Browsing by Author "Padulo, Mattia"

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    Computational engineering design under uncertainty: an aircraft conceptual design perspective
    (Cranfield University, 2009-07) Padulo, Mattia; Guenov, Marin D.
    Presented in this thesis is a novel methodology for aircraft design optimization in the presence of uncertainty, with emphasis on the conceptual design stage. In the initial part of the thesis, the uncertainty typologies of interest for aircraft design are identied within a broader epistemological framework. The main implications for non-deterministic computational design are also outlined. The focus is then restricted to uncertainties that can be modeled by probability theory. In this context, a methodology is developed to enhance robust design optimization (RDO). Firstly, the problem is formulated in order to relax, when required, the common RDO assumption about the normality of objectives and constraints. Secondly, starting from engineering considerations about the risk related with design unfeasibility, suitable estimates of tail conditional expectation are introduced in the set of robustness metrics. The proposed formulation requires the estimation of mean and variance of objec¬tives and constraints. To calculate such moments, a novel uncertainty propaga¬tion technique is proposed, which achieves a favorable trade-obetween the ac-curacy of the estimates and the required computational cost. Peculiar features of the propagation technique are exploited to couple the propagation and the opti¬mization phases for the classes of gradient-based methods and the derivative-free pattern search methods. Also analyzed are the possible advantages achievable when the two types of algorithms are hybridized. The usefulness of the proposed methodology for conceptual design optimization is demonstrated with the aid of two engineering design problems, concerning the sizing of passenger aircraft and the design of transonic airfoils.
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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.
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    Robust aircraft conceptual design using automatic differentiation in Matlab
    (2008-08-17T00:00:00Z) Padulo, Mattia; Forth, Shaun A.; Guenov, Marin D.; Bischof, C. H.; Bücker, H. M.; Hovland, P.; Naumann, U.; Utke, J.
    The need for robust optimisation in aircraft conceptual design, for which the design parameters are assumed stochastic, is introduced. We highlight two approaches, first-order method of moments and Sigma-Point reduced quadrature, to estimate the mean and variance of the design’s outputs. The method of moments requires the design model’s differentiation and here, since the model is implemented in Matlab, is performed using the AD tool MAD. Gradient-based constrained optimisation of the stochastic model is shown to be more efficient using AD-obtained gradients than finite-differencing. A post-optimality analysis, performed using ADenabled third-order method of moments and Monte-Carlo analysis, confirms the attractiveness of the Sigma-Point technique for uncertainty propagation.