A New Development of Cross-Correlation-Based Flow Estimation Validated and Optimized by CFD Simulation

The accurate measurement of mass flow rates is important in nuclear power plants. Flow meters have been invented and widely applied in several industries; however, the operating environment in advanced nuclear power plants is especially harsh due to high temperatures, high radiation, and potentially corrosive conditions. Traditional flow meters are largely limited to deployment at the outlet of pumps, on pipes, or in limited geometries. Cross-correlation function (CCF) flow estimation, on the other hand, can estimate the flow velocity indirectly without any specific instruments for flow measurement and in any geometry of the flow region. CCF flow estimation relies on redundant instruments, typically temperature sensors, in series in the direction of flow. One challenge for CCF flow estimation is that the accuracy of the flow measurement is mainly determined by inherent, common local process variation across the sensors, which may be small compared to the uncorrelated measurement noise. To differentiate the process variations from the uncorrelated noise, this research implements periodic fluid injection at a different temperature than the bulk fluid before the temperature sensors to amplify process variation. The feasibility and accuracy of this method are investigated through flow loop experiments and Computational Fluid Dynamics (CFD) simulations. This paper focuses on a CFD simulation model to verify the previous experimental results and optimize CCF flow estimation with different configurations. The optimization study is carried out to perform a grid search on the optimal location of the sensor pair under different flow rates. The CFD results show that the optimal sensor spacing depends on the flow rate being measured and provides guidance for sensor location implementation under various anticipated flow rates.