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Fluidized beds are pivotal in the process industry and chemical engineering, with Computational Fluid Dynamics (CFD) playing a crucial role in their design and optimization. Challenges in CFD modeling stem from the scarcity or inconsistency of experimental data for validation, along with the uncertainties introduced by numerous parameters and assumptions across different CFD codes. This study navigates these complexities by comparing simulation results from the open-source MFIX and OpenFOAM, and the commercial ANSYS FLUENT, against experimental data. Utilizing a Eulerian–Eulerian framework and the kinetic theory of granular flow (KTGF), the investigation focuses on solid-phase properties through the classical drag laws of Gidaspow and Syamlal–O’Brien across varied parameters. Findings indicate that ANSYS Fluent, MFiX, and OpenFOAM can achieve reasonable agreement with experimental benchmarks, each showcasing distinct strengths and weaknesses. The study also emphasizes that both the Syamlal–O’Brien and Gidaspow drag models exhibit reasonable agreement with experimental benchmarks across the examined CFD codes, suggesting a moderated sensitivity to the choice of drag model. Moreover, analyses were also carried out for 2D and 3D simulations, revealing that the dimensional approach impacts the predictive accuracy to a certain extent, with both models adapting well to the complexities of each simulation environment. The study highlights the significant influence of restitution coefficients on bed expansion due to their effect on particle–particle collisions, with a value of 0.9 deemed optimal for balancing simulation accuracy and computational efficiency. Conversely, the specularity coefficient, impacting particle–wall interactions, exhibits a more subtle effect on bed dynamics. This finding emphasizes the critical role of carefully choosing these coefficients to effectively simulate the nuanced behaviors of fluidized beds.

Organic Rankine Cycle (ORC)–based small-scale power plants are becoming a promising instrument in the recent drive to utilize renewable sources and reduce carbon emissions. But the effectiveness of such systems is limited by the low efficiency of gas expanders, which are the main part of an ORC system. Limaçon-based expansion machines with a fast inlet control valve have great prospects as they could potentially offer efficiencies over 50%. However, the lack of a highly reliable and significantly fast control valve is hindering its possible application. In this paper, a push–pull solenoid valve is optimized using a stochastic optimization technique to provide a fast response. The optimization yields about 56–58% improvement in overall valve response. A performance comparison of the initial and optimized valves applied to a limaçon expander thermodynamic model is also presented. Additionally, the sensitivity of the valve towards a changing inlet pressure and expander rotor velocity is analyzed to better understand the effectiveness of the valve and provide clues to overall performance improvement.

High-pressure gas quenching is widely used in the metals industry during the heat treatment processing of steel specimens to improve their material properties. In a gas quenching process, a preheated austenised metal specimen is rapidly cooled with a gas such as nitrogen, helium, etc. The resulting microstructure relies on the temporal and spatial thermal history during the quenching. As a result, the corresponding material properties such as hardness are achieved. Challenges reside with the selection of the proper process parameters. This research focuses on the heat treatment of steel sample batches. The gas quenching process is fundamentally investigated in experiments and numerical simulations. Experiments are carried out to determine the heat transfer coefficient and the cooling curves as well as the local flow fields. Quenched samples are analyzed to derive the material hardness. CFD and FEM models numerically determine the conjugate heat transfer, flow behavior, cooling curve, and material hardness. In a novel approach, the experimental and simulation results are adopted to train artificial neural networks (ANNs), which allow us to predict the required process parameters for a targeted material property. The steels 42CrMo4 (1.7225) and 100Cr6 (1.3505) are investigated, nitrogen is the quenching gas, and geometries such as a disc, disc with a hole and ring are considered for batch series production.

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.

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 (R2) 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.

Buildings are responsible for around 30% and 42% of the consumed energy at the global and European levels, respectively. Accurate building power consumption estimation is crucial for resource saving. This research investigates the combination of graph convolutional networks (GCNs) and long short-term memory networks (LSTMs) to analyze power building consumption, thereby focusing on predictive modeling. Specifically, by structuring graphs based on Pearson’s correlation and Euclidean distance methods, GCNs are employed to discern intricate spatial dependencies, and LSTM is used for temporal dependencies. The proposed models are applied to data from a multistory, multizone educational building, and they are then compared with baseline machine learning, deep learning, and statistical models. The performance of all models is evaluated using metrics such as the mean absolute error (MAE), mean squared error (MSE), R-squared (R2), and the coefficient of variation of the root mean squared error (CV(RMSE)). Among the proposed computation models, one of the Euclidean-based models consistently achieved the lowest MAE and MSE values, thus indicating superior prediction accuracy. The suggested methods seem promising and highlight the effectiveness of GCNs in improving accuracy and reliability in predicting power consumption. The results could be useful in the planning of building energy policies by engineers, as well as in the evaluation of the energy management of structures.

In chaos control, one usually seeks to stabilize the unstable periodic orbits (UPOs) that densely inhabit the attractors of many chaotic dynamical systems. These orbits collectively play a significant role in determining the dynamics and properties of chaotic systems and are said to form the skeleton of the associated attractors. While UPOs are insightful tools for analysis, they are naturally unstable and, as such, are difficult to find and computationally expensive to stabilize. An alternative to using UPOs is to approximate them using cupolets. Cupolets, a name derived from chaotic, unstable, periodic, orbit-lets, are a relatively new class of waveforms that represent highly accurate approximations to the UPOs of chaotic systems, but which are generated via a particular control scheme that applies tiny perturbations along Poincaré sections. Originally discovered in an application of secure chaotic communications, cupolets have since gone on to play pivotal roles in a number of theoretical and practical applications. These developments include using cupolets as wavelets for image compression, targeting in dynamical systems, a chaotic analog to quantum entanglement, an abstract reducibility classification, a basis for audio and video compression, and, most recently, their detection in a chaotic neuron model. This review will detail the historical development of cupolets, how they are generated, and their successful integration into theoretical and computational science and will also identify some unanswered questions and future directions for this work.

Thermo-vortices (bright spots, blobs, swirls) in cosmic fluids (planetary atmospheres, or even black hole accretion disks) are sometimes observed as clustered into quasi-symmetrical quasi-stationary groups but conceptualized in models as autonomous items. We demonstrate—using the (analytical) Sharp Boundaries Evolution Method and a generic model of a thermo-vorticial field in a rotating “thin” fluid layer in a spacetime that may be curved or flat—that these thermo-vortices may be not independent but represent interlinked parts of a single, coherent, multi-petal macro-structure. This alternative conceptualization may influence the designs of numerical models and image-reconstruction methods.

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 Bi2Te2.70Se0.30 thermoelectric (TE) materials. The specific focus is on evaluating diverse aspects of the Bi2Te2.70Se0.30 thermoelectric generator (TEG). The TEM design incorporates Bi2Te2.70Se0.30 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 Bi2Te2.70Se0.30 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 Bi2Te2.70Se0.30 TEM’s high potential for TEG applications, offering valuable insights into design and performance considerations crucial for advancing TE technology.

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 γsd, the Lewis acid γs+, and base γs− polar surface energies of PMMA/silica composites as a function of the temperature and the recovery fraction θ of PMMA. The polar acid-base surface energy γsAB 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 γsd, γs+, γs−, and γsAB 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 γsd(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 γsd(T,θ) of the systems PMMA/silica to the recovery fraction and the temperature was proposed.

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.

A semilinear quadratic equation of the form Aij(x)uij=Bi(x,u)ui+F(x,u) defines a metric Aij; therefore, it is possible to relate the Lie point symmetries of the equation with the symmetries of this metric. The Lie symmetry conditions break into two sets: one set containing the Lie derivative of the metric wrt the Lie symmetry generator, and the other set containing the quantities Bi(x,u),F(x,u). From the first set, it follows that the generators of Lie point symmetries are elements of the conformal algebra of the metric Aij, while the second set serves as constraint equations, which select elements from the conformal algebra of Aij. Therefore, it is possible to determine the Lie point symmetries using a geometric approach based on the computation of the conformal Killing vectors of the metric Aij. In the present article, the nonlinear Poisson equation Δgu−f(u)=0 is studied. The metric defined by this equation is 1 + 2 decomposable along the gradient Killing vector ∂t. It is a conformally flat metric, which admits 10 conformal Killing vectors. We determine the conformal Killing vectors of this metric using a general geometric method, which computes the conformal Killing vectors of a general 1+(n−1) decomposable metric in a systematic way. It is found that the nonlinear Poisson equation Δgu−f(u)=0 admits Lie point symmetries only when f(u)=ku, and in this case, only the Killing vectors are admitted. It is shown that the Noether point symmetries coincide with the Lie point symmetries. This approach/method can be used to study the Lie point symmetries of more complex equations and with more degrees of freedom.

Characteristic dynamics in lava flows, such as the formation processes of lava levees, toe-like tips, and overlapped structures, were reproduced successfully through numerical simulation using the smoothed particle hydrodynamics (SPH) method. Since these specific phenomena have a great influence on the flow direction of lava flows, it is indispensable to elucidate them for accurate predictions of areas where lava strikes. At the first step of this study, lava was expressed using a molten metal with known physical properties. The computational results showed that levees and toe-like tips formed at the fringe of the molten metal flowing down on a slope, which appeared for actual lava flows as well. The dynamics of an overlapped structure formation were also simulated successfully; therein, molten metal flowed down, solidified, and changed the surface shape of the slope, and the second molten metal flowed over the changed surface shape. It was concluded that the computational model developed in this study using the SPH method is applicable for simulating and clarifying lava flow phenomena.

For many engineering applications, it is sufficient to use the concept of simple materials. However, higher gradients of the kinematic variables are taken into account to model materials with internal length scales as well as to describe localization effects using gradient theories in finite plasticity or fluid mechanics. In many approaches, length scale parameters have been introduced that are related to a specific micro structure. An alternative approach is possible, if a thermodynamically consistent framework is used for material modeling, as shown in the present contribution. However, even if sophisticated and thermodynamically consistent material models can be established, there are still not yet standard experiments to determine higher order material constants. In order to contribute to this ongoing discussion, system identification based on the method of self-adaptive filtering is proposed in this paper. To evaluate the effectiveness of this approach, it has been applied to second-order gradient materials considering longitudinal vibrations. Based on thermodynamically consistent models that have been solved numerically, it has been possible to prove that system identification based on self-adaptive filtering can be used effectively for both narrow-band and broadband signals in the field of second-order gradient materials. It has also been found that the differences identified for simple materials and gradient materials allow for condition monitoring and detection of gradient effects in the material behavior.

The emergence of electric vehicles has brought new issues such as the problem of rolling element bearings (REBs) operating at high speeds. Losses due to these components in mechanical transmissions are a key issue and must therefore be taken into account right from the design stage of these systems. Among these losses, the one induced by the motion of rolling elements, known as drag loss, becomes predominant in high-speed REBs. Although an experimental approach is still possible, it is difficult to isolate this loss in order to study it properly. A numerical approach based on CFD is therefore a possible way forward, even if other issues arise. The aim of this article is to study the ability of such an approach to correctly estimate the drag coefficient associated with the motion of rolling elements. The influence of the numerical domain extension, the mesh refinement, the simplification of the ring shape, and the presence of the cage on the values of the drag coefficient is presented. While it seems possible to compromise on the calculation domain and mesh size, it appears that the other parameters must be taken into account as much as possible to obtain realistic results.

This paper discusses the auto-correlation functions of chaotic binary sequences obtained by a one-dimensional chaotic map and two binary functions. The two binary functions are alternately used to obtain a binary sequence from a chaotic real-valued sequence. We consider two similar methods and give the theoretical auto-correlation functions of the new binary sequences, which are expressed by the auto-/cross-correlation functions of the two chaotic binary sequences generated by a single binary function. Furthermore, some numerical experiments are performed to confirm the validity of the theoretical auto-correlation functions.

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.

Zeolites have wide application in industry, agriculture, medicine for ion exchange, for catalysis, and even for drug delivery so that it is important to understand their stability and synthesis. Although the standard thermochemical quantities (formula volume, enthalpies, entropies, and heat capacities) of zeolites are quite widely reported, there have been no general predictive procedures for the potentially vast numbers of possible examples of zeolites.We here consider some simple additive schemes for prediction of such properties. These demonstrate that zeolites behave in broad outline as standard ionic solids with dominant coulombic interactions. The Atom Sum method provides the necessary data for first thermochemical prediction with no need for inclusion of special group contributions.

In the initial two publications in this series we established predictive additive Atom Sum thermochemical quantities for inorganic solids for each of the chemical elements. With the chemical elements being a finite set, these data cover the whole of the domain of inorganic solids so far as reference data is currently available.We here introduce a dataset augmented in hydrates, and divide our analysis between the hydrates and anhydrates. The Atom Sum terms are better directed independently to these disparate inorganic groups thus providing improved estimates of the corresponding predicted thermochemical quantities. In this paper we provide both updated formation reaction entropies and previously unpublished Gibbs energies.

The potential usage of activated carbon from plantain peel (Musa paradisiaca) (TPPC) and unactivated carbon from plantain peel (UPPC) for the removal of Hexachlorocyclohexanes (HCHs) from water systems was investigated. The TPPC and UPPC were characterized using Fourier transform infrared spectroscopy (FT-IR), and scanning electron microscopy (SEM). Adsorption experiments were conducted as a function of adsorbent weight (2 – 10 g), temperature (30 -50 °C), and solution pH (2 - 9) under an adsorbent packed column. The Optimum removal efficiency of 98.23 % was achieved in the column studies under the following conditions: pH = 5, the dosage of adsorbent = 5 g/100 mL, temperature = 30 °C. The batch adsorption process was employed to evaluate the kinetics, equilibrium, and thermodynamics of the adsorption processes. The equilibrium study showed that Langmuir among other isotherm models applied performed better in fitting the data. Additionally, the kinetic data was best described by the pseudo-second-order model (R2 > 0.97), indicating a chemisorption mechanism. Furthermore, the thermodynamic calculations of the adsorption process suggest that HCH adsorption was exothermic (ΔH = -110.87) and spontaneous (-ΔG).

The objective of the study is to synthesize economical and facile ethylenediamine (ED) based protic ionic liquids (PILs) containing various carboxylate anions for CO2 capture for post-combustion method. The PILs were synthesized using a straightforward process, and the behavior of their CO2 absorption was investigated in relation to the alkyl chain of odd and even chain anions. The PILs were characterized using 1H NMR, 13C NMR, and HRMS techniques. The study results indicate that ED-ILs with longer alkyl chains are better at absorbing CO2. Among all the PILs, [ED][Hep] exhibited the highest absorption capacity except [ED][But], absorbing 0.26 CO2/mol of IL at 298K and 0.1 MPa. The thermophysical properties, including density, sound speed, viscosity, and refractive index of neat ED-based PILs, were tested at different temperatures and 0.1 MPa. Furthermore, the derived parameters such as expansion coefficient (α), molecular volume (V), isentropic compressibility (βs), lattice potential energy (UPOT), standard entropy (S0), Intermolecular free length (Lf) and free volume (Vm) were determined using experimental parameters. It was shown that these characteristics were directly related to the PILs ability to absorb CO2. Overall, the investigation demonstrated that [ED][But] offers a great potential for utilization as a CO2 absorption solvent because of its high absorption capacity and special qualities as a PIL.

The paper presents a semi-empirical approach to determining densities of chlorinated brines in the 0–4 molal concentration range and 293.15–323.15 K temperature range. This model incorporates continuous density functions for chlorinated salts and temperature, offering enhanced accuracy and reduced errors in density estimations for complex brine systems, including ternary systems. The model provides precise density estimations for applications like seawater and salt flats by considering ion activities and specific concentration ranges. Comparative analysis shows the model's superior performance with error adjustments consistently below 0.2 %. The continuous variable temperature model proves more effective than discrete approaches, contributing to more accurate density predictions.

Vapor pressure (P) data of pure substances in gas/solid-liquid equilibrium is important for scientific research and practical applications. Due to the large temperature range going from melting point to critical point, it is an impossible task to determine the P of a pure substance at each temperature point. Up to now, there is no general equation that can accurately describe the relation between temperature (T) and P for all pure substances. Utilizing Boltzmann distribution, we presented a general expression, ln P = a + bT + cln(T) + d(1/T), for the P of pure substances, that is, the P-T dependence is nearly exponential over the entire range of liquid and solid existence. Furthermore, on the base of above equation, we established a corrected general equation to express the liquid vapor pressure within temperature in going from melting point to critical point.lnP=a+bT+cTT/Tb+d(ln(T))+f(ln(T))T/Tb+g(1/T)The results show that the corrected equation has the advantages of universality, simplicity, and high calculation accuracy for the vapor pressure involving liquid simple substances, liquid inorganic and liquid organic compounds.

Protonation and deprotonation reactions play an important role in the biological processes. Such processes are fully thermodynamic and occur in complex media where functional groups are surrounded by other chemicals such as molecules, ions, water, etc. In this paper, the compound of interest is the methylprolithospermate (MPL) molecule, which was recently isolated from salvia yunnanensis CH Wright. The intended functional groups for deprotonation are -OH and -COOH. These are the potential groups depicted at four sites located in MPL. Three thermodynamic cycles were required for the estimation of MPL's pKa by the means of DFT at the levels of B3LYP and PW6B95 with the dual basis set 6–311++G(d,p)//6–31+G(d). Cycle 1 is the direct pKa calculation, Cycle 2 is also the direct pKa calculation with proton hydration, and Cycle 3, the proton exchange process with hydroxyl anion hydration. In these cycles, water molecules as, reagents as well as products, were held as monomers in one hand (monomer cycle approaches), and cluster on the other hand (cluster cycle approaches). The isodesmic scheme was used as benchmark method for pKa calculation and yielded: pKa1 = 3.6 ± 0.3, pKa2 = 6.6 ± 0.2, pKa3 = 9.3 ± 0.2, and pKa4 = 14.4. The inclusion of binding energies of MPL anions in the pKa calculation has improved the results. The best cycles depend on the level of waters and the level of acidity. The more the number of waters clustered to MPL anions, the more the deviation of pKa values.

It has been shown that an improved prediction of the thermodynamic quantity for the hard homonuclear diatomics can be performed with an interpolation scheme that relates the thermodynamic property of the hard sphere to that of tangent hard spheres at the same density. Using the analytic expressions based on the solution of an integral equation an excess Helmholtz free energy per particle and chemical potential for hard homonuclear diatomic fluid have been computed. Calculations are carried out for hard homonuclear diatomics with reduced internuclear separations of 0.1 to 1 at reduced densities of 0.2 to 0.9. Our findings for the excess Helmholtz free energy from the Percus–Yevickand Martynov–Sarkisov approximations presents good agreement with available accurate data, having maximum deviations of 15% from it over the separation and density ranges of the calculations. The excess chemical potential from the Martynov–Sarkisov approximation shows better agreement with accurate available data than those from the Percus–Yevick approximation. For the excess chemical potential, a maximum deviation of 9.5% over the range of the calculations has been shown up for the Percus–Yevick approximation.

Given the exponential rise in the amount of data requiring processing in all engineering fields, phenomenological models have become computationally cumbersome. For this reason, more efficient data-driven models have been recently used with the purpose of substantially reducing simulation computational times. However, especially in process engineering, the majority of the proposed surrogate models address steady-state problems, while poor studies refer to dynamic simulation modeling. For this reason, using a response function-based approach, a crystallization unit case study was set up in order to derive a dynamic data-driven model for crystal growth whose characteristic differential parameters are derived via Response Surface Methodology. In particular, multiple independent variables were considered, and a well-established sampling technique was exploited for sample generation. Then, different sample sizes were tested and compared in terms of accuracy indicators. Finally, the domain partition strategy was exploited in order to show its relevant impact on the final model accuracy. In conclusion, the outcome of this study proved that the proposed procedure is a suitable methodology for dynamic system metamodeling, as it shows good compliance and relevant improvement in terms of computational time. In terms of future research perspectives, testing the proposed procedure on different systems and in other research fields would allow for greater improvement and would, eventually, extend its validity.

This paper introduces a robust deposition model designed for exploring the growth dynamics of deposits on surfaces under practical conditions. The study addresses the challenge of characterizing the intricate morphology of deposits, exhibiting significant visual variations. A generative approach is deployed to create diverse natural and engineered surface textures, governed by probabilistic principles. The model’s formulation addresses key questions related to deposition initiation, nucleation point behaviour, spatial scaling, deposit growth rates, spread dynamics, and surface mobility. A versatile algorithm, relying on six parameters and employing nested loops and Gaussian sampling, is developed. The algorithm’s efficacy is examined through extensive simulations, involving variations in nucleation scaling densities, aggregate scaling scenarios, spread factors, and diffusion rates. Surface statistics are computed for simulated deposits and analyzed using Fast Fourier Transform (FFT). The resulting database enables quantitative comparisons of surfaces generated with different parameters, where the database-derived parallel coordinates offer guidance for selecting optimal model parameters to achieve desired surface morphologies. The proposed approach is validated against urea-derived deposits, exhibiting statistical consistency and agreement with experimental observations. Overall, the model’s adaptable framework holds promise for understanding and predicting deposit growth on surfaces in diverse practical scenarios.

The paper concerns the dynamics and stability of double-walled carbon nanotubes conveying fluid. The equations of motion adopted in the current study to describe the dynamics of such nano-pipes stem from the classical Bernoulli–Euler beam theory. Several additional terms are included in the basic equations in order to take into account the influence of the conveyed fluid, the impact of the surrounding medium and the effect of the van der Waals interaction between the inner and outer single-walled carbon nanotubes constituting a double-walled one. In the present work, the flow-induced vibrations of the considered nano-pipes are studied for different values of the length of the pipe, its inner radius, the characteristics of the ambient medium and the velocity of the fluid flow, which is assumed to be constant. The critical fluid flow velocities are obtained at which such a cantilevered double-walled carbon nanotube embedded in an elastic medium loses stability.

This paper presents an innovative entanglement-based protocol to address the Dining Cryptographers problem, utilizing maximally entangled |GHZn⟩ tuples as its core. This protocol aims to provide scalability in terms of both the number of cryptographers n and the amount of anonymous information conveyed, represented by the number of qubits m within each quantum register. The protocol supports an arbitrary number of cryptographers n, enabling scalability in both participant count and the volume of anonymous information transmitted. While the original Dining Cryptographers problem focused on a single bit of information—whether a cryptographer paid for dinner—the proposed protocol allows m, the number of qubits in each register, to be any arbitrarily large positive integer. This flexibility allows the transmission of additional information, such as the cost of the dinner or the timing of the arrangement. Another noteworthy aspect of the introduced protocol is its versatility in accommodating both localized and distributed versions of the Dining Cryptographers problem. The localized scenario involves all cryptographers gathering physically at the same location, such as a local restaurant, simultaneously. In contrast, the distributed scenario accommodates cryptographers situated in different places, engaging in a virtual dinner at the same time. Finally, in terms of implementation, the protocol accomplishes uniformity by requiring that all cryptographers utilize identical private quantum circuits. This design establishes a completely modular quantum system where all modules are identical. Furthermore, each private quantum circuit exclusively employs the widely used Hadamard and CNOT quantum gates, facilitating straightforward implementation on contemporary quantum computers.

In this paper, we explore the iterated Crank–Nicolson (ICN) algorithm for the one-dimensional peridynamic model. The peridynamic equation of motion is an integro-differential equation that governs structural deformations such as fractures. The ICN method was originally developed for hyperbolic advection equations. In peridynamics, we apply the ICN algorithm for temporal discretization and the midpoint quadrature method for spatial integration. Several numerical tests are carried out to evaluate the performance of the ICN method. In general, the ICN method demonstrates second-order accuracy, consistent with the Störmer–Verlet (SV) method. When the weight is 1/3, the ICN method behaves as a third-order Runge–Kutta method and maintains strong stability-preserving (SSP) properties for linear problems. Regarding energy conservation, the ICN algorithm maintains at least second-order accuracy, making it superior to the SV method, which converges linearly. Furthermore, selecting a weight of 0.25 results in fourth-order superconvergent energy variation for the ICN method. In this case, the ICN method exhibits energy variation similar to that of the fourth-order Runge–Kutta method but operates approximately 20% faster. Higher-order convergence for energy can also be achieved by increasing the number of iterations in the ICN method.

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 (T5%on) 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 T5%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.

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.

The development and deployment of the next generation of wind energy systems calls for simulation tools that model the entire wind farm while balancing accuracy and computational cost. A full-system wind farm simulation must consider the atmospheric inflow, the wakes and consequent response of the multiple turbines, and the implementation of the appropriate farm-collective control strategies that optimize the entire wind farm’s output. In this article, we present a novel vortex lattice model that enables the effective representation of the complex vortex wake dynamics of the turbines in a farm subject to transient inflow conditions. This work extends the capabilities of our multi-physics suite, CODEF, to include the capability to simulate the wakes and the high-fidelity aeroelastic response of multiple turbines in a wind farm. Herein, we compare the results of our GVLM technique with the LiDAR measurements obtained at Sandia National Laboratories’ SWiFT facility. The comparison shows remarkable similarities between the simulation and field measurements of the wake velocity. These similarities demonstrate our model’s capabilities in capturing the entire wake of a wind turbine at a significantly reduced computational cost as compared to other techniques.

Two neural networks were trained to predict, respectively, the Euler characteristic and the curvature of nuclear pastas in neutron star crust conditions generated by molecular dynamics simulations of neutron star matter with 0.1 < x < 0.5, 0.040 fm−3 < ρ < 0.085 fm−3 (0.68 × 1014 g/cm3 < ρ < 1.43 × 1014 g/cm3), and 0.2 MeV < T < 4.0 MeV, where x is proton content, the density is ρ, and the temperature is T. The predictions of the two networks were combined to determine the nuclear pasta phase that is thermodynamically stable at a given x, ρ, and T, and a three-dimensional phase diagram that extrapolated slightly the regions of existing molecular dynamics data was computed. The jungle gym and anti-jungle gym structures are prevalent at high temperature and low density, while the anti-jungle gym and anti-gnocchi structures dominate at high temperature and high density. A diversity of structures exist at low temperatures and intermediate density and proton content. The trained models used in this work are open access and available at a public repository to promote comparison to pastas obtained with other models.

An approach based on the OpenFOAM library has been developed to solve a high-speed, multicomponent mixture of a reacting, compressible flow. This work presents comprehensive validation of the newly developed solver, called compressibleCentralReactingFoam, with different supersonic flows, including shocks, expansion waves, and turbulence–combustion interaction. The comparisons of the simulation results with experimental and computational data confirm the fidelity of this solver for problems involving multicomponent high-speed reactive flows. The gas dynamics of turbulence–chemistry interaction are modeled using a partially stirred reactor formulation and provide promising results to better understand the complex physics involved in supersonic combustors. A time-scale analysis based on local Damköhler numbers reveals different regimes of turbulent combustion. In the core of the jet flow, the Damköhler number is relatively high, indicating that the reaction time scale is smaller than the turbulent mixing time scale. This means that the combustion is controlled by turbulent mixing. In the shear layer, where the heat release rate and the scalar dissipation rate have the highest value, the flame is stabilized due to finite rate chemistry with small Damköhler numbers and a limited fraction of fine structure. This solver allows three-dimensional gas dynamic simulation of high-speed multicomponent reactive flows relevant to practical combustion applications.

Reservoir computing (RC) systems can efficiently forecast chaotic time series using the nonlinear dynamical properties of an artificial neural network of random connections. The versatility of RC systems has motivated further research on both hardware counterparts of traditional RC algorithms and more-efficient RC-like schemes. Inspired by the nonlinear processes in a living biological brain and using solitary waves excited on the surface of a flowing liquid film, in this paper, we experimentally validated a physical RC system that substitutes the effect of randomness that underpins the operation of the traditional RC algorithm for a nonlinear transformation of input data. Carrying out all operations using a microcontroller with minimal computational power, we demonstrate that the so-designed RC system serves as a technically simple hardware counterpart to the ‘next-generation’ improvement of the traditional RC algorithm.

This paper addresses the often overlooked, yet critical, aspect of designing and optimizing the surface pipeline network for the transportation of geothermal fluids from the wellheads to the delivery point, such as greenhouses, food drying plants, or fish farming units. While research on the geothermal industry predominately focuses on the reservoir and well engineering aspects of exploitation, insufficient attention has been given to the design of the pipeline network, leading to improper design and significant, yet avoidable, energy losses. Thus, this paper presents a comprehensive methodology for modeling and simulating geothermal fluid flow within the pipeline network by fully considering all hydraulic (friction, viscous flow, and gravity effects) and thermal (open air and underground pipeline heat loss) phenomena. These two aspects are handled simultaneously by setting up and solving the coupled set of the governing (differential) equations. We also demonstrate the difficulties that arise when attempting the solution of the mathematical problem, such as potential instability or lack of convergence. Finally, a fully detailed study of the real-world geothermal production system is presented utilizing the developed methodology to optimize the design and operation conditions of the system. By integrating debottlenecking strategies into the analysis, this approach not only maximizes power output, but also identifies and mitigates constraints within the system, ensuring efficient operation and performance increase.

To ascertain the significance of temperature and residence time of hydrothermal carbonization (HTC) in controlling hydrochar production, multiple regression was employed based on central composite design (CCD) to model the responses of mass yield (MY) and higher heating value (HHV). The hydrothermal reaction was explored at temperatures and times ranging from 150 to 250 °C and 0.5 to 3.5 h. Sorghum bagasse (SB) and microalgae (MA) were used to complex the reaction due to their differences in organic constituents. Simultaneously, the operating parameters were optimized by maximizing the response values under domain constraints in the HHV models. The results show that at least temperature and time in the linear system played a significant role in determining the solids recovery and the energy generation of hydrochars (p-values = 0.00), regardless of the biomass type. Moreover, the optimum conditions of SB and MA hydrochars can be achieved by increasing the temperature to the limit of 250 °C and prolonging the time to 3.5 and 3.25 h, respectively. Both respective conditions resulted in maximum HHVs of 27.54 and 35.83 MJ kg−1.

Polymer thermal conductivities play an important role for their potential use in industrial applications. Therefore, great efforts have been made to investigate fundamental structure–property relationships to understand and predict thermal conductivities for polymers and their composites. The review summarizes selected well-proven microscopic theoretical approaches to calculate thermal conductivities such as EMD, NEMD, EMT, and BTE, and cites examples to focus on different qualitative aspects of recent polymer theoretical research. Examples other than polymer materials are given as supplemental information to support the general discussion of heat transport phenomena in solid materials.

Preheating is important to improve the mechanical properties of nickel-based superalloys processed by additive manufacturing. The microstructure of IN718 was found to be influenced by the preheating temperature. Different preheating temperatures affect mechanical properties by changing microstructures. This work aims to clarify the thermal behavior for two preheated base plate temperatures (200 °C and 600 °C) on the IN718 superalloy built by the selective laser melting (SLM) process using the finite element method and experiments. The simulation findings indicate that the preheated 600 °C model has a deeper melt pool, a slower transformation of liquid to solid, and a slower cooling rate compared to the 200 °C model. As a result, the interdendritic Niobium (Nb) segregation of IN718 is reduced, thus improving the mechanical properties of additive-manufactured IN718 using the laser. The solidification map derived from the simulation indicates a columnar microstructure for the IN718 superalloy. Preheating increased the size of the dendrite structure and reduced elemental segregation, but it did not affect the morphology or size of crystal grains. We focused on comparing the temperature gradient and cooling rate for the two preheated base plate temperatures using the solidification map of IN718. The simulation confirmed that preheating does not affect the grain structure.

In this work, the explicit boundary-condition-enforced immersed boundary method (EIBM) and the lattice Boltzmann flux solver (LBFS) are integrated into OpenFOAM to efficiently solve incompressible flows with complex geometries and moving boundaries. The EIBM applies the explicit technique to greatly improve the computational efficiency of the original boundary-condition-enforced immersed boundary method. In addition, the improved EIBM inherits the accurate interpretation of the no-slip boundary condition and the simple implementation from the original one. The LBFS uses the finite volume method to discretize the recovered macroscopic governing equations from the lattice Boltzmann equation. It enjoys the explicit relationship between the pressure and density, which avoids solving the pressure Poisson equation and thus saves much computational cost. Another attractive feature of the LBFS lies in its simultaneous evaluation of the inviscid and viscous fluxes. OpenFOAM, as an open-source CFD platform, has drawn increasing attention from the CFD community and has been proven to be a powerful tool for various problems. Thus, implementing the EIBM and LBFS into such a popular platform can advance the practical application of these two methods and may provide an effective alternative for complicated incompressible flow problems. The performance of the integrated solver in OpenFOAM is comprehensively assessed by comparing it with the widely used numerical solver in OpenFOAM, namely, the Pressure-Implicit with Splitting of Operators (PISO) algorithm with the IBM. A series of representative test cases with stationary and moving boundaries are simulated. Numerical results confirm that the present method does not have any streamline penetration and achieves the second-order accuracy in space. Therefore, the present method implemented in the open-source platform OpenFOAM may have good potential and can serve as a powerful tool for practical engineering problems.

The possibility of a shock wave recovery at a discrete closed interface with a heated gas has been investigated. A two-dimensional model applied to conditions of optical discharges featuring spherical, elliptical, and drop-like configurations demonstrated that non-symmetry in the shock refraction contributes to the specific mechanism of recovery other than simply its compensation. Even though the full restoration of the hypersonic flow state does not occur in a strict sense of it, clear reverse changes toward the initial shape of the shock front eventually take place, thus creating an appearance of a full recovery seen in experiments. From analysis of different interface symmetries, the factors determining the recovery dynamics are identified. The results are directly applicable to the problem of energy deposition into a hypersonic flow; however, it can be useful anywhere else where the flow modifications following the interaction are important. The dimensionless form of the equations allows applications on any scale other than that demonstrated for the optical discharges.

The symmetries of a Riemann surface Σ∖{ai} with n punctures ai are encoded in its fundamental group π1(Σ). Further structure may be described through representations (homomorphisms) of π1 over a Lie group G as globalized by the character variety C=Hom(π1,G)/G. Guided by our previous work in the context of topological quantum computing (TQC) and genetics, we specialize on the four-punctured Riemann sphere Σ=S2(4) and the ‘space-time-spin’ group G=SL2(C). In such a situation, C possesses remarkable properties: (i) a representation is described by a three-dimensional cubic surface Va,b,c,d(x,y,z) with three variables and four parameters; (ii) the automorphisms of the surface satisfy the dynamical (non-linear and transcendental) Painlevé VI equation (or PVI); and (iii) there exists a finite set of 1 (Cayley–Picard)+3 (continuous platonic)+45 (icosahedral) solutions of PVI. In this paper, we feature the parametric representation of some solutions of PVI: (a) solutions corresponding to algebraic surfaces such as the Klein quartic and (b) icosahedral solutions. Applications to the character variety of finitely generated groups fp encountered in TQC or DNA/RNA sequences are proposed.

The long-term, large-scale behavior in a problem of stochastic nonlinear dynamics corresponding to the Abelian sandpile model is studied with the use of the quantum-field theory renormalization group approach. We prove the multiplicative renormalization of the model including an infinite number of coupling parameters, calculate an infinite number of renormalization constants, identify a plane of fixed points in the infinite dimensional space of coupling parameters, discuss their stability and critical scaling in the model, and formulate a simple law relating the asymptotic size of an avalanche to a model exponent quantifying the time-scale separation between the slow energy injection and fast avalanche relaxation processes.

We derive a new system of integrable derivative non-linear Schrödinger equations with an L operator, quadratic in the spectral parameter with coefficients belonging to the Kac–Moody algebra A2(1). The construction of the fundamental analytic solutions of L is outlined and they are used to introduce the scattering data, thus formulating the scattering problem for the Lax pair L,M.

Multiwalled carbon nanotubes (MWCNTs) exhibit outstanding physical properties, including high thermal conductivity, excellent mechanical strength, and low electrical resistivity, which make them suitable candidates for a variety of applications. The work presented in this paper focuses on the pool boiling performance of refrigerant R-134a on microporous Cu-MWCNT composite surface layers. A two-stage electrodeposition technique was used to fabricate Cu-MWCNT composite coatings. In order to achieve variation in the surface properties of the Cu-MWCNT composite surface layer, electrodeposition was carried out at various bath temperatures (25 °C, 30 °C, 35 °C, and 40 °C). All surfaces coated with the Cu-MWCNT composite demonstrated superior boiling performance compared to the uncoated surface. Heat transfer coefficient (HTC) values for Cu-MWCNT composite surface layers, prepared at bath temperatures of 25 °C, 30 °C, 35 °C, and 40 °C, exhibited improvements of up to 1.75, 1.88, 2.06, and 2.22, respectively, in comparison to the plain Cu surface.

In this work, different CFD models to compute flows inside a steam ejector were investigated. The results were compared to the analytical models as well as the experimental results from the literature. All the simulations gave realistic results from the hydrodynamic perspective with a relative error of the entrainment ratio between 25% and 40% compared to reference experimental data. However, an analysis of the temperature profiles showed that only realistic results from the thermodynamic perspective were given by multiphase calculations. The first multiphase model tested was the so-called Wet-Steam model from ANSYS Fluent. This model gave inconsistent results for the steam ejector CFD simulation due to the physical boundaries of this model. The second model tested was the Eulerian mixture model, which gave the most realistic results in terms of the physical conditions of the liquid and vapor phases inside the ejector. It also showed that the phase change could have a significant impact on the value of the critical output pressure as a way to improve the performance of the ejector.

The current study explores the impact of two different imidazolium based ionic liquids, having diverse side chains and anions: 1-ethyl-3-methylimidazolium chloride ([EMIm]Cl) and 1‑butyl‑3-methylimidazolium bromide ([BMIm]Br) on the thermodynamic and transport properties of sodium salicylate. This inquiry examines the sodium salicylate's density, sound velocity, and viscosity within aqueous solutions of these ionic liquids across a temperature range of 283.15 K to 313.15 K at atmospheric pressure. Various parameters, including apparent molar volume (Vϕ), limiting apparent molar volume (Voϕ), transfer volume (ΔtrV∘ϕ), limiting apparent molar expansibility (E∘∅), Hepler's constant, apparent molar isentropic compression (Kϕ,s), limiting apparent molar isentropic compression (K∘ϕ,s), transfer parameter of isentropic compression (ΔtrK∘ϕ,S), and viscosity B-coefficient, were computed from experimental values. Additionally, we have analyzed the trends in computed parameters, considering the interactions between `solute and solvent along with solute-solute interactions in the systems under investigation. Our results suggest a predominance of hydrophilic-hydrophilic and ion-hydrophilic interactions within studied systems. The observed magnitudes of these parameters affirm a stronger interaction between sodium salicylate and [BMIm]Br in comparison to those with [EMIm]Cl.

Generation during the production process and emissions into the atmosphere with flue gases (CO2,SOx,NOx) cause the “greenhouse effect” and the problem of global warming. Despite the existing practice of using zeolite as an adsorbent for a number of gases in projects such as Carbon Capture, Utilization and Storage to prevent climate change, the thermodynamic functions and parameters of the zeolite tuff containing the mineral Khekordzula clinophtholite in the inner Kartli region of Georgia are unknown and have not been studied. According to the research data, the corresponding thermodynamic parameters were determined, namely the enthalpy of formation of the mineral clinophthylolite in the Khekordzula zeolite tuff ΔfH2980=−39,825.838kj·mol−1, Gibbs energy ΔfG2980=−36,734.784kj·mol−1, entropy S2980=−10.373j·K·mol−1; Dehydration and as well as physical sorption (CO2, SOx, NOx) start parameters: temperature T=352.90K, pressure P=6.84mmHg. The thermodynamic parameters of the Khekordzula natural clinoptilolite tuff, taking into account its basic mineralogical composition, were determined: ΔfH2980=−30,403.76kj·mol−1; ΔfG2980=−29,963.01kj·mol−1; S2980=−1.48j·K·mol−1; T=374.22K, P=0.0003mmHg. It has been confirmed that chemisorption (CO2, SOx, NOx) by Khekordzula clinophthylolite should begin at temperatures above 2000 K. The results of the conducted studies make it possible to evaluate the prospects of using Khekordzula and natural zeolites of similar composition as an adsorbent for harmful industrial gases, which will help plan and implement effective solutions to environmental problems.

In this work, thermal conductivity (TC), viscosity, and rheological properties of an ethylene glycol (EG) based single-walled carbon nanotube (SWCNT) nanofluid (NF) have been computed using equilibrium molecular dynamics (EMD) simulation. In SWCNT, for the interaction between carbon atoms, Tersoff potential is used. Results indicate that TC and viscosity increase in nonlinear fashion with volume fraction. However, with temperature, TC increases but viscosity decreases. Increased interaction between CNT and liquid atoms of EG, and the high heat conductance ability of SWCNT nanoparticles enhance the effective conductivity and viscosity of NFs. Longer CNTs provide more efficient heat transfer pathways and more interactions between CNT & base fluid molecules, which contribute to enhanced TC and viscosity of NFs. Weakening of intermolecular forces within the NF with increasing temperature decreases viscosity. To validate the results, radial distribution function (RDF) and stress autocorrelation function (SACF) have been estimated. Mean square displacement (MSD) investigation demonstrates that the diffusion of liquid atoms (or molecules) serves as the fundamental mechanism for heat conduction in nanofluid. The results have been compared with experimental findings for analogous dispersive medium. Broadly, an attempt has been made to explore how interactions between the base fluid and nanoparticles (NPs) can enhance the thermal and rheological efficiencies of nanofluids.