Thermo

Due to modern comfort demands and global warming, heating, ventilation, and air conditioning (HVAC) systems are widely used in many homes and buildings. However, HVAC based on the Vapor Compression System (VCS) is a major energy consumer, accounting for 20–50% of a building’s energy consumption and responsible for 29% of the world’s CO₂ emissions. Dew-point evaporative coolers offer a sustainable alternative yet face challenges, e.g., dew point and wet bulb effectiveness. Given the above, dew point evaporative cooling systems may find a place to dethrone conventional air conditioning systems. This research aims to design a dew point evaporative cooler system with better performance in terms of dew point and wet bulb effectiveness. In terms of methodology, a heat exchanger as part of a counter-flow dew point cooling system was designed and analyzed using COMSOL simulations under different representative climatic, geometric, and dimensional conditions, taking into account turbulent flow. Next, our model was compared with other cooling systems. The results show that our model performs similarly to other cooling systems, with an error of around 6.89% in the output temperature at low relative humidity (0–21%). In comparison, our system is more sensitive to humidity in the climate, whereas heat pumps can operate in high humidity. The average dew point and wet bulb effectiveness were also higher than reported in the literature, at 91.38% and 147.84%, respectively. In addition, there are some potential limitations of the simulations in terms of the assumptions made about atmospheric conditions. For this reason, the results cannot be generalized but must be considered as a starting point for future research and technology development projects. Full article

Here, we investigate the performance of phase-change materials (PCMs) in the passive thermal control of space habitats. PCMs are able to absorb and release large amounts energy in the form of latent heat during their (typically, solid-to-liquid) phase transition, which makes them an ideal choice for passive temperature control. In this study, a conceptual design of an igloo-shaped habitat is proposed. A scaled model for laboratory experiments is manufactured via 3D printing, using tap water as the PCM. The setup is used to conduct experiments and analyze PCM performance, based on temperature measurements inside and outside the habitat. Results demonstrate the effectiveness of PCMs in increasing thermal inertia and stabilizing the habitat interior temperature around the melting temperature, confirming that PCMs can be a suitable alternative for passive thermal control. The present study holds significant interest for the future of space exploration, with the emerging need to design habitats that are capable of accommodating astronauts. Full article

Global efforts aiming to shift towards renewable energy and smart grid configurations require accurate unit commitment schedules to guarantee power balance and ensure feasible operation under different complex constraints. Intelligent systems utilizing hybrid and high-level techniques have arisen as promising solutions to provide optimum exploration–exploitation trade-offs at the expense of computational complexity. To ameliorate this requirement, which is extremely expensive in non-interconnected renewable systems, radically different approaches based on enhanced priority schemes and Boolean encoding/decoding have to take place. This compilation encompasses various mappings that convert multi-valued clausal forms into Boolean expressions with equivalent satisfiability. Avoiding any need to introduce prior parameter settings, the solution utilizes state-of-the-art advancements in the field of artificial intelligence models, namely Boolean mapping. It allows for the efficient identification of the optimal configuration of a non-convex system with binary and discontinuous dynamics in the fewest possible trials, providing impressive performance. In this way, Boolean mapping becomes capable of providing global optimum solutions to unit commitment utilizing fully tractable procedures without deteriorating the computational time. The results, considering a non-interconnected power system, show that the proposed model based on artificial intelligence presents advantageous performance in terms of generating cost and complexity. This is particularly important in isolated networks, where even a-not-so great deviation between production and consumption may reflect as a major disturbance in terms of frequency and voltage. Full article

Textile heaters are made from knitted conductive yarns integrated into their fabric, making them stretchable, washable, breathable and suitable for close-to-skin wear. However, the non-zero resistance in the lead wires causes non-uniform power distribution, which presents a design challenge. To address this, the electrical performance of the heaters is modeled as an n-ladder resistor network. By using the finite difference method, simple, closed-form expressions are derived for networks with their power source connected to input terminals A₁B₁ and A₁B_n, respectively. The exact results are then used to derive approximations and design criteria. The solutions for the ladder networks presented in this paper apply to a wider class of physical problems, such as irrigation systems, transformer windings, and cooling fins. Full article

This review provides a critical assessment of the current state of affairs regarding the solvation thermodynamics involving mixed-solvent systems. It focuses specifically on (i) its rigorous molecular-based foundations, (ii) the underlying connections between the microstructural behavior of the mixed-solvent environment and its thermodynamic responses, (iii) the microstructural characterization of the behavior of the mixed-solvent environment around the dilute solute via unique fundamental structure-making/-breaking functions and the universal preferential solvation function, (iv) the discussion of potential drawbacks associated with the molecular simulation-based determination of thermodynamic preferential interaction parameters, and (v) the forensic examination of frequent modeling pitfalls behind the interpretation of preferential solvation from experimental data of Gibbs free energy of solute transfer. Full article

A new transient technique of the thermal conductivity and diffusivity measurement for anisotropic materials is presented and validated. It is based on measuring the through-plane properties using the extended dynamic plane source (EDPS) method and in-plane conductivity employing the transient plane source (TPS) and modified dynamic plane source (MDPS) methods. The key advantage of this technique is that only one pair of specimens is required for measurements. While the EDPS method is implemented on real measurements, the TPS and MDPS are applied to the finite elements method (FEM) simulation of the experiment. The accuracy of the results is enhanced by the application of the FEM and is better than 1% for materials with through-plane conductivity of less than 2 W m⁻¹ K⁻¹ and a specimen thickness of 9 mm. Full article

One method of nuclear energy development involves using helium. Its properties make using extended surfaces obligatory. However, currently nuclear technology does not typically use finned tubes. This study explores ways of enhancing heat transfer efficiency in a high-temperature gas-cooled reactor system by using novel fin designs in the heat exchanger for residual heat removal. Four different types of fins were studied: annular, serrated, square, and helical. The effect of fin height, thickness, and number was evaluated. Serrated and helical fins demonstrated superior performance compared to conventional annular fin designs, which was expressed in enhanced efficiency. The thickness of fins was found to have the strongest influence on the efficiency, while the height and number of fins per meter had weaker effects. In addition, the study emphasized the significance of considering complex effects when optimizing fin design, like the effect of fin geometry on the velocity of helium. The findings highlight the potential of creative fin designs to greatly enhance the efficiency and dependability of gas-cooled reactor systems, opening up possibilities for advancements in nuclear power plant technology. Full article

Thermogravimetry (TGA) and differential scanning calorimetry (DSC) are used broadly to study the kinetics of thermally stimulated processes such as thermal decomposition (pyrolysis) or thermal polymerization. These studies typically yield the activation energy (E) and preexponential factor (A). The resulting experimental values of E and A are oftentimes used to determine the so-called “thermodynamic parameters”, i.e., the enthalpy, entropy, and Gibbs free energy. Attention is called to the persistent and mistaken trend to interpret the resulting quantities as the thermodynamic parameters of the conversion of reactants to products. In fact, these quantities are specific to the conversion of reactants to the activated complex and, as such, provide no insights into the thermodynamics of the conversion of reactants to products. The basics of the activated complex (transition state) theory are provided to explain the meaning of the equations used for evaluating the thermodynamic parameters from the experimental values of E and A. Typical examples of misinterpretation are highlighted and discussed briefly. The applicability of the theory to the systems studied by the thermal analysis kinetics is also discussed. Full article

Microwave heating, which is caused by the interaction of electromagnetic radiation and materials, has become an important component in industrial operations across numerous industries. Despite their importance, conventional numerical simulations of microwave heating are computationally intensive. Concurrently, advances in artificial intelligence (AI), particularly machine learning algorithms, have transformed data processing by increasing accuracy while decreasing computational time. This study tackles the difficulty of efficient and accurate modelling in microwave heating by combining convolutional neural networks (CNNs) with traditional simulation techniques. The major goal of this research is to use CNNs to forecast temperature profiles in a variety of industrial materials, including susceptors, semi-transparent, and microwave-transparent materials, under varying power settings and heating periods. This unique strategy greatly reduces prediction times, with up to 60-fold speed increases over standard methods. Our research is based on examining the electromagnetic and thermal responses of these materials under microwave heating. This study’s findings emphasise the need for extensive datasets and show the transformational potential of CNNs in optimising material processing. It uses artificial intelligence to pave the way for more effective and exact simulations, supporting breakthroughs in industrial microwave heating applications. Full article

In thermodynamic theory, free energy (i.e., available energy) is the concept facilitating the combined applications of the theory’s two fundamental laws, the first and the second laws of thermodynamics. The critical step was taken by Kelvin, then by Helmholtz and Gibbs—that in natural processes, free energy dissipates spontaneously. With the formulation of the second law of entropy growth, this may be referred to as the dissymmetry proposition manifested in the spontaneous increase of system/environment entropy towards equilibrium. Because of Kelvin’s pre-entropy law formulation of free energy, our concept of free energy is still defined, within a framework on the premise of primacy of energy, as “body’s internal energy or enthalpy, subtracted by energy that is not available”. This primacy of energy is called into question because the driving force to cause a system’s change is the purview of the second law. This paper makes a case for an engineering thermodynamics framework, instead, to be based on the premise of the primacy of dissymmetry over free energy. With Gibbsian thermodynamics undergirded with dissymmetry proposition and engineering thermodynamics with a dissymmetry premise, the two branches of thermodynamics are unified to become classical thermodynamics. Full article

The use of thermal energy storage (TES) contributes to the ongoing process of integrating various types of energy resources in order to achieve cleaner, more flexible, and more sustainable energy use. Numerical modelling of hot storage packed bed storage systems has been conducted in this paper in order to investigate the optimum design of the hot storage system. In this paper, the effect of varying design parameters, including the diameter of the packed bed, the storage material, the void fraction, and the aspect ratio of the packed bed, on storage performance was investigated. COMSOL Multiphysics 5.6 software has been used to design, simulate, and validate an axisymmetric model, which was then applied to evaluate the performance of the storage system based on the total energy stored, the heat transfer efficiency, and the capacity factor. In this paper, a novel-packed bed was proposed based on the parametric analysis. This involved a 0.2 void fraction, 4 mm porous media particle diameter, and Magnesia as the optimum storage material with air as the heat transfer fluid. Full article

The paper revisits the formulation of the second law in continuum physics and investigates new methods of exploitation. Both the entropy flux and the entropy production are taken to be expressed by constitutive equations. In three-dimensional settings, vectors and tensors are in order and they occur through inner products in the inequality representing the second law; a representation formula, which is quite uncommon in the literature, produces the general solution whenever the sought equations are considered in rate-type forms. Next, the occurrence of the entropy production as a constitutive function is shown to produce a wider set of physically admissible models. Furthermore the constitutive property of the entropy production results in an additional, essential term in the evolution equation of rate-type materials, as is the case for Duhem-like hysteretic models. This feature of thermodynamically consistent hysteretic materials is exemplified for elastic–plastic materials. The representation formula is shown to allow more general non-local properties while the constitutive entropy production proves essential for the modeling of hysteresis. Full article

In this work, an artificial neural network (ANN)-based model is proposed to describe the input–output relationships in a Limaçon-To-Circular (L2C) gas expander with an inlet valve. The L2C gas expander is a type of energy converter that has great potential to be used in organic Rankine cycle (ORC)-based small-scale power plants. The proposed model predicts the different performance indices of a limaçon gas expander for different input pressures, rotor velocities, and valve cutoff angles. A network model is constructed and optimized for different model parameters to achieve the best prediction performance compared to the classic mathematical model of the system. An overall normalized mean square error of 0.0014, coefficient of determination ($R^2$) of 0.98, and mean average error of 0.0114 are reported. This implies that the surrogate model can effectively mimic the actual model with high precision. The model performance is also compared to a linear interpolation (LI) method. It is found that the proposed ANN model predictions are about 96.53% accurate for a given error threshold, compared to about 91.46% accuracy of the LI method. Thus the proposed model can effectively predict different output parameters of a limaçon gas expander such as energy, filling factor, isentropic efficiency, and mass flow for different operating conditions. Of note, the model is only trained by a set of input and target values; thus, the performance of the model is not affected by the internal complex mathematical models of the overall valved-expander system. This neural network-based approach is highly suitable for optimization, as the alternative iterative analysis of the complex analytical model is time-consuming and requires higher computational resources. A similar modeling approach with some modifications could also be utilized to design controllers for these types of systems that are difficult to model mathematically. Full article

Keyhole mode laser welding is a valuable technique for welding thick materials in industrial applications. However, its susceptibility to fluctuations and instabilities poses challenges, leading to defects that compromise weld quality. Observing the keyhole during laser welding is challenging due to bright process radiation, and existing observation methods are complex and expensive. This paper alternatively presents a novel numerical modeling approach for laser spot welding of aluminum through a modified mixture theory, a modified level-set (LS) method, and a thermal enthalpy porosity technique. The effects of laser parameters on keyhole penetration depth are investigated, with a focus on laser power, spot radius, frequency, and pulse wave modulation in pulsed wave (PW) versus continuous wave (CW) laser welding. PW laser welding involves the careful modulation of power waves, specifically adjusting the pulse width, pulse number, and pulse shapes. Results indicate a greater than 80 percent increase in the keyhole penetration depth with higher laser power, pulse width, and pulse number, as well as decreased spot radius. Keyhole instabilities are also more pronounced with higher pulse width/numbers and frequencies. Notably, the rectangular pulse shape demonstrates substantially deeper penetration compared to CW welding and other pulse shapes. This study enhances understanding of weld pool dynamics and provides insights into optimizing laser welding parameters to mitigate defects and improve weld quality. Full article

Inverse gas chromatography at infinite dilution was used to determine the surface thermodynamic properties of silica particles and PMMA adsorbed on silica, and more particularly, to quantify the London dispersive energy ${\gamma }_s^d$, the Lewis acid ${\gamma }_s^{+}$, and base ${\gamma }_s^{-}$ polar surface energies of PMMA/silica composites as a function of the temperature and the recovery fraction $\theta$ of PMMA. The polar acid-base surface energy ${\gamma }_s^{AB}$ and the total surface energy of the different composites were then deduced as a function of the temperature. In this paper, the Hamieh thermal model was used to quantify the surface thermodynamic energy of polymethyl methacrylate (PMMA) adsorbed on silica particles at different recovery fractions. A comparison of the new results was carried out with those obtained by applying other molecular models of the surface areas of organic molecules adsorbed on the different solid substrates. An important deviation of these molecular models from the thermal model was proved. The determination of ${\gamma }_s^d$, ${\gamma }_s^{+}$, ${\gamma }_s^{-}$, and ${\gamma }_s^{AB}$ of PMMA in both the bulk and adsorbed phases showed an important non-linearity variation of these surface parameters as a function of the temperature. The presence of maxima in the curves of ${\gamma }_s^d(T)$ highlighted the second-order transition temperatures in PMMA showing beta-relaxation, glass transition, and liquid–liquid temperatures. These three transition temperatures depended on the adsorption rate of PMMA on silica. The proposed method gave a new relation between the recovery fraction of PMMA and its London dispersive energy, showing an important effect of the temperature on the surface energy parameters of the adsorption of PMMA on silica. A universal equation relating ${\gamma }_s^d(T,\theta )$ of the systems PMMA/silica to the recovery fraction and the temperature was proposed. Full article

This research employs the COMSOL Multiphysics software (COMSOL 6.2) to conduct rigorous simulations and assess the performance of a thermoelectric module (TEM) meticulously crafted with alumina (Al₂O₃), copper (Cu), and Bi₂Te_2.70Se_0.30 thermoelectric (TE) materials. The specific focus is on evaluating diverse aspects of the Bi₂Te_2.70Se_0.30 thermoelectric generator (TEG). The TEM design incorporates Bi₂Te_2.70Se_0.30 for TE legs of the p- and n-type positioned among the Cu layers, Cu as the electrical conductor, and Al₂O₃ serving as an electrical insulator between the top and bottom layers. A thorough investigation is conducted into critical parameters within the TEM, which include arc length, electric potential, normalized current density, temperature gradient, total heat source, and total net energy rate. The geometric configuration of the square-shaped Bi₂Te_2.70Se_0.30 TEM, measuring 1 mm × 1 mm × 2.5 mm with a 0.25 mm Al₂O₃ thickness and a 0.125 mm Cu thickness, is scrutinized. This study delves into the transport phenomena of TE devices, exploring the impacts of the Seebeck coefficient (S), thermal conductivity (k), and electrical conductivity (σ) on the temperature differential across the leg geometry. Modeling studies underscore the substantial influence of S = ±2.41 × 10⁻³ V/K, revealing improved thermal conductivity and decreased electrical conductivity at lower temperatures. The findings highlight the Bi₂Te_2.70Se_0.30 TEM’s high potential for TEG applications, offering valuable insights into design and performance considerations crucial for advancing TE technology. Full article

Condensation heat transfer is involved in many industrial applications. Therefore, it is important to know the relative accuracy of the available methods for predicting heat transfer. Condensation can occur with saturated as well as superheated vapors. Predictive methods for both conditions were evaluated using a wide range of data. Twelve well-known correlations for the condensation of saturated vapor, including the most recent ones, were compared with data for 51 pure fluids and mixtures from 132 sources in horizontal and vertical channels of many shapes. Channel hydraulic diameters were 0.08–49 mm, the mass flux was 1.1–1400 kg/m²s, and the reduced pressure range was 0.0006–0.949. The fluids included water, CO₂, ammonia, hydrocarbons, halocarbon refrigerants, various chemicals, and heat transfer fluids. The best predictive technique was identified. The three most commonly used models for heat transfer during the condensation of superheated vapors were studied. They were first compared with test data using measured saturated condensation and forced convection heat transfer coefficients to select the best model. The selected model was then compared with test data using various correlations for heat transfer coefficients needed in the model. The best correlations to use in the model were identified. The results of this research are presented, as are recommendations for use in design. Full article

The phase behavior of lipids extracted from Astrocaryum vulgare (AV) and Astrocaryum aculeatum (AA) pulp and kernels and their microstructural, thermal and flow properties were studied. The lipid profiles, crystal structures, microstructures, thermal stabilities and flow behaviors of these lipids provided important structure–function information that are useful to assess potential applications in the food, cosmetic and pharmaceutical industries. AV and AA fruits were sourced from the lowlands and rainforests, respectively, of Guyana. AV and AA pulp oils (AVP and AAP) were distinguished from each other in composition and unsaturation, with AVP oils being predominated by a di-unsaturated TAG (2-(palmitoyloxy)propane-1,3-diyl dioleate (POO)) and AAP oils predominated by propane-1,2,3-triyl trioleate (OOO); there were unsaturation levels of 65% and 80%, respectively. The main fatty acids in AVP oils were oleic, palmitic and stearic; for AAP, these were oleic, linoleic, palmitic and stearic. The kernel fats of AV and AA were similar in composition and had saturation levels of 80%, being mainly comprised of tri-saturated TAGs propane-1,2,3-triyl tridodecanoate (LLL) and 3-(tetradecanoyloxy)propane-1,2-diyl didodecanoate (LML). The onset of mass loss $\left(T_{5\%}^{on}\right)$ of AV and AA pulp oils were similar at 328 ± 6 °C, which were 31 °C ± 9 higher compared to that of the kernel fats, which demonstrated similar $T_{5\%}^{on}$ = 293 ± 7 °C. AA and AV pulp oils were liquid at room temperature, with melting points of −5 ± 1 °C and 3 ± 1 °C, respectively; both kernel fats were solid at room temperature, packing in β′ (90% of crystals) and β (10% of crystals) polymorphic forms and melting almost identically at 30 ± 1 °C. Pulp oils demonstrated sporadic nucleation at the onset of crystallization with slow growth into rod-shaped crystallites, leading to an approximately 50% degree of crystallization at undercooling of approximately 40K. Nucleation for kernel fats was instantaneous at undercooling of approximately 23K, demonstrating a spherulitic growth pattern incorporating crystalline lamella and a 90% degree of crystallization. Kernel fats and pulp oils demonstrated Newtonian flow behavior and similar dynamic viscosity in the melt, approximately 28.5 mPa·s at 40 °C. The lipid profiles of AVP and AAP oils were dominated by unsaturated TAGs, suggesting potential nutrition and health benefits, particularly compared to other tropical oils with higher saturation levels, such as palm oil. AAP oil in particular is as unsaturated as olive oil, contains high levels of beta carotene and provides a unique flavor profile. The AAK and AVK lipid profiles and phase transformation indicate potential for applications where a high solid fat content and medium-chain fatty acids are required. Their high lauric and myristic acid content makes them similar to industrially important tropical oils (coconut and palm kernel), suggesting their use in similar formulations. The melting point and plasticity of the kernel fats are similar to that of cocoa and shea butters, suggesting use as replacements in cosmetics, foods and confections. There is, however, the need to better understand their nutritional status and effects on health. Full article

Given the climate change in recent decades and the ever-increasing energy consumption in the building sector, research is widely focused on the green revolution and ecological transition of buildings. In this regard, artificial intelligence can be a precious tool to simulate and optimize building energy performance, as shown by a plethora of recent studies. Accordingly, this paper provides a review of more than 70 articles from recent years, i.e., mostly from 2018 to 2023, about the applications of machine/deep learning (ML/DL) in forecasting the energy performance of buildings and their simulation/control/optimization. This review was conducted using the SCOPUS database with the keywords “buildings”, “energy”, “machine learning” and “deep learning” and by selecting recent papers addressing the following applications: energy design/retrofit optimization, prediction, control/management of heating/cooling systems and of renewable source systems, and/or fault detection. Notably, this paper discusses the main differences between ML and DL techniques, showing examples of their use in building energy simulation/control/optimization. The main aim is to group the most frequent ML/DL techniques used in the field of building energy performance, highlighting the potentiality and limitations of each one, both fundamental aspects for future studies. The ML approaches considered are decision trees/random forest, naive Bayes, support vector machines, the Kriging method and artificial neural networks. The DL techniques investigated are convolutional and recursive neural networks, long short-term memory and gated recurrent units. Firstly, various ML/DL techniques are explained and divided based on their methodology. Secondly, grouping by the aforementioned applications occurs. It emerges that ML is mostly used in energy efficiency issues while DL in the management of renewable source systems. Full article