Search Results (7,362)

Fuel cell trucks are a promising solution to reduce the disproportionately high greenhouse gas emissions of heavy-duty long-haul transportation. However, unlike conventional diesel vehicles, they lack combustion engine waste heat for cabin heating. As a result, electric heaters are often employed, which increase auxiliary energy consumption and reduce driving range. To address this challenge, advanced control strategies are needed to improve heating efficiency while maintaining passenger comfort. This study proposes and validates a methodology for implementing Model Predictive Control (MPC) in the cabin heating system of a fuel cell truck. Vehicle experiments were conducted to characterize dynamic heating behavior, passenger comfort indices, and to provide validation data for the mathematical models. Based on these models, an MPC strategy was developed in a Model-in-the-Loop simulation environment. The proposed approach achieves energy savings of up to 8.1% compared with conventional control using purely electric heating, and up to 21.7% when cabin heating is coupled with the medium-temperature cooling circuit. At the same time, passenger comfort is maintained within the desired range (PMV within ±0.5 under typical winter conditions). The results demonstrate the potential of MPC to enhance the energy efficiency of fuel cell trucks. The methodology presented provides a validated foundation for the further development of predictive thermal management strategies in heavy-duty zero-emission vehicles. Full article

Degradation data plays a crucial role in the reliability assessment and condition monitoring of engineering systems. The stage-wise changes in degradation rates often signal turning points in system performance or potential fault risks. To address the issue of structural changes during the degradation process, this paper constructs a degradation modeling framework based on a two-stage Inverse Gaussian (IG) process and proposes a change-point detection method based on an adjusted CUSUM (cumulative sum) statistic to identify potential stage changes in the degradation path. This method does not rely on complex prior information and constructs statistics by accumulating deviations, utilizing a binary search approach to achieve accurate change-point localization. In simulation experiments, the proposed method demonstrated superior detection performance compared to the classical likelihood ratio method and modified information criterion, verified through a combination of experiments with different change-point positions and degradation rates. Finally, the method was applied to real degradation data of a hydraulic piston pump, successfully identifying two structural change points during the degradation process. Based on these change points, the degradation stages were delineated, thereby enhancing the model’s ability to characterize the true degradation path of the equipment. Full article

In this study, the modeling of rough surfaces by eddy-viscosity-based roughness models is investigated, specifically focusing on surfaces representative of deterioration in aero-engines. In order to test these models, experimental measurements from a rough T106C blade section at a Reynolds number of 400 K are adopted. The modeling framework is based on the k–ω–SST with Dassler’s roughness transition model. The roughness model is recalibrated for the k–ω–SST model. As a complement to the available experimental data, a high-fidelity test rig designed for scale-resolving simulations is built. This allows us to examine the local flow phenomenon in detail, enabling the identification and rectification of shortcomings in the current RANS models. The scale-resolving simulations feature a high-order flux-reconstruction scheme, which enables the use of curved element faces to match the roughness geometry. The wake-loss predictions, as well as blade pressure profiles, show good agreement, especially between LES and the model-based RANS. The slight deviation from the experimental measurements can be attributed to the inherent uncertainties in the experiment, such as the end-wall effects. The outcomes of this study lend credibility to the roughness models proposed. In fact, these models have the potential to quantify the influence of roughness on the aerodynamics and the aero-acoustics of aero-engines, an area that remains an open question in the maintenance, repair, and overhaul (MRO) of aero-engines. Full article

Professional certification of engineers plays a crucial role in verifying competencies and ensuring the safety and quality of engineering outputs. However, most existing certification systems assign fixed validity periods (e.g., 3–5 years) without considering individual engineer characteristics or the intensity of technological progress in specific fields. This study examines the key factors influencing the optimal validity period of engineering certifications and proposes it as a measurable indicator to support safety in engineering practice. A new model is introduced that integrates expert judgment, fuzzy set theory, and bibliometric analysis of Q1/Q2 Scopus-indexed publications. The model incorporates three main factors: competence level, professional experience, and the technological intensity of the discipline. A case study from the engineering certification system of Kazakhstan demonstrates the model’s practical applicability. Certification bodies, policymakers, and engineering organizations can use these findings to establish more flexible certification validity periods, thereby ensuring timely reassessment of competencies and reducing safety risks. For example, for mechanical engineers, the optimal validity period is 3 years rather than the statutory 5 years; in other words, the model recommends a 40% reduction in certification validity. This reduction reflects the combined effects of competency level, professional experience, and technology intensity on certification renewal schedules. Overall, the proposed factorial approach supports a more personalized and safety-oriented certification process and offers insights into improving national qualification systems. Full article

Trust plays a pivotal role in individuals’ interactions with technological systems, and those incorporating artificial intelligence present significantly greater challenges than traditional systems. The current landscape of higher education is increasingly shaped by the integration of AI assistants into students’ classroom experiences. Their appropriate use is closely tied to the level of trust placed in these tools, as well as the strategies adopted to critically assess the accuracy of AI-generated content. However, scholarly attention to this dimension remains limited. To explore these dynamics, this study applied the POTDAI evaluation framework to a sample of 132 engineering and social sciences students at Camilo José Cela University in Madrid, Spain. The findings reveal a general lack of trust in AI assistants despite their extensive use, common reliance on inadequate verification methods, and a notable skepticism regarding professors’ ability to detect AI-related errors. Additionally, students demonstrated a concerning misperception of the capabilities of different AI models, often favoring less advanced or less appropriate tools. These results underscore the urgent need to establish a reliable verification protocol accessible to both students and faculty, and to further investigate the reasons why students opt for limited tools over the more powerful alternatives made available to them. Full article

With the continuous progress and innovation of petroleum engineering technology, the development of new oilfield additives with superior environmental benefits has attracted widespread attention. Konjac glucomannan (KGM) is a natural resource characterized by abundant availability, low cost, biodegradability, and environmental compatibility. Konjac gum easily forms a weak gel network in water, but its water solubility and thermal stability are poor, and it is easily degraded at high temperatures. Therefore, its application in drilling fluid and fracturing fluid is limited. In this paper, a method of carboxymethyl modification of KGM was developed, and a carboxymethyl group was introduced to adjust KGM’s hydrogel forming ability and stability. Carboxymethylated Konjac glucomannan (CMKG) is a water-soluble anionic polysaccharide derived from natural Konjac glucomannan. By introducing carboxymethyl groups, CMKG overcomes the limitations of the native polymer, such as poor solubility and instability, while retaining its safe and biocompatible nature, making it an effective natural polymer additive for oilfield applications. The results show that when used as a drilling fluid additive, CMKG can form a stable three-dimensional gel network through molecular chain cross-linking, significantly improving the rheological properties of the mud. Its unique gel structure can enhance the encapsulation of clay particles and inhibit clay hydration expansion. When used as a fracturing fluid thickener, the viscosity of the gel system formed by CMKG at 0.6% (w/v) is superior to that of the weak gel system of KGM. The heat resistance/shear resistance tests confirm that the gel structure remains intact under high-temperature and high-shear conditions, meeting the sand-carrying capacity requirements for fracturing operations. The gel-breaking experiment shows that the system can achieve controlled degradation within 300 min, in line with on-site gel-breaking specifications. This modification process not only improves the rheological properties and water solubility of the CMKG gel but also optimizes the gel stability and controlled degradation through molecular structure adjustment. Full article

To address the challenge of coordinating economic cost control and low-carbon objectives in microgrid scheduling, while overcoming the performance limitations of the traditional Zebra Optimization Algorithm (ZOA) in complex problems, this paper proposes a Synergistic Zebra Optimization Algorithm (SZOA) and integrates it with innovative management concepts to enhance the microgrid scheduling process. The SZOA incorporates three core strategies: a multi-population cooperative search mechanism to strengthen global exploration, a vertical crossover–mutation strategy to meet high-dimensional scheduling requirements, and a leader-guided boundary control strategy to ensure variable feasibility. These strategies not only improve algorithmic performance but also provide technical support for innovative management in microgrid scheduling. Extensive experiments on the CEC2017 (d = 30) and CEC2022 (d = 10, 20) benchmark sets demonstrate that the SZOA achieves higher optimization accuracy and stability compared with those of nine state-of-the-art algorithms, including IAGWO and EWOA. Friedman tests further confirm its superiority, with the best average rankings of 1.20 for CEC2017 and 1.08/1.25 for CEC2022 (d = 10, 20). To validate practical applicability, the SZOA is applied to grid-connected microgrid scheduling, where the system model integrates renewable energy sources such as photovoltaic (PV) generation and wind turbines (WT); controllable sources including fuel cells (FC), microturbines (MT), and gas engines (GS); a battery (BT) storage unit; and the main grid. The optimization problem is formulated as a bi-objective model minimizing both economic costs—including fuel, operation, pollutant treatment, main-grid interactions, and imbalance penalties—and carbon emissions, subject to constraints on generation limits and storage state-of-charge safety ranges. Simulation results based on typical daily data from Guangdong, China, show that the optimized microgrid achieves a minimum operating cost of USD 5165.96, an average cost of USD 6853.07, and a standard deviation of only USD 448.53, consistently outperforming all comparison algorithms across economic indicators. Meanwhile, the SZOA dynamically coordinates power outputs: during the daytime, it maximizes PV utilization (with peak output near 35 kW) and WT contribution (30–40 kW), while reducing reliance on fossil-based units such as FC and MT; at night, BT discharges (−20 to −30 kW) to cover load deficits, thereby lowering fossil fuel consumption and pollutant emissions. Overall, the SZOA effectively realizes the synergy of “economic efficiency and low-carbon operation”, offering a reliable and practical technical solution for innovative management and sustainable operation of microgrid scheduling. Full article

This study addresses the challenges in machining the raceways and oil holes of aircraft engine bearing rings by conducting abrasive flow machining experiments on main bearing rings which had undergone ultra-precision grinding. Viscoelastic abrasive media containing cubic boron nitride of different particle sizes is used during the experiments. The results show that bearing performance is improved significantly in terms of surface roughness and residual compressive stress consequently; the overall surface quality is raised. The machining process meets the precision requirements for the main bearings of this type of aircraft engine, validating the feasibility and effectiveness of Abrasive Flow Machining (AFM), and the foundation for further optimization of this process is set through this research. Full article

In recent decades, simulation has emerged as a pivotal educational tool, bolstering scientific knowledge and honing decision-making skills across diverse disciplines. Surgery and flight simulators are well-known tools used to practice and train safely in surgeries and piloting. Meanwhile, the development of simulation games advances in other scientific fields, such as economics, management, engineering, and mathematics. These simulations offer learners a risk-free virtual platform to apply and refine their knowledge, leveraging animations, graphics, and interactive environments to enrich the learning experience. In engineering, while simulation is widely utilized as a powerful training tool for heavy equipment and process handling, the creation of strategy games for educational purposes is less frequent. This gap primarily stems from the challenge of converting complex engineering concepts and theories into a user-friendly yet comprehensive setup that preserves the more difficult aspects. This study adopts a design-based research approach to develop and evaluate an educational simulation game aimed at enhancing probabilistic and spatial reasoning in mineral exploration. The application generates random scenarios, within which users deploy strategies based on their knowledge, while accommodating the randomness of physical phenomena. The simulation game is adopted as an educational tool in the course “Introduction to Mineral Exploration” in the School of Mining and Metallurgical Engineering of the National Technical University of Athens. Additionally, we present the outcomes of game analytics and a qualitative evaluation derived from three workshops at higher education institutions in Greece. Full article

This study evaluates the impact of mineralogy, elemental composition, and reaction pathways on hydrogen (H₂) generation in seven ultramafic and mafic (basaltic) rocks. Experiments were conducted under typical low-temperature hydrothermal conditions (150 °C) and captured early and evolving stages of fluid–rock interaction. Pre- and post-interactions, the solid phase was analyzed using X-ray Diffraction (XRD) and X-ray Photoelectron Spectroscopy (XPS), while Inductively Coupled Plasma Mass Spectrometry (ICP-MS) was used to determine the composition of the aqueous fluids. Results show that not all geologic H₂-generating reactions involving ultramafic and mafic rocks result in the formation of serpentine, brucite, or magnetite. Our observations suggest that while mineral transformation is significant and may be the predominant mechanism, there is also the contribution of surface-mediated electron transfer and redox cycling processes. The outcome suggests continuous H₂ production beyond mineral phase changes, indicating active reaction pathways. Particularly, in addition to transition metal sites, some ultramafic rock minerals may promote redox reactions, thereby facilitating ongoing H₂ production beyond their direct hydration. Fluid–rock interactions also regenerate reactive surfaces, such as clinochlore, zeolite, and augite, enabling sustained H₂ production, even without serpentine formation. Variation in reaction rates depends on mineralogy and reaction kinetics rather than being solely controlled by Fe oxidation states. These findings suggest that ultramafic and mafic rocks may serve as dynamic, self-sustaining systems for generating H₂. The potential involvement of transition metal sites (e.g., Ni, Mo, Mn, Cr, Cu) within the rock matrix may accelerate H₂ production, requiring further investigation. This perspective shifts the focus from serpentine formation as the primary driver of H₂ production to a more complex mechanism where mineral surfaces play a significant role. Understanding these processes will be valuable for refining experimental approaches, improving kinetic models of H₂ generation, and informing the site selection and design of engineered H₂ generation systems in ultramafic and mafic formations. Full article

Early and accurate detection of distributed bearing faults is essential to prevent equipment failures and reduce downtime in industrial environments. This study explores the optimal selection of input signal sources for high-accuracy distributed fault classification, employing wavelet transform and machine learning algorithms. The primary contribution of this work is to demonstrate that robust distributed bearing fault diagnosis can be achieved through optimal sensor fusion and wavelet-based feature engineering, without the need for deep learning or high-dimensional inputs. This approach provides interpretable, computationally efficient, and generalizable fault classification, setting it apart from most existing studies that rely on larger models or more extensive data. All experiments were conducted in a controlled laboratory environment across multiple loads and speeds. A comprehensive dataset, including three-axis vibration, stray magnetic flux, and two-phase current signals, was used to diagnose six distinct bearing fault conditions. The wavelet transform is applied to extract frequency-domain features, capturing intricate fault signatures. To identify the most effective input signal combinations, we systematically evaluated Random Forest, XGBoost, and Support Vector Machine (SVM) models. The analysis reveals that specific signal pairs significantly enhance classification accuracy. Notably, combining vibration signals with stray magnetic flux consistently achieved the highest performance across models, with Random Forest reaching perfect test accuracy (100%) and SVM showing robust results. These findings underscore the importance of optimal source selection and wavelet-transformed features for improving machine learning model performance in bearing fault classification tasks. While the results are promising, validation in real-world industrial settings is needed to fully assess the method’s practical reliability and impact on predictive maintenance systems. Full article

Human-database interaction is inevitable in intelligent system applications, and accurately converting user-entered natural language into database query language is a critical step. To improve the accuracy, generalization, and robustness of text-to-SQL, we propose Nabil (a model for natural language conversion query language based on brain-inspired computing technology and a large language model). This model first leverages the spatiotemporal encoding capabilities of spiking neural networks to capture semantic features of natural language, then fuses these features with those generated by a large language model. Finally, a champion model is designed to select the optimal query from multiple candidate SQLs. Experiments were conducted on three database engines, DuckDB, MySQL, and PostgreSQL, and the model’s effectiveness was verified on benchmark datasets such as BIRD. The results show that Nabil outperforms existing baseline methods in both execution accuracy and effective efficiency scores. Furthermore, our proposed normalization and syntax tree abstraction algorithms further enhance the champion model’s discriminative capabilities, providing new insights for text-to-SQL research. Full article

Lavender species are economically valuable plants, widely cultivated for their essential oils (EOs), which include sesquiterpenes. The sesquiterpenes caryophyllene and cadinol are major constituents, contributing woody and balsamic notes. However, the specific enzymes catalyzing their formation in lavender have not been elucidated. This study reports the comprehensive functional and structural characterization of two pivotal sesquiterpene synthases from Lavandula angustifolia (lavender): caryophyllene synthase (CARS) and cadinol synthase (CADS). Mutation experiments were performed based on molecular docking predictions, revealing that negatively charged residues interact electrostatically with magnesium ions (Mg²⁺). Both deletion of 1–226 and 1–228 (∆1–226 and ∆1–228) display activity levels equivalent to their corresponding wild-type proteins, while deletions at positions 522–548 and 529–555 significantly enhanced enzyme activity. Additionally, the highest expression levels of CARS were in the flowers under white light for 8 h, while CADS exhibited peak expression in the leaves under white light for 12 h. These findings deepen our understanding of the regulatory mechanisms involved in sesquiterpene biosynthesis in lavender and provide insights for genetic engineering strategies aimed at enhancing EO production. Such advances could also inform the development of cosmetic, personal care, and medicinal products. Full article

The enhancement effect and mechanism of porous frameworks on hydrogel thermal control performance are key factors in evaluating their engineering applications and performance improvements. This study investigates the enhancement mechanism of porous framework composite phase-change materials (CPCM) on hydrogel thermal control performance through multi-scale visualization comparison experiments. Results indicate that pure hydrogels, due to their dense internal structure, hinder water vapor escape, thereby impeding overall fluidity and mass transfer rates. The introduction of a porous framework significantly improves internal heat transfer and moisture transport pathways within the hydrogel, enabling smooth water vapor release during heating and preventing localized heat accumulation. Under 100 °C heating conditions, CPCM exhibited a 65% reduction in mass-specific dehydration rate compared to pure hydrogel, with a 25% lower temperature drop. Energy efficiency increased by 13.5% over hydrogel, while the coefficient of variation decreased by 34.1%, demonstrating superior thermal stability and temperature control capabilities. This study elucidates from a mechanistic perspective how porous frameworks regulate the thermal and mass transfer behaviors of hydrogels, providing a theoretical basis and experimental support for their advanced application and optimization in the thermal control systems of electronic devices. Full article

Understanding the mechanisms of injection-induced fault slip is critical for managing subsurface energy technologies. This study experimentally investigates the influences of the intermediate principal stress (σ_y), minimum principal stress (σ_x), and injection pressure (P) on fault slip initiation stress and velocity. Experiments were conducted on pre-faulted granite specimens (100 mm cubes) using a true triaxial apparatus, simulating in situ stress conditions. The results reveal a two-stage slip process: an initial stable stage dominated by elastic energy accumulation, followed by a slip stage characterized by rapid energy release and stick–slip oscillations. We found that slip initiation stress increases linearly with both σ_y and σ_x, but decreases linearly with increasing P. A higher σ_y delays slip initiation but can lead to larger stress drops and higher slip velocities upon failure. Conversely, fluid injection weakens the fault by reducing effective normal stress, exhibiting a dual effect: it lowers the stress required for slip and enhances the instantaneous slip velocity after initiation. Our findings provide quantitative, mechanistic insights into fault slip behavior, serving as a critical benchmark for numerical simulations and contributing to improved assessment and mitigation of injection-induced seismicity across various engineering applications. Full article