Model predictive control of resistive wall mode for ITER

Active feedback stabilization of the dominant resistive wall mode (RWM) for an ITER H-mode scenario at high plasma pressure using infinite-horizon model predictive control (MPC) is presented. The MPC approach is closely-related to linear-quadratic-Gaussian (LQG) control, improving the performance in the vicinity of constraints. The control-oriented model for MPC is obtained with model reduction from a high-dimensional model produced by CarMa code. Due to the limited time for on-line optimization, a suitable MPC formulation considering only input (coil voltage) constraints is chosen, and the primal fast gradient method is used for solving the associated quadratic programming problem. The performance is evaluated in simulation in comparison to LQG control. Sensitivity to noise, robustness to changes of unstable RWM dynamics, and size of the domain of attraction of the initial conditions of the unstable modes are examined.Comment: Original manuscript as submitted to Fusion Engineering and Desig

Structural health monitoring of civil infrastructure

Structural health monitoring (SHM) is a term increasingly used in the last decade to describe a range of systems implemented on full-scale civil infrastructures and whose purposes are to assist and inform operators about continued 'fitness for purpose' of structures under gradual or sudden changes to their state, to learn about either or both of the load and response mechanisms. Arguably, various forms of SHM have been employed in civil infrastructure for at least half a century, but it is only in the last decade or two that computer-based systems are being designed for the purpose of assisting owners/operators of ageing infrastructure with timely information for their continued safe and economic operation. This paper describes the motivations for and recent history of SHM applications to various forms of civil infrastructure and provides case studies on specific types of structure. It ends with a discussion of the present state-of-the-art and future developments in terms of instrumentation, data acquisition, communication systems and data mining and presentation procedures for diagnosis of infrastructural 'health'

Towards Real-Time Detection and Tracking of Spatio-Temporal Features: Blob-Filaments in Fusion Plasma

A novel algorithm and implementation of real-time identification and tracking of blob-filaments in fusion reactor data is presented. Similar spatio-temporal features are important in many other applications, for example, ignition kernels in combustion and tumor cells in a medical image. This work presents an approach for extracting these features by dividing the overall task into three steps: local identification of feature cells, grouping feature cells into extended feature, and tracking movement of feature through overlapping in space. Through our extensive work in parallelization, we demonstrate that this approach can effectively make use of a large number of compute nodes to detect and track blob-filaments in real time in fusion plasma. On a set of 30GB fusion simulation data, we observed linear speedup on 1024 processes and completed blob detection in less than three milliseconds using Edison, a Cray XC30 system at NERSC.Comment: 14 pages, 40 figure

Review of current Severe Accident Management (SAM) approaches for Nuclear Power Plants in Europe

The Fukushima accidents highlighted that both the in-depth understanding of such sequences and the development or improvement of adequate Severe Accident Management (SAM) measures are essential in order to further increase the safety of the nuclear power plants operated in Europe. To support this effort, the CESAM (Code for European Severe Accident Management) R&D project, coordinated by GRS, started in April 2013 for 4 years in the 7th EC Framework Programme of research and development of the European Commission. It gathers 18 partners from 12 countries: IRSN, AREVA NP SAS and EDF (France), GRS, KIT, USTUTT and RUB (Germany), CIEMAT (Spain), ENEA (Italy), VUJE and IVS (Slovakia), LEI (Lithuania), NUBIKI (Hungary), INRNE (Bulgaria), JSI (Slovenia), VTT (Finland), PSI (Switzerland), BARC (India) plus the European Commission Joint Research Center (JRC). The CESAM project focuses on the improvement of the ASTEC (Accident Source Term Evaluation Code) computer code. ASTEC,, jointly developed by IRSN and GRS, is considered as the European reference code since it capitalizes knowledge from the European R&D on the domain. The project aims at its enhancement and extension for use in severe accident management (SAM) analysis of the nuclear power plants (NPP) of Generation II-III presently under operation or foreseen in near future in Europe, spent fuel pools included. In the frame of the CESAM project one of the tasks consisted in the preparation of a report providing an overview of the Severe Accident Management (SAM) approaches in European Nuclear Power Plants to serve as a basis for further ASTEC improvements. This report draws on the experience in several countries from introducing SAMGs and on substantial information that has become available within the EU “stress test”. To disseminate this information to a broader audience, the initial CESAM report has been revised to include only public available information. This work has been done with the agreement and in collaboration with all the CESAM project partners. The result of this work is presented here.JRC.F.5-Nuclear Reactor Safety Assessmen

Anisotropic Neutron Imaging

Anisotropic neutron imaging presents a unique method to process images in order to produce a human-readable, singular, isotropic image from a set of anisotropic neutron images. These images were created using a Sӧller-slit collimator in a rotating device to change the azimuthal orientation of the slits with respect to the imaging plane. A multi-level, 2D-discreet wavelet transform (2D-DWT) was used to extract the information contained within each image and fuse the data into an isotropic resolution image. The footprint of the experimental system is small when compared to other neutron radiography facilities of comparable L/D and has the advantage of a relatively low acquisition time while maintaining high resolution image capture. The 2D-DWT algorithm developed within this dissertation is able to enhance the sharpness of the edges within the final image and remove the artifacts created by the slit-type collimator, which increased human readability in the test circumstanc

Near Real-Time Optimal Prediction of Adverse Events in Aviation Data

The prediction of anomalies or adverse events is a challenging task, and there are a variety of methods which can be used to address the problem. In this paper, we demonstrate how to recast the anomaly prediction problem into a form whose solution is accessible as a level-crossing prediction problem. The level-crossing prediction problem has an elegant, optimal, yet untested solution under certain technical constraints, and only when the appropriate modeling assumptions are made. As such, we will thoroughly investigate the resilience of these modeling assumptions, and show how they affect final performance. Finally, the predictive capability of this method will be assessed by quantitative means, using both validation and test data containing anomalies or adverse events from real aviation data sets that have previously been identified as operationally significant by domain experts. It will be shown that the formulation proposed yields a lower false alarm rate on average than competing methods based on similarly advanced concepts, and a higher correct detection rate than a standard method based upon exceedances that is commonly used for prediction