Turbulence remains the last frontier in nonlinear classical mechanics. We highlight how large-scale computing on supercomputers (specifically the CRAY XC40 Shaheen II at KAUST) has contributed to discoveries in turbulence. We discuss the limitations of brute force simulations and how employing turbulence models have enabled us to reach realistic flow Reynolds number and led to new insights in complex multi-scale nonlinear turbulent flows. Specifically, we will present two case studies. One is about the phenomenon of the drag crisis (wherein at below a specific critical Reynolds number there is a catastrophic increase in drag) and how subtle changes at the boundary translate to large scale effects. The second case study is on the generation and sustenance of electric fields in sandstorms, and the validation of our hypothesis that turbulence is the key driving force leading to large scale electric fields measured in sandstorms.

We present double enriched finite volume spaces for the simulation of free particles in a fluid. This involves forces beeing exchanged between the particles and the fluid at the interface. In an earlier work we derived a monotithic scheme which includes the interaction forces into the Navier-Stokes equations by direct coupling. In multiphase flows oscillations and spurious velocities are a common issue. The surface force term yields a jump in the pressure and therefore the oscillations are usually resolved by extending the spaces on cut elements in order to resolve the discontinuity. For the construction of the enriched spaces proposed in this paper we exploit the Petrov-Galerkin formulation of the vertex-centered finite volume method (PG-FVM). From the perspective of the finite volume scheme we argue that wrong discrete normal directions at the interface are the origin of the oscillations. The new perspective of normal vectors suggests to look at gradients rather than values of the enriching shape functions. The crucial parameter of the enrichement functions therefore is the gradient of the shape functions and especially the one of the test space. The distinguishing feature of our construction therefore is an enrichment that is based on the choice of shape functions with consistent gradients. These derivations finally yield a fitted scheme for the immersed interface. Numerical tests were conducted using the PG-FVM. We demonstrate that the enriched spaces are able to eliminate the oscillations. We apply the method to the parallel computation of the “Drafting, kissing and tumbling” benchmark.

We present the successful deployment of high-fidelity Large-Eddy Simulation (LES) technologies based on spectral/hp element methods to a real automotive car, namely the Elemental Rp1 model [1]. The simulation presents the common challenges of an industry-relevant simulation, namely high Reynolds number and complex geometry. To the best of the authors' knowledge, this simulation represents the first fifth-order accurate transient LES of an entire real car geometry. Moreover, this constitutes a key milestone towards considerably expanding the computational design envelope currently allowed in industry, where steady-state modelling remains the standard. In this talk, we highlight the key developments that were required to achieve the simulation, from mesh generation to improvements in solver and numerical technology.

In the close proximity of the liquid-vapour saturation curve and critical point, well-known thermodynamic phenomena including large compressibility and critical point effects results in very unusual fluid dynamics features, including non-ideal or rarefaction shock waves, mixed and split waves. This unconventional behaviour, which cannot occur in the ideal flow of dilute gases, is referred to as Non-Ideal Compressible-Fluid Dynamics or NICFD. The focus of this contribution is to review the theoretical background of NICFD and to discuss the impact of highly non-ideal conditions on the design and properties of numerical schemes for compressible flows. Exemplary flow fields will be presented and compared to available experimental data from the Test-Rig for Organic VApours (TROVA) of Politecnico di Milano, a unique facility in which supersonic flows in non-ideal conditions can be measured and observed. The quantification of the uncertainty of operating conditions and of fluid properties is discussed.

We will review physics-informed neural network and summarize available extensions for applications in fluid dynamics. We will also introduce new NNs that learn functionals and nonlinear operators from functions and corresponding responses for system identification. The universal approximation theorem of operators is suggestive of the potential of NNs in learning from scattered data any continuous operator or complex system. We first generalize the theorem to deep neural networks, and subsequently we apply it to design a new composite NN with small generalization error, the deep operator network (DeepONet), consisting of a NN for encoding the discrete input function space (branch net) and another NN for encoding the domain of the output functions (trunk net). We demonstrate that DeepONet can learn multiscale operators spanning across many scales and trained by diverse sources of data simultaneously; we wil include examples from hyoersonics and bubble dynamics.

President's speech to open the conference.

Brief Biography

Professor Chan assum