A Regression Approach for Extending ASME V&V 20 Validation Metrics to Application Conditions ASME V&V 20 Writing Committee on Regression

Abstract Engineering simulations based on high fidelity mathematical models support safety, performance, and mission critical decisions in nuclear energy, aerospace, and defense. The ASME V&V 20 standard quantifies a range that bounds true model error for a scalar quantity of interest at discrete validation setpoints where experimental data exist. Because decisions are made at application points where direct experimental data are unavailable, a method is needed to extend model error knowledge from validation setpoints to application points for margin analyses. This article develops and analyzes a regression based procedure extending ASME V&V 20 scalar validation metrics from validation setpoints to application points within a validation space. The approach performs weighted polynomial regression on the upper and lower bounds of the V&V 20 modeling error interval, quantifies uncertainty from residual scatter and coefficient estimation, and propagates uncertainty to a selected application point. Numerical uncertainty at the application point is incorporated consistent with V&V 20, yielding a modeling error interval suitable for safety and performance margin assessments.