3D-LIF Experiments in an Open Wet Clutch by means of Defocusing PTV

Defocusing particle tracking velocimetry (DPTV) is applied to sub-millimetre rotor-stator gap flows on a test rig of an open wet clutch model. A recently proposed in-situ calibration approach (Fuchs et al., 2016) is modified to account for both the rotating no-slip condition and the grooves of the clutch lamella. Fluorescent particles are used to suppress the reflection issues at the walls and the groove patterns. The results demonstrate that DPTV is capable to derive sufficiently accurate velocity information in the smooth Couette-like gap region. Moreover, the experiments uncover formerly unknown flow properties inside the lamella grooves. The results provide evidence that the application of DPTV is a promising means to achieve a deeper insight into cause-effect relations between the flow fields inside an open wet clutch and the resulting adverse drag-torque characteristics

Laser-Optical Shear-Flow Analysis across the Annular Gap of a Simplified Displacement Compressor Model

The present experimental feasibility study testifies the two flow measurement techniques Defocusing Particle Tracking Velocimetry (DPTV) and Interferometric Particle Imaging (IPI) for their applicability to measure the two-phase flow of thin (sub-millimeter) annular rotor-stator gaps such as occur across for the leakage flow e.g. in the housing gap of oil-injected rotary positive displacement ompressors (RPDC). To provide unrestriced optical access to the annular gap and in turn eliminate secondary effects, a simplified displacement compressor model has been developed and fabricated from perspex. The proof-of-concept results of both experimental campaigns (DPTV & IPI) are discussed and avenues for future efforts towards a straight-forward and accurate applicability of either method are elaborated

Flow-structure identification in a radially grooved open wet clutch by means of defocusing particle tracking velocimetry

The volumetric defocusing particle tracking velocimetry (DPTV) approach is applied to measure the flow in the sub-millimeter gap between the disks of a radially grooved open wet clutch. It is shown that DPTV is capable of determining the in-plane velocities with a spatial resolution of 12μm 12μm along the optical axis, which is sufficient to capture the complex and small flow structures in the miniature clutch grooves. A Couette-like velocity profile is identified at sufficient distance from the grooves. Moreover, the evaluation of the volumetric flow information in the rotor-fixed frame of reference uncovers a vortical structure inside the groove, which resembles a cavity roller. This vortex is found to extend well into the gap, such that the gap flow is displaced towards the smooth stator wall. Hence, the wall shear stress at the stator significantly increases in the groove region by up to 15% 15% as compared to the ideal linear velocity profile. Midway between the grooves, the wall shear stress is around 4% 4% lower than the linear reference. Furthermore, significant amounts of positive radial fluxes are identified inside the groove of the rotor; their counterpart are negative fluxes in the smooth part of the gap. The interaction of the roller in the groove and the resulting manipulation of the velocity profile has a strong impact on the wall shear stress and therefore on the drag torque production. In summary, this DPTV study demonstrates the applicability of such particle imaging approaches to achieve new insights into physical mechanisms of sub-millimeter gap flow scenarios in technical applications. These results help to bring the design- and performance-optimization processes of such devices to a new level

Deep Learning and Hybrid Approach for Particle Detection in Defocusing Particle Tracking Velocimetry

The present work aims at the improvement of particle detection in defocusing particle tracking velocimetry (DPTV) by means of a novel hybrid approach. Two deep learning approaches, namely faster R-CNN and RetinaNet are compared to the performance of two benchmark conventional image processing algorithms for DPTV. For the development of a hybrid approach with improved performance, the different detection approaches are evaluated on synthetic and images from an actual DPTV experiment. First, the performance under the influence of noise, overlaps, seeding density and optical aberrations is discussed and consequently advantages of neural networks over conventional image processing algorithms for image processing in DPTV are derived. Furthermore, current limitations of the application of neural networks for DPTV are pointed out and their origin is elaborated. It shows that neural networks have a better detection capability but suffer from low positional accuracy when locating particles. Finally, a novel Hybrid Approach is proposed, which uses a neural network for particle detection and passes the prediction onto a conventional refinement algorithm for better position accuracy. A third step is implemented to additionally eliminate false predictions by the network based on a subsequent rejection criterion. The novel approach improves the powerful detection performance of neural networks while maintaining the high position accuracy of conventional algorithms, combining the advantages of both approaches

Particle detection by means of neural networks and synthetic training data refinement in defocusing particle tracking velocimetry

The presented work addresses the problem of particle detection with neural networks (NNs) in defocusing particle tracking velocimetry. A novel approach based on synthetic training data refinement is introduced, with the scope of revising the well documented performance gap of synthetically trained NNs, applied to experimental recordings. In particular, synthetic particle image (PI) data is enriched with image features from the experimental recordings by means of deep learning through an unsupervised image-to-image translation. It is demonstrated that this refined synthetic training data enables the neural-network-based particle detection for a simultaneous increase in detection rate and reduction in the rate of false positives, beyond the capability of conventional detection algorithms. The potential for an increased accuracy in particle detection is revealed with NNs that utilise small scale image features, which further underlines the importance of representative training data. In addition, it is demonstrated that NNs are able to resolve overlapping PIs with a higher reliability and accuracy in comparison to conventional algorithms, suggesting the possibility of an increased seeding density in real experiments. A further finding is the robustness of NNs to inhomogeneous background illumination and aberration of the images, which opens up defocusing PTV for a wider range of possible applications. The successful application of synthetic training-data refinement advances the neural-network-based particle detection towards real world applicability and suggests the potential of a further performance gain from more suitable training data