Journal of Thermal Analysis and Calorimetry最新文献

Thermal analysis and calorimetry techniques with sealed sample cells are frequently employed to investigate the thermal stability of hazardous materials. However, the influence of sample mass on thermal decomposition is often ignored. To investigate the effect of sample mass on autocatalytic decomposition substances under sealed condition, 2,4-DNT was chosen for test and analysis. The techniques including differential scanning calorimetry (DSC), rapid screening calorimetry(RSC), high-performance liquid chromatography-mass spectrometry (HPLC–MS), and gas chromatography-mass spectrometry (GC–MS) were utilized for in-depth research on the decomposition of 2,4-DNT. The results revealed that an increase in sample mass led to an significant elevation in gaseous products such as CO₂ and CO with a negative heat of formation (ΔH_f). Then the system pressure and decomposition heat of 2,4-DNT increase significantly, thereby enhancing the thermal decomposition process. After that, it has been determined that o–nitrotoluene, m–nitroaniline, and o–nitrobenzoic acid can catalyze the decomposition of 2,4-DNT. Finally, it is advisable to increase the sample mass as much as possible when testing and analyzing such nitro compounds which would help ensure that the predicted results are closer to the actual conditions of production, transport, storage.

The polymorphic behavior of triacylglycerol (TAG) crystals formed during the manufacturing process of lipid-based food products relates directly to their textural and melting properties. In this work, we analyzed the polymorphic crystallization and transformation behavior of 1,2-dipalmitoyl-3-oleoyl-rac-glycerol (PPO), a widespread TAG in edible fats and oils, during the application dynamic thermal treatments of cooling and heating. By implementing calorimetric, X-ray diffraction, and microscopy techniques, we mapped the polymorphic occurrence and the polymorphic transformation pathways of PPO as a function of the rate of thermal treatments. The results obtained were later compared to that reported for diverse TAGs in previous studies. Despite the overall crystallization and transformation behavior of PPO following a similar trend to other TAGs close in fatty acid composition, we can highlight the much lower influence of varying cooling and heating conditions on the crystallization properties of this TAG. In more detail, crystalline forms of low stability were generally promoted during crystallization, whereas transformations occurred always through the melt independently of the heating conditions. One may expect this behavior to influence the industrial processing and final properties of food products based on edible fats containing PPO.

The investigation of velocity slip combined with the dissipative heat corresponds to the non-Newtonian Williamson hybrid nanofluids utilizing the “Cattaneo-Christov heat flux model” is crucial in advanced applications in several sectors. The proposed analysis focuses on the hybrid nanofluid comprised of magnesium oxide (MgO) and zirconium dioxide (ZrO₂) in water which boosts the thermal conductivity along with the performance of the fluid. The magnetized Williamson fluid is a particular type of non-Newtonian fluid that exhibits essential applications to biomedical engineering. The insertion of magnetization along with porosity suggests considering the dissipative heat impact associated with Joule and Darcy which energies the heat transport phenomena. The limitation of classical Fourier laws is addressed by the consideration of the Cattaneo–Christov heat flux framework along with the thermal radiation. The designed flow model with dimensional terms is transformed into a corresponding non-dimensional form by implementing similarity functions. Further, these transmuted equations are solved numerically via the shooting-based Runge–Kutta technique. The parametric analysis of the flow phenomena is obtained and arranged graphically. The validation with earlier investigation displays a valid association in particular scenarios. The main outcomes reveal that the resistivity characteristics produced by the interplay between permeability and magnetization regulate fluid velocity, especially when combined with the non-Newtonian Williamson parameter. Furthermore, in both nanofluid and hybrid nanofluid scenarios, the fluid temperature is greatly raised by the effects of thermal radiation and the Eckert number.

Optimizing the process of flow boiling and improving heat transfer efficiency require preventing the onset of flow instability (OFI) during the operation. The aim of this work was to examine the instability of upward flow in a rectangular mini-channel with a cross-section of 2 (times ) 2 mm when the heat flux and mass flux were gradually increased. The study examined the periodic transition of flow patterns during OFI, calculated the impact of bubble coalescence on OFI using the growth rate of bubbles with the potential to form gas columns, and introduced entropy generation to assess the irreversibility and disorder of the system. The findings revealed that the formation of gas columns during OFI is the main cause of the periodic transition of flow patterns. When the inlet subcooling ({Delta T}_{text{sub}}) is 12.1 (text{K}), the average number of bubble coalescences during OFI is 240% higher than during stable flow. Pressure drop fluctuations are significantly impacted by bubble coalescence, which also contributes to the formation of gas columns and an increase in system instability. The pressure drop and the heat transfer coefficient are inversely related. Lowering the inlet subcooling and reducing mass flux are like to cause OFI. Entropy generation analysis indicates that reducing inlet subcooling and increasing fluid velocity can reduce the system's irreversibility. When OFI occurs, entropy generation rises sharply.

The effect of hydrogen peroxide (H₂O₂) on the thermal decomposition characteristics of N-methylmorpholine-N-oxide (NMMO) was investigated using thermal experiments and quantum chemical calculations. The thermal decomposition characteristics of the NMMO and NMMO/H₂O₂ systems were determined at different heating rates using a microreaction calorimeter. Firstly, H₂O₂ decreased the onset temperature and increased the reaction enthalpy of NMMO decomposition. Secondly, the apparent activation energy decreased from 97.49–76.79 kJ mol⁻¹ to 80.76‒89.87 kJmol⁻¹ based on the Kissinger and Starink models. Finally, density functional theory was used to investigate the effect of H₂O₂ on NMMO decomposition from a microscopic perspective. It was found that the length of the N–O bond in NMMO increased, and the ability of oxygen atoms to obtain electrons was enhanced in the presence of H₂O₂, decreasing the stability of the NMMO molecules. The nucleophilicity of the oxygen atom in the NMMO molecule was enhanced in the NMMO/ H₂O₂ system, which is beneficial for electron transfer to the H₂O₂ molecule. The energy-gap of the frontier molecular orbital decreased under the influence of H₂O₂, intensifying the interaction between H₂O₂ and NMMO to form π bonds. Overall, the stability of the NMMO molecular structure was reduced and the reactivity of NMMO was enhanced at lower temperatures in the presence of H₂O₂, which provides a theoretical reference for the safe production of NMMO.

A broad and impactful application in designing and optimizing thermal system in engineering is due to the utility of the nanoparticles. These include advanced cooling technologies in electronics and enhanced recovery processes where managing heat flow in porous medium. Based on the above-mentioned features and utilities, a study is carried out in examining the flow characteristics involving the Marangoni convection of a radiative tri-hybrid nanofluid passing via a Riga plate by considering the variable thermal conductivity and the effect of Darcy–Forchheimer inertial drag. The incorporation of heat source/sink relating to both space and temperature dependent with the imposition of a magnetic field enriches the flow phenomena of a nanofluid consisting of composite nanoparticles. The thermal properties combined with the effect of thermal conductivity, density, etc., enrich the transport phenomena. The utilization of the specific similarity rules is effective in transforming the designed model into a dimensionless. Further, a numerical technique is introduced for the solution of these transmuted equations and the numerical values correlating to the established results show a good relationship in a particular case. The important characteristics of several factors about the flow phenomena are presented briefly through graphs. The observations reveal that the enhanced Hartmann number gives rise to increase the fluid velocity and the radiative heat for the inclusion of thermal radiation also favours in enhancing the fluid temperature.

This paper presents a detailed numerical modeling of a hybrid photovoltaic-thermal (PVT) unit combined with a TEG (thermoelectric generator), focusing on two key performance indicators: profit and CO₂ mitigation (CM). The study investigates how the unit's electrical and thermal outputs vary with different geometrical configurations of the cooling duct's cross sections. Four distinct geometries—circular, elliptical, triangular, and square—were analyzed, with results highlighting the effects of these shapes on system performance. The cooling medium used in the ducts is a hybrid nanofluid composed of copper and aluminum oxide nanoparticles suspended in water. This hybrid nanofluid was selected for its enhanced heat transfer properties, which directly impact the system's efficiency. The findings reveal that among the examined geometries, the triangular duct provides the best overall performance in terms of both profit and CM. Transitioning from a circular to a triangular duct results in a profit increase of approximately 2.58%, while CM improves by around 2.14%. Furthermore, increasing the inlet velocity of the coolant within the duct contributes to further gains, with profits and CM both enhanced by approximately 6% and 5%, respectively. The importance of current work lies in its demonstration that optimizing the cooling duct geometry, coupled with the use of hybrid nanofluids, can substantially improve both the economic and environmental performance of PVT-TEG systems.

The present work explores a design process of new medium-Mn alloy for forgings and its heat treatment optimization by thermodynamic simulations and experimental approach. The selection of specific chemical composition was performed on the basis of thermodynamic simulation for alloys with different additions of Mn and Al. The aim was to design an alloy allowing for production of at least 25% retained austenite in an intercritical annealing process, without deteriorating technological properties and economic indicators. Next simulations of intercritical annealing in a temperature range between 600 and 1000 °C, and their experimental verification were performed. For the thermodynamical simulations of different chemical compositions of steel and its intercritical annealing in a wide temperature range the JMatPro software was used. To verify the characteristic temperatures of steel such as A_c1, A_c3 and M_s, and for experimental investigation of intercritical annealing in a temperature range from 660 to 740 °C dilatometry was used. Obtained microstructures were characterized by means of X-ray diffraction and scanning electron microscopy. It was observed that with an initial increase in soaking temperature a fraction of retained austenite increases; however, its stability decreases, which leads to formation of large martensite fraction during cooling after soaking at high temperatures. The results of thermodynamic simulations and experimental tests showed the moderate agreement. Large differences were revealed for A_c1, M_s temperatures and the amount of retained austenite obtained at a given annealing temperature. The results clearly indicate that at the moment of software development and available databases for novel medium-Mn steels, simulations of their heat treatment can only be used to estimate results and be a guide for experimental research. However, they cannot be used to optimize heat treatment.

Nanofluids have the potential to improve heat transfer in automobile radiators, but issues such as scale formation in nanofluids, and inefficient tube design limit their effectiveness. Hence, this research introduces an Enriched Nanofluid with IERO process and Waist Tube Heat exchanger to enhance heat transmission in heat exchangers and disables the limitations of conventional nanofluids. The existing nanofluids faces issues with scale generation due to nanoparticle interaction with coolant ions, resulting in lower system efficiency and possible overheating. To address these challenges, the Enriched Nanofluid with IERO approach is used for eliminating the efficiency concerns and the risk of overheating. In this nanofluid contains Al₂O₃ nanoparticles in a mixture of water and ethylene glycol, and it is stabilized with a graphene oxide (GO) surfactant for ensuring optimal dispersion of nanoparticles. The Ion Exchange Reverse Osmosis (IERO) process continues to treat the coolant using Organic Polymers, reducing scale growth and improving coolant purity, which further mitigates overheating risks for improving system efficiency. Moreover, insufficient heat exchange and airflow coverage in the existing wasp waist tubes leads to flow separation of the tube surfaces. Thus, a novel wasp waist elliptic section tube design is implemented with an elliptical back end to reduce flow separation and the airflow line covers a larger area of the tube, thereby improving heat transfer efficiency. As a result, the proposed design surpasses existing heat exchanger designs with a higher pressure drop of 5100 Pa at Reynolds number 6500, heat transfer coefficient of 182 W/m²K, and greatest heat transfer rate of 85 W.

The estimation of heat transfer coefficients (HTC) and pressure drop (ΔP) in flow boiling processes is essential for the effective design and operation of refrigeration systems. In this study, the artificial neural network (ANN), locally weighted regression (LWR), and gradient boosted machine (GBM) methods are employed to predict the boiling heat transfer coefficient (HTC) and pressure drop ((Delta P)) in flow boiling of R134a. The study focuses on horizontally positioned both straight and microfin tubes. The ANN, LWR, and GBM methodologies are utilized to ascertain the parameters of boiling HTC and ΔP as outputs. These parameters are determined by considering the mass flux, saturation pressure, heat flux, vapor quality, Reynolds number, Lockhart–Martinelli parameter, Froud number, Weber number, and Bond number as inputs. The training dataset is partitioned into 5 sections for the purpose of hyperparameter tweaking for each model. Out of these sections, 4 parts, consisting of approximately 111 samples, are utilized for training, while 1 part, including around 27 samples, is allocated for validation. The optimal hyperparameters are determined by calculating the average R² score over the 5 validation sets. Using raw measurements, HTC and ΔP are successfully modeled using a relatively much smaller dataset of 174 measurements, with 82.4% R² score and 0.7% weighted average relative deviation for HTC, and 88.9% R² score and 4.1% weighted average relative deviation for ΔP across multiple tube types, achieved by LWR algorithm. Model performances are validated with an extrapolation test and found to be consistent with traditional train–validation–test sampling scheme with 75.9% R² score and −6.2% weighted average relative deviation for HTC, and 89.3% R² score and −3.9% weighted average relative deviation for ΔP, showing the consistency of the hypotheses created by a hybrid of parametric and nonparametric model families even outside the observed measurement range for multiple tube types. Local weighted regression models are the most performant, especially for limited data availability. However, calculated measurements increase error rates, suggesting that HTC and ΔP models work best with raw measurements.