LDV(laser Doppler velocimetry) measurements were conducted at a total of four planes at 0.4 speed ratio. The speed ratio of 0.4 is 1600rpm of impeller and 633rpm of turbine speed. Even at the speed ratio 0.4, fluid flow at the gap region between the impeller and turbine and impeller exit was leaving the impeller and flowing up behind the turbine, and flows were affected by the turbine blade as it passed, negatively effecting converter efficiency. In the gap region, fluctuations make a clear sinusoidal trend unclear. The rise and fall of the flow rates in a broad sense, indicate a dependency based on the passage of the turbine blade in front of the impeller passage exit but a sinusoidal trend is not evident from this data.

LDV(laser Doppler velocimetry) measurements were conducted on the exit region of the impeller passage and the gap between the impeller and turbine blades under 0.8 speed ratio. The 0.8 speed ratio has an impeller speed of 2000rpm and a turbine speed of 1600rpm. A periodic variation of the mass flow rate is present in many of the measurements made. The frequency of this variation is the same as the frequency of the turbine blades passing the impeller passage exit. It is found that the instantaneous position of the turbine had effect on fluid flow inside the impeller passage and gap region. This study would aid in the construction of higher accuracy CFD models of this complex turbomachinery device.

A dynamic displacement estimation system is developed by integrating laser Doppler vibromter (LDV) and light detection and ranging (LiDAR). The system includes hardware level integration for simultaneous measurement of two devices and data fusion of two measurement signals based on Kalman filter smoothing algorithms. For hardware integration of two devices, the laser beam directions and the triggering of measurement of LDV and LiDAR are controlled on the level of built-in commands of the devices. The distance data sequentially measured by LiDAR is converted to dynamic displacement of high noise and low sampling rate, and fused with the velocity measured by LDV which has high sampling rate and low noise but accumulated bias error when integrated. Using the Kalman filter based data fusion algorithm, it is able to estimate dynamic displacement in which the drawbacks of two devices are effectively removed. The proposed system is applied to a dynamic loading test on a highway bridge and the performance is verified.