Part 1. Particle-laden Turbulence in a Radiation Environment - the PSAAP program at Stanford

Work by: L. Jofre, A. Doostan, H. Fairbanks, G. Geraci, G. Iaccarino

In the framework of the Predictive Science Academic Alliance Program (PSAAP) Stanford Center's research portfolio blends efforts in computer science, uncertainty quantization, and computational physics to tackle a challenging physical problem: the transfer of radiative energy to a turbulent mixture of air and solid particles. The context is provided by a relatively untested and poorly understood method of harvesting solar energy. Traditional solar-thermal systems  use mirrors to concentrate solar radiation on a solid surface and transfer energy to a fluid, the first step toward generating electricity. In the proposed system, fine particles suspended within the fluid would absorb sunlight and directly transfer the heat evenly throughout the fluid volume. The talk will describe the Center's effort to perform uncertainty quantification using multilevel and multifidelity strategies to characterize parametric uncertainty in the particle properties and heating conditions. Some  aspects of the computer science effort will also be described.

Part 2. Quantifying Uncertainty in Turbulent Flow Predictions based on RANS/LES Closures

Work by: A. Mishra, L. Jofre, M. Emory

Despite recent developments in high-fidelity turbulent flow simulations, Reynolds Averaged Navier-Stokes (RANS) closures  remain broadly used in real-world applications, due to their inherent low cost. However, RANS models are based on assumptions (model-form) that are typically difficult to verify, leading to potential uncertainty in the predictions. Applying the spectral decomposition to the modeled Reynolds-Stress Tensor (RST) allows for the introduction of decoupled perturbations into the baseline turbulence intensity (kinetic energy), shape (eigenvalues), and orientation (eigenvectors) of the stresses. This constitutes a natural methodology to evaluate the model form uncertainty associated to different aspects of RST modeling. In a predictive setting, one frequently encounters an absence of any relevant reference data. To make data-free predictions with quantified uncertainty we employ physical bounds to a-priori define maximum spectral perturbations. When propagated, these perturbations yield conservative intervals with engineering utility. Detailed experiments and high-fidelity data open up the possibility of inferring a distribution of uncertainty, by means of various data-driven machine-learning techniques. We will demonstrate our framework on a number of flow problems where RANS models are prone to failure using both the data-free and data-driven approaches. Recent extensions of the same framework to subgrid closures used in Large Eddy Simulations (LES) will be briefly described.

Bio:

Gianluca Iaccarino is Director of the Institute for Computational Mathematical Engineering (https://icme.stanford.edu) and a professor in the Mechanical Engineering Department at Stanford University. He received his PhD in Italy from the Politecnico di Bari (Italy) and has worked for several years at the Center for Turbulence Research (NASA Ames & Stanford) before joining the faculty at Stanford in 2007. Since 2014, he is the Director of the PSAAP Center at Stanford, funded by the US Department of Energy focused on multiphysics simulations, uncertainty quantification and exascale computing (http://exascale.stanford.edu). In 2010, he received the Presidential Early Career Award for Scientists and Engineers (PECASE) award, and in the last couple of years, he has received best paper awards from AIAA, ASME and Turbo Expo Conferences.