Investigation on the Vibration Response to High-Intensity Pressure Waves During a Solid Rocket Motor Operation

Ignatius, Jopaul K.; Venkata, Ramakrishna V.; Sankaran, Sindhuja

doi:10.1007/978-981-15-5693-7_17

Cited by 1 publication

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“…Numerous works have highlighted the significance of nozzles concerning the flow analyses (see for instance [1,2,3]), the jet noise source and acoustics (see for instance [4,5,6,7,8]), the nozzle materials [9,10,11], the nozzle vibrations [12], the fluid-structure interactions and vibroacoustics [13,14,15,16], the nozzle optimization [17,18,19,20,21,22]. The effects of uncertainties in nozzle models have also been studied, see for instance [23,24,25,26].…”

Section: Introductionmentioning

confidence: 99%

Updating Nonlinear Stochastic Dynamics of an Uncertain Nozzle Model Using Probabilistic Learning With Partial Observability and Incomplete Dataset

Capiez-Lernout,

Ezvan,

Soize

2024

Journal of Computing and Information Science in Engineering

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This paper introduces a methodology for updating the nonlinear stochastic dynamics of a nozzle with uncertain computational model. The approach focuses on a high-dimensional nonlinear computational model constrained by a small target dataset. Challenges include the large number of degrees-of-freedom, geometric nonlinearities, material uncertainties, stochastic external loads, under-observability, and high computational costs. A detailed dynamic analysis of the nozzle is presented. An updated statistical surrogate model relating the observations of interest to the control parameters is constructed. Despite small training and target datasets, and partial observability, the study successfully applies Probabilistic Learning on Manifolds (PLoM) to address these challenges. PLoM captures geometric nonlinear effects and uncertainty propagation, improving conditional mean statistics compared to training data. The conditional confidence region demonstrates the ability of the methodology to accurately represent both observed and unobserved output variables, contributing to advancements in modeling complex systems.

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