arXiv subjects

Maximilian Dreisbach

Publications and source records attributed to Maximilian Dreisbach.

4 recordsLinked to original sources

arXiv2025

PINNs4Drops: Video-conditioned physics-informed neural networks for two-phase flow reconstruction

Two-phase flow phenomena underpin critical technologies such as hydrogen fuel cells, spray cooling, and combustion, where droplet dynamics govern performance and efficiency. Conventional optical diagnostics, including shadowgraphy and particle image velocimetry, provide valuable insights but are limited to two-dimensional projections of inherently three-dimensional flows. We employ a specialized optical technique that encodes droplet surface information through color-coded glare points, enabling enhanced reconstruction of gas-liquid interfaces. To interpret these measurements, we introduce video-conditioned physics-informed neural networks (VcPINNs), which integrate experimental observations with governing fluid dynamics equations. This hybrid framework leverages the strengths of both data-driven learning and physical constraints, allowing accurate volumetric flow reconstruction from limited input images. Applied to droplet impingement experiments, our method yields highly resolved and physically consistent 3D interface and flow dynamics. The combined imaging and PINN reconstruction strategy provides a powerful platform for advancing multiphase-flow analysis, with broad potential impact across energy, cooling, and propulsion applications.

physics.flu-dyn↗

arXiv2025

Interface reconstruction of adhering droplets for distortion correction using glare points and deep learning

The flow within adhering droplets subjected to external shear flows has a significant influence on the stability and eventual detachment of the droplets from the surface. Most commonly, the velocity field inside adhering droplets is measured by means of particle image velocimetry (PIV), which requires a correction step for distortion caused by refraction of light at the gas-liquid interface. Current methods for distortion correction based on ray tracing are limited to low external flow velocities. However, the ray-tracing method can be extended to arbitrarily deformed droplet shapes if the instantaneous three-dimensional droplet interface is availble. In the present work, a previously introduced method for the image-based reconstruction of gas-liquid interfaces by means of deep learning is adapted to determine the instantaneous interface of adhering droplets in external shear flows. In this regard, a purposefully developed optical measurement technique based on the shadowgraphy method is employed that encodes additional three-dimensional (3D) information of the interface in the images via glare points from lateral light sources. On the basis of the images recorded in the experiments, the volumetric shape of the droplet is reconstructed by a neural network that was trained on the spatio-temporal dynamics of the gas-liquid interface from a synthetic dataset obtained by numerical simulation. The results for experiments with adhering droplets at different velocities of external flow demonstrate that the combination of the learned droplet geometry with the depth encoding through the glare points facilitates a robust and flexible reconstruction. The proposed method reconstructs the instantaneous three-dimensional interface of adhering droplets at both high resolution and spatial accuracy and thereby enables the distortion correction of PIV measurements at high external flow velocities.

physics.flu-dyn↗

arXiv2023

Spatio-temporal reconstruction of drop impact dynamics by means of color-coded glare points and deep learning

The present work introduces a deep learning approach for the three-dimensional reconstruction of the spatio-temporal dynamics of the gas-liquid interface in two-phase flows on the basis of monocular images obtained via optical measurement techniques. The dynamics of liquid droplets impacting onto structured solid substrates are captured through high-speed imaging in an extended shadowgraphy setup with additional reflective glare points from lateral light sources that encode further three-dimensional information of the gas-liquid interface in the images. A neural network is learned for the physically correct reconstruction of the droplet dynamics on a labelled dataset generated by synthetic image rendering on the basis of gas-liquid interface shapes obtained from direct numerical simulation. The employment of synthetic image rendering allows for the efficient generation of training data and circumvents the introduction of errors resulting from the inherent discrepancy of the droplet shapes between experiment and simulation. The accurate reconstruction of the gas-liquid interface during droplet impingement on the basis of images obtained in the experiment demonstrates the practicality of the presented approach based on neural networks and synthetic training data generation. The introduction of glare points from lateral light sources in the experiments is shown to improve the reconstruction accuracy, which indicates that the neural network learns to leverage the additional three-dimensional information encoded in the images for a more accurate depth estimation. Furthermore, the physically reasonable reconstruction of unknown gas-liquid interface shapes indicates that the neural network learned a versatile model of the involved two-phase flow phenomena during droplet impingement.

physics.flu-dyn↗

arXiv2023

Interferometric Particle Imaging for Particle Sizing in the Front-, Side-, and Back-Scatter Region

Interferometric particle imaging is a widely used optical measuring technique for the sizing of poly-dispersed spherical particles like droplets and bubbles. In its conventional approach, the method is limited to forward-scattering angles and therefore, requiring a second optical access, restricting the range of possible applications. In the present work, this limitation of the scattering angle is addressed, showing that also other scattering angles, especially in the back-scatter region are applicable, expanding the technique to applications with only a single optical access. A general method for the identification of suitable scattering angles both for droplets and bubbles is proposed. The visibility criterion for interference patterns from particles is generalized and possible glare point parings and their separation in the forward-, side- and back-scatter regimes are discussed for droplets and bubbles. Due to being the most popular examples, different scattering angles are proposed for water droplets and air bubbles in water. In the last part, the method is validated on a bubble sizing experiment.

physics.flu-dyn↗