Gregory A. Kopp

Long-Duration Time-Resolved PIV to Study Unsteady Aerodynamics

A time-resolved particle image velocimetry (PIV) system has been developed at the University of Western Ontario, London, ON, Canada, with long-recording-time capabilities. This system is uniquely suited to the study of unsteady aerodynamics and hydrodynamics, such as avian aerodynamics or bluff-body oscillations. Measurements have been made on an elongated bluff body through the initial build-up phase of flutter. The possibilities to study this instability, which was responsible for the collapse of the Tacoma Narrows Bridge, are significantly broadened by the use of this system. The long-time recording capability of the system allows for novel results since it yields data that are spatially and temporally resolved over a long record length. The buildup of flutter is shown to exhibit complex dynamics that are heavily influenced by the flow-induced motion of the body. Features of the wake turbulence as a function of time are presented and shown to substantially vary.

The effects of wall proximity on vortex shedding from a square cylinder: Three-dimensional effects

The three-dimensional nature of turbulent vortex shedding from a square cylinder in the vicinity of a solid wall is investigated for a Reynolds number of 18 900 as a function of the gap height, S/D. Spanwise surface pressure measurements on the cylinder faces and on the solid wall are complemented by velocimetry data. It is observed that parallel and oblique shedding modes arise naturally. The number of vortex dislocations is clearly related to the variations in the oblique shedding angle. Dislocations occur with increasing probability as the gap height is decreased to S/D≈0.7. The dislocations are strongly associated with Type A instabilities and vortex splitting, which contribute significantly to phase-jitter. For gap heights close to that for vortex shedding suppression (0.5<S/D<0.7), dislocations occur less frequently. The vortex formation process is increasingly two-dimensional in this range, resulting in strong spanwise correlations and lower phase-jitter. These changes are observed as more sharply ...

The simulation and interpretation of free turbulence with a cognitive neural system

An artificial neural network, based on fuzzy ARTMAP, that is capable of learning the basic nonlinear dynamics of a turbulent velocity field is presented. The neural system is capable of generating a detailed multipoint time record with the same structural characteristics and basic statistics as those of the original instantaneous velocity field used for training. The good performance of the proposed architecture is demonstrated by the generation of synthetic two-dimensional velocity data at eight different positions along the homogeneous (spanwise) direction in the far region (x/D=420) of a turbulent wake flow generated behind a cylinder at Re=1 200. The analysis of the synthetic velocity field, carried out with spectral techniques, POD and pattern recognition, reveals that the proposed neural system is capable of capturing the highly nonlinear dynamics of free turbulence and of reproducing the sequence of individual classes of relevant events present in turbulent wake flows. The trained neural system als...

Use of the Wind Tunnel Test Method for Obtaining Design Wind Loads on Roof-Mounted Solar Arrays

AbstractASCE 7 does not provide design wind loads for roof-mounted solar panels. This paper discusses the use of the wind tunnel test method, called Method 3 in ASCE 7-05, which was originally intended for obtaining design wind loads for individual buildings. Because roof-mounted solar arrays are generally mounted in many configurations on many buildings of many different shapes, additional requirements are necessary to use Method 3 in this situation. The paper describes these additional requirements.

Prediction of pressure coefficients on roofs of low buildings using artificial neural networks

This paper describes an artificial neural network (ANN) approach for the prediction of mean and root-mean-square (rms) pressure coefficients on the gable roofs of low buildings. The ANN models, which employ a backpropagation training algorithm, are capable of generalizing the complex, nonlinear functional relationships between the pressure coefficients and eave height, wind direction and spatial location on the roof. The performance of the ANN is demonstrated by the prediction of the pressure coefficients for roof tap locations in a corner bay. The mean bay uplift can be predicted accurately with an average error less than 2% for three cornering wind directions not seen by the ANN during training. The mean-square errors of all of the individual pressure taps in the corner bay were 12% and 9% for the mean and rms coefficients, respectively. This approach could be used to expand aerodynamic databases to a larger variety of geometries and increase its practical feasibility.

Vortex shedding from a square cylinder near a wall

Wake velocity and cylinder pressure measurements are used to investigate the changes in the vortex shedding from a square cylinder near a solid wall at a Reynolds number of 19 000. The phase-averaged velocity field shows that vortex formation is displaced downstream due to the presence of the wall. Reattachment of the shear layer on the lower cylinder face interrupts the coupling of the upper and lower shear layers so that suppression of vortex shedding occurs for a gap height to diameter ratio, S/D < 0.4. Circulation ratios at suppression are consistent with the extension of von Karmans stability analysis for two rows of alternating vortices of unequal strength. It is observed that vortex shedding becomes increasingly intermittent for gap height ratios below 0.5 as a result of the straightening of the separated shear layers and reduced coupling between them. This article was chosen from selected Proceedings of the Second International Symposium on Turbulence and Shear Flow Phenomena (KTH-Stockholm, 27-2...

The Use of Pattern Recognition and Proper Orthogonal Decomposition in Identifying the Structure of Fully-Developed Free Turbulence

The eigenvectors obtained from proper orthogonal decomposition (POD) are used as the selection criteria to classify the individual events contained in data files of two-component velocity signals recorded in the non-homogeneous (vertical) and homogeneous (horizontal) planes of a fully-developed turbulent cylinder wake. This procedure uses the dominant eigenvectors from POD as initial templates to perform a pattern recognition (PR) analysis of the signals so that the individual coherent events appearing randomly in the signals can be educed more objectively. The prototype or ensemble average of the group of events classified as the large-scale structure in the vertical plane has a circulatory motion with strong negative streamwise and outward lateral velocity fluctuations. In the horizontal plane, the average structure is a double roller with negative streamwise velocity fluctuations in its centerplane. The class of instantaneous events selected contribute significantly to the variance in the outer intermittent region, but much less in the fully turbulent core.

Three-dimensional structure and momentum transfer in a turbulent cylinder wake

Simultaneous velocity and temperature measurements were made with rakes of sensors that sliced a slightly heated turbulent wake in the spanwise direction, at different lateral positions 150 diameters downstream of the cylinder. A pattern recognition analysis of hotter-to-colder transitions was performed on temperature data measured at the mean velocity half-width. The velocity data from the different ‘slices’ was then conditionally averaged based on the identified temperature events. This procedure yielded the topology of the average three-dimensional large-scale structure which was visualized with iso-surfaces of negative values of the second eigenvector of [S2+Ω2]. The results indicate that the average structure of the velocity fluctuations (using a triple decomposition of the velocity field) is found to be a shear-aligned ring-shaped vortex. This vortex ring has strong outward lateral velocities in its symmetry plane which are like Grants mixing jets. The mixing jet region extends outside the ring-like vortex and is bounded by two foci separated in the spanwise direction and an upstream saddle point. The two foci correspond to what has been previously identified in the literature as the double rollers.The ring vortex extracts energy from the mean flow by stretching in the mixing jet region just upstream of the ring boundary. The production of the small-scale (incoherent) turbulence by the coherent field and one-component energy dissipation rate occur just downstream of the saddle point within the mixing jet region. Incoherent turbulence energy is extracted from the mean flow just outside the mixing jet region, but within the core of the structure. These processes are highly three-dimensional with a spanwise extent equal to the mean velocity half-width.When a double decomposition is used, the coherent structure is found to be a tube-shaped vortex with a spanwise extent of about 2.5l0. The double roller motions are integral to this vortex in spite of its shape. Spatial averages of the coherent velocity field indicate that the mixing jet region causes a deficit of mean streamwise momentum, while the region outside the foci of the double rollers has a relatively small excess of streamwise momentum.

A wavelet pattern recognition technique for identifying flow structures in cylinder generated wakes

Abstract A pattern recognition technique, applied in wavelet space, with enhanced capabilities of identifying multiple flow templates based on scale is introduced and applied to the turbulent, intermediate wake region of a circular cylinder. The advantage of this enhanced pattern recognition technique is two-folded: (i) the wavelet templates can be easily identified based on the sharp definition of modulus maxima lines and (ii) the scale separation in wavelet space allows for the identification of multiple structures. This new technique is used to investigate the relationship between the primary (spanwise) and secondary (streamwise) wake structures. The analysis relates the interactions between these two flow structures to a local loss of periodicity in the Karman vortex street.

Interpolation of wind-induced pressure time series with an artificial neural network

Abstract This paper presents an approach for interpolating wind-induced pressure time series on a model low-rise building. The approach involves using artificial neural networks (ANN) that is capable of capturing the complex variations of the pressure time series and then predicting them over a long time. The ANN is trained with the time series data from adjacent taps and historical data and optimized at a single location within the corner vortex for a single (cornering) wind direction. The good performance and robustness of the proposed neural network is demonstrated by the prediction of pressure time series data at other roof locations within the corner vortex (except in the corner itself) and for slightly altered wind directions and terrains. Comparison of the results with those obtained via linear interpolation (LI) clearly indicates that the ANN approach overcomes the problem of spatial filtering associated with LI when low-resolution data is used. The main downside of the technique is the higher level of complexity and computational effort.