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Keywords = Latin Hypercube sampling

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18 pages, 7605 KiB  
Article
Multi-Objective Optimization of Thin-Walled Connectors in Injection Molding Process Based on Integrated Algorithms
by Size Peng, Mingbo Tan, Daohong Zhang and Maojun Li
Materials 2025, 18(9), 1991; https://doi.org/10.3390/ma18091991 - 28 Apr 2025
Viewed by 203
Abstract
For the manufacturing of thin-walled connectors, warpage represents an inherent challenge in injection molding, significantly affecting dimensional accuracy and shape consistency. This study introduces an optimization methodology that combines Latin Hypercube Sampling (LHS), numerical simulation, a DBO-BP neural network prediction model, and integrated [...] Read more.
For the manufacturing of thin-walled connectors, warpage represents an inherent challenge in injection molding, significantly affecting dimensional accuracy and shape consistency. This study introduces an optimization methodology that combines Latin Hypercube Sampling (LHS), numerical simulation, a DBO-BP neural network prediction model, and integrated multi-objective optimization algorithms (NSGA-II). Initially, LHS is employed to select experimental sample points, followed by numerical simulations to evaluate the influence of process parameters on the response variables. Based on the simulation outcomes and response data, a DBO-BP neural network prediction model is developed to enhance the precision of multi-objective optimization. Subsequently, the NSGA-II algorithm is utilized for multi-objective optimization to analyze the effects of various process parameter combinations on warpage, shrinkage, and clamping force, ultimately identifying the optimal Pareto front solutions. The optimization results demonstrate that the model’s prediction accuracy for warpage and volume shrinkage is within 5%. The clamping force remains relatively high, with the optimal values for warpage, volume shrinkage rate, and clamping force being 0.173 mm, 7.5%, and 15.83 tons, respectively. This approach facilitates the optimization of injection molding process parameters while ensuring the quality of thin-walled connectors, thereby improving production efficiency and minimizing defects. Full article
(This article belongs to the Special Issue Physical Metallurgy of Metals and Alloys (3rd Edition))
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30 pages, 5733 KiB  
Article
Two-Stage Distributionally Robust Optimal Scheduling for Integrated Energy Systems Considering Uncertainties in Renewable Generation and Loads
by Keyong Hu, Qingqing Yang, Lei Lu, Yu Zhang, Shuifa Sun and Ben Wang
Mathematics 2025, 13(9), 1439; https://doi.org/10.3390/math13091439 - 28 Apr 2025
Viewed by 152
Abstract
To effectively account for the impact of fluctuations in the power generation efficiency of renewable energy sources such as photovoltaics (PVs) and wind turbines (WTs), as well as the uncertainties in load demand within an integrated energy system (IES), this article develops an [...] Read more.
To effectively account for the impact of fluctuations in the power generation efficiency of renewable energy sources such as photovoltaics (PVs) and wind turbines (WTs), as well as the uncertainties in load demand within an integrated energy system (IES), this article develops an IES model incorporating power generation units such as PV, WT, microturbines (MTs), Electrolyzer (EL), and Hydrogen Fuel Cell (HFC), along with energy storage components including batteries and heating storage systems. Furthermore, a demand response (DR) mechanism is introduced to dynamically regulate the energy supply–demand balance. In modeling uncertainties, this article utilizes historical data on PV, WT, and loads, combined with the adjustability of decision variables, to generate a large set of initial scenarios through the Monte Carlo (MC) sampling algorithm. These scenarios are subsequently reduced using a combination of the K-means clustering algorithm and the Simultaneous Backward Reduction (SBR) technique to obtain representative scenarios. To further manage uncertainties, a distributionally robust optimization (DRO) approach is introduced. This method uses 1-norm and ∞-norm constraints to define an ambiguity set of probability distributions, thereby restricting the fluctuation range of probability distributions, mitigating the impact of deviations on optimization results, and achieving a balance between robustness and economic efficiency in the optimization process. Finally, the model is solved using the column and constraint generation algorithm, and its robustness and effectiveness are validated through case studies. The MC sampling method adopted in this article, compared to Latin hypercube sampling followed by clustering-based scenario reduction, achieves a maximum reduction of approximately 17.81% in total system cost. Additionally, the results confirm that as the number of generated scenarios increases, the optimized cost decreases, with a maximum reduction of 1.14%. Furthermore, a comprehensive cost analysis of different uncertainties modeling approaches is conducted, demonstrating that the optimization results lie between those obtained from stochastic optimization (SO) and robust optimization (RO), effectively balancing conservatism and economic efficiency. Full article
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20 pages, 9009 KiB  
Article
Calibration of RNG k-ε Model Constants Based on Experimental Data Assimilation: A Study on the Flow Characteristics of Air-Lifted Plunger Interstitial Flow
by Jinglong Zhang, Yucheng Song, Yan Xu, Yanli Yang and Jiahuan Wang
Appl. Sci. 2025, 15(8), 4515; https://doi.org/10.3390/app15084515 - 19 Apr 2025
Viewed by 117
Abstract
This study optimized the constants of the RNG k-ε model using the Ensemble Kalman Filter (ENKF) data assimilation method to improve the accuracy of air-lift plunger gap flow predictions. For high Reynolds number turbulent flow, we conducted numerical simulations integrating experimental data with [...] Read more.
This study optimized the constants of the RNG k-ε model using the Ensemble Kalman Filter (ENKF) data assimilation method to improve the accuracy of air-lift plunger gap flow predictions. For high Reynolds number turbulent flow, we conducted numerical simulations integrating experimental data with a library of predicted data generated via optimal Latin hypercube sampling. ENKF was employed to assimilate these data and adjust the model constants, significantly reducing prediction errors and enhancing the accuracy of plunger models. Specifically, mean square errors for rectangular and circular plungers decreased from 60.67 and 61.48 to 7.12 and 7.20, respectively. The study also revealed significant changes in vortex dynamics and flow distribution following data assimilation, providing insights for optimizing plunger design and improving system energy efficiency. These findings underscore the potential of data assimilation in advancing oil and gas production. Full article
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20 pages, 2734 KiB  
Article
Surrogate-Assisted Multi-Objective Optimization of Interior Permanent Magnet Synchronous Motors with a Limited Sample Size
by Zhiyong Li, Mingfeng Huang and Ziyi Wang
Appl. Sci. 2025, 15(8), 4259; https://doi.org/10.3390/app15084259 - 12 Apr 2025
Viewed by 255
Abstract
Interior permanent magnet synchronous motors (IPMSMs) are critical for electric vehicle traction and industrial systems, yet optimizing their performance under high-dimensional design spaces remains computationally challenging. This study proposes a surrogate-assisted multi-objective optimization framework tailored for limited sample sizes. The methodology integrates random [...] Read more.
Interior permanent magnet synchronous motors (IPMSMs) are critical for electric vehicle traction and industrial systems, yet optimizing their performance under high-dimensional design spaces remains computationally challenging. This study proposes a surrogate-assisted multi-objective optimization framework tailored for limited sample sizes. The methodology integrates random forest (RF) and analysis of variance (ANOVA) for variable importance analysis to reduce model complexity, followed by a Generalized Regression Neural Network (GRNN) to establish an efficient surrogate model. A multi-objective particle swarm optimization (MOPSO) algorithm generates Pareto-optimal solutions, while an entropy-weighted distance metric objectively selects the final design. Experimental results demonstrate that the optimized IPMSM achieves a 4.62% increase in average output torque, a 0.15% improvement in efficiency, and a 10.48% reduction in torque ripple compared to the prototype. Finite element analysis validates the consistency between predicted and simulated outcomes, with relative errors below 2.92%. The framework effectively balances computational efficiency and accuracy, offering a data-driven approach for motor optimization under constrained experimental resources. Full article
(This article belongs to the Section Electrical, Electronics and Communications Engineering)
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26 pages, 482 KiB  
Article
Computational Construction of Sequential Efficient Designs for the Second-Order Model
by Norah Alshammari, Stelios Georgiou and Stella Stylianou
Mathematics 2025, 13(7), 1190; https://doi.org/10.3390/math13071190 - 4 Apr 2025
Viewed by 249
Abstract
Sequential experimental designs enhance data collection efficiency by reducing resource usage and accelerating experimental objectives. This paper presents a model-driven approach to sequential Latin hypercube designs (SLHDs) tailored for second-order models. Unlike traditional model-free SLHDs, our method optimizes a conditional A-criterion to improve [...] Read more.
Sequential experimental designs enhance data collection efficiency by reducing resource usage and accelerating experimental objectives. This paper presents a model-driven approach to sequential Latin hypercube designs (SLHDs) tailored for second-order models. Unlike traditional model-free SLHDs, our method optimizes a conditional A-criterion to improve efficiency, particularly in higher dimensions. By relaxing the restriction of non-replicated points within equally spaced intervals, our approach maintains space-filling properties while allowing greater flexibility for model-specific optimization. Using Sobol sequences, the algorithm iteratively selects good points, enhancing conditional A-efficiency compared to distance minimization methods. Additional criteria, such as D-efficiency, further validate the generated design matrices, ensuring robust performance. The proposed approach demonstrates superior results, with detailed tables and graphs illustrating its advantages across applications in engineering, pharmacology, and manufacturing. Full article
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20 pages, 1788 KiB  
Article
Stochastic Optimal Scheduling of Flexible Traction Power Supply System for Heavy Haul Railway Considering the Online Degradation of Energy Storage
by Zhe Li, Yanlin He, Gaoqiang Peng and Jie Yin
World Electr. Veh. J. 2025, 16(4), 206; https://doi.org/10.3390/wevj16040206 - 1 Apr 2025
Viewed by 278
Abstract
The heavy-haul flexible traction power supply system (HFTPSS), integrated with an energy storage system (ESS) and power flow controller (PFC), offers significant potential for improving energy efficiency and reducing costs. However, the state of ESS capacity and the uncertainty of traction [...] Read more.
The heavy-haul flexible traction power supply system (HFTPSS), integrated with an energy storage system (ESS) and power flow controller (PFC), offers significant potential for improving energy efficiency and reducing costs. However, the state of ESS capacity and the uncertainty of traction power significantly affect HFTPSS operation, creating challenges in fully utilizing flexibility to achieve economic system operation. To address this challenge, a classical scenario generation approach combining long short-term memory (LSTM), Latin hypercube sampling (LHS), and fuzzy c-means (FCM) is proposed to quantitatively characterize traction power uncertainty. Based on the generated scenarios, and considering the energy balance and safe operation constraints of HFTPSS, a stochastic optimal energy dispatch model is developed. The model aims to minimize the operational cost for heavy-haul electrified railways (HERs) while accounting for the impact of online ESS capacity degradation on the energy scheduling process. Finally, the effectiveness of the proposed strategy and model is validated using operational data from a real HER system. Full article
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20 pages, 8274 KiB  
Article
A Structural Optimization Framework for Biodegradable Magnesium Interference Screws
by Zhenquan Shen, Xiaochen Zhou, Ming Zhao and Yafei Li
Biomimetics 2025, 10(4), 210; https://doi.org/10.3390/biomimetics10040210 - 28 Mar 2025
Viewed by 202
Abstract
Biodegradable magnesium alloys have garnered increasing attention in recent years, with magnesium alloy–based biomedical devices being clinically used. Unlike biologically inert metallic materials, magnesium-based medical devices degrade during service, resulting in a mechanical structure that evolves over time. However, there are currently few [...] Read more.
Biodegradable magnesium alloys have garnered increasing attention in recent years, with magnesium alloy–based biomedical devices being clinically used. Unlike biologically inert metallic materials, magnesium-based medical devices degrade during service, resulting in a mechanical structure that evolves over time. However, there are currently few computer-aided engineering methods specifically tailored for magnesium-based medical devices. This paper introduces a structural optimization framework for Mg-1Ca interference screws, accounting for degradation using a continuum damage model (CDM). The Optimal Latin Hypercube Sampling (OLHS) technique was employed to sample within the design space. Pull-out strengths were used as the optimization objective, which were calculated through finite element analysis (FEA). Both Response Surface Methodology (RSM) and Kriging models were employed as surrogate models and optimized using the Sequential Quadratic Programming (SQP) algorithm. The results from the Kriging model were validated through FEA, and were found to be acceptable. The relationships between the design parameters, the rationale behind the methodology, and its limitations are discussed. Finally, a final design is proposed along with recommendations for interference screw design. Full article
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26 pages, 11990 KiB  
Article
Bluff Body Size Parameters and Vortex Flowmeter Performance: A Big Data-Based Modeling and Machine Learning Methodology
by Haoran Yu
Symmetry 2025, 17(4), 510; https://doi.org/10.3390/sym17040510 - 27 Mar 2025
Viewed by 330
Abstract
This study investigates the correlation between bluff body parameters and vortex flowmeter performance through big data modeling and machine learning techniques. Vortex flowmeters are widely used in industry due to their high accuracy and minimal pressure loss. Nonetheless, optimizing their design remains challenging [...] Read more.
This study investigates the correlation between bluff body parameters and vortex flowmeter performance through big data modeling and machine learning techniques. Vortex flowmeters are widely used in industry due to their high accuracy and minimal pressure loss. Nonetheless, optimizing their design remains challenging due to the complex relationship between input and output parameters. Symmetry in bluff body design is crucial for vortex formation and stability. In this study, Latin Hypercube Sampling (LHS) was employed to generate 10,000 symmetry bluff bodies, and efficient serial simulations were conducted using Ansys Fluent, significantly reducing computational costs compared to traditional CFD methods. A regression model was developed using scikit-learn to map eight geometric parameters to eight performance indicators, achieving excellent fitting accuracy with residuals far smaller than the simulation accuracy of ANSYS Fluent. Through Grey Relational Analysis (GRA), objective function analysis, and in conjunction with CFD contour maps, this study has analyzed the relationships between input and output parameters and their impact on the Karman vortex street. This work has significantly improved the speed of big data collection and provided a solid theoretical foundation for data-driven optimization through big data analysis. In addition, the improvement of existing machine learning methods has achieved high-precision prediction and system parameter optimization, promoting the design of vortex flowmeters. Full article
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24 pages, 7462 KiB  
Article
Multi-Time-Scale Layered Energy Management Strategy for Integrated Production, Storage, and Supply Hydrogen Refueling Stations Based on Flexible Hydrogen Load Characteristics of Ports
by Zhuoyu Jiang, Rujie Liu, Weiwei Guan, Lei Xiong, Changli Shi and Jingyuan Yin
Energies 2025, 18(7), 1583; https://doi.org/10.3390/en18071583 - 22 Mar 2025
Viewed by 284
Abstract
Aiming at resolving the problem of stable and efficient operation of integrated green hydrogen production, storage, and supply hydrogen refueling stations at different time scales, this paper proposes a multi-time-scale hierarchical energy management strategy for integrated green hydrogen production, storage, and supply hydrogen [...] Read more.
Aiming at resolving the problem of stable and efficient operation of integrated green hydrogen production, storage, and supply hydrogen refueling stations at different time scales, this paper proposes a multi-time-scale hierarchical energy management strategy for integrated green hydrogen production, storage, and supply hydrogen refueling station (HFS). The proposed energy management strategy is divided into two layers. The upper layer uses the hourly time scale to optimize the operating power of HFS equipment with the goal of minimizing the typical daily operating cost, and proposes a parameter adaptive particle swarm optimization (PSA-PSO) solution algorithm that introduces Gaussian disturbance and adaptively adjusts the learning factor, inertia weight, and disturbance step size of the algorithm. Compared with traditional optimization algorithms, it can effectively improve the ability to search for the optimal solution. The lower layer uses the minute-level time scale to suppress the randomness of renewable energy power generation and hydrogen load consumption in the operation of HFS. A solution algorithm based on stochastic model predictive control (SMPC) is proposed. The Latin hypercube sampling (LHS) and simultaneous backward reduction methods are used to generate and reduce scenarios to obtain a set of high-probability random variable scenarios and bring them into the MPC to suppress the disturbance of random variables on the system operation. Finally, real operation data of a HFS in southern China are used for example analysis. The results show that the proposed energy management strategy has a good control effect in different typical scenarios. Full article
(This article belongs to the Special Issue Energy Storage Technologies and Applications for Smart Grids)
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26 pages, 1567 KiB  
Article
A Stochastic Continuous-Time Markov Chain Approach for Modeling the Dynamics of Cholera Transmission: Exploring the Probability of Disease Persistence or Extinction
by Leul Mekonnen Anteneh, Mahouton Norbert Hounkonnou and Romain Glèlè Kakaï
Mathematics 2025, 13(6), 1018; https://doi.org/10.3390/math13061018 - 20 Mar 2025
Viewed by 322
Abstract
In this paper, a stochastic continuous-time Markov chain (CTMC) model is developed and analyzed to explore the dynamics of cholera. The multitype branching process is used to compute a stochastic threshold for the CTMC model. Latin hypercube sampling/partial rank correlation coefficient (LHS/PRCC) sensitivity [...] Read more.
In this paper, a stochastic continuous-time Markov chain (CTMC) model is developed and analyzed to explore the dynamics of cholera. The multitype branching process is used to compute a stochastic threshold for the CTMC model. Latin hypercube sampling/partial rank correlation coefficient (LHS/PRCC) sensitivity analysis methods are implemented to derive sensitivity indices of model parameters. The results show that the natural death rate (μv) of a vector is the most sensitive parameter for controlling disease outbreaks. Numerical simulations indicate that the solutions of the CTMC stochastic model are relatively close to the solutions of the deterministic model. Numerical simulations estimate the probability of both disease extinction and outbreak. The probability of cholera extinction is high when it emerges from bacterial concentrations in non-contaminated/safe water in comparison to when it emerges from all infected groups. Thus, any intervention that focuses on reducing the number of infections at the beginning of a cholera outbreak is essential for reducing its transmission. Full article
(This article belongs to the Special Issue Stochastic Models in Mathematical Biology, 2nd Edition)
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27 pages, 5761 KiB  
Article
Optimization Scheduling of Hydro–Wind–Solar Multi-Energy Complementary Systems Based on an Improved Enterprise Development Algorithm
by Guohan Zhao, Chuanyang Yu, Haodong Huang, Yi Yu, Linfeng Zou and Li Mo
Sustainability 2025, 17(6), 2691; https://doi.org/10.3390/su17062691 - 18 Mar 2025
Viewed by 308
Abstract
To address the challenges posed by the direct integration of large-scale wind and solar power into the grid for peak-shaving, this paper proposes a short-term optimization scheduling model for hydro–wind–solar multi-energy complementary systems, aiming to minimize the peak–valley difference of system residual load. [...] Read more.
To address the challenges posed by the direct integration of large-scale wind and solar power into the grid for peak-shaving, this paper proposes a short-term optimization scheduling model for hydro–wind–solar multi-energy complementary systems, aiming to minimize the peak–valley difference of system residual load. The model generates and reduces wind and solar output scenarios using Latin Hypercube Sampling and K-means clustering methods, capturing the uncertainty of renewable energy generation. Based on this, a new improved algorithm, Tent–Gaussian Enterprise Development Optimization (TGED), is introduced by incorporating chaotic initialization and Gaussian random walk mechanisms, which enhance the optimization capability and solution accuracy of the traditional enterprise development optimization algorithm. In a practical case study of a certain hydropower station, the TGED algorithm outperforms other benchmark algorithms in terms of solution accuracy and convergence performance, reducing the residual load peak–valley difference by over 600 MW. This effectively mitigates the volatility of wind and solar power output and significantly enhances system stability. The TGED algorithm demonstrates strong applicability in complex scheduling environments and provides valuable insights for large-scale renewable energy integration and short-term optimization scheduling of hydro–wind–solar complementary systems. Full article
(This article belongs to the Section Energy Sustainability)
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20 pages, 4857 KiB  
Article
From Battlefield to Building Site: Probabilistic Analysis of UXO Penetration Depth for Infrastructure Resilience
by Boules N. Morkos, Magued Iskander, Mehdi Omidvar and Stephan Bless
Appl. Sci. 2025, 15(6), 3259; https://doi.org/10.3390/app15063259 - 17 Mar 2025
Viewed by 291
Abstract
Remediation of formerly used war zones requires knowledge of the depth of burial (DoB) of unexploded ordnances (UXOs). The DoB can vary greatly depending on soil and ballistic conditions, and their associated uncertainties. In this study, the well-known physics-based Poncelet equation is used [...] Read more.
Remediation of formerly used war zones requires knowledge of the depth of burial (DoB) of unexploded ordnances (UXOs). The DoB can vary greatly depending on soil and ballistic conditions, and their associated uncertainties. In this study, the well-known physics-based Poncelet equation is used to set a framework for stochastic prediction of the DoB of munitions in sandy, clayey sand, and clayey sediments using Monte Carlo simulations (MCSs). First, the coefficients of variation (COVs) of the empirical parameters affecting the model were computed, for the first time, from published experimental data. Second, the behavior of both normal and lognormal distributions was investigated and it was found that both distributions yielded comparable DoB predictions for COVs below 30%. However, a lognormal distribution was preferred, to avoid negative value sampling, since COVs of the studied parameters can easily exceed this threshold. Third, the performance of several MCS sampling techniques, including the Pseudorandom Generator (PRG), Latin Hypercube Sampling (LHS), and Gaussian Process Response Surface Method (GP_RSM), in predicting the DOB was explored. Different probabilistic sampling techniques produced similar DoB predictions for each soil type, but GP_RSM was the most computationally efficient method. Finally, a sensitivity analysis was conducted to determine the contribution of each random variable to the predicted DoB. Uncertainty of the density, drag coefficient, and bearing coefficient dominated the DoB in sandy soil, while uncertainty in the bearing coefficient controlled DoB in clayey sand soils. In clayey soil, all variables under various distribution conditions resulted in approximately identical predictions, with no single variable appearing to be dominant. It is recommended that Monte Carlo simulations using GP_RSM sampling from lognormally distributed effective variables be used for predicting DoB in soils with high COVs. Full article
(This article belongs to the Special Issue Infrastructure Resilience Analysis)
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17 pages, 6769 KiB  
Article
Study on Gearbox Fault Warning Based on the Improved M-IALO-GRU Model
by Yunhao Wang, Wenlei Sun, Han Liu, Shuai Wang and Qingsong Zhou
Appl. Sci. 2025, 15(6), 3175; https://doi.org/10.3390/app15063175 - 14 Mar 2025
Viewed by 379
Abstract
To address the limitations of traditional predictive maintenance for large wind turbines, a fault prediction method that combines a gated recurrent unit (GRU) network with an improved ant lion optimization (IALO) algorithm is proposed. Traditional fault monitoring primarily relies on the supervisory control [...] Read more.
To address the limitations of traditional predictive maintenance for large wind turbines, a fault prediction method that combines a gated recurrent unit (GRU) network with an improved ant lion optimization (IALO) algorithm is proposed. Traditional fault monitoring primarily relies on the supervisory control and data acquisition (SCADA) system to monitor parameters such as oil temperature using threshold-based alarm methods. However, this approach suffers from low accuracy in judgment and delayed fault detection. To enhance the accuracy and timeliness of fault warnings, this paper selects SCADA feature variables using the Pearson correlation coefficient (PCC) and optimizes the hyperparameters of the GRU model using the IALO algorithm, which is enhanced by Latin hypercube sampling and random sampling ranking. The method is based on historical data during normal operation, and the residuals and normal distribution are used to set warning thresholds for fault prediction. The results indicate that this method overcomes the issue of traditional hyperparameter tuning falling into local optima and surpasses conventional methods in terms of prediction accuracy and timeliness. It can effectively improve the gearbox fault-warning performance. Full article
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24 pages, 13464 KiB  
Article
The Mooring Optimization and Hydrodynamic Characteristics of the Combined Concept of a 15 MW FOWT with WECs
by Yi Yang, Shi Liu, Xinran Guo, Wen Chen, Tao Tao, Hao Wu and Kai Wang
J. Mar. Sci. Eng. 2025, 13(3), 545; https://doi.org/10.3390/jmse13030545 - 12 Mar 2025
Cited by 1 | Viewed by 510
Abstract
To reduce the cost of offshore wind and wave power, an innovative combined wind–wave energy generation system constituting of a 15 MW semi-submersible floating offshore wind turbine (FOWT) and four torus-type wave energy converters (WECs) is proposed. A wholly coupled numerical model of [...] Read more.
To reduce the cost of offshore wind and wave power, an innovative combined wind–wave energy generation system constituting of a 15 MW semi-submersible floating offshore wind turbine (FOWT) and four torus-type wave energy converters (WECs) is proposed. A wholly coupled numerical model of aero-hydro-elastic-servo-mooring was built to evaluate the mooring line and motion dynamics of this novel combined system. Additionally, a practical mooring optimization framework is proposed with the Latin Hypercube sampling method, Kriging model, and the combined optimization techniques of the Genetic Algorithm and Gradient Algorithm. The optimization results demonstrate that the optimized mooring scheme satisfies all the strict constraints, validating the effectiveness of the optimization method. Moreover, the hydrodynamic characteristics of the combined system and the effects of the WECs on the mooring system under both rated and extreme conditions are discussed, including changes in time-series mooring tension, power spectral density, and statistical characteristics. The research findings provide a reference for the further development and optimization of this novel combined system, contributing to the efficient utilization of offshore renewable energy. Full article
(This article belongs to the Special Issue Floating Wave–Wind Energy Converter Plants)
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18 pages, 5132 KiB  
Article
Ensemble-Based Model-Agnostic Meta-Learning with Operational Grouping for Intelligent Sensory Systems
by Mainak Mallick, Young-Dae Shim, Hong-In Won and Seung-Kyum Choi
Sensors 2025, 25(6), 1745; https://doi.org/10.3390/s25061745 - 12 Mar 2025
Viewed by 480
Abstract
Model-agnostic meta-learning (MAML), coupled with digital twins, is transformative for predictive maintenance (PdM), especially in robotic arms in assembly lines, where rapid and accurate fault classification of arms is essential. Despite gaining significant traction, the framework faces significant challenges, like hypersensitivity to learning [...] Read more.
Model-agnostic meta-learning (MAML), coupled with digital twins, is transformative for predictive maintenance (PdM), especially in robotic arms in assembly lines, where rapid and accurate fault classification of arms is essential. Despite gaining significant traction, the framework faces significant challenges, like hypersensitivity to learning parameters and limited generalization during meta-testing. To address these challenges, we proposed an ensemble-based meta-learning approach integrating majority voting with model-agnostic meta-learning (MAML), and operational grouping was implemented via Latin hypercube sampling (LHS) to enhance few-shot learning ability and generalization along with maintaining stable output. This approach demonstrates superior accuracy in classifying a significantly larger number of defective mechanical classes, particularly in cross-domain few-shot (CDFS) learning scenarios. The proposed methodology is validated using a synthetic vibration signal dataset of robotic arm faults generated via a digital twin. Comparative analysis with existing frameworks, including ANIL, Protonet, and Reptile, confirms that our approach achieves higher accuracy in the given scenario. Full article
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