Velocimetry Method Research Articles

Is the proper orthogonal decomposition suitable to validate simulation of turbulent wake?

To evaluate the suitability of the proper orthogonal decomposition (POD) for validating a simulated flow dynamics, we selected the flow in the wake behind a circular cylinder at Reynolds number of ≈5000 as a well studied canonical problem. The flow was simulated via k-ωSST detached-eddy simulation (DES) and, on selected planes of measurement (PoM), investigated experimentally by a time-resolved variant of the particle image velocimetry (PIV) and stereo-PIV methods. A standard model validation comprising, among other, comparison of the drag coefficient, or separation angle, and of the averaged flow properties and turbulence spectra, was carried out. Subsequently, both the PIV and the numerical data on the selected planes in the geometry were analyzed by POD, and a fully 3D POD analysis of the numerical data was used to evaluate the credibility of the planar POD as a tool for dynamic model validation.

Sensitivity of wavelet-based optical flow velocimetry (wOFV) to common experimental error sources

Abstract The influence of several potential error sources and non-ideal experimental effects on the accuracy of a wavelet-based optical flow velocimetry (wOFV) method when applied to tracer particle images is evaluated using data from a series of synthetic flows. Out-of-plane particle displacements, severe image noise, laser sheet thickness reduction, and image intensity non-uniformity are shown to decrease the accuracy of wOFV in a similar manner to correlation-based particle image velocimetry (PIV). For the error sources tested, wOFV displays a similar or slightly increased sensitivity compared to PIV, but the wOFV results are still more accurate than PIV when the magnitude of the non-ideal effects remain within expected experimental bounds. For the majority of test cases, the results are significantly improved by using image pre-processing filters and the magnitude of improvement is consistent between wOFV and PIV. Flow divergence does not appear to have an appreciable effect on the accuracy of wOFV velocity estimation, even though the underlying fluid transport equation on which wOFV is based implicitly assumes that the motion is divergence-free. This is a significant finding for the broader applicability of planar velocimetry measurements using wOFV. Finally, it is noted that the accuracy of wOFV is not reduced notably in regions of the image between tracer particles, as long as the overall seeding density is not too sparse i.e. below 0.02 particles per pixel. This explicitly demonstrates that wOFV (when applied to particle images) yields an accurate whole field measurement, and not only at or adjacent to the discrete particle locations.

Self-mixing thinly sliced ruby laser for laser Doppler velocimetry with high optical sensitivity

In self-mixing laser Doppler velocimetry (LDV), the motion of a moving target is observed by using intensity-modulated laser light detected by a simple photodetector. Here, the self-mixing laser output modulation takes place, reflecting the pronounced effective loss modulation index, which is proportional to the fluorescence-to-photon lifetime ratio. The fluorescence lifetime of a ruby laser is extremely long, so if a ruby crystal can be used as a laser light source for a self-mixing LDV system, high-sensitivity LDV measurements can be performed with it. We describe a method for velocimetry of moving targets using self-mixing LDV in which a CW oscillating ruby laser is the light source. The oscillation mechanism of the thin-slice ruby laser with a large fluorescence-to-photon lifetime ratio, which is suitable for LDV measurements, is clarified and the results of highly sensitive LDV measurements are presented, featuring nonlinear dynamics observed associated with the self-mixing velocimetry experiment. The measurement accuracy is clarified by measuring the rotating disc with various conditions using self-mixing LDV.

Research PIV-based model on subsidence caused by coal mining

The subsidence caused by coal mining could cause the destruction of roads and houses, and even the failure of infrastructures. Understanding of the mechanism of coal mining subsidence may provide early protecting to infrastructures on coming failure, but dynamic analysis of subsidence due to coal mining is currently needed. In this study we apply particle image velocimetry (PIV) method to reveal strata movement and subsidence according to the prototype and indoor physical model similarity experiment of Henan. Our result shows magnitude of the subsidence of overlying strata during the coal mining at different excavation thickness, that more coal mining thickness may produce more subsidence, and that shallower coal may cause more significant subsidence. Our result suggests that further PIV test combined with field monitoring data may be an effective measure to study subsidence mechanism and pattern helping to predict disaster caused by subsidence.

4D particle tracking velocimetry of an undular hydraulic jump

Undular hydraulic jumps are characterized by a three-dimensional flow field due to shock waves generated near the sidewalls. Basic flow properties have been described in the literature with experimental data, mostly gathered with classical sensors which provide local information only. A continuous description of the 3D flow field is still missing. In this study, instantaneous and three-dimensional velocity data in an undular jump were obtained between the inflow section and second wave crest using a volumetric particle tracking velocimetry method with high sample rates. A special feature of the tracking algorithm is its capability to process images with very high particle density yielding a highly resolved Lagrangian description of the flow. Velocity fields, turbulent kinetic energy, dissipation rates, Reynolds stresses and turbulent scales are presented for a volume of 10 cm width in cross-direction. Some differences were found when compared to data obtained with an intrusive acoustic Doppler velocimeter.

Experimental investigation of OH* emission spectrum characteristics and transient ignition dynamics in methane and coal dust mixtures explosions

Combustible gases, dusts and their mixtures are widely present in human production and life，and the fire and explosion disasters caused by them pose a serious threat to the field of energy security applications. Studying the ignition process of mixtures is essential for disaster risk assessment and safety protection. In this work, the explosion characteristics and OH* emission spectra of the mixtures were experimentally tested by varying the fuel equivalence ratio ( ER ≈ 0.79 ∼ 1.71), and the evolution of the transient flow field structure during the ignition process was quantitatively analyzed using the schlieren image velocimetry method. The results indicate that the emission spectrum of OH* is closely correlated with the maximum explosion pressure, and the spectral intensity of OH* at 306.4 nm is consistent with the maximum rate of explosion pressure rise. The flow field during the ignition process of the mixtures shows that a small amount of coal dust (concentration≤30 g/m3) can significantly promote flame acceleration and instability as the methane concentration is in lean combustion or stoichiometric ratio. However, when the concentration of coal dust increases (concentration≥40 g/m3), coal dust will suppress flame acceleration and instability. For methane concentration in fuel-rich combustion state, coal dust always suppresses flame acceleration and instability. The experimental results contribute to a further understanding of gas and coal dust mixed explosions and provide a verification database for the construction of chemical kinetic mechanisms.

Effects of velocity regularization on neural network performance in processing particle images

Recent studies have witnessed remarkable progress in harnessing convolutional neural networks (CNNs) to overcome the inherent limitations of conventional particle image velocimetry (PIV) methods. Traditional PIV techniques often suffer from compromised resolution and precision, hindering their ability to capture the complexities of fluid dynamics within the observation frame. While CNNs offer promise in addressing these challenges, they face obstacles such as limited accuracy, weak generalization, and a dearth of physical interpretability. In our prior research, we presented a CNN architecture that incorporates optical flow algorithms as supplementary physical constraints, thereby bolstering the model interpretability and precision. Nevertheless, the practical implications of this approach, especially when dealing with multi-dimensional, low-quality particle image data and restricted training sets, have yet to be fully explored. To address this knowledge gap, we have assembled a comprehensive dataset that simulates a wide array of experimental scenarios. We have systematically assessed the influence of velocity regularization on neural network performance, taking into account variations in image quality and the size of training datasets. The results underscore the pivotal importance of velocity regularization in enhancing the predictive prowess of neural networks, particularly when dealing with poor image quality and smaller data sizes. This study provides useful insights into the effective application of CNNs with velocity regularization in the field of experimental fluid dynamics.

Improvement in the Number of Velocity Vector Acquisitions Using an In-Picture Tracking Method for 3D3C Rainbow Particle Tracking Velocimetry

Particle image velocimetry and particle tracking velocimetry (PTV) have developed from two-dimensional two-component (2D2C) velocity vector measurements to 3D3C measurements. Rainbow particle tracking velocimetry is a low-cost 3D3C measurement technique adopting a single color camera. However, the vector acquisition rate is not so high. To increase the number of acquired vectors, this paper proposes a high probability and long-term tracking method. First, particles are tracked in a raw picture instead of in three-dimensional space. The tracking is aided by the color information. Second, a particle that temporarily cannot be tracked due to particle overlap is compensated for using the positional information at times before and after. The proposed method is demonstrated for flow under a rotating disk with different particle densities and velocities. The use of the proposed method improves the tracking rate, number of continuous tracking steps, and number of acquired velocity vectors. The method can be applied under the difficult conditions of high particle density (0.004 particles per pixel) and large particle movement (maximum of 60 pix).

Influence of outer large-scale motions on near-wall structures in compressible turbulent channel flows

The influence of outer large-scale motions (LSMs) on near-wall structures in compressible turbulent channel flows is investigated. To separate the compressibility effects, velocity fluctuations are decomposed into solenoidal and dilatational components using the Helmholtz decomposition method. Solenoidal velocity fluctuations manifest as near-wall streaks and outer large-scale structures. The spanwise drifting of near-wall solenoidal streaks is found to be driven by the outer LSMs, while LSMs have a trivial influence on the spanwise density of solenoidal streaks, consistent with the outer LSM impacts found in incompressible flows (Zhou et al., J. Fluid Mech., vol. 940, 2022, p. A23). Dilatational motions are characterized by the near-wall small-scale travelling-wave packets and the large-scale parts in the outer region. The streamwise advection velocity of the near-wall structures remains at $16 \sim 18u_{\tau }$ , hardly influenced by Mach numbers, Reynolds numbers and wall temperatures. The spanwise drifting of near-wall dilatational structures, quantified by the particle image velocimetry method, follows a mechanism distinct from solenoidal streaks. This drifting velocity is notably larger than those of the solenoidal streaks, and the influence of outer LSMs is not the primary trigger for this drifting.

KF-PEV: a causal Kalman filter-based particle event velocimetry

Event-based pixel sensors asynchronously report changes in log-intensity in microsecond-order resolution. Its exceptional speed, cost effectiveness, and sparse event stream make it an attractive imaging modality for particle tracking velocimetry. In this work, we propose a causal Kalman filter-based particle event velocimetry (KF-PEV). Using the Kalman filter model to track the events generated by the particles seeded in the flow medium, KF-PEV yields the linear least squares estimate of the particle track velocities corresponding to the flow vector field. KF-PEV processes events in a computationally efficient and streaming manner (i.e., causal and iteratively updating). Our simulation-based benchmarking study with synthetic particle event data confirms that the proposed KF-PEV outperforms the conventional frame-based particle image/tracking velocimetry as well as the state-of-the-art event-based particle velocimetry methods. In a real-world water tunnel event-based sensor data experiment performed on what we believe to be the widest field view ever reported, KF-PEV accurately predicted the expected flow field of the SD7003 wing, including details such as the lower velocity in the wake and the flow separation around the underside of an angled wing.

Multi-Scale Flow Estimation Within Stereoscopic PIV Processing Based On Deep Learning

Classical methods of particle-image velocimetry (PIV) based on state-of-the-art cross-correlation algorithms constitute a spatial filtering operation that leads to a loss of spatial resolution of the estimated velocity fields. This is associated with numerous limitations, e.g., when it comes to aligning high-fidelity numerical simulations, since the detection of small turbulent scales is often precluded. An optical flow neural network adapted for PIV processing, called RAFT-PIV, can overcome the drawback of reduced spatial resolution while maintaining the reliability and robustness of the classical approach. The present work aims to extend the application of the aforementioned deep learning based method by integrating it within a stereoscopic PIV processing workflow. Experimental data from an optically accessible single-cylinder combustion engine the flow field of which is characterized by complex, three-dimensional flow structures and a broad range turbulent scales are processed using RAFT-PIV. The 2D-3C vector fields obtained from stereoscopic reconstruction are compared against a classical cross-correlation based PIV evaluation, providing insight into instantaneous quantities and turbulent structures. A qualitative comparison of the instantaneous in-plane and out-of-plane velocity components reveals that the reconstructed flow obtained from the deep learning based estimation includes a wide range of resolved turbulent length scales. It outperforms the classical method. Furthermore, the findings of the present study demonstrate that the pixel accuracy has an effect on both physical quantities and turbulence characteristics. In particular, an increase in turbulent kinetic energy is observed across the engine cycles of the investigated engine flow states. Moreover, a higher fraction of three-dimensional isotropic turbulent structures is derived from RAFT-PIV compared to the results from the cross-correlation based technique.

A Full Meshless Algorithm For Super-Resolution In Image Velocimetry Based On KNN-PTV And Constrained RBFs

"A novel full meshless super-resolution method for image velocimetry is proposed. This method builds upon the combination, presented in Tirelli et al. (2023a), of K-nearest neighbour Particle Tracking Velocimetry (Tirelli et al., 2023b, KNN-PTV) and constrained Radial Basis Functions regression (Sperotto et al., 2022, c-RBFs). The main novelty is that the algorithm is implemented here in a fully meshless version, i.e. it does not require the definition of a common Eulerian grid at any step of the process. KNN-PTV enhances the spatial resolution of vector fields by exploiting data coherence in space and time, even for non-time-resolved measurements. On the other and, c-RBFs provide an analytical representation of the field from scattered data while allowing the introduction of physical constraints. In this new version, the dictionary to blend data from different snapshots is computed with a meshless POD approach developed by the authors and presented in another contribution to this conference. This approach removes the last constraint of Eulerian grids and paves the way for a fully meshless algorithm. The algorithm is validated on a challenging 2D synthetic case, such as turbulent channel flow, and an experimental case involving the separation bubble around the frontal portion of a Ground Transportation System (GTS)."

Evaluation of seedless wavelet-based optical flow velocimetry for schlieren images

In harsh flow environments, traditional particle-based velocimetry methods face challenges. This study explores the use of seedless schlieren images for velocimetry through a novel algorithm, namely, wavelet-based optical flow velocimetry (wOFV). Various data term constraints for wOFV were examined. It is found that the data term derived from the integrated continuity equation (ICE) outperformed the conventional displaced frame difference constraint and the schlieren-tailored constraints (SE and SSE). Evaluation based on the root mean square error (RMSE) and turbulence energy spectrum (TES) reveals that the choice of wavelet becomes insignificant for the optimal estimated velocity field when the wavelet support length is sufficiently long. In addition, the implementation of a proper truncation in wOFV shows little dependence of the RMSE on the weighting coefficient, therefore alleviating the uncertainty associated with selecting an appropriate weighting coefficient. It is found that the retrieved flow field from schlieren images approximates a down-sampled result based on available structural scales in images. Considering the prevalence of under-resolved velocity field in practical applications, schlieren-based wOFV offers a reasonable alternative to particle-based velocimetry, particularly in harsh flow environments.

Towards understanding effects of ultrasonic waves on subcooled boiling using particle image velocimetry

In this study, effects of ultrasonic waves on heat transfer, bubble behavior and temporal variation of liquid flow near the heating surface in subcooled boiling was investigated with the help of high-speed particle image velocimetry method. Experimental results show that the enhancement effect of ultrasound on heat transfer and critical heat flux considerably depends on subcooling and boiling space height. In nucleate boiling regime, the ultrasound-induced acoustic streaming alters the upward liquid flow and changes the vortex structure associated with bubble growth, departure and rising. As heat flux increases, the bubble-induced convection accelerates and competes with the acoustic streaming near the heating surface, weakening the ultrasonic effect on heat transfer. In transition boiling regime, effects of ultrasonic waves on heat transfer of microbubble emission boiling depends on the competition between acoustic streaming and oscillating flow resulting from vapor film oscillation, with enhancement occurring when acoustic streaming dominates the liquid flow near the heating surface. Based on the visualization results, a scaling law is developed to determine the potential effects of ultrasound on critical heat flux.

Machine learning approach to predict flow fields induced by internal solitary waves acting on mid-water structures based on particle image velocimetry experiments

Mid-water structures (MWSs) have important applications in the ocean to minimize the effects of environmental factors in the surface ocean and to avoid the effects of high pressure on the seabed. However, their operational positions are vulnerable to internal solitary waves (ISWs), which poses a significant challenge to the safe functioning of MWSs. Consequently, there is a pressing need to explore the flow field characteristics surrounding MWSs under the action of ISWs to establish a reliable foundation for structural design. In this study, experimental investigations were conducted to analyze the flow fields induced by ISWs acting on MWSs, employing the particle image velocimetry (PIV) method. The intricate details of the flow fields around the structure were thoroughly analyzed, and the impact of the structure's submerged depth on the ISW-induced flow fields was discussed. A machine learning approach is utilized to develop an Artificial Neural Network (ANN) model aimed at predicting the flow fields of ISWs. The experimental results are compared and analyzed alongside the predicted outcomes, focusing on the anomaly region, the tail field region, and the occluded region. The results show that the presence of the structure complicates the flow characteristics in the tail flow regions, and the flow patterns are constantly changing. The characteristics of the ISW flow fields around the structure vary with the different submerged depths of the MWS. Positioning the MWS above the wave trough results in an ascending trend followed by a descending trend in the strength of the surrounding flow field with increasing submerged depth. The ANN predictions for different types of regions of the flow fields show satisfactory accuracy. The model closely matches the PIV results in predicting the extremes and positions of horizontal velocities in the tail flow fields at different stages of ISW trough propagation. The ANN model can be recommended for predicting the flow fields of ISWs acting on MWSs. This study offers comprehensive insights into the characteristics of the flow fields induced by ISWs acting on MWSs, emphasizing the valuable contribution of machine learning methods to the field of fluid dynamics.

Soft-sensor model for indoor temperature prediction under heating conditions

Temperature information is important in everyday life. However, the local temperature at a certain point cannot be determined directly without using a physical thermometer, which can often disturb activity at that point and consume additional energy. Moreover, it would be costly to equip a room with many thermometers simply to measure local temperature. To overcome this problem, this study developed a model for prediction of local indoor temperature under heating conditions based on the heat transfer process mechanism using the soft-sensing method. To simulate the heating of air conditioning in winter, water at temperature of 30/50 °C was fed into water at 10 °C in an acrylic box. The soft-sensor model was trained and validated with transient temperature data and the multiple linear regression method. The particle image velocimetry method was used to obtain the velocity field. Temperature inhomogeneity in the vertical direction was evident for the feed temperature of 50 °C. The temperature variation in the bottom layer was divided into the dead time, rising, and settling periods, which was caused by variation of flow state. Multiple evaluation indicators suggested that the model performed with acceptable accuracy for different feed temperatures and locations. The bottom layer model at 50 °C required correction for the dead time, and model accuracy was improved by separately modeling the rising and settling periods. This soft sensor could be installed in air-conditioning systems to achieve optimal control, provide user comfort, and save energy.

Anchor chain profiles in sand based on centrifuge model tests

The rapid development of floating wind turbines (FWTs) has brought new challenges to their mooring systems in shallow water of 50-200 m. The sand seabed, which is widely distributed in offshore regions, is often encountered in the service of FWTs and influences the anchor and mooring line design. The anchor chain is primarily adopted as the part interacting with the seabed, and its configuration in the seabed is vital for anchor design. However, a complete understanding of the chain configuration in sand is yet to be achieved. To address this, a group of centrifuge model tests using a model chain were conducted to simulate the chain embedment behavior in carbonate sand. During the tests, the chain position and soil deformation were captured using the Particle Image Velocimetry (PIV) method. A calculation model was adopted to predict the chain configuration in the sand, compare it with the centrifuge test results, and then analyze the differences between them. Finally, suggestions for model improvement were discussed.

Interaction of stream-wise vortices generated by swirler grid

A system of stream-wise vortices has been created using a grid of swirling elements with alternating orientations (like a chessboard). The particle image velocimetry method has been used to map the velocity field in several planes perpendicular to the stream. The mesh-based Reynolds number is 1.35×104 and 2.71×104, respectively. The stream-wise development of turbulent kinetic energy (TKE) shows first an increase in a distance of x≈10M, followed by power-law decay. Individual vortices are detected in each snapshot. The radial profile of TKE transformed to a vortex coordinate system is almost constant, either with maximum as in static frame or zero as observed by previous research. The properties of detected vortices are studied statistically: the meandering amplitude expressed as the standard deviation of vortex positions grows roughly as ∼ex, i.e., faster than expected random-walk growth ∼x. Vortex circulation decays exponentially as predicted by classical Helmholtz theorem. The interaction between neighboring vortices is expressed via correlation of selected quantities. Correlation of energy develops downstream from anticorrelation to a positive correlation. The strongest correlation is observed between the first vortex circulation and the second vortex position perpendicular to their connection line. Other correlations are weak.

Gravitational modes of convection in water liquid bridges

The modes of pure buoyant (thermogravitational) convection emerging in a liquid bridge of water (Pr ≅ 6.1), uniformly heated from below and cooled from above are investigated experimentally by means of a microscale facility, a related laser-cut technique (used to illuminate isodense tracers dispersed in the liquid) and a particle image velocimetry method. In particular, the following conditions are examined: aspect ratio (A = length/diameter) in the range 0.3 ≤ A ≤ 0.9, volume ratio 0.7 ≤ S ≤ 1.3, and Rayleigh number spanning the interval from the initial quiescent state up to the development of oscillatory motion. A multitude of patterns is obtained, revealing the coexistence of different branches of steady flows in the space of parameters in the form of multiple solutions. These can evolve into oscillatory states featured by disturbances with the characteristics of standing waves (a kind of rocking motion). The analysis largely relies on a novel approach where the position of the center of the main vortex of buoyant nature established in the liquid bridge is carefully monitored in space. The related trajectory is used to discern the flow spatial degrees of freedom, which are progressively enabled as the temperature difference is increased. It is shown that the effective volume of liquid held by surface tension between the hot and cold walls can have an appreciable impact on the onset of unsteadiness and the related oscillation frequency.

Travel time after photobleaching velocimetry

In interfacial science and microfluidics, there is an increasing need for improving the ability to measure flow velocity profiles in the sub-micrometer range to better understand transport phenomena at interfaces, such as liquid–solid interfaces. Current standard methods of velocimetry typically use particles as tracers. However, seed particles can encounter issues at liquid and solid interfaces, where charge interactions between particles and surfaces can limit their ability to measure near-wall flows accurately. Furthermore, in many flows, seed particles have a different velocity from that of their surrounding fluid, which the particles are intended to represent. Several molecular tracer-based velocimeters have been developed which can bypass these issues. However, they either have limited resolution for measurement near solid surfaces, such as for slip flows, or require pre-calibration. Laser-induced fluorescence photobleaching anemometry (LIFPA) is one such technique that is noninvasive and has achieved unprecedented nanoscopic resolution for flow velocity profile measurement. However, it also requires pre-calibration, which is unavailable for unknown flows. Here, we present a novel, calibration-free technique called travel time after photobleaching (TTAP) velocimetry, which can measure flow velocity profiles and near-wall flow with high spatiotemporal resolution. Furthermore, TTAP velocimetry is compatible with LIFPA, and thus, the two systems can be coupled to satisfy LIFPA’s long-anticipated need for pre-calibration, enabling measurement of flow velocity profiles in unknown flows with salient resolution.