A Component-Based Parametric Reduced-Order Modeling Method Combined with Substructural Matrix Interpolation and Automatic Sampling

Parametric Reduced Order Models Using Adaptive Sampling and Interpolation

Over the past decade, a number of approaches have been put forth to improve the accuracy of projection-based reduced order models over parameter ranges. These can be classified as either i.) building a global basis that is suitable for a large parameter set by applying sampling strategies, ii.) identifying parameter dependent coefficient functions in the reduced order model, or iii.) changing t...

A Black-Box method for parametric model order reduction based on matrix interpolation with application to simulation and control

Accurate modeling of dynamical systems can lead to large-scale systems of ordinary differential equations. For many applications, these systems additionally depend on parameters, which, for example, describe geometric attributes. In order to be able to perform tasks such as optimization, simulation or control, it is necessary to reduce the computational effort of solving the system. For that re...

Numerical Simulation and Parametric Reduced Order Modeling of the Natural Convection of Water-Copper Nanofluid

In this article, a coupled computational framework is presented for the numerical simulation of mass transfer under the effects of natural convection phenomena in a field contains water-copper Nano-fluid. This CFD model is build up based on accurate algorithms for spatial derivatives and time integration. The spatial derivatives have been calculated using first order upwind and second order cen...

Improved Reduced Order Modeling Strategies for Coupled and Parametric Systems

Parametric Reduced-Order Models

We address the problem of propagating input uncertainties through a computational fluid dynamics model. Methods such as Monte Carlo simulation can require many thousands (or more) of computational fluid dynamics solves, rendering them prohibitively expensive for practical applications. This expense can be overcome with reduced-order models that preserve the essential flow dynamics. The specific...