• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.15 no.7 s.100
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• pp.788-798
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• 2005

The monopole theory has long been used to model air-pumped effect from the elastic cavities in car tire. This approach models the change of an air as a Piston moving backward and forward on a spring and equates local air movements exactly with the volume changes of the system. Thus, the monopole theory has a restricted domain of applicability due to the usual assumption of a small amplitude acoustic wave equation and acoustic monopole theory This paper describes an approach to predict the air-pumping noise of a car tyre with CFD/Kirchhoff integral method. The tyre groove is simply modeled as piston-cavity-sliding door geometry and with the aid of CFD technique flow properties in the groove of rolling car tyre are acquired.'rhese unsteady flow data are used as a air-pumping source in the next CFD calculation of full tyre-road geometry. Acoustic far field is predicted from Kirchhoff integral method by using unsteady flow data in space and time which is provided by the CFD calculation of full tyre-road domain. This approach can cover the non-linearity of acoustic monopole theory with the aid of Non-linear governing equation in CFD calculation. The method proposed in this paper is applied to the prediction of air-pumping noise of simply modeled car tyre and through the predicted results, the influence of nonlinear effect on air-pumping noise propagation is investigated.

• Geomechanics and Engineering
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• v.12 no.3
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• pp.441-464
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• 2017

The determination of the mixture parameters of stabilization has become a great concern in geotechnical applications. This paper presents an effort about the application of artificial intelligence (AI) techniques including radial basis neural network (RBNN), multi-layer perceptrons (MLP), generalized regression neural network (GRNN) and adaptive neuro-fuzzy inference system (ANFIS) in order to predict the unconfined compressive strength (UCS) of silty soil stabilized with bottom ash (BA), jute fiber (JF) and steel fiber (SF) under different freeze-thaw cycles (FTC). The dosages of the stabilizers and number of freeze-thaw cycles were employed as input (predictor) variables and the UCS values as output variable. For understanding the dominant parameter of the predictor variables on the UCS of stabilized soil, a sensitivity analysis has also been performed. The performance measures of root mean square error (RMSE), mean absolute error (MAE) and determination coefficient ($R^2$) were used for the evaluations of the prediction accuracy and applicability of the employed models. The results indicate that the predictions due to all AI techniques employed are significantly correlated with the measured UCS ($p{\leq}0.05$). They also perform better predictions than nonlinear regression (NLR) in terms of the performance measures. It is found from the model performances that RBNN approach within AI techniques yields the highest satisfactory results (RMSE = 55.4 kPa, MAE = 45.1 kPa, and $R^2=0.988$). The sensitivity analysis demonstrates that the JF inclusion within the input predictors is the most effective parameter on the UCS responses, followed by FTC.

• Composites Research
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• v.27 no.6
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• pp.213-218
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• 2014

This paper predicted the strength of mechanical joint of composites under bending load by means of the characteristic curve method. The method has been employed only for tensile and compression load conditions, but in this study, this method was extended to the bending load condition. For the finite element analysis (FEA), the nonlinear analysis was conducted considering the contact and friction effects between composite material and pin. The failure strength and mode on characteristic curve were evaluate with Tsai-Wu failure theory. To validate the results of FEA, the experiments were conducted to find out the failure load by applying bending moment on the composite specimens. The results showed reasonable agreements with theoretical results. These results lead to a conclusion that the characteristic curve method can be applied to predict the bending strength of mechanical joint of composites.

• Computers and Concrete
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• v.25 no.1
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• pp.67-73
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• 2020

Traditionally used analytical approach to predict the fatigue failure of reinforced concrete (RC) structure is generally conservative and has certain limitations. The nonlinear finite element method (FEM) offers less expensive solution for fatigue analysis with sufficient accuracy. However, the conventional implicit dynamic analysis is very expensive for high level computation. Whereas, an explicit dynamic analysis approach offers a computationally operative modelling to predict true responses of a structural element under periodic loading and might be perfectly matched to accomplish long life fatigue computations. Hence, this study simulates the fatigue behaviour of RC beams with finite element (FE) assemblage presenting a simplified explicit dynamic numerical solution to show computer aided fatigue behaviour of RC beam. A commercial FEM package, ABAQUS has been chosen for this complex modelling. The concrete has been modelled as a 8-node solid element providing competent compression hardening and tension stiffening. The steel reinforcements are simulated as two-node truss elements comprising elasto-plastic stress-strain behaviour. All the possible nonlinearities are duly incorporated. Time domain analysis has been adopted through an automatic Newmark-β time incremental technique. The program consists of twelve RC beams to visualize the real behaviour during fatigue process and to obtain the reliability of the study. Both the numerical and experimental results indicate a redistribution of stresses along the time and damage accumulation of beam which severely affect the serviceability and ultimate capacity of RC beam. The output of the FEM analysis demonstrates good match with the experimental consequences which affirm the efficacy of the computer aided model. The controlled fatigue damage evolution at service fatigue load limits makes the FE model an efficient tool in predicting high cycle fatigue behaviour of RC structures.

• Transactions of the Korean Society of Automotive Engineers
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• v.13 no.5
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• pp.142-151
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• 2005

To predict the fatigue life of the Hub Bearing Unit(HBU), preload effect and initial axial clearance have to be considered. Various theory and equations for the HBU design used in the passenger car are well developed in many literatures. But most design hand book for bearings or bearing catalogues do not consider the initial axial clearance and preload effect. So there are limits and difficulties to use those data in actual bearing design. To consider the preload effect and initial axial clearance, complex elliptic integrals and nonlinear equations are involved. These equations are difficult to solve during the design process. In order to solve these problems effectively, a program is developed to solve these equations reliably and to help the designer in obtaining the performance data of the HBU such as load distribution, maximum contact stress and fatigue life. The preprocessor of the program helps users to prepare the input data through a dialog box and the post processor makes graphical presentation of the result. In this paper, theoretical and numerical background for the prediction of the fatigue life of the HBU is explained. A simple example is presented to show the usefulness of developed program.

• Applied Chemistry for Engineering
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• v.31 no.5
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• pp.520-525
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• 2020

In this study, we developed a machine learning-based model for predicting the production stage temperature of distillation process. It is necessary to predict an accurate temperature for control because the control of the distillation process is done through the production stage temperature. The temperature in distillation process has a nonlinear complex relationship with other variables and time series data, so we used the recurrent neural network algorithms to predict temperature. In the model development process, by adjusting three recurrent neural network based algorithms, and batch size, we selected the most appropriate model for predicting the production stage temperature. LSTM₁₂₈ was selected as the most appropriate model for predicting the production stage temperature. The prediction performance of selected model for the actual temperature is RMSE of 0.0791 and R² of 0.924.

• Journal of the Korea Society of Computer and Information
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• v.16 no.8
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• pp.137-146
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• 2011

In order to support increased consumer awareness regarding energy consumption, we present new ways of monitoring and predicting with energy in electric appliances. The proposed system is a design of a common electrical power outlet called smart plug that measures the amount of current passing through current sensor at 0.5 second. To acquire data for training and testing the proposed neural network, weather parameters used include average temperature of day, min and max temperature, humidity, and sunshine hour as input data, and power consumption as target data from smart plug. Using the experimental data for training, the neural network model based on Back-Propagation algorithm was developed. Multi layer perception network was used for nonlinear mapping between the input and the output data. It was observed that the proposed neural network model can predict the power consumption quite well with correlation coefficient was 0.9965, and prediction mean square error was 0.02033.

• Journal of the Korean Solar Energy Society
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• v.27 no.4
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• pp.1-9
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• 2007

Sensitivity analysis of wind resource micrositing has been performed through the application case at the Antarctic King Sejong station with the most representative micrositing softwares: WAsP, WindSim and Meteodyn WT. The wind data obtained from two met-masts separated 625m were applied as a climatology input condition of micro-scale wind mapping. A tower shading effect on the met-mast installed 20m apart from the warehouse has been assessed by the CFD software Fluent and confirmed a negligible influence on wind speed measurement. Theoretically, micro-scale wind maps generated by the two met-data located within the same wind system and strongly correlated meteor-statistically should be identical if nothing influenced on wind prediction but orography. They, however, show discrepancies due to nonlinear effects induced by surrounding complex terrain. From the comparison of sensitivity analysis, Meteodyn WT employing 1-equation turbulence model showed 68% higher RMSE error of wind speed prediction than that of WindSim using the ${\kappa}-{\epsilon}$ turbulence model, while a linear-theoretical model WAsP showed 21% higher error. Consequently, the CFD model WindSim would predict wind field over complex terrain more reliable and less sensitive to climatology input data than other micrositing models. The auto-validation method proposed in this paper and the evaluation result of the micrositing softwares would be anticipated a good reference of wind resource assessments in complex terrain.

• Journal of the Korean Institute of Landscape Architecture
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• v.30 no.3
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• pp.73-85
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• 2002

The purpose of this study was to develop prediction models for plant species abundance by stream restoration. Generally the stream plant is affected by stream gemophology. So in this study, the relationship between the vegetation abundance and stream gemophology was developed by multiple regression analysis. The stream characteristics utilized in this study were longitudinal slope, transectional slope, micro-landforms through the longitudinal direction, riparian width and geometric mean diameter and biggest diameter of bed material, and cumulated coarse and fine sand weight portion. The Pyungchang River with mountainous watershed and the Kyungan stream and the Bokha stream in the agricultural region were selected and vegetation species abundance and stream characteristics were documented from the site at 2~3km intervals from the upper stream to the lower. The Models for predicting the vegetation abundance were developed by multiple regression analysis using SPSS statistics package. The linear relationship between the dependant(species abundance) and independant(stream characteristics) variables was tested by a graphical method. Longitudinal and transectional slope had a nonlinear relationship with species abundance. In the next step, the independance between the independant variables was tested and the correlation between independant and dependant variables was tested by the Pearson bivariate correlation test. The selected independant variables were transectional slope, riparian width, and cumulated fine sand weight portion. From the multiple regression analysis, the $R^2$for the Pyungchang river, Kyungan stream, Bokga stream were 0.651, 0.512 and 0.240 respectively. The natural stream configuration in the Pyungchang river had the best result and the lower $R^2$for Kyunan and Bokha stream were due to human impact which disturbed the natural ecosystem. The lowest $R^2$for the Bokha stream was due to the shifting sandy bed. If the stream bed is fugitive, the prediction model may not be valid. Using the multiple regression models, the vegetation abundance could be predicted with stream characteristics such as, transection slope, riaparian width, cumulated fine sand weigth portion, after stream restoration.

• Geomechanics and Engineering
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• v.2 no.4
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• pp.253-280
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• 2010

A new prediction model is derived for the uplift capacity of suction caissons using a hybrid method coupling genetic programming (GP) and simulated annealing (SA), called GP/SA. The predictor variables included in the analysis are the aspect ratio of caisson, shear strength of clayey soil, load point of application, load inclination angle, soil permeability, and loading rate. The proposed model is developed based on well established and widely dispersed experimental results gathered from the literature. To verify the applicability of the proposed model, it is employed to estimate the uplift capacity of parts of the test results that are not included in the modeling process. Traditional GP and multiple regression analyses are performed to benchmark the derived model. The external validation of the GP/SA and GP models was further verified using several statistical criteria recommended by researchers. Contributions of the parameters affecting the uplift capacity are evaluated through a sensitivity analysis. A subsequent parametric analysis is carried out and the obtained trends are confirmed with some previous studies. Based on the results, the GP/SA-based solution is effectively capable of estimating the horizontal, vertical and inclined uplift capacity of suction caissons. Furthermore, the GP/SA model provides a better prediction performance than the GP, regression and different models found in the literature. The proposed simplified formulation can reliably be employed for the pre-design of suction caissons. It may be also used as a quick check on solutions developed by more time consuming and in-depth deterministic analyses.