Julien Cortial

Towards Real-Time Computational-Fluid-Dynamics-Based Aeroelastic Computations Using a Database of Reduced-Order Information

This paper describes a computational-fluid-dynamics-based computational methodology for fast on-demand aeroelastic predictions of the behavior of a full aircraft configuration at variable flight conditions and demonstrates its feasibility. The methodology relies on the offline precomputation of a database of reduced-order bases and models associated with a discrete set of flight parameters, and its training for an interpolation method suitable for reduced-order information. The potential of this near-real-time computational methodology for assisting flutter flight testing is highlighted with the aeroelastic identification of an F-16 configuration in the subsonic, transonic, and supersonic regimes.

On-Demand CFD-Based Aeroelastic Predictions Using a Database of Reduced-Order Bases and Models

This paper demonstrates the feasibility of a CFD-based computational strategy aimed at on-demand predictions of aeroelastic responses of full aircraft configurations for variable flight conditions. The strategy relies on the pre-computation of a database of reduced-order bases and models for discrete flight parameters, and an interpolation method suitable for adapting in real-time the stored reduced-order information to parameter values not populated in the database. It also features a database training and reduction scheme based on concepts from machine learning to maximize both the robustness and performance of local interpolations. The application of this computational strategy to the broad aeroelastic identification of a complete F-16 fighter configuration highlights its near-real-time processing capability and demonstrates its potential for assisting flutter flight testing.

Compressed Sensing and Time-Parallel Reduced-Order Modeling for Structural Health Monitoring Using a DDDAS

This paper discusses recent progress achieved in two areas related to the development of a Dynamic Data Driven Applications System (DDDAS) for structural and material health monitoring and critical event prediction. The first area concerns the development and demonstration of a sensor data compression algorithm and its application to the detection of structural damage. The second area concerns the prediction in near real-time of the transient dynamics of a structural system using a nonlinear reduced-order model and a time-parallel ODE (Ordinary Differential Equation) solver.

Corrigendum: Corrigendum to The GNAT method for nonlinear model reduction: Effective implementation and application to computational fluid dynamics and turbulent flows [J. Comput. Phys. 242 (2013) 623-647]

0021-9991/

An On-Line Method for Interpolating Linear Reduced-Order Structural Dynamics Models

see front matter Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.jcp.2013.05.022 DOI of original article: http://dx.doi.org/10.1016/j.jcp.2013.02.028 ⇑ Corresponding author. Tel.: +1 925 2946677. E-mail addresses: ktcarlb@sandia.gov (K. Carlberg), cfarhat@stanford.edu (C. Farhat), jcortia@sandia.gov (J. Cortial), amsallem@stan (D. Amsallem). URL: http://sandia.gov/~ktcarlb (K. Carlberg). 1 7011 East Ave., MS 9159, Livermore, CA 94550. Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Compan United States Department of Energy under contract DE-AC04-94-AL85000. 2 Durand Building, 496 Lomita Mall, Stanford University, Stanford, CA 94305-3035, United States. Kevin Carlberg a,⇑,1, Charbel Farhat , Julien Cortial , David Amsallem b,2

The GNAT method for nonlinear model reduction: Effective implementation and application to computational fluid dynamics and turbulent flows

A rigorous method for interpolating a set of parameterized linear structural dynamics reduced-order models (ROMs) is presented. By design, this method does not operate on the underlying set of parameterized full-order models. Hence, it is amenable to a real-time and on-line implementation. It is based on mapping appropriately the ROM data onto a tangent space to the manifold of symmetric positive definite matrices, interpolating the mapped data in this space and mapping back the result to the aforementioned manifold. Algorithms for computing the forward and backward mappings are oered for the case where the ROMs are derived from a general Galerkin projection method and the case where they are constructed from modal reduction. The proposed interpolation method is illustrated with applications ranging from the fast dynamic characterization of a parameterized structural model to the fast evaluation of its response to a given input. In all cases, good accuracy is demonstrated at real-time processing speeds.