LaVision (Germany)

“Flow over an espresso cup” is a modern classic in the background-oriented schlieren (BOS) literature, demonstrating that three-dimensional, time-resolved velocity, pressure, and temperature fields can be reconstructed from multi-camera BOS measurements. In that study, a buoyancy-driven flow above a heated cup was measured using BOS tomography, and density reconstructions were subsequently post-processed with a physics-informed neural network based on the Boussinesq approximation of the Navier–Stokes equations. In the present work, we revisit this canonical dataset using the neural-implicit reconstruction technique in both standard and physics-informed configurations, enabling a direct assessment of the benefits and limitations of physical constraints in the reconstruction workflow. Comparisons are made between historical results and modern best practices. The influence of temporal resolution on the inferred flow fields is systematically evaluated.

Several automated pollen monitors have recently become available, most using conventional and/or machine-learning algorithms to detect pollen and classify their taxa. An international intercomparison campaign was organised by the EUMETNET AutoPollen Programme and the ADOPT COST Action in Munich (March–July 2021) (Maya-Manzano et al. 2023). The study showed that some automatic systems, especially those that have built-in correction factors to compensate for losses of sampling, detection and classification, agreed with the averaged pollen concentration of four manual Hirst-type measurements. The manual pollen counting method, relying on human operators, is still used today due to a lack of traceable metrological standards for pollen monitoring. Additionally, existing reference particle counters have been validated up to particle sizes around 20 µm only (Vasilatou et al. 2022). In this study, we evaluated the shake-the-box particle tracking/detection algorithm (Novara et al. 2023) to serve as a reference measurement of particle number concentration. A volume is illuminated with an expanded light sheet produced by a double-pulse laser, and four calibrated double-frame cameras record images of the particles inside this volume. The algorithm determines the position and velocity of each particle, allowing the measurement of particle number concentration directly in the air without sampling inlets that could influence the measurement.Particle tracking was validated against the Inkjet Aerosol Generator at the Japanese National Metrology Institute NMIJ and the reference optical particle counter at METAS using an 11-D spectrometer (Grimm GmbH, Germany) as a transfer standard and size-certified polystyrene particles. Expanded uncertainties (coverage factor k=2) were 24 %, 10 %, and 7 % for particle sizes 15 μm, 20 μm, and 26 μm, respectively. The good agreement among the three methods shows that the shake-the-box particle tracking method can be applied as a reference for particle number concentration.We used a laboratory-based method for characterising the performance of bioaerosol monitors as a whole unit (hardware plus identification algorithms) using the particle tracking method and, in a separate experiment, freshly sampled pollen. Experiments were carried out with the SwisensPoleno Jupiter, which combines light scattering, inline digital holography and ultraviolet laser-induced fluorescence with machine learning to classify different particles. For a pollen grain to be measured, it must be sampled, detected and correctly classified.  Each stage is subject to particle losses, leading to a measurement efficiency below 100 %. Particle tracking measurements of pollen (Pinus, Zea Mays) resulted in an average counting efficiency of about 43 %, neglecting particle losses in the Sigma-2 sampling head and issues with the classification of particles. The unit-to-unit variability of the Poleno instruments was 30 % based on measurement with three units. Independent experiments with Alnus glutinosa, Betula pendula and Corylus avellana showed that the major source of losses, however, originates from the pollen classification algorithm, which is trimmed to best correlate with Hirst measurements (currently the defacto reference for pollen monitoring). This highlights the need for an independent, standardised method for evaluating classifier losses.

<bold>Background:</bold> Inhalation errors are a common source of underdosing in asthma patients, leading to poor asthma control. Thus, regular inhalation training is recommended. The Kata^® app is a digital inhalation coach that provides step-by-step guidance on how to inhale correctly. <bold>Objective:</bold> Evaluate the effect of the Kata^® app on inhalation technique and clinical endpoints (e.g., FeNO; ACQ-5) in a clinical trial. <bold>Methods:</bold> This post hoc analysis of Kata^® app data collected during a phase II randomized controlled trial, included adult patients with moderate to severe, uncontrolled type-2 high asthma (N=78). Patients were followed for up to 12 weeks. A smartphone with the Kata^® app was provided at study start. Patients were asked to record each inhalation of their asthma maintenance treatment. Inhalation errors were scored according to predefined criteria. Rates for total and critical errors (significantly affect drug delivery) were calculated as the number of errors per inhalation. Analysis of clinical endpoints used data from the run-in phase only. <bold>Results:</bold> Univariate regression analysis showed a significant total error rate decrease of 1.4% per study day (p<.001; 95%CI: 0.8-1.9). Similarly, a significant decrease of the critical error rate of 2.0% per study day was found (p<.001; 95%CI: 1.0-2.6). There was a significant improvement of the ACQ-5 (p=.036; N=59). No significant improvement of FeNO was found (p=.170; N=60), but 26 of 60 patients (43%) showed a clinically relevant decrease (>20%) of FeNO. <bold>Conclusion:</bold> The Kata^® app improves inhalation technique in a clinical trial and is associated with improved asthma control. The Kata^® app is a useful tool for patients using inhalers to treat their asthma.

A data assimilation approach is proposed to enhance the dynamic range of the Vortex-In-Cell (VIC) method by simulating future- and past- instances. The VIC method mainly considers a vorticity field from which velocity and acceleration fields are calculated through Poisson equations, respectively bounded by prescribed conditions. In addition, a vorticity time derivative is also available by the vorticity transport equation. The proposed approach focuses on such already available data, i.e., the vorticity and its time derivative fields, for simulating additional instances and getting feedbacks from the corresponding measurement instances, e.g., particle image velocimetry (PTV). However, the self-simulated flow field can be depleted due to a lack of incoming information, which is out of the reconstruction domain at the source instance. To supply that kind of information and thus sustain the simulation, boundary conditions of the simulated instances are required and considered. As a result, the proposed approach can gather corrections from multiple PTV instances while optimizing a single vorticity volume and time-resolved boundary conditions. Since the boundary grid points are much smaller in number than that of the whole volume, one can expect an increased dynamic range. A former work, VIC# (Jeon et al. 2018), which supplements additional constraints and coarse-grid approximation to VIC+ (Schneiders and Scarano 2016), is selected as a 3D method to which the proposed 4D approach is applied. Two explicit Eulerian time-marching methods are tested as a simulation scheme: the forward Euler and the Runge-Kutta methods. A numerical assessment is conducted using the synthetic PTV data, whose ground truth is known, and returns reconstruction qualities based on the velocity and the identified vortical structures. Other practical features regarding convergence and computation complexity are also reported. To visually verify an improvement by the proposed approach, two kinds of time-resolved Shake-the-Box (STB) measurements, which were acquired in high-speed systems, are processed and discussed.

In this paper a new approach for 3D flame structure diagnostics using tomographic laser-induced fluorescence (Tomo-LIF) of the OH radical was evaluated. The approach combined volumetric illumination with a multi-camera detection system of eight views. Single-shot measurements were performed in a methane/air premixed laminar flame and in a non-premixed turbulent methane jet flame. 3D OH fluorescence distributions in the flames were reconstructed using the simultaneous multiplicative algebraic reconstruction technique. The tomographic measurements were compared and validated against results of OH-PLIF in the laminar flame. The effects of the experimental setup of the detection system and the size of the volumetric illumination on the quality of the tomographic reconstructions were evaluated. Results revealed that the Tomo-LIF is suitable for volumetric reconstruction of flame structures with acceptable spatial resolution and uncertainty. It was found that the number of views and their angular orientation have a strong influence on the quality and accuracy of the tomographic reconstruction while the illumination volume thickness influences mainly the spatial resolution.

We present a spatially and temporally highly resolved flow measurement covering a large volume (~0.6 m3) in a pure thermal plume in air. The thermal plume develops above an extended heat source and is characterized by moderate velocities (U ~ 0.35 m/s) with a Reynolds number of $$\text{Re} \sim 500$$ and a Rayleigh number of $${\text{Ra}}\sim 10^{6}$$ . We demonstrate the requirements and capabilities of the measurement equipment and the particle tracking approach to be able to probe measurement volumes up to and beyond one cubic meter. The use of large tracer particles (300 μm), helium-filled soap bubbles (HFSBs), is crucial and yields high particle image quality over large-volume depths when illuminated with arrays of pulsed high-power LEDs. The experimental limitations of the HFSBs—their limited lifetime and their intensity loss over time—are quantified. The HFSBs’ uniform particle images allows an accurate reconstruction of the flow using Shake-The-Box particle tracking with high particle concentrations up to 0.1 particles per pixel. This enables tracking of up to 275,000 HFSBs simultaneously. After interpolating the scattered data onto a regular grid with a Navier–Stokes regularization, the velocity field of the thermal plume reveals a multitude of vortices with a smooth temporal evolution and a remarkable coherence in time (see animation, supplementary data). Acceleration fields are also derived from interpolated particle tracks and complement the flow measurement. Additionally, the flow map, the basis of a large class of Lagrangian coherent structures, is computed directly from observed particle tracks. We show entrainment regions and coherent vortices of the thermal plume in the flow map and compute fields of the finite-time Lyapunov exponent.

Recently, progress has been made to reliably compute uncertainty estimates for each velocity vector in planar flow fields measured with 2D-or stereo-PIV. This information can be used for a post-processing denoising scheme to reduce errors by a spatial averaging scheme preserving true flow fluctuations. Starting with a 5 × 5 vector kernel, a second-order 2D-polynomial function is fitted to the flow field. Vectors just outside will be included in the filter kernel if they lie within the uncertainty band around the fitted function. Repeating this procedure, vectors are added in all directions until the true flow field can no longer be approximated by the second-order polynomial function. The center vector is then replaced by the value of the fitted function. The final shape and size of the filter kernel automatically adjusts to local flow gradients in an optimal way preserving true velocity fluctuations above the noise level. This anisotropic denoising scheme is validated first on synthetic vector fields varying spatial wavelengths of the flow field and noise levels relative to the fluctuation amplitude. For wavelengths larger than 5–7 times the spatial resolution, a noise reduction factor of 2–4 is achieved significantly increasing the velocity dynamic range. For large noise levels above 50% of the flow fluctuation, the denoising scheme can no longer distinguish between true flow fluctuations and noise. Finally, it is shown that the procedure performs well for typical experimental PIV vector fields. It provides an effective alternative to more complicated adaptive PIV algorithms optimizing interrogation window sizes and shapes based on seeding density, local flow gradients, and other criteria.

A typical challenge with subset based DIC algorithms is to find the optimal subset size for the problem. Small subsets may improve spatial resolution, but also increase noise. Larger subsets offer better noise rejection at the cost of resolved features. In general, the product of spatial resolution and noise is approximately constant. Therefore, changing the subset size offers limited options to achieve good spatial resolution and smooth data. To resolve this dilemma, an anisotropic denoising technique is presented which utilizes a DIC uncertainty estimator as an input. The anisotropic denoising will smooth the vector field only where local gradients are smaller than the uncertainty and preserve regions with strong gradients. We will give several examples of the performance of the anisotropic denoising, including DIC challenge sample 14.

As a follow-up to a previous proof-of-principle study, a novel Lagrangian pressure-extraction technique is analytically evaluated, and experimentally validated using dense 4D-PTV data. The technique is analytically evaluated using the semi-three-dimensional Taylor–Green vortex, and it is found that the Lagrangian technique out-performs the standard Eulerian technique when Dirichlet boundary conditions are enforced. However, the Lagrangian technique produces worse estimates of the pressure field when Neumann boundary conditions are enforced on boundaries with strong pressure gradients. The technique is experimentally validated using flow data obtained for the case of a free-falling, index-matched sphere at \(Re=2100\). The experimental data were collected using a four-camera particle tracking velocimetry measurement system, and processed using 4D-PTV. The pressure field is then extracted using both the Eulerian and Lagrangian techniques, and the resulting pressure fields are compared. Qualitatively, the pressure fields agree; however, quantitative differences are found with respect to the magnitude of the pressure minima on the side of the sphere. Finally, the pressure-drag coefficient is estimated using each technique, and the two techniques are found to be in very close agreement. A comparison to a reference value from literature confirms that the drag coefficient estimates are reasonable, demonstrating the validity of the technique.

A posteriori uncertainty quantification of particle image velocimetry (PIV) data is essential to obtain accurate estimates of the uncertainty associated with a given experiment. This is particularly relevant when measurements are used to validate computational models or in design and decision processes. In spite of the importance of the subject, the first PIV uncertainty quantification (PIV-UQ) methods have been developed only in the last three years. The present work is a comparative assessment of four approaches recently proposed in the literature: the uncertainty surface method (Timmins et al ), the particle disparity approach (Sciacchitano et al ), the peak ratio criterion (Charonko and Vlachos ) and the correlation statistics method (Wieneke ). The analysis is based upon experiments conducted for this specific purpose, where several measurement techniques are employed simultaneously. The performances of the above approaches are surveyed across different measurement conditions and flow regimes.