PSI - Issue 57

ScienceDirect Structural Integrity Procedia 00 (2023) 000–000 Structural Integrity Procedia 00 (2023) 000–000 Available online at www.sciencedirect.com Available online at www.sciencedirect.com Procedia Structural Integrity 57 (2024) 711–717 Available online at www.sciencedirect.com

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www.elsevier.com / locate / procedia www.elsevier.com / locate / procedia

© 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0 ) Peer-review under responsibility of the scientific committee of the Fatigue Design 2023 organizers Abstract Global Sensitivity Analysis (GSA) is a well-established approach to support simulation-driven design decisions where the de pendency between the simulation’s output and the model’s input is quantified. However, classical GSA approaches, such as Sobol’ indices based on Monte Carlo Simulations (MCS), are not convenient when computationally expensive simulation models such as Representative Volume Elements (RVE) are used as the model to analyze. A simulation framework is developed with a metamodeling-based GSA to overcome the aforementioned cost of the MCS approaches. The developed framework has been ap plied in a Multi-Scale Modeling (MSM) framework replacing a micromechanical RVE simulation with three di ff erent metamodels for performing GSA. The micromechanical model predicts the sti ff ness of a Carbon Fiber Reinforced Polymer (CFRP) material and the GSA can quantify how the experimental material parameters a ff ect the material properties. The obtained sensitivity anal ysis demonstrates that void size is the most influential parameter on the outputs of interest, and the metamodel-based GSA is a computationally convenient approach. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http: // creativecommons.org / licenses / by-nc-nd / 4.0 / ) Peer-review under responsibility of the scientific committee of the Fatigue Design 2023 organizers. Keywords: Global Sensitivity Analysis; Probabilistic Modeling; Metamodeling ;Porosity; Fatigue Behavior Abstract Global Sensitivity Analysis (GSA) is a well-established approach to support simulation-driven design decisions where the de pendency between the simulation’s output and the model’s input is quantified. However, classical GSA approaches, such as Sobol’ indices based on Monte Carlo Simulations (MCS), are not convenient when computationally expensive simulation models such as Representative Volume Elements (RVE) are used as the model to analyze. A simulation framework is developed with a metamodeling-based GSA to overcome the aforementioned cost of the MCS approaches. The developed framework has been ap plied in a Multi-Scale Modeling (MSM) framework replacing a micromechanical RVE simulation with three di ff erent metamodels for performing GSA. The micromechanical model predicts the sti ff ness of a Carbon Fiber Reinforced Polymer (CFRP) material and the GSA can quantify how the experimental material parameters a ff ect the material properties. The obtained sensitivity anal ysis demonstrates that void size is the most influential parameter on the outputs of interest, and the metamodel-based GSA is a computationally convenient approach. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http: // creativecommons.org / licenses / by-nc-nd / 4.0 / ) Peer-review under responsibility of the scientific committee of the Fatigue Design 2023 organizers. Keywords: Global Sensitivity Analysis; Probabilistic Modeling; Metamodeling ;Porosity; Fatigue Behavior Fatigue Design 2023 (FatDes 2023) Enhancement of fatigue life modeling using a metamodel-based global sensitivity analysis framework Khashayar Shahrezaei a,b, ∗ , Sara Eliasson a,b,c , Per Wennhage a,b , Zuheir Barsoum a,b Fatigue Design 2023 (FatDes 2023) Enhancement of fatigue life modeling using a metamodel-based global sensitivity analysis framework Khashayar Shahrezaei a,b, ∗ , Sara Eliasson a,b,c , Per Wennhage a,b , Zuheir Barsoum a,b a KTH Royal Institute of Technology, Centre for ECO 2 Vehicle Design, SE-100 44 Stockholm, Sweden b KTH Royal Institute of Technology, Department of Engineering Mechanics, SE-100 44 Stockholm, Sweden c Scania CV AB, SE151 87 So¨derta¨lje, Sweden a KTH Royal Institute of Technology, Centre for ECO 2 Vehicle Design, SE-100 44 Stockholm, Sweden b KTH Royal Institute of Technology, Department of Engineering Mechanics, SE-100 44 Stockholm, Sweden c Scania CV AB, SE151 87 So¨derta¨lje, Sweden

1. Introduction 1. Introduction

There is a growing consumer demand for more environmentally responsible products and the industry needs to put a special focus on sustainable development to increase their economic growth. A choice of material must consider the complete life cycle and with the possibility to intervene early in the design process, the industries have the opportunity to e.g. lower manufacturing costs, improve the quality of their product, and reduce waste. In order to facilitate the capability of influencing designs in an early stage of the design development process robust simulation methods are There is a growing consumer demand for more environmentally responsible products and the industry needs to put a special focus on sustainable development to increase their economic growth. A choice of material must consider the complete life cycle and with the possibility to intervene early in the design process, the industries have the opportunity to e.g. lower manufacturing costs, improve the quality of their product, and reduce waste. In order to facilitate the capability of influencing designs in an early stage of the design development process robust simulation methods are

† First and second authors have contributed equally to the study. ∗ Corresponding author. E-mail address: khasha@kth.se † First and second authors have contributed equally to the study. ∗ Corresponding author. E-mail address: khasha@kth.se

2452-3216 © 2024 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Fatigue Design 2023 organizers 10.1016/j.prostr.2024.03.077 2210-7843 © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http: // creativecommons.org / licenses / by-nc-nd / 4.0 / ) Peer-review under responsibility of the scientific committee of the Fatigue Design 2023 organizers. 2210-7843 © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http: // creativecommons.org / licenses / by-nc-nd / 4.0 / ) Peer-review under responsibility of the scientific committee of the Fatigue Design 2023 organizers.