A Fully Automated Chain from MDAO Problem Formulation to Workflow Execution

In this paper, a methodology to connect the multidisciplinary design analysis and optimization (MDAO) problem formulation tool KADMOS and the commercial Process Integration and Design Optimization (PIDO) platform Optimus is presented. This capability has been developed in the context of the EU project AGILE. The aim of this development is to create a combined environment that gives the MDAO design team the ability to define and formalize an MDAO problem and directly execute it with ease, without the need of the otherwise needed manual operations typically required to define the workflow in the PIDO system. The combination of problem formulation and PIDO platform execution have been tested on a small analytical MDAO problem to demonstrate its viability. Furthermore, a realistic aerostructural MDAO system of industrial relevance was also used to demonstrate the scalability of the approach for a bigger and more complex MDAO system. Results indicate that a fully automated chain is indeed possible which will make it easier for design teams to define, execute and compare different MDAO problem definitions and architectures in the time usually necessary to implement one MDAO system. Future work will focus on extending the proven capabilities of the automated chain to a wider variety of design problems and MDAO architectures.

[1]  John Haymaker,et al.  A comparison of multidisciplinary design, analysis and optimization processes in the building construction and aerospace industries , 2007 .

[2]  Gianfranco La Rocca,et al.  Composing MDAO symphonies : Graph-based generation and manipulation of large multidisciplinary systems , 2017 .

[3]  Joaquim R. R. A. Martins,et al.  Multidisciplinary design optimization: A survey of architectures , 2013 .

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

[5]  Brian J. German,et al.  A graph theoretic approach to problem formulation for multidisciplinary design analysis and optimization , 2014 .

[6]  Michel van Tooren,et al.  Beyond Quasi-Analytical Methods for Preliminary Structural Sizing and Weight Estimation of Lifting Surfaces , 2015 .

[7]  Joaquim R. R. A. Martins,et al.  Extensions to the design structure matrix for the description of multidisciplinary design, analysis, and optimization processes , 2012, Structural and Multidisciplinary Optimization.

[8]  Terence Macquart,et al.  Aeroelastic Tailoring of Blended Composite Structures using Lamination Parameters , 2016 .

[9]  Ali Elham,et al.  Quasi-Three-Dimensional Aerodynamic Solver for Multidisciplinary Design Optimization of Lifting Surfaces , 2014 .

[10]  Gary Belie Non-Technical Barriers to Multidisciplinary Optimization in the Aerospace Industry , 2002 .

[11]  Pier Davide Ciampa,et al.  Knowledge architecture supporting collaborative MDO in the AGILE paradigm , 2017 .

[12]  Joaquim R. R. A. Martins,et al.  Multidisciplinary Design Optimization for Complex Engineered Systems: Report From a National Science Foundation Workshop , 2011 .

[13]  S. Shahpar,et al.  Challenges to overcome for routine usage of automatic optimisation in the propulsion industry , 2011, The Aeronautical Journal (1968).

[14]  Arthur Rizzi,et al.  Implementation of a heterogeneous, variable-fidelity framework for flight mechanics analysis in preliminary aircraft design , 2011 .