Contour extraction of Doppler velocity profiles using neural networks

Author(s):  
S. Gazula ◽  
A.F. Hall ◽  
S.J. Kovacs
2018 ◽  
Vol 40 ◽  
pp. 23-32 ◽  
Author(s):  
Vedrana Baličević ◽  
Hrvoje Kalinić ◽  
Sven Lončarić ◽  
Maja Čikeš ◽  
Bart Bijnens

2014 ◽  
Vol 2 (1) ◽  
pp. 167-180 ◽  
Author(s):  
M. Stagnaro ◽  
M. Bolla Pittaluga

Abstract. We present a series of detailed experimental observations of saline and turbidity currents flowing in a straight channel. Experiments are performed by continuously feeding the channel with a dense mixture until a quasi-steady configuration is obtained. The flume, 12 m long, is characterized by a concrete fixed bed with a uniform slope of 0.005. Longitudinal velocity profiles are measured in ten cross sections, 1 m apart, employing an ultrasound Doppler velocity profiler. We also measure the density of the mixture using a rake of siphons sampling at different heights from the bottom in order to obtain the vertical density distributions in a cross section where the flow already attained a quasi-uniform configuration. We performed 27 experiments changing the flow discharge, the fractional excess density, the character of the current (saline or turbidity) and the roughness of the bed in order to observe the consequences of these variations on the vertical velocity profiles and on the overall characteristics of the flow. Dimensionless velocity profiles under quasi-uniform flow conditions were obtained by scaling longitudinal velocity with its depth averaged value and the vertical coordinate with the flow thickness. They turned out to be influenced by the Reynolds number of the flow, by the relative bed roughness, and by the presence of sediment in suspension. Unexpectedly, the densimetric Froude number of the current turned out to have no influence on the dimensionless velocity profiles.


1995 ◽  
Vol 117 (1) ◽  
pp. 62-67 ◽  
Author(s):  
Hyung Jin Sung ◽  
Chong Kuk Chun ◽  
Jae Min Hyun

An experimental study is made of two-dimensional uniform-shear flow (U = Uc + Gy) past a rotating cylinder of diameter D. A water-tunnel, equipped with a shear generator, was constructed. Laser-Doppler velocity measurements were undertaken to describe the wake characteristics. Data are compiled over the ranges of 600≤Re≤1,200, the shear parameter K[≡GD/Uc] up to 0.15, and the value of the cylinder rotation parameter α[≡ωD/2Uc], – 2.0≤α≤2.0. The power spectra of velocity measurements at downstream locations were analyzed to examine the vortex shedding patterns. In general, the dominant shedding frequency is shifted to a higher value as |α| and K increase. When |α| increases beyond a certain threshold value, the dominant frequency becomes less distinct. If |α| takes a value larger than around 1.5, the velocity field becomes randomized and diffuse, and the organized Karman vortex street activity weakens. The variations of the Strouhal number with K and α are described. The evolution of mean velocity profiles in the wake field is depicted. Characterizations of the velocity profiles, as K and α vary, are made based on the measurement data.


Author(s):  
Harjit S. Hura ◽  
Narendra D. Joshi ◽  
Hukam C. Mongia ◽  
Jon Tonouchi

Computational Combustion Dynamics has been used extensively at General Electric Company for combustion applications. This paper demonstrates an application of Advanced Combustion Code to GE’s lean premixed dry low NOx emissions LM2500 and LM6000 gas turbine combustors. A methodology for anchoring the Double Annular Counter-Rotating Swirler (DACRS) exit conditions to Laser Doppler Velocity data from a reacting single cup experiment is described. The DACRS exit velocity profiles and turbulence parameters are inlet boundary conditions for the annular combustor simulation. Since over 80 per cent of the total air enters the combustor via the premixers, inaccuracies in these boundary conditions have a significant impact on the predicted flame shape, liner temperatures and emissions. The paper shows comparisons between measured and predicted velocity in a rectangular duct equipped with a single DACRS. The k-ε turbulence model and the two-step eddy break up/eddy dissipation combustion models are used to predict the reacting flow field of the natural gas/air flame. The inlet velocity profiles are developed first to match the LV data and the observed flame impingement location at nominal settings of the inlet turbulence parameters. The sum square error between measured and predicted velocity is used as the optimization function. Next, a design of experiment computational study is conducted to determine the inlet turbulence length scale and kinetic energy in order to further improve the data match. The eddy break up model is shown to be more robust than the eddy dissipation model. The eddy dissipation model resulted in slow combustion rates, and high fuel and carbon monoxide emissions.


2011 ◽  
Vol 50 (11) ◽  
pp. 2338-2342 ◽  
Author(s):  
Vincent T. Wood ◽  
Rodger A. Brown

AbstractA tornadic vortex signature (TVS) is a degraded Doppler velocity signature that occurs when the tangential velocity core region of a tornado is smaller than the effective beamwidth of a sampling Doppler radar. Early Doppler radar simulations, which used a uniform reflectivity distribution across an idealized Rankine vortex, showed that the extreme Doppler velocity peaks of a TVS profile are separated by approximately one beamwidth. The simulations also indicated that neither the size nor the strength of the tornado is recoverable from a TVS. The current study was undertaken to investigate how the TVS might change if vortices having more realistic tangential velocity profiles were considered. The one-celled (axial updraft only) Burgers–Rott vortex model and the two-celled (annular updraft with axial downdraft) Sullivan vortex model were selected. Results of the simulations show that the TVS peaks still are separated by approximately one beamwidth—signifying that the TVS not only is unaffected by the size or strength of a tornado but also is unaffected by whether the tornado structure consists of one or two cells.


2001 ◽  
Author(s):  
Tanja N. Dreischuh ◽  
Luan L. Gurdev ◽  
Dimitar V. Stoyanov

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