Impact of nuclear data uncertainties in the Phase-1B Benchmark

Accurate control over the spent nuclear fuel content is essential for its safe and optimized transportation, storage and management. Consequently, the reactivity of spent fuel and its isotopic content must be accurately determined. Nowadays, to predict isotopic evolution throughout irradiation and decay periods is not a problem thanks to the development of powerful codes and methodologies. In order to have a realistic confidence level in the prediction of spent fuel isotopic content, it is desirable to determine how uncertainties in the basic nuclear data affect isotopic prediction calculations by quantifying their associated uncertainties

Propagation of Nuclear Data Uncertainties in Transmutation Calculations Using ACAB Code

The assessment of the accuracy of parameters related to the reactor core performance (e.g., ke) and f el cycle (e.g., isotopic evolution/transmutation) due to the uncertainties in the basic nuclear data (ND) is a critical issue. Different error propagation techniques (adjoint/forward sensitivity analysis procedures and/or Monte Carlo technique) can be used to address by computational simulation the systematic propagation of uncertainties on the final parameters. To perform this uncertainty assessment, the ENDF covariance les (variance/correlation in energy and cross- reactions-isotopes correlations) are required. In this paper, we assess the impact of ND uncertainties on the isotopic prediction for a conceptual design of a modular European Facility for Industrial Transmutation (EFIT) for a discharge burnup of 150 GWd/tHM. The complete set of uncertainty data for cross sections (EAF2007/UN, SCALE6.0/COVA-44G), radioactive decay and fission yield data (JEFF-3.1.1) are processed and used in ACAB code.

Importance of Nuclear Data Uncertainties in Criticality Calculations

To study the propagation of the uncertainty from basic data across different scale and physics phenomena -> through complex coupled multi-physics and multi-scale simulations

Impact of Nuclear Data Uncertainties in the Phase-1B Benchmark

Accurate control over the spent nuclear fuel content is essential for its safe and optimized transportation, storage and management. Consequently, the reactivity of spent fuel and its isotopic content must be accurately determined.

Propagation of nuclear data uncertainties in fuel cycle calculations using MONTE-CARLO Technique

The uncertainty propagation in fuel cycle calculations due to Nuclear Data (ND) is a important important issue for : issue for : • Present fuel cycles (e.g. high burnup fuel programme) • New fuel cycles designs (e.g. fast breeder reactors and ADS) Different error propagation techniques can be used: • Sensitivity analysis • Response Response Surface Method Surface Method • Monte Carlo technique Then, p p , , in this paper, it is assessed the imp y pact of ND uncertainties on the decay heat and radiotoxicity in two applications: • Fission Pulse Decay ( y Heat calculation (FPDH) • Conceptual design of European Facility for Industrial Transmutation (EFIT)

Characterization and Test of a Data Acquisition System for PET

A small Positron Emission Tomography demonstrator based on LYSO slabs and Silicon Photomultiplier matrices is under construction at the University and INFN of Pisa. In this paper we present the characterization results of the read-out electronics and of the detection system. Two SiPM matrices, composed by 8 × 8 SiPM pixels, 1.5 mm pitch, have been coupled one to one to a LYSO crystals array. Custom Front-End ASICs were used to read the 64 channels of each matrix. Data from each Front-End were multiplexed and sent to a DAQ board for the digital conversion; a motherboard collects the data and communicates with a host computer through a USB port. Specific tests were carried out on the system in order to assess its performance. Futhermore we have measured some of the most important parameters of the system for PET application.

Generating Automated News to Explain the Meaning of Sensor Data

An important competence of human data analysts is to interpret and explain the meaning of the results of data analysis to end-users. However, existing automatic solutions for intelligent data analysis provide limited help to interpret and communicate information to non-expert users. In this paper we present a general approach to generating explanatory descriptions about the meaning of quantitative sensor data. We propose a type of web application: a virtual newspaper with automatically generated news stories that describe the meaning of sensor data. This solution integrates a variety of techniques from intelligent data analysis into a web-based multimedia presentation system. We validated our approach in a real world problem and demonstrate its generality using data sets from several domains. Our experience shows that this solution can facilitate the use of sensor data by general users and, therefore, can increase the utility of sensor network infrastructures.

Two way clustering of Microarray Data using a Hybrid Approach

The Microarray technique is rather powerful, as it allows to test up thousands of genes at a time, but this produces an overwhelming set of data files containing huge amounts of data, which is quite difficult to pre-process, separate, classify and correlate for interesting conclusions to be extracted. Modern machine learning, data mining and clustering techniques based on information theory, are needed to read and interpret the information contents buried in those large data sets. Independent Component Analysis method can be used to correct the data affected by corruption processes or to filter the uncorrectable one and then clustering methods can group similar genes or classify samples. In this paper a hybrid approach is used to obtain a two way unsupervised clustering for a corrected microarray data.

Importance of nuclear data uncertainties in criticality calculations

The aim of this paper is to study the importance of nuclear data uncertainties in the prediction of the uncertainties in keff for LWR (Light Water Reactor) unit-cells. The first part of this work is focused on the comparison of different sensitivity/uncertainty propagation methodologies based on TSUNAMI and MCNP codes; this study is undertaken for a fresh-fuel at different operational conditions. The second part of this work studies the burnup effect where the indirect contribution due to the uncertainty of the isotopic evolution is also analyzed.

Timing performances of a data acquisition system for time of flight PET

We are investigating the performances of a data acquisition system for Time of Flight PET, based on LYSO crystal slabs and 64 channels Silicon Photomultipliers matrices (1.2 cm2 of active area each). Measurements have been performed to test the timing capability of the detection system (SiPM matices coupled to a LYSO slab and the read-out electronics) with both test signal and radioactive source.

Estimation of functional connectivity from electromagnetic signals and the amount of empirical data required

An increasing number of neuroimaging studies are concerned with the identification of interactions or statistical dependencies between brain areas. Dependencies between the activities of different brain regions can be quantified with functional connectivity measures such as the cross-correlation coefficient. An important factor limiting the accuracy of such measures is the amount of empirical data available. For event-related protocols, the amount of data also affects the temporal resolution of the analysis. We use analytical expressions to calculate the amount of empirical data needed to establish whether a certain level of dependency is significant when the time series are autocorrelated, as is the case for biological signals. These analytical results are then contrasted with estimates from simulations based on real data recorded with magnetoencephalography during a resting-state paradigm and during the presentation of visual stimuli. Results indicate that, for broadband signals, 50–100 s of data is required to detect a true underlying cross-correlations coefficient of 0.05. This corresponds to a resolution of a few hundred milliseconds for typical event-related recordings. The required time window increases for narrow band signals as frequency decreases. For instance, approximately 3 times as much data is necessary for signals in the alpha band. Important implications can be derived for the design and interpretation of experiments to characterize weak interactions, which are potentially important for brain processing.

Feature selection in the reconstruction of complex network representations of spectral data

Complex networks have been extensively used in the last decade to characterize and analyze complex systems, and they have been recently proposed as a novel instrument for the analysis of spectra extracted from biological samples. Yet, the high number of measurements composing spectra, and the consequent high computational cost, make a direct network analysis unfeasible. We here present a comparative analysis of three customary feature selection algorithms, including the binning of spectral data and the use of information theory metrics. Such algorithms are compared by assessing the score obtained in a classification task, where healthy subjects and people suffering from different types of cancers should be discriminated. Results indicate that a feature selection strategy based on Mutual Information outperforms the more classical data binning, while allowing a reduction of the dimensionality of the data set in two orders of magnitude

Implementation of intelligent data acquisition system for ITER fast controllers using RIO devices.

A basic requirement of the data acquisition systems used in long pulse fusion experiments is the real time physical events detection in signals. Developing such applications is usually a complex task, so it is necessary to develop a set of hardware and software tools that simplify their implementation. This type of applications can be implemented in ITER using fast controllers. ITER is standardizing the architectures to be used for fast controller implementation. Until now the standards chosen are PXIe architectures (based on PCIe) for the hardware and EPICS middleware for the software. This work presents the methodology for implementing data acquisition and pre-processing using FPGA-based DAQ cards and how to integrate these in fast controllers using EPICS.

Impact of Nuclear Data Uncertainties on Advanced Fuel Cycles and Their Irradiated Fuel - A comparison between Libraries

The uncertainties on the isotopic composition throughout the burnup due to the nuclear data uncertainties are analysed. The different sources of uncertainties: decay data, fission yield and cross sections; are propagated individually, and their effect assessed. Two applications are studied: EFIT (an ADS-like reactor) and ESFR (Sodium Fast Reactor). The impact of the uncertainties on cross sections provided by the EAF-2010, SCALE6.1 and COMMARA-2.0 libraries are compared. These Uncertainty Quantification (UQ) studies have been carried out with a Monte Carlo sampling approach implemented in the depletion/activation code ACAB. Such implementation has been improved to overcome depletion/activation problems with variations of the neutron spectrum.

Towards a complete propagation of nuclear data uncertainties Fission yields and decay data

Propagation of nuclear data uncertainties to calculated values is interesting for design purposes and libraries evaluation. XSUSA, developed at GRS, propagates cross section uncertainties to nuclear calculations. In depletion simulations, fission yields and decay data are also involved and suppose a possible source of uncertainty that must be taken into account. We have developed tools to generate varied fission yields and decay libraries and to propagate uncertainties trough depletion in order to complete the XSUSA uncertainty assessment capabilities. A simple test to probe the methodology is defined and discussed.