Sep, 2026
Conference Paper
Publications
Showing 29 results for Author: Justin R. Weinmeister
Mar, 2026
Conference Paper
The Fluid Dynamics Uncertainty Quantification Challenge Problem: XFOIL vs. MFOIL
Uncertainty quantification (UQ) has become more critical in aerospace engineering due to the growing dependence on computational tools for design optimization and performance analyses of aerospace vehicles. Even though the significance of UQ in assessing the credibility of computational analyses is well recognized, its costs and complexity impede its integration into stand…
Mar, 2026
Conference Paper
Robust Solution Verification Experiments on Nonuniform Meshes
The activities of verification, validation, and uncertainty quantification (VVUQ) provide a comprehensive means to assess the credibility of computational models. Within VVUQ, solution verification assesses numerical errors and evaluates whether the simulation is sufficiently accurate for its intended applications. As computational modeling gains traction in the developmen…
Mar, 2026
Conference Paper
An Open-Source Python Package for CFD Solution Verification
Informed decision-making using computational fluid dynamics (CFD) results requires quantifying the errors and uncertainties of a simulation. Verification, validation, and uncertainty quantification (VVUQ) methods were developed to address this need and have matured. However, these VVUQ analyses are often non-trivial and require CFD analysts and practitioners to have specif…
Mar, 2026
Conference Paper
Successive Procedure for Solution Verification Based on User Needs
This paper discusses a revised solution verification procedure for computational fluid dynamics simulations to estimate the uncertainties in the quantities of interest based on discretization error models. This proposed procedure builds upon current procedures described in ASME V&V 20 but provides more guidance in determining the necessary number of mesh levels to build re…
Mar, 2026
Conference Paper
Comparison of Full-Field and Integrated CFD Convergence Based on Richardson Extrapolation
This work investigated the usefulness of Richardson extrapolation--based discretization error estimates across all points in a solution field to produce a spatial convergence field for a computational fluid dynamics (CFD) simulation. The presented work used previously developed methods for Richardson extrapolation to compute the convergence orders of a CFD simulation at al…
Dec, 2023
Conference Paper
Deep Learning for Intelligent Bubble Size Detection in the Spallation Neutron Source Visual Target
The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory (ORNL) will undergo proton power upgrade (PPU), increasing the proton beam power from 1.4 MW to 2.8 MW. From 2.8 MW, 2.0 MW will go to the current First Target Station and the rest will go to the future Second Target Station (STS). The First Target Station uses a liquid mercury target that is contained in…