Multidisciplinary design optimization (MDO) is concerned with solving design problems involving coupled numerical models of complex engineering systems. While various MDO software frameworks exist, none of them take full advantage of state-of-the-art algorithms to solve coupled models efficiently. Furthermore, there is a need to facilitate the computation of the derivatives of these coupled models for use with gradient-based optimization algorithms to enable design with respect to large numbers of variables. In this paper, we present the theory and architecture of OpenMDAO, an open-source MDO framework that uses Newton-type algorithms to solve coupled systems and exploits problem structure through new hierarchical strategies to achieve high computational efficiency. OpenMDAO also provides a framework for computing coupled derivatives efficiently and in a way that exploits problem sparsity. We demonstrate the framework's efficiency by benchmarking scalable test problems. We also summarize a number of OpenMDAO applications previously reported in the literature, which include trajectory optimization, wing design, and structural topology optimization, demonstrating that the framework is effective in both coupling existing models and developing new multidisciplinary models from the ground up. Given the potential of the OpenMDAO framework, we expect the number of users and developers to continue growing, enabling even more diverse applications in engineering analysis and design.
This paper describes the progress made in the development of OpenMDAO, an open source framework for performing Multidisciplinary Analysis and Optimization (MDAO). NASA intends to use OpenMDAO to aid in the design of unconventional aircraft, but the general structure and methods may be applied to solve any number of engineering-related design problems. The framework currently supports data passing capabilities, and several example problems have been executed with it. Recent work has focused on enabling the creation of more complex MDAO strategies, such as collaborative optimization and surrogate modeling techniques. An example is presented that demonstrates an implementation of the surrogate model generation using Kriging surrogate models augmented with the expected improvement method.
Reliable engine-weight estimation at the conceptual design stage is critical to the development of new aircraft engines. It helps to identify the best engine concept amongst several candidates. At NASA Glenn (GRC), the Weight Analysis of Turbine Engines (WATE) computer code, originally developed by Boeing Aircraft, has been used to estimate the engine weight of various conceptual engine designs. The code, written in FORTRAN, was originally developed for NASA in 1979. Since then, substantial improvements have been made to the code to improve the weight calculations for most of the engine components. Most recently, to improve the maintainability and extensibility of WATE, the FORTRAN code has been converted into an object-oriented version. The conversion was done within the NASA’s NPSS (Numerical Propulsion System Simulation) framework. This enables WATE to interact seamlessly with the thermodynamic cycle model which provides component flow data such as airflows, temperatures, and pressures, etc. that are required for sizing the components and weight calculations. The tighter integration between the NPSS and WATE would greatly enhance system-level analysis and optimization capabilities. It also would facilitate the enhancement of the WATE code for next-generation aircraft and space propulsion systems. In this paper, the architecture of the object-oriented WATE code (or WATE++) is described. Both the FORTRAN and object-oriented versions of the code are employed to compute the dimensions and weight of a 300-passenger aircraft engine (GE90 class). Both versions of the code produce essentially identical results as should be the case.
This paper presents the motivation for the development of an open source framework for performing Multidisciplinary Analysis and Optimization (MDAO) to aid in the design of unconventional aircraft. While a number of frameworks already exist, critical and desirable requirements are still not satisfactorily met. For developing a new framework, an open source development is the most logical choice, in particular because it provides the means to rapidly develop a community of users who can also contribute enhancements, components, and knowledge to the framework. Presently, the framework development project has finished the requirements gathering stage and has begun prototyping some of the capability in the cross-platform scripting language Python, which already provides many of the basic building blocks needed to create an MDAO framework. I
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