Knowledge architecture supporting the next generation of MDO in the AGILE paradigm

Abstract After almost three decades of evolution, it is not yet possible to apply MDO in collaborative projects within large, heterogeneous and distributed teams of experts, whilst nowadays necessary for the development of any complex product. The H2020 project AGILE took the challenge of devising a novel paradigm to swiftly set up and deploy large distributed MDO systems, that are easy to (re)configure and monitor during the whole process, from requirements definition to data post-processing. The main outcome is an advanced set of tools and methods contributing to a 3rd generation MDO environment, specifically tailored to the aerospace industry. The AGILE paradigm is built on top of two main pillars, the so-called knowledge architecture and the collaborative architecture. The former, which is the main focus of this paper, provides a structured approach and the related workbench to formulate and inspect any automated design process, including fully formalized MDO systems. The latter includes the tools and methods to translate these formulations into executable workflows and deploy them across distributed networks. Although AGILE aims specifically at aircraft MDO, the proposed knowledge architecture provides a general conceptual framework that is suitable for the development of any complex product. The knowledge architecture has a multi-level hierarchical structure, consisting of four layers: development process, automated design, design competences and data schemas. Interfaces between the various layers are defined to achieve a fully.interconnected development process. This paper provides first a description of the knowledge architecture as a generalized paradigm to formulate collaborative and distributed MDO systems. Then, the specific implementation of such a paradigm within the AGILE project is illustrated: four knowledge architecture applications and two data schemas are described in detail. Finally, the whole approach is demonstrated by means of a realistic aircraft design case. This implementation proved successful in multiple aspects. First of all, in allowing heterogeneous teams of experts to generate complete and correct MDO system formulations involving large amount of distributed disciplinary tools, while maintaining full control and systematic overview of the complete system archi- tecture. Second, in offering the necessary agility to adjust and reconfigure the formulated MDO systems, such to support the iterative and evolutionary nature of their development process. Finally, by dramatically accelerating the setup time of the MDO system, thanks to the automation of the complex, lengthy and repetitive operations involved in the partitioning and coordination process, and to the effective support in inspecting and resolving the eventual inconsistencies in the data flow, arising every time tools are added or modified, or different solution strategies are implemented.

[1]  Graeme J. Kennedy,et al.  Kona: A Parallel Optimization Library for Engineering-Design Problems , 2016 .

[2]  R. Siezen,et al.  others , 1999, Microbial Biotechnology.

[3]  Ilan Kroo,et al.  Collaborative Optimization: Status and Directions , 2000 .

[4]  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.

[5]  Wouter Joosen,et al.  Adaptive and reflective middleware for the cloudification of simulation & optimization workflows , 2017, ARM@Middleware.

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

[7]  D. V. Steward,et al.  The design structure system: A method for managing the design of complex systems , 1981, IEEE Transactions on Engineering Management.

[8]  Eike Stumpf,et al.  Graph-based algorithms and data-driven documents for formulation and visualization of large MDO systems , 2018, CEAS Aeronautical Journal.

[9]  Michael Harpham.  December , 1855, The Hospital.

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

[11]  Danny Holten,et al.  Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data , 2006, IEEE Transactions on Visualization and Computer Graphics.

[12]  E. J. Schut,et al.  Design and implementation of a new generation multi-agent task environment framework , 2008 .

[13]  R H Thurston,et al.  The Thermal Efficiency of Steam Engines , 1924, Nature.

[14]  Pier Davide Ciampa,et al.  Avoiding Inconsistencies between Data Models in Collaborative Aircraft Design Processes , 2016 .

[15]  Pier Davide Ciampa,et al.  The AGILE Paradigm: the next generation of collaborative MDO , 2017 .

[16]  Gianfranco La Rocca,et al.  Formulation and integration of MDAO systems for collaborative design: A graph-based methodological approach , 2019, Aerospace Science and Technology.

[17]  D 2 . 3 : Industrial service-oriented process methodology , 2017 .

[18]  Joseph Morlier,et al.  An adaptive optimization strategy based on mixture of experts for wing aerodynamic design optimization , 2017 .

[19]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[20]  S. Laurence,et al.  Concepts: Core Readings , 1999 .

[21]  Panos Y. Papalambros,et al.  General framework for decomposition analysis in optimal design , 1993 .

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

[23]  Volker Gollnick,et al.  Communication in aircraft design : Can we establish a common language? , 2012 .

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

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

[26]  G. La Rocca,et al.  A Fully Automated Chain from MDAO Problem Formulation to Workflow Execution , 2017 .

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

[28]  R. H. Thurston,et al.  THERMAL EFFICIENCY OF STEAM-ENGINES. , 1898 .

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

[30]  Martin Spieck,et al.  MDO: assessment and direction for advancement—an opinion of one international group , 2009 .

[31]  Pier Davide Ciampa,et al.  Collaborative Architecture supporting the next generation of MDAO within the AGILE paradigm , 2020 .

[32]  Susan X. Ying,et al.  REPORT FROM ICAS WORKSHOP ON COMPLEX SYSTEMS INTEGRATION IN AERONAUTICS , 2016 .

[33]  Gianfranco La Rocca,et al.  A critical look at design automation solutions for collaborative MDO in the AGILE paradigm , 2018 .

[34]  Maurice F. M. Hoogreef,et al.  CMDOWS: a proposed new standard to store and exchange MDO systems , 2018 .