On scaling and system identification of flexible aircraft dynamics.

The use of subscale models has been common practice in the industry and has helped engineers gain more confidence in their design processes. However, each subscale model is developed for a specifc test, and consequently, different types of models are needed for observing aerodynamic, structural and aeroelastic characteristics of a full-scale aircraft. Yet, traditional aircraft design methods face serious challenges when a novel aircraft de- sign emerges and a proof-of-concept is needed for investigating this multi-disciplinary problem. An example of such a problem is the development of aircraft configurations with high aspect ratio wings for which the disciplines of aeroelastic and flight mechanics are strongly interconnected. Moreover, if the prediction of dynamic behaviour is of interest, a method that utilises system identification for analysing experimental data is of importance. Therefore, this thesis aims to develop a methodology to investigate the complex flight dynamic behaviour of flexible aircraft by combining techniques for developing subscale models and methods with the field of system identification. This aim is achieved through three objectives: 1) assessment of system identification methods for subscale flexible aircraft, 2) theoretical development of subscale modelling in terms of scaling laws and aeroelastic simulation framework and, 3) wind tunnel testing of the subscale model. Aspects of System Identification have been explored through use-cases where experimental data for a rigid aircraft both in full-scale and subscale configuration is used. The results highlight the fact that in testing a subscale model, dynamics are more prone to exhibit non-linear behaviour when compared to the full-scale model. It followed by the application of system identification for a flexible aircraft based on a simulation framework. This study emphasised the need for non-linear identification methods, such as an output error method, to characterise a flexible aircraft system. The work continues with the exploration of scaling laws applied to a simple aerofoil that is free to pitch and plunge. These results build the foundation for the development of a subscale high aspect ratio wing for wind tunnel experiments. The work highlights the trade-o s and compromises faced during the development of a dynamically subscaled model and the practice of system identification. The main contribution lies in the development of a low-cost methodology in building a subscale model that allows the use of dynamically scaled models at the early design stages. This practice provides the designer with a means to de-risk novel aircraft concepts as early as possible and in doing so, reduce overall development costs.PhD in Aerospac

Fast Landmark Localization with 3D Component Reconstruction and CNN for Cross-Pose Recognition

Two approaches are proposed for cross-pose face recognition, one is based on the 3D reconstruction of facial components and the other is based on the deep Convolutional Neural Network (CNN). Unlike most 3D approaches that consider holistic faces, the proposed approach considers 3D facial components. It segments a 2D gallery face into components, reconstructs the 3D surface for each component, and recognizes a probe face by component features. The segmentation is based on the landmarks located by a hierarchical algorithm that combines the Faster R-CNN for face detection and the Reduced Tree Structured Model for landmark localization. The core part of the CNN-based approach is a revised VGG network. We study the performances with different settings on the training set, including the synthesized data from 3D reconstruction, the real-life data from an in-the-wild database, and both types of data combined. We investigate the performances of the network when it is employed as a classifier or designed as a feature extractor. The two recognition approaches and the fast landmark localization are evaluated in extensive experiments, and compared to stateof-the-art methods to demonstrate their efficacy.Comment: 14 pages, 12 figures, 4 table

Discrete curvature approximations and segmentation of polyhedral surfaces

The segmentation of digitized data to divide a free form surface into patches is one of the key steps required to perform a reverse engineering process of an object. To this end, discrete curvature approximations are introduced as the basis of a segmentation process that lead to a decomposition of digitized data into areas that will help the construction of parametric surface patches. The approach proposed relies on the use of a polyhedral representation of the object built from the digitized data input. Then, it is shown how noise reduction, edge swapping techniques and adapted remeshing schemes can participate to different preparation phases to provide a geometry that highlights useful characteristics for the segmentation process. The segmentation process is performed with various approximations of discrete curvatures evaluated on the polyhedron produced during the preparation phases. The segmentation process proposed involves two phases: the identification of characteristic polygonal lines and the identification of polyhedral areas useful for a patch construction process. Discrete curvature criteria are adapted to each phase and the concept of invariant evaluation of curvatures is introduced to generate criteria that are constant over equivalent meshes. A description of the segmentation procedure is provided together with examples of results for free form object surfaces