Search Results (225)

This study investigated the motion stability of underwater gliders and optimized their shape to enhance hydrodynamic performance. Given the critical role of stability in underwater operations, a multi-objective optimization framework was developed, focusing on the geometric configuration of hydrofoils. Computational fluid dynamics (CFD) simulations were employed, with stability assessed based on hydrodynamic moments in roll and pitch motions. A surrogate model was constructed using Kriging interpolation, leveraging Latin hypercube sampling (LHS) to generate 60 design points. Sensitivity analysis identified key shape parameters influencing stability, guiding a multi-objective particle swarm optimization (MOPSO) algorithm to explore optimal design configurations. Improvements of up to 68.91% in roll stability and 51.63% in pitch stability are achieved compared to the original model, which demonstrates the effectiveness of the proposed optimization approach. The findings provide valuable insights into the hydrodynamic design of underwater gliders, facilitating enhanced maneuverability and stability in complex marine environments. Full article

Software-defined networking (SDN) is a transformative approach for managing modern network architectures, particularly in Internet-of-Things (IoT) applications. However, ensuring the optimal SDN performance and security often needs a robust sensitivity analysis (SA). To complement existing SA methods, this study proposes a new SA framework that integrates design of experiments (DOE) and machine-learning (ML) techniques. Although existing SA methods have been shown to be effective and scalable, most of these methods have yet to hybridize anomaly detection and classification (ADC) and data augmentation into a single, unified framework. To fill this gap, a targeted application of well-established existing techniques is proposed. This is achieved by hybridizing these existing techniques to undertake a more robust SA of a typified SDN-reliant IoT network. The proposed hybrid framework combines Latin hypercube sampling (LHS)-based DOE and generative adversarial network (GAN)-driven data augmentation to improve SA and support ADC in SDN-reliant IoT networks. Hence, it is called DOE-GAN-SA. In DOE-GAN-SA, LHS is used to ensure uniform parameter sampling, while GAN is used to generate synthetic data to augment data derived from typified real-world SDN-reliant IoT network scenarios. DOE-GAN-SA also employs a classification and regression tree (CART) to validate the GAN-generated synthetic dataset. Through the proposed framework, ADC is implemented, and an artificial neural network (ANN)-driven SA on an SDN-reliant IoT network is carried out. The performance of the SDN-reliant IoT network is analyzed under two conditions: namely, a normal operating scenario and a distributed-denial-of-service (DDoS) flooding attack scenario, using throughput, jitter, and response time as performance metrics. To statistically validate the experimental findings, hypothesis tests are conducted to confirm the significance of all the inferences. The results demonstrate that integrating LHS and GAN significantly enhances SA, enabling the identification of critical SDN parameters affecting the modeled SDN-reliant IoT network performance. Additionally, ADC is also better supported, achieving higher DDoS flooding attack detection accuracy through the incorporation of synthetic network observations that emulate real-time traffic. Overall, this work highlights the potential of hybridizing LHS-based DOE, GAN-driven data augmentation, and ANN-assisted SA for robust network behavioral analysis and characterization in a new hybrid framework. Full article

This paper presents a comprehensive design approach for optimizing the pole configuration of a coaxial magnetic gear (CMG) structure with an overhang to enhance torque characteristics. Five CMG models were designed, and their characteristics were analyzed. A three-dimensional finite element method analysis was conducted to account for axial leakage flux. To efficiently explore the design space, we utilized an optimal Latin hypercube sampling method to generate experimental points and constructed a kriging-based metamodel owing to its low root-mean-square error. We analyzed torque characteristics across the design variables to identify characteristic trends and performed a parametric sensitivity analysis to evaluate the influence of each variable on the torque. We derived an optimal solution that satisfied the objective function and constraints using the design variables. The characteristics of the proposed model were validated through electromagnetic field analysis, fast Fourier transform analysis of the air-gap magnetic flux density, and structural analysis. The optimal model achieved an average torque of 61.75 Nm, representing a 21.15% improvement over the initial model, while simultaneously reducing the ripple factor by 0.41%. These findings indicate that the proposed CMG design with an overhang effectively enhances torque characteristics. Full article

This study addresses the challenge of minimizing carbon emissions in designing prestressed road flyovers by comparing advanced predictive modeling techniques for surrogate-based optimization. The research develops a two-stage optimization approach. First, a response surface is generated using Latin-hypercube sampling. Second, that response surface is optimized to identify design configurations with the lowest CO₂ emissions. The optimal configuration (deck #37)—base width 3.40 m, deck depth 1.10 m, and concrete grade C-35 MPa—achieved a carbon footprint of 386,515 kg CO₂, representing a reduction of 12% compared to the reference bridge. Among the models tested, the artificial neural network (ANN) achieved the highest predictive accuracy (RMSE = 8372 kg, MAE = 7356 kg), closely followed by the Kriging 1 model (RMSE = 9235 kg, MAE = 7236 kg). Results indicate that emissions remain minimal for deck depths between 1.10 and 1.30 m, base widths between 3.20 and 3.80 m, and concrete grades of C-35 to C-40 MPa. This study provides practical guidelines for reducing the carbon footprint of prestressed bridges and highlights the value of robust surrogate models in sustainable structural optimization. Full article

In deep-sea mining operations, rigid risers operate in a complex and uncertain ocean environment where vessel–riser interactions present significant structural challenges. This study develops a coupled dynamic modeling framework that integrates vessel motions and environmental loads to evaluate the probabilistic risk of riser failure. Using frequency-domain RAOs derived from AQWA and time-domain simulations in OrcaFlex 11.0, we analyze the riser’s effective tension, bending moment, and von Mises stress under a range of wave heights, periods, and directions, as well as varying current and wind speeds. A Monte Carlo simulation framework based on Latin hypercube sampling is used to generate 10,000 sea state scenarios. The response distributions are approximated using probability density functions to assess structural reliability, and global sensitivity is evaluated using a Sobol-based approach. Results show that the wave height and period are the primary drivers of riser dynamic response, both with sensitivity indices exceeding 0.7. Transverse wave directions exert stronger dynamic excitation, and the current speed notably affects the bending moment (sensitivity index = 0.111). The proposed methodology unifies a coupled time-domain simulation, environmental uncertainty analysis, and reliability assessment, enabling clear identification of dominant factors and distribution patterns of extreme riser responses. Additionally, the workflow offers practical guidance on key monitoring targets, alarm thresholds, and safe operation to support design and real-time decision-making. Full article

The prestressed concrete-filled double skin steel tube (CFDST) lattice tower has emerged as a promising structural solution for large-capacity wind turbine systems due to its superior load-bearing capacity and economic efficiency. The steel–concrete composite adapter (SCCA) is a key component that connects the upper tubular steel tower to the lower lattice segment, transferring axial loads. However, the compressive behaviour of the SCCA remains underexplored due to its complex multi-shell configuration and steel–concrete interaction. This study investigates the axial compression behaviour of SCCAs through refined finite element simulations, identifying diagonal extrusion as the typical failure mode. The analysis clarifies the distinct roles of the outer and inner shells in confinement, highlighting the dominant influence of outer shell thickness and concrete strength. A sensitivity-based parametric study highlights the significant roles of outer shell thickness and concrete strength. To address the high cost of FE simulations, a 400-sample database was built using Latin Hypercube Sampling and engineering-grade material inputs. Using this dataset, five neural networks were trained to predict SCCA capacity. The Dropout model exhibited the best accuracy and generalization, confirming the feasibility of physics-informed, data-driven prediction for SCCAs and outperforming traditional empirical approaches. A graphical prediction tool was also developed, enabling rapid capacity estimation and design optimization for wind turbine structures. This tool supports real-time prediction and multi-objective optimization, offering practical value for the early-stage design of composite adapters in lattice turbine towers. Full article

In this study, a new approach was developed for the estimation of optimum parameters (ODP), in terms of materials and design in livestock barns, and for optimal design. For this purpose, two thousand simulations were run using Monte Carlo (MC) techniques and Latin hypercube methods using the Energy Plus program on a 50-head closed dairy farm. In this study, the heat balance in the barn was adapted to Energy Plus using an innovative approach, using heat balance equations according to the ASHRAE Standard. First, data normality was determined using the Shapiro–Wilk (SW) and Kolmogorov–Smirnov (KS) tests. Data on thermal stress duration and energy consumption for dairy cattle welfare were estimated directly from the simulations, and sensitivity (SA) and uncertainty (UA) analyses were conducted. Furthermore, the statistical relationship between thermal comfort and energy consumption was determined using Pearson correlation. The predicted values obtained from the simulations were validated with barn values, and time-series overlay plots and histograms were generated. Furthermore, interpretations of the validation processes were made based on MBE, RSME, and R² statistical values. The study estimated an indoor thermal comfort temperature of 12 °C, and this value was taken into account in the innovatively developed simulations. The estimated optimum design parameters in the study resulted in energy reductions of 25% and 41% for walls and roofs, 48% and 19% for cooling and heating setpoint temperatures, 43% and 37% for window areas, and 75% and 40% for natural and mechanical ventilation, respectively. When the design parameters were evaluated holistically and analyzed in terms of average values, the new simulation model achieved approximately 50% energy savings. We believe that the newly developed approach will guide future planning for countries, the public, and private sectors to ensure animal welfare and reduce energy consumption. Full article

Unmanned Aerial Vehicles (UAVs) are widely used in meteorology and logistics due to their unique advantages nowadays. During their lifecycle, uncertainties—such as flight condition variations—can significantly affect both design and performance, making Robust Aerodynamic Design Optimization (RADO) essential. However, existing RADO methodologies face high computational cost of uncertainty analysis and inefficiency of conventional optimization algorithms. To address these challenges, this paper proposed a novel RADO methodology integrating a Stochastic Kriging (SK) surrogate model with the PPO-Clip reinforcement learning algorithm, targeting atmospheric uncertainties encountered by turbojet-powered UAVs in transonic cruise. The SK surrogate model, constructed via Maximin Latin Hypercube Sampling and refined using the Expected Improvement infill criterion, enabled efficient uncertainty quantification. Based on the trained surrogate model, a PPO-Clip-based RADO framework with tailored reward and state transition functions was established. Applied to the RAE2822 airfoil under Mach number perturbations, the methodology demonstrated superior reliability and efficiency compared with L-BFGS-B and PSO algorithms. Full article

A significant amount of heat is generated during the braking process of a kilometer-deep well hoist, which causes a large temperature rise and then thermal deformation and cracks in the brake disc. Thus, improving the surface performance of the brake disc is necessary to ensure reliable braking under high-speed and heavy-load conditions. In this paper, thermal barrier coating technology is applied to a brake disc, and the friction and wear characteristics of a yttria-stabilized zirconia (YSZ) thermal barrier-coated brake disc is studied. A coupled thermomechanical model of the hoist disc brake is established, and a temperature field simulation analysis of uncoated and coated brake discs under emergency braking conditions is carried out. Then, a surrogate model of the maximum temperature of the brake disc surface with respect to the random parameters of the brake disc is constructed based on a Latin hypercube experimental design and the Kriging method. The reliability of the brake disc under emergency braking conditions is estimated based on saddlepoint approximation (SPA), and the feasibility of applying a YSZ thermal barrier coating to a hoist disc brake is verified. Full article

Winding hot-spot temperature (HST) is a key factor affecting the insulation life of transformers. This paper proposes an optimization method based on the Kriging response surface model, which minimizes HST by adjusting the key structural parameters of the number of winding zones, vertical oil channel width, and horizontal oil channel height. First, a two-dimensional axisymmetric temperature–fluid field coupling model is established, and the finite volume method is used to solve the HST under the actual structure, which is 92.59 °C. A total of 50 sample datasets are designed using Latin hypercube sampling, and the whale optimization algorithm (WOA) is used to determine the optimal kernel parameters of Kriging with the goal of minimizing the root mean square error (RMSE) under 5-fold cross-validation. Combined with the genetic algorithm (GA) global optimization of structural parameters, the Kriging model predicts that the optimized HST is 89.77 °C, which is verified by simulation to be 89.79 °C, achieving a temperature drop of 2.80 °C, proving the effectiveness of the structural optimization method. Full article

This study developed a novel folding light shelf system that integrates reflectors, photovoltaic (PV) modules, and adaptive louvers that adjust based on solar altitude, aiming to improve daylight distribution, minimize glare, and reduce energy consumption in office buildings. The research employed an advanced optimization approach, utilizing Non-dominated Sorting Genetic Algorithm III (NSGA-III) and Latin Hypercube Sampling, a highly effective method suitable for managing complex multi-objective scenarios involving numerous variables, to efficiently identify high-performance configurations with increased precision. Key design variables across all three components of the system included angle, width, distance, and the number of folds in the light shelf, along with the number of louvers. The proposed method successfully integrates PV technology into light shelves without compromising their functionality, enabling both daylight control and energy generation. The optimization results demonstrate that the system achieved up to a 15% improvement in useful daylight illuminance (UDI) and a 16% reduction in cooling energy consumption. Furthermore, the PV modules generated 509.5 kWh/year, ensuring improved efficiency and sustainability in building performance. Full article

This paper investigates the inverse reconstruction of temperature fields under both steady-state and transient heat conduction scenarios. The central contribution lies in the structured development and validation of the Gappy Clustering-based Proper Orthogonal Decomposition (Gappy C-POD) method—an approach that, despite its conceptual origin alongside the clustering-based dimensionality reduction method guided by POD structures (C-POD), had previously lacked an explicit algorithmic framework or experimental validation. To this end, the study constructs a comprehensive solution framework that integrates sparse sensor layout optimization with data-driven field reconstruction techniques. Numerical models incorporating multiple internal heat sources and heterogeneous boundary conditions are solved using the finite difference method. Multiple sensor layout strategies—including random selection, S-OPT, the Correlation Coefficient Filtering Method (CCFM), and uniform sampling—are evaluated in conjunction with database generation techniques such as Latin Hypercube sampling, Sobol sequences, and maximum–minimum distance sampling. The experimental results demonstrate that both Gappy POD and Gappy C-POD exhibit strong robustness in low-modal scenarios (1–5 modes), with Gappy C-POD—when combined with the CCFM and maximum distance sampling—achieving the best reconstruction stability. In contrast, while POD-MLP and POD-RBF perform well at higher modal numbers (>10), they show increased sensitivity to sensor configuration and sample size. This research not only introduces the first complete implementation of the Gappy C-POD methodology but also provides a systematic evaluation of reconstruction performance across diverse sensor placement strategies and reconstruction algorithms. The results offer novel methodological insights into the integration of data-driven modeling and sensor network design for solving inverse temperature field problems in complex thermal environments. Full article

This research introduces an innovative passive vibration control methodology employing acoustic black hole (ABH) structures to mitigate vibration transmission in electric automotive steering machines—a prevalent issue adversely affecting driving comfort and vehicle safety. Leveraging the inherent bending wave manipulation properties of ABH configurations, we conceive an integrated vibration suppression framework synergizing advanced computational modeling with intelligent optimization algorithms. A high-fidelity finite element (FEM) model integrating ABH-attached steering machine system was developed and subjected to experimental validation via rigorous modal testing. To address computational challenges in design optimization, a hybrid modeling strategy integrating parametric design (using Latin Hypercube Sampling, LHS) with Kriging surrogate modeling is proposed. Systematic parameterization of ABH geometry and damping layer dimensions generated 40 training datasets and 12 validation datasets. Surrogate model verification confirms the model’s precise mapping of vibration characteristics across the design space. Subsequent multi-objective genetic algorithm optimization targeting RMS velocity suppression achieved substantial vibration attenuation (29.2%) compared to baseline parameters. The developed methodology provides automotive researchers and engineers with an efficient suitable design tool for vibration-sensitive automotive component design. Full article

This study presents a design and optimization methodology to enhance the power density and efficiency of wound field synchronous machines (WFSMs) by selectively applying grain-oriented electrical steel (GOES). Unlike conventional non-grain-oriented electrical steel (NOES), GOES exhibits significantly lower core loss along its rolling direction, making it suitable for regions with predominantly alternating magnetic fields. Based on magnetic field analysis, four machine configurations were investigated, differing in the placement of GOES within stator and rotor teeth. Finite element analysis (FEA) was employed to compare electromagnetic performance across the configurations. Subsequently, a multi-objective optimization was conducted using Latin Hypercube Sampling, meta-modeling, and a genetic algorithm to maximize power density and efficiency while minimizing torque ripple. The optimized WFSM achieved a 13.97% increase in power density and a 1.0% improvement in efficiency compared to the baseline NOES model. These results demonstrate the feasibility of applying GOES in rotating machines to reduce core loss and improve overall performance, offering a viable alternative to rare-earth permanent magnet machines in xEV applications. Full article

As sea levels rise and extreme weather events become more frequent due to climate change, coastal urban areas are increasingly vulnerable to wave overtopping and flooding. Retrofitting existing vertical seawalls with retreated storm walls represents a key adaptive strategy, especially in the Mediterranean, where steep foreshores and limited public space constrain conventional coastal defenses. This study investigates the effectiveness of storm walls in reducing wave overtopping on vertical walls with steep foreshores (1:7 to 1:10) through high-fidelity numerical simulations using the SWASH model. A comprehensive parametric study, involving 450 test cases, was conducted using Latin Hypercube Sampling to explore the influence of geometric and hydrodynamic variables on overtopping rate. Model validation against Eurotop/CLASH physical data demonstrated strong agreement (r = 0.96), confirming the reliability of SWASH for such applications. Key findings indicate that longer promenades (Gc) and reduced impulsiveness of the wave conditions reduce overtopping. A new empirical reduction factor, calibrated for integration into the Eurotop overtopping equation for plain vertical walls, is proposed based on dimensionless promenade width and water depth. The modified empirical model shows strong predictive performance (r = 0.94) against SWASH-calculated overtopping rates. This work highlights the practical value of integrating storm walls into urban seawall design and offers engineers a validated tool for enhancing coastal resilience. Future research should extend the framework to other superstructure adaptations, such as parapets or stilling basins, to further improve flood protection in the face of climate change. Full article