This mainly theoretical work is devoted to the study of the contribution of the cell-free layer (CFL) near the vessel wall to hemodynamics in a large coronary artery with stenosis to assess the relevance of CFL modeling to the needs of interventional cardiology. An Euler–Euler model considering blood as a two-component fluid with a discrete phase of erythrocytes and a liquid plasma phase was applied to a simple 2d vessel with 65% stenosis. It was found that both the CFL thickness and the local contribution of the CFL thickness to hemodynamics are inhomogeneous along the vessel. The effects of CFL on the velocity profiles, vortex formation, hematocrit, viscosity, and wall shear stresses in the area of stenosis were determined. To demonstrate the significance of CFL modeling for prognostic purposes, the same hemodynamic conditions, analyzed using a one-component model, were also considered. A comparison analysis showed that the existence of CFL resulted in a significant overestimation (up to over 100%) of the main hemodynamic characteristics of the flow obtained using the model based on the Carreau equation.

A computer vision algorithm to determine the parameters of a two-phase turbulent jet of a water-gas mixture traveling at a velocity in the range of 5–10 m/s was developed in order to evaluate the hydrodynamic efficiency of mass exchange apparatuses in real time, as well as to predict the gas exchange rate. The algorithm is based on threshold segmentation, the active contours method, the regression of principal components method, and the comparison of feature overlays, which allows the stable determination of jet boundaries and is a more efficient method when working with low-quality data than traditional implementations of the Canny method. Based on high-speed video recordings of jets, the proposed algorithm allows the calculation of key characteristics of jets: the velocity, angle of incidence, structural density, etc. Both the algorithm’s description and a test application based on video recordings of a real jet created on an experimental prototype of a jet bioreactor are discussed. The results are compared with computational fluid dynamics modeling and theoretical predictions, and good agreement is demonstrated. The presented algorithm itself represents the basis for a real-time control system for aerator operation in jet bioreactors, as well as being used in laboratory jet stream installations for the accumulation of big data on the structure and dynamic properties of jets.

An innovative method for bubble detection and characterization in multiphase flows using advanced computer vision and neural network algorithms is introduced. Building on the research group’s previous findings, this study combines high-speed video capture with advanced deep learning techniques to enhance bubble detection accuracy and dynamic analysis. In order to further develop a robust framework for detecting and analyzing bubble properties in multiphase flows, enabling accurate estimation of essential mass transfer parameters, a YOLOv9-based neural network was implemented for bubble segmentation and trajectory analysis, achieving high accuracy. Key contributions include the development of an averaged mass transfer model integrating experimental data, neural network outputs, and scaling algorithms, as well as validation of the proposed methodology through experimental studies, including high-speed video imaging and comparisons with mass transfer coefficients obtained via the sulfite method. By precisely characterizing critical parameters, the algorithm enables accurate gas transfer rate calculations, ensuring optimal conditions in various industrial applications. The neural network-based algorithm serves as a non-invasive platform for detailed characterization of bubble media, demonstrating high accuracy in experimental validation and significantly outperforming traditional techniques. This approach provides a robust tool for real-time monitoring and modeling of bubble flows, laying the foundation for novel, non-invasive methods to measure multiphase media properties.

Bubble multiphase systems are crucial in industries such as biotechnology, medicine, oil and gas, and water treatment. Optical data analysis provides critical insights into bubble characteristics, such as the shape and size, complementing physical sensor data. Existing detection techniques rely on classical computer vision algorithms and neural network models. While neural networks achieve a higher accuracy, they require extensive annotated datasets, and classical methods often struggle with complex systems due to their lower accuracy. This study proposes a novel framework to address these limitations. Using Superformula parameter regression, we introduce an advanced border detection method for accurately identifying gas inclusions and complex-shaped objects in multiphase environments. The framework also includes a new approach for generating realistic artificial bubble images based on physical flow conditions, leveraging the Superformula to create extensive, labeled datasets without manual annotation. Tested on real bubble flows in mass transfer equipment, the algorithms enable bubble classification by shape and size, enhance detection accuracy, and reduce development time for neural network solutions. This work provides a robust method for object detection and dataset generation in multiphase systems, paving the way for more precise modeling and analysis.

Our study investigates how non-spherical bubble shapes influence gas–liquid mass transfer across the bubble interface. An analytical shape descriptor, namely Superformula, is used to parametrically define the bubble interface, enabling efficient CFD simulations over a range of Reynolds ($Re$) and Eötvös ($Eo$) numbers. By prescribing the bubble geometry analytically, we avoid expensive interface-capturing simulations and directly compute the concentration field without transient boundary shape pre-equilibration. The represented approach is computationally efficient and captures the impact of bubble shape and flow parameters on the dissolved gas concentration gradients in the surrounding liquid. Results show that bubble deformation alters the distribution of dissolved gas around the bubble and the overall mass transfer rate, with higher $Re$ enhancing convective transport and higher $Eo$ (more deformed bubbles), leading to anisotropic concentration boundary layers. The developed framework not only advances a fundamental understanding of bubble-driven mass transfer mechanisms but also directly addresses industrial needs, particularly in optimizing oxygen delivery within bioreactors contour and similar aerated processes. The proposed efficient modeling strategy provides a basis for developing fast surrogate tools in hybrid modeling frameworks, where high-fidelity CFD insights are incorporated into larger-scale multiphase process simulations.

Micrometer-sized particles are widely introduced as fluid flow markers in experimental studies of convective flows. The tracks of such particles demonstrate a high contrast in the optical range and well illustrate the direction of fluid flow at local vortices. This study addresses the theoretical justification on the use of large particles for obtaining vortex phenomena and its characterization in stenotic arteries by the Echo Particle Velocimetry method. Calcite particles with an average diameter of 0.15 mm were chosen as a marker of streamlines using a medical ultrasound device. The Euler–Euler model of particle motion was applied to simulate the mechanical behavior of calcite particles and 20 µm aluminum particles. The accuracy of flow measurement at vortex regions was evaluated by computational fluid dynamics methods. The simulation results of vortex zone formation obtained by Azuma and Fukushima (1976) for aluminum particles with the use of the optical velocimetry method and calcite particles were compared. An error in determining the size of the vortex zone behind of stenosis does not exceed 5%. We concluded that the application of large-size particles for the needs of in vitro studies of local hemodynamics is possible.