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A domain-specific language for model composition and verification of multidisciplinary models

Published

Author(s)

Peter O. Denno, Anantha Narayanan Narayanan, Amogh Kulkarni, Daniel Balasubramanian, Gabor Karsai

Abstract

Complex, engineered products and manufacturing processes often necessitate integrated analysis that cuts across physical domains and engineering disciplines. When the domain-specific models that contribute to the overall analysis process are available then the problem can be addressed by composing them into an analysis workflow which then can be executed using some execution platform. Such a composition and integrated analysis is essentially a systems engineering approach applied to an engineering process. In this paper we describe a model integration language that allows the rapid composition of models, the verification of the composition and the generation of executable code and other engineering artifacts that are needed for model execution on a software platform. The language is based on OpenMDAO, a widely-used model execution framework, and it improves the engineering process by checking composition constraints that must be satisfied by the integrated model and by automatically generating executable code that facilitates the run-time integration of the models. This paper describes the design of the language, illustrates its use through a running example and outlines future work
Proceedings Title
Conference on Systems Engineering Research (CSER 2016)
Conference Dates
March 21-24, 2016
Conference Location
Huntsville, AL
Conference Title
2016 Conference on Systems Engineering Research

Keywords

MDAO, OpenMDAO, DSML, Modeling, Model composition and verification

Citation

Denno, P. , , A. , Kulkarni, A. , Balasubramanian, D. and Karsai, G. (2016), A domain-specific language for model composition and verification of multidisciplinary models, Conference on Systems Engineering Research (CSER 2016), Huntsville, AL (Accessed December 7, 2024)

Issues

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Created March 21, 2016, Updated February 19, 2017