Rayleigh–Taylor and Richtmyer–Meshkov instability induced flow, turbulence, and mixing. II

Y Zhou - Physics Reports, 2017 - Elsevier
Abstract Rayleigh–Taylor (RT) and Richtmyer–Meshkov (RM) instabilities are well-known
pathways towards turbulent mixing layers, in many cases characterized by significant mass …

Closed-loop turbulence control: Progress and challenges

SL Brunton, BR Noack - Applied Mechanics …, 2015 - asmedigitalcollection.asme.org
Closed-loop turbulence control is a critical enabler of aerodynamic drag reduction, lift
increase, mixing enhancement, and noise reduction. Current and future applications have …

Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification

RK Tripathy, I Bilionis - Journal of computational physics, 2018 - Elsevier
State-of-the-art computer codes for simulating real physical systems are often characterized
by vast number of input parameters. Performing uncertainty quantification (UQ) tasks with …

[图书][B] Uncertainty quantification: theory, implementation, and applications

RC Smith - 2024 - SIAM
Uncertainty quantification serves a central role for simulation-based analysis of physical,
engineering, and biological applications using mechanistic models. From a broad …

Stochastic analysis of the fracture toughness of polymeric nanoparticle composites using polynomial chaos expansions

KM Hamdia, M Silani, X Zhuang, P He… - International Journal of …, 2017 - Springer
The fracture energy is a substantial material property that measures the ability of materials to
resist crack growth. The reinforcement of the epoxy polymers by nanosize fillers improves …

Review of multi-fidelity models

MG Fernández-Godino - arXiv preprint arXiv:1609.07196, 2016 - arxiv.org
This article provides an overview of multi-fidelity modeling trends. Fidelity in modeling refers
to the level of detail and accuracy provided by a predictive model or simulation. Generally …

The Wiener--Askey polynomial chaos for stochastic differential equations

D Xiu, GE Karniadakis - SIAM journal on scientific computing, 2002 - SIAM
We present a new method for solving stochastic differential equations based on Galerkin
projections and extensions of Wiener's polynomial chaos. Specifically, we represent the …

Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion

S Oladyshkin, W Nowak - Reliability Engineering & System Safety, 2012 - Elsevier
We discuss the arbitrary polynomial chaos (aPC), which has been subject of research in a
few recent theoretical papers. Like all polynomial chaos expansion techniques, aPC …

Combustion kinetic model uncertainty quantification, propagation and minimization

H Wang, DA Sheen - Progress in Energy and Combustion Science, 2015 - Elsevier
The current interest in the combustion chemistry of hydrocarbon fuels, including the various
alcohol and biodiesel compounds, motivates this review of the methods and application of …

High-order collocation methods for differential equations with random inputs

D Xiu, JS Hesthaven - SIAM Journal on Scientific Computing, 2005 - SIAM
Recently there has been a growing interest in designing efficient methods for the solution of
ordinary/partial differential equations with random inputs. To this end, stochastic Galerkin …