Engineering reports : open access最新文献

Hybrid manufacturing techniques that merge selective laser melting (SLM) with conventional casting give opportunities for engineering aluminium parts with enhanced design flexibility and performance. This study uniquely evaluates the corrosion behavior and microstructural features of each section of a hybrid aluminium component—the SLM-built region and the cast substrate—individually, rather than treating the hybrid as a single entity. Electrochemical methods, comprising open circuit potential (OCP), electrochemical impedance spectroscopy (EIS), and potentiodynamic polarization (PDP), were applied to determine how each region responds to corrosive environments. Complementary X-ray diffraction (XRD) identified phase composition, while scanning electron microscopy (SEM) with energy dispersive spectroscopy (EDS) examined surface morphology and grain structure before and after corrosion exposure. The findings revealed that regions with finer, more evenly dispersed grain structures tended to show lower corrosion current densities and more stable passivation, while areas with coarser grains were more vulnerable to localized corrosion, likely due to microstructural irregularities that compromised the protective oxide layer. Notably, one cast substrate region exhibited the highest corrosion resistance, with a corrosion rate of 0.0548 mm/year, followed by the SLM-built zones and the other cast substrate regions, which showed corrosion rates of 0.0976, 0.1635, and 0.1873 mm/year, respectively. These findings reveal the novelty of section-specific analysis, how each section of a hybrid aluminium structure behaves differently, hence providing insight for material design and optimization.

The acoustic optimization of mufflers in marine propulsion systems requires rigorous evaluation of transmission loss (TL) prediction methodologies, yet engineers often face challenges in selecting appropriate computational tools balancing accuracy and efficiency. This tutorial review provides a systematic comparison of three dominant approaches—Transfer Matrix Method (TMM), Finite Element Method (FEM), and Transient Computational Fluid Dynamics (CFD)—through the lens of both theoretical foundations and industrial applications. Beginning with didactic expositions of their mathematical formulations (plane wave theory, 3D Helmholtz solvers, and flow-acoustic coupling), we establish standardized validation protocols using a canonical expansion chamber muffler case. Experimental measurements reveal distinct performance boundaries: TMM achieves rapid convergence (< 2 dB error below 2000 Hz) but neglects 3D wave effects; FEM maintains < 10% deviation up to 3500 Hz with 90% reduced computational cost versus CFD, while transient CFD captures broadband attenuation trends despite 10× higher resource demands. These findings deliver actionable workflows for muffler designers to strategically leverage TMM for conceptual screening, FEM for mid-frequency optimization, and experimental-correlated CFD for extreme flow conditions. The synthesis of theoretical rigor, numerical benchmarking, and marine-specific implementation guidelines establishes this work as a foundational reference for both academic researchers and industrial practitioners in propulsion system acoustics.

This paper introduces FG-LSTM-TA, a hybrid deep learning model that integrates feature-gated LSTM networks with a temporal attention mechanism for high-accuracy short-term residential load forecasting. The architecture combines selective feature gating, sequential temporal modeling, and attention-based context weighting to enhance learning efficiency and adaptivity. The model was evaluated using real-world 30-min interval electricity consumption data from Ahvaz, Iran, across four scenarios involving both individual and aggregated households. Experimental results demonstrate that FG-LSTM-TA consistently outperforms baseline models (LSTM and CNN) and advanced benchmarks (GRU, Transformer, and CNN-LSTM), achieving R² scores up to 98%, lower training losses, and shorter computation times. Compared to other models, it maintains robust performance even in high-noise or low-amplitude environments. A compact sensitivity analysis further confirms the model's responsiveness to dominant temporal and weather-related features. These results establish FG-LSTM-TA as a scalable, efficient, and interpretable forecasting solution for smart grid applications such as demand-side control and energy optimization.

The shear parameters at rock–concrete interfaces are key technical indicators for rock-based structures and are of great significance to the antisliding stability of such constructions. Through in situ shear tests and multidimensional mechanistic experiments, this study investigates the effects of different rubber powder dosages on the shear behavior between concrete and rock foundations and reveals the mechanism by which rubber powder enhances the antisliding stability of rock–concrete interfaces. The results show that when the rubber powder dosage is 20 and 30 kg/m³, the shear friction coefficient increases to 1.46 and 1.63 times that of the reference group, corresponding to an improvement of 53.4%–58.0% in antisliding friction. Rubber powder imparts strain-hardening characteristics to concrete; the fracture initiation toughness of rubberized concrete is 1.17 times that of ordinary concrete, and the instability toughness is 1.24 times, indicating higher fracture energy. In addition, the base reaction distribution of rubberized concrete helps reduce stress concentration at specimen edges. By redistributing internal stresses and mitigating stress concentration, rubber powder enhances the antisliding stability of rock–concrete interfaces, providing a new approach for improving the stability of rock-based engineering structures.

Advanced thermal management systems in automotive and energy sectors demand high-performance cooling fluids capable of withstanding intense thermal loads. The integration of copper and copper–ferro oxide nanoparticles in engine oil offers a promising route to enhance heat dissipation, minimize energy loss, and ensure operational stability in magneto-thermal environments. This study presents a detailed thermal and flow analysis of a magnetohydrodynamics (MHD) couple stress hybrid nanofluid containing copper (Cu) and copper–ferrite (Cu-Fe₃O₄) nanoparticles suspended in an engine oil (10W40C) base fluid. The model incorporates the effects of thermal radiation, internal heat generation, and the Cattaneo–Christov heat flux theory to capture non-Fourier heat conduction behavior. The fluid is assumed to exhibit couple stress characteristics, suitable for simulating microstructural effects in lubricating and thermal processing systems. A truncated system containing the dimensionless parameters has been attained. The numerical simulations task against this system is complied with the help of the shooting method. A comparative investigation is carried out between mono nanofluid (Cu/engine oil) and hybrid nanoparticle ([Cu-Fe₃O₄]/engine oil) nanofluids to assess their thermal performance. It has been observed that relaxation time effects play a novel role to control the heat transfer phenomenon. The hybrid nanofluid significantly enhances heat transfer rates compared to the mono nanofluid. Furthermore, the concentration profile declined due to the Schmidt number and concentration relaxation parameter. These findings offer useful insights for advanced thermal management technologies, particularly in automotive and energy-related applications.

With the growing use of unmanned aerial vehicles (UAVs) in both civil and military applications, optimizing electric powertrain systems is essential to enhance endurance, efficiency ($��\eta �$), and mission adaptability. This study explores the impact of commercially available propeller variations on UAV powertrain performance, keeping the battery, ESC, and brushless DC motor fixed. The hypothesis is that propeller geometry plays a critical role in thrust production and flight endurance. To validate this, several off-the-shelf propellers are experimentally tested using a dynamometer setup that measured thrust, RPM, voltage, and current at various throttle levels. Results show that propeller P3_30 delivered the highest Thrust to Mechanical Power Ratio (TMPR) (8.48%), while P1_26 offered the longest endurance (16.17 min). Notably, efficiency ($��\eta �$) peaked at 93.1% for the P1_26 propeller configuration, further supporting its suitability for long-duration missions. These insights provide a practical performance guide for UAV designers and emphasize the value of experimental benchmarking in selecting propulsion components for mission-specific UAV configurations.

The existing fault diagnosis methods for 12 kV ring main units face several limitations, including slow perception speed, low cross-modal data fusion accuracy, and ineffective avoidance of signal interference. The accuracy of fault diagnosis is low, and the isolation speed is slow. This article proposes an integrated design method for the 12 kV intelligent ring main unit and constructs a fault diagnosis and isolation model. Firstly, an integrated hardware platform is constructed that significantly improves the perception speed and fusion potential. Then, an innovative fusion diagnostic model, named GAT-Transformer-DRSN, is proposed. The proposed model consists of three core components: a GAT-based topology fault locator, which analyzes fault propagation paths; a spatiotemporal Transformer-based cross-modal evolution analyzer, which improves fusion accuracy; and a DRSN-based anti-interference feature extractor, which suppresses the impact of signal interference. Finally, a self-optimizing closed loop is formed based on weighted fusion output diagnosis results and isolation instructions. The experimental validation utilizes fault data from the American Electric Power Research Institute (EPRI) and actual operational data from a provincial distribution network in China. The proposed GAT-Transformer-DRSN achieved the highest performance across all datasets, with fault diagnosis metrics including a Macro-F1 score of 0.9531, recognition accuracies of 96.28% and 96.45%, and false alarm rates of 0.75% and 0.72%, outperforming all other comparative methods.

The rapid growth of agri-food industries has led to an alarming increase in waste generation, posing environmental, economic, and sustainability challenges. This review explores recent advancements in the valorization of agri-food by-products into value-added products through green extraction and biorefinery technologies. It emphasizes the recovery of bioactive compounds such as polyphenols, flavonoids, carotenoids, and dietary fibers from fruit, vegetable, dairy, meat, and seafood wastes, highlighting their potential applications in the food, pharmaceutical, cosmetic, and bioenergy sectors. Emerging eco-friendly extraction techniques—including supercritical and subcritical fluid extraction, enzyme-assisted extraction, microwave- and ultrasound-assisted methods, and pulsed electric field processing—offer improved yield, purity, and energy efficiency while reducing ecological impact. Despite technological progress, large-scale adoption remains constrained by high costs, lack of standardization, and limited industrial integration. Key research gaps include the need for techno-economic assessments, solvent recovery strategies, and life-cycle evaluations to ensure process scalability and sustainability. Future research should focus on developing hybrid extraction systems, AI-driven process optimization, and pilot-scale biorefineries supported by robust policy frameworks and industry–academia collaboration. Overall, agri-food waste valorization presents a viable pathway toward achieving environmental sustainability and circular bioeconomy goals, enabling a transition from waste-intensive practices to resource-efficient and climate-resilient production systems.

This study presents a four-parameter distribution which is called the Harris generalized Kappa distribution, which is flexible and tractable. The new distribution extends the two-parameter Kappa distribution, and it can be explored to model highly skewed data, especially those exhibiting decreasing, increasing, reversed J, and non-monotone (bathtub) failure rates. Mathematical expressions were derived for the statistical properties of the Harris generalized Kappa distribution and studied in detail, namely: moments, incomplete moments, moment generating functions, characteristic function, mean residual life, average waiting time, Bonferroni and Lorenz curves, Gini index, Renyi and q-entropies, and stress-strength reliability function. The estimate of the parameters of the proposed model is obtained using the maximum likelihood estimation procedure with the adequacy model in R. The application of the Harris generalized Kappa model to two lifetime data sets, namely the taxes revenue's data and the fracture toughness data sets, demonstrated its applicability in modeling real-life data, as it provides the best fit among other competitive models considered in the study.

Demand-side management in conjunction with plug-in electric vehicles, along with renewable energy sources, has been explored in relation to dynamic optimal power flow (DOPF). The renewable energy sources considered here are solar PV plants (SPVPs) along with hydropower plants (HPPs). All of the bottlenose dolphin optimizer (BDO), gray wolf optimization (GWO), and the self-organizing Hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC) are the techniques utilized to solve DOPF. 57-bus, 118-bus, as well as IEEE 30-bus systems are employed for authentication. The cost acquired with DSM is about 4.35%, 12.98%, and 5.25% less than the cost obtained without DSM in the case of the IEEE 30-bus, 57-bus, and 118-bus systems, respectively. The cost derived by BDO is less than that of the other two methods.