Hessian-based dimension reduction for uncertainty propagation and robust design optimization
Authors
Panda, Kinshuk
ORCID
Loading...
Other Contributors
Hicken, Jason
Sahni, Onkar
Picu, Catalin R.
Martins, Joaquim R. R. A.
Sahni, Onkar
Picu, Catalin R.
Martins, Joaquim R. R. A.
Issue Date
2019-12
Keywords
Mechanical engineering
Degree
PhD
Terms of Use
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
Full Citation
Abstract
This dissertation describes a dimension reduction method and its application to problems in uncertainty propagation and robust design optimization. For a given problem, the nonlinear behavior of a quantity of interest (QoI) with respect to the random variables is identified by computing the dominant eigenmodes of the Hessian of a QoI. A modified Arnoldi's method is used to estimate these dominant eigenmodes of the Hessian by utilizing only the gradient information of the QoI, thereby avoiding the explicit computation of the Hessian matrix. The eigenvectors corresponding to these dominant eigenmodes can then be used for approximating the statistical moments, such as the mean and the variance, by integrating the QoI in the stochastic space only along these dominant directions, therefore mitigating the potentially intractable cost of computing the high-dimensional integrals associated with these statistical moments. The dimension reduction method is first applied to simple quadratic functions and its performance in estimating the statistical moments is examined. Subsequently, this method is applied to two practical problems. The first problem demonstrates the application of the dimension reduction method to the aerostructural robust design optimization of a tailless aircraft. The second problem investigates the impact of uncertainty in the atmospheric density when solving an aircraft minimum-time-to-climb problem. It is observed that the proposed dimension reduction method performs well when accurate gradient information is available and the eigenmodes of the Hessian decay rapidly.
Description
December 2019
School of Engineering
School of Engineering
Department
Dept. of Mechanical, Aerospace, and Nuclear Engineering
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
Access
Restricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.