Machine Learning and Deep Learning applications for the protection of nuclear fusion devices

This Thesis addresses the use of artificial intelligence methods for the protection of nuclear fusion devices with reference to the Joint European Torus (JET) Tokamak and the Wendenstein 7-X (W7-X) Stellarator. JET is currently the world's largest operational Tokamak and the only one operated with the Deuterium-Tritium fuel, while W7-X is the world's largest and most advanced Stellarator. For the work on JET, research focused on the prediction of “disruptions”, and sudden terminations of plasma confinement. For the development and testing of machine learning classifiers, a total of 198 disrupted discharges and 219 regularly terminated discharges from JET. Convolutional Neural Networks (CNNs) were proposed to extract the spatiotemporal characteristics from plasma temperature, density and radiation profiles. Since the CNN is a supervised algorithm, it is necessary to explicitly assign a label to the time windows of the dataset during training. All segments belonging to regularly terminated discharges were labelled as 'stable'. For each disrupted discharge, the labelling of 'unstable' was performed by automatically identifying the pre-disruption phase using an algorithm developed during the PhD. The CNN performance has been evaluated using disrupted and regularly terminated discharges from a decade of JET experimental campaigns, from 2011 to 2020, showing the robustness of the algorithm. Concerning W7-X, the research involved the real-time measurement of heat fluxes on plasma-facing components. THEODOR is a code currently used at W7-X for computing heat fluxes offline. However, for heat load control, fast heat flux estimation in real-time is required. Part of the PhD work was dedicated to refactoring and optimizing the THEODOR code, with the aim of speeding up calculation times and making it compatible with real-time use. In addition, a Physics Informed Neural Network (PINN) model was proposed to bring thermal flow computation to GPUs for real-time implementation

A model-based method for visualization, monitoring, and diagnosis of fouling in heat exchangers

A critical review of current methods for monitoring the performance of heat exchangers in the presence of fouling highlights a number of pitfalls. An improved analysis method and visualization of operation data (the TH-λ plot) are proposed, which enable to accurately and rapidly estimate the location and extent of fouling, the properties of the deposit, and their impact on exchanger performance. The method uses advanced dynamic thermo-hydraulic models to analyze the data. The visualization presents this information in a way easily interpreted by field engineers. The superior features are demonstrated on various applications, where traditional methods give poor visibility or outright wrong information about underlying events. These include organic fouling deposition and aging, incomplete cleaning, multicomponent deposits, and changes in fouling behavior. First, the basic concepts are illustrated with idealized examples (constant inlet conditions, using simulated data). The approach is then applied to three real refining case studies, with pressure drop either measured or generated via soft sensors. The results show that the advanced dynamic models used enable to properly integrate and interpret highly variable data measurements, explain complex underlying thermal and hydraulic effects, adequately monitor performance, and rapidly detect changes in fouling behavior. The approach provides a new practical tool for monitoring of heat exchanger performance and early fouling diagnosis

Estimation of the remaining useful life of hydro generators

O monitoramento da condição dos geradores é muito desejável para uma operação confiável de uma usina hidrelétrica. As atividades de manutenção podem ser programadas para evitar falhas inesperadas que podem levar a meses ou anos de máquinas paradas sem geração. Estudos indicam que o isolamento do estator é a principal causa de falha do gerador. Nesse sentido, a base da metodologia proposta é o monitoramento do estado atual do sistema de isolamento do estator de hidrogeradores. Testes de descarga parcial nos enrolamentos do estator são aplicados para acessar a condição de isolamento. Um algoritmo para estimar a vida útil remanescente é a principal contribuição deste trabalho. Esta estimativa é baseada em avaliações estatísticas de hidro-geradores e na condição real do sistema de isolamento do estator. Testes de envelhecimento acelerado em amostras de estator com ampla aquisição de variáveis são realizados para entender o processo de envelhecimento. O algoritmo proposto é testado em casos simulados e em dados reais de um ensaio de ciclo térmico, no qual foi observado a ruptura do isolamento.Agência 1The monitoring of generators’ condition is very desirable for a reliable operation of a hydropower plant. Maintenance activities can be scheduled to avoid unexpected failures that can lead to months or years of machines stopped without generation. Studies indicate that stator insulation is the leading cause of generator failure. In this sense, the proposal methodology’s base is the monitorization of the actual health stage of the stator insulation system of hydro generators. Partial discharge tests in stator windings are applied to access the insulation condition. An algorithm to estimate the remaining useful life is the main contribution of this work. This estimation is based on both statistical evaluations of hydro generators and the stator insulation system’s actual condition. Accelerated aging tests in stator specimens with wide variables acquisition are performed to understand the aging process. The proposed algorithm is tested in simulated cases and real data from a thermal cycle test, which observed an insulation breakdown

Nuclear Power - Control, Reliability and Human Factors

Advances in reactor designs, materials and human-machine interfaces guarantee safety and reliability of emerging reactor technologies, eliminating possibilities for high-consequence human errors as those which have occurred in the past. New instrumentation and control technologies based in digital systems, novel sensors and measurement approaches facilitate safety, reliability and economic competitiveness of nuclear power options. Autonomous operation scenarios are becoming increasingly popular to consider for small modular systems. This book belongs to a series of books on nuclear power published by InTech. It consists of four major sections and contains twenty-one chapters on topics from key subject areas pertinent to instrumentation and control, operation reliability, system aging and human-machine interfaces. The book targets a broad potential readership group - students, researchers and specialists in the field - who are interested in learning about nuclear power

Chemistry and the Worm: Caenorhabditis elegans as a Platform for Integrating Chemical and Biological Research

This Review discusses the potential usefulness of the worm Caenorhabditis elegans as a model organism for chemists interested in studying living systems. C. elegans, a 1 mm long roundworm, is a popular model organism in almost all areas of modern biology. The worm has several features that make it attractive for biology: it is small (<1000 cells), transparent, and genetically tractable. Despite its simplicity, the worm exhibits complex phenotypes associated with multicellularity: the worm has differentiated cells and organs, it ages and has a well-defined lifespan, and it is capable of learning and remembering. This Review argues that the balance between simplicity and complexity in the worm will make it a useful tool in determining the relationship between molecular-scale phenomena and organism-level phenomena, such as aging, behavior, cognition, and disease. Following an introduction to worm biology, the Review provides examples of current research with C. elegans that is chemically relevant. It also describes tools—biological, chemical, and physical—that are available to researchers studying the worm.Chemistry and Chemical Biolog

TECHNART 2017. Non-destructive and microanalytical techniques in art and cultural heritage. Book of abstracts

440 p.TECHNART2017 is the international biannual congress on the application of Analytical Techniques in Art and Cultural Heritage. The aim of this European conference is to provide a scientific forum to present and promote the use of analytical spectroscopic techniques in cultural heritage on a worldwide scale to stimulate contacts and exchange experiences, making a bridge between science and art. This conference builds on the momentum of the previous TECHNART editions of Lisbon, Athens, Berlin, Amsterdam and Catania, offering an outstanding and unique opportunity for exchanging knowledge on leading edge developments. Cultural heritage studies are interpreted in a broad sense, including pigments, stones, metal, glass, ceramics, chemometrics on artwork studies, resins, fibers, forensic applications in art, history, archaeology and conservation science. The meeting is focused in different aspects: - X-ray analysis (XRF, PIXE, XRD, SEM-EDX). - Confocal X-ray microscopy (3D Micro-XRF, 3D Micro-PIXE). - Synchrotron, ion beam and neutron based techniques/instrumentation. - FT-IR and Raman spectroscopy. - UV-Vis and NIR absorption/reflectance and fluorescence. - Laser-based analytical techniques (LIBS, etc.). - Magnetic resonance techniques. - Chromatography (GC, HPLC) and mass spectrometry. - Optical imaging and coherence techniques. - Mobile spectrometry and remote sensing