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Gradient-based constrained optimization using a database of linear reduced-order models
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DOI：10.1016/j.jcp.2020.109787.png)
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Abstract
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A methodology grounded in model reduction is presented for accelerating the gradient-based solution of a family of linear or nonlinear constrained optimization problems where the constraints include at least one linear Partial Differential Equation (PDE). A key component of this methodology is the construction, during an offline phase, of a database of pointwise, linear, Projection-based Reduced-Order Models (PROM)s associated with a design parameter space and the linear PDE(s). A parameter sampling procedure based on an appropriate saturation assumption is proposed to maximize the efficiency of such a database of PROMs. A real-time method is also presented for interpolating at any queried but unsampled parameter vector in the design parameter space the relevant sensitivities of a PROM. The practical feasibility, computational advantages, and performance of the proposed methodology are demonstrated for several realistic, nonlinear, aerodynamic shape optimization problems governed by linear aeroelastic constraints. (C) 2020 Elsevier Inc. All rights reserved.
Keywords:
Constrained optimization
Flutter
Gradient-based optimization
Interpolation on a matrix manifold
Model reduction
Parameter sampling
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