As a critical feature, FFCM-1 not only reconciles a large body of fundamental combustion data, it also has rigorously evaluated uncertainties for the rate coefficients, the combustion experimental targets used for model optimization and uncertainty minimization, and most importantly, an optimized reaction model with quantified uncertainties.
The method, therefore, is ideally suited for optimization and uncertainty studies.
Both the optimization and uncertainty quantification workflows require (typically multiple) multi-scenario simulations, and are therefore very compute intensive.
Repeated evaluations of expensive computer models in applications such as design optimization and uncertainty quantification can be computationally infeasible.
Design optimization and uncertainty quantification, among other applications of industrial interest, require fast or multiple queries of some parametric model.
More recently, Gholaminezhad et al. [20] applied a multi-objective optimization and uncertainty analysis of methane fed SOFCs for maximum power density and efficiency performance achievement.
Sensitivity analysis (SA) aims to identify the key parameters that affect model performance and it plays important roles in model parameterization, calibration, optimization, and uncertainty quantification.
These decisions can be influenced by the risk preferences and technical preferences of the enterprises, thus deviating traditional estimation with respect to both economic optimization and uncertainty.
The proposed tool improves industry-standard clustering and provides a basis for design optimization and uncertainty analysis in large wind farms.
Therefore, using the parametric ROM to replace the full-order model (FOM) in simulation and other analyses like optimization and uncertainty quantification leads to significant speedup and high accuracy.
