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pyMDO: An Object-Oriented Framework for Multidisciplinary Design Optimization

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ACM Transactions on Mathematical Software (TOMS) archive
Volume 36 , Issue 4 (August 2009) table of contents

Article No. 20

Year of Publication: 2009

ISSN:0098-3500

Authors

Joaquim R. R. A. Martins	University of Toronto Institute for Aerospace Studies
Christopher Marriage	University of Toronto Institute for Aerospace Studies
Nathan Tedford	University of Toronto Institute for Aerospace Studies

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ACM New York, NY, USA

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ABSTRACT

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.

REFERENCES

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	1	Alexandrov, N. M. and Kodiyalam, S. 1998. Initial results of an MDO evaluation survey. AIAA Paper, AIAA, Reston, VA, 98--4884.
	2	Alexandrov, N. M. and Lewis, R. M. 2002. Analytical and computational aspects of collaborative optimization for multidisciplinary design. AIAA J. 40, 2, 301--309.
	3	Alexandrov, N. M. and Lewis, R. M. 2004a. Reconfigurability in MDO problem synthesis, part 1. In Proceedings of the 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. AIAA, Reston, VA, 2004--4307.
	4	Alexandrov, N. M. and Lewis, R. M. 2004b. Reconfigurability in MDO problem synthesis, part 2. In Proceedings of the 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. (Albany, NY). AIAA, Reston, VA, 2004--4308.
	5	Alonso, J. J., LeGresley, P., van der Weide, E., Martins, J. R. R. A., and Reuther, J. J. 2004. pyMDO: A framework for high-fidelity multi-disciplinary optimization. In Proceedings of the 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. AIAA, Reston, VA, 2004--4480.
	6	Beazley, D. M. 2006. Python Essential Reference, 3rd ed. Sams, Indianapolis, IN.
	7	Blezek, D. 1998. Rapid prototyping with SWIG. C/C++ Users J. 16, 11, 61--66.
	8	Braun, R. D. and Kroo, I. M. 1997. Development and application of the collaborative optimization architecture in a multidisciplinary design environment. In Multidisciplinary Design Optimization: State of the Art, N. Alexandrov and M. Y. Hussaini, Eds. SIAM, Philadelphia, PA. 98--116.
	9	Brown, N. F. and Olds, J. R. 2006. Evaluation of multidisciplinary optimization techniques applied to a reusable launch vehicle. J. Space. Rock. 43, 6, 1289--1300.
	10	Cramer, E. J., Dennis, J. E., Frank, P. D., Lewis, R. M., and Shubin, G. R. 1994. Problem formulation for multidisciplinary optimization. SIAM J. Opt. 4, 4, 754--776.
	11	DeMiguel, V. and Murray, W. 2006. A local convergence analysis of bilevel decomposition algorithms. Opt. Eng. 7, 2, 99--133.
	12	Eldred, M. S., Brown, S. L., Adams, B. M., Dunlavy, D. M., Gay, D. M., Swiler, L. P., Giunta, A. A., Hart, W. E., Watson, J.-P., Eddy, J. P., Griffin, J. D., Hough, P. D., Kolda, T. G., Martinez-Canales, M. L., and Williams, P. J. 2006. DAKOTA: A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis, Version 4.0 User’s Manual. Sandia National Laboratories, Albuquerque, NM.
	13	Eldred, M. S., Outka, D. E., Bohnhoff, W. J., Witkowski, W. R., Romero, V. J., Ponslet, E. R., and Chen, K. S. 1996. Optimization of complex mechanics simulations with object-oriented software design. Comput. Model. Sim. Eng. 1, 3, 323--352.
	14	Gansner, E. R. and North, S. C. 2000. An open graph visualization system and its applications to software engineering. Softw.---Pract. Exper. 30, 11, 1203--1233.
	15	Gill, P. E., Murray, W., and Saunders, M. A. 2002. SNOPT: An SQP algorithm for large-scale constrained optimization. SIAM J. Opt. 12, 4, 979--1006.
	16	Haftka, R. T. and Gürdal, Z. 1993. Elements of Structural Optimization, 3rd ed. Kluwer, Dordrecht, The Netherlands, Chapter 10.
	17	Kodiyalam, S. 1998. Evaluation of methods for multidisciplinary design optimization (MDO), part 1. NASA Report CR-2000-210313. NASA, Washington, DC.
	18	Kodiyalam, S. and Yuan, C. 2000. Evaluation of methods for multidisciplinary design optimization (MDO), part 2. NASA Report CR-2000-210313. Nov. Washington, DC.
	19	Kroo, I. M. 1997. MDO for large-scale design. In Multidisciplinary Design Optimization: State-of-the-Art, N. Alexandrov and M. Y. Hussaini, Eds. SIAM, Philadelphia, PA. 22--44.
	20	Langtangen, H. P. 2004. Python Scripting for Computational Science. Springer, Berlin, Germany.
	21	Martins, J. R. R. A., Sturdza, P., and Alonso, J. J. 2003. The complex-step derivative approximation. ACM Trans. Math. Softw. 29, 3, 245--262.
	22	Meza, J. C., Oliva, R. A., Hough, P. D., and Williams, P. J. 2007. OPT++: An object-oriented toolkit for nonlinear optimization. ACM Trans. Math. Softw. 33, 2, 12.
	23	O’Docherty, M. 2005. Object-Oriented Analysis and Design. John Wiley and Sons, New York, NY.
	24	Padula, S. L., Alexandrov, N., and Green, L. L. 1996. MDO test suite at NASA Langley Research Center. In Proceedings of the 6th AIAA/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization. AIAA, Reston, VA, 1996-4028.
	25	Perez, R. E., Liu, H. H. T., and Behdinan, K. 2004. Evaluation of multidisciplinary optimization approaches for aircraft conceptual design. In Proceedings of the 10th AIAA/ISSMO Muldisiciplinary Analysis and Optimization Conference (Albany, NY). AIAA, Reston, VA, 2004-4537.
	26	Peterson, P., Martins, J. R. R. A., and Alonso, J. J. 2001. Fortran to Python interface generator with an application to aerospace engineering. In Proceedings of the 9th International Python Conference (Long Beach, CA).
	27	Schmit, Jr., L. A. and Ramanathan, R. K. 1978. Multilevel approach to minimum weight design including buckling constraints. AIAA J. 16, 2, 97--104.
	28	Sellar, R. S., Batill, S. M., and Renaud, J. E. 1996. Response surface based, concurrent subspace optimization for multidisciplinary system design. In Proceedings of the 34th AIAA Aerospace Sciences Meeting and Exhibit (Reno, NV). AIAA, Reston, VA, 1996-0714.
	29	Sobieski, I. P. and Kroo, I. M. 2000. Collaborative optimization using response surface estimation. AIAA J. 38, 10, 1931--1938.
	30	Sobieszczanski-Sobieski, J. 1988. Optimization by decomposition: A step from hierarchic to non-hierarchic systems. NASA tech. rep. CP-3031. NASA, Washington, DC.
	31	Sobieszczanski-Sobieski, J. and Haftka, R. T. 1997. Multidisciplinary aerospace design optimization: Survey of recent developments. Struct. Opt. 14, 1, 1--23.
	32	Squire, W. and Trapp, G. 1998. Using complex variables to estimate derivatives of real functions. SIAM Rev. 40, 1, 110--112.
	33	Tedford, N. P. and Martins, J. R. R. A. 2006a. Comparison of MDO architectures within a universal framework. In Proceedings of the 2nd AIAA Multidisciplinary Design Optimization Specialist Conference (Newport, RI). AIAA, Reston, VA, 2006--1617.
	34	Tedford, N. P. and Martins, J. R. R. A. 2006b. On the common structure of MDO problems: A comparison of architectures. In Proceedings of the 11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference (Portsmouth, VA). AIAA, Reston, VA, 2006--7080.
	35	Thareja, R. and Haftka, R. T. 1986. Numerical difficulties associated with using equality constraints toachieve multi-level decomposition in structural optimization. In Proceedings of the 27th Structures, Structural Dynamics and Materials Conference (San Antonio, TX). AIAA, Reston, VA. 21--28.
	36	Wujek, B., Renaud, J., and Batill, S. 1997. A concurrent engineering approach for multidisciplinary design in a distributed computing environment. In Multidisciplinary Design Optimization: State of the Art, N. Alexandrov and M. Y. Hussaini, Eds. SIAM, Philadelphia, PA. 189--208.