TU Darmstadt / ULB / TUprints

Quantification of Uncertainty in the Mathematical Modelling of a Multivariable Suspension Strut Using Bayesian Interval Hypothesis-Based Approach

Mallapur, Shashidhar ; Platz, Roland (2022)
Quantification of Uncertainty in the Mathematical Modelling of a Multivariable Suspension Strut Using Bayesian Interval Hypothesis-Based Approach.
In: Applied Mechanics and Materials, 2018, 885
doi: 10.26083/tuprints-00020445
Article, Secondary publication, Publisher's Version

[img] Text
AMM.885.3.pdf
Copyright Information: CC BY 4.0 International - Creative Commons, Attribution.

Download (840kB)
Item Type: Article
Type of entry: Secondary publication
Title: Quantification of Uncertainty in the Mathematical Modelling of a Multivariable Suspension Strut Using Bayesian Interval Hypothesis-Based Approach
Language: English
Date: 2022
Place of Publication: Darmstadt
Year of primary publication: 2018
Publisher: Trans Tech Publications
Journal or Publication Title: Applied Mechanics and Materials
Volume of the journal: 885
DOI: 10.26083/tuprints-00020445
Corresponding Links:
Origin: Secondary publication service
Abstract:

Mathematical models of a suspension strut such as an aircraft landing gear are utilized by engineers in order to predict its dynamic response under different boundary conditions. The prediction of the dynamic response, for example the external loads, the stress and the strength as well as the maximum compression in the spring-damper component aids engineers in early decision making to ensure its structural reliability under various operational conditions. However, the prediction of the dynamic response is influenced by model uncertainty. As far as the model uncertainty is concerned, the prediction of the dynamic behavior via different mathematical models depends upon various factors such as the model's complexity in terms of the degrees of freedom, material and geometrical assumptions, their boundary conditions and the governing functional relations between the model input and output parameters. The latter can be linear or nonlinear, axiomatic or empiric, time variant or time-invariant. Hence, the uncertainty that arises in the prediction of the dynamic response of the resulting different mathematical models needs to be quantified with suitable validation metrics, especially when the system is under structural risk and failure assessment. In this contribution, the authors utilize the Bayesian interval hypothesis-based method to quantify the uncertainty in the mathematical models of the suspension strut.

Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-204459
Additional Information:

Keywords: Bayesian Interval Hypothesis, Marginal Likelihood, Mathematical Model, Model Validation, Suspension Strut, Uncertainty

Classification DDC: 600 Technology, medicine, applied sciences > 600 Technology
600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
Divisions: 16 Department of Mechanical Engineering > Research group System Reliability, Adaptive Structures, and Machine Acoustics (SAM)
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 805: Control of Uncertainty in Load-Carrying Structures in Mechanical Engineering
Date Deposited: 10 Feb 2022 13:29
Last Modified: 22 Mar 2023 14:43
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/20445
PPN: 506193500
Export:
Actions (login required)
View Item View Item