Animation model of Krško nuclear power plant for RELAP5 calculations

Today most software applications, also in the nuclear field, come with a graphical user interface. The first graphical user interface for the RELAP5 thermal-hydraulic computer code was called the Nuclear Plant Analyzer (NPA). Later, Symbolic Nuclear Analysis Package (SNAP) was developed. The purpose of the present study was to develop SNAP animation model of Krško nuclear power plant (NPP) for RELAP5 calculations with the aim to help analyze the results. In addition, the reference calculations for Krško full scope simulator validation were performed with the latest RELAP5/MOD3.3 Patch 03 code and compared to previous RELAP5 versions to provide verified source data, needed to demonstrate animation model. In total six scenarios were analyzed: two scenarios of the small-break loss-of-coolant accident, two scenarios of the loss of main feedwater, a scenario of the anticipated transient without scram, and a scenario of the steam generator tube rupture. The use of SNAP for animation of Krško nuclear power plant analyses showed several benefits, especially better understanding of the calculated physical phenomena and processes. It can be concluded that an animation tool was created, which enables to analyze very complex accident scenarios. The graphical surface helps keeping the overview and focusing on the main influences. Also, the use of such support tools to system codes may significantly contribute to better quality of safety analysis.     

A Systematic Review of Parkinson’s Disease Pharmacogenomics: Is There Time for Translation into the Clinics?

Background: Parkinson’s disease (PD) is the second most frequent neurodegenerative disease, which creates a significant public health burden. There is a challenge for the optimization of therapies since patients not only respond differently to current treatment options but also develop different side effects to the treatment. Genetic variability in the human genome can serve as a biomarker for the metabolism, availability of drugs and stratification of patients for suitable therapies. The goal of this systematic review is to assess the current evidence for the clinical translation of pharmacogenomics in the personalization of treatment for Parkinson’s disease. Methods: We performed a systematic search of Medline database for publications covering the topic of pharmacogenomics and genotype specific mutations in Parkinson’s disease treatment, along with a manual search, and finally included a total of 116 publications in the review. Results: We analyzed 75 studies and 41 reviews published up to December of 2020. Most research is focused on levodopa pharmacogenomic properties and catechol-O-methyltransferase (COMT) enzymatic pathway polymorphisms, which have potential for clinical implementation due to changes in treatment response and side-effects. Likewise, there is some consistent evidence in the heritability of impulse control disorder via Opioid Receptor Kappa 1 (OPRK1), 5-Hydroxytryptamine Receptor 2A (HTR2a) and Dopa decarboxylase (DDC) genotypes, and hyperhomocysteinemia via the Methylenetetrahydrofolate reductase (MTHFR) gene. On the other hand, many available studies vary in design and methodology and lack in sample size, leading to inconsistent findings. Conclusions: This systematic review demonstrated that the evidence for implementation of pharmacogenomics in clinical practice is still lacking and that further research needs to be done to enable a more personalized approach to therapy for each patient.     

The stochastic walk algorithms for point location in pseudo-triangulations

A pseudo-triangulation is a planar subdivision into polygons with three convex vertices, useful for ray shooting, visibility problems and kinetic collision detection. As pseudo-triangulations are quite young, there is a lack of specialized algorithms for them. In this paper, we address the question of location in pseudo-triangulations. We propose two location algorithms based on the so-called stochastic walk and present their experimental results. The class of walk location algorithms is very popular for triangulations, namely in engineering applications, due to simplicity and low memory requirements, in spite of their non-optimality. As far as we know, no walk algorithm specialized on pseudo-triangulations has been developed before.     

A GIS-based human health risk assessment for urban green space planning—An example from Grugliasco (Italy)

The need to develop approaches for risk-based management of soil contamination, as well as the integration of the assessment of the human health risk (HHR) due to the soil contamination in the urban planning procedures has been the subject of recent attention of scientific literature and policy makers. The spatial analysis of environmental data offers multiple advantages for studying soil contamination and HHR assessment, facilitating the decision making process. The aim of this study was to explore the possibilities and benefits of spatial implementation of a quantitative HHR assessment methodology for a planning case in a typical urban environment where the soil is contaminated. The study area is located in the city of Grugliasco a part of the Turin (Italy) metropolitan area.The soils data were derived from a site specific soil survey and the land-use data from secondary sources. In the first step the soil contamination data were geo-statistically analysed and a spatial soil contamination data risk modelling procedure designed. In order to spatially assess the HHR computer routines were developed using GIS raster tools. The risk was evaluated for several different land uses for the planned naturalistic park area.The HHR assessment indicated that the contamination of soils with heavy metals in the area is not sufficient to induce considerable health problems due to typical human behaviour within the variety of urban land uses. An exception is the possibility of direct ingestion of contaminated soil which commonly occurs in playgrounds.The HHR evaluation in a planning case in the Grugliasco Municipality confirms the suitability of the selected planning option. The construction of the naturalistic park presents one solution for reducing the impacts of soil contamination on the health of citizens. The spatial HHR evaluation using GIS techniques is a diagnostic procedure for assessing the impacts of urban soil contamination, with which one can verify planning options, and provides an important step in the integration of human health protection within urban planning procedures.     

Multi-objective evolutionary algorithm using problem-specific genetic operators for community detection in networks

Automatic network clustering is an important method for mining the meaningful communities of complex networks. Uncovered communities help to understand the potential system structure and functionality. Many algorithms that use multiple optimization criteria and optimize a population of solutions are difficult to apply to real systems because they suffer a long optimization process. In this paper, in order to accelerate the optimization process and to uncover multiple significant community structures more effectively, a multi-objective evolutionary algorithm is proposed and evaluated using problem-specific genetic mutation and group crossover, and problem-specific initialization. Since crossover operators mainly contribute to performance of genetic algorithms, more problem-specific group crossover operators are introduced and evaluated for intelligent evolution of population. The experiments on both artificial and real-world networks demonstrate that the proposed evolutionary algorithm with problem-specific genetic operations has effective performance on discovering the community structure of networks.

     

Improved solidification influence modelling for Eulerian fuel-coolant interaction codes

Steam explosion experiments revealed important differences in the efficiency between simulant alumina and oxidic corium melts. The experimentally observed differences are importantly attributed to the differences in the melt droplets solidification and void production, which are limiting phenomena in the steam explosion process and have to be adequately modelled in fuel-coolant interaction codes. This article focuses on the modelling of the solidification effect. An improved solidification influence modelling approach for Eulerian fuel-coolant interaction codes was developed and is presented herein.The solidification influence modelling in fuel-coolant interaction codes is strongly related to the modelling of the temperature profile and the mechanical effect of the crust on the fragmentation process. Therefore the first objective was to introduce an improved temperature profile modelling and a fragmentation criterion for partly solidified droplets. The fragmentation criterion was based on the established modified Weber number, which considers the crust stiffness as a stabilizing force acting to retain the crust under presence of the hydrodynamic forces. The modified Weber number was validated on experimental data. The application of the developed improved solidification influence modelling enables an improved determination of the melt droplet mass, which can be efficiently involved in the fine fragmentation during the steam explosion process. Additionally, also the void production modelling is improved, because it is strongly related to the temperature profile modelling in the frame of the solidification influence modelling. Therefore the second objective was to enable an improved solidification influence modelling in codes with an Eulerian formulation of the droplet field. Two additional transported model parameters based on the most important droplets features regarding the fuel-coolant interaction behaviour, were derived. First, the crust stiffness was considered as an important property, because it enables the correct prediction of the amount of droplets participating in the fine fragmentation process during the explosion phase. Second, the heat flux from the droplet interior to the surface was considered as an important feature, because it enables to improve the surface temperature determination and reflects the history of the droplet's cooling. The last objective was to implement the improved solidification influence modelling into the Eulerian code MC3D. The first demonstrative simulations with the implemented modelling are promising and are showing improvements in the simulation capability.     

Genetic algorithm optimisation of the maintenance scheduling of generating units in a power system

A new method for optimisation of the maintenance scheduling of generating units in a power system is developed. Maintenance is scheduled to minimise the risk through minimisation of the yearly value of the loss of load expectation (LOLE) taken as a measure of the power system reliability. The proposed method uses genetic algorithm to obtain the best solution resulting in a minimal value of the annual LOLE value for the power system in the analysed period. The operational constraints for generating units are included in the method. The proposed algorithm was tested on a Macedonian power system and the obtained results were compared with the results received from the approximate methodology. The results show the improved reliability of a power system with the maintenance schedule obtained by the new method compared to the results from the approximate methodology.     

Model-based Predictive Control of Hybrid Systems: A Probabilistic Neural-network Approach to Real-time Control

This paper proposes an approach for reducing the computational complexity of a model-predictive-control strategy for discrete-time hybrid systems with discrete inputs only. Existing solutions are based on dynamic programming and multi-parametric programming approaches, while the one proposed in this paper is based on a modified version of performance-driven reachability analyses. The algorithm abstracts the behaviour of the hybrid system by building a ’tree of evolution’. The nodes of the tree represent the reachable states of a process, and the branches correspond to input combinations leading to designated states. A cost-function value is associated with each node and based on this value the exploration of the tree is driven. For any initial state, an input sequence is thus obtained, driving the system optimally over a finite horizon. According to the model predictive strategy, only the first input is actually applied to the system. The number of possible discrete input combinations is finite and the feasible set of the states of the system may be partitioned according to the optimization results. In the proposed approach, the partitioning is performed offline and a probabilistic neural network (PNN) is then trained by the set of points at the borders of the state-space partitions. The trained PNN is used as a system-state-based control-law classifier. Thus, the online computational effort is minimized and the control can be implemented in real time.     

A sweep-line algorithm for spatial clustering

This paper presents an agglomerative hierarchical clustering algorithm for spatial data. It discovers clusters of arbitrary shapes which may be nested. The algorithm uses a sweeping approach consisting of three phases: sorting is done during the preprocessing phase, determination of clusters is performed during the sweeping phase, and clusters are adjusted during the post processing phase. The properties of the algorithm are demonstrated by examples. The algorithm is also adapted to the streaming algorithm for clustering large spatial datasets.