Planar Laser-Induced Fluorescence fuel concentration measurements in isothermal Diesel sprays

Application of SLIPI-Based Techniques for Droplet Size, Concentration, and Liquid Volume Fraction Mapping in Sprays

Structured laser illumination planar imaging (SLIPI)-based techniques have been employed during the past decade for addressing multiple light scattering issues in spray imaging. In this article, SLIPI droplet sizing based on the intensity ratio of laser-induced fluorescence (LIF) over Mie scattering (SLIPI-LIF/Mie) and SLIPI-Scan for extinction-coefficient (µe) mapping are applied simultaneously. In addition, phase Doppler anemometry (PDA) and numerical calculations based on the Lorenz–Mie theory are also employed in order to extract the droplets Sauter mean diameter (SMD), the droplets number density (N), and the liquid volume fraction (LVF) in a steady asymmetric hollow cone water spray. The SLIPI-LIF/Mie ratio is converted to droplets SMD by means of a calibration procedure based on PDA measurements. The droplet SMD for the investigated spray varies from 20 µm to 60 µm, the N values range from 5 to 60 droplets per mm3, and the LVF varies between 0.05 × 10−4 and 5.5 × 10−4 within the probed region of the spray. To generate a series of two-dimensional images at different planes, the spray scanning procedure is operated in a “bread slicing” manner by moving the spray perpendicularly to the light sheet axis. From the resulting series of images, the procedure described here shows the possibility of obtaining three-dimensional reconstructions of each scalar quantity, allowing a more complete characterization of droplet clouds forming the spray region.

Segmentation of diesel spray images with log-likelihood ratio test algorithm for non-Gaussian distributions

A methodology for processing images of diesel sprays under different experimental situations is presented. The new approach has been developed for cases where the background does not follow a Gaussian distribution but a positive bias appears. In such cases, the lognormal and the gamma probability density functions have been considered for the background digital level distributions. Two different algorithms have been compared with the standard log-likelihood ratio test (LRT): a threshold defined from the cumulative probability density function of the background shows a sensitive improvement, but the best results are obtained with modified versions of the LRT algorithm adapted to non-Gaussian cases.

Optimal feature extraction for segmentation of Diesel spray images

A one-dimensional simplification, based on optimal feature extraction, of the algorithm based on the likelihood-ratio test method (LRT) for segmentation in colored Diesel spray images is presented. If the pixel values of the Diesel spray and the combustion images are represented in RGB space, in most cases they are distributed in an area with a given so-called privileged direction. It is demonstrated that this direction permits optimal feature extraction for one-dimensional segmentation in the Diesel spray images, and some of its advantages compared with more-conventional one-dimensional simplification methods, including considerably reduced computational cost while accuracy is maintained within more than reasonable limits, are presented. The method has been successfully applied to images of Diesel sprays injected at room temperature as well as to images of sprays with evaporation and combustion. It has proved to be valid for several cameras and experimental arrangements.