Multi-fidelity optimization of super-cavitating hydrofoils

Bonfiglio, Luca; Perdikaris, Paris; Brizzolara, Stefano; Karniadakis, George

doi:10.1016/j.cma.2017.12.009

Cited by 51 publications

(28 citation statements)

References 27 publications

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“…These response surfaces (surrogate models) are capable of describing quantities of interest such as motions in high-dimensional spaces (for instance operating conditions or designs). Some good examples of this are [38] where the fidelity of a URANS model was tuned by changing the mesh resolution and [39] where potential flow and RANSE calm water predictions were used to optimize the shape of a SWATH vessel. This proves that it is reasonable to think that such optimization techniques can make URANS simulations a practical design and optimization tool.…”

Section: Discussionmentioning

confidence: 99%

Influence of Viscosity and Non-Linearities in Predicting Motions of a Wind Energy Offshore Platform in Regular Waves

Ferrandis

Bonfiglio

Rodríguez

et al. 2020

Journal of Offshore Mechanics and Arctic Engineering

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Motion predictions of floating bodies in extreme waves represent a challenging problem in naval hydrodynamics. The solution of the seakeeping problem involves the study of complex non-linear wave-body interactions that require large computational costs. For this reason, over the years, many seakeeping models have been formulated in order to predict ship motions using simplified flow theories, usually based on potential flow theories. Neglecting viscous effects in the wave-induced forces might largely underestimate the energy dissipated by the system. This problem is particularly relevant for unconventional floating bodies at resonance. In these operating conditions, the linear assumption is no longer valid, and conventional boundary element methods, based on potential flow, might predict unrealistic large responses if not corrected with empirical viscous damping coefficients. The application considered in this study is an offshore platform to be operated in a wind farm requiring operability even in extreme meteorological conditions. In this paper, we compare heave and pitch response amplitude operators predicted for an offshore platform using three different seakeeping models of increasing complexity, namely, a frequency-domain boundary element method (BEM), a partly nonlinear time domain BEM, and a non-linear viscous model based on the solution of the unsteady Reynolds-averaged Navier–Stokes (URANS) equations. Results are critically compared in terms of accuracy, applicability, and computational costs.

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Section: Discussionmentioning

confidence: 99%

Influence of Viscosity and Non-Linearities in Predicting Motions of a Wind Energy Offshore Platform in Regular Waves

Ferrandis

Bonfiglio

Rodríguez

et al. 2020

Journal of Offshore Mechanics and Arctic Engineering

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“…Multifidelity methods have much broader applications, not only Monte Carlo‐based methods, but also more general UQ aspects, for example, optimization with uncertainty (Bonfiglio, Perdikaris, Brizzolara, & Karniadakis, 2018; Heinkenschloss, Kramer, Takhtaganov, & Willcox, 2018; Pang, Perdikaris, Cai, & Karniadakis, 2017), multifidelity surrogate modeling (Chaudhuri, Lam, & Willcox, 2018; Giselle Ferńandez‐Godino, Park, Kim, & Haftka, 2019; Guo, Song, Park, Li, & Haftka, 2018; Parussini, Venturi, Perdikaris, & Karniadakis, 2017; Perdikaris, Venturi, Royset, & Karniadakis, 2015; Tian et al, 2020) and multifidelity information reuse, and fusion (Cook, Jarrett, & Willcox, 2018; Perdikaris, Venturi, & Karniadakis, 2016). We refer to (Park et al, 2017; Peherstorfer, Willcox, & Gunzburger, 2018) for a comprehensive introduction and in‐depth discussion of multifidelity methods for uncertainty propagation.…”

Section: Modern MC Methods For Uqmentioning

confidence: 99%

Modern Monte Carlo methods for efficient uncertainty quantification and propagation: A survey

Zhang

2020

WIREs Computational Stats

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Uncertainty quantification (UQ) includes the characterization, integration, and propagation of uncertainties that result from stochastic variations and a lack of knowledge or data in the natural world. Monte Carlo (MC) method is a sampling‐based approach that has widely used for quantification and propagation of uncertainties. However, the standard MC method is often time‐consuming if the simulation‐based model is computationally intensive. This article gives an overview of modern MC methods to address the existing challenges of the standard MC in the context of UQ. Specifically, multilevel Monte Carlo (MLMC) extending the concept of control variates achieves a significant reduction of the computational cost by performing most evaluations with low accuracy and corresponding low cost, and relatively few evaluations at high accuracy and corresponding high cost. Multifidelity Monte Carlo (MFMC) accelerates the convergence of standard Monte Carlo by generalizing the control variates with different models having varying fidelities and varying computational costs. Multimodel Monte Carlo method (MMMC), having a different setting of MLMC and MFMC, aims to address the issue of UQ and propagation when data for characterizing probability distributions are limited. Multimodel inference combined with importance sampling is proposed for quantifying and efficiently propagating the uncertainties resulting from small data sets. All of these three modern MC methods achieve a significant improvement of computational efficiency for probabilistic UQ, particularly uncertainty propagation. An algorithm summary and the corresponding code implementation are provided for each of the modern MC methods. The extension and application of these methods are discussed in detail. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Monte Carlo Methods Statistical and Graphical Methods of Data Analysis > Sampling

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“…The motivation for developing improved strategies for constructing metamodels relevant to mechanical data is twofold. First, meta- models enable unprecedented exploration of the model parameter space [21,22] and enhanced metamodel performance will lead to improvements in the computational methods that rely on them [23]. Second, similar to synthetic datasets in computer vision [24], the synthetic datasets generated by mechanical models are a proxy for real world data.…”

Section: Introductionmentioning

confidence: 99%

Mechanical MNIST: A benchmark dataset for mechanical metamodels

Lejeune

2020

Extreme Mechanics Letters

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Multi-fidelity optimization of super-cavitating hydrofoils

Cited by 51 publications

References 27 publications

Influence of Viscosity and Non-Linearities in Predicting Motions of a Wind Energy Offshore Platform in Regular Waves

Influence of Viscosity and Non-Linearities in Predicting Motions of a Wind Energy Offshore Platform in Regular Waves

Modern Monte Carlo methods for efficient uncertainty quantification and propagation: A survey

Mechanical MNIST: A benchmark dataset for mechanical metamodels

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