Search Results (2,914)

Reliable simulations of any flow require proper preservation of the fundamental principles governing the mechanics of its motion, whether in differential or integral form. When these principles are solved in differential form, discretization schemes introduce errors by transforming the continuous physical domain into a discrete representation that only approximates it. This paper analyzes the numerical performance of the solver for supersonic chemically active flows, rhoCentralRfFoam, using integral conservation principles of mass, momentum, energy, and chemical species as a validation tool in a classical test case with a highly refined mesh under nonlinear pre-established reference conditions. The analysis is conducted on this specific test case; however, the methodology presented here can be applied to any problem under study. It may serve as an a posteriori verification tool or be integrated into the solver’s workflow, enabling automatic verification of conservation at each time step. The resulting deviations are evaluated, and it is observed that the numerical errors remain below $0.25\%$, even in cases with a high degree of nonlinearity. These results provide preliminary validation of the solver’s accuracy, as well as its ability to capture physically consistent solutions using only information generated internally by the solver for validation. This represents a significant advantage over validation methods that require external comparison with reference solutions, numerical benchmarks, or exact solutions. Full article

Water scarcity is a relevant issue whose impact can be mitigated through sustainable solutions. Humidification–dehumidification (HDH) cycles powered by photovoltaic thermal (PVT) modules enable pure water production in remote areas. In this study, models have been developed and validated for the main components of the system, the humidifier and the dehumidifier. A unique HDH-PVT prototype was built and experimentally tested at the SolarTech Lab of Politecnico di Milano in Milan, Italy. The experimental system is a Closed Air Closed Water—Water Heated (CACW-WH) that mimics a Closed Air Open Water—Water Heated (CAOW-WH) cycle through brine cooling, pure water mixing, and recirculation, avoiding a continuous waste of water. Tests were performed varying the mass flow ratio (MR) between 0.346 and 2.03 during summer and autumn in 2023 and 2024. The experimental results enabled the verification of the developed models. The optimal system performance was obtained for an MR close to 1 and a maximum cycle temperature of $44 °\mathrm{C}$, enabling a 0.51 gain output ratio (GOR) and 0.72% recovery ratio (RR). The electrical and thermal energy generation of the PVT modules satisfied the whole consumption of the system enabling pure water production exploiting only the solar resource available. The PVT-HDH system proved the viability of the proposed solution for a sustainable self-sufficient desalination system in remote areas, thus successfully addressing water scarcity issues exploiting a renewable energy source. Full article

The short-circuit withstanding capability of a transformer is a critical indicator for evaluating its operational reliability. This study investigates the influence of the low-voltage winding height, a key structural parameter, on the electromagnetic forces induced by short-circuit currents and the resultant short-circuit withstand capability. First, theoretical calculation formulas for the transformer leakage magnetic field and winding electromagnetic forces were derived, establishing a foundation for subsequent analysis. Subsequently, two 110 kV transformers, identical in all structural parameters except for their low-voltage winding heights, were selected as case studies. Three-dimensional finite element models were constructed to perform detailed simulations and a comparative analysis of the leakage magnetic field distribution and electromagnetic forces under short-circuit conditions. Finally, practical short-circuit tests were conducted on both transformers for experimental validation, monitoring and comparison of their short-circuit reactance variation curves. Furthermore, a CNN-LSTM model, utilizing the winding axial height of a 110 kV three-phase three-limb transformer as the characteristic parameter, is developed to detect short-circuit fault damage in such transformers with varying winding heights. Through a combined approach of theoretical analysis, simulation, and experimental verification, this study confirms that the low-voltage winding height was a crucial factor affecting the transformer’s short-circuit withstand capability of the transformer. Studies have shown that with the increase in the height of low-voltage windings, the leakage magnetic flux of the low-voltage windings increases by 36%, the radial electromagnetic force increases by 37.5%, and the axial electromagnetic force increases by 8.5%. Excessively tall windings amplify radial electromagnetic forces, compromising mechanical stability and consequently increasing the risk of damage during short-circuit faults. Full article

In the source location of underground explosions, the phase non-consistency among sensors can cause significant errors in the extraction of the time difference in the arrival of seismic waves, seriously affecting the accuracy of source location. To address the above-mentioned problem, this paper proposes a phase compensation method based on the Multi-strategy Quantum behaved Particle Swarm Optimization (MQPSO) algorithm. First, this method calibrates the phases of vibration sensors to obtain the phase differences among sensors. Second, it uses the MQPSO intelligent optimization algorithm to correct the phase differences among vibration sensors. Finally, simulations and field tests are carried out for verification. The experimental results show that after adopting the phase compensation method with MQPSO, the range of phase differences in sensors is reduced by an average of 91% compared with the uncompensated state. This fully verifies that the phase compensation method of MQPSO can effectively complete the phase consistency calibration of sensors, providing important support for the source location of underground explosions. Full article

A mathematical model was established to describe the sublimation and diffusion of water molecules and their adsorption onto cold traps. This model was used to analyze the combined influence mechanisms of sublimation temperature and ambient pressure on the vapor deposition process of water ice. Tunable Diode Laser Absorption Spectroscopy (TDLAS) was employed to provide real-time feedback on water vapor concentration within the experimental apparatus. Based on this feedback, the sublimation temperature was dynamically adjusted to maintain the concentration dynamically stabilized around the target value. A dedicated apparatus for generating controlled water vapor flow fields and detecting concentration was constructed. The accuracy of both the mathematical model and Finite Element Analysis (FEA) simulations was verified through comparative experiments. Laser triangulation was utilized as a method to detect the thickness of the adsorbed ice film on the sample surface. Leveraging this technique, a water vapor deposition and adsorption verification system was developed. This system was used to test the differences in water adsorption performance across various materials and to measure the correlation between the thickness of the adsorbed/deposited ice film on the samples and both deposition time and sublimation temperature. Full article

Cryogenic industries handling liquid hydrogen and helium require rigorous safety verification. However, current standards (ASTM, ASME, ISO) are optimized for LNG at −163 °C and remain inadequate for extreme cryogenic conditions such as −253 °C. As the temperature decreases, materials experience ductile-to-brittle transition, raising the risk of sudden fracture in testing equipment. This study presents a fuzzy-integrated reliability framework that combines fault tree analysis (FTA) and Failure Modes, Effects, and Criticality Analysis (FMECA). The method converts qualitative expert judgments into quantitative risk indices for use in data-scarce conditions. When applied to a cryogenic impact testing apparatus, the framework produced a total failure probability of 1.52 × 10⁻³, about 7.5% lower than the deterministic FTA result (1.64 × 10⁻³). These results confirm the framework’s robustness and its potential use in cryogenic testing and hydrogen systems. Full article

Designing UHPC beams for shear is challenging because many factors—geometry, concrete strength, fibers, and stirrups—act together. In this study, we compile a large, curated database of laboratory tests and develop machine learning models to predict shear capacity. The best models provide accurate point predictions and, importantly, a 95% prediction band that tells how much uncertainty to expect; in tests, about 95% of results fall inside this band. For day-to-day design, we also offer a short, design-oriented formula with explicit coefficients and variables that can be used in a spreadsheet. Together, these tools let engineers screen options quickly, check designs with an uncertainty margin, and choose a conservative value when needed. The approach is transparent, easy to implement, and aligned with common code variables, so it can support preliminary sizing, verification, and assessment of UHPC members. Full article

(1) Background: This study compared subjective and objective texture classifications of hospital-provided pureed meat dishes and evaluated the impact of adding a food-shaping agent on the consistency of the food. (2) Methods: In total, 18 common pureed meat dishes (pork, chicken, and fish) from a medical center were tested. Subjective classification was conducted according to the International Dysphagia Diet Standardisation Initiative (IDDSI) level 4 criteria, and an objective texture analysis was performed using a Texture Profile Analysis (TPA), with hardness values interpreted via the Universal Design Foods (UDF) framework. (3) Results: Only six of the 18 dishes (33%) met all IDDSI level 4 tests in their original form, despite visually resembling purees. After the addition of 1% of a food-shaping agent, all samples passed IDDSI criteria, indicating enhanced textural consistency and a reduced risk of swallowing complications. TPA data confirmed that all samples, both with and without the food-shaping agent, met UDF stage 4 hardness standards (<5 × 10³ N/m²), ensuring appropriate structural integrity for safe swallowing. The addition of food-shaping agents significantly increased the hardness and adhesiveness (p < 0.001), while the cohesiveness remained unchanged. (4) Conclusions: These findings highlight discrepancies between visual/subjective assessments and objective measurements and support the use of combined IDDSI- and TPA-based verification to improve dietary safety and reproducibility in dysphagia care. Full article

Text recognition on coffee bean package labels is of great importance for product tracking and brand verification, but it poses a challenge due to variations in image quality, packaging materials, and environmental conditions. In this paper, we propose a pipeline that combines several image enhancement techniques and is followed by an Optical Character Recognition (OCR) model based on vision–language (VL) Qwen VL variants, conditioned by structured prompts. To facilitate the evaluation, we construct a coffee bean package image set containing two subsets, namely low-resolution (LRCB) and high-resolution coffee bean image sets (HRCB), enclosing multiple real-world challenges. These cases involve various packaging types (bottles and bags), label sides (front and back), rotation, and different illumination. To address the image quality problem, we design a dedicated preprocessing pipeline for package label situations. We develop and evaluate four Qwen-VL OCR variants with prompt engineering, which are compared against four baselines: DocTR, PaddleOCR, EasyOCR, and Tesseract. Extensive comparison using various metrics, including the Levenshtein distance, Cosine similarity, Jaccard index, Exact Match, BLEU score, and ROUGE scores (ROUGE-1, ROUGE-2, and ROUGE-L), proves significant improvements upon the baselines. In addition, the public POIE dataset validation test proves how well the framework can generalize, thus demonstrating its practicality and reliability for label recognition. Full article

Precise online measurement of large structural components is urgently needed in modern manufacturing and intelligent construction, requiring a measurement range over 1 m, near-millimeter accuracy, second-level measurement speed, and adaptability to complex environments. In this paper, three mainstream measurement technologies, namely the image method, line laser scanning method, and structured light method, are comparatively analyzed. The structured light method exhibits remarkable comprehensive advantages in terms of accuracy and speed; however, it suffers from the issue of occlusion during contour measurement. To tackle this problem, multi-camera stitching is employed, wherein the accuracy of camera calibration plays a crucial role in determining the quality of point cloud stitching. Focusing on the cable tightening scenario of meter-diameter cables in cable-stayed bridges, this study develops a contour measurement system based on the collaboration of multiple structured light cameras. Measurement indicators are optimized through modeling analysis, system construction, and performance verification. During verification, four structured light scanners were adopted, and measurements were repeated 11 times for the test workpieces. Experimental results demonstrate that although the current measurement errors have not yet been stably controlled within the millimeter level, this research provides technical exploration and practical experience for high-precision measurement in the field of intelligent construction, thus laying a solid foundation for subsequent accuracy improvement. Full article

Using a firm-level panel of China’s heavily polluting industries from 2014 to 2023, this paper employs two-way fixed-effects regressions and a battery of robustness checks to examine how ESG performance affects corporate financing constraints and the channels through which effects operate. We uncover a paradox: overall ESG performance is associated with reduced financing constraint, whereas the environmental subcomponent alone significantly aggravates firms’ financing difficulties. Moderating analyses show that stricter regional environmental regulations and higher persistence in firms’ innovation outputs weaken the easing effect of aggregate ESG performance and may even fully offset it under certain conditions. Mechanism tests reveal that ESG mitigates constraints mainly by enhancing corporate reputation and curbing green agency costs. Heterogeneity analyses further indicate that the environmental-induced tightening effect is more pronounced in state-owned enterprises, firms in eastern provinces, and those located in regions with lower levels of new-quality productivity. These findings point to a trade-off between the short-term compliance costs of environmental investment and the longer-run signaling and informational benefits of ESG disclosure. Policy implications include the need for targeted green-finance support, improved ESG transparency and verification, and measures to accelerate innovation pathways that shorten the payback period for environmental investments. Full article

Receiver operating characteristic (ROC) surfaces provide a natural extension of ROC curves to three-class diagnostic problems. A key summary index is the volume under the surface (VUS), representing the probability that a randomly chosen observation from each of the three ordered groups is correctly classified. A parametric estimation of VUS typically assumes trinormality of the class distributions. However, a formal method for the verification of this composite assumption has not appeared in the literature. Our approach generalizes the two-class AUC-based GOF test of Zou et al. to the three-class setting by exploiting the parallel structure between empirical and trinormal VUS estimators. We propose a global goodness-of-fit (GOF) test for trinormal ROC models based on the difference between empirical and trinormal parametric estimates of the VUS. To improve stability, a probit transformation is applied and a bootstrap procedure is used to estimate the variance of the difference. The resulting test provides a formal diagnostic for assessing the adequacy of trinormal ROC modeling. Simulation studies illustrate the robustness of the assumption via the empirical size and power of the test under various distributional settings, including skewed and multimodal alternatives. The method’s application to COVID-19 antibody level data demonstrates the practical utility of it. Our findings suggest that the proposed GOF test is simple to implement, computationally feasible for moderate sample sizes, and a useful complement to existing ROC surface methodology. Full article

The Blockchain-Based Medical Insurance Transaction System (BMIT) developed in this study addresses key issues in traditional medical insurance—information silos, data tampering, and privacy breaches—through innovative blockchain architectural design and technical infrastructure reconstruction. Built on a consortium blockchain architecture with FISCO BCOS (Financial Blockchain Shenzhen Consortium Blockchain Open Source Platform) as the underlying platform, the system leverages FISCO BCOS’s distributed ledger, granular access control, and efficient consensus algorithms to enable multi-stakeholder on-chain collaboration. Four node roles and data protocols are defined: hospitals (on-chain data providers) generate 3D coordinate hashes of medical data via an algorithmically enhanced Bloom Filter for on-chain certification; patients control data access via blockchain private keys and unique parameters; insurance companies verify eligibility/claims using on-chain Bloom filters; the blockchain network stores encrypted key data (public keys, Bloom filter coordinates, and timestamps) to ensure immutability and traceability. A 3D-enhanced Bloom filter—tailored for on-chain use with user-specific hash functions and key control—stores only 3D coordinates (not raw data), cutting storage costs for 100 records to 1.27 KB and reducing the error rate to near zero ($1.77\%$ lower than traditional schemes for 10,000 entries). Three core smart contracts (identity registration, medical information certification, and automated verification) enable the automation of on-chain processes. Performance tests conducted on a 4-node consortium chain indicate a transaction throughput of 736 TPS (Transactions Per Second) and a per-operation latency of 181.7 ms, which meets the requirements of large-scale commercial applications. BMIT’s three-layer design (“underlying blockchain + enhanced Bloom filter + smart contracts”) delivers a balanced, efficient blockchain medical insurance prototype, offering a reusable technical framework for industry digital transformation. Full article

Transient dynamic balancing of flexible rotors is of significant practical value in engineering. Traditional dynamic balancing requires repeated trial weights and start–stop cycles, resulting in inefficient balancing. This paper proposed a multi-layer LSTM (Long Short-Term Memory) network-based method for transient dynamic balancing of flexible rotors. Firstly, a dynamic model of the rotor bearing system is established using the finite element method. Secondly, leveraging the relationship between transient unbalance force and quantity, a formula using the force zero-crossing to identify unbalance magnitude and phase is derived, and a multi-layer LSTM architecture is established to predict the unbalance force from the rotor dynamic response. Finally, a complete transient dynamic balancing solution based on the multi-layer LSTM network is developed. Simulation verification is conducted on a three-disc rotor, and the identification errors of unbalance phase and eccentricity are both less than 5%. After balancing, the maximum amplitude of the rotor decreases by about 93%. Additional verification is carried out on the rotor test rig. The experimental results show that the multi-layer LSTM flexible rotor transient dynamic balancing method can significantly reduce unbalance vibration: the transient amplitude of the rotor decreases by 70.22%, and the steady-state amplitude decreases by 69.3% after balancing. Full article

The integration of renewable energy systems and electrified transportation requires advanced energy storage solutions capable of providing both high energy density and fast dynamic response. Hybrid energy storage systems offer a promising approach by combining complementary battery chemistries, exploiting their respective strengths while mitigating individual limitations. This study presents the design, modeling, and optimization of a hybrid energy storage system composed of two high-energy lithium nickel manganese cobalt batteries and one high-power lithium titanate oxide battery, interconnected through a triple dual-active multi-port converter. A nonlinear model predictive control strategy was employed to optimally distribute battery currents while respecting constraints such as state of charge limits, current bounds, and converter efficiency. Equivalent circuit models were used for real-time state of charge estimation, and converter losses were explicitly included in the optimization. The main contributions of this work are threefold: (i) verification of the model predictive control strategy in diverse applications, including residential renewable energy systems with photovoltaic generation and electric vehicles following the World Harmonized Light-duty Vehicle Test Procedure driving cycle; (ii) explicit inclusion of the power converter model in the system dynamics, enabling realistic coordination between batteries and power electronics; and (iii) incorporation of converter efficiency into the cost function, allowing for simultaneous optimization of energy losses, battery stress, and operational constraints. Simulation results demonstrate that the proposed model predictive control strategy effectively balances power demand, extends system lifetime by prioritizing lithium titanate oxide battery during transient peaks, and preserves lithium nickel manganese cobalt cell health through smoother operation. Overall, the results confirm that the proposed hybrid energy storage system architecture and control strategy enables flexible, reliable, and efficient operation across diverse real-world scenarios, providing a pathway toward more sustainable and durable energy storage solutions. Full article