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Title: Development and application of a standardized flow measurement uncertainty analysis framework to various low-head short-converging intake types across the United States federal hydropower fleet

Turbine discharges at low-head short converging intakes are difficult to measure accurately. The proximity of the measurement section to the intake entrance admits large uncertainties related to asymmetry of the velocity profile, swirl, and turbulence. Existing turbine performance codes [10, 24] do not address this special case and published literature is largely silent on rigorous evaluation of uncertainties associated with this measurement context. The American Society of Mechanical Engineers (ASME) Committee investigated the use of Acoustic transit time (ATT), Acoustic scintillation (AS), and Current meter (CM) in a short converging intake at the Kootenay Canal Generating Station in 2009. Based on their findings, a standardized uncertainty analysis (UA) framework for velocity-area method (specifically for CM measurements) is presented in this paper given the fact that CM is still the most fundamental and common type of measurement system. Typical sources of systematic and random errors associated with CM measurements are investigated, and the major sources of uncertainties associated with turbulence and velocity fluctuations, numerical velocity integration technique (bi-cubic spline), and the number and placement of current meters are being considered for an evaluation. Since the velocity measurements in a short converging intake are associated with complex nonlinear and time varying uncertaintiesmore » (e.g., Reynolds stress in fluid dynamics), simply applying the law of propagation of uncertainty is known to overestimate the measurement variance while the Monte Carlo method does not. Therefore, a pseudo-Monte Carlo simulation method (random flow generation technique [8]) which was initially developed for the purpose of establishing upstream or initial conditions in the Large-Eddy Simulation (LES) and the Direct Numerical Simulation (DNS) is used to statistically determine uncertainties associated with turbulence and velocity fluctuations. This technique is then combined with a bi-cubic spline interpolation method which converts point velocities into a continuous velocity distribution over the measurement domain. Subsequently the number and placement of current meters are simulated to investigate the accuracy of the estimated flow rates using the numerical velocity-area integration method outlined in ISO 3354 [12]. The authors herein consider that statistics on generated flow rates processed with bi-cubic interpolation and sensor simulations are the combined uncertainties which already accounted for the effects of all those three uncertainty sources. A preliminary analysis based on the current meter data obtained through an upgrade acceptance test of a single unit located in a mainstem plant has been presented.« less
  1. ORNL
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Conference: HydroVision International, Portland, OR, USA, 20150714, 20150717
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Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
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United States