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Objectives – part II The proposed approach will leverage on the experience and demonstration tools being developed in the EC FP7 project iProd and other currently running research initiatives. 3. An interactive MDO architecture design system Can assist in the actual definition of a MDO architecture and allow the user to obtain an MDO framework implementation that is ready for use 4. “Smart” Aircraft Optimization Algorithm Algorithm that considers aircraft design knowledge for the generation of new iterates and the handling of constraint violations. Methodological approach to support aircraft MDO with knowledge-based technology Background Multidisciplinary Design Optimization (MDO) can provide designers with the structured approach and mathematical formulations to further improve the performance of already mature solutions, and to support the exploration of innovative complex designs, fully considering interactions between disciplines [1]. Although the very first MDO implementations have been presented about 50 years ago, the discipline is not yet fully exploited at industrial level [2]. Some of the reasons for the limited exploitation of MDO in industry are: 1. Lack of adequately flexible, accurate and robust parametric models to support MDO using high fidelity simulations. 2. Limited availability of computation resources to solve complex problems of industrial interest 3. Intrinsic complexity of such discipline and its mathematical foundations 4. Lack in awareness and understanding of the many available MDO architectures and their specific suitability to problem of different nature Advances in knowledge-based parametric CAD modelling, e.g. Knowledge Based Engineering (KBE) applications [3], and robust pre-processing tools to support High Fidelity analysis seem to successfully address the first issue. References 1. N. P. Tedford and J. R. R. A. Martins, Benchmarking Multidisciplinary Design Optimization Algorithms, Optimization and Engineering, vol. 11, p. 159–183, 2010 2. J. Agte et al., MDO: Assessment and direction for advancement - An opinion of one international group. Structural and Multidisciplinary Optimization, 40:17-33, 2010. 3. G. La Rocca, Knowledge based engineering: Between AI and CAD. Review of a language based technology to support engineering design. Advanced Engineering Informatics, 26:159- 179, 2012. 4. OpenMDAO, NASA Glenn Research Center, http://openmdao.org/ 5. Master Thesis Patrick Chan, A New Methodology for the Development of Simulation Workflows, TU Delft, 2013 6. J. R. R. A. Martins, A. B. Lambe; Multidisciplinary Design Optimization: Survey of Architectures, AIAA Journal, Vol. 51, No 9, September 2013 The iProd Project iProd – Integrated Management of product heterogeneous data - FP7 project of the European Community (Grant agreement no. 257657) The aim is to improve efficiency and quality of the PDP through a flexible and service oriented software framework, reasoning and operating on a well-structured knowledge base, that will be the backbone of the computer systems associated with the PDP. PhD Candidate: Maurice Hoogreef Department: AWEP Section: Flight Performance and Propulsion Supervisor: Gianfranco La Rocca Promoter: Leo Veldhuis Start date: 15-10-2012 Funding: iProd/ IDEALISM Cooperations: KE-Works, Noesis, Fokker Type: √ Engineering □ Scientific Aerospace Engineering Progress Currently, focus is on the MDO architectures and the algorithms involved in solving MDO problems. At the same time, experience in working with and building knowledge bases, ontologies, KBE applications and the required software and software architecture is obtained from the work performed in the iProd project, which was finished after the final review in April 2014. The IDEaliSM Project IDEaliSM - Integrated & Distributed Engineering Services framework for MDO - ITEA2 project (NO. 13040) IDEaliSM aims to improve the time-to-market and cost-efficiency of the PDP by enabling continuous integration of distributed and highly specialized development teams. To this purpose the project will deliver a new distributed flexible and service- oriented development framework for multi-disciplinary design and optimization that is capable of integrating people, process and technology. To achieve these goals, the project will rely on knowledge management (KM), knowledge based engineering (KBE) and process integration and automation technologies. 1. A knowledge base Can find knowledge related to MDO system architecture and can also store knowledge relative to (other) processes. 2. An MDO advisory system Can give advice on the most suitable method for the problem at hand. Objectives – part I The main purpose of this PhD research is the development of an innovative methodological approach based on Knowledge Technologies to address issues 3 and 4. As main results, this PhD research will deliver:
