Reduced-order modelling for high-speed aerial weapon aerodynamics

In this work a high-fidelity low-cost surrogate of a computational fluid dynamics analysis tool was developed. This computational tool is composed of general and physics- based approximation methods by which three dimensional high-speed aerodynamic flow- field predictions are made with high efficiency and an accuracy which is comparable with that of CFD. The tool makes use of reduced-basis methods that are suitable for both linear and non-linear problems, whereby the basis vectors are computed via the proper orthogonal decomposition (POD) of a training dataset or a set of observations. The surrogate model was applied to two flow problems related to high-speed weapon aerodynamics. Comparisons of surrogate model predictions with high-fidelity CFD simulations suggest that POD-based reduced-order modelling together with response surface methods provide a reliable and robust approach for efficient and accurate predictions. In contrast to the many modelling efforts reported in the literature, this surrogate model provides access to information about the whole flow-field. In an attempt to reduce the up-front cost necessary to generate the training dataset from which the surrogate model is subsequently developed, a variable-fidelity POD- based reduced-order modelling method is proposed in this work for the first time. In this model, the scalar coefficients which are obtained by projecting the solution vectors onto the basis vectors, are mapped between spaces of low and high fidelities, to achieve high- fidelity predictions with complete flow-field information. In general, this technique offers an automatic way of fusing variable-fidelity data through interpolation and extrapolation schemes together with reduced-order modelling (ROM). Furthermore, a study was undertaken to investigate the possibility of modelling the transonic flow over an aerofoil using a kernel POD–based reduced-order modelling method. By using this type of ROM it was noticed that the weak non-linear features of the transonic flow are accurately modelled using a small number of basis vectors. The strong non-linear features are only modelled accurately by using a large number of basis vectors

State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners

Multi-fidelity probabilistic optimisation of composite structures

In this thesis, novel multi-fidelity modelling-based probabilistic optimisation methods are presented to address the computational challenge of stochastic design philosophies applied to complex aircraft composite structures. Novel multi-fidelity formulations developed in this thesis, blending High-Fidelity Model (HFM) and Low-Fidelity Model (LFM), are shown to significantly improve computational efficiency by making use of machine learning techniques, such as Artificial Neural Networks (ANN) and Non-linear Auto-Regressive Gaussian Process (NARGP). To further improve the computational efficiency compared to the conventional probabilistic optimisation methods, a multi-level optimisation approach and a new sampling strategy to collect training data points are incorporated into the multi- fidelity formulations for the first time. In the developed optimisation methods, the HFM covers part of the design space whilst the LFM explores the whole design space to fill the lack of high-fidelity information. This improvement enables the multi-fidelity formulations to request a much smaller number of high-fidelity information causing considerable computational costs. Several engineering examples such as aircraft mono-stringer composite panels are used to demonstrate the accuracy and computational efficiency of the developed methods when used with different reliability and robustness analysis techniques, including Monte Carlo Simulation (MCS), the First-Order Reliability Method (FORM) and the Second-Order Reliability Method (SORM). The composite panels are subjected to mechanical and thermomechanical loads to show the broad range of potential applications. It is shown that the newly developed multi-fidelity probabilistic optimisation methods offer substantial computational time savings ranging from 50 % to 70 % and levels of error typically less than 1 % when compared with traditional probabilistic optimisation methods. Results demonstrate that the newly developed multi-fidelity probabilistic optimisation methods herein provide significant computational benefits and accurately predict the influence of uncertainties associated with design and manufacturing stages. As a result, the presented methods confidently carry out reliability-based and robust design optimisation of large-scale and complex aircraft composite structures.Open Acces

Acoustic and Device Feature Fusion for Load Recognition

Appliance-specific Load Monitoring (LM) provides a possible solution to the problem of energy conservation which is becoming increasingly challenging, due to growing energy demands within offices and residential spaces. It is essential to perform automatic appliance recognition and monitoring for optimal resource utilization. In this paper, we study the use of non-intrusive LM methods that rely on steady-state appliance signatures for classifying most commonly used office appliances, while demonstrating their limitation in terms of accurately discerning the low-power devices due to overlapping load signatures. We propose a multilayer decision architecture that makes use of audio features derived from device sounds and fuse it with load signatures acquired from energy meter. For the recognition of device sounds, we perform feature set selection by evaluating the combination of time-domain and FFT-based audio features on the state of the art machine learning algorithms. The highest recognition performance however is shown by support vector machines, for the device and audio recognition experiments. Further, we demonstrate that our proposed feature set which is a concatenation of device audio feature and load signature significantly improves the device recognition accuracy in comparison to the use of steady-state load signatures only