We present pyMDO, an object-oriented framework that facilitates the usage and development of algorithms for multidisciplinary optimization (MDO). The resulting implementation of the MDO methods is efficient and portable. The main advantage of the proposed framework is that it is flexible, with a strong emphasis on object-oriented classes and operator overloading, and it is therefore useful for the rapid development and evaluation of new MDO methods. The top layer interface is programmed in Python and it allows for the layers below the interface to be programmed in C, C++, Fortran, and other languages. We describe an implementation of pyMDO and demonstrate that we can take advantage of object-oriented programming to obtain intuitive, easy-to-read, and easy-to-develop codes that are at the same time efficient. This allows developers to focus on the new algorithms they are developing and testing, rather than on implementation details. Examples demonstrate the user interface and the corresponding results show that the various MDO methods yield the correct solutions.
πMDO is a software framework which allows the automatic implementation of multidisciplinary design optimization (MDO) architectures to find the optimum of any appropriately defined multidisciplinary problem. A significant improvement and extension of the πMDO framework is presented. Using the inherent advantages of πMDO's object oriented and highly flexible structure, several semi-analytic sensitivity methods are implemented for the MDO architectures within the framework. Their generalized nature allows the application of these powerful and efficient methods to any problem defined within πMDO, without modification to the existing structure of the problem. Further, exploiting information gathered by these methods, a new "meta" MDO architecture is proposed which dynamically reconfigures the problem to speed the optimization while maintaining the fidelity of the original analysis. This hybrid approach uses the existing architectures in πMDO encapsulated within each other to reduce the dimensionality of coupling between disciplines. Again, due to the object oriented nature of πMDO, no modifications are required to the problem statement or the MDO architectures within the framework. Initial results suggest that both these additions produce valuable performance gains while maintaining the general flexibility and simplicity characteristic of πMDO.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.