首页|New Findings from University of Stuttgart Update Understanding of Machine Learning (Machine Learning Based Spray Process Quantification)
New Findings from University of Stuttgart Update Understanding of Machine Learning (Machine Learning Based Spray Process Quantification)
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NETL
NSTL
Elsevier
Fresh data on Machine Learning are presented in a new report. According to news reporting originating in Stuttgart, Germany, by NewsRx journalists, research stated, “Droplet size distribution and liquid loading are two of the most important properties to characterise spray processes. For example, nitrogen oxide (NOx) formation of liquid fuel-operated combustion systems is dominated by mixture homogenisation that is heavily dependent on atomization.” Financial supporters for this research include German Research Foundation (DFG), German Aerospace Centre (DLR), Stuttgart Center for Simulation Science (SimTech). The news reporters obtained a quote from the research from the University of Stuttgart, “While phase Doppler interferometry (PDI) is the technique of choice to measure droplet size distribution, shadowgraphy enables the detection of liquid bodies of arbitrary shape, e.g. ligaments, and thus can be used to delineate primary atomization. For this purpose, a data analysis tool is developed that enables a quantitative description of droplet size and the primary break-up process simultaneously. In this context, machine learning-based (ML) object detection provides a generally applicable approach that is used here to characterise a pressure swirl water spray. The deep neural networks are trained using synthetic training data with physics-informed domain randomisation. Two different network architectures (namely Mask R-CNN and SparseInst), backbones (ResNet50 and ResNet101) and several different training approaches are evaluated. The inference accuracy of the ML models is validated against PDI measurements in terms of droplet size distribution. The best-performing ML model provides a robust detection method for a wide range of measurement conditions. The validated ML model is used to characterise the primary break-up to delineate the effect of aerodynamic forces on the atomization of a hollow cone pressure swirl spray in high momentum gaseous co-flow.”
StuttgartGermanyEuropeCyborgsEmerging TechnologiesMachine LearningUniversity of Stuttgart
NETL
NSTL
Elsevier
