Journal of Thermal Analysis and Calorimetry最新文献

Effective thermal management is necessary for maximizing both the performance and longevity of solar cells and batteries. The present research explores novel cooling methodologies through the utilization of heat sinks integrated with nanofluids to enhance thermal regulation and improve overall efficiency. Specifically, by integrating heat sinks and nanofluids, the present research seeks to advance thermal regulation, thereby increasing the overall efficiency and reliability of solar cells and batteries. Moreover, the findings present substantial enhancements in thermal dissipation, thereby enhancing energy conversion efficiencies and increasing the lifespan of the devices. The study provides a comprehensive examination of the mechanisms of these enhancements and discusses future applications and developments in the thermal management of renewable energy systems. In this regard, five geometries of fins are introduced, and for the better case (Case 2), the effect of using ternary hybrid nanofluids with particle loading of 0.03, 0.05, 0.07 and 0.09 on the heat sink performance for Reynolds numbers between 600 and 1400 has been investigated. The results indicate that for Reynolds number 600, the heat transfer coefficients for Cases 1, 2, 3, and 4 were enhanced by 19.67%, 26.80%, 22.90%, and 11.84%, respectively, compared with the reference case. Furthermore, as the Reynolds number increased from 600 to 800, 1000, 1200, and 1400 in Case 2, the heat transfer coefficient increased by 19.57%, 36.95%, 52.78%, and 67.49%, respectively. For Reynolds number 800, the performance of Cases 1, 2, and 3 improved by 13.36%, 14.33%, and 7.55% over the base case, while the performance of Case 4 decreased by 4.92% compared to the base case. R_th decreased by up to 34.5% and Nu increased consistently across all cases. Additionally, the use of THNFs further improved HTC by up to 37.7% and reduced R_th by 32% albeit with higher ΔP. The performance evaluation criterion (PEC) confirmed Case 2 with 0.03–0.05 at Re = 800–1000 as the optimal configuration. Overall, the outcomes clearly demonstrate that the optimized fin geometry (Case 2) combined with ternary hybrid nanofluids significantly improves cooling efficiency by enhancing (HTC) and reducing thermal resistance, although accompanied by an increase in pressure drop. This provides a reliable strategy for solar cell and battery thermal management in high-performance.

The binary phase diagrams phenanthrene/naphthalene and phenanthrene/biphenyl at 1 atm were determined experimentally by combination of structural (XRD, TR-XRD) and thermal (DSC) analyses, to complete data points of the existing literature. While full discrimination at the solid state seems to be characterized in phenanthrene/naphthalene binary section (i.e., “usual” eutectic diagram), a total solid solution was characterized in phenanthrene/biphenyl diagram, with a drastic impact of biphenyl doping on phenanthrene solid–solid transition temperature.

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The research investigates the Casson fluid model for mixed convection flow in artificial neural networks, emphasizing non-Fourier double-diffusion theories alongside ion slip and Hall effects. The research investigates the dynamics of a Casson nanofluid within a Darcy–Forchheimer porous medium characterized by significant inertial and viscous stresses. The trained neural networks forecast velocity, temperature, and concentration profiles, offering a reliable computer substitute for traditional approaches, with achieved mean square errors (MSE) on the order of 10⁻⁹ to 10⁻¹⁰. The study demonstrates an inverse relationship between fluid velocity and the Schmidt number, while ion slip and Hall parameters exhibit direct correlations with vertical velocity. Furthermore, temperature profiles are directly correlated with the thermophoresis parameter ((Nt)) but inversely related to the Prandtl number (left( {Pr} right)), Hall parameter ((alpha_{text{e}})), and buoyancy ratio parameter (left( {Nr} right)). Concentration profiles increase with the mixed convection parameter (left( lambda right)), Hall parameter, Schmidt number (left( {Sc} right),), and thermal relaxation parameter (left( {lambda_{2} } right).) Neural networks utilizing backpropagation employ the Levenberg–Marquardt optimization (LMS) technique to minimize absolute errors, which are consistently contained within a range of 10⁻³ to 10⁻¹⁰, hence ensuring precision and dependability. The enhanced heat transfer properties informed by these AI-driven predictions can be utilized to develop more efficient systems in industries such as HVAC and power plants. The dataset for the proposed LMS-ANN model was partitioned into 70% for training, 15% for validation, and 15% for testing. The findings illustrate the capability of AI-driven neural networks in tackling complex, multi-parameter fluid dynamics issues and encourage additional exploration of AI-based solutions in computational fluid dynamics.

Magnetic nanoparticle hyperthermia has emerged as a promising method in cancer treatment. However, the aggregation behavior of Fe₃O₄ nanoparticles under intense magnetic fields remains poorly understood. This study mathematically models the flow of a Casson fluid embedded with both aggregated and non-aggregated Fe₃O₄ nanoparticles to assess their effect on heat transfer efficiency. Porosity ((beta _{1})), magnetic field (M), Prandtl number (Pr), heat generation parameter ((Q_0)), and nanoparticle volume fraction ((phi )) are analyzed, considering velocity and temperature profiles, skin friction ((C_textrm{fr})), and Nusselt number (Nu). The coupled nonlinear partial differential equations are nondimensionalized and solved using the bivariate spectral weighted residual method, and validation is confirmed through the finite element method. The results show that with aggregation, (C_textrm{fr}) decreases by 16.13% as (phi ) rises from 0.05 to 0.06, compared to 0.73% without aggregation. As (Q_0) increases, (C_textrm{fr}) rises by approximately 0.13% with aggregation, compared to 0.15% without aggregation. Furthermore, as (phi ) increases with aggregation, Nu decreases by 16.14% compared to 0.73% without aggregation. The higher (beta _{1}) and stronger M decrease velocity. The temperature profile increases with (phi ) for both aggregated and non-aggregated nanoparticles, indicating enhanced energy dissipation. These results demonstrate the potential of ({text{F}}{{text{e}}_3}{{text{O}}_4}) nanofluids to improve hyperthermia in cancer treatment.

This study examines the effect of the biosurfactant coco glucoside on saturated pool boiling of acetone, methanol, and ethanol on an aluminium surface. Experiments were carried out at concentrations of 0, 500, 1000, and 1500 PPM. The results show that the response of each liquid is different and the effect is not linear. Methanol shows the highest improvement at 1.0 mL, where the heat transfer coefficient increases from 7096.77 to 8943.09 W m⁻² K⁻¹. Ethanol shows a steady increase in HTC with concentration and reaches 6156.72 W m⁻² K⁻¹ at 1500 PPM. Acetone shows a small rise at low concentration and a clear drop at 1.5 mL. These trends are linked to changes in surface tension and bubble behaviour caused by the surfactant. The study also includes the development of an empirical correlation for predicting PBHTC and the use of machine learning algorithms to improve HTC prediction. The k nearest neighbour, random forest and gradient boosted tree models were trained using the experimental data. Among these, the gradient boosted tree algorithm gave the best performance with an R squared value of 0.9922 and a mean absolute percentage error of 3.27 percent. These findings show that selecting the correct surfactant concentration is important for improving boiling performance in practical applications such as heat exchangers, power plants and refrigeration systems.

The need for effective solutions for water heating presents a significant opportunity for solar energy technology, especially parabolic trough collector (PTC). Nevertheless, improving their thermal performance is the most challenging undertaking. The main objective of this study is to find the best arrangement of dimpled absorber tubes to raise a PTSC’s thermal performance for water heating applications. An experimental investigation was conducted to raise the thermal performance of a (PTC) by using dimpled absorber tube with dimples in the shape of (truncated cone). During the study, four absorber tubes are used (smooth, dimples in-line 2 sides, dimples in-line 4 sides, and dimples staggered 4 sides). The experiments were conducted in (Tikrit–Iraq) for the period (27 August 2023) to (26 September 2023) during the time from (8:00 AM) to (4:00 PM). The study’s findings demonstrated that the highest water outlet temperature is (364.15 K) obtained using a dimpled absorber tube staggered 4 sides at a volume flow rate of 0.2 L min⁻¹ and time (1:30 PM), and the highest percentage increase in water outlet temperature using dimpled absorber tube staggered 4 sides compared to a smooth tube is 23.81% at a volume flow rate of 0.6 L min⁻¹ and time (10:00 AM). Additionally, the experimental study demonstrated that the highest value of Nusselt number (Nu) and efficiency of the (PTC) is (11.1) and (39.428%), consequently using a dimpled absorber tube staggered 4 sides and a volume flow rate of (0.6 L min⁻¹) and time (1:30 PM).

Form-stable phase change materials (FSPCMs) with limited thermal management temperature ranges restrict their applications in terms of large temperature differences; therefore, the development of FSPCMs with wide phase change temperature ranges and high latent heat is vital for practical applications in thermal energy storage. Eicosane–paraffin/expanded graphite (EG) FSPCMs were prepared by vacuum adsorption method, and the samples were characterized by FT-IR, XRD, SEM, thermal conductivity test, DSC, TGA, and thermal cycling test. There was no chemical reaction during the synthesis of FSPCMs, and the obtained eicosane–paraffin PCM was successfully adsorbed in the EG structures. The FSPCM with 10% EG was the best ratio. The synthesized FSPCMs had good phase change qualities with a ΔHm of 155.6 J g⁻¹ and a phase transition temperature range of 15.18–61.85 °C, which was much wider than that of eicosane (22.23–44.40 °C). The FSPCMs maintained good thermal stability up to 140 °C. Furthermore, the thermal cycling properties of FSPCMs remain steady. The introduction of EG can improve heat transfer. In conclusion, the synthesized FSPCMs with a wide phase change range exhibit promising potential for practical applications in the field of thermal energy storage.

This study investigates the formulation, stability and thermophysical properties of graphene-water nanofluids and its impact on heat transfer efficiency in a shell-and-tube counterflow heat exchanger. Nanofluids were prepared using concentrations of 0.1%, 0.2%, 0.3% and 0.5%. As the volume fraction increased, density and viscosity rose, while both parameters decreased with rise in temperature. Initially, when tested with water, the overall convection heat transfer coefficient (Ui) on the tube side increased with the flow rate. At a temperature of 70 °C and a flow rate of 2.37 L min^-1, the maximum Ui of 633 W m^-2 K^-1 was measured, signifying a 65.2% increase in performance over 40 °C. The total heat transfer coefficient was improved by 37.4% and the heat transfer coefficient in comparison to water was increased by 45.7% when 0.5 mass percent graphene nanofluids were used as the cooling medium. The Nusselt number increased by 43.2% at this concentration. However, the friction factor increased by 16%, while pressure dropped by 8.3%. There was a maximum gain of 6% in the thermal performance factor. Not with standing these compromises, the results indicates that the graphene nanofluids have energy-saving potential in heat exchanger applications and significantly increase heat transfer efficiency.

The micellization process of a series of cationic gemini surfactants (left[{C}_{{rm m}}{H}_{2m+1}{left(C{H}_{3}right)}_{2}N{left(C{H}_{2}right)}_{{rm s}}N{left(C{H}_{3}right)}_{2}{C}_{{rm n}}{H}_{2{rm n}+1}right]B{r}_{2}) (designated as m-s–n with m = 10, s = 4, 6, 8 10, and n = 6, 10, and 12, symmetric with m = n) has been investigated using conductivity and isothermal titration calorimetry (ITC) as a function of temperature. The CMC values and the degrees of counterion dissociation, determined from conductivity experiments,  represent perhaps the first complete assessment for a series of symmetric and dissymmetric amphiphiles as a function of temperature and spacer length from any technique. The thermodynamic properties of micelle formation were obtained by calculating the Gibbs energies of micelle formation ((Delta_{{{text{mic}}}} G)) from the conductivity and the counterion dissociation data at each temperature and applying the Gibbs–Helmholtz equation and the Gibbs equation to obtain the enthalpies and entropies of micelle formation ((Delta_{{{text{mic}}}} H) and (Delta_{{{text{mic}}}} S)), respectively. The (Delta_{{{text{mic}}}} H) values were also obtained directly for the same systems using isothermal titration calorimetry; from the calorimetrically determined CMC values and the degrees of counterion dissociation from conductance measurements, the calorimetric (Delta_{{{text{mic}}}} G) and (Delta_{{{text{mic}}}} S) values were obtained. As expected, the values of the micellar thermodynamic properties from both techniques are not comparable! However, the results from both techniques agree in that only a minor effect on the CMC (and hence, the (Delta_{{{text{mic}}}} G) values) is found with changing temperature, spacer length, and the ratio of m/n (the degree of dissymmetry). However, we see large effects on both the aggregate enthalpy and entropy as both the spacer length and the degree of dissymmetry changes. The trends in the thermodynamic properties of micelle formation can be rationalized in terms of the intermolecular and intramolecular contributions to hydrophobic interactions versus hydrophobic effects and electrostatic interactions because of packing differences in the micelles.

This study presents a comparative analysis of energy destruction in integral rolled spiral finned tube (IRSFT) bundles in heat exchangers, employing advanced machine learning techniques to enhance efficiency. Traditional normal finned tube (NFT) designs often encounter significant energy losses due to suboptimal geometric parameters. To address this, machine learning algorithms were applied to optimize the design and performance of integral rolled SFT bundles. The research involved extensive numerical simulations using computational fluid dynamics (CFD) to model heat transfer and energy destruction. Key geometric parameters, including fin tip and root thickness, were varied and analysed. Machine learning models, particularly deep neural networks (DNNs), were then trained on this simulation data to predict the optimal configurations that minimize energy destruction. The results demonstrate that machine learning techniques can significantly improve the thermal performance of integral rolled SFT bundles. The optimized designs, identified through machine learning, exhibited lower entropy generation and higher heat transfer efficiency compared to traditional designs. Specifically, the optimized SFT bundles achieved a reduction in energy destruction by up to 20%, highlighting the potential of these advanced techniques in enhancing heat exchanger performance. Furthermore, the study developed performance evaluation criteria (PEC) to assess and predict the impact of various fin configurations on system efficiency. These criteria provided a comprehensive framework for understanding and mitigating energy losses. The integration of machine learning techniques in the design of integral rolled SFT bundles results in substantial improvements in efficiency of 7%. This approach not only reduces energy destruction but also offers a robust methodology for optimizing heat exchanger performance in various industrial applications.