Level measurement of liquid radwaste is essential for inventory management of treatment system. Among various methods, level measurement based on differential pressure has many advantages. First, it is possible to measure the liquid level of the system regardless of liquid type. Second, as the instrument doesn’t need to be installed near the tank, there is no need to contact the tank when managing it. Therefore, workers’ radiation dose from the system can be decreased. Finally, although it depends on the accuracy, the price of the instrument is relatively low. With these advantages, in general, liquid radwaste level in a tank is measured using differential pressure in the treatment system. Not only the advantages described above, there are some disadvantages. As the liquid in the system is waste, it is not pure but has some suspended materials. These materials can be accumulated in tanks and pipes where the liquids move to come into direct contact with pneumatic pipes that are essential in differential pressure instruments. As a result, in case of a treatment using heat source, the accumulated materials may become sludge causing interference in pneumatic pipes. And this can change the pressure which also affects the level measured. In conclusion, in case of liquid storage tanks in which the situation cannot be checked, the proficiency of an operator becomes important.

There are various types of level gauging method such as using float, differential pressure, hypersonic, displacement and so on. In this study, among them, the method utilizing the differential pressure was reviewed. The strengths include: the differential pressure type level gauge can measure the level without direct contact of the sensor with media. That is to say, the level can be measured even if the sensor is far away from the tank. And regardless of the size of the tank, the level can be measured if the pneumatic pipes are installed. The weaknesses include: the sensor needs intermedium to recognize the level. The intermedium utilizes a fluid, which is compressed air. It is difficult to handle that compressed air has the properties of a gas. And to make compressed air needs compressor, tank and pneumatic pipes. So if you have many tanks, you need to install exponentially the pneumatic pipes. As well, level measurement range is limited to the points where the pneumatic pipes of the tank is installed. And if a compressed air that supplies to the sensor leaks, uncertainty will increase. A compressed air is colorless and odorless, so it’s difficult to pinpoint the leak. Finally, events like cracks and clogging can cause inaccurate measurement. Rather than using only differential pressure, it is better to use another measurement method according to the situation of the facility.

A dynamic tube pressure method was proposed for a liquid level measurement. The reliability of our in-house manufactured prototype level measurement system was investigated for water samples in a vial as a preliminary study. The prototype instrument, equipped with a stepper motor and a differential pressure sensor, was used to measure the travel distance of the tube from an initial zero position to the liquid surface. Unlike a conventional bubbler method, our dynamic tube pressure method is based on the abrupt changes in the tube pressure to directly detect the liquid surface. Optimum conditions were determined from the measurements of the travel distance with different-sized tubes at various ambient base pressures and various descending tube speeds. In addition, we proposed a gravimetric calibration method. In the gravimetric calibration method, the travel distances are used instead of the liquid level, which can be obtained from the measurement data of the travel distance. The travel distance versus the weight calibration curve showed a good linear relationship (R2 = 0.9999), and standard deviations of the travel distance over the whole range of experimental conditions were less than 0.1 mm. In a further study, our present system will also be used in the measurement of density and surface tension by minimizing the contact time with high-temperature and highly-corrosive molten salts.

PURPOSES: This study is to predict the Sound Pressure Level(SPL) obtained from the Noble Close ProXimity(NCPX) method by using the Extended Kalman Filter Algorithm employing the taylor series and Linear Regression Analysis based on the least square method. The objective of utilizing EKF Algorithm is to consider stochastically the effect of error because the Regression analysis is not the method for the statical approach. METHODS: For measuring the friction noise between the surface and vehicle’s tire, NCPX method was used. With NCPX method, SPL can be obtained using the frequency analysis such as Discrete Fourier Transform(DFT), Fast Fourier Transform(FFT) and Constant Percentage Bandwidth(CPB) Analysis. In this research, CPB analysis was only conducted for deriving A-weighted SPL from the sound power level in terms of frequencies. EKF Algorithm and Regression analysis were performed for estimating the SPL regarding the vehicle velocities. RESULTS : The study has shown that the results related to the coefficient of determination and RMSE from EKF Algorithm have been improved by comparing to Regression analysis. CONCLUSIONS : The more the vehicle is fast, the more the SPL must be high. But in the results of EKF Algorithm, SPLs are irregular. The reason of that is the EKF algorithm can be reflected by the error covariance from the measurements.

The first purpose was to identify the plantar pressure distributions (peak pressure, pressure integral time, and contact area) during level walking, and stair ascent and descent in asymptomatic flexible flatfoot (AFF). The second purpose was to investigate whether peak pressure data during level walking could be used to predict peak pressure during stair walking by identifying correlations between the peak pressures of level walking and stair walking. Twenty young adult subjects (8 males and 12 females, age 21.0±1.7 years) with AFF were recruited. A distance greater than 10 ㎜ in a navicular drop test was defined as flexible flatfoot. Each subject performed at least 10 steps during level walking, and stair ascent and descent. The plantar pressure distribution was measured in nine foot regions using a pressure measurement system. A two-way repeated analysis of variance was conducted to examine the differences in the three dependent variables with two within-subject factors (activity type and foot region). Linear regression analysis was conducted to predict peak pressure during stair walking using the peak pressure in the metatarsal regions during level walking. Significant interaction effects were observed between activity type and foot region for peak pressure (F=9.508, p<.001), pressure time integral (F=5.912, p=.003), and contact area (F=15.510, p<.001). The regression equations predicting peak pressure during stair walking accounted for variance in the range of 25.7% and 65.8%. The findings indicate that plantar pressures in AFF were influenced by both activity type and foot region. Furthermore the findings suggest that peak pressure data during level walking could be used to predict the peak pressure data during stair walking. These data collected for AFF can be useful for evaluating gait patterns and for predicting pressure data of flexible flatfoot subjects who have difficulty performing activities such as stair walking. Further studies should investigate plantar pressure distribution during various functional activities in symptomatic flexible flatfoot, and consider other predictors for regression analysis.

PURPOSES : The objective of this study is to provide for the overall SPL (Sound Pressure Level) prediction model by using the NCPX (Noble Close Proximity) measurement method in terms of regression equations. METHODS: Many methods can be used to measure the traffic noise. However, NCPX measurement can powerfully measure the friction noise originated somewhere between tire and pavement by attaching the microphone at the proximity location of tire. The overall SPL(Sound Pressure Level) calculated by NCPX method depends on the vehicle speed, and the basic equation form of the prediction model for overall SPL was used, according to the previous studies (Bloemhof, 1986; Cho and Mun, 2008a; Cho and Mun, 2008b; Cho and Mun, 2008c). RESULTS : After developing the prediction model, the prediction model was verified by the correlation analysis and RMSE (Root Mean Squared Error). Furthermore, the correlation was resulted in good agreement. CONCLUSIONS: If the polynomial overall SPL prediction model can be used, the special cautions are required in terms of considering the interpolation points between vehicle speeds as well as overall SPLs.