Aerodynamic CFD Based Optimization of Police Car Using Bezier Curves

This paper investigates the optimization of the aerodynamic design of a police car, BMW 5-series which is popular police force across the UK. A Bezier curve fitting approach is proposed as a tool to improve the existing design of the warning light cluster in order to reduce drag. A formal optimization technique based on Computational Fluid Dynamics (CFD) and moving least squares (MLS) is used to determine the control points for the approximated curve to cover the light-bar and streamline the shape of the roof. The results clearly show that improving the aerodynamic design of the roofs will offer an important opportunity for reducing the fuel consumption and emissions for police vehicles. The optimized police car has 30% less drag than the non-optimized counter-part

A Subspace Projection Methodology for Nonlinear Manifold Based Face Recognition

A novel feature extraction method that utilizes nonlinear mapping from the original data space to the feature space is presented in this dissertation. Feature extraction methods aim to find compact representations of data that are easy to classify. Measurements with similar values are grouped to same category, while those with differing values are deemed to be of separate categories. For most practical systems, the meaningful features of a pattern class lie in a low dimensional nonlinear constraint region (manifold) within the high dimensional data space. A learning algorithm to model this nonlinear region and to project patterns to this feature space is developed. Least squares estimation approach that utilizes interdependency between points in training patterns is used to form the nonlinear region. The proposed feature extraction strategy is employed to improve face recognition accuracy under varying illumination conditions and facial expressions. Though the face features show variations under these conditions, the features of one individual tend to cluster together and can be considered as a neighborhood. Low dimensional representations of face patterns in the feature space may lie in a nonlinear constraint region, which when modeled leads to efficient pattern classification. A feature space encompassing multiple pattern classes can be trained by modeling a separate constraint region for each pattern class and obtaining a mean constraint region by averaging all the individual regions. Unlike most other nonlinear techniques, the proposed method provides an easy intuitive way to place new points onto a nonlinear region in the feature space. The proposed feature extraction and classification method results in improved accuracy when compared to the classical linear representations. Face recognition accuracy is further improved by introducing the concepts of modularity, discriminant analysis and phase congruency into the proposed method. In the modular approach, feature components are extracted from different sub-modules of the images and concatenated to make a single vector to represent a face region. By doing this we are able to extract features that are more representative of the local features of the face. When projected onto an arbitrary line, samples from well formed clusters could produce a confused mixture of samples from all the classes leading to poor recognition. Discriminant analysis aims to find an optimal line orientation for which the data classes are well separated. Experiments performed on various databases to evaluate the performance of the proposed face recognition technique have shown improvement in recognition accuracy, especially under varying illumination conditions and facial expressions. This shows that the integration of multiple subspaces, each representing a part of a higher order nonlinear function, could represent a pattern with variability. Research work is progressing to investigate the effectiveness of subspace projection methodology for building manifolds with other nonlinear functions and to identify the optimum nonlinear function from an object classification perspective

Computational fluid dynamics based optimisation of emergency response vehicles

Formal optimisation studies of the aerodynamic design of Emergency Response Vehicles, typically encountered within the United Kingdom, were undertaken. The objectives of the study were to optimise the aerodynamics of the Emergency Response Vehicles such as Ambulance and Police cars, in terms of drag force. A combination of wind tunnel tests and the Computational Fluid Dynamics (CFD) simulations were used to analyse the flow field and aerodynamic characteristics of Emergency Response Vehicles. The experimental data were used to validate the computer simulations and the good agreement observed gave confidence in the results obtained. Results from computer simulations on the scale models and full-scale models, were also characteristically similar to those of the validated scale model. Computational Fluid Dynamics (CFD) was combined with an efficient optimisation framework to minimize the drag force of three different types of Emergency Response Vehicles, Ambulance Van Conversion, Police Van Conversion and Police Sedan car Conversion. The benefits of employing an airfoil-based roof design and Bezier curve fitting approach which minimizes the deleterious aerodynamic effects of the required front and rear light-bars, were investigated. Optimal Latin Hypercube (OLH) Design of Experiments, the Multipoint Approximation Method (MAM) and surrogate modelling were used for the optimisation. Optimisation results demonstrated a clear improvement of the aerodynamic design of the Emergency Response Vehicles named above. It was also clearly demonstrated that improving the aerodynamic design of Emergency Response Vehicles roof offers a significant opportunity for reducing the fuel consumption and emissions for Emergency Response Vehicles