Engineering topics

Saracco, P.

Publications and source records attributed to Saracco, P..

3 records

DOE OSTI2022

Formulation of the density eigenvalue problem in neutron transport for relevant engineering applications

A new formulation of the density eigenvalue problem for the neutron transport equation is presented. This new eigenvalue, named ζ eigenvalue can be introduced freely in the transport model, acting on a selected portion of the phase space. Despite its broader applications and its connection with the nature of the transport operator, the ζ eigenvalue has been presented here mainly as a design-oriented technique for the efficient evaluation of the critical concentration for a specific nuclide (or mixture of nuclides). This new eigenvalue is particularly adequate to study the definition of the material composition in the criticality design process of a multiplying system. The method is then applied for the study of classical problems such as the critical moderation ratio and the poison concentration to control the reactor. Some results are presented in one dimensional configuration using the multi- group spherical harmonics approach. This eigenvalue formulation proves to be a convenient and useful way to attain criticality, also for complex, realistic systems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2014

Uncertainty Quantification for Monte Carlo simulations - Paper 18

Uncertainty quantification (UQ) generally speaking refers to a wide variety of specific problems, also pertaining different scientific areas: then studies on Uncertainty Quantification involve many different methodologies, techniques and even terminologies: to be specific we are interested in studying how uncertainties on input physical data needed for Monte Carlo simulations transfer into uncertainties on the physical output of the simulation. We mention that other sources of uncertainty are be present in a simulation environment: they can be ascribed either to model inadequacy (for instance when one neglects atomic lattice at very low energies) or to the numerical procedure involved: for the case of MC simulation the latter corresponds to the statistical error affecting the simulation itself. So in general parameter uncertainties are mixed with other kind of uncertainties. The problem then remains seemingly quite complicated, even in this more specific domain: moreover we should better specify the meaning of the rather undetermined words physical data needed for Monte Carlo simulation. In fact they may refer both those data needed to specify the experimental setup that is going to be simulated - like, e.g., geometrical or composition data, but including also some external conditions applied to the system, like temperature, pressure, electromagnetic fields and so on - and physical data - cross sections or often physical models - required to describe particle transport. All these data are generally affected by uncertainties, which through the process of simulation transfer into uncertainty on its physical outcome. Then, from the MC user's own perspective, input data refers both to those that are under his direct experimental control and to many others, whose values and uncertainties will affect the result of the simulation. In this contribution we will summarize previous results, together with some new ones. (authors)

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2013

PIXE simulation: Models, methods and technologies

The simulation of PIXE (Particle Induced X-ray Emission) is discussed in the context of general-purpose Monte Carlo systems for particle transport. Dedicated PIXE codes are mainly concerned with the application of the technique to elemental analysis, but they lack the capability of dealing with complex experimental configurations. General-purpose Monte Carlo codes provide powerful tools to model the experimental environment in great detail, but so far they have provided limited functionality for PIXE simulation. This paper reviews recent developments that have endowed the Geant4 simulation toolkit with advanced capabilities for PIXE simulation, and related efforts for quantitative validation of cross sections and other physical parameters relevant to PIXE simulation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗