Browsing by Author "Fantini, Paolo"

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    Effective multiobjective MDO for conceptual design - An aircraft design perspective
    (Cranfield University, 2007) Fantini, Paolo; Guenov, Marin D.
    Once the requirements for a new aircraft have been defined, the Conceptual design phase is launched. During this phase one or more designers have the goal of defining and investigating a number of alternative solutions. Through discussion with industry it has become apparent that optimisation tools are seldom used, even though these could greatly enhance the work of the designers. The objective of the work carried forward has been of identifying, comparing and where necessary improving the most suitable techniques, as well as schemes for their integration, in order to perform effectively Multidisciplinary Design and Optimisation (MDO) in the Conceptual phase of the aircraft design. The techniques that have been investigated include: multi-objective optimisation algorithms, MDO algorithms for treating non-hierarchically decomposable systems and Automatic Differentiation (AD). As a result an algorithm for performing multiobjective MDO has been developed. Given a complete model for a complex non-hierarchically decomposable system and given a number of objectives and constraints, the algorithm is capable of determining a set of well distributed solutions, representative of both local and global Pareto frontiers. A number of test cases have been used for evaluating the alternative methodologies and the proposed algorithm. These include a set of complex algebraic test cases typically used for evaluating global optimisation algorithms and a simplified aircraft conceptual design model, which was provided by industry. The results demonstrate the unique capability of the algorithm of determining well distributed solutions on the global and local Pareto frontiers for global multiobjective continuous nonlinear constrained optimisation problems. The results also show this capability when the algorithm is applied to non-hierarchically decomposable systems, as typically encountered when performing MDO. Further work could extend the approach in order to handle mixed discrete/continuous variables.
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    A method for generating a well-distributed Pareto set in nonlinear multiobjective optimization
    (Elsevier Science B.V., Amsterdam., 2009-01-15T00:00:00Z) Utyuzhnikov, S. V.; Fantini, Paolo; Guenov, Marin D.
    A method is presented for generating a well-distributed Pareto set in nonlinear multiobjective optimization. The approach shares conceptual similarity with the Physical Programming-based method, the Normal-Boundary Intersection and the Normal Constraint methods, in its systematic approach investigating the objective space in order to obtain a well-distributed Pareto set. The proposed approach is based on the generalization of the class functions which allows the orientation of the search domain to be conducted in the objective space. It is shown that the proposed modification allows the method to generate an even representation of the entire Pareto surface. The generation is performed for both convex and nonconvex Pareto frontiers. A simple algorithm has been proposed to remove local Pareto solutions. The suggested approach has been verified by several test cases, including the generation of both convex and concave Pareto frontiers.
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    Multidisciplinary design optimization framework for the pre design stage
    (Springer Science Business Media, 2010-09-30T00:00:00Z) Guenov, Marin D.; Fantini, Paolo; Balachandran, Libish Kalathil; Maginot, Jeremy; Padulo, Mattia; Nunez, Marco
    Presented is a novel framework for performing flexible computational design studies at preliminary design stage. It incorporates a workflow management device (WMD) and a number of advanced numerical treatments, including multi-objective optimization, sensitivity analysis and uncertainty management with emphasis on design robustness. The WMD enables the designer to build, understand, manipulate and share complex processes and studies. Results obtained after applying the WMD on various test cases, showed a significant reduction of the iterations required for the convergence of the computational system. The tests results also demonstrated the capabilities of the advanced treatments as follows: The novel procedure for global multi-objective optimization has the unique ability to generate well-distributed Pareto points on both local and global Pareto fronts simultaneously. The global sensitivity analysis procedure is able to identify input variables whose range of variation does not have significant effect on the objectives and constraints. It was demonstrated that fixing such variables can greatly reduce the computational time while retaining a satisfactory quality of the resulting Pareto front. The novel derivative-free method for uncertainty propagation, which was proposed for enabling multi-objective robust optimization, delivers a higher accuracy compared to the one based on function linearization, without altering significantly the cost of the single optimization step.