Comparative performance of complex-valued B-spline and polynomial models applied to iterative frequency-domain decision feedback equalization of Hammerstein channels

Complex-valued (CV) B-spline neural network approach offers a highly effective means for identifying and inverting practical Hammerstein systems. Compared with its conventional CV polynomial-based counterpart, a CV B-spline neural network has superior performance in identifying and inverting CV Hammerstein systems, while imposing a similar complexity. This paper reviews the optimality of the CV B-spline neural network approach. Advantages of B-spline neural network approach as compared with the polynomial based modeling approach are extensively discussed, and the effectiveness of the CV neural network-based approach is demonstrated in a real-world application. More specifically, we evaluate the comparative performance of the CV B-spline and polynomial-based approaches for the nonlinear iterative frequency-domain decision feedback equalization (NIFDDFE) of single-carrier Hammerstein channels. Our results confirm the superior performance of the CV B-spline-based NIFDDFE over its CV polynomial-based counterpart

Microwave resonant sensors

Microwave resonant sensors use the spectral characterisation of a resonator to make high sensitivity measurements of material electromagnetic properties at GHz frequencies. They have been applied to a wide range of industrial and scientific measurements, and used to study a diversity of physical phenomena. Recently, a number of challenging dynamic applications have been developed that require very high speed and high performance, such as kinetic inductance detectors and scanning microwave microscopes. Others, such as sensors for miniaturised fluidic systems and non-invasive blood glucose sensors, also require low system cost and small footprint. This thesis investigates new and improved techniques for implementing microwave resonant sensor systems, aiming to enhance their suitability for such demanding tasks. This was achieved through several original contributions: new insights into coupling, dynamics, and statistical properties of sensors; a hardware implementation of a realtime multitone readout system; and the development of efficient signal processing algorithms for the extraction of sensor measurements from resonator response data. The performance of this improved sensor system was verified through a number of novel measurements, achieving a higher sampling rate than the best available technology yet with equivalent accuracy and precision. At the same time, these experiments revealed unforeseen applications in liquid metrology and precision microwave heating of miniature flow systems.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Microwave resonant sensors

Microwave resonant sensors use the spectral characterisation of a resonator to make high sensitivity measurements of material electromagnetic properties at GHz frequencies. They have been applied to a wide range of industrial and scientific measurements, and used to study a diversity of physical phenomena. Recently, a number of challenging dynamic applications have been developed that require very high speed and high performance, such as kinetic inductance detectors and scanning microwave microscopes. Others, such as sensors for miniaturised fluidic systems and non-invasive blood glucose sensors, also require low system cost and small footprint. This thesis investigates new and improved techniques for implementing microwave resonant sensor systems, aiming to enhance their suitability for such demanding tasks. This was achieved through several original contributions: new insights into coupling, dynamics, and statistical properties of sensors; a hardware implementation of a realtime multitone readout system; and the development of efficient signal processing algorithms for the extraction of sensor measurements from resonator response data. The performance of this improved sensor system was verified through a number of novel measurements, achieving a higher sampling rate than the best available technology yet with equivalent accuracy and precision. At the same time, these experiments revealed unforeseen applications in liquid metrology and precision microwave heating of miniature flow systems

Multiphase flow modelling for enhanced oil and gas drilling and production

From the exploration to the abandonment of an oil and gas discovery, operators and engineers are constantly faced with the challenge of achieving the best commercial potential of oil fields. Although the petroleum engineering community has significantly contributed towards maximising the potential of discovered prospects, the approach adopted so far has been compartmentalised with little (heuristics-based) or no quality integration. The highly interconnected nature of the decision factors affecting the management of any field requires increased implementation of Computer-Aided Process Engineering (CAPE) methods, thus presenting a task for which chemical engineers have the background to make useful contributions. Drilling and production are the two primary challenging operations of oilfield activities, which span through different time horizons with both fast and slow-paced dynamics. These attributes of these systems make the application of modelling, simulation, and optimisation tasks difficult. This PhD project aims to improve field planning and development decisions from a Process Systems Engineering (PSE) perspective via numerical (fluid dynamics) simulations and modelbased deterministic optimisation of drilling and production operations, respectively. Also demonstrated in this work is the importance of deterministic optimisation as a reliable alternative to classical heuristic methods. From a drilling operation perspective, this project focuses on the application of Computational Fluid Dynamics (CFD) as a tool to understand the intricacies of cuttings transport (during wellbore cleaning) with drilling fluids of non-Newtonian rheology. Simulations of two-phase solid-liquid flows in an annular domain are carried out, with a detailed analysis on the impact of several drilling parameters (drill pipe eccentricity, inclination angle, drill pipe rotation, bit penetration rate, fluid rheology, and particle properties) on the cuttings concentration, pressure drop profiles, axial fluid, and solid velocities. The influence of the flow regime (laminar and turbulent) on cuttings transport efficiency is also examined using the Eulerian-Eulerian and Lagrangian-Eulerian modelling methods. With experimentally validated simulations, this aspect of the PhD project provides new understanding on the interdependence of these parameters; thus facilitating industrial wellbore cleaning operations. The second part of this project applies mathematical optimisation techniques via reduced-order modelling strategies for the enhancement of petroleum recovery under complex constraints that characterise production operations. The motivation for this aspect of the project stems from the observation that previous PSE-based contributions aimed at enhancing field profitability, often apply over-simplifications of the actual process or neglect some key performance indices due to problem complexity. However, this project focuses on a more detailed computational integration and optimisation of the models describing the whole field development process from the reservoir to the surface facilities to ensure optimal field operations. Nonlinear Programs (NLPs), Mixed-Integer Linear Programs (MILPs), and Mixed-Integer Nonlinear Programs (MINLPs) are formulated for this purpose and solved using high-fidelity simulators and algorithms in open-source and commercial solvers. Compared to previous studies, more flow physics are incorporated and rapid computations obtained, thus enabling real-time decision support for enhanced production in the oil and gas industry