Quantifying and assessing the computational accuracy of coarse-graining simulations of turbulence is challenging and imperative to achieve prediction – computations and results with a quantified and adequate degree of uncertainty that can be confidently used in projects without reference data. Verification, validation, and uncertainty quantification (VVUQ) provide the tools and metrics to accomplish such an objective. This chapter reviews these methods and illustrates their importance to coarse-graining models. Toward this end, we first describe the sources of computational errors and uncertainties in coarse-graining simulations of turbulence, followed by the concepts of VVUQ. Next, we utilize the modified equation analysis and the physical interpretation of a complex problem to demonstrate the role of VVUQ in evaluating and enhancing the fidelity and confidence in numerical simulations. This is crucial to achieving predictive rather than postdictive simulations.

We live in a turbulent world observed through coarse-grained lenses. Coarse graining (CG), however, is not only a limit but also a need imposed by the enormous amount of data produced by modern simulations. Target audiences for our survey are graduate students, basic research scientists, and professionals involved in the design and analysis of complex turbulent flows. The ideal readers of this book are researchers with a basic knowledge of fluid mechanics, turbulence, computing, and statistical methods, who are disposed to enlarging their understanding of the fundamentals of CG and are interested in examining different methods applied to managing a chaotic world observed through coarse-grained lenses.

We live in a turbulent world observed through coarse-grained lenses. Coarse graining (CG), however, is not only a limit but also a need imposed by the enormous amount of data produced by modern simulations. Target audiences for our survey are graduate students, basic research scientists, and professionals involved in the design and analysis of complex turbulent flows. The ideal readers of this book are researchers with a basic knowledge of fluid mechanics, turbulence, computing, and statistical methods, who are disposed to enlarging their understanding of the fundamentals of CG and are interested in examining different methods applied to managing a chaotic world observed through coarse-grained lenses.

Scale-resolving simulation (SRS) methods of practical interest are coarse-graining formulations widely used in science and engineering. These methods aim to efficiently predict complex flows by only resolving the phenomena not amenable to modeling, unleashing the concept of accuracy on demand. This chapter provides an overview of the SRS methods best suited for engineering applications: hybrid and bridging models. It starts by reviewing basic turbulence modeling concepts. Following on from that is an overview of hybrid and bridging models, discussing their main advantages and limitations. The challenges to the predictive application of these models are enumerated, as well as possible strategies to solve or mitigate them. Several examples are provided to illustrate the potential of these classes of SRS methods. Overall, the chapter intends to help new and experienced SRS modelers and users obtain predictive turbulence computations.

Longstanding design and reproducibility challenges in inertial confinement fusion (ICF) capsule implosion experiments involve recognizing the need for appropriately characterized and modeled three-dimensional initial conditions and high-fidelity simulation capabilities to predict transitional flow approaching turbulence, material mixing characteristics, and late-time quantities of interest – for example, fusion yield. We build on previous coarse-graining (CG) simulations of the indirect-drive national ignition facility (NIF) cryogenic capsule N170601 experiment – a precursor of N221205 which resulted in net energy gain. We apply effectively combined initialization aspects and multiphysics coupling in conjunction with newly available hydrodynamics simulation methods, including directional unsplit algorithms and low Mach-number correction – key advances enabling high fidelity coarse-grained simulations of radiation-hydrodynamics driven transition.