Analysis of thermoelastic stresses in filament wound composite pressure vessels using evolutionary deep learning and many-objective optimisation

Thermoelastic stresses induced in filament wound composite pressure vessels by temperature changes significantly influence the resulting load of the pressure vessel. This study presents the analytical approaches based on classical lamination theory and netting theory, which can be used for computing...

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Main Authors: Dominik Vondráček, Zdeněk Padovec, Tomáš Mareš, Nirupam Chakraborti
Format: Article
Language:English
Published: Taylor & Francis Group 2025-12-01
Series:Philosophical Magazine Letters
Subjects:
Online Access:https://www.tandfonline.com/doi/10.1080/09500839.2025.2476499
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author Dominik Vondráček
Zdeněk Padovec
Tomáš Mareš
Nirupam Chakraborti
author_facet Dominik Vondráček
Zdeněk Padovec
Tomáš Mareš
Nirupam Chakraborti
author_sort Dominik Vondráček
collection DOAJ
description Thermoelastic stresses induced in filament wound composite pressure vessels by temperature changes significantly influence the resulting load of the pressure vessel. This study presents the analytical approaches based on classical lamination theory and netting theory, which can be used for computing these thermoelastic stresses in the basic parts of the pressure vessel, such as the cylinder part, the end dome, and the junction area between them. Two material configurations were considered – glass-epoxy and carbon-epoxy. Concerning the knowledge of the thermoelastic stresses in analysed essential parts of the vessel, the Hoffman failure index analysis was performed. Using this failure index analysis, the critical places of the pressure vessel can be easily detected. Using data-driven evolutionary algorithms, the invariance of the pressure vessel geometry concerning the thermoelastic stresses was verified. Knowing the critical places of the pressure vessel may improve the designer’s decision during the development and design process.
format Article
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institution OA Journals
issn 0950-0839
1362-3036
language English
publishDate 2025-12-01
publisher Taylor & Francis Group
record_format Article
series Philosophical Magazine Letters
spelling doaj-art-e480ce897a9a43119ed79884dd808c092025-08-20T02:29:59ZengTaylor & Francis GroupPhilosophical Magazine Letters0950-08391362-30362025-12-01105110.1080/09500839.2025.2476499Analysis of thermoelastic stresses in filament wound composite pressure vessels using evolutionary deep learning and many-objective optimisationDominik Vondráček0Zdeněk Padovec1Tomáš Mareš2Nirupam Chakraborti3Department of Mechanics, Biomechanics and Mechatronics, Faculty of Mechanical Engineering, Czech Technical University in Prague, Prague, Czech RepublicDepartment of Mechanics, Biomechanics and Mechatronics, Faculty of Mechanical Engineering, Czech Technical University in Prague, Prague, Czech RepublicDepartment of Mechanics, Biomechanics and Mechatronics, Faculty of Mechanical Engineering, Czech Technical University in Prague, Prague, Czech RepublicDepartment of Mechanics, Biomechanics and Mechatronics, Faculty of Mechanical Engineering, Czech Technical University in Prague, Prague, Czech RepublicThermoelastic stresses induced in filament wound composite pressure vessels by temperature changes significantly influence the resulting load of the pressure vessel. This study presents the analytical approaches based on classical lamination theory and netting theory, which can be used for computing these thermoelastic stresses in the basic parts of the pressure vessel, such as the cylinder part, the end dome, and the junction area between them. Two material configurations were considered – glass-epoxy and carbon-epoxy. Concerning the knowledge of the thermoelastic stresses in analysed essential parts of the vessel, the Hoffman failure index analysis was performed. Using this failure index analysis, the critical places of the pressure vessel can be easily detected. Using data-driven evolutionary algorithms, the invariance of the pressure vessel geometry concerning the thermoelastic stresses was verified. Knowing the critical places of the pressure vessel may improve the designer’s decision during the development and design process.https://www.tandfonline.com/doi/10.1080/09500839.2025.2476499Composite pressure vesselfilament windingthermoelastic stressesdata-driven evolutionary algorithmsdeep learning
spellingShingle Dominik Vondráček
Zdeněk Padovec
Tomáš Mareš
Nirupam Chakraborti
Analysis of thermoelastic stresses in filament wound composite pressure vessels using evolutionary deep learning and many-objective optimisation
Philosophical Magazine Letters
Composite pressure vessel
filament winding
thermoelastic stresses
data-driven evolutionary algorithms
deep learning
title Analysis of thermoelastic stresses in filament wound composite pressure vessels using evolutionary deep learning and many-objective optimisation
title_full Analysis of thermoelastic stresses in filament wound composite pressure vessels using evolutionary deep learning and many-objective optimisation
title_fullStr Analysis of thermoelastic stresses in filament wound composite pressure vessels using evolutionary deep learning and many-objective optimisation
title_full_unstemmed Analysis of thermoelastic stresses in filament wound composite pressure vessels using evolutionary deep learning and many-objective optimisation
title_short Analysis of thermoelastic stresses in filament wound composite pressure vessels using evolutionary deep learning and many-objective optimisation
title_sort analysis of thermoelastic stresses in filament wound composite pressure vessels using evolutionary deep learning and many objective optimisation
topic Composite pressure vessel
filament winding
thermoelastic stresses
data-driven evolutionary algorithms
deep learning
url https://www.tandfonline.com/doi/10.1080/09500839.2025.2476499
work_keys_str_mv AT dominikvondracek analysisofthermoelasticstressesinfilamentwoundcompositepressurevesselsusingevolutionarydeeplearningandmanyobjectiveoptimisation
AT zdenekpadovec analysisofthermoelasticstressesinfilamentwoundcompositepressurevesselsusingevolutionarydeeplearningandmanyobjectiveoptimisation
AT tomasmares analysisofthermoelasticstressesinfilamentwoundcompositepressurevesselsusingevolutionarydeeplearningandmanyobjectiveoptimisation
AT nirupamchakraborti analysisofthermoelasticstressesinfilamentwoundcompositepressurevesselsusingevolutionarydeeplearningandmanyobjectiveoptimisation