Sin-Young Lee

FLUID - STRUCTURE INTERACTION IN A U-TUBE WITH SURFACE ROUGHNESS AND PRESSURE DROP

In this research, the surface roughness affecting the pressure drop in a pipe used as the steam generator of a PWR was studied. Based on the CFD (Computational Fluid Dynamics) technique using a commercial code named ANSYS-FLUENT, a straight pipe was modeled to obtain the Darcy frictional coefficient, changed with a range of various surface roughness ratios as well as Reynolds numbers. The result is validated by the comparison with a Moody chart to set the appropriate size of grids at the wall for the correct consideration of surface roughness. The pressure drop in a full-scale U-shaped pipe is measured with the same code, correlated with the surface roughness ratio. In the next stage, we studied a reduced scale model of a U-shaped heat pipe with experiment and analysis of the investigation into fluid-structure interaction (FSI). The material of the pipe was cut from the real heat pipe of a material named Inconel 690 alloy, now used in steam generators. The accelerations at the fixed stations on the outer surface of the pipe model are measured in the series of time history, and Fourier transformed to the frequency domain. The natural frequency of three leading modes were traced from the FFT data, and compared with the result of a numerical analysis for unsteady, incompressible flow. The corresponding mode shapes and maximum displacement are obtained numerically from the FSI simulation with the coupling of the commercial codes, ANSYS-FLUENT and TRANSIENT_STRUCTURAL. The primary frequencies for the model system consist of three parts: structural vibration, BPF(blade pass frequency) of pump, and fluid-structure interaction.

Acoustic diagnosis of a pump by using neural network

A fundamental study for developing a fault diagnosis system of a pump is performed by using neural network. Acoustic signals were obtained and converted to frequency domain for normal products and artificially deformed products. The neural network model used in this study was 3-layer type composed of input, hidden, and output layer. The normalized amplitudes at the multiples of real driving frequency were chosen as units of input layer. And the codes of pump malfunctions were selected as units of output layer. Various sets of teach signals made from original data by eliminating some random cases were used in the training. The average errors were approximately proportional to the number of untaught data. Neural network trained by acoustic signals can detect malfunction or diagnose fault of a given machine from the results.

Vibration Measurement of an Automobile Exhaust System in Operation

In this work, the operational deflection shape(ODS) of an automobile exhaust system is measured by using a recently-developed magnetic sensor. The magnetic sensor is composed of a solenoid and two pairs of permanent magnets generating an antisymmetric magnetic field in the lateral direction inside the solenoid. Lateral movement of a ferromagnetic pipe inside the magnetic field of the suggested sensor induces an electromotive force in the solenoid corresponding to the lateral velocity of the pipe. Due to the simplicity and non-contact characteristics of the magnetic sensor, dynamic behaviors of the structures operating under high temperature such as an exhaust pipe can be efficiently observed. It is shown that the lateral ODS of an exhaust system can be successfully measured by the suggested sensors.

Vibration evaluation of nonlinear system under random parametric excitations by probabilistic method

Vibration of a non-linear system under random parametric excitations was evaluated by probabilistic methods. The nonlinear characteristic terms of a system were quasi-linearized and excitation terms were remained as they were. An analytical method where the square mean of error was minimized was used. An alternative method was an energy method where the damping energy and restoring energy of the linearized system were equalized to those of the original non-linear system. The numerical results were compared with those obtained by Monte Carlo simulation. The comparison showed the results obtained by Monte Carlo simulation located between those by the analytical method and those by the energy method.