Keywords: Fluid machinery, fluid instability phenomena, data science, flow control, jet flow
We are conducting research to estimate the state of fluids from observational data and control their flow. As specific examples, I will introduce research on estimating urinary flow rate and the internal state of turbo machinery.
First, we apply the phenomenon of fluid interface instability as a non-contact method for measuring urine flow. Specifically, when liquid flows out from a nozzle with a cross-section that is not circular but elongated elliptical, we can observe the phenomenon where the major and minor axes of the elongated elliptical cross-section switch immediately after the outflow. This phenomenon, called "axis switching," is also observed in actual urination, and there is a correlation between the wavelength of urination and the urine flow rate. By recording the time series data of the wavelength, it becomes possible to estimate the urine flow rate.
Furthermore, by combining not only the wavelength but also the width of urination and sound information, we enhance the accuracy of urine flow rate estimation. In addition, by using data science, we accurately reproduce the state variables (pressure, flow velocity) inside turbo machinery from data collected at limited observation points.