Dynamic PRA: an Overview of New Algorithms to Generate, Analyze and Visualize Data

State of the art PRA methods, i.e. Dynamic PRA (DPRA) methodologies, largely employ system simulator codes to accurately model system dynamics. Typically, these system simulator codes (e.g., RELAP5 ) are coupled with other codes (e.g., ADAPT, RAVEN that monitor and control the simulation. The latter codes, in particular, introduce both deterministic (e.g., system control logic, operating procedures) and stochastic (e.g., component failures, variable uncertainties) elements into the simulation. A typical DPRA analysis is performed by: 1. Sampling values of a set of parameters from the uncertainty space of interest 2. Simulating the system behavior for that specific set of parameter values 3. Analyzing the set of simulation runs 4. Visualizing the correlations between parameter values and simulation outcome Step 1 is typically performed by randomly sampling from a given distribution (i.e., Monte-Carlo) or selecting such parameter values as inputs from the user (i.e., Dynamic Event Tre

Dynamic PRA: An Overview of New Algorithms to Gene

State of the art PRA methods, i.e. Dynamic PRA (D

Local fusion of an ensemble of semi-supervised self organizing maps for post-processing accidental scenarios

Integrated Deterministic and Probabilistic Safety Analysis (IDPSA) of dynamic systems is challenged by the need of implementing efficient methods for accidental scenarios generation (that are to be increased with respect to conventional PSA, due to the necessary consideration of failure events timing and sequencing along the scenarios) and for their post-processing for retrieving safety relevant information regarding the system behavior (that, in the context of IDPSA consists in the classification of the generated scenarios as safe, failed, Near Misses (NMs) and Prime Implicants (PIs)). The large amount of generated scenarios makes the computational cost for scenario post-processing enormous and the retrieved information difficult to interpret. To address this issue, in this paper we propose the use of an ensemble of Semi-Supervised Self Organizing Maps (SSSOM) whose outcomes are combined by a locally weighted aggregation: we resort to the Local Fusion (LF) principle for accounting the classification reliability of the different SSSOM classifiers, for the type of scenario to be classified. The strategy is applied for the post-processing of the accidental scenarios of a dynamic U-Tube Steam Generator (UTSG)

Postprocessing of Accidental Scenarios by Semi-Supervised Self-Organizing Maps

Integrated Deterministic and Probabilistic Safety Analysis (IDPSA) of dynamic systems calls for the development of efficient methods for accidental scenarios generation. The necessary consideration of failure events timing and sequencing along the scenarios requires the number of scenarios to be generated to increase with respect to conventional PSA. Consequently, their postprocessing for retrieving safety relevant information regarding the system behavior is challenged because of the large amount of generated scenarios that makes the computational cost for scenario postprocessing enormous and the retrieved information difficult to interpret. In the context of IDPSA, the interpretation consists in the classification of the generated scenarios as safe, failed, Near Misses (NMs), and Prime Implicants (PIs). To address this issue, in this paper we propose the use of an ensemble of Semi-Supervised Self-Organizing Maps (SSSOMs) whose outcomes are combined by a locally weighted aggregation according to two strategies: a locally weighted aggregation and a decision tree based aggregation. In the former, we resort to the Local Fusion (LF) principle for accounting the classification reliability of the different SSSOM classifiers, whereas in the latter we build a classification scheme to select the appropriate classifier (or ensemble of classifiers), for the type of scenario to be classified. The two strategies are applied for the postprocessing of the accidental scenarios of a dynamic U-Tube Steam Generator (UTSG)

Doctor of Philosophy

dissertationWith the ever-increasing amount of available computing resources and sensing devices, a wide variety of high-dimensional datasets are being produced in numerous fields. The complexity and increasing popularity of these data have led to new challenges and opportunities in visualization. Since most display devices are limited to communication through two-dimensional (2D) images, many visualization methods rely on 2D projections to express high-dimensional information. Such a reduction of dimension leads to an explosion in the number of 2D representations required to visualize high-dimensional spaces, each giving a glimpse of the high-dimensional information. As a result, one of the most important challenges in visualizing high-dimensional datasets is the automatic filtration and summarization of the large exploration space consisting of all 2D projections. In this dissertation, a new type of algorithm is introduced to reduce the exploration space that identifies a small set of projections that capture the intrinsic structure of high-dimensional data. In addition, a general framework for summarizing the structure of quality measures in the space of all linear 2D projections is presented. However, identifying the representative or informative projections is only part of the challenge. Due to the high-dimensional nature of these datasets, obtaining insights and arriving at conclusions based solely on 2D representations are limited and prone to error. How to interpret the inaccuracies and resolve the ambiguity in the 2D projections is the other half of the puzzle. This dissertation introduces projection distortion error measures and interactive manipulation schemes that allow the understanding of high-dimensional structures via data manipulation in 2D projections

A Framework to Expand and Advance Probabilistic Risk Assessment to Support Small Modular Reactors

During the early development of nuclear power plants, researchers and engineers focused on many aspects of plant operation, two of which were getting the newly-found technology to work and minimizing the likelihood of perceived accidents through redundancy and diversity. As time, and our experience, has progressed, the realization of plant operational risk/reliability has entered into the design, operation, and regulation of these plants. But, to date, we have only dabbled at the surface of risk and reliability technologies. For the next generation of small modular reactors (SMRs), it is imperative that these technologies evolve into an accepted, encompassing, validated, and integral part of the plant in order to reduce costs and to demonstrate safe operation. Further, while it is presumed that safety margins are substantial for proposed SMR designs, the depiction and demonstration of these margins needs to be better understood in order to optimize the licensing process

Effective Investment Planning in Waste-to-Energy Systems

Disertační práce se zabývá aplikací simulačních a optimalizačních metod v oblasti energetického využití odpadů. V úvodu práce je popsán současný stav v oblasti odpadového hospodářství v EU se zaměřením na ČR. Následující kapitola pojednává o hodnotících kritériích investičních záměrů a základních principech stochastického programování. Jádrem práce jsou matematické modely zaměřené na plánování a provoz jednotlivých zařízení a problematiku spojenou se svozem odpadu. Dopravní úloha dává do souvislostí všechny uvažované projekty v hodnoceném zájmovém území a je možné díky ní simulovat toky odpadu mezi producenty a zpracovateli. Přístup je demonstrován na pěti případových studiích. V prvních třech studiích byly uvedeny výpočty pro potenciálního investora. Hlavním výstupem bylo určení míry atraktivity investice a identifikace největších rizik. Další případová studie byla věnována analýze z pohledu statní koncepce. V poslední případové studii je detailně analyzována problematika nakládání s odpadem z pohledu producentů.PhD thesis deals with the application of the simulation and optimization methods in the waste-to-energy field. An introduction describes the current state of the waste management in the EU with the focus on the Czech Republic. In the following chapter the evaluation criteria for investment intentions and the basic principles of stochastic programming are discussed. The core of the work lays in the mathematical models for the planning and operation of the process plants as well as in the mathematical models for the waste collection. The transportation problem involves all considered technological elements and therefore it is possible to simulate the waste streams between the producers and processors. This approach is demonstrated with five case studies. In the first three studies the calculations for the potential investor are presented. The main outcome of these case studies is the determination of the level of attractiveness of investment and the identification the greatest risks. Another case study is devoted to an analysis with the focus on perspective of government policies and in the last case study the issue of the waste management is analyzed in detail from the perspective of the waste producers. Developed computational tools are flexible and can be further developed and adapted based on the objectives of the specific tasks.

Preliminary framework for the run-ahead predictive simulation software (RAPSS)

The Run-Ahead Predictive Simulation Software (RAPSS) is an architecture designed for faster-than-real-time decision support for operators of complex networks. To enable further development of the RAPSS methodology, the necessary proof of principle is illustrated in two applications: decision support for shift technical advisors in nuclear power plant control rooms (RAPSS-STA), and in the event of a release outside of containment, decision support for emergency operation centers (RAPSS-EOC)