Search Results (13)

When examined at the nanometer length scale, metallic liquids exhibit extensive ordering. Bonding enthalpies are balanced against entropic tendencies resulting in a rich complicated behavior that leads to clustering that depends on temperature but evolves on picosecond time scales. The structural organization of metallic liquids affects their thermophysical properties, such as viscosity and density, thus influencing the ability of a metallic liquid to form useful technological phases, such as metallic glasses. The time-dependent pair correlation function (the Van Hove function) was determined for metallic-glass forming Cu₄₉Zr₄₅Al₆ at 1060 °C from time-of-flight inelastic neutron scattering measurements made using the Neutron Electrostatic Levitation facility at the Spallation Neutron Source. The time for changes in local atomic connectivity, which is the timescale of atomic ordering, was determined by examining the decay of the nearest neighbor peak. The results of rigorous statistical analyses were used to distinguish between competing models of ordering, suggesting that a stretched exponential model of coordination number change is valid for this system. Full article

This study explores the optimization of a Cu–Al₂O₃/water hybrid nanofluid within an irregular wavy enclosure under inclined periodic MHD effects. Hybrid nanofluids, with different mixture ratios of copper (Cu) and alumina (Al₂O₃) nanoparticles in water, are used in this study. Numerical simulations using the Galerkin residual-based finite-element method (FEM) are conducted to solve the governing PDEs. At the same time, artificial neural networks (ANNs) and response surface methodology (RSM) are employed to optimize thermal performance by maximizing the average Nusselt number (Nu_av), the key indicator of thermal transport efficiency. Thermophysical properties such as viscosity and thermal conductivity are evaluated for validation against experimental data. The results include visual representations of heatlines, streamlines, and isotherms for various physical parameters. Additionally, Nu_av, friction factors, and thermal efficiency index are analyzed using different nanoparticle ratios. The findings show that buoyancy and MHD parameters significantly influence heat transfer, friction, and thermal efficiency. The addition of Cu nanoparticles improves heat transport compared to Al₂O₃ nanofluid, demonstrating the superior thermal conductivity of the Cu–Al₂O₃/water hybrid nanofluid. The results also indicate that adding Al₂O₃ nanoparticles to the Cu/water nanofluid diminishes the heat transport rate. The waviness of the geometry shows a significant impact on thermal management as well. Moreover, the statistical RSM analysis indicates a high R² value of 98.88% for the response function, which suggests that the model is well suited for predicting Nu_av. Furthermore, the ANN model demonstrates high accuracy with a mean squared error (MSE) of 0.00018, making it a strong alternative to RSM analysis. Finally, this study focuses on the interaction between the hybrid nanofluid, a wavy geometry, and MHD effects, which can optimize heat transfer and contribute to energy-efficient cooling or heating technologies. Full article

In the present work, hybrid Cellulose Nanocrystal–MXene (CNC–MXene) nanolubricants were prepared via a two-step method and investigated as potential heat-transfer hybrid nanofluids for the first time. CNC–MXene nanolubricants were synthesized via a two-step method by varying the weight percentage of CNC–MXene nanoparticles (ranging from 0.01 to 0.05 wt%) and characterized using Fourier-Transform Infrared Spectroscopy and TGA (Thermogravimetric Analysis). Response surface methodology (RSM) was used in conjunction with the miscellaneous design model to identify prediction models for the thermophysical properties of the hybrid CNC–MXene nanolubricant. Minitab 18 statistical analysis software and Response Surface Methodology (RSM) based on Central Composite Design (CCD) were utilized to generate an empirical mathematical model investigating the effect of concentration and temperature. The analysis of variance (ANOVA) results indicated significant contributions from the type of nanolubricant (p < 0.001) and the quadratic effect of temperature (p < 0.001), highlighting non-linear interactions that affect viscosity and thermal conductivity. The findings showed that the predicted values closely matched the experimental results, with a percentage of absolute error below 9%, confirming the reliability of the optimization models. Additionally, the models could predict more than 85% of the nanolubricant output variations, indicating high model accuracy. The optimization analysis identified optimal conditions for maximizing both dynamic viscosity and thermal conductivity. The predicted optimal values (17.0685 for dynamic viscosity and 0.3317 for thermal conductivity) were achieved at 30 °C and a 0.01% concentration, with a composite desirability of 1. The findings of the percentage of absolute error (POAE) reveal that the model can precisely predict the optimum experimental parameters. This study contributes to the growing field of advanced nanolubricants by providing insights into the synergistic effects of CNC and MXene in enhancing thermophysical properties. The developed models and optimization techniques offer valuable tools for tailoring nanolubricant formulations to specific tribological applications, potentially leading to improved efficiency and durability in various industrial settings. Full article

Prospective environmental and technological assessment of emerging chemical processes is necessary to identify, analyze and evaluate the technologies that are highly imperative in the transition towards climate neutrality. The investigation of the environmental impacts and material and energy requirements of the processes at the low technology readiness level (TRL) is important in making early decisions about the feasibility of adapting and upscaling the process to the industrial level. However, the upscaling of new chemical processes has always been a major challenge; and in this context, there is no general methodological guidance available in the literature. Hence, a new comprehensive methodological framework for upscaling of novel chemical processes is designed and presented based on thermodynamic process modeling and simulation. The practical implementation of the proposed methodology is extensively discussed by developing a scaled-up novel carbon capture and utilization (CCU) process comprised of sequestration of carbon dioxide (CO₂) from blast furnace gas with a capacity of 1000 liter per hour (L/h) using methanol and its utilization as a precursor to produce methane (CH₄). It was found that thermodynamic process modeling and simulations based on the perturbed-chain statistical associating (PC-SAFT) equation of state (EOS) can precisely estimate the CO₂ solubility in methanol and conversion to CH₄ at various temperature and pressure conditions. The achieved thermophysical property and kinetics parameters can be employed in process simulations to estimate scaled-up environmental flows and material and energy requirements of the process. Full article

Finding eco-friendly products that are beneficial to the environment and serve as tools for sustainable development is a contemporary challenge. This work illustrates the recovery of bio-waste-based materials, which not only improve the hygrothermal properties of gypsum but also promote the paper and wood recycling processes in a circular economy approach. The samples were subjected to tests for density, water absorption, ultrasonic pulse velocity, flexural strength, compressive strength, and thermophysical property characterization. A statistical analysis of variance was used to study the impact of waste on the physico-mechanical behavior of gypsum, leading to the development of predictive models that can be used to predict and optimize the performance of bio-composites in various applications. The results revealed a reduction in mechanical strength with the addition of waste, but the samples still exhibit superior insulation properties, surpassing commonly used standard boards. By adding ouate and wood wastes to a mass of 20% in its natural state, the gypsum becomes lighter and acts as a better insulator with a reduced density, thermal conductivity, and ultrasound velocity of up to 50%, 57%, and 83%, respectively. These findings show the significant implication of reducing environmental impacts while contributing to the promotion of sustainable building practices, both in new construction projects and in building renovations. Full article

Accurate determination of the thermophysical properties of hydrogen is a crucial issue in hydrogen system design. By developing computational programs, a statistical thermodynamic model based on fundamental equations of state was implemented to determine hydrogen’s thermophysical properties, including the ortho-hydrogen fraction in equilibrium hydrogen, para-ortho hydrogen conversion heat, isobaric heat capacities and enthalpies. The deviations of calculated para-hydrogen enthalpies from REFPROP data were within 2.22%, ranging from 20 K to 300 K at 0.1 MPa, and within 2.32% between 100 K and 1500 K at pressures from 0.1 MPa to 20 MPa. To quantitatively assess the convenience of the statistical thermodynamic method, the running speeds of programs with different methods for determining hydrogen’s thermophysical properties were compared. The time required for statistical thermodynamic calculation was 7.95% that required for treading REFPROP data when the performance of the variable density multilayer insulation combined with a one vapor-cooled shield and para-ortho hydrogen conversion was calculated. The programs developed based on the statistical thermodynamic method can be used to determine the thermophysical properties of hydrogen or other fluids. Full article

Thermophysical molding (TPM) treatments can significantly improve the surface properties of thick wood veneer. To understand the effects of TPM treatments on the surface properties of thick veneer, the roughness, contact angles, and chemical changes were determined. The results indicated that the roughness of the thick veneer decreased when the temperature and the duration increased. The contact angles decreased when the temperature increased, resulting in better wettability. X-ray photoelectron spectroscopic (XPS) results provided information about the significant chemical changes in the surface with different TPM temperatures of 160–190 °C and durations of 5–11 min. Increases in temperature and duration increased the C content and decreased the O content during the treatment process. The most significant changes in the thick veneer that resulted from increasing the temperature and the duration were the increase in the C1 component and the decrease in the C2 component. Thus, the oxygen to carbon (O/C) ratio decreased and the ratio of aromatic carbon to aliphatic carbon (C1/C2) notably increased with the increasing TPM temperature. The TPM duration slightly affected the O/C ratio, but it had a stronger linear relation with the C1/C2 ratio. Additionally, the C1/C2 ratio and the O/C ratio had a linear statistical relationship with the initial wettability. These findings could provide useful information for the future utilization of thick veneers treated with TPM. Full article

The design of processing routes involving the presence of the liquid phase is mainly associated with the knowledge of its surface and transport properties. Despite this need, due to experimental difficulties related to high temperature measurements of metallic melts, for many alloy systems neither thermodynamic nor thermophysical properties data are available. A good example of a system lacking these datasets is the Ir-Si system, although over the last fifty years, the structures and properties of its solid phases have been widely investigated. To compensate the missing data, the Gibbs free energy of mixing of the Ir-Si liquid phase was calculated combining the model predicted values for the enthalpy and entropy of mixing using Miedema’s model and the free volume theory, respectively. Subsequently, in the framework of statistical mechanics and thermodynamics, the surface properties were calculated using the quasi-chemical approximation (QCA) for the regular solution, while to obtain the viscosity, the Moelwyn-Hughes (MH) and Terzieff models were applied. Subsequently, the predicted values of the abovementioned thermophysical properties were used to model the non-reactive infiltration isotherm of Ir-Si (eutectic)/SiC system. Full article

The deployment of low-carbon hydrogen in gas grids comes with strategic benefits in terms of energy system integration and decarbonization. However, hydrogen thermophysical properties substantially differ from natural gas and pose concerns of technical and regulatory nature. The present study investigates the blending of hydrogen into distribution gas networks, focusing on the steady-state fluid dynamic response of the grids and gas quality compliance issues at increasing hydrogen admixture levels. Two blending strategies are analyzed, the first of which involves the supply of NG–H₂ blends at the city gate, while the latter addresses the injection of pure hydrogen in internal grid locations. In contrast with traditional case-specific analyses, results are derived from simulations executed over a large number (i.e., one thousand) of synthetic models of gas networks. The responses of the grids are therefore analyzed in a statistical fashion. The results highlight that lower probabilities of violating fluid dynamic and quality restrictions are obtained when hydrogen injection occurs close to or in correspondence with the system city gate. When pure hydrogen is injected in internal grid locations, even very low volumes (1% vol of the total) may determine gas quality violations, while fluid dynamic issues arise only in rare cases of significant hydrogen injection volumes (30% vol of the total). Full article

The effects of using 100% larch bark (Larix decidua Mill) as a raw material for composite boards on the thermophysical properties of this innovative material were investigated in this study. Panels made of larch bark with 4–11 mm and 10–30 mm particle size, with ground bark oriented parallel and perpendicular to the panel’s plane at densities varying from 350 to 700 kg/m³ and bonded with urea-formaldehyde adhesive were analyzed for thermal conductivity, thermal resistivity and specific heat capacity. It was determined that there was a highly significant influence of bulk density on the thermal conductivity of all the panels. With an increase in the particle size, both parallel and perpendicular to the panel´s plane direction, the thermal conductivity also increased. The decrease of thermal diffusivity was a consequence of the increasing particle size, mostly in the parallel orientation of the bark particles due to the different pore structures. The specific heat capacity is not statistically significantly dependent on the density, particle size, glue amount and particle orientation. Full article

This paper reports the use of a numerical solution of nanofluid flow. The boundary layer flow over a stretching sheet in combination of two nanofluids models is studied. The partial differential equation that governs this model was transformed into a nonlinear ordinary differential equation by using similarity variables, and the numerical results were obtained by applying the shooting technique. Copper (Cu) nanoparticles (water-based fluid) were used in this study. This paper presents and discusses all numerical results, including those for the local Sherwood number and the local Nusselt number. Additionally, the effects of the nanoparticle volume fraction, Brownian motion Nb, and thermophoresis Nt on the performance of heat transfer are discussed. The results show that the stretching sheet has a unique solution: as the nanoparticle volume fraction φ (φ = 0), Nt (Nt = 0.1), and Nb decrease, the rate of heat transfer increases. Furthermore, as φ (φ = 0) and Nb decrease, the rate of mass transfer increases. The data of the Nusselt and Sherwood numbers were tested using different statistical distributions, and it is found that both datasets fit the Weibull distribution for different values of Nt and rotating φ. Full article

The main purpose of the present paper is to improve the performance of the adaptive neuro-fuzzy inference system (ANFIS) in predicting the thermophysical properties of Al₂O₃-MWCNT/thermal oil hybrid nanofluid through mixing using metaheuristic optimization techniques. A literature survey showed that the use of an artificial neural network (ANN) is the most widely used method, although there are other methods that showed better performance. Moreover, it was found in the literature that artificial intelligence methods have been widely used for predicting the thermal conductivity of nanofluids. Thus, in the present study, genetic algorithms (GAs) and particle swarm optimization (PSO) have been utilized to search and determine the antecedent and consequent parameters of the ANFIS model. Solid concentration and temperature were considered as input variables, and thermal conductivity, dynamic viscosity, heat transfer performance, and pumping power in both the internal laminar and turbulent flow regimes were the outputs. In order to evaluate and compare the performance of the models, two statistical indices of root mean square error (RMSE) and determination coefficient (R) were utilized. Based on the results, both of the models are able to predict the thermophysical properties appropriately. However, the ANFIS-PSO model had a better performance than the ANFIS-GA model. Finally, the studied thermophysical properties were developed by the trained ANFIS-PSO model. Full article

Some known results from statistical thermophysics as well as from hydrology are revisited from a different perspective trying: (a) to unify the notion of entropy in thermodynamic and statistical/stochastic approaches of complex hydrological systems and (b) to show the power of entropy and the principle of maximum entropy in inference, both deductive and inductive. The capability for deductive reasoning is illustrated by deriving the law of phase change transition of water (Clausius-Clapeyron) from scratch by maximizing entropy in a formal probabilistic frame. However, such deductive reasoning cannot work in more complex hydrological systems with diverse elements, yet the entropy maximization framework can help in inductive inference, necessarily based on data. Several examples of this type are provided in an attempt to link statistical thermophysics with hydrology with a unifying view of entropy. Full article