Multidisciplinary design optimization (MDO) is an approach that usesoptimization methods to design complex engineering systems involving
multiple disciplines simultaneously.
The coupled nature of multidisciplinary systems and the large number of
design variables involved in complex systems present
unique challenges to solving MDO problems.
One of these challenges is the implementation of software necessary to
evaluate multidisciplinary models within an optimization framework.
When gradient-based optimization approaches are used, efficient and
accurate derivatives must be computed for each model evaluation.
This dissertation presents an approach that significantly reduces the
manual effort required to implement computational models for use within
a gradient-based MDO framework, especially in large-scale problems.
This dissertation introduces a novel approach to address these
challenges and automate the process, enabling accurate and efficient
adjoint-based sensitivity analysis for gradient-based MDO in particular.
To address these challenges, a three-stage compiler
methodology is proposed.
The methodology centers around a graph representation that provides a
foundation for automating sensitivity analysis in MDO.
In addition, the Computational System Design Language (CSDL) is
introduced, which allows for a concise description of the
physical system.
The adoption of CSDL demonstrates a twofold reduction in code
complexity for engineering models, significantly reducing the barrier to
entry for MDO practitioners.
The three-stage compiler also provdes users of CSDL with the ability to measure the effect of the model structure
on run-time performance and memory complexity using the graph representation.
Finally, the methodology developed in this dissertation is applied to
the design of a space-based virtual telescope comprised of two
spacecraft flying in formation.
A reformulation of the orbit dynamics of the spacecraft is found to
avoid the introduction of truncation errors due to tight formation
constraints that render solving the
optimization problem impossible.
A sequential approach to applying MDO to the design of a space-based
virtual telescope is also found to be more robust than solving the MDO
problem where all
disicplines are considered simultaneously.