Use of massively parallel computing to improve modelling accuracy within the nuclear sector

The extreme environments found within the nuclear sector impose large safety factors on modelling analyses to ensure components operate in their desired manner. Improving analysis accuracy has clear value of increasing the design space that could lead to greater efficiency and reliability. Novel materials for new reactor designs often exhibit non-linear behaviour; additionally material properties evolve due to in-service damage a combination that is difficult to model accurately. To better describe these complex behaviours a range of modelling techniques previously under-pursued due to computational expense are being developed. This work presents recent advancements in three techniques: Uncertainty quantification (UQ); Cellular automata finite element (CAFE); Image based finite element methods (IBFEM). Case studies are presented demonstrating their suitability for use in nuclear engineering made possible by advancements in parallel computing hardware that is projected to be available for industry within the next decade costing of the order of $100k

Spectral-spatial classification of n-dimensional images in real-time based on segmentation and mathematical morphology on GPUs

The objective of this thesis is to develop efficient schemes for spectral-spatial n-dimensional image classification. By efficient schemes, we mean schemes that produce good classification results in terms of accuracy, as well as schemes that can be executed in real-time on low-cost computing infrastructures, such as the Graphics Processing Units (GPUs) shipped in personal computers. The n-dimensional images include images with two and three dimensions, such as images coming from the medical domain, and also images ranging from ten to hundreds of dimensions, such as the multiand hyperspectral images acquired in remote sensing. In image analysis, classification is a regularly used method for information retrieval in areas such as medical diagnosis, surveillance, manufacturing and remote sensing, among others. In addition, as the hyperspectral images have been widely available in recent years owing to the reduction in the size and cost of the sensors, the number of applications at lab scale, such as food quality control, art forgery detection, disease diagnosis and forensics has also increased. Although there are many spectral-spatial classification schemes, most are computationally inefficient in terms of execution time. In addition, the need for efficient computation on low-cost computing infrastructures is increasing in line with the incorporation of technology into everyday applications. In this thesis we have proposed two spectral-spatial classification schemes: one based on segmentation and other based on wavelets and mathematical morphology. These schemes were designed with the aim of producing good classification results and they perform better than other schemes found in the literature based on segmentation and mathematical morphology in terms of accuracy. Additionally, it was necessary to develop techniques and strategies for efficient GPU computing, for example, a block–asynchronous strategy, resulting in an efficient implementation on GPU of the aforementioned spectral-spatial classification schemes. The optimal GPU parameters were analyzed and different data partitioning and thread block arrangements were studied to exploit the GPU resources. The results show that the GPU is an adequate computing platform for on-board processing of hyperspectral information

Neural Cellular Automata for Solidification Microstructure Modelling

We propose Neural Cellular Automata (NCA) to simulate the microstructure development during the solidification process in metals. Based on convolutional neural networks, NCA can learn essential solidification features, such as preferred growth direction and competitive grain growth, and are up to six orders of magnitude faster than the conventional Cellular Automata (CA). Notably, NCA delivers reliable predictions also outside their training range, which indicates that they learn the physics of the solidification process. While in this study we employ data produced by CA for training, NCA can be trained based on any microstructural simulation data, e.g. from phase-field models