Random field simulation over curved surfaces: Applications to computational structural mechanics

Scarth, Carl; Adhikari, Sondipon; Cabral, Pedro H.; Silva, Gustavo H C; Prado, Alex P.

doi:10.1016/j.cma.2018.10.026

Cited by 39 publications

(22 citation statements)

References 51 publications

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“…Let us now give more details about the construction of the fluctuation modes {δT i } 1≤i≤4 . First, surface fluctuation modes are computed on the boundary ∂Ω using the method given in [65] for the construction of random fields on a curved surface. The correlation function is defined as a function of the geodesic distance d G along the surface ∂Ω:…”

Section: Stochastic Model For the Thermal Loadingmentioning

confidence: 99%

Uncertainty quantification for industrial numerical simulation using dictionaries of reduced order models

Daniel

Casenave

Akkari

et al. 2022

Mechanics & Industry

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We consider the dictionary-based ROM-net (Reduced Order Model) framework [Daniel et al., Adv. Model. Simul. Eng. Sci. 7 (2020) https://doi.org/10.1186/s40323-020-00153-6] and summarize the underlying methodologies and their recent improvements. The object of interest is a real-life industrial model of an elastoviscoplastic high-pressure turbine blade subjected to thermal, centrifugal and pressure loadings. The main contribution of this work is the application of the complete ROM-net workflow to the quantification of the uncertainty of dual quantities on this blade (such as the accumulated plastic strain and the stress tensor), generated by the uncertainty of the temperature loading field. The dictionary-based ROM-net computes predictions of dual quantities of interest for 1008 Monte Carlo draws of the temperature loading field in 2 h and 48 min, which corresponds to a speedup greater than 600 with respect to a reference parallel solver using domain decomposition, with a relative error in the order of 2%. Another contribution of this work consists in the derivation of a meta-model to reconstruct the dual quantities of interest over the complete mesh from their values on the reduced integration points.

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Section: Stochastic Model For the Thermal Loadingmentioning

confidence: 99%

Uncertainty quantification for industrial numerical simulation using dictionaries of reduced order models

Daniel

Casenave

Akkari

et al. 2022

Mechanics & Industry

View full text Add to dashboard Cite

show abstract

“…Let us now give more details about the construction of the fluctuation modes {δT i } 1≤i≤4 . First, surface fluctuation modes are computed on the boundary ∂Ω using the method given in [78] for the construction of random fields on a curved surface. The correlation function is defined as a function of the geodesic distance d G along the surface ∂Ω:…”

Section: Stochastic Model For the Thermal Loadingmentioning

confidence: 99%

Uncertainty quantification for industrial design using dictionaries of reduced order models

Daniel,

Casenave,

Akkari

et al. 2021

Preprint

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We consider the dictionary-based ROM-net (Reduced Order Model) framework [T. Daniel, F. Casenave, N. Akkari, D. Ryckelynck, Model order reduction assisted by deep neural networks (ROMnet), Advanced modeling and Simulation in Engineering Sciences 7 (16), 2020] and summarize the underlying methodologies and their recent improvements. The main contribution of this work is the application of the complete workflow to a real-life industrial model of an elastoviscoplastic highpressure turbine blade subjected to thermal, centrifugal and pressure loadings, for the quantification of the uncertainty on dual quantities (such as the accumulated plastic strain and the stress tensor), generated by the uncertainty on the temperature loading field. The dictionary-based ROM-net computes predictions of dual quantities of interest for 1008 Monte Carlo draws of the temperature loading field in 2 hours and 48 minutes, which corresponds to a speedup greater than 600 with respect to a reference parallel solver using domain decomposition, with a relative error in the order of 2%. Another contribution of this work consists in the derivation of a meta-model to reconstruct the dual quantities of interest over the complete mesh from their values on the reduced integration points.

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“…The term ∆L traditionally refers to Euclidean distance, but as we are concerned with thin-walled structures, the geodesic length is used instead. Using geodesic lengths creates a more accurate measure for generating random fields than Euclidian distance, particularly for highly curved structures [47]. Structures which are relatively flat might have a bottom and top which are close to each other in a Euclidian sense, but distant in a geodesic sense.…”

Section: B Random Field Generationmentioning

confidence: 99%

Enhanced Deterministic Performance of Panels Using Stochastic Variations of Geometric and Material Parameters

Broek¹,

Minera

Pirrera

et al. 2019

AIAA Scitech 2019 Forum

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The effect of stochastic variation in material and geometric properties on structural performance is important for robust design. Knowledge of such effects can be acquired by applying variation patterns to a structure using random fields through a Monte Carlo analysis. The output is postprocessed to show the correlation pattern between the stochastic variation of a structural property and a chosen mechanical response measure. The resulting patterns are used to identify areas most susceptible to variations, as well as areas which have the most potential to increase structural performance by varying the material parameter or geometry.By using these maps of local sensitivity to variations with respect to the structural response it is possible to redistribute material properties or geometry to promote certain behavior. This is demonstrated on a flat plate and curved panel, by either varying the Young's modulus, or thickness of the structure to increase the linear buckling load. In both of these variations the average property is set to remain the same as the original structure. Applying the redistribution increased the linear buckling load by up to 29%.

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Random field simulation over curved surfaces: Applications to computational structural mechanics

Cited by 39 publications

References 51 publications

Uncertainty quantification for industrial numerical simulation using dictionaries of reduced order models

Uncertainty quantification for industrial numerical simulation using dictionaries of reduced order models

Uncertainty quantification for industrial design using dictionaries of reduced order models

Enhanced Deterministic Performance of Panels Using Stochastic Variations of Geometric and Material Parameters

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