A GIS based analysis of meandering channel evolution focussing on channel curvature, meander cut-off events and bank erosion rate interactions over a 125-year time period

The development and evolution of meandering channels has long held a fascination for scientists of many disciplines. Researchers have used field, archival and modelling sources to investigate the dynamics and long-term behaviour of meandering channels to help understand and predict future changes. There is still a great deal of uncertainty about why different meander bends are active and why similar bends are stable. Questions also remain about how the impact of change on one part of the river is propagated upstream and downstream. These questions are important as dynamic rivers can be challenging for human settlements, but also provide an opportunity to increase biodiversity and habitat opportunity in the riparian zone. The aims of this thesis are to explore the variability of meander migration in space and time for two actively meandering rivers in the UK. The long-term evolutionary behaviour of individual bends is explored using historic Ordnance Survey maps. Finally, a data modelling approach is used to predict channel change within a large dataset, with rivers from a third catchment, the River Ribble included, with a view to predicting future channel change. It is shown a high degree of variability exists both along channel reaches and between different periods. The mean annual migration rate for a reach varied from a minimum of 0.04ma-1 and 0.15ma-1 in the Lugg and Till catchments respectively to a maximum 1.29ma-1 and 0.92ma-1 for the catchments. The River Migration Toolbox is used to measure the migration rate of individual bends for each reach and rates of up to 3ma-1 were measured, which represent some of the most active rivers in the UK. There appears to be a strong relationship between the rate of migration and length of period between two map dates as the shortest time between the map dates tended to have the highest migration rates. The implications for management of this phenomenon is discussed. The evolutionary behaviour of meander bends is then investigated and the relationship between channel radius of curvature and migration rate quantified. It is shown that the migration rate tends to increase as the radius of curvature decreased, although there was a high amount of scatter present in the results. The trajectories of individual bends in a bend curvature-migration rate phase space were analysed to help understand the behaviour of different types of bends. The changes of the curvature profile of individual bends was measured and showed bends would develop a long section with a low radius of curvature, before becoming compound or double-headed. The final section of the research uses a machine learning approach to understand the importance of the different factors on the migration rate of individual bends and to predict channel change. The approach showed human infrastructure has an important control on migration rate, along with channel curvature and riparian vegetation. The machine learning approach was able to predict the location of active meandering channels but had a worse performance when predicting the rate of erosion. The potential future applications of the approach are discussed

Flow-3D CFD model of bifurcated open channel flow: setup and validation

Bifurcation is a morphological feature present in most of fluvial systems; where a river splits into two channels, each bearing a portion of the flow and sediments. Extensive theoretical studies of river bifurcations were performed to understand the nature of flow patterns at such diversions. Nevertheless, the complexity of the flow structure in the bifurcated channel has resulted in various constraints on physical experimentation, so computational modelling is required to investigate the phenomenon. The advantages of computational modelling compared with experimental research (e.g. simple variable control, reduced cost, optimize design condition etc.) are widely known. The great advancement of computer technologies and the exponential increase in power, memory storage and affordability of high-speed machines in the early 20th century led to evolution and wide application of numerical fluid flow simulations, generally referred to as Computational Fluid Dynamics {CFD). In this study, the open-channel flume with a lateral channel established by Momplot et al (2017) is modelled in Flow-3D. The original investigation on divided flow of equal widths as simulated in ANSYS Fluent and validated with velocity measurements

8th Annual Jackson School of Geosciences Student Research Symposium, February 2, 2019

ConocoPhillipsGeological Science

VI Workshop on Computational Data Analysis and Numerical Methods: Book of Abstracts

The VI Workshop on Computational Data Analysis and Numerical Methods (WCDANM) is going to be held on June 27-29, 2019, in the Department of Mathematics of the University of Beira Interior (UBI), Covilhã, Portugal and it is a unique opportunity to disseminate scientific research related to the areas of Mathematics in general, with particular relevance to the areas of Computational Data Analysis and Numerical Methods in theoretical and/or practical field, using new techniques, giving especial emphasis to applications in Medicine, Biology, Biotechnology, Engineering, Industry, Environmental Sciences, Finance, Insurance, Management and Administration. The meeting will provide a forum for discussion and debate of ideas with interest to the scientific community in general. With this meeting new scientific collaborations among colleagues, namely new collaborations in Masters and PhD projects are expected. The event is open to the entire scientific community (with or without communication/poster)

3-D Inorganic Crystal Structure Generation and Property Prediction via Representation Learning

Generative models have been successfully used to synthesize completely novel images, text, music and speech. As such, they present an exciting opportunity for the design of new materials for functional applications. So far, generative deep-learning methods applied to molecular and drug discovery have yet to produce stable and novel 3-D crystal structures across multiple material classes. To that end, we herein present an autoencoder-based generative deep-representation learning pipeline for geometrically optimized 3-D crystal structures that simultaneously predicts the values of eight target properties. The system is highly general, as demonstrated through creation of novel materials from three separate material classes: binary alloys, ternary perovskites and Heusler compounds. Comparison of these generated structures to those optimized via electronic-structure calculations shows that our generated materials are valid and geometrically optimized