• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.05a
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• pp.1156-1161
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• 2001

A significant trial has been performed for estimation of noise level of a cross flow fan for air conditioning system. In general, measurements of noise level of machinery require rigorous equipment involving an anechoic chamber with precision gauges. The apparatus is expensive to utilize and is not easy to construct. In this work, we adopt static pressure sensing from an ordinary pressure transducer for prediction of noise level of a rotating fan. The present procedure is finding sound pressure from the static pressure by manipulating Light-Curle equation depicts noisy energy in terms of pressure on surfaces of noise generators. Sound power level near core unit of the fan is evaluated with the present methodology in a normal laboratory room without any sound absorbers. The method is easy and shows good prediction results compared with precise measurements by using microphones.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.10 no.6
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• pp.95-101
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• 2001

This work is concerned with construction of the intelligence stress predictor far compression strength evaluation using neural network-ultrasonic waves. The contact pressure in jointed plates was measured by using ultrasonic technique. Neural network is used to evaluate and predict contact pressure from the results of the calibration curves. The organized neural system was leaned with the accuracy of 99%, as a result of learning the ultrasonic echo ratio to the contact pressure measurement between SM45C and STS410 materials. And it could be evaluated and predicted with the accuracy of 90% in the evaluation of ultrasonic echo ratio difference in the same surface roughness and contact pressure, and 85% in the prediction of virtual ultrasonic echo ratio. Thus the proposed stress predictor is very useful for the evaluation and prediction of the contact pressure between SM45C and STS410 materials.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.24 no.9
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• pp.695-705
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• 2014

In this paper, two types of techniques for the prediction of radiated sound pressure due to vibration of a structure are investigated. The prediction performance using wave-number sensing technique is compared to that of conventional prediction method, such as Rayleigh's integral method, for the prediction of far-field radiated sound pressure. For a coupled plate, wave-number components are predicted by the vibration response of plate and the prediction performance of far-field sound is verified. In addition, the applicability of distributed sensors that are not allowable to Rayleigh's integral method is considered and these can replace point sensors. Experimental implementation verified the prediction accuracy of far-field sound radiation by the wave-number sensing technique. Prediction results from the technique are as good as those of Rayleigh's integral method and with distributed sensors, more reduced computation time is expected. To predict the radiated sound by the efficient configuration of structural sensors, composed(synthesized) mode considering sound power contribution is determined and from this size and location of sensors are chosen. Four types of sensor configuration are suggested, simulated and compared.

• Nuclear Engineering and Technology
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• v.55 no.7
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• pp.2642-2649
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• 2023

A real-time unmeasured dynamic response prediction process for the nuclear power plant pressure pipeline is proposed and its performance is tested in the test-loop system (KAERI). The aim of the process is to predict unmeasurable or unreachable dynamic responses such as acceleration, velocity, and displacement by using a limited amount of directly measured physical responses. It is achieved by combining a well-constructed finite element model and robust inverse force identification algorithm. The pressure pipeline system is described by using the displacement-pressure vibro-acoustic formulation to consider fully filled liquid effect inside the pipeline structure. A robust multiphysics modal projection technique is employed for the real-time sensor synchronized prediction. The inverse force identification method is also derived and employed by using Bathe's time integration method to identify the full-field responses of the target system from the modal domain computation. To validate the performance of the proposed process, an experimental test is extensively performed on the nuclear power plant pressure pipeline test-loop under operation conditions. The results show that the proposed identification process could well estimate the unmeasured acceleration in both frequency and time domain faster than 32,768 samples per sec.

• Particle and aerosol research
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• v.19 no.4
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• pp.145-154
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• 2023

Granular bed filters are widely used to remove particulate matter in flue gas and are filled with various shapes of packing material. The packing material plays an important role in determining the overall collection performance, such as pressure drop and collection efficiency. The pressure drop of a granular bed filter has been calculated using the Ergun equation, while the collection efficiency has been predicted using the log-penetration equation based on the single sphere theory. However, a prediction equation of collection efficiency for a granular bed filter filled with non-spherical packing materials has not been suggested yet. Therefore, in this study, three different shapes of packing materials (sphere, cylinder, and irregular) were prepared to propose a prediction equation. The pressure drop and collection efficiency in a granular bed filter filled with each shape of packing material were measured experimentally and compared with theoretically predicted values. We found that experimentally measured pressure drops matched well with values theoretically predicted using the Ergun equation considering the shape factor. However, experimental collection efficiencies were higher than theoretical ones predicted by the log-penetration equation using the single sphere theory. We modified the log-penetration equation by employing a shape factor and found a good relationship between experimental and theoretical collection efficiencies.

• The Journal of Society for e-Business Studies
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• v.23 no.2
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• pp.33-47
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• 2018

City gas pipelines are buried underground. Because of this, pipeline is hard to manage, and can be easily damaged. This research proposes a real time prediction system that helps experts can make decision about pressure anomalies. The gas pipline pressure data of Jungbu City Gas Company, which is one of the domestic city gas suppliers, time variables and environment variables are analysed. In this research, regression models that predicts pipeline pressure in minutes are proposed. Random forest, support vector regression (SVR), long-short term memory (LSTM) algorithms are used to build pressure prediction models. A comparison of pressure prediction models' preformances shows that the LSTM model was the best. LSTM model for Asan-si have root mean square error (RMSE) 0.011, mean absolute percentage error (MAPE) 0.494. LSTM model for Cheonan-si have RMSE 0.015, MAPE 0.668.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.1431-1432
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• 2013

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11a
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• pp.102-107
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• 2001

Recently, with the increasing of construction works, large construction equipment are used to reduce the term of work and labor cost in construction field. Therefore, construction equipment noise has caused much annoyance for a number of dweller in nearby construction field and it has become a very serious problem in our living environment. Neverthless, in our country, adequite guidelines for the construction equipment noise are very deficiency because of the lack of basic data and insufficient research works. From this point of view, this study attempts to survey the characteristics and prediction of piling noise in oil pressure method. On the basis of measurement value, we analysed about prediction possibility of piling noise in oil pressure method.

• Bulletin of the Korean Chemical Society
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• v.28 no.9
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• pp.1477-1484
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• 2007

Artificial neural networks (ANNs), for a first time, were successfully developed for the prediction partial molar heat capacity of aqueous solutions at infinite dilution for various polar aromatic compounds over wide range of temperatures (303.55-623.20 K) and pressures (0.1-30.2 MPa). Two three-layered feed forward ANNs with back-propagation of error were generated using three (the heat capacity in T = 303.55 K and P = 0.1 MPa, temperature and pressure) and six parameters (four theoretical descriptors, temperature and pressure) as inputs and its output is partial molar heat capacity at infinite dilution. It was found that properly selected and trained neural networks could fairly represent dependence of the heat capacity on the molecular descriptors, temperature and pressure. Mean percentage deviations (MPD) for prediction set by the models are 4.755 and 4.642, respectively.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2003.09a
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• pp.107-114
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• 2003

There are many problems in the prediction of dynamic behaviors of saturated soils because undrained excess pore water pressure builds up and then the strain softening behavior is occurred simultaneously. A few analytical constitutive models based on the effective stress concept have been proposed but most models hardly predict the excess pore water pressure and strain softening behaviors correctly In this study, the disturbed state concept (DSC) model proposed by Dr, Desai was modified to predict the saturated soil behaviors under the dynamic loads. Also, back-prediction program was developed for verification of modified DSC model. Cyclic triaxial tests were carried out to determine DSC parameters and test result was compared with the result of back-prediction. Through this research, it is proved that the proposed model based on the modified disturbed state concept can predict the realistic soil dynamic characteristics such as stress degradation and strain softening behavior according to dynamic process of excess pore water pressure.