PSI - Issue 19

Available online at www.sciencedirect.com Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia 00 (2019) 000–000 Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia 00 (2019) 000–000

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

ScienceDirect

Procedia Structural Integrity 19 (2019) 150–167

Fatigue Design 2019 Probabilistic Fatigue and Reliability Simulation Dr A. Halfpenny a , A. Chabod a , P. Czapski a , J. Aldred a , K. Munson a , Dr M. Bonato b * a HBM Prenscia, FRANCE, UK, USA b Valeo Thermal Systems, La Verrière, FRANCE Abstract This paper demonstrates the advantages of using ‘probabilistic fatigue simulation’ and ‘stochastic’ design over the traditional ‘deterministic’ design approach. It demonstrates how ‘Monte Carlo’ simulation with ‘Latin hypercube sampling’ are effective for obtaining simulated reliability tests based on standard FEA models. Particular attention is paid to recommendations for addressing the effects of fatigue endurance and static failure in the reliability analysis. It also describes how design ‘robustness’ is ensured by using a ‘factorial sampling’ technique in conjunction with a ‘response surface’ model. The requirements for a ‘Reduced Order Model’, obtainable from FEA, are discussed. Recommendations are made to address the effects of fatigue endurance, static failure and non-linearities in the fatigue life curves during Weibull analysis. A case study demonstrates how the methods are applied to an air-cooled intercooler. A comparison is made between measured reliability test results and simulations performed using both deterministic and stochastic approaches. The simulation offers excellent correlation with the experimental measurements. No further improvements in the model were deemed necessary and the model is considered a suitable platform for performing additional design simulations and for extrapolation of the reliability statistics. Fatigue Design 2019 Probabilistic Fatigue and Reliability Simulation Dr A. Halfpenny a , A. Chabod a , P. Czapski a , J. Aldred a , K. Munson a , Dr M. Bonato b * a HBM Prenscia, FRANCE, UK, US b Valeo Thermal Systems, La Verrière, FRANCE Abstract This paper demonstrates the advantages of using ‘probabilistic fatigue simulation’ and ‘stochastic’ design over th traditional ‘deterministic’ design approach. It demonstrates how ‘Monte Carlo’ simulation with ‘Latin hypercube sampling’ are effective for obtaining simulated reliability tests based on standard FEA models. Particular attention is paid to r commendations for addressing the effects of fatigue endurance a d static failure in the reliability analysis. It also describes how design ‘robustness’ is ensured by using a ‘factorial sampling’ technique in conjunction with a ‘response surface’ model. The requirements for ‘Reduced Order Model’, obtainable from FEA, are discussed. Recommendations are made to ad ress the effects of fatigue nduranc , static failure nd non-linearities in the fatigue life curves during Weibull analysis. A case study demonstrates how the methods are applied to an air-cooled intercooler. A com arison is made between measured reliability test results and simulations performed usi g both deterministic and stochastic approaches. The simulation offers excellent correlation with the experimental easurements. No further improvements in the model were deemed necessary and the model is considered a suitable platform for performing additional design simulations and for extrapolation of the reliability statistics.

© 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Fatigue Design 2019 Organizers. © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Fatigue Design 2019 Organizers. © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Fatigue Design 2019 Organizers.

Keywords: Fatigue, Reliability, Monte Carlo, Design of Experiment Keywords: Fatigue, Reliability, Monte Carlo, Design of Experiment

1. Introduction 1. Introduction

 Deterministic and stochastic design methodologies  Deterministic and stochastic design methodologies

* Corresponding author. Tel.: +33-1-30-18-20-20. E-mail address: Amaury.chabod@hbmprenscia.com * Correspon ing author. Tel.: +33-1-30-18-20-20 E-mail address: Amaury.chabod@hbmprenscia.com

2452-3216 © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Fatigue Design 2019 Organizers. 2452-3216 © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Fatigue Design 2019 Organizers.

2452-3216 © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Fatigue Design 2019 Organizers. 10.1016/j.prostr.2019.12.018