2018/03/02 by Aashwin Ananda Mishra, Aashwin Mishra, Jayant Mukhopadhaya +7 · 3 citations
Computer Science · Decision Sciences · Engineering · Physics and Astronomy · #Advanced Multi-Objective Optimization Algorithms #Aerospace #Aerospace engineering #Benchmark (surveying) #Computational Fluid Dynamics and Aerodynamics #Computational fluid dynamics #Computer science #Engineering #Engineering design process #Estimation #FOS: Physical sciences #Fluid Dynamics (physics.flu-dyn) #Machine learning #Mechanical engineering #Probabilistic and Robust Engineering Design #Process (computing) #Range (aeronautics) #Reynolds-averaged Navier–Stokes equations #Simulation #Suite #Surrogate model #Systems engineering #Turbulence #Uncertainty analysis #Uncertainty quantification #physics.flu-dyn
paper · pdf · doi:10.48550/arxiv.1803.00725
published in arXiv (Cornell University) (Cornell University) · Submitted to the AIAA Journal. Under review
arxiv created 2018/03/02 · openalex publication_date 2018/03/02 · arxiv updated 2018/03/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
With the advent of improved computational resources, aerospace design has testing-based process to a simulation-driven 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.