An introduction to heat transfer

The development of the method of particle image velocimetry (PIV) is traced by describing some of the milestones that have enabled new and/or better measurements to be made. The current status of PIV is summarized, and some goals for future advances are addressed.

Twenty years of particle image velocimetry

The size specifications for suitable tracer particles for particle image velocimetry (PIV), particularly with respect to their flow tracking capability, are discussed and quantified for several examples. A review of a wide variety of tracer materials used in recent PIV experiments in liquids and gases indicates that appropriately sized particles have normally been used. With emphasis on gas flows, methods of generating seeding particles and for introducing the particles into the flow are described and their advantages are discussed.

https://iopscience.iop.org/article/10.1088/0957-0233/8/12/005/pdf

Tracer particles and seeding for particle image velocimetry

To improve the performance of particle image velocimetry in measuring instantaneous velocity fields, direct cross-correlation of image fields can be used in place of auto-correlation methods of interrogation of double- or multiple-exposure recordings. With improved speed of photographic recording and increased resolution of video array detectors, cross-correlation methods of interrogation of successive single-exposure frames can be used to measure the separation of pairs of particle images between successive frames. By knowing the extent of image shifting used in a multiple-exposure and by a priori knowledge of the mean flow-field, the cross-correlation of different sized interrogation spots with known separation can be optimized in terms of spatial resolution, detection rate, accuracy and reliability.

Theory of cross-correlation analysis of PIV images : Image analysis as measuring technique in flows

Very large-scale motions in the form of long regions of streamwise velocity fluctuation are observed in the outer layer of fully developed turbulent pipe flow over a range of Reynolds numbers. The premultiplied, one-dimensional spectrum of the streamwise velocity measured by hot-film anemometry has a bimodal distribution whose components are associated with large-scale motion and a range of smaller scales corresponding to the main turbulent motion. The characteristic wavelength of the large-scale mode increases through the logarithmic layer, and reaches a maximum value that is approximately 12–14 times the pipe radius, one order of magnitude longer than the largest reported integral length scale, and more than four to five times longer than the length of a turbulent bulge. The wavelength decreases to approximately two pipe radii at the pipe centerline. It is conjectured that the very large-scale motions result from the coherent alignment of large-scale motions in the form of turbulent bulges or packets of...

Very large-scale motion in the outer layer

We introduce an approach that enables continuous monitoring of airborne nanoparticles by online detection and quantification of the collected species. Our method uniquely combines ultra-high flow rate sampling (up to thousands of liters per minute) with sensitive detection based on localized fluorescent complexation, permitting rapid quantitative measurement of airborne nanoparticle concentration. By coupling these components, we show initial results demonstrating detection of airborne ultrafine Al2O3 nanoparticles at environmental concentrations below 200 μg m−3 in air sampled at 200 L min−1. This capability suggests potential for online monitoring, making it possible to establish dynamic exposure profiles not readily obtainable using current-generation personal sampling instruments. The underlying fluorescent complexation interactions are inherently size and composition dependent, offering potential to straightforwardly obtain continuous detailed characterization.

/pdf/localized-fluorescent-complexation-enables-rapid-monitoring-3g8l9rrvc6.pdf

Localized fluorescent complexation enables rapid monitoring of airborne nanoparticles

Flow induced vibrations can be detrimental to all manner of engineering applications with fluid flow. Heat exchangers with bundles of thin tubes experiencing a crossflow are especially susceptible to the unexpected onset of vibration. Heat-exchanger design necessarily involves extensive modeling and testing to ensure that significant vibrations cannot develop for any expected flow conditions. A properly validated numerical simulation can work in conjunction with physical experiments to identify problematic vibrations and allow for rapid iteration of design at relatively low expense. A high-fidelity fluid–structure interaction code has been developed by fully coupling CFD LES/DNS code Nek5000 and CSM code Diablo. The coupled code is used to simulate crossflow through a tube bundle in a geometry recreated after a physical experiment. Validation against this experiment involves comparing the amplitude and frequency spectrum for three different flow velocities. The velocities compared straddle the onset of large magnitude vibrations. The simulation accurately captures the onset of the vibrations with a marginally lower predicted frequency of vibration. When the onset velocity is compensated for the difference in natural frequency, the simulation results closely match the experiment. The continuous nature of the simulation measurements helps illustrate the fluid mechanics behind the pin motion and reveals the front to back propagation of the vibrations as the fluid velocity increases.

High fidelity simulation and validation of crossflow through a tube bundle and the onset of vibration

Experimental measurements of turbulent flows in a rod bundle with a 3-D printed channel-type spacer grid

Large Eddy Simulation of the Flow Behavior in a Simplified Helical Coil Steam Generator

Generalizability of machine-learning (ML) based turbulence closures to accurately predict unseen practical flows remains an important challenge. It is well recognized that the neural network (NN) architecture and training protocol profoundly influence the generalizability characteristics. At the Reynolds-averaged Navier–Stokes level, NN–based turbulence closure modeling is rendered difficult due to two important reasons: inherent complexity of the constitutive relation arising from flow-dependent non-linearity and bifurcations; and, inordinate difficulty in obtaining high-fidelity data covering the entire parameter space of interest. Thus, a predictive turbulence model must be robust enough to perform reasonably outside the domain of training. In this context, the objective of the work is to investigate the approximation capabilities of standard moderate‐sized fully connected NNs. We seek to systematically investigate the effects of (i) intrinsic complexity of the solution manifold; (ii) sampling procedure (interpolation vs extrapolation); and (iii) optimization procedure. To overcome the data acquisition challenges, three proxy-physics turbulence surrogates of different degrees of complexity (yet significantly simpler than turbulence physics) are employed to generate the parameter-to-solution maps. Lacking a strong theoretical basis for finding the globally optimal NN architecture and hyperparameters in the presence of non-linearity and bifurcations, a “brute‐force” parameter-space sweep is performed to determine a locally optimal solution. Even for this simple proxy-physics system, it is demonstrated that feed-forward NNs require more degrees of freedom than the original proxy-physics model to accurately approximate the true model even when trained with data over the entire parameter space (interpolation). Additionally, if deep fully connected NNs are trained with data only from part of the parameter space (extrapolation), their approximation capability reduces considerably and it is not straightforward to find an optimal architecture. Overall, the findings provide a realistic perspective on the utility of ML turbulence closures for practical applications and identify areas for improvement.

Yassin A. Hassan

Papers

Localized fluorescent complexation enables rapid monitoring of airborne nanoparticles

High fidelity simulation and validation of crossflow through a tube bundle and the onset of vibration

Experimental measurements of turbulent flows in a rod bundle with a 3-D printed channel-type spacer grid

Large Eddy Simulation of the Flow Behavior in a Simplified Helical Coil Steam Generator

Turbulence closure modeling with data-driven techniques: Investigation of generalizable deep neural networks