot Disk method, the proposed method exhibits higher accuracy.

The use of reverse osmosis (RO) membranes for desalination has gained popularity in generating drinking water from seawater sources. This study assesses the performance of a single-module feed-forward reverse osmosis (RO) system, representing the membrane module as a tubular module with feed flow on the tube side. A superstructure for the single-module feed-forward RO system forms the basis for a comprehensive mathematical model of the RO system. Mass, materials, and energy balances are meticulously applied to all system components. The study also explores external factors’ influence, such as feed parameters, utility costs, and product costs, on RO system performance and optimal design. It delves into parameters affecting unit performance, including feed characteristics and operational conditions. Additionally, the impact of feed specifications and operating conditions on concentration polarization within each module is investigated. The obtained results showed that the total permeate from the unit decreases with higher salt concentration on the membrane wall as the feed concentration increases, while the unit cost remains constant. In addition, the rise in feed flow rate and feed temperature led to a decrease in wall concentration. Finally, a substantial 20% reduction in wall concentration was generated with approaching the upper limits endorsed by module manufacturers for feed temperature.

Accidental leakage of liquid fuel frequently results in spill fire accidents, with radiation playing a pivotal role in flame propagation and environmental hazard. Conducted in a scale tunnel, ethanol spill fire experiment utilized five stainless steel rectangular channels, with length of 1 m, widths ranging from 0.1 to 0.3 m, and height of 0.03 m. The study focused on aspects such as flame area, bifurcation and fusion behaviors, flame height, and the distribution of flame heat radiation. Notably, as the channel width increased, the flame area and bifurcation phenomenon decreased, leading to taller flames. Drawing comparisons with the trapezoid flame thermal radiation model, we introduced a weighted multi-point source flame thermal radiation model that takes into account flame shape. In terms of predicting thermal radiation, weighted multi-point source model demonstrates a slightly higher degree of accuracy compared to trapezoid model, providing results closer to experimental values. It not only accurately predicted near-distance radiation from the spill fire but also distant radiation, with an error margin of less than 20%. This work offers crucial insights into the spatial distribution of flame heat radiation in spill fire accidents.

Nanofluid (NF) as a new type of high thermal conductivity fluid, macroscopic research methods can only observe the macroscopic change of thermal conductivity of NF, but cannot further reveal the microscopic mechanism of nanoparticles. In this paper, the microscopic mechanism of thermal conductivity enhancement of NF was simulated based on non-equilibrium molecular dynamics method (NEMD), and the thermal conductivities of argon-based gold (Au–Ar) NF with different volume fractions and Au nanoparticle sizes are simulated separately, and the radial distribution functions, system densities, and tracking atom trajectories are computed to explore the mechanism of the action of the change in thermal conductivity of the nanofluids induced by nanoparticles at the microscopic level. It was found that the thermal conductivity of Au–Ar–NF system is positively correlated with the volume fraction of nanoparticles and negatively correlated with the particle size. When the NP particle size was 0.8 nm and the volume fraction was 6.0%, the NF thermal conductivity increased by 65.7% compared to the base solution. The key finding of the study was that the underlying liquid atoms on the surface of the nanoparticles form a non-fugitive adsorption layer, and that their arrangement resembles the ordered arrangement of a solid. In the model with r (NP) = 0.8 nm, the highest thermal conductivity was 1.21 times that of the base solution, and the thickness of the adsorption layer on the particle surface was about 0.35 nm. Generally speaking, the addition of nanoparticles alters the atomic configuration of NF, resulting in NF displaying a solid-like microstructure, which significantly increases the thermal conductivity of NF.

High-temperature heat damage is a common phenomenon in the field of mine mining, and as the mining depth increases, the degree of underground heat damage is also increasing, which seriously restricts the productivity of the mine and affects the physical and mental health of workers. Based on the mining situation of mineral resources, this paper summarized the formation mechanism and heat dissipation characteristics of different heat sources of mine high-temperature heat damage and analyzed the influence of heat damage on the mine production process and human physiological and biochemical indicators. Then, we summarized the existing cooling technologies, mainly divided into non-artificial and artificial cooling technology. The cooling mechanism and application status of cooling technology were introduced and analyzed and compared the characteristics and application scope of each cooling technology, which have specific guiding and reference significance for the selection of cooling technology for different degrees of heat damage mines. Finally, building upon the low-temperature rock formation pre-cooling technology, a novel concept for quantifying the mine inlet airflow volume was advanced, along with the formulation of a relationship model that correlates the geometry of the roadway with the temperature alteration of the airflow. This development laid a theoretical foundation for harnessing the ground temperature effect in shallow roadways to regulate the temperature of the mine’s inlet airflow, thereby enhancing the working environment at the mine’s working face.

This study delved into the influence of starch botanic origin on the ultimate physicochemical properties and energetic attributes of modified starches. To accomplish this, starches extracted from potato (PS), corn (CS), and wheat (WS) underwent chemical modification via nitration, resulting in the creation of high-energy polysaccharides (NPS), (NCS), and (NWS), respectively, which were then subjected to comprehensive characterization. The principal finding of this investigation underscored the substantial impact of starch botanical origin on the properties of the resultant starch nitrate. Notably, the synthesized starch nitrate exhibited promising characteristics, including heightened density and elevated nitrogen content. Moreover, it is observed that both the short and long-term thermal stability of starch nitrate are influenced by the botanical origin of the starch, alongside their mechanical sensitivities, which diminish with higher nitrogen content. Through kinetic degradation analysis of all prepared nitro-starches, it was observed that the Arrhenius parameters decreased as nitrogen content increased. Specifically, starch nitrate derived from corn (NCS), with lower nitrogen content, displayed a higher energy of activation (E_a), while potato-derived starch nitrate (NPS), with the highest nitrogen concentration, exhibited the lowest activation energy value, indicative of its heightened reactivity.

The surge in electricity generation demand has led to heightened CO₂ emissions and climate change; thus, the emphasis on transitioning to renewable energy (solar energy) and enhancing energy efficiency (hybrid nanofluids) is emerged as the most significant solutions. The investigation examines MWCNTs-Al₂O₃-water hybrid nanofluid laminar forced convection in a circular duct subject to a uniform heat flux. The effect of MWCNTs nanoparticles percentage ratio (0 to 100%), total nanoparticles volume fraction (1 to 4%), and Reynolds number (100 to 2100) on thermal and hydraulic performance, entropy generation, and CO₂ emissions, embodied energy, and water saving is investigated numerically. ANSYS Fluent was employed to solve this issue using the finite volume method; validation of the current work demonstrates strong concordance with experimental, numerical, and theoretical investigations. Outcomes show that increasing Reynolds number, total nanoparticles volume fraction, and percentage ratio of MWCNT in hybrid nanofluid significantly affects the hydrodynamic and thermal entry region in terms of average velocity, outlet temperature, and the temperature gap in the system. The heat transfer coefficient enhances by up to 50.96%. However, the maximum pressure drop, Nusselt number, and thermal efficiency increased by 769.97%, 24.75%, and 24.75%, respectively. Moreover, the entropy production due to the thermal irreversibility was reduced by 32.65% compared with water showed for 4% of (100%:0) MWCNTs-Al₂O₃–water at Reynolds number about 2100. Furthermore, the embodied energy and water consumption, tube mass, and CO₂ emissions are reduced by 1.81041 MJ, 9.00691 m³, 0.00831 kg, and 1.09892 kg, respectively.