Search Results (2,998)

The key to metallic fuel development is the fabrication of uranium metal and alloys into fuel forms. U-Nb alloys are one of the best candidates for a metallic fuel alloy with high-temperature strength sufficient to support the core, acceptable nuclear properties, good fabricability, and compatibility with usable coolant media. Melt processing has been a key component of the metallic fuel cycle, and process models require thermophysical parameters at elevated temperatures, particularly above the melting temperatures, regarding which experimental data are scarce, for accurate simulations and process development. By means of ab initio density-functional theory (DFT) quantum molecular dynamics (QMD), we have calculated the main thermophysical parameters—the density, thermal expansion coefficient, specific heat, thermal conductivity, melting temperature, latent heat of fusion, and viscosity—used in the modeling of the U-6 wt.% Nb alloy casting. The melting temperature of the U-6 wt.% Nb alloy at ambient pressure is obtained by means of QMD simulations using the Z-method. The ambient volume change and latent heat of melting of U-6 wt.% Nb are also derived from QMD simulations in conjunction with analytical fitting for the energy and pressure. The thermal conductivity for the solid U-Nb alloy is calculated from the semi-classical Boltzmann transport equation combined with an estimate of the electron relaxation time obtained from DFT simulations. Full article

Quantitative characterization of permeability in porous media represents a central problem in filtration theory, geosciences, and materials engineering. Standard numerical approaches, including finite element methods and Lattice Boltzmann simulations, typically require extensive domain-specific expertise together with specialized computational software. This motivates the development of computationally simpler and more accessible geometric approaches applicable directly to binary volumetric data. We introduce a novel algorithmic framework for the analysis of porous structures that reformulates permeability-related characterization in terms of discrete geometry and graph-based computation. The method combines parallel raster-grid and graph representations of a binarized three-dimensional CT image. The principal transport-limiting feature of the pore network, interpreted as the minimal constriction governing connectivity, is identified through iterative morphological dilation coupled with a three-dimensional scanline seed-fill procedure. In addition, a dichotomous bisection strategy is proposed to accelerate the determination of the critical bottleneck scale. The proposed methodology was evaluated on five volumetric datasets of size 100 × 100 × 100 voxels obtained from CT-derived porous structures. Experimental results demonstrate that dilation- and erosion-based formulations yield equivalent estimates of the bottleneck parameter in four of the five investigated samples. Furthermore, incorporation of the bisection optimization reduces computational time in three-dimensional experiments by approximately 50% relative to sequential iteration. The presented approach provides a computationally efficient and fully open-source alternative to conventional physics-based permeability solvers for binary porous media. The resulting bottleneck parameter b should be interpreted as a discrete geometric invariant characterizing the pore-network connectivity and minimal transport cross-section. It is not intended to replace the absolute permeability coefficient K appearing in Darcy’s law, but rather to serve as an independent structural descriptor suitable for comparative and topological analysis of porous systems. Full article

The Internet of Vehicles (IoV) is rapidly emerging as a core component of intelligent transportation systems, enabling real-time communication among vehicles, infrastructure, and cloud platforms. However, the increasing interconnectivity of vehicular systems and the advancement of quantum computing introduce significant security challenges to existing cryptographic mechanisms. Conventional schemes such as RSA and Elliptic Curve Cryptography (ECC) are vulnerable to quantum attacks and are computationally inefficient for resource-constrained vehicular environments. To address these limitations, this paper proposes a Double-Ring Hybrid Sparse NTRU (DRH-SNTRU) framework, a lightweight and quantum-resistant cryptographic scheme for secure IoV communication. The proposed framework introduces three key enhancements: (i) controlled-support sparse polynomial structures to reduce polynomial multiplication complexity while improving entropy distribution; (ii) a double-ring algebraic architecture that separates key operations from message processing to enhance structural security and minimize coefficient leakage; and (iii) hybrid ephemeral keys derived from contextual entropy to strengthen forward secrecy and adaptive security. An optional ciphertext evaluation mechanism is further incorporated to detect malformed and replayed ciphertexts prior to decryption. Security analysis demonstrates that the proposed framework achieves IND-CPA security under the hardness assumption of the NTRU lattice problem and can be extended to resist chosen-ciphertext attacks through the integrated validation mechanism. Experimental benchmarking across polynomial dimensions N = 512 to 8192 demonstrates that DRH-SNTRU achieves low setup overhead below 3 μs, efficient decryption latency of approximately 305.64 μs at N = 8192, and compact sparse private key representation of only 117 bytes at higher dimensions. Compared with Standard NTRUEncrypt, NTRU-HRSS, and Ring-LWE Encryption, the proposed framework demonstrates improved decryption efficiency, lightweight storage overhead, and enhanced ciphertext integrity protection while maintaining practical scalability for resource-constrained post-quantum IoV environments. Full article

The modernization of global energy infrastructure within the Industry 5.0 framework requires the use of high-strength steels and reliable joining technologies to ensure safe, sustainable pipeline transport. This study focuses on the analysis of heterogeneous welded joints formed between high-strength alloy steel (34KhN2MA/EN 34CrNiMo6) and an austenitic welded seam (ER 307). While austenitic welds mitigate the risk of cold cracking, they introduce significant structural and mechanical heterogeneity. To address this, the research proposes and validates a material homogeneity criterion (MHC) derived from the LM-hardness methodology. By analyzing the statistical dispersion of macrohardness (HRC) through indicators such as the Weibull homogeneity coefficient (m) and the coefficient of variation (ν), the study establishes a quantitative approach to assess material degradation and structural uniformity across key weld zones. Results demonstrate that macrohardness profiling effectively distinguishes between structurally heterogeneous regions near the weld axis characterized by low homogeneity coefficients (m = 4.04 < 10, A_m = 0.742 < 0.878), elevated variability (ν = 29.68% > 11.6%), and high technological damageability (D = 0.92 > 0.81, j_D = 11.87 > 4.38) with pronounced step-like variation in macrohardness (HRC ∈ [12.6; 47]), on the one hand, and stabilized homogeneous zones in the base material, where m = 24.89 > 10, A_m = 0.947 > 0.878, ν = 4.39% < 11.6%, D = 0.52 ⟶ 0, j_D = 1.09 ⟶ 0, and characteristic range of HRC = 47–55, on the other hand. This methodology provides a robust, quasi-non-destructive tool for enhancing predictive maintenance, digital twins, and the overall integrity management of “smart” pipeline systems. Full article

High friction composite brake shoes containing reinforced steel fibers are now widely used in freight train tread braking systems. With the demand for higher transportation efficiency on railway lines with long steep slopes, it is necessary to explore the braking capabilities of existing general freight train high friction composite brake shoes under continuous braking conditions. In this paper, continuous braking tests at different speed levels were conducted using a friction and wear test rig. Through material characterization and interface damage analysis, it was found that reinforced steel fibers can exist as a contact platform at the brake shoe friction interface. Due to the strip-like morphology and high strength features of steel fibers, even after the steel fiber layer is fragmented, it can still promote the formation of a continuous contact platform with complex material composition on the surface, maintaining the progress of the braking process. For existing general freight train high friction composite brake shoes, at speeds up to 80 km/h, although the friction coefficient decreases to some extent, the wear rate maintains a relatively low range. When the speed increases to 100 km/h, the friction coefficient of the braking interface deteriorates severely, and the wear rate of the brake shoe increases sharply, seriously endangering braking safety. The research results reveal the evolution of wear behavior of high friction composite brake shoes containing reinforced steel fibers at different speed levels, providing theoretical support for exploring the braking capabilities and design optimization of brake shoes. Full article

Thermal barrier coatings (TBCs) require materials with intrinsically low thermal conductivity and high grain-boundary thermal resistance to maximize the temperature gradient across the top coat. In this work, the effective thermal conductivity of more than 40 prospective TBC oxides belonging to seven structural families (YSZ/YSH, pyrochlores/fluorites A₂B₂O₇, defective fluorites A₃BO₇, fergusonite/monazite ABO₄, and perovskites ABO₃) was systematically deconvoluted into intrinsic grain thermal conductivity (k_grain) and grain-boundary (R_gb) contributions. It is shown that grain-boundary Kapitza resistance dominates heat transport in virtually all advanced oxides, contributing 60–90% to the total thermal resistance of polycrystalline samples. The lowest k_grain values (4–12 W m⁻¹ K⁻¹) are found for cerates and certain tantalates, while the highest R_gb (up to 7.2 × 10⁻⁶ m² K W⁻¹) are characteristic of high-entropy and heavily doped perovskites. Orthorhombically distorted SrCeO₃-based and high-entropy perovskites combine moderate k_grain (4.7–27.9 W m⁻¹ K⁻¹), high R_gb, and tunable thermal-expansion coefficients (10–13 × 10⁻⁶ K⁻¹), making them the most promising candidates for next-generation TBCs. These findings provide a rational basis for microstructure engineering and composition design aimed at maximizing the temperature drop across TBC layers while maintaining phase stability and CMAS resistance. Full article

Accurate prediction of steady-state relative permeability via pore-scale modeling is fundamental to understanding multiphase flow processes in diverse engineering applications. However, the stochastic nature of the initial fluid distribution (IFD) in simulations is frequently overlooked, creating uncertainties that may obscure the physical influence of critical parameters on transport behavior. In this study, a color-gradient lattice Boltzmann method was employed to conduct extensive steady-state simulations across two porous media of varying geometric complexity. The investigation focused on evaluating three representative IFD patterns across different capillary numbers (Ca) and viscosity ratios (M). By introducing the coefficient of variation (CV) and distribution interval overlap analysis, the IFD-induced uncertainty was systematically quantified. The results demonstrate that the IFD is a primary source of statistical variance in relative permeability, exhibiting a strong nonlinear coupling with Ca, M, and structural complexity. CV analysis reveals that uncertainty peaks within specific saturation windows, which shift according to the pore geometry. Specifically, the peak uncertainty window for total relative permeability shifts from S_w$\in$ [0.5, 0.7] in the simple model to S_w$\in$ [0.3, 0.5] in the heterogeneous model. Notably, the wetting phase exhibits pronounced instability in the low-saturation regime, with the wetting-phase CV reaching its maximum at S_w = 0.3 in the simple model. At low Ca conditions, IFD-induced errors can entirely mask the physical sensitivity of relative permeability to Ca and M within certain saturation intervals. Furthermore, variations in initial configurations lead to divergent evolutions of the fluid-fluid interfacial area relative to wetting saturation, highlighting the role of microscopic topological memory in governing flow behavior. This research provides a quantitative foundation for IFD sensitivity in pore-scale modeling and proposes the integration of a CV-based uncertainty framework into macro-scale models to enhance the robustness and reliability of multiphase flow predictions. Full article

Electric vehicles (EVs) are playing a crucial role in decarbonising the transportation industry by cutting down on toxic emissions from vehicles. Increasing the range of EVs is still a major hurdle in the widespread adoption of such vehicles, and serious efforts are underway across the globe in order to address this issue. A potential solution to this is the integration of small wind turbines with EVs to extract wind power and help charge the batteries. However, serious efforts in this regard are severely lacking in the published literature. This study aims to bridge this gap through systematic numerical investigations on a drag-based vertical-axis wind turbine (VAWT) installed on top of an EV. Utilising Computational Fluid Dynamic (CFD)-based solvers, the flow fields associated with the turbine are analysed in detail. Instantaneous and average power produced by the turbine have been critically evaluated over its entire operational range and at different vehicle speeds. The results obtained show that the VAWT has a peak power coefficient (Cp) of 0.46 at a tip speed ratio (λ) of 0.55. The average power produced by the VAWT at 30 mph, 50 mph, and 70 mph is about 160 W, 700 W, and 2 kW, respectively. Full article

Oil enterprises face the challenge of reconciling escalating energy conservation targets with persistent production requirements, necessitating sophisticated electricity management solutions. The conventional ton-per-kWh allocation approach, often manually adjusted based on historical production and planning data, lacks a scientific basis and fails to accurately identify efficiency differences or assess energy-saving potential, making it difficult to convince participating units. To address this, we propose a dynamic spatiotemporal allocation scheme and develop a multi-objective optimization model that integrates electricity efficiency, operational stability, and production priority. The model incorporates nonlinear efficiency terms, stability components, and priority-weighted items, with constraints including total balance, monthly adjustment limits, and key area protection. Central to the efficiency term is the accurate prediction of liquid production from electricity consumption. We decompose electricity use into three components—core production electricity, auxiliary production electricity, and product transportation electricity—and derive their proportional coefficients through regression of historical data, enabling high-precision liquid production prediction via machine learning using the Light Gradient Boosting Machine (LGBM). The resulting constrained optimization problem is solved using the Sequential Least Squares Programming (SLSQP) algorithm. Validation using both simulated data and Daqing Oilfield field data demonstrates that the scheme effectively achieves electricity reduction targets while significantly mitigating associated liquid production loss, reducing it by 18.0% in simulated experiments and 32.5% in field validation compared to the conventional ton-per-kWh method. This offers a scientific and adaptive electricity management framework that supports refined energy control and facilitates the petroleum industry’s green and low-carbon transformation. Full article

Single-phase Cu₄Bi₄Se₉ was successfully synthesized through a simple and rapid process combining mechanical alloying (MA) and hot pressing (HP). The phase formation behavior, microstructural evolution, charge transport characteristics, and thermoelectric properties were systematically investigated. X-ray diffraction analysis as a function of MA time confirmed that all powders crystallized into a single orthorhombic phase with space group Pnma. No decompositions or secondary phases were observed after HP sintering, indicating high phase stability. Thermogravimetric and differential scanning calorimetric analyses revealed distinct endothermic peaks at 714–717 K for all samples, corresponding to the onset of the decomposition of Cu₄Bi₄Se₉. Microstructural observations showed that the relative density decreased with increasing HP temperature (>573 K), accompanied by grain growth and pore formation, reflecting the competition between Cu–Se interdiffusion and pore coarsening during high-temperature sintering. Hall effect measurements indicated p-type conduction for all samples, with carrier concentrations on the order of 10¹⁷ cm⁻³ and carrier mobilities of approximately 10² cm² V⁻¹ s⁻¹. With increasing temperature, the electrical conductivity increased monotonically, while the Seebeck coefficient gradually decreased, resulting in a maximum power factor of 0.12 mW m⁻¹ K⁻² at 573 K. The total thermal conductivity remained extremely low, ranging from 0.33 to 0.48 W m⁻¹ K⁻¹, with the electronic contribution accounting for less than 10%, indicating that lattice thermal transport is dominant. The suppressed lattice thermal conductivity is attributed to the combined effects of Cu atomic rattling, asymmetric bonding induced by Bi 6s² lone-pair electrons, and strong anharmonic phonon scattering arising from the complex crystal structure. Consequently, Cu₄Bi₄Se₉ achieved a peak dimensionless figure of merit ZT of 0.19 in the temperature range of 573–623 K, demonstrating that the MA–HP process enables stable phase formation and competitive thermoelectric performance without post-annealing. Full article

Poly(β-L-malic acid) (PMLA) has attracted considerable industrial attention due to its promising applications in biomedicine, bioplastics, and environmental fields. However, its biosynthesis is highly dependent on elevated dissolved oxygen (DO) levels, while the simultaneous production of pullulan represents a major obstacle. This study introduces a novel strategy to enhance PMLA production in Aureobasidium melanogenum by selectively inhibiting pullulan biosynthesis. We demonstrate that excessive pullulan accumulation severely impairs fermentation performance by significantly reducing oxygen transfer efficiency—an uncharacterized bottleneck in PMLA production. To address this, an ARTP-induced mutant, designated No. H13, was generated, exhibiting an 82.1% reduction in pullulan synthesis. This metabolic shift led to an 86.93% increase in the oxygen mass transfer coefficient (K_La), ultimately enhancing PMLA yield by 72.1% to 45.0 g/L with a specific production of 1.09 g/g. Transcriptomic analysis suggested a potential redirection of carbon flux toward PMLA biosynthesis through coordinated up-regulation of glycolysis and TCA cycle genes, alongside down-regulation of gluconeogenesis and pullulan-exporting ABC transporters. This work presents an alternative to enzymatic approaches by employing a consolidated mutagenesis strategy to reconfigure metabolic networks, offering a strategy for PMLA overproduction. Full article

Large-eddy simulation (LES) of wind farms is often limited by the computational cost required to represent many turbine rows and to obtain statistically converged wake and power statistics. Here, we present LES of an extended wind-farm configuration using the PALM model system, where cyclic lateral boundary conditions are employed to emulate interior-farm interaction in an idealized neutral boundary layer. The setup consists of nine identical horizontal-axis wind turbines arranged in a staggered array within the computational domain. Time-averaged hub-height fields show coherent wake corridors with a mean inflow-speed reduction of $23.7$% (array-mean across turbines) relative to an undisturbed background wind speed, and peak wake deficits reaching $71.4$% in the near-wake region. Turbulence levels increase markedly in the wake shear layers, with hub-height turbulence intensity enhanced by $32.2$% in the rotor region compared to background conditions; correspondingly, the peak hub-height SGS-TKE increases by a factor of 6.74 relative to background. Vertically averaged profiles indicate a momentum deficit within the turbine layer and gradual recovery aloft; the streamwise turbulent momentum flux remains predominantly negative, demonstrating the downward transport of higher-momentum air from above as a key recovery mechanism. Turbine rotor-power statistics show an initial adjustment followed by a quasi-stationary regime, with a farm-mean rotor power of $1.93$ MW and persistent inter-turbine variability characterized by a mean coefficient of variation of $61.2$%. Overall, the results demonstrate that the proposed extended-farm LES approach enables computationally efficient quantification of wake dynamics, vertical momentum transport, and their impact on power variability under idealized neutral wind-farm conditions. Full article

With the development of autonomous driving and intelligent connected vehicle technologies, the vehicle cabin is shifting from a simple transportation space to an intelligent mobile space integrating infotainment, interaction, and rest, and passenger comfort has gradually become an important factor affecting user experience, system trust, and perceived safety. Focusing on three categories of cabin environmental factors, namely the acoustic, optical, and thermal environments, this study develops an experimental design and comprehensive modeling method for passenger comfort evaluation. First, controlled single-factor experiments were conducted to establish quantitative mapping relationships between physical environmental parameters and subjective comfort ratings. The analytic hierarchy process (AHP) was then used to determine the weights of each indicator, and a penalty-based aggregation mechanism was introduced to construct a comprehensive comfort evaluation model. Finally, external validation was performed on an independent vehicle platform to examine the model’s applicability and consistency. The results show that acoustic comfort decreases as the sound pressure level increases, whereas optical and thermal comfort exhibit nonlinear behavior with optimal intervals. AHP weight results show that the thermal environment has the highest weight (0.4280), followed by the acoustic environment (0.3305) and the optical environment (0.2415). The external validation results indicate that the proposed model exhibits good predictive consistency across three steady-state operating conditions, with a mean absolute error of 0.122, a root-mean-square error of 0.150, and a Pearson correlation coefficient of 0.960. The findings show that the penalty-based aggregation model can effectively characterize the limiting-factor effect under the joint action of multiple environmental factors, providing a computable and interpretable evaluation framework for intelligent cockpit environmental control and automotive engineering experimental teaching. The conclusions of this study are mainly applicable to the current experimental platform and steady-state operating conditions, and further validation is still required with more vehicle models, dynamic road scenarios, and complex multi-environment factor disturbances. Full article

With the global emphasis on sustainable development, supply chain management is facing new challenges and opportunities. Enterprises often face a large number of suppliers when selecting suppliers, which makes the selection process complex. Considering the crucial role of supplier selection in sustainable supply chains, a sustainable supplier selection model based on multi-attribute utility analysis and a fuzzy approximation ideal solution ranking method is proposed to reduce carbon emissions and environmental pollution. This model helps companies scientifically evaluate and select suppliers by comprehensively considering three aspects: environment, economy, and society. Meanwhile, the study utilizes an optimized genetic algorithm-based order allocation model to raise the efficacy and fairness of order allocation. Reducing procurement costs often relies on improving resource utilization and reducing production waste, which directly lowers the energy consumption and carbon emission intensity per unit of product. At the same time, reducing product damage and delivery delay rates can avoid additional greenhouse gas emissions caused by rework, abandonment, and emergency transportation. By improving supplier productivity and optimizing order allocation, the developed model can not only reduce economic costs but also control environmental pollution and carbon footprints from the source of the supply chain. The outcomes indicate that technological level is a crucial factor influencing supplier selection, with a significant positive impact on supplier willingness to choose, and its standard path coefficient is 0.199, with a significance level of 0.001. Meanwhile, the optimized genetic algorithm exhibits strong stability and convergence in order allocation. This optimization model has high efficiency in handling large-scale orders. This provides strong support for the decision-making of enterprises in sustainable supply chain management and a valuable reference for China’s exploration and practice in the field of sustainable development. Full article

The composition and interface quality of jointed concrete can significantly influence chloride ion penetration, especially in coastal environments. This study investigates the transport behavior of chloride ions in concrete flexural members with varying joint configurations—no joint, smooth wet joint, and roughened wet joint—under different bending loads. After 28 days of curing, specimens were subjected to bending loads and immersed in an 8% NaCl solution for 300 days. Chloride ion concentrations were then measured at different depths and locations. Results revealed that joints, particularly smooth wet joints, significantly accelerate chloride ion transmission, and that chloride accumulation at the joint is consistently higher than in adjacent areas or jointless concrete. The apparent diffusion coefficient of chloride ions was notably higher at joint interfaces and increased with bending load level due to microcrack formation. Notably, the incorporation of Cementitious Capillary Crystalline Waterproofing (CCCW) in the concrete mix improved resistance to chloride ion penetration. A dosage of 1% CCCW proved most effective, reducing the diffusion coefficient at the joint by approximately 10%—demonstrating an optimal balance between performance and material efficiency. These findings provide practical guidance for improving the durability of jointed concrete structures in chloride-rich environments. Full article