• Transactions of Materials Processing
• /
• v.27 no.4
• /
• pp.227-235
• /
• 2018

The flow stresses have been identified prior to a numerical simulation for predicting a deformation of materials using the experimental or analytical analysis. Recently, the flow stress models considering the temperature effect have been developed to reduce the number of experiments. Artificial neural network can provide a simple procedure for solving a problem from the analytical models. The objective of this paper is the prediction of flow stress on the fiber metal laminate using the artificial neural network. First, the training data were obtained by conducting the uniaxial tensile tests at the various temperature conditions. After, the artificial neural network has been trained by Levenberg-Marquardt method. The numerical results of the trained model were compared with the analytical models predicted at the previous study. It is noted that the artificial neural network can predict flow stress effectively as compared with the previously-proposed analytical models.

• Proceedings of the Korean Society For Composite Materials Conference
• /
• 2003.04a
• /
• pp.184-187
• /
• 2003

Complete prediction of second order permeability tensor for three dimensional circular braided preform is critical to understand the resin transfer molding process of composites. The permeability can be predicted by considering resin flow through the multi-axial fiber structure. In this study, permeability tensor for a 3-D circular braided preform is calculated by solving a boundary problem of a periodic unit cell. Flow field through the unit cell is obtained by using a 3-D finite volume method (FVM) and Darcy's law is utilized to obtain permeability tensor. Flow analysis for two cases that a fiber tow is regarded as impermeable solid and permeable porous medium is carried out respectively. It is found that the flow within the intra-tow region of the braided preform is negligible if inter-tow porosity is relatively high but the flow through the tow must be considered when the porosity is low. To avoid checkerboard pressure field and improve the efficiency of numerical computation, a new interpolation function for velocity variation is proposed on the basis of analytic solutions. Permeability of the braided preform is measured through a radial flow experiment and compared with the permeability predicted numerically.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.27 no.3
• /
• pp.286-293
• /
• 2003

The unsteady flow characteristics and associated blade tonal noise of a cross-flow fan are predicted by computational methods. The incompressible Navier-Stokes equations are time-accurately solved for obtaining the pressure fluctuations between the rotating blades and the stabilizer. and the sound pressure is predicted using Curie's equation. The discrete noise characteristics of three impellers with a uniform and two random pitch (type-A and -B) blades are compared by their SPL (Sound Pressure Level) spectra. and the frequency modulation characteristics of the BPF (Blade Passing Frequency) noise are discussed. Besides. a mathematical model is proposed for the prediction of discrete blade tonal noise and is validated with available experimental data. The fan performance is also compared with experimental data. indicating that the random pitch effect does not significantly alter the performance characteristics at ${\phi}$ 〉 0.4

• Transactions of the Korean Society of Automotive Engineers
• /
• v.4 no.4
• /
• pp.118-129
• /
• 1996

In this study, three dimensional flow analysis in a HVAC duct was performed computationally using various turbulence models and compared numerical predictions such as outlet flow split, surface pressure distribution along the duct to experimental data. It's well known that accuracy of computational predictions of flow heavily dependent on turbulent models and discritization method. Therefore, in this work, to assess the ability of turbulent models to predict characteristics of duct flow, three kinds of models, namely standard $k-\varepsilon$, RNG $k-\varepsilon$ and modified $k-\varepsilon$, containing parameter for the effect of streamline curvature were employed and validated one another by comparing with experimental data. In results, modified $k-\varepsilon$ turbulence model allows a successful prediction of static pressure distribution particulary at around strong curvature but little improvement flow split. In the futrue, adoption of CFD to design HVAC duct with modified $k-\varepsilon$ model will bring benefits of producing more accurate prediction, and also give designers more detail information much more than now.

• Journal of Mechanical Science and Technology
• /
• v.20 no.10
• /
• pp.1576-1589
• /
• 2006

Most of surface water ways in Egypt suffer from the infestation of aquatic weeds especially submerged ones which cause lots of problems for the open channels and the water structures such as increasing water losses, obstructing the water flow, and reducing the efficiency of the water structures. Accurate simulation of the water flow behavior in such channels is very essential for water distribution decision makers. Artificial Neural Network (ANN) has been widely utilized in the past ten years in civil engineering applications for the simulation and prediction of the different physical phenomena and has proven its capabilities in the different fields. The present study aims towards introducing the use of ANN technique to model and predict the impact of the existence of submerged aquatic weeds on the hydraulic performance of open channels. Specifically the current paper investigates utilizing the ANN technique in developing a simulation and prediction model for the flow behavior in an open channel experiment that simulates the existence of submerged weeds as branched flexible elements. This experiment was considered as an example for implementing the same methodology and technique in a real open channel system. The results of current manuscript showed that ANN technique was very successful in simulating the flow behavior of the pre-mentioned open channel experiment with the existence of the submerged weeds. In addition, the developed ANN models were capable of predicting the open channel flow behavior in all the submerged weeds' cases that were considered in the ANN development process.

• Journal of the Korean Society of Industry Convergence
• /
• v.24 no.5
• /
• pp.573-579
• /
• 2021

In this study, flow analysis was performed on three types of strainers for ships with different flow rates to predict the pressure drop of the strainer due to the filter of strainer. In the case of flow analysis, the flow analysis was performed by applying the porous media method by applying the resistance value derived by Ergun's equation to the filter position. As a result of the analysis, it was found that when the dimensions of the strainer body were small, the influence of the flow rate on the pressure drop was large. In addition, the amount of pressure drop and the flow rate are almost linearly proportional, and an analysis formula that can predict the amount of pressure drop was derived. In order to predict the amount of pressure drop of the strainer when blockage exist in the strainer filter, the analysis was performed by introducing the resistance ratio to derive the prediction equation. Using this equation, it is thought that it will be possible to predict the performance of the strainer due to blockage in the future strainer design and field application.

• Journal of Korea Water Resources Association
• /
• v.53 no.2
• /
• pp.97-105
• /
• 2020

Recently, Artificial Neural Network receives attention as a data prediction method. Among these, a Long Shot-term Memory (LSTM) model specialized for time-series data prediction was utilized as a prediction method of hydrological time series data. In this study, the LSTM model was constructed utilizing deep running open source library TensorFlow which provided by Google, to predict inflows of multipurpose dams. We predicted the inflow of the Yongdam Multipurpose Dam which is located in the upper stream of the Geumgang. The hourly flow data of Yongdam Dam from 2006 to 2018 provided by WAMIS was used as the analysis data. Predictive analysis was performed under various of variable condition in order to compare and analyze the prediction accuracy according to four learning parameters of the LSTM model. Root mean square error (RMSE), Mean absolute error (MAE) and Volume error (VE) were calculated and evaluated its accuracy through comparing the predicted and observed inflows. We found that all the models had lower accuracy at high inflow rate and hourly precipitation data (2006~2018) of Yongdam Dam utilized as additional input variables to solve this problem. When the data of rainfall and inflow were utilized together, it was found that the accuracy of the prediction for the high flow rate is improved.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.27 no.8
• /
• pp.1150-1157
• /
• 2003

Nonlinear relationship between Reynolds stresses and the rate of strain of nonlinear k-$\varepsilon$models is evaluated theoretically by using the boundary layer assumptions against the turbulence-driven secondary flows in noncircular ducts and then their prediction performance is validated numerically through the application to the fully developed turbulent flow in a square duct. Typical predicted quantities such as mean axial and secondary velocities, turbulent kinetic energy and Reynolds stresses are compared with available experimental data. The nonlinear k-$\varepsilon$ model adopted in a commercial code is found to be unable to predict accurately duct flows with the prediction level of secondary flows one order less than that of the experiment.

• The Transactions of the Korea Information Processing Society
• /
• v.2 no.1
• /
• pp.34-44
• /
• 1995

One of the most important problems faced by software developers and users is the prediction of the size of programming system and its development effort. This article define the identical characteristics for structured specification which is consisted of Data Flow Diagram, Data Dictionary and Mini Specification and apply quantitative estimation factor of structured specification to program code metrics, Moreover, concerning DFD which is made up of component element of structured specification executed quantitative estimation experiment. In the result, we propose man/month prediction model of lower progression with production on analysis phase of upper progression.

• Journal of Mechanical Science and Technology
• /
• v.15 no.9
• /
• pp.1292-1301
• /
• 2001

The mean-line method using empirical models is the most practical method of predicting off-design performance. To gain insight into the empirical models, the influence of empirical models on the performance prediction results is investigated. We found that, in the two-zone model, the secondary flow mass fraction has a considerable effect at high mass flow-rates on the performance prediction curves. In the TEIS model, the first element changes the slope of the performance curves as well as the stable operating range. The second element makes the performance curves move up and down as it increases or decreases. It is also discovered that the slip factor affects pressure ratio, but it has little effect on efficiency. Finally, this study reveals that the skin friction coefficient has significant effect on both the pressure ratio curve and the efficiency curve. These results show the limitations of the present empirical models, and more resonable empirical models are reeded.