Search Results (778)

Mesh generation is a critical preprocessing step in Computational Fluid Dynamics. In ship hydrodynamics, existing mesh generation methods lack adaptability to complex hull surface geometries, necessitating repeated optimization. To address these issues, a new hybrid mesh generation strategy was proposed, integrating Non-Uniform Rational B-Spline surface interpolation, advancing front technique, hull surface curvature features, and mesh quality evaluation parameters. Firstly, the ship hull surface was partitioned into multiple regions, and each region was assigned a specific mesh type. Subsequently, the adaptively sized mesh was generated based on local curvature variations. Finally, the angle skewness was employed as an objective function to improve the mesh quality. In addition, considering the actual ship model as an example, the mesh generated by our method and conventional Laplacian smoothing method were used to perform first-order potential flow simulations, and the results were compared against the convergence values. The results indicated that our method has lower root mean square errors in computing the total non-viscous force, steady drift force and ship hull free floating Response Amplitude Operator. This method is applicable to numerical simulations of the ship potential flow, providing high-quality hull meshes for hydrodynamic analysis. Full article

This study conducts a comprehensive bibliometric analysis of literature from 2000 to 2025, aiming to map the intellectual landscape and evolving trends in research on heavy metal transport within watershed ecosystems. By leveraging the Citespace literature visualization tool, we analyzed publication trends, intellectual bases, and, most importantly, the dynamic shifts in research fronts through keyword co-occurrence and clustering analysis. The findings reveal a clear trajectory from basic geochemical theories to specific applications, characterized by three prominent themes: (1) the evolution of pollution source tracking from single-method tracing to coupled multi-method quantitative modeling; (2) the establishment of a comprehensive risk evaluation chain spanning regional assessments to targeted analyses of sensitive receptors; and (3) the analysis indicates that the current research on heavy metal transport in watershed environments remains somewhat fragmented, with limited cross-comparative synthesis across different metal species and watershed contexts, and uneven progress in applying advanced data-driven and multi-model approaches. Addressing these issues is crucial for enhancing the predictive power of models and formulating effective strategies. This study thus provides a detailed overview of the field’s development while highlighting critical pathways for future research to strengthen the scientific foundation for preventing and controlling heavy metal pollution in watershed ecosystems. Full article

Protected agriculture increasingly requires solutions that reduce energy consumption and environmental impacts while maintaining stable microclimatic conditions. The integration of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) with solar technologies has emerged as a pathway toward autonomous and energy-efficient greenhouses and solar dryers. This study analyzes the scientific and technological evolution of this convergence using a mixed review approach bibliometric and systematic, following PRISMA 2020 guidelines. From Scopus records (2012–2025), 115 documents were screened and 79 met the inclusion criteria. Bibliometric results reveal accelerated growth since 2019, led by Engineering, Computer Science, and Energy, with China, India, Saudi Arabia, and the United Kingdom as dominant contributors. Thematic analysis identifies four major research fronts: (i) thermal modeling and energy efficiency, (ii) predictive control and microclimate automation, (iii) integration of photovoltaic–thermal (PV/T) systems and phase change materials (PCMs), and (iv) sustainability and agrivoltaics. Systematic evidence shows that AI, ML, and DL based models improve solar forecasting, microclimate regulation, and energy optimization; model predictive control (MPC), deep reinforcement learning (DRL), and energy management systems (EMS) enhance operational efficiency; and PV/T–PCM hybrids strengthen heat recovery and storage. Remaining gaps include long-term validation, metric standardization, and cross-context comparability. Overall, the field is advancing toward near-zero-energy greenhouses powered by Internet of Things (IoT), AI, and solar energy, enabling resilient, efficient, and decarbonized agro-energy systems. Full article

Microplastics (MPs) have emerged as persistent and ubiquitous contaminants in aquatic and terrestrial environments, yet existing reviews often focus narrowly on conventional removal methods and lack an integrated assessment of rapidly emerging technologies. This review addresses this critical gap by providing a comprehensive and comparative synthesis of both established and next-generation approaches for MP removal from water and wastewater systems. Conventional methods such as coagulation–flocculation, sedimentation, and filtration are compared with advanced approaches including membrane separation, adsorption using engineered biochar and nanomaterials, advanced oxidation processes (AOPs), and biodegradation using microbial or enzymatic pathways. Particular emphasis is placed on hybrid and integrated systems, an area seldom summarized in prior reviews, highlighting their synergistic potential to enhance removal efficiency, reduce energy demand, and improve operational stability. Promising front-runner technologies including membrane filtration coupled with coagulation pretreatment and biochar-based magnetic adsorption systems have been identified based on a balanced performance across the key criteria of removal efficiency, scalability, energy demand, cost, byproduct risk, and environmental sustainability. The review concludes by outlining key research priorities such as standardized testing protocols, scalable biophysicochemical integration strategies, and sustainability-oriented life-cycle assessments to guide future innovation in MP management. Full article

Municipal solid waste (MSW) composition and properties play a critical role in determining the efficiency and environmental impact of waste incineration processes. However, the effects of moisture variation in MSW on combustion performance in full-scale grate systems remain insufficiently understood. To reveal how the moisture variation in municipal solid waste (MSW) properties affects the combustion process in full-scale grate systems, a 50 t/d mechanical grate incinerator was modeled. The influence of MSW inlet moisture content (42.85%, 35.71%, and 28.57%) was investigated. When the moisture content is 35.71%, the horizontal and vertical temperature gradient of the incinerator was least pronounced, and the high-temperature zone in the incinerator would not be locally concentrated. The moderate ignition position could reduce the corrosion of the front and rear arches of the grate incinerator. In the combustion process of three moisture contents, the complete evaporation positions were located at X = 4.23 m in the combustion section, X = 3.15 m in the drying section and X = 2.63 m in the drying section, the corresponding ignition points were X = 6 m, X = 4.47 m, and X = 3.74 m in the combustion section, respectively. After the moisture content was reduced to 35.71% and 28.57%, the drying process was advanced by 25.5% and 37.8%, respectively; the ignition points were advanced by 25.5% and 37.7%, respectively. It is recommended that the moisture content of MSW be maintained within the range of 33.8% to 41.6% under practical operating conditions. With the decrease in the moisture content of the MSW, the O₂ content at the incinerator outlet decreased; the CO₂ content increased. The findings offer quantitative guidance on feed pre-treatment for MSW incineration plants. Full article

The design of a control system becomes more complex with the advancement of technology, and this requires optimization techniques to be developed. In particular, multi-objective optimal control (MOC) is a method that can be used to achieve a scheme for control system that coordinates several design objectives that can be in conflict with each other. In this study, a new hybrid scheme is presented that is a combination of non-dominated sorting genetic algorithm-II (NSGA-II) and the simple cell mapping (SCM) method. The combined method first starts a random search using the genetic algorithm and then proceeds by using the SCM method for a neighborhood-based search and recovery algorithm. An evaluation of the proposed method’s efficiency and performance was conducted on two benchmark problems and two multi-objective optimal control problems. We utilized two performance indicators (generational distance (GD) and a diversity metric) to assess the convergence to the Pareto front and the diversity of the solution set, respectively. The results demonstrated that the proposed method not only achieved superior efficiency but also produced a more uniform distribution of solutions along the Pareto front compared to the SCM and NSGA-II algorithms. Full article

Advanced deception countermeasures now enable adversaries to inject false targets into synthetic-aperture-radar (SAR) imagery, generating electromagnetic signatures virtually indistinguishable from genuine targets, thus destroying the separability essential for conventional recognition algorithms. To address this problem, we propose a versatile front-end Multi-Feature Extraction and Spatial Transformation Network (MFE-STN), specifically designed for the task of discriminating between true targets and deceptive false targets created by SAR jamming, which can be seamlessly integrated with existing CNN backbones without architecture modification. MFE-STN integrates three complementary operations: (i) wavelet decomposition to extract the overall geometric features and scattering distribution of the target, (ii) a manifold transformation module for non-linear alignment of heterogeneous feature spaces, and (iii) a lightweight deformable spatial transformer that compensates for local geometric distortions introduced by deceptive jamming. By analyzing seven typical parameter-mismatch effects, we construct a simulated dataset containing six representative classes—four known classes and two unseen classes. Experimental results demonstrate that inserting MFE-STN boosts the average F1-score of known targets by 12.19% and significantly improves identification accuracy for unseen targets. This confirms the module’s capability to capture discriminative signatures to distinguish genuine targets from deceptive ones while exhibiting strong cross-domain generalization capabilities. Full article

This study investigates the adaptive cycle fan (ACF), a key component enabling variable-cycle functionality in next-generation adaptive cycle engines (ACE). Despite its critical importance for whole-engine matching, the full operating-range performance of the ACF and the coupling effects among its parameters have not been systematically examined. This work addresses this gap. Owing to its dual-flowpath architecture and multiple adjustable variables, advanced modeling approaches are required; therefore, a neural-network-based surrogate model is developed to map parameter variations to ACF performance. Based on this model, the full operating-range performance of the ACF is analyzed. The constant-speed performance forms multi-line surfaces with distinct trends across rotational speeds. Core throttling provides wide-range total pressure ratio regulation, while bypass throttling enables broad bypass ratio modulation with relatively stable pressure ratio and efficiency. To interpret the neural network, the SHAP method is employed to quantify parameter sensitivity and multi-parameter coupling effects. Bypass outlet backpressure, core outlet backpressure, and front-fan tip clearance are identified as dominant factors, exhibiting strong coupling effects that must be jointly considered for optimal engine regulation. This study presents the first three-parameter coupling analysis of an ACF and provides guidance on ACE control design, component matching, and adjustable structure design. Full article

The fabrication of semiconductor devices with three-dimensional architectures imposes unprecedented demands on advanced plasma dry etching processes. These include the simultaneous requirements of high throughput, high material selectivity, and precise profile control. In conventional reactive ion etching (RIE), fluorocarbon plasma provides both accelerated ion species and reactive neutrals that etch the feature front, while the CF_x radicals promote polymerization that protects sidewalls and enhance selectivity to the amorphous carbon layer (ACL) mask. In this work, we present computational results on the role of CF₄ addition to hydrogen fluoride (HF) plasma for next-generation RIE, specifically cryogenic etching. Simulations were performed by varying the CF₄ concentration in the HF plasma to evaluate its influence on ion densities, neutral species concentration, and electron density. The results show that the densities of CF_x (x = 1–3) ions and radicals increase significantly with CF₄ addition (up to 20%), while the overall plasma density and the excited HF species remain nearly unchanged. The results of plasma density and atomic fluorine density are consistent with the experimental observations of the HF/CF₄ plasma using an absorption probe and the actimetry method. It was verified that the gas-phase reaction model proposed in this study can accurately reproduce the plasma characteristics of the HF/CF₄ system. The coupling of HF-based etchants with CF_x radicals enables polymerization that preserves SiO₂ etching throughput while significantly enhancing etch selectivity against the ACL mask from 1.86 to 5.07, with only a small fraction (~10%) of fluorocarbon gas added. The plasma simulation provides new insights into enhancing the etching performance of HF-based cryogenic plasma etching by controlling the CF₂ radicals and HF reactants through the addition of fluorocarbon gases. Full article

Traditional solid screw rotors suffer from excessive weight, structural redundancy, low material utilization, and high energy consumption, conflicting with the growing demand for efficient, sustainable manufacturing. To address these challenges, this study proposes a lightweight design method for hollow, internally supported male screw rotors that simultaneously enhances stiffness and static–dynamic performance. A parameterized structural model with four key design variables was established, and multi-physics simulations integrating fluid flow, heat transfer, and structural mechanics were conducted to obtain mass, maximum deformation, and first-order natural frequency. Based on these simulation results, a surrogate-assisted multi-objective evolutionary optimization framework was employed: an enhanced Newton–Raphson-based optimizer (SNRBO) was used to tune the extreme gradient boosting surrogate (XGBoost 1.5.2), and the tuned surrogate then guided the Nondominated Sorting Genetic Algorithm III (NSGA-III) to perform multi-objective search and construct the Pareto front. Compared with a conventional solid rotor, the optimized design reduces mass by 64.43%, decreases maximum deformation by 4.41%, and increases the first-order natural frequency by 82.14%. These findings indicate that the proposed method provides an effective pathway to balance lightweight design with structural safety and dynamic stability, offering strong potential for green manufacturing and high-performance applications in energy, aerospace, and industrial compressor systems, and providing robust support for further advances in this field. Full article

During the process of CO₂ displacing shear-thinning oil, the occurrence of fingering is a key factor contributing to a reduction in both displacement and sequestration efficiency. Existing studies typically use the average viscosity to calculate the viscosity ratio M for shear-thinning oil, overlooking the non-uniform viscosity distribution resulting from uneven shear stress. Consequently, a phase diagram based on M fails to accurately capture the underlying mechanism influencing fingering. We investigate the influence of shear-thinning on fingering patterns by analyzing viscosity heterogeneity during immiscible CO₂ flooding in porous media. The results showed the following: (1) An increase in zero-shear viscosity (μ₀) resulted in a greater viscosity difference between the two phases, which intensified interface instability, and the power-law index (n) diminished the shear-thinning effect, promoted fingering formation, and significantly reduced displacement efficiency, with a maximum reduction of 28.6% observed in this study. (2) Shear-thinning oil was more prone to capillary fingering compared to Newtonian oil under the same capillary number Ca and viscosity ratio M. (3) Intense pressure fluctuations at the displacement front combined with non-uniform viscosity distribution exacerbate interfacial instability and make shear-thinning oil more prone to capillary fingering. This study provides guidance for optimizing displacement strategies for shear-thinning fluids and advancing the practical implementation of CO₂ flooding technology. Full article

This study proposes a methodological approach to the redesign of a 1980s vehicle, the Lancia Beta HPE, integrating the TRIZ (Theory of Inventive Problem Solving) and the Industrial Design Structure (IDeS) frameworks within the design process. The redesign process focused on both the external morphology of the vehicle and its propulsion system, aligning the outcome with contemporary trends in market evolution, societal shifts, and environmental considerations. The objective of the project was to reinterpret stylistic elements that were typical of 1980s automotive design through a contemporary lens, while incorporating characteristics of the current aesthetic of electric vehicles (EVs). A pivotal element of the research involved a comparative stylistic analysis of past and present vehicle design languages. This facilitated the identification of design guidelines for adapting formal and stylistic details to the electric mobility paradigm, with emphasis on contemporary aesthetics and energy efficiency. The transition from internal combustion to electric propulsion necessitated a comprehensive re-evaluation of the vehicle’s key exterior features, encompassing the front end, body shape, and lighting systems, in order to reflect a novel ecological identity and convey technological advancement. In order to inform stylistic choices, an in-depth exploration of electric propulsion principles was conducted, leveraging AI-based tools such as GPT to support TRIZ-guided problem-solving. Full article

This study investigates the ballistic performance and energy-absorption behavior of advanced multilayer ceramic composite armor systems composed of silicon carbide (SiC) ceramics, composite metal foam (CMF), rolled homogeneous armor (RHA), ultra-high-molecular-weight polyethylene (UHMWPE), aluminum, and rubber interlayers. The objective is to enhance impact resistance and optimize energy dissipation efficiency against armor-piercing (AP) projectiles. Ballistic tests were performed following the NIJ Standard 0101.06 Level IV specifications using .30” caliber AP M2 rounds with an impact velocity of 784–844 m/s. Experimental results revealed that the SiC front layer effectively fragmented the projectile and dispersed its kinetic energy, while the CMF and UHMWPE layers were the primary energy absorbers, dissipating approximately 70% of the total impact energy (≈3660 J). The aluminum and RHA layers provided additional reinforcement, and the rubber interlayer significantly reduced stress-wave propagation and suppressed crack growth in the ceramic. The most efficient configuration 0.5 mm RHA + 7 mm SiC + 7 mm EPDM + 7 mm CMF + 5 mm UHMWPE achieved an areal density absorption of 77.2 J·m²/kg and a unit thickness absorption of 190.6 J/mm. These findings establish a quantitative layer-wise energy dissipation framework, highlighting the synergistic interaction between brittle, porous, and ductile layers. This work provides practical design principles for developing lightweight, high-efficiency composite armor systems applicable to defense, aerospace, and personal protection fields. Moreover, this study not only validates the NIJ Standard 0101.06 ballistic performance experimentally but also establishes a reproducible methodology for quantitative, layer-wise energy analysis of hybrid ceramic-CMF-fiber armor systems, offering a scientific framework for future model calibration and optimization. Full article

The aerodynamic performance of racing motorcycles plays a crucial role in improving speed, stability, and rider control under dynamic conditions. While most existing studies focus on front-mounted winglets and fairing extensions, the aerodynamic role of rear fairing appendages remains comparatively unexplored despite their potential influence on drag, downforce distribution, and wake behaviour. In this work, three alternative rear winglet configurations were parametrically designed in Siemens NX and systematically evaluated within a validated CFD framework based on Simcenter STAR-CCM+, with the aim of assessing how geometric variations influence aerodynamic performance and achieve a favourable trade-off between reduced aerodynamic resistance and enhanced rear downforce. The numerical setup employed has been previously validated against wind-tunnel measurements in similar aerodynamic applications, ensuring the reliability and accuracy of the predicted flow fields. A Design Space Exploration (DSE) was performed through an automated multi-software workflow, enabling systematic variation in key geometric parameters and real-time assessment of their aerodynamic effects. The study revealed distinct influences of the different configurations on drag and lift coefficients, as well as on wake structure and flow detachment, highlighting the critical aerodynamic mechanisms governing rear stability and flow closure. Through iterative design and simulation, the workflow identified the most effective configuration, achieving a balance between reduced aerodynamic resistance and increased downforce, both essential for competitive racing performance. The results demonstrate the potential of integrating parametric modelling, automated CFD simulation, and DSE optimization in the aerodynamic design phase. This methodology not only offers new insights into the scarcely studied rear aerodynamic region of racing motorcycles but also establishes a replicable framework for future developments involving advanced optimization algorithms, experimental validation, and wake-interaction analyses between leading and trailing riders. Full article

This study investigates the influence of suspension elastokinematics on vehicle handling and stability through a combined research of experimental testing and numerical simulation. Laboratory tests were conducted on the front suspension of a Mercedes-Benz S320 using a quarter-car test rig equipped with specialized sensors to measure wheel displacements, steering angles, camber, and accelerations. Complementary dynamic tests were carried out under real driving conditions, including braking in a turn and “fishhook” maneuvers, to capture suspension behavior under critical operating scenarios. Based on the experimental data, an MSC Adams/Car multibody simulation model was used, incorporating varying stiffness values of suspension elastomeric elements that replicated progressive aging and degradation effects. The simulation results were compared with experimental data to validate the model’s predictive capability. Key findings indicate that reductions in elastomer stiffness significantly affect wheel kinematics, vehicle yaw response, and lateral acceleration, particularly during high-intensity maneuvers. The results underline the critical importance of accounting for elastomeric component degradation in suspension modeling to ensure vehicle safety and performance over the operational lifespan. The developed methodology demonstrates the effectiveness of integrating experimental measurements with advanced simulation tools to assess elastokinematic effects on vehicle dynamics. Full article