Browsing by Author "Pagliuca, Giampaolo"

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    Blended wing body (BWB) planform multidisciplinary optimisation (MDO) for early stage aircraft design using model based engineering
    (International Council of the Aeronautical Sciences, 2021-09-10) Di Pasquale, Davide; Verma, D.; Pagliuca, Giampaolo
    A model-based engineering (MBE) framework has been developed for Multi-Disciplinary Optimisation (MDO) of a Blended Wing Body (BWB) configuration during early design stages. Specifically, a planform optimisation has been performed by focusing on three objective functions, namely, aerodynamic efficiency (Eff), drag coefficient (CD) and Operational Empty Weight (OEW). Particle Swarm Optimisation (PSO) has been used as algorithm for the optimisation, an open-source Vortex Lattice Method (VLM), with empirical corrections for compressibility, as aerodynamic module, along with a mass estimation model with respect to BWB considerations. A successful multidisciplinary optimisation has been performed for the BWB-11 configuration flying at cruise condition, specifically at Mach 0.85 and at an altitude of 10 km. Increment in Eff and decrement in CD and OEW compared to the baseline BWB has been achieved. The OEW has been calculated from a newly developed mass estimation model and successfully validated via statistical methods. The paper presents a rapid MDO framework for efficient BWB planform optimisation to be used at the early design stage, providing useful guidance to the designers. A detailed analysis of the integrated design system, the methods as well as the optimisation results are provided. In addition, further research to the current framework is also presented.
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    Surrogate modelling for wing planform multidisciplinary optimisation using model-based engineering
    (Hindawi Publishing Corporation, 2019-05-09) Pagliuca, Giampaolo; Kipouros, Timoleon; Savill, Mark
    Optimisation is aimed at enhancing aircraft design by identifying the most promising wing planforms at the early stage while discarding the least performing ones. Multiple disciplines must be taken into account when assessing new wing planforms, and a model-based framework is proposed as a way to include mass estimation and longitudinal stability alongside aerodynamics. Optimisation is performed with a particle swarm optimiser, statistical methods are exploited for mass estimation, and the vortex lattice method (VLM) with empirical corrections for transonic flow provides aerodynamic performance. Three surrogates of the aerodynamic model are investigated. The first one is based on radial basis function (RBF) interpolation, and it relies on a precomputed database to evaluate the performance of new wing planforms. The second one is based on an artificial neural network, and it needs precomputed data for a training step. The third one is a hybrid model which switches automatically between VLM and RBF, and it does not need any precomputation. Its switching criterion is defined in an objective way to avoid any arbitrariness. The investigation is reported for a test case based on the common research model (CRM). Reference results are produced with the aerodynamic model based on VLM for two- and three-objective optimisations. Results from all surrogate models for the same benchmark optimisation are compared so that their benefits and limitations are both highlighted. A discussion on specific parameters, such as number of samples for example, is given for each surrogate. Overall, a model-based implementation with a hybrid model is proposed as a compromise between versatility and an arbitrary level of accuracy for wing early-stage design.