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.

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This paper proposes a novel methodology and its software implementation, called KADMOS (Knowledge- and graph-based Agile Design for Multidisciplinary Optimization System), to increase the agility of design teams in collaborative Multidisciplinary Design Analysis and Optimization (MDAO). Agility here refers to the ease and flexibility to assemble, adjust and reconfigure MDAO computational systems. This is a necessary feature to comply with the complex and iterative nature of the (aircraft) design process. KADMOS has been developed on the notion that a formal specification of an MDAO system is required before proceeding with integration of the executable workflow. A thorough formulation of the system becomes essential when such system is built on the many contributions of large, heterogeneous design teams. KADMOS can automate the generation of such formulations through a graph-based methodological approach. The graph syntax and manipulation algorithms form the core content of this paper. First, a simple MDAO benchmark problem is used to illustrate KADMOS's working principles. Second, a wing aerostructural design case is discussed to demonstrate KADMOS's capabilities to enable collaborative MDAO on large problems of industry-representative complexity. Next to its graph-theoretic foundation, KADMOS makes use of two data schemas: one containing the parametric representation of the product being designed and a second to store the achieved formulation of the MDAO system. The latter enables the interchangeable use of different process integration and design optimization platforms to automatically integrate the generated MDAO system formulation as an executable workflow. The proposed approach has been estimated to be capable of halving the time typically required to set up and iteratively reconfigure a complex MDAO system, while allowing discipline experts and system architects to maintain constant oversight and control of the overall system and its components by means of human-readable dynamic visualizations.

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The need to continuously design better aircraft is challenging on multiple fronts: the existing tube-and-wing aircraft are difficult to improve further, while more creative configurations prove more cumbersome to design due to the lack of empirical knowledge and the (usually) more highly integrated components. Multidisciplinary Design Analysis and Optimization (MDAO) can offer major benefits to tackle these issues. However, MDAO is not yet widely applied in the industrial context due to two major hurdles: configuration hurdle the set-up of the first executable MDAO workflow takes too long —namely 60-80%of the available project time—in collaborative projects in which a large team of heterogeneous experts needs to operate a large, complex MDAO system; reconfiguration hurdle once the executable system has been established, it lacks agility and cannot be reconfigured easily to address ‘what if scenarios’ or answer new questions arising from the insights obtained from previously configured systems. Typical reconfigurations include the removal, addition, or modification of disciplinary tools, adjustment of the MDAO problem to be solved (e.g. new design variables, different objective, additional constraints), or the alternation of the underlying strategy (i.e. design convergence, design of experiments, optimization) that needs to be implemented as an executable workflow. @en

The research and innovation AGILE project developed the next generation of aircraft Multidisciplinary Design and Optimization processes, which target significant reductions in aircraft development costs and time to market, leading to more cost-effective and greener aircraft solutions. The high level objective is the reduction of the lead time of 40% with respect to the current state-of-the-art. 19 industry, research and academia partners from Europe, Canada and Russia developed solutions to cope with the challenges of collaborative design and optimization of complex products. In order to accelerate the deployment of large-scale, collaborative multidisciplinary design and optimization (MDO), a novel methodology, the so-called AGILE Paradigm, has been developed. Furthermore, the AGILE project has developed and released a set of open technologies enabling the implementation of the AGILE Paradigm approach. The collection of all the technologies constitutes AGILE Framework, which has been deployed for the design and the optimization of multiple aircraft configurations. This paper focuses on the application of the AGILE Paradigm on seven novel aircraft configurations, proving the achievement of the project’s objectives.

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AGILE Project is developing the 3^rd generation MDO processes, which will support the development of the next generation aerospace products. The establishment of effective collaborative design methodologies, is currently acknowledged as the key enabler for future product development processes. At the same time, the need to introduce collaborative design techniques within educational activities is also well recognized by the Academic, Research and Industrial communities. AGILE project supported by European Commission’s H2020 Programme, is setting the “AGILE Paradigm”, a conceptual framework which contains all the elements to implement a multidisciplinary collaborative design network. The AGILE Academy initiative is conceived to infuse into the Academic organizations and educational environments the “AGILE Paradigm”, and make available all the technologies developed within the AGILE Project, which support the implementation of such a Paradigm. This paper focus is on the inception, approach and results of the AGILE Academy participants from several universities around the world.

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A new system is presented that enables the visualization of large multidisciplinary design optimization (MDO) problems and their solution strategy. It was developed within the scope of the European project AGILE. In AGILE, collaborative MDO is performed in large, heterogeneous teams of experts by solving MDO problems using a collection of design and analysis tools. This paper focuses on the visualizations required to support the formulation phase of an MDO project. The Knowledge and graph-based AGILE Design for Multidisciplinary Optimization System (KADMOS), an open-source MDO support system developed by Delft University of Technology, uses graph-based analysis to formulate an MDO problem and its solution strategy, based on the disciplinary analyses available in a repository. The results of KADMOS are stored in the standardized format CMDOWS (Common MDO Workflow Schema), which comprises the entire information on an MDO system. Although, based on Extensible Markup Language, the readability of the CMDOWS file is quite poor also for MDO experts, especially for large MDO systems involving thousands of variables. Providing visualization capabilities to thoroughly inspect the outcome of the different MDO formulation steps becomes a key factor to enable the specification of large MDO systems in a heterogeneous team. Therefore, VISTOMS (VISualization TOol for MDO Systems), a dynamic visualization package, was developed by RWTH Aachen University to enable the visualization and inspection of the different MDO system specification steps, thereby removing one of the main hurdles for using MDO as a development process. The developed visualization capabilities are demonstrated by means of an aerostructural wing design optimization project.@en

This paper proposes a new format to store and exchange multidisciplinary design optimization (MDO) systems. Here, the generic term MDO system refers to the set of disciplinary tools, their exchanged data and process connections that, all together, define an MDO computational setup. In the process leading to the formal specification of such a computational system, the set of tools, data and connections evolves, until a complete MDO system formulation (not executable) is reached. The proposed open-source standard, called CMDOWS (Common MDO Workflow Schema), has been developed to support this process. The key aspect of the format is its neutral XML-based data representation, making any stored MDO system exchangeable between the design team members and applications (e.g., tool repositories, visualization packages) developed to support the team in setting up the MDO system. This exchangeability is a key enabler for the creation of a versatile MDO framework. Furthermore, CMDOWS provides the starting point to translate any MDO system formulation into an execut-able workflow using a workflow platform of choice. To the authors’ knowledge, such an exchange format does currently not exist, notwithstanding the enormous potential it would have for the exploitation of large-scale MDO in industry. A case study demonstrating the use of CMDOWS is presented in this paper. It was concluded that the current version of CMDOWS already provides a robust standard to store and exchange MDO systems. The schema will be extended to meet future developments and promote its adoption as a recognized standard in the broader MDO community.@en

In this paper methodological investigations regarding an innovative Multidisciplinary Design and Optimization (MDO) approach for conceptual aircraft design are presented. These research activities are part of the ongoing EU-funded research project AGILE. The next generation of aircraft MDO processes is developed in AGILE, which targets significant reductions in aircraft development cost and time to market, leading to cheaper and greener aircraft solutions. The paper introduces the AGILE project structure and recalls the achievements of the first year of activities where a reference distributed MDO system has been formulated, deployed and applied to the design and optimization of a reference conventional aircraft configuration. Then, investigations conducted in the second year are presented, all aiming at making the complex optimization workflows easier to handle, characterized by a high degree of discipline interdependencies, multi-level processes and multi-partner collaborative engineering activities. The paper focuses on an innovative approach in which knowledge-based engineering and collaborative engineering techniques are used to handle a complex aircraft design workflow. Surrogate models replacing clusters of analysis disciplines have been developed and applied to make workflow execution more efficient. The paper details the different steps of the developed approach to set up and operate this test case, involving a team of aircraft design and surrogate modelling specialists, and taking advantage of the AGILE MDO framework. To validate the approach, different executable workflows were generated automatically and used to efficiently compare different MDO formulations. The use of surrogate models for clusters of design competences have been proved to be efficient approach not only to decrease the computational time but also to benchmark different MDO formulations on a complex optimization problem.@en

This paper presents a critical discussion on the automated problem formulation and workflow creation approach developed within the European project AGILE to support and accelerate the setup of aircraft MDO workflows in a large, heterogeneous team of experts. The developed framework is based on a methodological approach, called the AGILE paradigm, where a complete MDO system is formulated and executed in five main steps. In Step I the requirements are collected, in Step II a repository of disciplinary tools is established, in Step III the design optimization problem is formulated and structured according to a selected MDO architecture, in Step IV an executable workflow is assembled and finally operated in Step V. All steps have been streamlined and highly automated through the development of a novel set of MDO support applications and data standards, addressed as the AGILE MDO framework. This framework was tested through a series of design campaigns culminating with four design tasks, where a variety of unconventional aircraft configurations is collaboratively designed using MDO. After a brief introduction on the AGILE paradigm and the four design tasks, this paper will focus on a set of AGILE framework core components enabling the automated process to formulate and execute collaborative MDO systems. The strengths and current limitations of these components are discussed, based on the extensive feedback from the heterogeneous set of specialists involved in the four design tasks. Although drastic reductions in the setup time of an MDO system (up to 40 percent) appear to be already achievable, recommendations are provided to improve the flexibility, usability and scalability of the framework.

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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.@en