The flow convergence method includes calculation of the proximal isovelocity surface area (PISA) and is widely used to classify mitral regurgitation (MR) with echocardiography. It constitutes a primary decision factor for determination of treatment and should therefore be a robust quantification method. However, it is known for its tendency to underestimate MR and its dependence on user expertise. The present work systematically compares different pulsatile flow profiles arising from different regurgitation orifices using transesophageal echocardiographic (TEE) probe and particle image velocimetry (PIV) as a reference in an in-vitro environment. It is found that the inter-observer variability using echocardiography is small compared to the systematic underestimation of the regurgitation volume for large orifice areas (up to 52%) where a violation of the flow convergence method assumptions occurs. From a flow perspective, a starting vortex was found as a dominant flow pattern in the regurgant jet for all orifice shapes and sizes. A series of simplified computational fluid dynamics (CFD) simulations indicate that selecting a suboptimal aliasing velocity during echocardiography measurements might be a primary source of potential underestimation in MR characterization via the PISA-based method, reaching up to 40%. In this study, it has been noted in clinical observations that physicians often select an aliasing velocity higher than necessary for optimal estimation in diagnostic procedures.

Lagrangian defocusing particle tracking velocimetry (DPTV) measurements are conducted in a thin, wall-parallel volume above a plasma actuator array that is applied to mimic the effect of wall oscillations by inducing alternating, wall-parallel forcing in opposite directions into the air above the actuator surface. The aim of the experiments is to capture the plasma-induced flow structures in otherwise quiescent air in order to increase the understanding of different actuation parameters. For this purpose, high-speed particle image velocimetry equipment with one camera is used in a DPTV setup, where the out-of-plane particle coordinate is obtained through the diameter of a defocused particle image. On this basis, an approach for continuous particle tracking in several consecutive frames is presented, allowing to derive three component, three dimensional velocity and acceleration data. Light reflections that occur on the adjacent actuator surface give raise to particular challenges concerning the measurement uncertainty estimation as well as the calibration procedure for the evaluation of the wall-normal coordinate of tracer particles. To overcome the latter, a calibration approach is presented for which solid particles are applied to the actuator surface and their particle image diameter is captured at different camera positions in a separate measurement. The estimation of in-plane and out-of-plane displacement measurement uncertainties is conducted following a newly-developed procedure where the deviation of particle displacements from a straight track is evaluated for measurements in quasi-quiescent air. The obtained results show the suitability of DPTV measurement technique for the practical application of the characterization of flow structures above a plasma actuator array. The measurement accuracy is found to be limited due to the available illumination, which depends on the used components. The measured flow fields together with optical and electrical measurement data allow for a further analysis of the present forcing strategy. Particularly, by recording phase-resolved, three-dimensional flow velocity and acceleration fields in the vicinity of the wall, the spatio-temporal occurrence and homogeneity of the near-wall forcing effects can be analyzed in future investigations.

Spatially and temporally resolved velocity measurements in wall-bounded turbulent flows remain a challenge. Contrary to classical laser Doppler velocimetry (LDV) measurements, the laser Doppler velocity profile sensor (LDV-PS) allows the combined measurement of tracer particle position and velocity, which makes it a promising tool. To assess its feasibility a commercial LDV-PS is employed in a turbulent channel flow at Re_τ =350 . Additionally, the measurement and signal-processing accuracies of velocity and location are evaluated for various tracer-object sizes and velocities. On this basis, the turbulent channel flow measurements are evaluated and compared to reference data from direct numerical simulations. Thus, potentials of the LDV-PS are investigated for different regions of the flow and various data processing routines as well as the experimental practice are discussed from an application perspective.

Time-resolved Shake-the-Box (STB) experiments have been conducted in the channel-flow facility at the Institute of Fluid Mechanics (ISTM) at f = 30 kHz repetition rate to study the near-wall velocity field at a viscous Reynolds number of Reτ = 350 (centerline velocity Ucl = 8.4 m/s, channel half-height h = 12.6 mm, viscous unit δv ≈ 36 μm) to testify the STB method for the applicability to provide high-resolution velocity information of the given flow field including the sublayer region of the channel flow. In addition to the extraction of Lagrangian particle tracks and corresponding Eulerian iso-surfaces of vortical structure the major emphasis of the study centers around an evaluation of the resulting near wall velocity profile. Particularly, it is discussed how the choice of data-processing steps and strategies affects the resulting velocity estimates in the immediate vicinity of the wall, where the major impact for the current study has been identified to result from image-arithmetic efforts during data pre-processing. The achieved results are discussed and further compared with direct numerical simulations (DNS) of similar Reτ and earlier experimental efforts at ISTM of the same flow configuration by means of stereoscopic particle image velocimetry (stereo PIV) and a laser-Doppler velocimetry profile sensor (LDV-PS). Finally, future perspectives of the experimental efforts for near-wall shear flow measurements are outlined on the grounds of the achieved insights.

Camera technology is rapidly enhancing within the last years, which led to the availability of sophisticated technical features within consumer cameras, that could only be found in scientific set-ups earlier. The present work explores the possibilities of utilizing the potentials of commercially-available camera drones as moving cameras for large-scale particle tracking velocimetry. We made use of the glare-point particle tracking approach introduced by Kaiser & Rival (2023), where only a single camera and natural illumination, e.g. the sun, could be used. The set-up demonstrated full suitability for large volumes in the order of 10-100 m³. The frame-to-frame camera movement, caused by the slight movement of the drone could be quantified and corrected by an imaged-based approach. The calibration strategy could be simplified due to the fixed set-up in the camera drone. The limits introduced through the tracer size and uncertainty caused by the glare-point approach are discussed accordingly. For the presented magnification and camera set-up given by the DJI Mini 3 pro, the limit in height can be determined when the two most dominant glare points collapse to one, which happened well above 10 m for the present set-up. The equipment together with the glare-point particle tracking approach is best suited for flow information extraction at large-scale facilities or difficult accessible terrain, where the current set-up could depict its strengths.

The optical measurement technique Defocusing Particle Tracking Velocimetry (Defocusing PTV) is applied to the sub-millimeter gap of an open wet clutch to gain deeper insights into the unknown flow, which causes a significant loss in nowadays automobiles. The present work improves the fundamental understanding of the flow and its contribution to the generated drag torque and the physical process of aeration. A set of governing analytical equations is revealed from in-depth theoretical considerations, which describe the general cause-effect relations of the flow. To gain deeper insights into the unknown intra-groove phenomena Defocusing PTV is successfully applied to (locally) extract precise vortex information and fine resolved wall shear stress values. Metrological insights are generated with the introduction of a new detection strategy and the proven flexibility of Defocusing PTV, which makes comprehensive magnification and location-accuracy studies possible. The work is completed with a flow analysis along the entire radial region of interest, and the consideration of a more complex groove geometry.

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.

AbstractWet clutches in their open state add losses caused by drag torque to the drive train, making the optimization of the disk design and drag torque reduction a core development aspect. The present work focuses on the influence of the chosen disk-groove geometry on the resulting flow topology in open wet clutches. Therefore, the flow topology of six different disk designs is investigated experimentally and numerically. Other influences of the operating conditions such as volume flow or other design elements such as wave springs are not considered. New parameters for the flow topology are derived, for a better description of the influence of the flow topology on the drag torque. Based on these insights strategies for further understanding of the complex flow topology on open wet clutches are derived and optimization approaches proposed.

Wet clutches in their open state add losses caused by drag torque to the drive train, making the optimization of the disk design and drag torque reduction a core development aspect. The present work focuses on the influence of the chosen disk-groove geometry on the resulting flow topology in open wet clutches. Therefore, the flow topology of six different disk designs is investigated experimentally and numerically. Other influences of the operating conditions such as volume flow or other design elements such as wave springs are not considered. New parameters for the flow topology are derived, for a better description of the influence of the flow topology on the drag torque. Based on these insights strategies for further understanding of the complex flow topology on open wet clutches are derived and optimization approaches proposed.

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 compressors (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.

The present experimental study revolves around the applicability of a Bragg-shifted laser Doppler velocimetry profile sensor (LDV-PS) in use for open wet clutch flow scenarios, where sub-millimeter gap height and textured surfaces are present. It is shown that the LDV-PS is capable to determine angular-resolved 1D3C velocity information, with all complex flow structures, depicted properly that are present in a radial groove. For the flow measurements the sensor is tilted to $$\pm 30^{\circ }$$ compared to the axial orientation to enable the opportunity to reconstruct angular-resolved 1D3C velocity fields from two consecutively conducted runs. This facilitates measurement results with high axial and angular resolution for the complete open clutch flow and proves for the first time, that a profile sensor is capable to extract 3C information with the mentioned method. The results show that all characteristic flow structures occurring in the investigated sub-millimeter rotor-stator gap flow can be recorded properly. This insight renders the LDV-PS a promising and straight-forward applicable means to support industry-relevant research so as to uncover formerly hidden flow features and thus contribute to advanced development approaches for the respectively considered applications.

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.

The accurate measurement of a fluid flow inside a measurement volume (MV) with limited optical access poses a challenge since the view on the MV is often partially obstructed for all but one viewing angle. Defocusing particle tracking velocimetry (DPTV) can be used to determine the instantaneous threedimensional velocity field of the flow with a standard PIV setup, requiring only a single optical axis. Current detection algorithms reach an out-of-plane accuracy in an order of magnitude lower than the planar accuracy, on top of a low rate of detected particles in comparison to other PTV approaches. These drawbacks originate from the low image quality due to noise, fluctuations in illumination, reflections and overlapping particle images. It has been shown that Machine Learning (ML) based detection is more robust against these adverse effects, due to the ability to leverage a higher amount of optical features for detection than conventional algorithms (Lecun et al. (1998)). Therefore, the present work addresses the applicability of ML algorithms in the post-processing of DPTV experiments, which will be evaluated on the ground of the DPTV experiments conducted by Leister and Kriegseis (2019). The setup of these experiments can be seen in Figure 1(a) and a section of a raw image recorded during the experiments in Figure 1(b).

Wet clutches are widely used in power transmission, but lack of the fact of an energy loss in open state condition. The flow conditions in the fluid flow of an open wet clutches are analyzed by analytical means. The requisite simplifications that result in an analytically integrateable solution are stated in detail. Special emphasis is put on the role of gravitation in the equations of fluid motion. This force component leads to a slightly earlier aeration than stated in earlier conditions. The simplifications and the resultant solutions are considered by means of dimensionless quantities. Despite the actual geometric parameters the drag torque can be described as $$\zeta_{\mathrm{m}}=\pi/\mathrm{Re}_{\mathrm{l}}$$ . An additional aeration condition is introduced, which is based on the back flow of the radial velocity. This quantity can be described as non-dimensional volumetric flow rate $$Q^{*}$$ . With these equations at hand the theoretical considerations are transferred to an evaluation with grooves, where a backward curved groove appears as beneficial for further investigations.

System-of-Systems-Engineering als Basis zukunftsfähiger Antriebs- und Kupplungsentwicklung . . . . .1

The trend to lower energy consumption in the automotive industry still offers potential in various fields of application. One powerful saving strategy is described by the idling behavior of wet clutches, where the speed difference between drive and output, and the cooling oil in combination with a sub-millimeter spacing leads to significant amounts of wall shear stress (WSS) and accordingly drag torque. Minimization of this adverse effect has been found to be possible by means of grooved clutch-disk geometries, which have been demonstrated to correlate with the drag torque (see e.g. Neupert et al., 2018). The main interplay between torque and fluid flow in open wet clutches has been analyzed by Leister et al. (2020) in a dimensionless way. Today, a detailed investigation of a clutch flow, however, is missing for a larger variety of groove patterns and the cause-effect relations remain yet to be fully understood. Especially, the clear identification of the so-called foot print of a particular groove geometry in the flow field and corresponding WSS – thus drag-torque predictions – still requires further research efforts.

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 $$12\;\upmu \mathrm{m}$$ 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 $$15\%$$ as compared to the ideal linear velocity profile. Midway between the grooves, the wall shear stress is around $$4\%$$ 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.

The remaining torque of disengaged wet clutches is a major source of energy loss and, therefore, an objective of current research. The present contribution describes the necessary simplifications to obtain an analytical solution of the governing equations by means of an order-of-magnitude analysis. The obtained results are brought to a dimensionless frame of reference, where formerly unknown simple dependencies of the drag torque and the aeration onset have been uncovered. The dimensionless description serves as promising a means to achieve a quantitative comparison of experimental data. Additionally, a new modelling concept for grooves is introduced, which is based on the hydraulic-diameter concept. The combination of either approach offers a robust prediction method for drag torque and aeration onset.