An Object-Oriented Framework for Multidisciplinary Design Optimization

We present πMDO, an object-oriented framework that facilitates the use of algorithms for multidisciplinary optimization (MDO). The resulting implementation of the MDO architectures is efficient, scalable, 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 thus ideal for the rapid development and evaluation of new MDO architectures, as well as the benchmarking of existing ones. The top layer interface is programmed in Python and it allows for the layers blow the interface to be programmed in C, C++, Fortran and other languages. We describe the implementation of πMDO and demonstrate that we can take advantage of object-oriented programming and operator overloading 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. Several numerical experiments verify that the various MDO architectures yield the correct solutions and allow the evaluation of their relative performance.

[1]  Emden R. Gansner,et al.  An open graph visualization system and its applications to software engineering , 2000, Softw. Pract. Exp..

[2]  Michael Jesse Chonoles,et al.  UML 2 For Dummies , 2003 .

[3]  Jaroslaw Sobieszczanski-Sobieski,et al.  Optimization by decomposition: A step from hierarchic to non-hierarchic systems , 1989 .

[4]  Joaquim R. R. A. Martins,et al.  The complex-step derivative approximation , 2003, TOMS.

[5]  Ilan Kroo,et al.  Collaborative optimization using response surface estimation , 1998 .

[6]  Natalia Alexandrov,et al.  Multidisciplinary design optimization : state of the art , 1997 .

[7]  Natalia Alexandrov,et al.  Reconfigurability in MDO Problem Synthesis, Part 2 , 2004 .

[8]  Pearu Peterson,et al.  Fortran to Python Interface Generator with an Application to Aerospace Engineering , 2001 .

[9]  Kamran Behdinan,et al.  Evaluation of Multidisciplinary Optimization Approaches for Aircraft Conceptual D esign , 2004 .

[10]  Ilan Kroo,et al.  Implementation and Performance Issues in Collaborative Optimization , 1996 .

[11]  Joaquim R. R. A. Martins,et al.  pyMDO: A framework for high-fidelity multi-disciplinary optimization , 2004 .

[12]  Joaquim R. R. A. Martins,et al.  On the Common Structure of MDO Problems: A Comparison of Architectures , 2006 .

[13]  J. Sobieszczanski-Sobieski,et al.  Bilevel Integrated System Synthesis for Concurrent and Distributed Processing , 2002 .

[14]  John E. Renaud,et al.  Response surface based, concurrent subspace optimization for multidisciplinary system design , 1996 .

[15]  John R. Olds,et al.  Evaluation of Multidisciplinary Optimization Techniques Applied to a Reusable Launch Vehicle , 2006 .

[16]  Michael A. Saunders,et al.  SNOPT: An SQP Algorithm for Large-Scale Constrained Optimization , 2002, SIAM J. Optim..

[17]  John E. Dennis,et al.  Problem Formulation for Multidisciplinary Optimization , 1994, SIAM J. Optim..

[18]  Natalia Alexandrov,et al.  Analytical and Computational Aspects of Collaborative Optimization for Multidisciplinary Design , 2002 .

[19]  Robert D. Braun,et al.  Collaborative optimization: an architecture for large-scale distributed design , 1996 .

[20]  Robert Michael Lewis,et al.  Reconfigurability in MDO Problem Synthesis. Part 1 , 2004 .

[21]  Ilan Kroo,et al.  Development and Application of the Collaborative Optimization Architecture in a Multidisciplinary Design Environment , 1995 .