Data driven techniques for modal decomposition and reduced-order modelling of fluids

In this thesis, a number of data-driven techniques are proposed for the analysis and extraction of reduced-order models of fluid flows. Throughout the thesis, there has been an emphasis on the practicality and interpretability of data-driven feature-extraction techniques to aid practitioners in flow-control and estimation. The first contribution uses a graph theoretic approach to analyse the similarity of modes extracted using data-driven modal decomposition algorithms to give a more intuitive understanding of the degrees of freedom in the underlying system. The method extracts clusters of spatially and spectrally similar modes by post-processing the modes extracted using DMD and its variants. The second contribution proposes a method for extracting coherent structures, using snapshots of high dimensional measurements, that can be mapped to a low dimensional output of the system. The importance of finding such coherent structures is that in the context of active flow control and estimation, the practitioner often has to rely on a limited number of measurable outputs to estimate the state of the flow. Therefore, ensuring that the extracted flow features can be mapped to the measured outputs of the system can be beneficial for estimating the state of the flow. The third contribution concentrates on using neural networks for exploiting the nonlinear relationships amongst linearly extracted modal time series to find a reduced order state, which can then be used for modelling the dynamics of the flow. The method utilises recurrent neural networks to find an encoding of a high dimensional set of modal time series, and fully connected neural networks to find a mapping between the encoded state and the physically interpretable modal coefficients. As a result of this architecture, the significantly reduced-order representation maintains an automatically extracted relationship to a higher-dimensional, interpretable state.Open Acces

Wind Energy Harvesting and Conversion Systems: A Technical Review

Wind energy harvesting for electricity generation has a significant role in overcoming the challenges involved with climate change and the energy resource implications involved with population growth and political unrest. Indeed, there has been significant growth in wind energy capacity worldwide with turbine capacity growing significantly over the last two decades. This confidence is echoed in the wind power market and global wind energy statistics. However, wind energy capture and utilisation has always been challenging. Appreciation of the wind as a resource makes for difficulties in modelling and the sensitivities of how the wind resource maps to energy production results in an energy harvesting opportunity. An opportunity that is dependent on different system parameters, namely the wind as a resource, technology and system synergies in realizing an optimal wind energy harvest. This paper presents a thorough review of the state of the art concerning the realization of optimal wind energy harvesting and utilisation. The wind energy resource and, more specifically, the influence of wind speed and wind energy resource forecasting are considered in conjunction with technological considerations and how system optimization can realise more effective operational efficiencies. Moreover, non-technological issues affecting wind energy harvesting are also considered. These include standards and regulatory implications with higher levels of grid integration and higher system non-synchronous penetration (SNSP). The review concludes that hybrid forecasting techniques enable a more accurate and predictable resource appreciation and that a hybrid power system that employs a multi-objective optimization approach is most suitable in achieving an optimal configuration for maximum energy harvesting

Lattice-Boltzmann simulations of cerebral blood flow

Computational haemodynamics play a central role in the understanding of blood behaviour in the cerebral vasculature, increasing our knowledge in the onset of vascular diseases and their progression, improving diagnosis and ultimately providing better patient prognosis. Computer simulations hold the potential of accurately characterising motion of blood and its interaction with the vessel wall, providing the capability to assess surgical treatments with no danger to the patient. These aspects considerably contribute to better understand of blood circulation processes as well as to augment pre-treatment planning. Existing software environments for treatment planning consist of several stages, each requiring significant user interaction and processing time, significantly limiting their use in clinical scenarios. The aim of this PhD is to provide clinicians and researchers with a tool to aid in the understanding of human cerebral haemodynamics. This tool employs a high performance fluid solver based on the lattice-Boltzmann method (coined HemeLB), high performance distributed computing and grid computing, and various advanced software applications useful to efficiently set up and run patient-specific simulations. A graphical tool is used to segment the vasculature from patient-specific CT or MR data and configure boundary conditions with ease, creating models of the vasculature in real time. Blood flow visualisation is done in real time using in situ rendering techniques implemented within the parallel fluid solver and aided by steering capabilities; these programming strategies allows the clinician to interactively display the simulation results on a local workstation. A separate software application is used to numerically compare simulation results carried out at different spatial resolutions, providing a strategy to approach numerical validation. This developed software and supporting computational infrastructure was used to study various patient-specific intracranial aneurysms with the collaborating interventionalists at the National Hospital for Neurology and Neuroscience (London), using three-dimensional rotational angiography data to define the patient-specific vasculature. Blood flow motion was depicted in detail by the visualisation capabilities, clearly showing vortex fluid ow features and stress distribution at the inner surface of the aneurysms and their surrounding vasculature. These investigations permitted the clinicians to rapidly assess the risk associated with the growth and rupture of each aneurysm. The ultimate goal of this work is to aid clinical practice with an efficient easy-to-use toolkit for real-time decision support

14th International Conference on Turbochargers and Turbocharging

14th International Conference on Turbochargers and Turbocharging addresses current and novel turbocharging system choices and components with a renewed emphasis to address the challenges posed by emission regulations and market trends. The contributions focus on the development of air management solutions and waste heat recovery ideas to support thermal propulsion systems leading to high thermal efficiency and low exhaust emissions. These can be in the form of internal combustion engines or other propulsion technologies (eg. Fuel cell) in both direct drive and hybridised configuration. 14th International Conference on Turbochargers and Turbocharging also provides a particular focus on turbochargers, superchargers, waste heat recovery turbines and related air managements components in both electrical and mechanical forms

ReSCon '12, Research Student Conference: Book of Abstracts

The fifth SED Research Student Conference (ReSCon2012) was hosted over three days, 18-20 June 2012, in the Hamilton Centre at Brunel University. The conference consisted of 130 oral and 70 poster presentations, based on the high quality and diverse research being conducted within the School of Engineering and Design by postgraduate research students. The conference is held annually, and ReSCon plays a key role in contributing to research and innovations within the School

Application of Artificial Intelligence Approaches in the Flood Management Process for Assessing Blockage at Cross-Drainage Hydraulic Structures

Floods are the most recurrent, widespread and damaging natural disasters, and are ex-pected to become further devastating because of global warming. Blockage of cross-drainage hydraulic structures (e.g., culverts, bridges) by flood-borne debris is an influen-tial factor which usually results in reducing hydraulic capacity, diverting the flows, dam-aging structures and downstream scouring. Australia is among the countries adversely impacted by blockage issues (e.g., 1998 floods in Wollongong, 2007 floods in Newcas-tle). In this context, Wollongong City Council (WCC), under the Australian Rainfall and Runoff (ARR), investigated the impact of blockage on floods and proposed guidelines to consider blockage in the design process for the first time. However, existing WCC guide-lines are based on various assumptions (i.e., visual inspections as representative of hy-draulic behaviour, post-flood blockage as representative of peak floods, blockage remains constant during the whole flooding event), that are not supported by scientific research while also being criticised by hydraulic design engineers. This suggests the need to per-form detailed investigations of blockage from both visual and hydraulic perspectives, in order to develop quantifiable relationships and incorporate blockage into design guide-lines of hydraulic structures. However, because of the complex nature of blockage as a process and the lack of blockage-related data from actual floods, conventional numerical modelling-based approaches have not achieved much success. The research in this thesis applies artificial intelligence (AI) approaches to assess the blockage at cross-drainage hydraulic structures, motivated by recent success achieved by AI in addressing complex real-world problems (e.g., scour depth estimation and flood inundation monitoring). The research has been carried out in three phases: (a) litera-ture review, (b) hydraulic blockage assessment, and (c) visual blockage assessment. The first phase investigates the use of computer vision in the flood management domain and provides context for blockage. The second phase investigates hydraulic blockage using lab scale experiments and the implementation of multiple machine learning approaches on datasets collected from lab experiments (i.e., Hydraulics-Lab Dataset (HD), Visual Hydraulics-Lab Dataset (VHD)). The artificial neural network (ANN) and end-to-end deep learning approaches reported top performers among the implemented approaches and demonstrated the potential of learning-based approaches in addressing blockage is-sues. The third phase assesses visual blockage at culverts using deep learning classifi-cation, detection and segmentation approaches for two types of visual assessments (i.e., blockage status classification, percentage visual blockage estimation). Firstly, a range of existing convolutional neural network (CNN) image classification models are imple-mented and compared using visual datasets (i.e., Images of Culvert Openings and Block-age (ICOB), VHD, Synthetic Images of Culverts (SIC)), with the aim to automate the process of manual visual blockage classification of culverts. The Neural Architecture Search Network (NASNet) model achieved best classification results among those im-plemented. Furthermore, the study identified background noise and simplified labelling criteria as two contributing factors in degraded performance of existing CNN models for blockage classification. To address the background clutter issue, a detection-classification pipeline is proposed and achieved improved visual blockage classification performance. The proposed pipeline has been deployed using edge computing hardware for blockage monitoring of actual culverts. The role of synthetic data (i.e., SIC) on the performance of culvert opening detection is also investigated. Secondly, an automated segmentation-classification deep learning pipeline is proposed to estimate the percentage of visual blockage at circular culverts to better prioritise culvert maintenance. The AI solutions proposed in this thesis are integrated into a blockage assessment framework, designed to be deployed through edge computing to monitor, record and assess blockage at cross-drainage hydraulic structures

Vortex flows around morphing foils: reshape yourself to effect a change

The unsteady production of vorticity is a unifying principle in biological locomotion in air and water. Shape-tailoring their aerodynamic/hydrodynamic surfaces, natural flyers/swimmers switch with ease between regimes of attached and separated flow. Modern engineering tries to mimic this shape control ability in applications such as micro air vehicles and oscillating energy harvesting devices. Owing to the complexity of morphing, fundamental research merits further efforts, to provide a clearer view of the link between shape variation and unsteady flow response. In this dissertation, classical theories (potential flow) are combined with numerical methods (vortex methods) in the development of a physics-based low-order model to simulate complex unsteady flows around foils undergoing large camber variations, as a first step towards exploring the capability of camber morphing to alter vortex characteristics, like formation time and strength. Discrete-vortex methods have risen as one of the most suitable numerical tools to establish a relation between camber definition, flow characteristics and aerodynamic loads produced, involving a reasonably low number of parameters. An existing discrete-vortex model for rigid foils is extended to variable-camber foils. A time-varying chord line is proposed, where the boundary condition in thin-aerofoil theory is to be satisfied. This enables large deformations of the camber line to be modelled. Computational fluid dynamics simulations at Reynolds numbers of O(104) are used to test the performance of the model. Furthermore, a new method is introduced to determine the rate at which vorticity is fed into leading-edge vortices in vortex models. The strength of nascent particles at the leading edge is here computed with the velocity at the edge of the shear layer, formulated in terms of the leading-edge suction parameter (an inviscid parameter from unsteady thin-aerofoil theory). This novel formulation allows postseparation flow behaviour, like vortex sheets dynamics, to be correctly captured. Finally, morphing and vortex modelling are combined to demonstrate the potential of this technique to affect leading-edge vortices: the amount of vorticity produced at the leading edge is modulated by suitably tailoring the shape of the camber line. This research aims to advance our theoretical understanding of the correlation between prescribed deflection at the trailing edge and alteration of flow properties at the leading edge, of interest to the design of flow control strategies inspired by nature