• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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• pp.963-971
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• 2022

Water pipeline network in local and metropolitan area is buried underground, by which it is hard to know the degree of pipe aging and leakage. In this study, assuming various sensor combinations installed in the water pipeline network, the optimal algorithm was derived by predicting the water flow rate and pressure through artificial intelligence algorithms such as linear regression and neuro fuzzy analysis to examine the possibility of detecting pipe leakage according to the data combination. In the case of leakage detection through water supply pressure prediction, Neuro fuzzy algorithm was superior to linear regression analysis. In case of leakage detection through water supply flow prediction, flow rate prediction using neuro fuzzy algorithm should be considered first. If flow meter for prediction don't exists, linear regression algorithm should be considered instead for pressure estimation.

• 유체기계공업학회:학술대회논문집
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• 2003.12a
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• pp.457-463
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• 2003

A general program of meanline design and/or performance prediction for centrifugal/mixed-flow compressors is successfully commercialized using various empirical loss models. 4 types of diffusers, 3 types of exit elements, shrouded/unshrouded impellers and real gas option are included in the program capabilities. Total 16 cases of benchmark test results proved its reliability to be effectively utilized in the development processes.

• Journal of information and communication convergence engineering
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• v.5 no.2
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• pp.171-176
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• 2007

To give a guarantee a consistently high level of quality and reliability of Telematics traffic service, traffic flow forecasting is very important issue. In this paper, we proposed an adaptable integrated prediction model to predict the traffic flow in the future. Our model combines two methods, short-term prediction model and long-term prediction model with different combining coefficients to reflect current traffic condition. Short-term model uses the Kalman filtering technique to predict the future traffic conditions. And long-term model processes accumulated speed patterns which means the analysis results for all past speeds of each road by classifying the same day and the same time interval. Combining two models makes it possible to predict future traffic flow with higher accuracy over a longer time range. Many experiments showed our algorithm gives a better precise prediction than only an accumulated speed pattern that is used commonly. The result can be applied to the car navigation to support a dynamic shortest path. In addition, it can give users the travel information to avoid the traffic congestion areas.

• Journal of the Korean Society of Visualization
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• v.21 no.1
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• pp.18-25
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• 2023

In these days, high dimensional data prediction technology based on neural network shows compelling results in many different kind of field including engineering. Especially, a lot of variants of convolution neural network are widely utilized to develop pixel level prediction model for high dimensional data such as picture, or physical field value from the sensors. In this study, velocity vector field of ideal flow on ship surface is estimated on pixel level by Unet. First, potential flow analysis was conducted for the set of hull form data which are generated by hull form transformation method. Thereafter, four different neural network with a U-shape structure were conFig.d to train velocity vectors at the node position of pre-processed hull form data. As a result, for the test hull forms, it was confirmed that the network with short skip-connection gives the most accurate prediction results of streamlines and velocity magnitude. And the results also have a good agreement with potential flow analysis results. However, in some cases which don't have nothing in common with training data in terms of speed or shape, the network has relatively high error at the region of large curvature.

• Tribology and Lubricants
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• v.26 no.1
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• pp.37-43
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• 2010

Precise prediction of rotordynamic coefficients for annular type seal of turbomachinery is necessary for enhancing their vibrational stability and various prediction methods have been developed. As the seal passage is designed complicatedly, the analysis based on Bulk-flow concept which has been mainly used in predicting seal dynamics is limited. In order to improve the seal rotordynamic prediction, full Navier-Stokes Equations with turbulent model derived in the seal flow passage have to be solved. In this study, 3D CFD(Computational Fluid Dynamics) analysis has been performed for predicting rotordynamic coefficients of non-contact type annular plain seal using FLUENT. Comparing with the results of Bulk-flow model analysis, the result of 3D CFD analysis shows good agreement.

• The KSFM Journal of Fluid Machinery
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• v.9 no.3 s.36
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• pp.14-21
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• 2006

Leakage reduction through annular type seals of turbomachinery is necessary for enhancing their efficiency and the precise prediction method of seal leakage is needed. The analysis based on Bulk-flow concept has been mainly used in predicting seal leakage. However, full Navier-Stokes Equations with turbulent model derived in the seal flow passage have to be solved for improving the prediction of seal leakage. FLUENT 6 which is commercial CFD(Computational Fluid Dynamics) code based on FVM(Finite Volume Method) and SIMPLE algorism has been used to analyze leakage of various non-contact-type seals in this presentation. Comparing with the results of Bulk-flow model analysis and experiment, the result of CFD analysis shows good agreement with that of existing theoretical analysis for the incompressible grooved seal and compressive plain and staggered seal. The CFD analysis also shows improvement on the leakage prediction of the incompressible plain seal and compressive see-through-type labyrinth seal.

• Computers and Concrete
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• v.5 no.5
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• pp.461-473
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• 2008

The paper explores the potential of Support Vector Machines (SVM) approach in predicting 28-day compressive strength and slump flow of self-compacting concrete. Total of 80 data collected from the exiting literature were used in present work. To compare the performance of the technique, prediction was also done using a back propagation neural network model. For this data-set, RBF kernel worked well in comparison to polynomial kernel based support vector machines and provide a root mean square error of 4.688 (MPa) (correlation coefficient=0.942) for 28-day compressive strength prediction and a root mean square error of 7.825 cm (correlation coefficient=0.931) for slump flow. Results obtained for RMSE and correlation coefficient suggested a comparable performance by Support Vector Machine approach to neural network approach for both 28-day compressive strength and slump flow prediction.

• Tribology and Lubricants
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• v.25 no.3
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• pp.150-156
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• 2009

Precise leakage prediction for annular type seals of turbomachinery is necessary for enhancing their efficiency and various prediction methods have been developed. As the seal passage is designed intricately, the analysis based on Bulk-flow concept which has been mainly used in predicting seal leakage is limited. In order to improve the seal leakage prediction, full Navier-Stokes Equations with turbulent model derived in the seal flow passage have to be solved. In this study, 3D CFD (Computational Fluid Dynamics) analysis has been performed for predicting leakage of various non-contact type anular seals using FLUENT. Compared to the results by Bulk-flow model analysis, experiment, and 2D CFD analysis, the result of 3D CFD analysis shows improvement in predicting seal leakage, especially for the parallel grooved pump seal.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.78-83
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• 2003

General algorithm is developed for the prediction of internal flow-induced noise. This algorithm is based on the integral formula derived by using the General Green Function, Lighthills acoustic analogy and Curls extension of Lighthills. Novel approach of this algorithm is that the integral formula is so arranged as to predict frequency-domain acoustic signal at any location in a duct by using unsteady flow data in space and time, which can be provided by the Computational Fluid Dynamics Techniques. This semi-analytic model is applied to the prediction of internal aerodynamic noise from a throttle valve in an automotive engine. The predicted noise levels from the throttle valve are compared with actual measurements. This illustrative computation shows that the current method permits generalized predictions of flow noise generated by bluff bodies and turbulence in flow ducts.

• International Journal of Fluid Machinery and Systems
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• v.2 no.1
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• pp.80-91
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• 2009

In this study, a prediction theory for specific noise that is the overall characteristic of the fan has been proposed. This theory is based on total pressure prediction and broadband noise prediction. The specific noises of two forward curved fans with different number of blades were predicted. The flow around the impeller having 120 blades (MF120) was more biased at a certain positions than the impeller with 40 blades (MF40). An effective domain of the energy conversion of MF40 has extended overall than MF120. The total pressure was affected by the slip factor and pressure loss caused by the vortex flow. The suppression of a major pressure drop by the vortex flow and expansion of the effective domain for energy conversion contributed to an increase in the total pressure of MF40 at the design point. The position of maximum relative velocity was different for each fan. The relative velocity of MF120 was less than that of MF40 due to the deviation angle. The specific noise of MF120 was 2.7 dB less than that of MF40 due to the difference in internal flow. It has been quantitatively estimated that the deceleration in the relative velocity contributed to the improvement in the overall performance.