The multi-phase flow within a steel diesel injector is imaged using synchrotron-produced X-rays from the Advanced Photon Source at Argonne National Laboratory. Projections gathered from several repeated injection events at up to 10,000 frames per second and with 2.1 micrometer resolution at various viewing angles resulted in a 4D data set. Photon statistics were improved by averaging the data over 200 injection events. Owing to significant attenuation caused by the injector body material, and the short exposure time, the images are obscured by various types of noise making tomographic reconstruction challenging. Attempts at denoising this data are discussed.
At each time step, translational and rotational image registration was performed to align projections obtained from different lines-of-sight. This is followed by a Fourier Transform method for computed tomography to reconstruct the 3D flow-field, from the start to end of fuel injection, which is a 2 millisecond long event. The X-ray phase contrast in the data was exploited by applying a low-pass filter. Segmentation is performed to track the location of the liquid-gas interface, thus distinctly revealing a highly asymmetric flow-separation layer affected by micron-scale features in the nozzle geometry. This complete data processing pipeline converted the images acquired at a signal-to-noise ratio of 1 into a unique tomographic dataset of internal fluid flow through approximately 2 millimeters of steel. The data shows excellent validation with computational fluid dynamics simulations of the flow profile previously obtained for this nozzle.
The X-ray Fuel Spray research at Argonne National Laboratory is aimed at utilizing synchrotron X-ray diagnostics for providing insights into automotive fuel injection. One important task is to generate high-fidelity geometries or iso-surfaces of steel fuel injector nozzles from X-ray Computed Tomography measurements, to be used as inputs to realistic CFD simulations of fuel injector flow. These fuel nozzles contain 3D features between 5 - 500 micron and are imaged at a pixel resolution of 1 micron. The main bottleneck to automated generation of an STL geometry from X-ray CT data is the segmentation or surface determination process – conversion of the CT volume into a binary map that classifies each voxel as belonging to either injector metal or flow domain, accurately locating the metal surface at the transitions between these domains. Here, we describe our recent success in automating the segmentation process itself, which is challenging because various artifacts that arise from X-ray imaging and CT reconstruction confound the identification of threshold values needed for traditional segmentation algorithms. A convolutional neural network (CNN) coupled with a tailored loss function is implemented to achieve state-of-the-art accuracy in surface localization with limited manual intervention. Through data augmentation, the model can be trained on less than 30% of the slices drawn from two CT scans of different automotive injectors that were manually segmented and is tested on a third. Our architecture achieves state-of-the-art accuracy at lower computation time and GPU memory requirement compared to U-net, one of the most popular architectures for image segmentation.
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