Alloy Design for a Fusion Power Plant

Fusion power is generated when hot deuterium and tritium nuclei react, producing alpha particles and 14 MeV neutrons. These neutrons escape the reaction plasma and are absorbed by the surrounding material structure of the plant, transferring the heat of the reaction to an external cooling circuit. In such high-energy neutron irradiation environments, extensive atomic displacement damage and transmutation production of helium affect the mechanical properties of materials. Among these effects are irradiation hardening, embrittlement, and macroscopic swelling due to the formation of voids within the material. To aid understanding of these effects, Bayesian neural networks were used to model irradiation hardening and embrittlement of a set of candidate alloys, reduced-activation ferritic-martensitic steels. The models have been compared to other methods, and it is demonstrated that a neural network approach to modelling the properties of irradiated steels provides a useful tool in the future engineering of fusion materials, and for the first time, predictions are made on irradiated property changes based on the full range of available experimental parameters rather than a simplified model. In addition, the models are used to calculate optimised compositions for potential fusion alloys. Recommendations on the most fruitful ways of designing future experiments have also been made. In addition, a classical nucleation theory approach was taken to modelling the incubation and nucleation of irradiation-induced voids in these steels, with a view to minimising this undesirable phenomenon in candidate materials. Using these models, recommendations are made with regards to the engineering of future reduced-activation steels for fusion applications, and further research opportunities presented by the work are reviewed

Heart Diseases Diagnosis Using Artificial Neural Networks

Information technology has virtually altered every aspect of human life in the present era. The application of informatics in the health sector is rapidly gaining prominence and the benefits of this innovative paradigm are being realized across the globe. This evolution produced large number of patients’ data that can be employed by computer technologies and machine learning techniques, and turned into useful information and knowledge. This data can be used to develop expert systems to help in diagnosing some life-threating diseases such as heart diseases, with less cost, processing time and improved diagnosis accuracy. Even though, modern medicine is generating huge amount of data every day, little has been done to use this available data to solve challenges faced in the successful diagnosis of heart diseases. Highlighting the need for more research into the usage of robust data mining techniques to help health care professionals in the diagnosis of heart diseases and other debilitating disease conditions. Based on the foregoing, this thesis aims to develop a health informatics system for the classification of heart diseases using data mining techniques focusing on Radial Basis functions and emerging Neural Networks approach. The presented research involves three development stages; firstly, the development of a preliminary classification system for Coronary Artery Disease (CAD) using Radial Basis Function (RBF) neural networks. The research then deploys the deep learning approach to detect three different types of heart diseases i.e. Sleep Apnea, Arrhythmias and CAD by designing two novel classification systems; the first adopt a novel deep neural network method (with Rectified Linear unit activation) design as the second approach in this thesis and the other implements a novel multilayer kernel machine to mimic the behaviour of deep learning as the third approach. Additionally, this thesis uses a dataset obtained from patients, and employs normalization and feature extraction means to explore it in a unique way that facilitates its usage for training and validating different classification methods. This unique dataset is useful to researchers and practitioners working in heart disease treatment and diagnosis. The findings from the study reveal that the proposed models have high classification performance that is comparable, or perhaps exceed in some cases, the existing automated and manual methods of heart disease diagnosis. Besides, the proposed deep-learning models provide better performance when applied on large data sets (e.g., in the case of Sleep Apnea), with reasonable performance with smaller data sets. The proposed system for clinical diagnoses of heart diseases, contributes to the accurate detection of such disease, and could serve as an important tool in the area of clinic support system. The outcome of this study in form of implementation tool can be used by cardiologists to help them make more consistent diagnosis of heart diseases

Perspectives on multiscale modelling and experiments to accelerate materials development for fusion

Prediction of material performance in fusion reactor environments relies on computational modelling, and will continue to do so until the first generation of fusion power plants come on line and allow long-term behaviour to be observed. In the meantime, the modelling is supported by experiments that attempt to replicate some aspects of the eventual operational conditions. In 2019, a group of leading experts met under the umbrella of the IEA to discuss the current position and ongoing challenges in modelling of fusion materials and how advanced experimental characterisation is aiding model improvement. This review draws from the discussions held during that workshop. Topics covering modelling of irradiation-induced defect production and fundamental properties, gas behaviour, clustering and segregation, defect evolution and interactions are discussed, as well as new and novel multiscale simulation approaches, and the latest efforts to link modelling to experiments through advanced observation and characterisation techniques.MRG, SLD, and DRM acknowledge funding by the RCUK Energy Programme [grant number EP/T012250/1]. Part of this work has been carried out within the framework of the EUROFusion Consortium and has received funding from the Euratom research and training programme 2014–2018 and 2019–2020 under grant Agreement No. 633053. The views and opinions expressed herein do not necessarily reflect those of the European Commission. JRT acknowledges funding from the US Department of Energy (DOE) through grant DE-SC0017899. ZB, LY,BDW, and SJZ acknowledge funding through the US DOE Fusion Energy Sciences grant DE-SC0006661ZB, LY and BDW also were partially supported from the US DOE Office of Science, Office of Fusion Energy Sciences and Office of Advanced Scientific Computing Research through the Scientific Discovery through Advanced Computing (SciDAC) project on Plasma-Surface Interactions. JMa acknowledges support from the US-DOEs Office of Fusion Energy Sciences (US-DOE), project DE-SC0019157. Pacific Northwest National Laboratory is operated by Battelle Memorial Institute for the US Department of Energy (DOE) under contract DE-AC05-76RL01830. YO and YZ were supported as part of the Energy Dissipation to Defect Evolution (EDDE), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under contract number DE-AC05-00OR22725. TS and TT are supported by JSPS KAKENHI Grant Number 19K05338

Dynamic risk-based analysis of petroleum reservoir production systems

Petroleum reservoirs are complex process systems defined by intrinsically uncertain data and a distinct pressure gradient. The upstream sector’s assets are with huge uncertainties and high risks. Thus, the investments in these complex geologic process systems majorly suffer severe dynamic risks due to the process’ complex dynamics, process data’s temporal and spatial variabilities, and data/model’s uncertainties. Over time, the complex dynamic risks of the reservoir production system have resulted in unforeseen severe production fluctuations, total process system failures, and/or abrupt well shut-in due to uncontrollable circumstances. Hence, the need to introduce a multipurpose dynamic risk-based smart production prognostic approach to address the outlined inherent petroleum production challenges. This thesis presents dynamic risks assessment models for dynamic risks-based analysis of petroleum reservoir production systems. Different possible production scenarios are captured with the developed adaptive hybrid model with the following highlighted novel scientific contributions. Firstly, a dynamic risk-based predictive model is introduced to forecast production and capture the parameters variabilities, data and model’s uncertainties, and dynamic risks of primary recovery processes. This is followed with an introduced novel model for dynamic risks monitoring and production forecast of secondary recovery processes. A novel model is also presented to incorporate dual reservoir energy support mechanisms in production predictions and associated dynamic risks forecast under lift mechanisms. In addition, a dynamic economic risks analysis model is proposed to consider economic risk assessment of the reservoir production systems. Lastly, a dynamic risks-based smart model is proposed to capture sand face pressure enhancement influence on the reservoir production system with production pump schemes

Proceedings Of The 18th Annual Meeting Of The Asia Oceania Geosciences Society (Aogs 2021)

The 18th Annual Meeting of the Asia Oceania Geosciences Society (AOGS 2021) was held from 1st to 6th August 2021. This proceedings volume includes selected extended abstracts from a challenging array of presentations at this conference. The AOGS Annual Meeting is a leading venue for professional interaction among researchers and practitioners, covering diverse disciplines of geosciences

Virginia Commonwealth University Courses

Listing of courses for the 2021-2022 year

Virginia Commonwealth University Undergraduate Bulletin

Undergraduate bulletin for Virginia Commonwealth University for the academic year 2021-2022. It includes information on academic regulations, degree requirements, course offerings, faculty, academic calendar, and tuition and expenses for undergraduate programs

Virginia Commonwealth University Undergraduate Bulletin

Undergraduate bulletin for Virginia Commonwealth University for the academic year 2022-2023. It includes information on academic regulations, degree requirements, course offerings, faculty, academic calendar, and tuition and expenses for undergraduate programs

Virginia Commonwealth University Undergraduate Bulletin

Undergraduate bulletin for Virginia Commonwealth University for the academic year 2002-2003. It includes information on academic regulations, degree requirements, course offerings, faculty, academic calendar, and tuition and expenses for undergraduate programs

Virginia Commonwealth University Courses

Listing of courses for the 2022-2023 year