Search Results (922)

In this paper, a double-mode self-calibration tension system is proposed, which adopts the conversion of hydraulic meter tension and the monitoring of standard force sensors. According to the material characteristics of the jack and the viscosity and temperature characteristics of the hydraulic oil, the differential model of heat conduction in the hydraulic cylinder and the mathematical model of oil film friction heat generation are established, and the internal thermodynamic characteristics of the jack are theoretically analyzed, which provides theoretical support for the temperature compensation of the hydraulic oil pressure gauge of the jack. A simulation analysis was conducted on the thermodynamic characteristics of the hydraulic jack, and the distribution patterns of the temperature field, thermal stress field, and thermal strain field inside the hydraulic cylinder during normal operation were determined by measuring the temperature changes in five different parts of the jack at different times (t = 200 s, 2600 s, 5000 s, 7400 s, and 10,000 s). For the issue of heat generation due to oil film friction in the hydraulic jack, a simulation calculation model is developed by integrating Computational Fluid Dynamics (CFD) techniques with dynamic grid and slip grid methods. By simulating and analyzing frictional heating under conditions where the inlet pressures are 0.1 MPa, 0.3 MPa, 0.5 MPa, 0.7 MPa, and 0.9 MPa, respectively, we can obtain the temperature distribution on the jack, determine the frictional resistance, and subsequently conduct a theoretical analysis of the simulation results. Using the high-precision standard force sensor after data processing and the hydraulic oil gauge after temperature compensation, the online self-calibration of the tensioning system is carried out, and the regression equation of the tensioning system under different oil temperatures is obtained. The double-mode self-calibration tensioning system with temperature compensation is used to verify the compensation accuracy of the proposed double-mode self-calibration tensioning system. Full article

The digital twin model, which serves as a virtual counterpart symmetric to the physical entity, enables high-fidelity simulation and real-time monitoring. However, digital twin implementation for marine steam turbines (MSTs) faces dual multi-domain simulation fidelity and computational efficiency challenges. This study establishes a MST digital twin modeling methodology through two interconnected innovations: (1) a Modelica-based modular architecture enabling cross-domain coupling across mechanical, thermodynamic, and hydrodynamic systems via hierarchical decomposition, ensuring bidirectional symmetry between physical components and their virtual representations; and (2) a hybrid support vector regression-bidirectional long short-term memory (SVR-BiLSTM) surrogate model combining Gaussian radial basis function-supported SVR for steady-state mapping with Bi-LSTM networks for dynamic error compensation. Experimental validation demonstrates: (a) the SVR component achieves <1.57% absolute error under step-load conditions with 85% computational time reduction versus physics-based models; and (b) Bi-LSTM integration improves transient prediction accuracy by 14.85% in maximum absolute error compared to standalone SVR, effectively resolving static–dynamic discrepancies in telemetry simulation. This dual-approach innovation successfully bridges the critical trade-off between real-time computation and predictive accuracy while maintaining symmetric consistency between the physical turbine and its digital counterpart, providing a validated technical foundation for the intelligent operation and maintenance of MSTs. Full article

This study integrates molecular dynamics (MD) simulations and density functional theory (DFT) computations to elucidate the unique adsorption characteristics of phenol and para-chlorophenol onto sepiolite by examining structural deformation, electronic properties, and adsorption energetics. The hydroxyl group (-OH) of phenol mainly determines its adsorption process since it has a quite negative Mulliken charge (−0.428) and significant electrophilic reactivity (fi⁺ = 0.090), therefore enabling strong hydrogen bonding with the silanol (-SiOH) groups of sepiolite. By π-π interactions with the electron-rich siloxane (-Si-O-Si-) surfaces, the aromatic carbons in phenol improve stability. The close molecular structure allows minimum deformation energy (E_def = 94.18 kcal/mol), hence optimizing alignment with the sepiolite surface. The much negative adsorption energy (E_ads = −349.26 kcal/mol) of phenol supports its further thermodynamic stability. Conversely, because of its copious chlorine (-Cl) component, para-chlorophenol runs against steric and electrical obstacles. The virtually neutral Mulliken charge (−0.020) limits electrostatic interactions even if the chlorine atom shows great electrophilicity (fi⁺ = 0.278). Chlorine’s electron-withdrawing action lowers the hydroxyl group’s (fi⁺ = 0.077) reactivity, hence lowering hydrogen bonding. Moreover, para-chlorophenol shows strong deformation energy (E_def = 102.33 kcal/mol), which causes poor alignment and less access to high-affinity sites. With less negative than phenol, the adsorption energy for para-chlorophenol (E_ads = −317.53 kcal/mol) indicates its reduced thermodynamic affinity. Although more evident in para-chlorophenol because of the polarizable chlorine atom, van der Waals interactions do not balance its steric hindrance and reduced electrostatic interactions. With a maximum Qmax = 0.78 mmol/g, isotherm models confirm the remarkable adsorption capability of phenol in contrast to Qmax = 0.66 mmol/g for para-chlorophenol. By hydrogen bonding and π-cation interactions, phenol builds a dense and structured adsorption layer, and para-chlorophenol shows a chaotic organization with reduced site use. Supported by computational approaches and experimental validation, the results provide a comprehensive knowledge of adsorption mechanisms and provide a basis for the design of adsorbents catered for particular organic pollutants. Full article

This paper discusses the thermal and exergy efficiency analysis of the hydrothermal liquefaction (HTL) process, which converts sewage sludge into biocrude oil in a continuous plug–flow reactor using a linear Fresnel solar collector. The investigation focuses on the influence of key operational parameters, including slurry flow rate, temperature, pressure, residence time, and the external heat transfer coefficient, on the overall efficiency of biocrude oil production. A detailed thermodynamic evaluation was conducted using process simulation principles and a kinetic model to assess mass and energy balances within the HTL reaction, considering heat and mass momentum exchange in a multiphase system using UDF. The reactor’s receiver, a copper absorber tube, has a total length of 20 m and is designed in a coiled configuration from the base to enhance heat absorption efficiency. To optimize the thermal performance of biomass conversion in the HTL process, a Computational Fluid Dynamics–Discrete Element Method (CFD-DEM) coupling numerical method approach was employed to investigate improved thermal performance by obtaining a heat source solely through solar energy. This numerical modeling approach allows for an in-depth assessment of heat transfer mechanisms and fluid-particle interactions, ensuring efficient energy utilization and sustainable process development. The findings contribute to advancing solar-driven HTL technologies by maximizing thermal efficiency and minimizing external energy requirements. Full article

High-entropy alloys (HEAs) are a novel class of metallic materials composed of five or more principal elements in near-equimolar ratios. This unconventional composition leads to high configurational entropy, which promotes the formation of solid solution phases with enhanced mechanical properties, thermal stability, and corrosion resistance. Phase stability plays a critical role in determining their structural integrity and performance. This study provides a focused review of HEA phase transitions, emphasizing the role of lattice gas models in predicting phase behavior. By integrating statistical mechanics with thermodynamic principles, lattice gas models enable accurate modeling of atomic interactions, phase segregation, and order-disorder transformations. The combination of computational simulations (e.g., Monte Carlo, molecular dynamics) with experimental validation (e.g., XRD, TEM, APT) improves predictive accuracy. Furthermore, advances in data-driven methodologies facilitate high-throughput exploration of HEA compositions, accelerating the discovery of alloys with optimized phase stability and superior mechanical performance. Beyond structural applications, HEAs demonstrate potential in functional domains, such as catalysis, hydrogen storage, and energy technologies. This review brings together theoretical modeling—particularly lattice gas approaches—and experimental validation to form a unified understanding of phase behavior in high-entropy alloys. By highlighting the mechanisms behind phase transitions and their implications for material performance, this work aims to support the design and optimization of HEAs for real-world applications in aerospace, energy systems, and structural materials engineering. Full article

: Predicting the mechanical response of industrial alloys is crucial for optimizing manufacturing processes and improving material performance. Traditional, solely experimental approaches, though effective, are inefficient as they are resource-intensive, requiring extensive laboratory testing and the iterative calibration of processing conditions. These costs can be avoided through computational/virtual experiments based on a multiscale hierarchical framework that integrates macroscopic approaches, mesoscale modelling as well as atomic level and advanced thermodynamical simulations to study and predict the mechanical response of metallic systems. In the context of this work, a framework for studying the effect of forming on metallic materials is proposed, applied, and validated on the hot extrusion of AA6063. Coupling thermodynamic simulations (including Phase Field) results with literature data establishes a microstructurally accurate representative volume element (RVE) design. This way, the phase fraction and the grain size of the RVE are determined by thermodynamic simulations (ThermoCalc, MICRESS), which can be validated via microstructure characterization. It is known that the mechanical properties of the individual phases affect the macroscopical properties of the material. Using atomic level simulations (i.e., molecular dynamics), the dislocation density of the material is calculated and utilized as an input for a Crystal Plasticity Fast Fourier Transformation simulation. This iterative process can be applied to match all stages of manufacturing processes. The hierarchical and systematic integration of these computational methodologies enables a rigorous analysis of the effect that processing parameters have on the microstructure. This work contributes to the broader effort of creating experiment-free workflows for designing materials and processes by leveraging a multiscale modeling approach. Coupled with experimental data, the predictive accuracy of the mechanical behavior can be further enhanced. Full article

The exchange antisymmetry of identical fermions leads to an exponential computational bottleneck in ab initio simulations, known as the fermion sign problem. The thermodynamic approach of fictitious identical particles (Y. Xiong and H. Xiong, J. Chem. Phys. 157, 094112 (2022)) provides an efficient and accurate means to simulate some fermionic systems by overcoming the fermion sign problem. This method has been significantly promoted and used by National Ignition Facilities for the ab initio simulations and is believed to have wide application prospects in warm dense quantum matter (T. Dornheim et al., arXiv: 2402.19113 (2023)). By utilizing the fictitious identical particles in the bosonic regime and constant energy extrapolation method (Y. Xiong and H. Xiong, Phys. Rev. E 107, 055308 (2023); T. Morresi and G. Garberoglio, Phys. Rev. B 111, 014521 (2025)), there are promising results in simulating the energy of strongly quantum degenerate fermionic systems. The previous works mainly concern the energy of Fermi systems or only consider situations of weak quantum degeneracy. In this study, we extend the concept of the constant energy extrapolation method and demonstrate the potential of the constant density extrapolation method to accurately simulate the density distribution of fermionic systems in strongly quantum degenerate conditions. Furthermore, based on the energy derived from the constant energy extrapolation method, we present simulation results for the entropy of fermions. Full article

The maintenance of gas turbines relies a lot on gas path diagnostics (GPD), which includes two approaches. The first approach employs a physics-based model (thermodynamic model) to convert measurement shifts (deviations) induced by deterioration into fault parameters, which drastically simplify diagnostics. The second approach relies on data-driven models, makes diagnosis in the space of measurement deviations, and involves pattern recognition techniques. Although a thermodynamic model is an essential element of GPD, it has limitations. This model is a complex software critical to computer resources, and the computation sometimes does not converge. Therefore, it is difficult to use the model in online applications. Since the 1990s, we have developed many thermodynamic models for different engines. Since the 2000s, simplified data-driven models were investigated. This paper proposes to substitute a thermodynamic model for novel simplified data-driven models that have the same functionality, i.e., take into consideration the influence of both operating conditions and engine faults. The proposed models are formed and compared with the underlying thermodynamic model. To obtain a solid conclusion about these models, they are verified in twelve test cases formed by three test-case engines, two model types, and two approximation functions. Although the accuracy of the simplified models varies from 1.15% to 0.0082%, it was found acceptable even for the worst case. Thus, these simple-but-accurate models with the functionality of a physics-based model represent a good replacement for the latter. It is expected that the models will stimulate the further development of advanced diagnostic systems. Full article

Most transmission system operators (TSOs) use seasonally static models considering extreme weather conditions, serving as a reference for computing the transmission capacity of power lines. The use of dynamic line rating (DLR) models can avoid the construction of new lines, market splitting, false congestions and the degradation of lines in a cost-effective way. The operation of power systems is planned based on market results, which consider transactions hours ahead of real-time operation using forecasts with errors. The same is true for the DLR. So, during real-time operation TSOs should rapidly compute the DLR of overhead lines to avoid considering an ampacity above their lines’ design, reflecting the real-time weather conditions. Considering that the DLR of the lines can affect the power flow of an entire region, the use of the complete indirect DLR methodology has a high computation burden for all sectors and lines in a region. So, this article presents and tests three pre-solve methodologies able to rapidly identify the critical sector of each line. These methodologies solve the problem of the high computation burden of the CIGRÉ thermodynamic model of overhead lines. They have been tested by using real data of the transmission grid and the weather conditions for two different regions in Portugal, leading to errors in the computation of the DLR lower than 1% in relation to the complete CIGRÉ model, identifying the critical sector in significantly less time. Full article

We study the thermodynamic properties of the generalized non-convex multispecies Curie–Weiss model, where interactions among different types of particles (forming the species) are encoded in a generic matrix. For spins with a generic prior distribution, we compute the thermodynamic limit of the generating functional for the moments of the Boltzmann–Gibbs measure using simple interpolation techniques. For Ising spins, we further analyze the fluctuations of the magnetization in the thermodynamic limit under the Boltzmann–Gibbs measure. It is shown that a central limit theorem (CLT) holds for a rescaled and centered vector of species magnetizations, which converges to either a centered or non-centered multivariate normal distribution, depending on the rate of convergence of the relative sizes of the species. Full article

Sulfamic acid (SA) is extensively utilised in industry as a component in the production of flameproof materials, a catalyst for swift and highly efficient synthesis, in dye and pigment manufacturing processes, or as herbicide. Under ambient conditions, this compound exists as a solid in zwitterionc form, undergoing pressure-induced isosymmetric polymorphic phase transition (IPT), starting at approximately 10.0 GPa. In this work, multiple computational approaches were used to predict and describe this transition. While geometry optimisation at an increased pressure using periodic DFT-level calculations have not resulted in the anticipated IPT, the comparison of the experimental and theoretical Raman spectra confirmed this transformation. Thermodynamic calculations enabled the comparison of the stability of the modelled phases and explained the experimental observations. Ab initio molecular dynamics simulations revealed the mechanisms behind the observed transition. This work presents a complex methodology that can be successfully used to predict the IPT of molecular crystals. Full article

This study investigates the physical interactions and between forest fires and the atmosphere, which often lead to conditions favourable to instability and the formation of pyrocumulus (PyCu). Using the coupled atmosphere–fire spread modelling framework, WRF-SFIRE, the Portuguese October 2017 Quiaios wildfire, in association with tropical cyclone Ophelia, was simulated. Fire spread was imposed via burnt area data, and the fire’s influence on the vertical and surface atmosphere was analysed. Simulated local atmospheric conditions were influenced by warm and dry air advection near the surface, and moist air in mid to high levels, displaying an inverted “V” profile in thermodynamic diagrams. These conditions created a near-neutrally unstable atmospheric layer in the first 3000 m, associated with a low-level jet above 1000 m. Results showed that vertical wind shear tilted the plume, resulting in an intermittent, high-based, shallow pyroconvection, in a zero convective available potential energy environment (CAPE). Lifted parcels from the fire lost their buoyancy shortly after condensation, and the presence of PyCu was governed by the energy output from the fire and its updrafts. Clouds formed above the lifted condensation level (LCL) as moisture fluxes from the surface and released from combustion were lifted along the fire plume. Clouds were primarily composed of liquid water (1 g/kg) with smaller traces of ice, graupel, and snow (up to 0.15 g/kg). The representation of pyroconvective dynamics via coupled models is the cornerstone of understanding the phenomena and field applications as the computation capability increases and provides firefighters with real time extreme fire conditions or predicting ahead of time. Full article

The potential of utilizing the rejected heat of a fuel cell system to improve the aircraft propulsive efficiency is discussed for various flight conditions. The thermodynamic background of the process and the connection of power consumption in the fan of the ducted propulsor and fuel cell heat are given, and a link between these two components is presented. A concept that goes beyond the known ram heat exchanger is discussed, which outlines the potential benefits of integrating a fan upstream of the heat exchanger. The influence of the fan pressure ratio, flight speed, and altitude, as well as the temperature level of the available fuel cell heat on the propulsive efficiency, is presented. A correlation between the fan pressure ratio, flight speed, and exchangeable fuel cell heat is established, providing a simplified computational approach for evaluating feasible operating conditions within this process. This paper identifies the challenges of heat exchanger integration at International Standard Atmosphere sea level conditions and its benefits for cruise flight conditions. The results show that for a flight Mach number of 0.8 and a fan pressure ratio of 1.5 at a cruising altitude of 11,000 m, the propulsion efficiency increases by approximately 8 percentage points compared to a ducted propulsor without heat utilization. Under sea-level conditions, the concept does not offer any performance advantages over a ducted propulsor. Instead, it exhibits either comparable or reduced propulsive efficiency. Full article

Cross sections and thermally averaged rate coefficients for the vibrational excitation and de-excitation by electron impact on the HDO molecule are computed using a theoretical approach based entirely on first principles. This approach combines scattering matrices obtained from the UK R-matrix codes for various geometries of the target molecule, three-dimensional vibrational states of HDO, and the vibrational frame transformation. The vibrational states of the molecule are evaluated by solving the Schrödinger equation numerically, without relying on the normal-mode approximation, which is known to be inaccurate for water molecules. As a result, couplings and transitions between the vibrational states of HDO are accurately accounted for. From the calculated cross sections, thermally averaged rate coefficients and their analytical fits are provided. Significant differences between the results for HDO and H₂O are observed. Additionally, an uncertainty assessment of the obtained data is performed for potential use in modeling non-local thermodynamic equilibrium (non-LTE) spectra of water in various astrophysical environments. Full article

Boiling heat transfer plays a crucial role in various engineering applications, requiring accurate numerical modeling to capture phase-change dynamics. This study employs the pseudopotential lattice Boltzmann method (LBM) to simulate boiling heat transfer at different reduced temperatures, aiming to provide deeper insights into bubble dynamics and heat transfer mechanisms. The LBM framework incorporates a multi-relaxation-time approach and the Peng–Robinson equation of state to enhance numerical stability and thermodynamic consistency. Simulations were performed to analyze bubble nucleation, growth, and detachment across varying reduced temperatures, considering the influence of surface wettability, surface tension and gravitational acceleration. The results indicate a strong dependence of bubble behavior on the reduced temperature, affecting both heat flux and boiling regimes. The numerical findings show reasonable agreement with theoretical predictions and experimental trends, validating the effectiveness of the LBM approach for phase-change simulations. Additionally, this study highlights the role of contact angle variation in modifying boiling characteristics, emphasizing the necessity of accurate surface interaction modeling. The outcomes of this work contribute to advancing computational methodologies for boiling heat transfer, supporting improved thermal management in industrial applications. Full article