Optimal laser intensity profiles for a uniform target illumination in direct-drive inertial confinement fusion

A numerical method providing the optimal laser intensity profiles for a direct-drive inertial confinement fusion scheme has been developed. The method provides an alternative approach to phase-space optimization studies, which can prove computationally expensive. The method applies to a generic irradiation configuration characterized by an arbitrary number NB of laser beams provided that they irradiate the whole target surface, and thus goes beyond previous analyses limited to symmetric configurations. The calculated laser intensity profiles optimize the illumination of a spherical target. This paper focuses on description of the method, which uses two steps: first, the target irradiation is calculated for initial trial laser intensities, and then in a second step the optimal laser intensities are obtained by correcting the trial intensities using the calculated illumination. A limited number of example applications to direct drive on the Laser MegaJoule (LMJ) are described.

Caracterización de colectores para sistemas de concentración fotovoltaica

Este proyecto, titulado “Caracterización de colectores para concentración fotovoltaica”, consiste en una aplicación en Labview para obtener las características de los elementos ópticos utilizados en sistemas de concentración fotovoltaica , atendiendo a la distribución espacial del foco de luz concentrado que generan. Un sistema de concentración fotovoltaica utiliza un sistema óptico para transmitir la radiación luminosa a la célula solar aumentando la densidad de potencia luminosa. Estos sistemas ópticos están formados por espejos o lentes para recoger la radiación incidente en ellos y concentrar el haz de luz en una superficie mucho menor. De esta manera se puede reducir el área de material semiconductor necesario, lo que conlleva una importante reducción del coste del sistema. Se pueden distinguir diferentes sistemas de concentración dependiendo de la óptica que emplee, la estructura del receptor o el rango de concentración. Sin embargo, ya que el objetivo es analizar la distribución espacial, diferenciaremos dos tipos de concentradores dependiendo de la geometría que presenta el foco de luz. El concentrador lineal o cilíndrico que enfoca sobre una línea, y el concentrador de foco puntual o circular que enfoca la luz sobre un punto. Debido a esta diferencia el análisis en ambos casos se realizará de forma distinta. El análisis se realiza procesando una imagen del foco tomada en el lugar del receptor, este método se llama LS-CCD (Difusión de luz y captura con CCD). Puede utilizarse en varios montajes dependiendo si se capta la imagen por reflexión o por transmisión en el receptor. En algunos montajes no es posible captar la imagen perpendicular al receptor por lo que la aplicación realizará un ajuste de perspectiva para obtener el foco con su forma original. La imagen del foco ofrece información detallada acerca de la uniformidad del foco mediante el mapa de superficie, que es una representación en 3D de la imagen pero que resulta poco manejable. Una representación más sencilla y útil es la que ofrecen los llamados “perfiles de intensidad”. El perfil de intensidad o distribución de la irradiancia que representa la distribución de la luz para cada distancia al centro, y el perfil acumulado o irradiancia acumulada que representa la luz contenida en relación también al centro. Las representaciones de estos perfiles en el caso de un concentrador lineal y otro circular son distintas debido a su diferente geometría. Mientras que para un foco lineal se expresa el perfil en función de la semi-anchura del receptor, para uno circular se expresa en función del radio. En cualquiera de los casos ofrecen información sobre la uniformidad y el tamaño del foco de luz necesarios para diseñar el receptor. El objetivo de este proyecto es la creación de una aplicación software que realice el procesado y análisis de las imágenes obtenidas del foco de luz de los sistemas ópticos a caracterizar. La aplicación tiene una interfaz sencilla e intuitiva para que pueda ser empleada por cualquier usuario. Los recursos necesarios para realizar el proyecto son: un PC con sistema operativo Windows, el software Labview 8.6 Professional Edition y los módulos NI Vision Development Module (para trabajar con imágenes) y NI Report Generation Toolkit (para realizar reportes y guardar datos de la aplicación). ABSTRACT This project, called “Characterization of collectors for concentration photovoltaic systems”, consists in a Labview application to obtain the characteristics of the optical elements used in photovoltaic concentrator, taking into account the spatial distribution of concentrated light source generated. A concentrator photovoltaic system uses an optical system to transmit light radiation to the solar cell by increasing the light power density. This optical system are formed by mirrors or lenses to collect the radiation incident on them and focus the beam of light in a much smaller surface area. In this way you can reduce the area of semiconductor material needed, which implies a significant reduction in system cost. There are different concentration systems depending on the optics used, receptor structure or concentration range. However, as the aim is to analyze the spatial distribution, distinguish between two types of concentrators depending on the geometry that has the light focus. The linear or cylindrical concentrator that focused on a line, and the circular concentrator that focused light onto a point. Because this difference in both cases the analysis will be carried out differently. The analysis is performed by processing a focus image taken at the receiver site, this method is called “LS-CCD” (Light Scattering and CCD recording). Can be used in several mountings depending on whether the image is captured by reflection or transmission on the receiver. In some mountings it is not possible to capture the image perpendicular to the receivers so that the application makes an adjustment of perspective to get the focus to its original shape. The focus image provides detail information about the uniformity of focus through the surface map, which is a 3D image representation but it is unwieldy. A simple and useful representation is provided by so called “intensity profiles”. The intensity profile or irradiance distribution which represents the distribution of light to each distance to the center. The accumulated profile or accumulated irradiance that represents the cumulative light contained in relation also to the center. The representation of these profiles in the case of a linear and a circular concentrator are different due to their distinct geometry. While for a line focus profile is expressed in terms of semi-width of the receiver, for a circular concentrator is expressed in terms of radius. In either case provides information about the uniformity and size of focus needed to design the receiver. The objective of this project is the creation of a software application to perform processing and analysis of images obtained from light source of optical systems to characterize.The application has a simple and a intuitive interface so it can be used for any users. The resources required for the project are: a PC with Windows operating system, LabVIEW 8.6 Professional Edition and the modules NI Vision Development Module (for working with images) and NI Report Generation Toolkit (for reports and store application data .)

Automatic motion compensation of free breathing acquired myocardial perfusion data by using independent component analysis

Images acquired during free breathing using first-pass gadolinium-enhanced myocardial perfusion magnetic resonance imaging (MRI) exhibit a quasiperiodic motion pattern that needs to be compensated for if a further automatic analysis of the perfusion is to be executed. In this work, we present a method to compensate this movement by combining independent component analysis (ICA) and image registration: First, we use ICA and a time?frequency analysis to identify the motion and separate it from the intensity change induced by the contrast agent. Then, synthetic reference images are created by recombining all the independent components but the one related to the motion. Therefore, the resulting image series does not exhibit motion and its images have intensities similar to those of their original counterparts. Motion compensation is then achieved by using a multi-pass image registration procedure. We tested our method on 39 image series acquired from 13 patients, covering the basal, mid and apical areas of the left heart ventricle and consisting of 58 perfusion images each. We validated our method by comparing manually tracked intensity profiles of the myocardial sections to automatically generated ones before and after registration of 13 patient data sets (39 distinct slices). We compared linear, non-linear, and combined ICA based registration approaches and previously published motion compensation schemes. Considering run-time and accuracy, a two-step ICA based motion compensation scheme that first optimizes a translation and then for non-linear transformation performed best and achieves registration of the whole series in 32 ± 12 s on a recent workstation. The proposed scheme improves the Pearsons correlation coefficient between manually and automatically obtained time?intensity curves from .84 ± .19 before registration to .96 ± .06 after registration