Journal of Thermal Analysis and Calorimetry最新文献

Graphene-based coating systems have emerged as a new generation of flame-retardant materials with outstanding thermal stability, mechanical robustness, and multifunctionality. This review provides a comprehensive analysis of the thermal behavior, degradation pathways, and flame-retardant performance of graphene-enhanced nanocomposite coatings. The discussion begins with fundamental flame-retardant mechanisms—char formation, heat dissipation, and radical trapping—highlighting how graphene facilitates the development of compact, thermally stable carbonized layers that act as effective barriers to heat and oxygen. The synergistic interactions between graphene and conventional flame retardants, including phosphorus-, nitrogen-, and metal oxide-based additives, are critically examined to explain their dual-phase (gas and condensed) protective actions. Advanced coating architectures—polymeric (epoxy, polyurethane, and polyaniline), metal oxide/graphene, ceramic/graphene, and MXene–graphene hybrids—are reviewed with emphasis on fire resistance, smoke suppression, and thermal endurance. These hybrid systems exhibit significant reductions in peak heat release rate (20–40%) and increases in char yield (15–30%) compared with pristine polymers. Next-generation fabrication methods—ranging from layer-by-layer assembly and plasma-assisted deposition to 3D printing, electrospinning, and spray coating—are paving the way for scalable, uniform, and high-performance flame-retardant coatings. Additionally, smart graphene-based coatings capable of self-healing, real-time fire detection and multifunctional properties—such as electromagnetic shielding and corrosion protection—are discussed. Finally, a comparative analysis with conventional flame-retardant coatings underscores the superior stability, environmental compatibility, and long-term durability of graphene-based systems. The review concludes with future perspectives on green synthesis, multicomponent hybridization, and intelligent monitoring integration for next-generation fire-safe materials in aerospace, automotive, and electronic applications.

The efficiency of photovoltaic thermal (PVT) systems is often hindered by high operating temperatures, which can be effectively addressed through advanced cooling methods. This study explored the use of a water-based tin dioxide (SnO₂) nanofluid at a 0.1% concentration as an enhanced coolant to boost the system’s exergy efficiency. The research involved experimental testing under three distinct flow rates—0.5, 1.0 and 1.5 LPM—to evaluate the nanofluid’s performance. The results confirmed that the nanofluid offered a significant advantage over conventional pure water cooling. Specifically, at the highest flow rate of 1.5 LPM, the maximum exergy efficiency improved remarkably from 11.1 to 18.9%. In addition to the experimental work, the study also developed and tested several machine learning (ML) models to predict the system’s performance. Two primary models, K-Nearest Neighbor (KNN) and Support Vector Regression (SVR), were utilized. The researchers also investigated the impact of integrating Wavelet Transform (WT), a signal-processing technique, with these ML models. The results demonstrated that the SVR model combined with Wavelet Transform (SVR-WT) provided the most accurate predictions on the test dataset. This model achieved an impressive coefficient of determination (R2) of 0.885, indicating a strong correlation between the predicted and actual values. Its predictive capability was further highlighted by a low root mean square error (RMSE) of 2.196 and a mean absolute error (MAE) of 3.086. Overall, the findings conclusively establish that SnO2 nanofluid is an excellent coolant for enhancing PVT system performance, and that the SVR-WT model offers a reliable predictive framework for optimizing these systems.

This study presents a three-dimensional numerical model for analyzing the heat transfer behavior of lithium-ion battery (LIB) modules, with applications in electric vehicles and energy storage systems. A computational fluid dynamics (CFD) approach, coupled with a user-defined heat generation model, was employed to investigate the influence of cooling geometry, discharge rate, fluid type, and flow conditions on thermo-fluid performance. The analysis focuses on a 4 × 4 module of 18,650 cylindrical cells simulated in ANSYS Fluent 19.0 by solving the energy, momentum, and turbulence equations. Three air cooling configurations (AC-1–AC-3), two liquid cooling configurations (LC-1 and LC-2), and one hybrid configuration (HC) were developed and analyzed. Simulations were performed for air inlet velocities of 2–4 m.s⁻¹, water velocities of 0.1–0.7 m.s⁻¹, C-rates of 0.5C–5C, and inter-cell spacing of 2 mm. The results demonstrate that both air and liquid velocities have a significant impact on temperature rise and pressure loss. Among all tested configurations, the hybrid air–liquid (HC) system demonstrated the most efficient thermal behavior, achieving up to a 25% reduction in maximum temperature and keeping it below 310 K while maintaining acceptable pressure losses. This CFD-based framework provides a reliable and cost-effective approach for optimizing the design of battery thermal management systems (BTMS).

Indoor thermal comfort and air quality are essential for occupant well-being, while simultaneously optimizing energy consumption in buildings. Achieving a balance between these factors presents a significant challenge, as indoor environments are dynamic and energy demands fluctuate. By modifying ventilation rates in response to real-time data, demand-controlled ventilation systems can reduce energy consumption and enhance indoor comfort and air quality. However, optimizing these systems with advanced predictive models remains a complex task. To address this challenge, this publication proposes a Dual-Stream Multi-Dependency Graph Neural Network (DMGNN)-based energy-efficient ventilation management technique that maximizes indoor air quality and thermal comfort. The suggested method seeks to enhance thermal comfort and air quality by maximizing heating, ventilation, and air conditioning (HVAC) operations while reducing energy consumption. Initially data are collected from an Indoor Air Quality Monitoring Dataset. The DMGNN is employed to capture the complex dependencies between environmental factors such as temperature, humidity, and CO₂ concentrations, considering both temporal and spatial relationships. Implementing the proposed system and evaluating it through simulations in various building environments demonstrates notable improvements in thermal comfort, indoor air quality, and energy economy. The suggested system’s performance is contrasted with that of other current methods, showing superior energy efficiency and optimization of both indoor air quality and occupant comfort. This study presents an innovative, scalable framework for smart building management, promoting sustainable energy solutions.

Power plants frequently operate under off-design conditions, necessitating comprehensive performance assessments across varying load levels. This study applies energy, conventional exergy, conventional exergoeconomic, advanced exergy, and advanced exergoeconomic analyses to a three-pressure combined cycle power plant in Rudshur, Tehran, comparing full load (100%) and partial-load (75%) performance. At full load, the plant achieves high net power output and improved exergy efficiency, albeit with increased component cost rates and exergy destruction. Advanced exergy analysis identifies the low-pressure steam turbine as the primary source of endogenous avoidable exergy destruction, while the gas turbine incurs the highest endogenous avoidable investment cost. Under partial load, energy and exergy efficiencies decline by 2–3%, with proportional reductions in investment and exergy destruction costs, while the ratios of avoidable to unavoidable and endogenous to exogenous losses remain stable. Notably, the condenser assumes a greater share of investment cost at partial load, while the low-pressure steam turbine remains the dominant source of endogenous avoidable exergy destruction. To our knowledge, relatively few studies have integrated advanced exergy decomposition with exergoeconomic valuation across both full load and representative part load conditions for an actual three-pressure combined cycle plant. This work applies that integrated approach to the Rudshur power plant (Tehran) and identifies component-level thermoeconomic priorities under realistic load variation.

Addressing the global water crisis requires efficient desalination technologies. This study introduces a novel, multi-level nanomaterial integration method to significantly enhance the productivity of a solar still. The system employs a synergistic approach: a nano-paint coating (graphite/activated carbon) in a checked pattern on the glass cover, a preheater for the inlet saline water, and nanoparticles (graphite, ZnO, Cu, and AC) dispersed directly in the basin water at varying concentrations (5–20 g). Experimental results demonstrate that a 15 g nanoparticle concentration yields optimal performance, with the integrated system particularly using an 8 mm thick nano-coated glass cover achieving the highest freshwater yield. This multi-enhancement strategy presents a viable and sustainable path for high-efficiency solar desalination in water-scarce regions.

This study presents a comprehensive thermal analysis of a solar-assisted gas turbine power plant. The central concept involves integrating parabolic trough collector (PTC) modules upstream of the combustion chamber to reduce naphtha fuel consumption and enhance the system's overall sustainability and efficiency. The analysis begins with the standalone gas turbine system and then progresses to a combined configuration with the solar collectors. A parametric investigation is conducted, with particular focus on pressure losses. Results indicate that the optimal  electrical efficiency is achieved at a pressure ratio of 7 to 9 with 20 collectors, 6 to 8 with 40 collectors, and 5 to 7 with 60 collectors in series, regardless of the number of collectors arranged in parallel. Additionally, the system yields a maximum electrical output of approximately in the range of 16.5 MW to 17 MW at a pressure ratio of 5 to 7 for 40 or 60 or 80 collectors in parallel regardless of the number of collectors arranged in series . The study demonstrates that incorporating solar collectors before the combustion chamber can lead to fuel savings of up to 50%, with only a 2% reduction in electricity production. Finally, a multi-objective optimization approach, based on the Pareto front methodology, is employed to identify the optimal configuration of the solar collector field and pressure ratio for maximizing the performance of the solar-assisted gas turbine power plant.

Borate minerals hold broad industrial significance, particularly in the production of high-performance ceramics and specialty glasses, where precise control over thermal and structural properties is critical. Detailed understanding of the thermal decomposition pathways of borate phases is essential for optimizing these applications. Priceite, calcium borate hydrate Ca₂B₅O₇(OH)₅·H₂O, is a borate mineral occurring in many regions. In this study, we investigated its thermal decomposition via thermogravimetric (TG) analysis and ex situ high-temperature synchrotron powder X-ray diffraction (XRD), revealing three dehydration steps, ultimately forming an amorphous anhydrous phase and subsequently crystallizing into CaB₂O₄ (I) at 700 °C. The XRD pattern remained stable until 200 ℃, whereas it changed significantly after heating at 250 ℃ for 60 min, forming the dehydrated phase Ca₂B₅O₇(OH)₅. Heating at 300 ℃ for 60 min significantly increased crystallinity and produced a second dehydrated phase Ca₂B₅O₈(OH)₃, which persisted until 400 ℃. Heating at 450 ℃ for 60 min fully converted Ca₂B₅O₈(OH)₃ to the amorphous phase Ca₂B₅O₉, consistent with the TG/DTA measurements. CaB₂O₄ (I) appeared at 700 ℃, with its crystallinity improving up to 1000 ℃. Therefore, through several dehydration, amorphization, and decomposition steps, the fundamental building block of priceite 〈3▢⟩–〈∆2▢⟩ finally transformed into the one-dimensional infinite chain of corner-sharing BO₃ triangles in CaB₂O₄ (I). The recent identification of Ca(BO₂)₂ as an outstanding deep-ultraviolet (DUV) birefringent material further highlights the importance of elucidating its formation mechanism from hydrated precursors. This study provides insights into the structural evolution leading to CaB₂O₄ (I), thereby advancing future material design and industrial applications in optics and thermal-resistant materials.

The oxy-steam combustion characteristics of ShenFu (SF) superfine pulverized coal with three average particle sizes (9.83, 21.63, and 35.26 μm) were studied by thermogravimetric analysis. Combustion tests were performed under different atmospheres (O₂/N₂, O₂/CO₂, and O₂/H₂O) with oxygen concentrations of 21, 30, 40, and 60%. Under the same oxygen concentration, the ignition and burnout temperatures in the O₂/H₂O atmosphere are lower than those in the O₂/N₂ and O₂/CO₂ atmospheres, which indicates that steam significantly enhances combustion reactivity. For samples of the same particle size, increasing the oxygen concentration shifts the combustion process to a lower temperature region, further enhancing reactivity. The influence of particle size varies across the different atmospheres. In the O₂/H₂O atmosphere at a fixed oxygen concentration, reactivity decreases rapidly as the average particle size increases from 21.63 to 35.26 μm, while only a slight decrease is observed when the size increases from 9.83 to 21.63 μm.

This study presents a comprehensive analysis of Cu–Al₂O₃/water hybrid nanofluid over a stretched cylinder surface, including microscale effects in terms of velocity slip and thermal jump conditions. An unsupervised deep learning framework called unsupervised deep learning physics-informed neural networks (UDL-PINN) was used in modeling the nonlinear system governing the momentum and energy transport processes. The physical effects for magnetohydrodynamics (MHD), viscous dissipation, curvature, and radiative heat transfer were incorporated, and the governing equations were transformed into a coupled system of dimensionless ordinary differential equations through similarity transformations. These differential equations were solved in a physics-informed neural network framework with automatic differentiation. A number of parametric studies in the regime of slip and jump conditions, Prandtl number, Eckert number, curvature, and magnetic field strength were investigated in order to determine the effect on the velocity and temperature distributions. The results benchmarked well to already existing numerical solutions, confirming the accuracy and convergence of the model. The results confirmed that hybrid nanofluids has optimal heat transfer enhancement given the same conditions, and reduced skin friction compared to classical nanofluids. The model was also a consistent operating framework for future deep learning integration into real-time digital twin systems, which could optimize thermal processes in engineering, biomedical, and bioenergy applications.