Screening and metamodeling of computer experiments with functional outputs. Application to thermal-hydraulic computations

TL;DR

This paper extends screening and metamodeling techniques to models with functional outputs, such as curves, enabling efficient uncertainty and sensitivity analysis for complex thermal-hydraulic computations.

Contribution

It introduces a novel methodology for screening and metamodeling of models with functional outputs, demonstrated on nuclear reactor thermal-hydraulic analysis.

Findings

Effective screening of influential inputs using variance and PCA.

Successful construction of functional metamodels for curve outputs.

Application to nuclear reactor safety analysis.

Abstract

To perform uncertainty, sensitivity or optimization analysis on scalar variables calculated by a cpu time expensive computer code, a widely accepted methodology consists in first identifying the most influential uncertain inputs (by screening techniques), and then in replacing the cpu time expensive model by a cpu inexpensive mathematical function, called a metamodel. This paper extends this methodology to the functional output case, for instance when the model output variables are curves. The screening approach is based on the analysis of variance and principal component analysis of output curves. The functional metamodeling consists in a curve classification step, a dimension reduction step, then a classical metamodeling step. An industrial nuclear reactor application (dealing with uncertainties in the pressurized thermal shock analysis) illustrates all these steps.