Experiment of Adiabatic Two-Phase Flow in an Annulus Under Low-Frequency Vibration

Abstract

In order to investigate the possible effect of seismic vibration on two-phase flow dynamics and thermal-hydraulics of a nuclear reactor, experimental tests of adiabatic air-water two-phase flow under low-frequency vibration were carried out in this study. An eccentric cam vibration module operated at low motor speed (up to 390rpm) was attached to an annulus test section which was scaled down from a prototypic BWR fuel assembly sub-channel. The inner and outer diameters of the annulus are 19.1mm and 38.1mm, respectively. The two-phase flow operating conditions cover the ranges of 0.03≤<jg> ≤1.46m/s and 0.25≤<jf>≤1.00m/s and the vibration displacement ranges from ±0.8mm to ±22.2mm. Steady-state area-averaged instantaneous and time-averaged void fraction was recorded and analyzed in stationary and vibration experiments. A neural network flow regime identification technique and fast Fourier transformation (FFT) analysis were introduced to analyze the flow regimes and void signals under stationary and vibration conditions. Experimental results reveal possible changes in flow regimes under specific flow and vibration conditions. In addition, the instantaneous void fraction signals were affected and shown by FFT analysis. Possible reasons for the changes include the applied high acceleration and/or induced resonance at certain ports under the specific flow and vibration conditions.

Measurement of Gas-Oil Two-Phase Flow Patterns by Using CNN Algorithm Based on Dual ECT Sensors with Venturi Tube

“…x is an independent variable, y is a dependent variable, ω is a weight vector, b is an offset, and φ(x) : R d → H is a nonlinear function that maps the data set S to a high-dimensional linear eigenspace and seeks optimality in the eigenvector. For the regression function, the optimization goals and constraints of the SVM are Equations (13) and (14), respectively. The ε insensitive loss function is used for a given training data set.…”

“…K is the penalty coefficient, the larger value indicates the higher requirement for error, by introducing Lagrangian function, the optimization problem of Equations (13) and (14) is transformed into dual problem, which is obtained by solving the dual problem (the solution of Equation (11) [38]:…”