Derek J. Dalle | Engineering Papers

NASA NTRSDate not supplied

Statistically Consistent Dispersions of Line Loads to Uncertain Integrated Forces and Moments

This work presents a method to generate line loads that are consistent with integrated force and moment constraints, while also following the statistical distribution underlying known line load data. The motivation for this method is to address a particular problem that can arise during the pre-flight analysis of an ascent vehicle. That problem is constructing separate databases, for line loads and integrated aerodynamic coefficients, that contain different data and thus different amounts of quantified uncertainty. A dispersed line load method is described and applied to a data set generated by 29 computational fluid dynamics simulations at various angles of attack and sideslip. The statistical similarity of the generated line loads to the known data is confirmed by using the Maximum Mean Discrepancy 2-sample test. The promising results from this novel method are compared against results from applying an existing line load adjustment procedure to the same data set. Finally, potential future avenues of research are briefly discussed.

Aaron C. Burkhead↗

NASA NTRSDate not supplied

Deep Neural Network Based Convergence Classification for Computational Fluid Dynamics

A supervised deep learning approach is coupled with heuristic convergence criteria to construct a classification model for detecting the completion (convergence) of computational fluid dynamics (CFD) simulations. Heuristic convergence criteria alone are not always sufficient and more complex decisions are often left to a human analyst. The proposed approach leverages heuristic convergence criteria as well as two deep neural network (DNN) models, one binary and one multi-class, to improve the efficiency and consistency of convergence classification across a wide range of flight regimes. The DNN models presented are each trained on a subset of ascent aerodynamic CFD simulations for NASA’s Space Launch System and were produced using NASA’s unstructured Navier-Stokes solver FUN3D. Individual solutions are analyzed intermittently and are classified as sufficiently converged, further iterations required, or switch from steady Reynolds Averaged Navier-Stokes (RANS) to unsteady RANS CFD based on the iterative histories of four aerodynamic coefficients. The implemented classification model is shown to produce solutions that closely correlate to solutions produced by a human analyst. This work lays groundwork for expanding the capabilities of DNNs for automating and improving more of the CFD process.

SLS↗