Search Results (1,002)

The increasing need for high-resolution, real-time radiative transfer (RT) modeling in climate science, remote sensing, and planetary exploration has exposed limitations of traditional solvers such as the Discrete Ordinate Radiative Transfer (DISORT) and Rapid Radiative Transfer Model for General Circulation Models (RRTMG), particularly in handling spectral complexity, non-local thermodynamic equilibrium (non-LTE) conditions, and computational scalability. Quantum-Inspired Neural Radiative Transfer (QINRT) frameworks, combining tensor-network parameterizations and quantum neural operators (QNOs), offer efficient approximation of high-dimensional radiative fields while preserving key physical correlations. This review highlights the advances of QINRT in enhancing spectral fidelity and computational efficiency, enabling energy-efficient, real-time RT inference suitable for satellite constellations and unmanned aerial vehicle (UAV) platforms. By integrating physics-informed modeling with scalable neural architectures, QINRT represents a transformative approach for next-generation Earth-system digital twins and autonomous climate intelligence. Full article

This review surveys recent progress in hybrid artificial intelligence (AI) approaches for gas turbine intelligent digital twins, with an emphasis on models that integrate physics-based simulations and machine learning. The main contribution is the introduction of a structured classification of hybrid AI methods tailored to gas turbine applications, the development of a novel comparative maturity framework, and the proposal of a layered roadmap for integration. The classification organizes hybrid AI approaches into four categories: (1) artificial neural network (ANN)-augmented thermodynamic models, (2) physics-integrated operational architectures, (3) physics-constrained neural networks (PcNNs) with computational fluid dynamics (CFD) surrogates, and (4) generative and model discovery approaches. The maturity framework evaluates these categories across five criteria: data dependency, interpretability, deployment complexity, workflow integration, and real-time capability. Industrial case studies—including General Electric (GE) Vernova’s SmartSignal, Siemens’ Autonomous Turbine Operation and Maintenance (ATOM), and the Electric Power Research Institute (EPRI) turbine digital twin—illustrate applications in real-time diagnostics, predictive maintenance, and performance optimization. Together, the classification and maturity framework provide the means for systematic assessment of hybrid AI methods in gas turbine intelligent digital twins. The review concludes by identifying key challenges and outlining a roadmap for the future development of scalable, interpretable, and operationally robust intelligent digital twins for gas turbines. Full article

The two-star random graph is the simplest exponential random graph model with nontrivial interactions between the graph edges. We propose a set of auxiliary variables that control the thermodynamic limit where the number of vertices N tends to infinity. Such ’master variables’ are usually highly desirable in treatments of ‘large N’ statistical field theory problems. For the dense regime when a finite fraction of all possible edges are filled, this construction recovers the mean-field solution of Park and Newman, but with explicit control over the $1/N$ corrections. We use this advantage to compute the first subleading correction to the Park–Newman result, which encodes the finite, nonextensive contribution to the free energy. For the sparse regime with a finite mean degree, we obtain a very compact derivation of the Annibale–Courtney solution, originally developed with the use of functional integrals, which is comfortably bypassed in our treatment. Full article

Liquefied natural gas plays a crucial role in global energy transitions due to its high efficiency and low emissions, especially in long-distance transportation. However, the thermal management of LNG storage tanks remains a significant challenge due to temperature fluctuations, which impact both efficiency and safety. Traditional methods rely on thermodynamic models or computational fluid dynamics simulations but are computationally expensive and time-consuming. This study proposes a hybrid approach that integrates machine learning techniques with CFD data to predict temperature variations inside LNG storage tanks. Several ML models, including Random Forest, XGBoost, and deep learning-based models like CNN and TCN, were tested. Results indicate that CNN and TCN models offer the best performance in predicting temperature changes, showing superior accuracy and computational efficiency. This approach significantly enhances the real-time prediction capability, offering a promising solution for improving LNG tank thermal management, ensuring both operational safety and efficiency. Full article

The chemical bath deposition of ZnO nanowires is of high interest for many functional devices, but the typical use of hexamethylenetetramine (HMTA) molecules forming formaldehyde as a harmful substance raises health, environment, and regulation issues. After a careful review of the multiple roles of HMTA molecules, we unambiguously show, using X-ray near-edge structure absorption spectroscopy with synchrotron radiation, that they do not form any complexes with the Zn(II) species, both in the low- and high-pH regions. In contrast and in agreement with thermodynamic computations, [Zn(H₂O)₆]²⁺ and Zn(NH₃)₄²⁺ ion complexes are revealed to be the predominant Zn(II) species in the low- and high-pH regions. The use of HMTA molecules is found to be critical to form ZnO nanowires with a high aspect ratio in the low-pH region. In contrast, HMTA molecules are shown to be fully substituted by ammonia in the high-pH region to form ZnO nanowires with a high structural and optical quality. The removal of HMTA molecules for the chemical bath deposition of ZnO nanowires in the high-pH region represents a significant step forward towards the development of a chemical synthesis fully compatible with green chemistry. Full article

Beginning with Mandelbrot’s insight that Fisher information may admit a thermodynamic interpretation, a growing body of work has connected this information-theoretic measure to fluctuation–dissipation relations, thermodynamic geometry, and phase transitions. Yet, these connections have largely remained at the level of formal analogies. In this work, we provide what is, to our knowledge, the first explicit realization of the epistemic-to-physical transition of Fisher information within a finite interacting quantum system. Specifically, we analyze a model of N fermions occupying two degenerate levels and coupled by a spin-flip interaction of strength V, treated in the grand canonical ensemble at inverse temperature $\beta$. We compute the Fisher information $F_N\left(V\right)$ associated with the sensitivity of the thermal state to changes in V, and show that it becomes an observer-independent, experimentally meaningful quantity: it encodes fluctuations, tracks entropy variations, and reveals structural transitions induced by interactions. Our findings thus demonstrate that Fisher information, originally conceived as an inferential and epistemic measure, can operate as a bona fide thermodynamic observable in quantum many-body physics, bridging the gap between information-theoretic foundations and measurable physical law. Full article

Cryogenic propellant sloshing presents significant challenges in aerospace systems, inducing vehicle instability, structural fatigue, energy losses, and complex thermal management issues. This review synthesizes experimental, analytical, and numerical advances with an emphasis on energy dissipation and conversion efficiency in propellant storage and transfer. Recent developments in computational fluid dynamics (CFD) and AI-driven digital-twin frameworks are critically examined alongside the influences of tank materials, baffle configurations, and operating conditions. Unlike conventional fluids, cryogenic propellants in microgravity and within composite overwrapped pressure vessels (COPVs) exhibit unique thermodynamic and dynamic couplings that remain only partially characterized. Prior reviews have typically treated these factors in isolation; here, they are unified through an integrated perspective linking cryogenic thermo-physics, reduced-gravity hydrodynamics, and fluid–structure interactions. Persistent research limitations are identified in the areas of data availability, model validation, and thermo-mechanical coupling fidelity, underscoring the need for scalable multi-physics approaches. This review’s contribution lies in consolidating these interdisciplinary domains while outlining a roadmap toward experimentally validated, AI-augmented digital-twin architectures for improved energy efficiency, reliability, and propellant stability in next-generation aerospace missions. Full article

This study examines the design and implementation of a sensor developed to measure total pressure in supersonic flow conditions using nitrogen as the working fluid. Using a combination of absolute and differential pressure sensors, the total pressure distribution downstream of a nozzle—where normal shock waves are generated—was characterized across a range of low-pressure regimes. The experimental results were employed to validate and calibrate computational fluid dynamics (CFD) models, particularly within pressure ranges approaching the limits of continuum mechanics. The validated analyses enabled a more detailed examination of shock-wave behavior under near-continuum conditions, with direct relevance to the operational environment of differentially pumped chambers in Environmental Scanning Electron Microscopy (ESEM). Furthermore, an entropy increase across the normal shock wave at low pressures was quantified, attributed to the extended molecular mean free path and local deviations from thermodynamic equilibrium. Full article

Excessive exhaust backpressure (EBP) in modern diesel engines disrupts gas exchange, increases residual gas fraction (RGF), and reduces combustion efficiency. Traditional experimental approaches, including simulations and bench testing, are often time-consuming and costly, which has driven growing interest in artificial neural networks (ANNs) for accurately modelling complex engine behavior. This research introduces an ANN model designed to predict the impact of EBP on the performance and emissions of a diesel engine across varying compression ratio (CR) of 12, 14, 16, and 18 and engine load (25%, 50%, 75%, and 100%) conditions. The ANN model was developed and optimised using genetic algorithms (GA) and particle swarm optimisation (PSO). It was then trained using data from an experimentally validated one-dimensional computational fluid dynamics (1D-CFD) model developed through GT-Power GT-ISE v2024, simulating engine responses under variation CR, load, and EBP conditions. The optimised ANN architecture, featuring an optimal (3-14-10) configuration, was trained using the Levenberg–Marquardt back propagation algorithm. The performance of the model was assessed using statistical criteria, including the coefficient of determination (R²), root mean square error (RMSE), and k-fold cross-validation, by comparing its predictions with both experimental and simulated data. Results indicate that the optimised ANN model outperformed the baseline ANN and other machine learning (ML) models, attaining an R² of 0.991 and an RMSE of 0.011. It reliably predicts engine performance and emissions under varying EBP conditions while offering insights for engine control, optimisation, diagnostics, and thermodynamic mechanisms. The overall prediction error ranged from 1.911% to 2.972%, confirming the model’s robustness in capturing performance and emission outcomes. Full article

This study investigates underground heat sources and develops effective strategies for mitigating heat hazards in coal mines, with a focus on the design and optimization of cooling systems. Using the 3107 fully mechanized mining face of Menkeqing Coal Mine as a case study, geological survey data and in situ measurements were combined to evaluate the severity of thermal hazards. Thermodynamic and heat transfer models were applied to quantify heat dissipation from multiple sources. Computational fluid dynamics (CFD) simulations, based on data-driven modeling and geometric reconstruction, tested different equipment layouts and spacing configurations to identify optimal cooling schemes. Field implementation of the designed cooling system confirmed its effectiveness, offering practical guidance for improving heat hazard control and cooling system optimization in deep coal mines. Full article

Lindbladian formalism models open quantum systems using a ‘bottom-up’ approach, deriving linear dynamics from system–environment interactions. We present a ‘top-down’ approach starting with phenomenological constraints, focusing on a system’s structure, subsystems’ interactions, and environmental effects and often using a non-equilibrium variational principle designed to enforce strict thermodynamic consistency. However, incorporating the second law’s requirement—that Gibbs states are the sole stable equilibria—necessitates nonlinear dynamics, challenging no-signaling principles in composite systems. We reintroduce ‘local perception operators’ and show that they allow to model signaling-free non-local effects. Using the steepest-entropy-ascent variational principle as an example, we demonstrate the validity of the ‘top-down’ approach for integrating quantum mechanics and thermodynamics in phenomenological models, with potential applications in quantum computing and resource theories. Full article

Alloys from the Ag-Ti system are extremely promising and offer the possibility of versatile applications owing to their attractive properties. However, due to the experimental difficulties caused, among others, by the significant difference in melting points of the components, most of the information on the thermodynamic properties available in the literature has been obtained by computer methods. Therefore, the main aim of this work is to extend the current knowledge about the experimentally determined thermodynamic properties of selected alloys from the Ag-Ti system. Within the scope of this work, calorimetric studies were carried out using Differential Scanning Calorimetry (DSC) and high-temperature drop calorimetry measurements. The first of the aforementioned methods was used to determine the characteristic temperature of the Ag_0.43Ti_0.57 alloy synthesized by mechanical alloying. Using titanium hydride instead of titanium for the preparation of alloys from the Ag-Ti system has not yet been reported in the literature. This paper presents a complete structural characterization (SEM, XRD studies) of the above alloy produced by this method. The second technique was applied to ascertain the mixing enthalpy change in the alloys in the composition range between x_Ti = 0.02–0.226, and for the measurements of the formation enthalpy of the AgTi intermetallic phase. Based on the calorimetric results obtained in this study, along with the relevant thermodynamic data from the literature, the Ag-Ti phase diagram was reoptimized. Full article

Amorphous solid dispersions (ASDs) represent a promising formulation strategy for improving the solubility and bioavailability of poorly water-soluble drugs, a major challenge in pharmaceutical development. This review provides a comprehensive analysis of the physicochemical principles underlying ASD stability, with a focus on drug–polymer miscibility, molecular mobility, and thermodynamic properties. The main manufacturing techniques including hot-melt extrusion, spray drying, and KinetiSol^® dispersing are discussed for their impact on formulation homogeneity and scalability. Recent advances in excipient selection, molecular modeling, and in silico predictive approaches have transformed ASD design, reducing dependence on traditional trial-and-error methods. Furthermore, machine learning and artificial intelligence (AI)-based computational platforms are reshaping formulation strategies by enabling accurate predictions of drug–polymer interactions and physical stability. Advanced characterization methods such as solid-state NMR, IR, and dielectric spectroscopy provide valuable insights into phase separation and recrystallization. Despite these technological innovations, ensuring long-term stability and maintaining supersaturation remain significant challenges for ASDs. Integrated formulation design frameworks, including PBPK modeling and accelerated stability testing, offer potential solutions to address these issues. Future research should emphasize interdisciplinary collaboration, leveraging computational advancements together with experimental validation to refine formulation strategies and accelerate clinical translation. The scientists can unlock the full therapeutic potential with emerging technologies and a data-driven approach. Full article

The intrinsic instability of organic electrolytes seriously impedes practical applications of lithium–oxygen (Li-O₂) batteries. Recent studies have shown that the use of high-concentration electrolytes can suppress the decomposition reaction of electrolytes and help enhance cell reversibility. However, the fundamental nature of concentrated electrolytes’ ability to improve the chemical durability and stability of Li-O₂ batteries remains unclear. In this work, we conducted computational studies to elucidate the origin of the enhanced oxidative/reductive stability of three representative solvents—DMSO, DME, and EC—in high-concentration electrolytes. The modeling results identify that Li⁺-solvent complexes, one of the solvate components, are the easiest to decompose in concentrated electrolytes. Thermodynamic and kinetic characterizations reveal that more anions in concentrated electrolytes are responsible for improving the oxidative and reductive stability of electrolytes. In addition, more Li⁺ ions, acting as a scavenging or stabilizing agent for superoxide anion (O₂⁻), also improve the stability of electrolytes against oxidation in Li-O₂ batteries. This work provides a mechanistic understanding of the enhanced cycle stability of a Li-O₂ battery using high-concentration electrolytes. Full article

Spin waves represent an important class of low-energy excitations in magnetic solids, which influence the thermodynamic properties and play a major role in technical applications, such as spintronics or magnetic data storage. Despite the enormous advances of ab initio simulations in materials science, quantitative calculations of spin-wave spectra still pose a significant challenge, because the collective nature of the spin dynamics requires an accurate treatment of the Coulomb interaction between the electrons. As a consequence, simple lattice models like the Heisenberg Hamiltonian are still widespread in practical investigations, but modern techniques like time-dependent density-functional theory or many-body perturbation theory also open a route to material-specific spin-wave calculations from first principles. Although both are in principle exact, actual implementations necessarily employ approximations for electronic exchange and correlation as well as additional numerical simplifications. In this review, we recapitulate the theoretical foundations of ab initio spin-wave calculations and analyze the common approximations that underlie present implementations. In addition, we survey the available results for spin-wave dispersions of various magnetic materials and compare the performance of different computational approaches. In this way, we provide an overview of the present state of the art and identify directions for further developments. Full article