With the advent of improved computational resources, aerospace design has testing-based process to a simulationdriven procedure, wherein uncertainties in design and operating conditions are explicitly accounted for in the design under uncertainty methodology. A key source of such uncertainties in design are the closure models used to account for fluid turbulence. In spite of their importance, no reliable and extensively tested modules are available to estimate this epistemic uncertainty. In this article, we outline the EQUiPS uncertainty estimation module developed for the SU2 CFD suite that focuses on uncertainty due to turbulence models. The theoretical foundations underlying this uncertainty estimation and its computational implementation are detailed. Thence, the performance of this module is presented for a range of test cases, including both benchmark problems and flows relevant to aerospace design. Across the range of test cases, the uncertainty estimates of the module were able to account for a significant portion of the discrepancy between RANS predictions and high fidelity data.

Computational strategies that explicitly quantify uncertainties are becoming increasingly used in aerospace applications to improve the consistency in reliability, robustness, and performance of designs. A major source of uncertainty in simulations is due to the structural assumptions invoked in the formulation of turbulence models. Accounting for the turbulence model-form uncertainty has been described as “the greatest challenge” in simulation-based engineering design. Despite its importance, design exploration and optimization under turbulence model-form uncertainty is an avenue that has not been investigated in depth in prior literature. In this investigation, we outline methodologies for the design analysis, exploration, and robust optimization under model-form uncertainty due to Reynolds averaged Navier–Stokes models. We exhibit how interval uncertainty estimates enable the use of alternative criteria for decision making under uncertainty in engineering design. It is shown that such criteria can lead to different design choices in design exploration. Finally, we carry out design optimization under mixed uncertainties by using the perturbation framework in conjunction with polynomial chaos expansions. We introduce an approach for engineering design optimization under uncertainty that utilizes physics-based uncertainty estimation along with decision theory criteria under uncertainty to produce designs that are more robust to turbulence model uncertainties. These methodologies are illustrated via their application to complex turbulent flow cases, pertinent to aerospace design applications.

No abstract

No abstract

No abstract