Journal of Thermal Analysis and Calorimetry最新文献

Prior research suggests that the use of nanotechnology may greatly improve the efficiency of enhanced oil recovery methods, especially hot fluid injection. The thermophysical characteristics of the nanofluid may have an enormous effect on how well the injection process works. However, it takes both time and resources to conduct laboratory analyses of the effects of thermophysical characteristics on the effectiveness of nanofluid-based improved oil recovery methods. Computational models can effectively forecast the thermophysical characteristics of nanofluids and how they affect oil recovery efficiency, which helps overcome this difficulty. The current study investigates the flow of vacuum residue (VR) fluid, which generates entropy when suspended graphene oxide (GO) nanoparticles. When mixed convection and variable thermal conductivity are present, a static/moving wedge allows the nanofluid to propagate. The continuity, energy, entropy, and momentum equations form the foundation of the governing model. We use certain similarity variables to simplify the suggested mathematical formulations into forms for nonlinear differential equations (DEs). We show the results of the reduced equations using the Chebyshev collocation method. We present the graphical and numerical results for all the emerging parameters. For enhanced oil recovery applications, the current results are beneficial.

This research article presents the experimental evaluation of thermal conductivity for hydraulic oil-based hybrid nanolubricants with an aim to enhance the heat transfer potential in engineering applications. The nanolubricant samples were formulated at concentrations ranging from 0.3 to 1.8%. Using transient hot wire method, the thermal conductivity of nanolubricants were evaluated for all the samples from 30 to 80 °C. The maximum enhancement in thermal conductivity was 62.93% for the highest concentration. In this paper, response surface methodology (RSM) and artificial neural network (ANN) have been employed for prediction of the thermal conductivity of nanolubricants. In RSM, analysis of variance (ANOVA) and 3D surface plot techniques were used to determine the significance of the interaction parameters on the output. A new correlation has been proposed to predict the thermal conductivity of the nanolubricants with a R² value of 0.9992. A combination of concentration and temperature (1.5783 vol% and 72.5695 °C) yielded to the maximum optimal thermal conductivity of 0.204526 Wm⁻¹ K⁻¹. In addition, multilayer perceptron, a type of neural network model, has been trained and tested to predict the thermal conductivity of the nanolubricants. Experiments have revealed that the ANN model consisting of only 10 hidden neurons has been able to achieve an average R² of 0.98567 and RMSE of 0.02463 thereby establishing its ingenuity. Comparatively, it turned out that the RSM model was slightly more accurate in predicting thermal conductivity than the ANN model.

Coal fire oxygen-lean combustion is a global catastrophe, well known and difficult to describe. To deepen the understanding of the stage characteristics under the competition between pyrolysis and oxidation in coal oxygen-lean combustion. In this study, a TA-Q600 simultaneous thermal analyzer was used to investigate the macroscopic mass characteristics of three typical low-rank coals during oxygen-lean combustion processes under different time-scale effects. Through a coupled competitive comparison of pyrolysis and combustion characteristic temperature points, the stage characteristics and evolution laws of coal in the oxygen-lean combustion process were comprehensively analyzed. The results showed that the stage development of coal pyrolysis can be divided into four stages; under different time-scale effect, the higher the coal rank, the better the separation between the thermal decomposition and the thermal polycondensation processes. The stage development patterns of coal structure conversion combustion were divided into three categories, and the stage development types were divided into six categories. The difference in the burnout state caused by the decrease in oxygen concentration includes to 4–7 different combustion progressions. When the oxygen concentration falls within the range of 5–1%, the coal combustion stage transitions and delays from semi-coke burnout to coal coke burnout. The evolution of the burnout state, induced by the oxygen concentration, remained unaffected by the coal rank but was relatively less influenced by the time-scale effect. With an increase in the coal rank under a 1% oxygen concentration, the stage progression total gradually diminishes. This characteristic remains unaffected by the time-scale effect. As the coal rank increased, the influence of the time-scale effect on the oxygen concentration of the stage development pattern evolution became increasingly evident. The results of the study guided the identification of the development progression in different areas of the fire zone and provided safer temperature and oxygen concentration indicators for fire suppression work and unsealing of the fire zone.

This paper reports on the experimental examination and optimization of a response surface methodology (RSM)-based predictive model for the thermal conductivity of aqueous multi-walled carbon nanotube (MWCNT)-based nanofluids for heat transfer applications. The design matrix was created with nanofluid temperature (°C) and nanoparticle concentration (mass/%) as independent variables, while thermal conductivity was considered as a response variable. Magnetic stirring and ultrasonication were used to produce nanofluid samples. The thermal conductivity of the prepared samples was measured, and quadratic models were selected through regression analysis. ANOVA was performed to validate the models. The maximum thermal conductivity value, i.e., 0.988 W m⁻¹ K⁻¹, was achieved at MWCNT particle content 0.5 mass/% and 60 °C temperature. A comprehensive optimization study was also performed for maximizing thermal conductivity. The optimal values for the thermal conductivity of nanofluids were found to be 0.8845 W m⁻¹ K⁻¹, whereas the optimal values for the control factors, i.e., nanofluid temperature and nanoparticles' concentration, were estimated to be 60 °C and 0.5 mass/%, respectively. The coefficient of determination R² for the thermal conductivity of the developed model was found to be 0.9866, which confirmed the suitability of the developed models. The optimized MWCNT/water nanofluid shows potential as an effective heat transfer fluid, particularly for solar thermal and hybrid photovoltaic/thermal applications.

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 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.

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.

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.