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Digital twin method for the stress field of a deep-diving spherical shell based on a simulation database

OCEAN ENGINEERING 2024年 300卷

作者： Cao, Yu Li, Jie Wu, Yu Wang, Fang Wang, Xueliang Shanghai Ocean Univ Coll Engn Sci & Technol Shanghai Engn Res Ctr Marine Renewable Energy 999 Huchenghuan Rd Shanghai 201306 Peoples R China Dalian Univ Technol State Key Lab Struct Anal Ind Equipment Dalian 116081 Liaoning Peoples R China China Ship Sci Res Ctr Wuxi 21408 Peoples R China

In this paper, a Digital Twin (DT) method is proposed for quickly predicting the stress fields of deep-diving spherical shells based on a simulation database. The DT method can completely cover the stress field distribution of the entire pressure-resistant shell and provide stress predictions for different sizes of deep-diving protection shells. The DT model has a three-level structure. The Level-1 DT maps the Finite Element Model to the digital model, which is verified by experiments. The difference between the experimental and numerical results is less than 9.4%. Then, the Level-2 DT deduces the digital model and expands the simulation database samples. The uncalculated stress field results in the simulation database could be obtained by the local Lagrangian interpolation method. Furthermore, the Level-3 DT can rapidly predict the stress field distribution in deep-diving spherical shell digital models. By comparison with different optimization algorithms, the Particle Swarm optimization algorithm demonstrates strong optimization capabilities and fast convergence, and the error compared with the simulation results is less than 1%. Finally, combined with actual sensor measurements and historical experience data, this method can be used to carry out real-time mapping and synchronous deduction of the service state of the dynamic stress digital twin.

关键词： Digital twin Deep-diving spherical shell Stress field distribution Simulation database optimization algorithm

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Modeling and Multi-Objective optimization of a Miniature Refrigeration System

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JOURNAL OF THERMAL SCIENCE AND ENGINEERING APPLICATIONS 2022年第10期14卷 101007页

作者： Moctezuma-Hernandez, J. A. Belman-Flores, J. M. Soria-Alcaraz, J. A. Espinal-Jimenez, A. Rubio-Maya, C. Univ Guanajuato Dept Mech Engn Engn Div Campus Irapuato Salamanca Salamanca 36885 Spain Univ Guanajuato Dept Estudios Org Div Ciencias Econ Adm Guanajuato 36885 TL Mexico Univ Michoacana Fac Mech Engn Grp Energy Efficiency & Renewable Energy Green ER Edif W Morelia 58030 Michoacan Mexico

In this study, the development of a physical model for a miniature refrigeration system is presented. The model is based on physical foundations and the incorporation of information provided in the manufacturers' catalogs. The model is able to adequately predict the energy behavior of the refrigeration system, showing a maximum error of 10% in comparison with the experimental information found in the literature. Thus, the model is used to perform a multi-objective optimization by comparing two algorithms, nondominated storing genetic algorithm II and multi-objective particle swarm optimization algorithm. For this purpose, three scenarios are proposed whose objective functions involve parameters such as coefficient of performance, compressor discharge temperature, energy consumption, and refrigerant mass flowrate. The decision variables are the evaporation and condensation temperatures, the rotational speed of the compressor, and the degrees of subcooling and superheating. Thus, an overview of the optimal operating conditions that represent the best energy performance of the refrigeration system is provided.

关键词： energy NSGA-II OMOPSO optimization algorithm vapor compression system energy efficiency

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Iterative feature mode decomposition: a novel adaptive denoising method for mechanical fault diagnosis

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第9期35卷 096101-096101页

作者： Ruan, Xiaolong Yuan, Rui Dang, Zhang Lv, Yong Jing, Xiaolong Wuhan Univ Sci & Technol Key Lab Met Equipment & Control Technol Minist Educ Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan 430081 Peoples R China

Remaining useful life prediction of rolling bearings highly relies on feature extraction of signals. The use of denoising algorithms helps to better eliminate noise and extract features, thereby constructing health indicators to predict remaining useful life. This paper proposes a novel adaptive denoising method based on iterative feature mode decomposition (IFMD) to accurately and efficiently extract fault features. The feature mode decomposition (FMD) employs correlation kurtosis (CK) as the objective function for iterative filter bank updates, enabling rapid identification of fault features. To achieve IFMD, the sparrow search algorithm combines sine-cosine algorithm and cauchy variation (SCSSA) to optimize two key parameters in FMD. During the continuous iteration process of the SCSSA algorithm, filter length and number of modes were determined. IFMD does not require empirical setting of initial parameters. During iterative process, the signal is accurately decomposed and the noise is eliminated. Compared with other optimization algorithms, SCSSA has obvious advantages in iterative rate and global optimization. The envelope spectrum feature energy ratio (ES-FER) is used to select decomposed modes, and the mode with the largest ES-FER is chosen as the optimal mode. Bearing fault diagnosis is realized by envelope spectrum analysis of the optimal mode. The numerical simulations and experimental verifications both validate the effectiveness and superiority of the proposed IFMD in mechanical fault diagnosis.

关键词： fault diagnosis iterative feature mode decomposition optimization algorithm denoising algorithm

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Hybrid Machine learning Techniques-Aided design of corroded reinforced concrete beams

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COMPUTERS & STRUCTURES 2024年 298卷

作者： Nguyen, Thuy-Anh Ly, Hai-Bang Univ Transport Technol Hanoi 100000 Vietnam

Shear strength (SS) is an essential component in the design of reinforced concrete structural elements, particularly in severe settings where reinforcements can occur and cause a loss in SS. The purpose of this work is to develop a framework for predicting the SS and optimal design of corroded reinforced concrete (CRCo) beams by using a machine learning (ML) approach. The model employed was Gradient Boosting (CGB), optimized using three metaheuristic algorithms. By optimizing the CGB model with the Hunger Games Search (HGS) algorithm, the study achieved the best ML model for predicting the SS of CRCo beams, with an R2 value of 0.996. The proposed model outperformed five other empirical SS models for CRCo beams, and sensitivity, partial dependence analyses were also conducted to explore the impact of various variables on SS. Finally, this work provided guidance on selecting appropriate beam sizes and corrosion rates for optimal CRCo beams design solutions.

关键词： Corrosion reinforced concrete beams Shear strength Gradient Boosting optimization algorithm

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A Comparison Study About Parameter optimization Using Swarm algorithms

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IEEE ACCESS 2022年 10卷 55488-55498页

作者： Pereira De Carvalho, Halcyon Davys Soares, Wedson L. Santos, Wylliams Barbosa Fagundes, Roberta Univ Pernambuco Dept Comp Engn BR-50720001 Recife PE Brazil

Adjusting the parameters of a machine learning algorithm can be difficult if the possible domain of expansion of these parameters is too high. In addition, if a sensible parameter is not adjusted correctly, the changes can be very impactful in the final results, making adjusting it manually not trivial. In order to adjust these features automatically, the current work proposes six models based on the use of optimization algorithms to adjust the models' parameters automatically. These models were built around two machine learning-based algorithms, an extreme learning machine neural network, and a support vector regression. The optimization algorithms used are Particle Swarm optimization, the Artificial Bee Colony, and the genetic algorithm. The models were compared with each other based on predictive precision in the criterion of Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and statistical tests. The experimental results on ten datasets in different contexts indicated that optimized algorithms models perform better in convergence, precision, and robustness than the non-optimized algorithms models. Therefore, the automatic adjustment of the parameters of optimized algorithms is a powerful tool for analyzing different data contexts. Thus, this study shows that the optimized algorithm models (in particular the ELM PSO model) are more accurate than all experimental evaluations.

关键词： optimization Training Particle swarm optimization Machine learning algorithms Genetic algorithms Prediction algorithms Predictive models Machine learning extreme learning machine support vector regression ensemble optimization algorithm

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Leveraging Machine Learning for Generating and Utilizing Motion Primitives in Adversarial Environments

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2024年第2期21卷 127-139页

作者： Goddard, Zachary C. Rajasekar, Rithesh Mocharla, Madhumita Manaster, Garrett Williams, Kyle Mazumdar, Anirban Georgia Inst Technol Woodruff Sch Mech Engn Atlanta GA 30332 USA Georgia Inst Technol Sch Comp Sci Atlanta GA 30332 USA Sandia Natl Labs Pathfinder Technol Albuquerque NM 87123 USA

Motion primitives enable fast planning for complex and dynamic environments. Adversarial environments pose a particularly challenging and unpredictable scenario. Motion-primitive-based planners have the potential to provide benefit in these types of environments. The key challenge is to design a library of maneuvers that effectively capture the necessary capabilities of the vehicle. This work presents a primitive-based game tree search to solve adversarial games in continuous state and action spaces and applies a reinforcement learning framework to autonomously generate effective primitives for the given task. The results demonstrate the ability of the learning framework to produce maneuvers necessary for competing against adversaries. Furthermore, we propose a method for learning a model to estimate the state-dependent value of each motion primitives and demonstrate how to incorporate this model to increase planning performance under time constraints. Additionally, we compare our primitive-based algorithm against forward simulated methods from existing literature and highlight the benefits of motion primitives.

关键词： Reinforcement Learning Search algorithm Artificial Neural Network optimization algorithm Proportional Control Computing and Informatics Monte Carlo Method

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A novel approach for underwater acoustic signal denoising based on improved time-variant filtered empirical mode decomposition and weighted fusion filtering

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OCEAN ENGINEERING 2024年 313卷

作者： Li, Guohui Han, Yaoyu Yang, Hong Xian Univ Posts & Telecommun Sch Elect Engn Xian 710121 Shaanxi Peoples R China

Denoising of underwater acoustic signal (UAS) has vital academic significance and practical value. To achieve effective denoising of UAS, a novel approach for UAS denoising based on improved time-variant filtered empirical mode decomposition and weighted fusion filtering is proposed. Firstly, to improve decomposition efficiency, time-variant filtered empirical mode decomposition (TVFEMD) based on an improved walrus optimization algorithm (IWaOA) (IWTVFEMD) is proposed. It decomposes signal into some intrinsic mode functions (IMFs), and IMFs are classified into high frequency IMFs and low frequency IMFs by energy analysis. Secondly, Gaussian-weighted moving average filtering (GWMAF) is used to filter boundary low frequency IMF and remaining low frequency IMFs are reconstructed as noise-free IMFs. Thirdly, all high frequency IMFs are reconstructed, and reconstructed high frequency IMFs are secondary decomposed by IWTVFEMD, and IMFs obtained by secondary decomposition are called SIMFs. K-means clustering and time-shift multi-scale amplitudeaware permutation entropy (TSMAAPE) are used to adaptively divide SIMFs into low complexity category and high complexity category. Then, weighted fusion filtering based on Gaussian mixture model clustering (GWFF) is proposed, which is used to filter low complexity category. High complexity category is discarded as noise. Finally, noise-free IMFs, boundary low frequency IMF after GWMAF and low complexity category after GWFF are reconstructed to obtain the denoised signal. Typical chaotic signal such as Chua and Duffing signal and actual measured five UAS are tested. The outcomes reveal that the proposed denoising method has achieved superior denoising results, which can improve signal-to-noise ratio of Chua and Duffing signal by 14 dB and 17 dB respectively, and make three-dimensional attractor phase diagram of actual measured UAS clearer and smoother.

关键词： Underwater acoustic signal Denoising Mode decomposition Clustering Entropy Filtering optimization algorithm

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Fuzzy deep learning for modeling uncertainty in character recognition using EEG signals

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APPLIED SOFT COMPUTING 2024年 159卷

作者： Latifi, Farzaneh Hosseini, Rahil Sharifi, Arash Islamic Azad Univ Dept Comp Engn Sci & Res Branch Tehran Iran Islamic Azad Univ Dept Comp Engn Shahr E Qods Branch Tehran Iran

Classification of brain signals for character recognition is a challenging task associated with uncertainties. This study aims to present an Improved Type-2 Fuzzy Deep Learning (IT2FDL) framework for uncertainty management in character recognition using EEG signals by learning rules from each other in the learning layer. The Evolutionary algorithm (EA) was employed for this study's optimum design of the proposed IT2FDL model. This algorithm was used to determine the T2F set parameters. The model parameters were tuned by Parallel Swarm optimization (PSO) algorithm. The present framework optimized the fusion layer of the model. This layer is a multi-layered Recurrent Type2 Fuzzy Neural Network (RT2FNN) with a learning layer composed of RNN cells. For performance evaluation, the models were examined using three public databases: BCI Competition IIIDataset II, BCI Competition II-Dataset IIb, and EEG Dataset. The optimized IT2FDL model outperformed the counterpart models with a significant improvement of test score of 2.5% for Competition III-Dataset II at epoch 15, 2.5% for Competition II-Dataset IIb at epoch 3, and 1% for EEG Dataset at epoch 15. The proposed IT2FDLPSO (GRU) model showed a mean test score of 96.2% at epoch 15 for all 58 subjects, which outperformed other models with many subjects for character recognition.

关键词： Brain-compute interfaces (BCI) Electroencephalogram (EEG) Spelling Classification Type2 fuzzy system Recurrent neural network (RNN) optimization algorithm PSO

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Enhancing stability and position control of a constrained magnetic levitation system through optimal fractional-order PID controller

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ALEXANDRIA ENGINEERING JOURNAL 2024年 107卷 730-746页

作者： Mughees, Abdullah Mughees, Neelam Mughees, Anam Mohsin, Syed Ali Ejsmont, Krzysztof Natl Univ Sci & Technol NUST Sch Elect Engn & Comp Sci SEECS Islamabad 44000 Pakistan Natl Text Univ Sch Engn & Technol Faisalabad 37610 Pakistan Govt Coll Univ Faisalabad Dept Elect Engn Faisalabad 38000 Pakistan Natl Univ Comp & Emerging Sci Dept Elect Engn Chiniot 34500 Pakistan Warsaw Univ Technol Fac Mech & Ind Engn PL-02524 Warsaw Poland

Magnetic levitation systems are complex and nonlinear, requiring sophisticated control methods to maintain the stability and position of the levitated object. This research presents an optimized fractional order PID (FOPID) control approach for position control of a freely-suspended ferromagnetic object. The dynamic system model is mathematically modeled in MATLAB using first-principle modeling and the grey box method. The FOPID controller has five degrees of freedom (DOFs) that allow for fine-tuning of the control gains and fractional orders, enabling the system to handle the nonlinearity inherent in the magnetic levitation system. The DOFs of FOPID and integer order PID controllers are optimized using the Artificial Bee Colony (ABC) algorithm and results are compared with state-of-the-art optimization methods. The results showed that the FOPID controller can effectively control the magnetic levitation system with constraints and outperforms other methods by up to 92.14% in terms of settling time with negligible steady-state error.

关键词： FOPID controller Fractional calculus Artificial bee colony optimization algorithm Maglev system

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A Reconstruction Method for Weak Magnetic Pipeline Inspection Signals Based on Adaptive Multiscale Signal Reconstruction

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Liu, Bin Liu, Yuze Lian, Zheng Wu, Zihan He, Luyao Yang, Lijian Shenyang Univ Technol Sch Informat Sci & Engn Shenyang 110870 Peoples R China

The weak magnetic pipeline stress detection technology is at the forefront of international pipeline safety maintenance. However, the noise embedded in weak magnetic stress detection signals significantly impacts detection accuracy. To address this issue, this article proposes a reconstruction method for weak magnetic inspection data that integrate adaptive parameter optimization and multiscale signal decomposition (MSD), aiming to enhance the detection accuracy of weak magnetic stress inspection systems. First, the method combines intrinsic computing expressive empirical mode decomposition with adaptive noise (ICEEMDAN) with MSD technology to handle residual complex noise. Second, fuzzy entropy (FE) is employed for the selection of intrinsic mode functions (IMFs) to tackle the nonstationarity of weak magnetic signals. Finally, an improved sparrow search algorithm (ISSA) is introduced to dynamically adjust key parameter configurations, effectively resolving tuning difficulties and significantly improving signal reconstruction performance. The method's performance is evaluated through the reconstruction of synthetic signals and weak magnetic simulation signals of external pipeline defects, as well as reconstruction experiments on weak excitation signals collected from Q235 pipelines. The results indicate that the method, while ensuring the highest signal-to-noise ratio (SNR), significantly reduces the total variation (TV) from 20887.5 to 1158.6 and reduces the peak distortion (Pd) factor to approximately 57, improving by about 27.62% compared to the second-best method. Furthermore, the inversion of pipeline stress distribution demonstrates that this method can effectively improve model accuracy and robustness.

关键词： Improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) optimization algorithm signal reconstruction stress distribution inversion weak magnetic signals Improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) optimization algorithm signal reconstruction stress distribution inversion weak magnetic signals

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