Integration, management and communication of heterogeneous design resources with WWW technologies

Recently, advanced information technologies have opened new pos-sibilities for collaborative designs. In this paper, a Web-based collaborative de-sign environment is proposed, where heterogeneous design applications can be integrated with a common interface, managed dynamically for publishing and searching, and communicated with each other for integrated multi-objective de-sign. The CORBA (Common Object Request Broker Architecture) is employed as an implementation tool to enable integration and communication of design application programs; and the XML (eXtensible Markup Language) is used as a common data descriptive language for data exchange between heterogeneous applications and for resource description and recording. This paper also intro-duces the implementation of the system and the encapsulating issues of existing legacy applications. At last, an example of gear design based on the system is il-lustrated to identify the methods and procedure developed by this research

Enhancing optimization capabilities using the AGILE collaborative MDO framework with application to wing and nacelle design

This paper presents methodological investigations performed in research activities in the field of Multi-disciplinary Design and Optimization (MDO) for overall aircraft design in the EU funded research project AGILE (2015–2018). In the AGILE project a team of 19 industrial, research and academic partners from Europe, Canada and Russia are working together to develop the next generation of MDO environment that targets significant reductions in aircraft development costs and time to market, leading to cheaper and greener aircraft. The paper introduces the AGILE project structure and describes the achievements of the 1st year that led to a reference distributed MDO system. A focus is then made on different novel optimization techniques studied during the 2nd year, all aiming at easing the optimization of complex workflows that are characterized by a high number of discipline interdependencies and a large number of design variables in the context of multi-level processes and multi-partner collaborative engineering projects. Three optimization strategies are introduced and validated for a conventional aircraft. First, a multi-objective technique based on Nash Games and Genetic Algorithm is used on a wing design problem. Then a zoom is made on the nacelle design where a surrogate-based optimizer is used to solve a mono-objective problem. Finally a robust approach is adopted to study the effects of uncertainty in parameters on the nacelle design process. These new capabilities have been integrated in the AGILE collaborative framework that in the future will be used to study and optimize novel unconventional aircraft configurations

Robust Mission Design Through Evidence Theory and Multi-Agent Collaborative Search

In this paper, the preliminary design of a space mission is approached introducing uncertainties on the design parameters and formulating the resulting reliable design problem as a multiobjective optimization problem. Uncertainties are modelled through evidence theory and the belief, or credibility, in the successful achievement of mission goals is maximised along with the reliability of constraint satisfaction. The multiobjective optimisation problem is solved through a novel algorithm based on the collaboration of a population of agents in search for the set of highly reliable solutions. Two typical problems in mission analysis are used to illustrate the proposed methodology

Decoupled UMDO formulation for interdisciplinary coupling satisfaction under uncertainty

International audienceAt early design phases, taking into account uncertainty for the optimization of a multidisciplinary system is essential to establish the optimal system characteristics and performances. Uncertainty Multidisciplinary Design Optimization (UMDO) formulations have to eciently organize the dierent disciplinary analyses, the uncertainty propagation, the optimization, but also the handling of interdisciplinary couplings under uncertainty. A decoupled UMDO formulation (Individual Discipline Feasible - Polynomial Chaos Expansion) ensuring the coupling satisfaction for all the instantiations of the uncertain variables is presented in this paper. Ensuring coupling satisfaction in instantiations is essential to ensure the equivalence between the coupled and decoupled UMDO problem formulations. The proposed approach relies on the iterative construction of surrogate models based on Polynomial Chaos Expansion in order to represent at the convergence of the optimization problem, the coupling functional relations as a coupled approach under uncertainty does. The performances of the proposed formulation is assessed on an analytic test case and on the design of a new Vega launch vehicle upper stage

OMD : Optimisation MultiDisciplinaire

http://www.emse.fr/~leriche/rapport_final_rntl_omd_public.pdfProgramme RNTL 2005 de l'Agence Nationale de la Recherch

APPROXIMATION ASSISTED MULTIOBJECTIVE AND COLLABORATIVE ROBUST OPTIMIZATION UNDER INTERVAL UNCERTAINTY

Optimization of engineering systems under uncertainty often involves problems that have multiple objectives, constraints and subsystems. The main goal in these problems is to obtain solutions that are optimum and relatively insensitive to uncertainty. Such solutions are called robust optimum solutions. Two classes of such problems are considered in this dissertation. The first class involves Multi-Objective Robust Optimization (MORO) problems under interval uncertainty. In this class, an entire system optimization problem, which has multiple nonlinear objectives and constraints, is solved by a multiobjective optimizer at one level while robustness of trial alternatives generated by the optimizer is evaluated at the other level. This bi-level (or nested) MORO approach can become computationally prohibitive as the size of the problem grows. To address this difficulty, a new and improved MORO approach under interval uncertainty is developed. Unlike the previously reported bi-level MORO methods, the improved MORO performs robustness evaluation only for optimum solutions and uses this information to iteratively shrink the feasible domain and find the location of robust optimum solutions. Compared to the previous bi-level approach, the improved MORO significantly reduces the number of function calls needed to arrive at the solutions. To further improve the computational cost, the improved MORO is combined with an online approximation approach. This new approach is called Approximation-Assisted MORO or AA-MORO. The second class involves Multiobjective collaborative Robust Optimization (McRO) problems. In this class, an entire system optimization problem is decomposed hierarchically along user-defined domain specific boundaries into system optimization problem and several subsystem optimization subproblems. The dissertation presents a new Approximation-Assisted McRO (AA-McRO) approach under interval uncertainty. AA-McRO uses a single-objective optimization problem to coordinate all system and subsystem optimization problems in a Collaborative Optimization (CO) framework. The approach converts the consistency constraints of CO into penalty terms which are integrated into the subsystem objective functions. In this way, AA-McRO is able to explore the design space and obtain optimum design solutions more efficiently compared to a previously reported McRO. Both AA-MORO and AA-McRO approaches are demonstrated with a variety of numerical and engineering optimization examples. It is found that the solutions from both approaches compare well with the previously reported approaches but require a significantly less computational cost. Finally, the AA-MORO has been used in the development of a decision support system for a refinery case study in order to facilitate the integration of engineering and business decisions using an agent-based approach

Multifidelity Uncertainty Propagation via Adaptive Surrogates in Coupled Multidisciplinary Systems

Fixed point iteration is a common strategy to handle interdisciplinary coupling within a feedback-coupled multidisciplinary analysis. For each coupled analysis, this requires a large number of disciplinary high-fidelity simulations to resolve the interactions between different disciplines. When embedded within an uncertainty analysis loop (e.g., with Monte Carlo sampling over uncertain parameters), the number of high-fidelity disciplinary simulations quickly becomes prohibitive, because each sample requires a fixed point iteration and the uncertainty analysis typically involves thousands or even millions of samples. This paper develops a method for uncertainty quantification in feedback-coupled systems that leverage adaptive surrogates to reduce the number of cases forwhichfixedpoint iteration is needed. The multifidelity coupled uncertainty propagation method is an iterative process that uses surrogates for approximating the coupling variables and adaptive sampling strategies to refine the surrogates. The adaptive sampling strategies explored in this work are residual error, information gain, and weighted information gain. The surrogate models are adapted in a way that does not compromise the accuracy of the uncertainty analysis relative to the original coupled high-fidelity problem as shown through a rigorous convergence analysis.United States. Army Research Office. Multidisciplinary University Research Initiative (Award FA9550-15-1-0038