Multi-Objective Optimization of Thin-Walled Composite Axisymmetric Structures Using Neural Surrogate Models and Genetic Algorithms DOI Open Access
Bartosz Miller, Leonard Ziemiański

Materials, Год журнала: 2023, Номер 16(20), С. 6794 - 6794

Опубликована: Окт. 20, 2023

Composite shells find diverse applications across industries due to their high strength-to-weight ratio and tailored properties. Optimizing parameters such as matrix-reinforcement orientation of the reinforcement is crucial for achieving desired performance metrics. Stochastic optimization, specifically genetic algorithms, offer solutions, yet computational intensity hinders widespread use. Surrogate models, employing neural networks, emerge efficient alternatives by approximating objective functions bypassing costly computations. This study investigates surrogate models in multi-objective optimization composite shells. It incorporates deep networks approximate relationships between input key metrics, enabling exploration design possibilities. Incorporating mode shape identification enhances accuracy, especially multi-criteria optimization. Employing network ensembles strengthens reliability mitigating model weaknesses. Efficiency analysis assesses required computations, managing trade-off cost accuracy. Considering complex comparing against Monte Carlo approach further demonstrates methodology’s efficacy. work showcases successful integration employed identification, enhancing engineering applications. The approach’s efficiency handling intricate designs accuracy has broad implications methodologies.

Язык: Английский

Efficient reliability-based concurrent topology optimization method under PID-driven sequential decoupling framework DOI

Zeshang Li,

Sheng Wang,

Kaixuan Gu

и другие.

Thin-Walled Structures, Год журнала: 2024, Номер 203, С. 112117 - 112117

Опубликована: Июнь 29, 2024

Язык: Английский

Процитировано

21
Machine Learning Applications in Building Energy Systems: Review and Prospects DOI Creative Commons

D. Li,

Zhenzhen Qi,

Yiming Zhou

и другие.

Buildings, Год журнала: 2025, Номер 15(4), С. 648 - 648

Опубликована: Фев. 19, 2025

Building energy systems (BESs) are essential for modern infrastructure but face significant challenges in equipment diagnosis, consumption prediction, and operational control. The complexity of BESs, coupled with the increasing integration renewable sources, presents difficulties fault detection, accurate forecasting, dynamic system optimisation. Traditional control strategies struggle low efficiency, slow response times, limited adaptability, making it difficult to ensure reliable operation optimal management. To address these issues, researchers have increasingly turned machine learning (ML) techniques, which offer promising solutions improving scheduling, real-time BESs. This review provides a comprehensive analysis ML techniques applied According results literature review, supervised methods, such as support vector machines random forest, demonstrate high classification accuracy detection require extensive labelled datasets. Unsupervised approaches, including principal component clustering algorithms, robust identification capabilities without data may complex nonlinear patterns. Deep particularly convolutional neural networks long short-term memory models, exhibit superior forecasting Reinforcement further enhances management by dynamically adjusting parameters maximise efficiency cost savings. Despite advancements, remain terms availability, computational costs, model interpretability. Future research should focus on hybrid integrating explainable AI enhancing adaptability evolving demands. also highlights transformative potential BESs outlines future directions sustainable intelligent building

Язык: Английский

Процитировано

3
Multi-Objective Optimization of Thin-Walled Composite Axisymmetric Structures Using Neural Surrogate Models and Genetic Algorithms DOI Open Access
Bartosz Miller, Leonard Ziemiański

Materials, Год журнала: 2023, Номер 16(20), С. 6794 - 6794

Опубликована: Окт. 20, 2023

Composite shells find diverse applications across industries due to their high strength-to-weight ratio and tailored properties. Optimizing parameters such as matrix-reinforcement orientation of the reinforcement is crucial for achieving desired performance metrics. Stochastic optimization, specifically genetic algorithms, offer solutions, yet computational intensity hinders widespread use. Surrogate models, employing neural networks, emerge efficient alternatives by approximating objective functions bypassing costly computations. This study investigates surrogate models in multi-objective optimization composite shells. It incorporates deep networks approximate relationships between input key metrics, enabling exploration design possibilities. Incorporating mode shape identification enhances accuracy, especially multi-criteria optimization. Employing network ensembles strengthens reliability mitigating model weaknesses. Efficiency analysis assesses required computations, managing trade-off cost accuracy. Considering complex comparing against Monte Carlo approach further demonstrates methodology’s efficacy. work showcases successful integration employed identification, enhancing engineering applications. The approach’s efficiency handling intricate designs accuracy has broad implications methodologies.

Язык: Английский

Процитировано

5