A hybrid method for fast and reliable contact detection between ellipsoids is developed.It computes the minimum distance and contact points, defined as the closest points on the interacting surfaces.The method targets particle-resolved simulations of disperse multiphase flows, where distance information is required at the moment of collision and also during particle approach and rebound for lubrication modeling.Its design follows criteria resulting from a detailed literature analysis.First, the contact problem is formulated as an unconstrained optimization based on common normals.Three parametric formulations of the ellipsoid-ellipsoid distance are derived.Second, these formulations are combined with several iterative methods adapted to improve convergence.A novel initialization strategy provides suitable starting guesses.Third, the resulting methods are assessed in terms of accuracy, robustness, and runtime relative to each other and to the GJK algorithm.For this purpose, a newly extended procedure is used to generate a comprehensive set of random ellipsoid pairs with prescribed contact points and a wide range of separation distances.The results show that the performance is governed jointly by the distance formulation and the iterative scheme.Based on this analysis, a hybrid method is constructed by combining the most effective formulations and solution strategies.It converges reliably across all geometric configurations considered.The method is implemented in a large-scale sediment transport simulation involving thousands of spatially resolved ellipsoidal particles, where it reduces computational cost of computing the distances by a factor of 28 compared to a previously used simpler approach while maintaining accurate collision handling.

The accuracy obtained with CFD and process simulations of flotation critically depends on the quality and robustness of the underlying models for the non-resolved sub-processes. An important issue in flotation is the collision between particles and air bubbles. Many models have been developed, but their accuracy for applications in flotation is limited. In particular, the significant size difference between particles and bubbles and their intricate coupling to the turbulent flow field pose severe challenges. The present paper first reviews presently employed collision models, highlighting their advantages and disadvantages when applied to flotation. On this basis, the "Integrated Multi-Size Collision model" (IMSC) is proposed. After a detailed evaluation, it combines existing approaches from various sources and introduces new developments designed to address present shortcomings. The model is validated by own DNS data as well as data from the literature. It is shown that, overall, the IMSC provides better predictions for the collision rate in typical flotation conditions than presently employed collision models and covers the entire parameter range of the flotation process very well. Using the available data, some of the underlying modelling assumptions are validated. Finally, a comprehensive overview of the model is provided for further use in Euler-Euler frameworks or process simulations.

The static reconfiguration of a weighted flexible ribbon of trapezoidal shape, exposed to fluid flow, was investigated by means of highly resolved numerical simulations, configured to match the experiment of Barois and de Langre [J. Fluid Mech. 735, R2, 2013]. Over a broad velocity range, the ribbon experiences a drag force nearly independent of the flow velocity. This phenomenon is regarded as a unique characteristic of tension-controlled reconfiguration, in contrast to the more prevalent case of elastic reconfiguration in literature. The present paper provides a detailed analysis of the flow field in general and the interaction with the ribbon in particular, for example in terms of distributed fluid loads acting on the ribbon. Situations with small and strong reconfiguration of the ribbon reveal fundamentally different wake regimes, hence strongly influencing the characteristics of fluid loading. The findings enabled to develop an enhanced theoretical model in a self-similar framework, especially including a physically justified, realistic local drag coefficient, termed streamline-based local drag model (SLDM). The modeling approach provides an even deeper understanding of the mechanisms during reconfiguration.

Optimizing the cleaning time of thin soil layers is an omnipresent challenge in the food processing industry. One approach to address this problem is to identify different prototypical modes of soil removal, called cleaning mechanisms, and to simulate the cleaning process with a dedicated model for each cleaning mechanism. Industrial cleaning procedures, however, involve chemicals and non-constant operating conditions that cause the cleaning mechanism to change. In the present paper, a cleaning model combining different cleaning mechanisms is developed to represent the use of hot aqueous solution of sodium hydroxide as cleaning fluid, which is often employed in the food industry. The model includes a new formulation of a swelling sub-model based on a pseudo-diffusion approach and accounts for the transport of water, sodium hydroxide, and heat into the soil and the model switches automatically between the different cleaning mechanisms. Various validation cases for sub-models are presented. These allow to assess the most important interactions between different sub-models. The cleaning model is applied to the case of a soiled wall in a straight rectangular duct with turbulent flow characterized by bulk Reynolds numbers between 5000 and 30000 for ketchup and starch soils, and a flushing process of chocolate with laminar flow. Sodium hydroxide concentrations between 0 and 2 wt ^∘ C are considered. Finally, in a further case study, the cleaning of a proteinaceous soil in a heat exchanger is simulated, where a simplified cleaning-in-place procedure is optimized using the new model.

Premixed submerged multiphase turbulent jet flows occur in a variety of important processes in chemical and mineral engineering to enhance mixing and mass transfer. Computational fluid dynamics (CFD) simulations of such processes on industrial scales are principally feasible within the Eulerian framework of interpenetrating continua. However, practical application requires suitable closure models to account for phenomena on the scale of individual particles or bubbles, which are not resolved in this approach. The present work applies closure relations, which were previously established for different geometries, such as bubble columns, pipe flows, and stirred tanks. CFD simulations based on these models are compared with experimental data from the literature for two-phase gas–liquid and solid–liquid turbulent jets. Overall, a reasonable agreement between the simulation and experiment is found. Possible causes for the remaining differences are discussed, and directions for further research are identified. Finally, simulations are also presented for three-phase gas–solid–liquid turbulent jets.

In the food industry, processing plants are cleaned daily, and considerable amounts of water and chemicals are used. The cleaning procedures usually consume more resources than necessary since they are not optimized. Optimization may be conducted using simulations, however, currently suitable models are not available. This contribution summarizes research results of recent years in the field of cleaning modeling for the simulation-based optimization of cleaning processes at TUD. The following achievements are discussed: i) two machine learning-based strategies for classifying soils according to their behavior during removal-termed cleaning mechanism, ii) basic simulation models for each cleaning mechanism and validation of the models on various flow configurations involving a duct flow, a duct flow with sudden cross-sectional expansion, a pipe flow, and an impinging jet and iii) a combined cleaning model allowing the transition between cleaning mechanisms. The model accounts for the influence of temperature and hydroxide ion concentration of the cleaning fluid on the cleaning process. Its potential is demonstrated in a case study. In all investigated scenarios, performing cleaning simulations takes less than ten minutes. The results are placed in the context of the current state of research, and challenges for the future are identified.

Improving the efficiency of gas turbines requires a detailed understanding of secondary flow phenomena, including tip leakage vortices (TLVs) and corner separation. While linear compressor cascades are commonly used for simplifying flow studies, their ability to replicate flow features in annular rotating cascades remains insufficiently explored. This study evaluates the transferability of flow characteristics between these configurations using wall-resolving large eddy simulations for identical blade geometries. Specifically, the effects of cascade geometry, relative end wall motion, and rotation on secondary flows are assessed. In our study, linear cascade results overpredict total pressure losses associated with the TLV by up to 10% compared to annular rotating cascades while reproducing the TLV structure with reasonable accuracy. Within the blade passage, rotation shifts the distribution of mass flow crossing the gap toward the blade's front half, increasing the TLV intensity by 50% and altering its roll-up location from 20% chord (non-rotating) to 10% chord (rotating). Oppositely, the TLV size remains similar. Furthermore, rotation reduces corner separation losses to a third of the non-rotating annular cascade, transforming its topology from double-sided to single-sided. Relative end wall motion, while exerting limited direct effects, induces flow redistribution, aligning TLV positions between the linear cascade with end wall motion and the annular rotating cascade. The present findings underscore the importance of incorporating relative end wall motion into linear cascade studies for improved TLV predictions. However, the pronounced effects of rotation on flow structure and loss mechanisms reveal fundamental limitations of linear cascades for precisely approximating secondary flows in rotating systems.

Predicting the cleaning time of a fouling layer, termed soil, is the subject of current research. One approach to tackle this problem for film-like soils is the identification of different, prototypical modes of removal, called cleaning mechanisms. This allows the employment of dedicated modeling approaches for each of the cleaning mechanisms. In the present paper, a model for the cleaning mechanism viscous shifting is presented. Existing approaches to model viscous shifting soils are reviewed. Compared to the existing models, the new model proposed here has three distinctive features: i) geometry-independent formulation for a range of geometries, ii) decoupling of flow computation and soil removal, iii) consideration of non-isothermal scenarios. The model is validated based on two representative cases: jet cleaning of a Newtonian oil layer and a flushing process of chocolates. In the latter also a non-isothermal scenario is considered. The presented model is able to capture the evolution of soil height over time for all cases investigated. The present model achieves relative.

Fluid mechanical conditions are crucial for cavitation formation, and significantly influence chemical reactivity. This study investigates process conditions such as pressure, degassing, cavitation and reaction volume, and the sound emission of oxidative dye degradation by cavitation. For ensuring comparability and scalability, dimensionless similarity numbers aligned to the process were introduced. A further focus of the paper is reproducibility with corresponding guidelines. Measurements of dye degradation were carried out without additional chemicals. The oxidation process was assessed by the chemiluminescence of luminol. For this purpose, configurations with three nozzle sizes at different pressure differences were investigated. The generated cavitating jet was captured by imaging techniques and correlated to degradation. The most energy-efficient configuration was obtained by the smallest nozzle diameter of 0.6 mm at a pressure difference of 40 bar. Significant degassing occurred during cavitation. It was more pronounced with smaller nozzle diameters, correlating with higher degradation. Furthermore, discontinuous treatment methods can improve efficiency. Scaling to higher flow rates through multiple reactors in parallel proved more effective, compared to increasing the nozzle diameter or the pressure difference. For the same treated volume, two parallel reactors increased degradation by a factor of 1.35. The insights provide perspectives for optimizing jet cavitation reactors for water treatment.

As an advanced oxidation process, hydrodynamic cavitation generates radicals inducing reduction of chemicals in water. In the present work dye degradation is investigated as a representative for such chemical. Cavitation intensity, outgassing and flow reactivity largely depend on pressure boundary conditions. The paper presents an experimental study aimed to investigate effects of outgassing on degradation through jet cavitation in a multiphase reactor by varying back pressure between 0.6 and 2 bar at a constant pressure difference of 40 bar. The measurements reveal that some outgassed air bubbles are recirculated into the jet, which may enhance the process as an oxidizing agent. Degradation is found to vary significantly by back pressure obtaining maximum degradation around ambient pressure in the experimental setup used. But outgassing also restricts reactivity at back pressures below ambient pressure. The influence of outgassing on degradation unlocks opportunities for energy-to-degradation efficient applications.

Experiments and fully resolved numerical simulations are reliable, yet costly ways of designing safe and efficient flushing processes. An alternative is the use of a simplified algebraic model. In this work a model previously developed by the present authors for combinations of fluids with identical properties is extended to different fluid properties of previous and flushing fluid. A realistic range of fluid properties is determined using chocolate as an example. The results from the simplified model are compared to highly resolved numerical data. The simplified model is able to predict the flushing process well, for phases where only the removal of previous fluid near the wall is relevant. This is achieved quickly, when the previous fluid has a lower apparent viscosity than the displacing one. In the opposite case, the prediction quality of the model for the preceding phase is low and other approaches should be considered.

Collisions between particles and bubbles are decisive for the performance of flotation processes. In this work Direct Numerical Simulations of a prototypical gravitation-driven flotation process are presented with bubbles fully resolved and modelled as rigid spheres while the solid-phase is represented as point-particles. Key bubble and particle parameters correspond to realistic setups and are varied to study their effect on the collision rate. The study addresses the main influencing parameters, such as bubble diameter, gas hold-up, particle diameter, and particle density. Locally around the bubble significant differences of the collision behaviour exist. Most collisions occur on the upper bubble half, but some also on the lower bubble half. This is caused by a high particle-bubble relative velocity towards the bubble from the upper bubble half and an accumulation of particles at these locations as they deviate around the bubble. The paper provides reference data for flotation modelling.

The paper presents a constraint-based collision model for Cosserat rods, able to handle dynamic or static contact between a large number of highly flexible structures. The model provides the required collision impulses prior to updating the solution of the rods, with the impulses accounted for as external loads. The procedure avoids the need to modify the structure solver itself and circumvents any iteration between the collision model and the solver for the Cosserat rods, maintaining the efficiency of any chosen Cosserat solver. The collision model is adopted from Tschisgale et al. (Arch. Appl. Mech. 89(2):167–193, 2019) and extended towards higher stability, which is found necessary in the case of very flexible rods. Furthermore, the model is supplemented with additional terms that arise when the colliding rods are immersed in a fluid. The latter is accounted for by an immersed-boundary method. A large number of tests are conducted to demonstrate the functionality of the final model. Beyond the present study, this set of cases may constitute a suitable test bench for dry and wet collisions of flexible structures.

The paper presents a simulation of the turbulent flow over and through a submerged aquatic canopy composed of 672 long, slender ribbons modelled as Cosserat rods. It is characterized by a bulk Reynolds number of 20 000, and a friction Reynolds number of 2638. Compared with a smooth turbulent channel at the same bulk Reynolds number, the canopy increases drag by a factor of 12. The ribbons are highly flexible, with a Cauchy number of 25 000, slightly buoyant, and densely packed. Their length exceeds the channel height by a factor of 1.6, while their average reconfigured height is only a quarter of the channel height. Different from lower-Cauchy-number cases, the movement of the ribbons, characterized by the motion of their tips, is very pronounced in the vertical direction, and even more in the spanwise direction, with root-mean-square fluctuations of the spanwise tip position 1.5 times the vertical ones. A canopy hull is defined to analyse the collective motion of the canopy and its interaction with the outer flow. Dominant spanwise wavelengths at this interface measure approximately one channel height, corresponding to twice the spacing of adjacent high- and low-speed streaks identified in two-point correlations of fluid velocity fluctuations. Conditional averages associated with troughs and ridges in the topography of the hull reveal streamwise-oriented counter-rotating vortices. They are reminiscent of the head-down structures related to the monami phenomenon in lower-Cauchy-number cases.

A novel Y-shaped membraneless flow-through electrolyzer is introduced to achieve a homogeneous electrochemical reaction across the entire electrode in a cost-efficient cell design with effective product separation. Numerical simulations of the electrolyte flow and electrical current within the already known I- and T-shaped cells motivate the newly proposed Y-shape cell. Furthermore, a new design criterion is developed based on the balance between bubble removal and gas generation. As proof-of-concept experimental results using the Y-shaped electrolyzer are presented, showing homogeneous gas distributions across the electrode and efficient product separation by the electrolyte flow.

Knowledge of the cleaning mechanism is necessary to choose a suitable model for a cleaning simulation. In the present work, an existing classification scheme for cleaning mechanisms is considered. Altough this framework is quite promising, the generation of training data constitutes a bottleneck, since the labeling was done manually and very roughly in order to supply the necessary amount of samples in a reasonable time. This, in turn, causes the scheme to be inaccurate when applied to more realistic data. The aim of the present work is to improve the preparation of training data preparation by introducing a semi-automatic labeling procedure. The labeling procedure involves a new perspective on the data and the application of a gradient filter procedure. Furthermore, fully convolutional networks (FCNs) are employed to generalize different gradient filter. The labeling procedure is significantly faster and more consistent than manual labeling. Also, a proof of concept is provided showing that the FCNs are a suitable technique for the present classification task.

Nasal airflow obstruction correlates with several ailments, such as higher patency, increased friction at the mucosal wall or the so-called Little’s area, improper air conditioning, and snoring. Nasal dilators are frequently employed, mainly due to their ease of access and use, combined with their non-permanent and non-surgical nature. Their overall efficacy, however, has not been clearly demonstrated so far, with some studies reporting conflicting outcomes, mainly because being based on subjective evaluations. This study employs Computational Fluid Dynamics simulations to analyze the flow inside a real nose, performs an objective assessment of a nasal dilator’s effect in terms of airflow and air conditioning, reporting flow paths, friction levels, heat and water fluxes and detailed temperature and humidity distributions. Coincidentally, the studied nose presents a septal deviation, with one nostril being wider than the other. The tubes of the dilator used in both nostrils are identical, as with any standard commercial dilator. Consequently, the dilator widens one nostril, as intended, but results in an obstruction in the other. This allows simultaneously addressing two situations, the nominal function of the dilator, as well as an off-design case. Results indicate a 24% increase in nasal patency in the design situation. The effect, however, is limited, as quantified by appropriate measures, such as the flow-generated friction at the nose surfaces and the temperature fluxes. Hence, the effect of such a dilator in nominal conditions is perhaps not as large as might be hoped. In the off-design situation, nasal resistance increases by 62%, an undesirable effect, illustrating the consequences of using an inappropriate dilator.

When dealing with modeling and simulation of surface cleaning processes knowledge of the specific cleaning mechanism at hand is necessary to apply the correct cleaning model. This paper presents an approach for the identification of cleaning mechanism based on fully convolutional networks. An efficient labelling strategy based on gradient filters is developed, allowing fast and efficient labelling of cleaning experiment video footage while achieving pixel-perfect labels. First, datasets are generated for model soils (starch 12410, ketchup, petroleum jelly) as well as standard soils (eggyolk, vanilla pudding, gelatine, starch 12616). A subset of the model soil dataset is used for training machine learning algorithms based on fully convolutional networks. The remaining data is subsequently employed as unseen data to assess the performance of the models. Independent testing on the model soils data achieves 93% accuracy and 82% intersection over union.With these values, selection of the correct cleaning model is possible. Application of the models to standard soils shows the ability of the models to generalize towards more realistic soils without further training or adaption. Finally, the models are applied to 89 experiments with ketchup under various operating conditions. The results were combined to construct a regime map for cleaning mechanisms constituting a new way to efficiently display the effect of different operating condition on the cleaning behaviour of soils for the practitioner.

Bubble deformation and breakup due to strain-rate-induced stress is investigated for a laminar flow configuration. The bubble shape is assumed to be a prolate ellipsoid. A new model for bubble deformation under dynamic load is introduced in the form of an ordinary differential equation for the deformation energy. Breakup is identified with a critical value of the deformation. As an application case, the flow in a joining T-junction is considered with the ratio of the volume flow rate being unity and the outflow Reynolds number being 1800. Dilute, dispersed bubbles with a diameter of 0.5 mm are injected. High-speed shadowgraphy is used and bubble parameters are evaluated via image processing. The capillary number is obtained from a single-phase flow simulation providing the instantaneous shear rate at the position of the bubble. The deformation resulting from the proposed model is then compared with the measured deformation for an exemplary bubble trajectory.

This work aims to improve the turbulence modeling in RANS simulations for particle-laden flows. Using DNS data as reference, the errors of the model assumptions for the Reynolds stress tensor and turbulence transport equations are extracted and serve as target data for a machine learning process called SpaRTA (Sparse Regression of Turbulent Stress Anisotropy). In the present work, the algorithm is extended so that additional quantities can be taken into account and a new modeling approach is introduced, in which the models can be expressed as a scalar polynomial. The resulting corrective algebraic expressions are implemented in the RANS solver SedFoam-2.0 for cross-validation. This study shows the applicability of the SpaRTA algorithm to multi-phase flows and the relevance of incorporating sediment-related quantities to the set of features from which the models are assembled. An average improvement of ca. thirty percent on various flow quantities is achieved, compared to the standard turbulence models.