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.
Highlights
Since the mid-1960s, mathematical simulation models have been used as a tool in the field of scientific research as well as in the design and development of engineering systems [1]
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 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
Summary
Since the mid-1960s, mathematical simulation models have been used as a tool in the field of scientific research as well as in the design and development of engineering systems [1]. For multivariate engineering systems under uncertainty different validation metrics based on the Bayesian hypothesis appproach and the area metric approach are discussed in [15], [16]. Data uncertainty occurs when the input parameters, such as state variables or the system parameters of the mathematical models are uncertain This can be represented using probabilistic and non-probabilistic approaches, [17], [18] and [19]. The sources of uncertainty can be broadly classified as aleatoric or epistemic [10] In this contribution, the authors consider the B interval hypothesis as a model validation metric according to [20] to quantify the uncertainty in the mathematical modeling of a suspension strut. The concept of MAFDS is registered as a Patent DE 10 2014 106 858.A1 ”Load transferring device” [21]
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