Comparison of Subgrid-scale Viscosity Models and Selective Filtering Strategy for Large-eddy Simulations

Explicitly filtered large-eddy simulations (LES), combining high-accuracy schemes with the use of a selective filtering without adding an explicit subgrid-scales (SGS) model, are carried out for the Taylor-Green-vortex and the supersonic-boundary-layer cases. First, the present approach is validated against direct numerical simulation (DNS) results. Subsequently, several SGS models are implemented in order to investigate if they can improve the initial filter-based methodology. It is shown that the most accurate results are obtained when the filtering is used alone as an implicit model, and for a minimal cost. Moreover, the tests for the Taylor-Green vortex indicate that the discretization error from the numerical methods, notably the dissipation error from the high-order filtering, can have a greater influence than the SGS models

The host galaxies of luminous radio-quiet quasars

We present the results of a deep K-band imaging study which reveals the host galaxies around a sample of luminous radio-quiet quasars. The K-band images, obtained at UKIRT, are of sufficient quality to allow accurate modelling of the underlying host galaxy. Initially, the basic structure of the hosts is revealed using a modified Clean deconvolution routine optimised for this analysis. 2 of the 14 quasars are shown to have host galaxies with violently disturbed morphologies which cannot be modelled by smooth elliptical profiles. For the remainder of our sample, 2D models of the host and nuclear component are fitted to the images using the chi-squared statistic to determine goodness of fit. Host galaxies are detected around all of the quasars. The reliability of the modelling is extensively tested, and we find the host luminosity to be well constrained for 9 quasars. The derived average K-band absolute K-corrected host galaxy magnitude for these luminous radio-quiet quasars is =-25.15+/-0.04, slightly more luminous than an L* galaxy. The spread of derived host galaxy luminosities is small, although the spread of nuclear-to-host ratios is not. These host luminosities are shown to be comparable to those derived from samples of quasars of lower total luminosity and we conclude that there is no correlation between host and nuclear luminosity for these quasars. Nuclear-to-host ratios break the lower limit previously suggested from studies of lower nuclear luminosity quasars and Seyfert galaxies. Morphologies are less certain but, on the scales probed by these images, some hosts appear to be dominated by spheroids but others appear to have disk-dominated profiles.Comment: 16 pages, 8 figures, revised version to be published in MNRA

Realistic FDTD GPR antenna models optimized using a novel linear/nonlinear Full-Waveform Inversion

Finite-Difference Time-Domain (FDTD) modelling of Ground Penetrating Radar (GPR) is becoming regularly used in model-based interpretation methods like full waveform inversion (FWI), and machine learning schemes using synthetic training data. Oversimplifications in such forward models can compromise the accuracy and realism with which real GPR responses can be simulated, and this degrades the overall performance of the aforementioned interpretation techniques. Therefore, a forward model must be able to accurately simulate every part of the GPR problem that can affect the resulting scattered field. A key element is the antenna system and excitation waveform, so the model must contain a complete description of the antenna including the excitation source and waveform, the geometry, and the dielectric properties of materials in the antenna. The challenge is that some of these parameters are not known or easily measured, especially for commercial GPR antennas that are used in practice. We present a novel hybrid linear/non-linear FWI approach which can be used, with only knowledge of the basic antenna geometry, to simultaneously optimise the dielectric properties and excitation waveform of the antenna, and minimise the error between real and synthetic data. The accuracy and stability of our proposed methodology is demonstrated by successfully modelling a Geophysical Survey Systems (GSSI) Inc. 1.5~GHz commercial antenna. Our framework allows accurate models of GPR antennas to be developed without requiring detailed knowledge of every component in the antenna. This is significant because it allows commercial GPR antennas, regularly used in GPR surveys, to be more readily simulated

Active inference, evidence accumulation, and the urn task

Deciding how much evidence to accumulate before making a decision is a problem we and other animals often face, but one that is not completely understood. This issue is particularly important because a tendency to sample less information (often known as reflection impulsivity) is a feature in several psychopathologies, such as psychosis. A formal understanding of information sampling may therefore clarify the computational anatomy of psychopathology. In this theoretical letter, we consider evidence accumulation in terms of active (Bayesian) inference using a generic model of Markov decision processes. Here, agents are equipped with beliefs about their own behavior--in this case, that they will make informed decisions. Normative decision making is then modeled using variational Bayes to minimize surprise about choice outcomes. Under this scheme, different facets of belief updating map naturally onto the functional anatomy of the brain (at least at a heuristic level). Of particular interest is the key role played by the expected precision of beliefs about control, which we have previously suggested may be encoded by dopaminergic neurons in the midbrain. We show that manipulating expected precision strongly affects how much information an agent characteristically samples, and thus provides a possible link between impulsivity and dopaminergic dysfunction. Our study therefore represents a step toward understanding evidence accumulation in terms of neurobiologically plausible Bayesian inference and may cast light on why this process is disordered in psychopathology

Gaussian Process Modelling for Improved Resolution in Faraday Depth Reconstruction

The incomplete sampling of data in complex polarization measurements from radio telescopes negatively affects both the rotation measure (RM) transfer function and the Faraday depth spectra derived from these data. Such gaps in polarization data are mostly caused by flagging of radio frequency interference and their effects worsen as the percentage of missing data increases. In this paper we present a novel method for inferring missing polarization data based on Gaussian processes (GPs). Gaussian processes are stochastic processes that enable us to encode prior knowledge in our models. They also provide a comprehensive way of incorporating and quantifying uncertainties in regression modelling. In addition to providing non-parametric model estimates for missing values, we also demonstrate that Gaussian process modelling can be used for recovering rotation measure values directly from complex polarization data, and that inferring missing polarization data using this probabilistic method improves the resolution of reconstructed Faraday depth spectra.Comment: 16 pages, 10 figures, submitted to MNRA

Automated analysis of non destructive evaluation data

Interpretation of NDE data can be unreliable and difficult due to the complex interaction between the instrument, object under inspection and noise and uncertainties about the system or data. A common method of reducing the complexity and volume of data is to use thresholds. However, many of these methods are based on making subjective assessments from the data or assumptions about the system which can be source of error. Reducing data whilst retaining important information is difficult and normally compromises have to be made. This thesis has developed methods that are based on sound mathematical and scientific principles and require the minimum use of assumptions and subjective choices. Optimisation has been shown to reduce data acquired from a multilayer composite panel and hence show the ply layers. The problem can be ill-posed. It is possible to obtain a solution close to optimum and obtain confidence on the result. Important factors are: the size of the search space, representation of the data and any assumptions and choices made. Further work is required in the use of model based optimisation to measure layer thicknesses from a metal laminate panel. A number of important factors that must be addressed have been identified. Two novel approaches to removing features from Transient Eddy-Current (TEC) data have been shown to improve the visibility of defects. The best approach to take depends on the available knowledge of the system. Principal Value Decomposition (PVD) has been shown to remove layer interface reflections from ultrasonic data. However, PVD is not suited to all problems such as the TEC data described. PVD is best suited in the later stages of data reduction. This thesis has demonstrated new methods and a roadmap for solving multivariate problems, these methods may be applied to a wide range of data and problems

Optical dosimeters for Radiotherapy with MRI-LINACs

In modern radiation therapy, treatment delivery techniques are getting increasingly complex to optimise patient outcomes. In modern radiation therapy clinics, there are conditions where accurate dosimetry is challenging, yet essential to ensure that optimal treatments are being delivered. These challenging dosimetry conditions require specialised dosimeters with a set of dosimetric qualities that allow them to remain accurate in such conditions. Fibre-coupled luminescent dosimeters possess a wealth of desirable qualities that make them advantageous for a wide range of dosimetry conditions. Due to their all-optical composition (i.e. no electronics or wires attached to the sensitive volume) and their typically compact sensitive volume sizes, fibre-coupled luminescent dosimeters have high spatial resolutions whilst minimising the perturbations of radiation fields in water. Dosimetric properties such as water equivalence, energy independence and dose-rate independence are inherited through their luminescent sensitive volumes, allowing for the luminescent material to be chosen to suit the measurement conditions. In this thesis, two fibre-coupled luminescent dosimeters are developed and investigated for two such challenging clinical dosimetry conditions. Firstly, plastic scintillation dosimeters (PSDs) are investigated for dosimetry with MRI-LINACs, a technology that combines an MRI scanner with a linear accelerator (LINAC) to provide the opportunity for real-time image guidance with optimal soft tissue contrast during radiotherapy treatments. Secondly, an in-house fibre-coupled BeO dosimeter is investigated for it’s potential as a real-time in vivo dosimeter during LINAC and brachytherapy treatments