Session UU03: V: Fluids III

Measuring Vorticity in Astrophysical Turbulence

Turbulence is a general properties of fluids in nature, and is observed to be present in many space and astrophysical fluids . Vorticity is a crucial aspect of turbulence. Measurement of vorticity in a turbulent astrophysical fluid would be very important, but is difficult. Most astronomical measurements of flow velocity rely on Doppler-shifted spectral lines, which provide a path-integrated measurement of one component of the velocity. Raymond et al (ApJ 903,2,2020) report an estimate of vorticity from spatially-varying Doppler shifts in the Cygnus Loop supernova remnant. I further consider this matter by calculating the spectrum of an idealized vortex for which the vorticity is known. The estimate of the vorticity from the "observed" spectrum is a good estimate of the true value, but the inferred vorticity from the spectrum of a model irrotational flow is comparable. Position-dependent Doppler shifts may give accurate estimates of the vorticity in astrophysical turbulence, but caution with this conclusion would seem warranted.   

Uncertainty Quantification Integrated with the Reduced-Dimensional Modeling of Supersonic Shear Flows

The development of reduced-order models as fast running models for the simulation of fluid flows has been a research topic for several years [1,2] and their widespread application to supersonic aerothermodynamics is a topic of continued research. Though Reynolds-Averaged solutions are generally considered computationally affordable numerical methods, real-time results for complex systems are not possible with such solvers. For applications such as digital twin framework, real-time results are paramount and reduced-dimensional and reduced-order models can be a solution.

The authors present a novel approach where reduced-dimensional modeling leveraging K-means clustering [3] and uncertainty quantification has been integrated with reduced-order modeling. The approach is demonstrated by modeling the supersonic flow over a double-fin-on-cylinder test article [4] where shock-boundary layer interactions are dominant. Heterogeneous experimental data for 20-degree and 5-degree fins was used as the so-called training data to develop a reduced-order model of the surface pressure distribution for a fin with a 10-degree incident angle. Further, Reynolds averaged Navier Stokes (RANS) computational fluid dynamics (CFD) calculations are conducted for the same 10-degree configuration, and the results are leveraged as additional training data. The experimental data obtained from various facilities can have different dimensionality and CFD data can have different dimensionality depending upon the grid density and numerical methods employed. If the dimensionality of each dataset is reduced to the same dimensionality, all data can be fused and the resulting large dataset can be used to build the reduced-order model. The research challenges in performing this dimensionality reduction are (1) maintaining an acceptably accurate representation of the relevant physical interactions and (2) quantifying both the contribution of the error and imprecision in dimensionality reduction and uncertainty inherent in the training data to the uncertainty of a reduced-order model’s predictions. This research will show the results from dimensionality reduction and reduced-order modeling, and implement existing uncertainty quantification (UQ) methods that address the above research challenges.   

Probabilistic Machine Learning to Improve Generalisation of Data-Driven Turbulence Modelling

A probabilistic machine learning model is introduced to augment the k-ω SST turbulence model in order to improve the modelling of separated flows. Increasingly, machine learning methods have been used to leverage experimental and high-fidelity data, improving the accuracy of the Reynolds Averaged Navier Stokes (RANS) turbulence models widely used in industry. A significant challenge for such methods is their ability to generalise to unseen geometries. Furthermore, heterogeneous datasets containing a mix of experimental and simulation data must be efficiently handled. In this work, field inversion and an ensemble of Gaussian Process Emulators (GPEs) is employed to address both of these challenges. The ensemble model is applied to a range of benchmark test cases, demonstrating improved turbulence modelling for cases with separated flows with adverse pressure gradients, where RANS simulations are understood to be unreliable. Perhaps more significantly, the simulation reverted to the uncorrected model in regions of the flow exhibiting physics outside of the training data.

    

Aggregation of virus particles

Viral aggregation is affected by several physicochemical parameters of the aqueous medium, including but not limited to pH, ionic strength and composition, and temperature. Some studies investigating these parameters have shown that aggregation can be reversible for some viruses. For some viruses, these parameters also govern the degree of reversibility of viral aggregation.

Viruses are colloidal particles, and as such, their Brownian motion and subsequent collision rate rise at higher temperatures leading to more aggregation. Several studies exploring the kinetics of viral aggregation as a function of these physicochemical parameters show that the process is mainly virus-specific and depends on the surface properties of viral particles. For instance, four genogroups of F-specific RNA bacteriophages, MS2, GA, Qβ, and SP, showed different aggregation behaviors over a broad range of pH (1.5–7.5) and ionic strength (1–100 mM NaNO3) conditions tested.. While MS2 only aggregated near their isoelectric point (pH = 4) regardless of the ionic strength, Qβ aggregated at low pH and high ionic strength, and GA and SP both aggregated over the entire range of tested conditions.

Models describe the energies of interaction between viruses using the extended DLVO theory. We present a model considering collisions between virus particles with double layers. The results are compared with experimental observations.

    

Acoustic bubble for cell patterning and co-culture

Over the past few years, a variety of progress in tissue engineering has been achieved to construct artificial tissue models. As a powerful tool to control the position and organization of cells, cell patterning techniques play an important role in the development of various biomedical applications. Acoustic manipulation is a technique that can actively control the cells to the desired location using physical phenomena. Among these approaches, the acoustic bubble is a promising tool to manipulate cells and fluids with high biocompatibility. In this talk, we will introduce an acoustic bubble-based microfluidic platform for cell patterning and co-culture in complex structures. We will first introduce the approach of forming bubbles with different geometries. Then the two modes of bubbles under different driving frequencies and the mechanism of patterning will be introduced. Using this method, we will demonstrate the particle and cell patterning with complex geometric shapes in our microfluidic platform. Furthermore, a co-culture model is demonstrated using this approach, enabling patterning and co-culture of different types of cells in a pre-designed structure. This approach provides a simple, inexpensive, and effective way for cell patterning and co-culture. In future, this work will be further applied to pattern multiple types of cells for various in vitro biomedical research, such as studying the cell-cell interaction, multicellular tissue construction and drug testing.   

On the decay of propulsive performance of a pitching foil near the free surface

An experimental set-up has been designed to study the propulsive performance of a pitching foil near the water free surface. The system is actuated by a servo that rotates sinusoidally at a fixed amplitude and frequency. The experiment allows to adjust the gap between the upper edge of the foil and the water surface. The gap is varied from a condition in which there is virtually no influence of the free surface, to a point at which the upper edge of the foil is at the free surface.

Kinematics are measured using a precision potentiometer, whilst a six-axis load cell and a torque sensor provide hydrodynamic loads. The robotic system hangs from an air bearing that ensures that there is no friction between the supporting structure and the system. Thanks to this arrangement, the thrust force can be measured redundantly using a single axis precision load cell, showing excellent agreement with the output of the six-axis balance. A time resolved Particle Image Velocimetry system is used to study the flow generated by the moving foil in two planes, one parallel to the trailing edge and another perpendicular to the rotating shaft.

 

Results are presented in the form of dimensionless parameters of thrust, side force, power and efficiency, which are heavily influenced by the proximity of the foil to the free surface, showing a nonlinear behaviour with submergence depth. The vortex formation when the system is near the free surface is substantially modified, with the appearance of axial flow and the loss of symmetry in the wake.   

Collaborative locomotion of artificial microswimmers through reinforcement learning

Artificial microswimmers offer exciting opportunities for biomedical applications, such as microsurgery and targeted drug delivery. Designing artificial microswimmers that can navigate in complicated biological micro-environments such as blood vessels and tumor environments has been of great research interest over the past decades. Collaborative navigation of multiple artificial microswimmers is particularly challenging due to complex hydrodynamic interactions between swimmers. In this work, we utilize deep reinforcement learning to obtain the effective locomotory policy of two collaborative colinear microswimmers. Our learning results demonstrate that phase shift and relative distance are the key factors that influence the net propulsion of the two collaborative swimmers. This reinforcement learning approach opens an alternative avenue to investigate the collaborative navigation of multiple artificial microswimmers.   

Vortex dynamics in the wake of a finite span wing in stable stratification.

A numerical investigation is carried out to study the evolution of wing-tip vortices in a stably stratified environment. Earlier studies have mostly

modeled the wake behind a wing using a pair of counter rotating vortices(Spalart 1996, Ortiz et al. 2015). In the present work we simulate the full

wake behind a finite span wing upto a downstream distance of 60 chord lengths for various levels of stratification. In the unstratified case, nearwake

consists of the vortex rollup and far wake consists of fully developed wing-tip vortices (Navrose et al. 2019). The calculations are carried out at Re

(Reynolds number)=1000 and 1< Fr(Froude number) <10. The results show that as strength of stratification increases, the rollup process of wing-

tip vortex formation is inhibited. The stratification suppresses the vertical motion and baroclinic vorticity of opposite sense as that of wing-tip vortices

is generated that persists along with vortex sheet. At relatively large stratification elongated flat 'V'-shaped structures are observed in the near

wake. The far wake in this case comprises of a mixture of pancake like strutures (Pal et al. 2017) and streamwise elongated vortical structures. The

vortical structures appears to have a well defined wavelength in the streamwise direction.