• Journal of ILASS-Korea
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• v.21 no.1
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• pp.47-52
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• 2016

This paper describes the modeling of CNG direct injection using gaseous sphere injection model. Simulation of CNG direct injection does not need break up and evaporation model compared to that of liquid fuel injection. And very fine mesh is needed near the injector nozzle to resolve the inflow boundary. Therefore it takes long computation time for gaseous fuel injection simulation. However, simulation of CNG direct injection could be performed with the coarse mesh using gaseous sphere injection model. This model was integrated in KIVA-3V code and RNG $k-{\varepsilon}$ turbulence model needs to be modified because this model tends to over-predict gas jet diffusion. Furthermore, we preformed experiments of gaseous fuel injection using PLIF (planar laser induced fluorescence)method. Gaseous fuel injection model was validated against experiment data. The simulation results agreed well with the experiment results. Therefore gaseous sphere injection model has the reliability about gaseous fuel direct injection. And this model was predicted well a general tendency of gaseous fuel injection.

• Design & Manufacturing
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• v.15 no.4
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• pp.24-31
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• 2021

In this study, an artificial neural network model was constructed to convert CAE analysis data into similar experimental data. In the analysis and experiment, the injection molding data for 50 conditions were acquired through the design of experiment and random selection method. The injection molding conditions and the weight, height, and diameter of the product derived from CAE results were used as the input parameters for learning of the convert model. Also the product qualities of experimental results were used as the output parameters for learning of the convert model. The accuracy of the convert model showed RMSE values of 0.06g, 0.03mm, and 0.03mm in weight, height, and diameter, respectively. As the next step, additional randomly selected conditions were created and CAE analysis was performed. Then, the additional CAE analysis data were converted to similar experimental data through the conversion model. An artificial neural network model was constructed to predict the quality of injection molded product by using converted similar experimental data and injection molding experiment data. The injection molding conditions were used as input parameters for learning of the predicted model and weight, height, and diameter of the product were used as output parameters for learning. As a result of evaluating the performance of the prediction model, the predicted weight, height, and diameter showed RMSE values of 0.11g, 0.03mm, and 0.05mm and in terms of quality criteria of the target product, all of them showed accurate results satisfying the criteria range.

• Design & Manufacturing
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• v.16 no.1
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• pp.62-69
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• 2022

In the present study, the injection molding optimization of a large thick LH type elastic frames for the reduction of warpage was performed. Two kinds of fine and coarse finite element models were prepared to investigate the efficiency of analysis time and quality on simulation results. In order to derive injection molding conditions that can minimize distortion of parts, it was investigated that the effects of mold temperature, resin temperature, injection time, hold pressure switching time, holding pressure and the hold time on deformation characteristics using the design of experiments. The main influential factors on the warpage were found from the optimization simulation and the geometry data of the warpage result was converted into an initial model for injection simulation. It was shown that a coarse model with good mesh quality could be adapted for mold design since the total analysis time using the proposed model was reduced to 1/10. The suggested inversed warpage model produced the best minimized result of warpage.

• Journal of ILASS-Korea
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• v.28 no.4
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• pp.169-175
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• 2023

Multi-injection scheme is being applied to GDI combustion to reduce PM and PN emission to meet the EU7 regulation. However, very short injection duration encounters the ballistic injection region, which injection quantity does not increase linearly with injection duration when applying multi-injection. In this study, numerical studies were conducted to reveal the cause of ballistic injection and the effect of design parameters on ballistic region using 1-D simulation, AMESim. Injection rate and injection quantity were compared with experiment to validate the established model, which showed the accuracy with 10% error. The model revealed that the tendency of ballistic region coincides with the needle motion behavior, which means that parameters at the upper part of needle such as electro-magnetic force, needle spring force and needle friction force have dominant effect on ballistic injection. To figure out the effect of electro-magnetic and needle friction force on ballistic, those parameters were varied to plus and minus 10% with model. The result showed that those parameters clearly changed the ballistic region characteristics, however, the impact became insignificant for outside of ballistic region, which means that the ballistic injection is mainly influenced by initial motion of injector needle.

• Journal of ILASS-Korea
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• v.13 no.2
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• pp.99-106
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• 2008

In this study, spray characteristics research of steady/pulsed injection in crossflow was performed experimentally in the model ramjet combustor. High-speed-camera photography was performed through a visualization window of model combustor, and then, steady and pulsed spray structures were observed and analyzed. Varying influx air temperature and fuel species, we could obtain the trajectory correlation in the steady injection case. In the experiment of pulsed injection, it is found that the pulsed frequency hardly influences spray trajectory. Also, it is found that, in the same injection pressure differential, the trajectory correlation of steady condition can be used for estimating pulsed spray trajectory.

• Design & Manufacturing
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• v.18 no.2
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• pp.64-73
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• 2024

The purpose of this paper is to introduce a fusion method that combines the design of experiments (DOE) and machine learning to optimize the bias of plastic products. The study focuses on the plastic motor housing used in automobiles, which is manufactured through plastic injection molding. Achieving optimal molding for the motor housing involves the optimization of various molding conditions, including injection pressure, injection time, holding pressure, mold temperature, and cooling time. Failure to optimize these conditions can lead to increased product deformation. To minimize the deformation of the motor housing, the widely used Taguchi method, which is one of the design of experiment techniques, was employed to identify the injection molding conditions that affect deformation. Machine learning was then applied to various models based on the identified molding conditions. Among the models, the Random Forest model emerged as the most effective in predicting deformation amounts. The validity of the Random Forest model was also confirmed through verification. The verification results demonstrated the excellent prediction accuracy of the trained Random Forest model. By utilizing the validated model, molding conditions that minimize deformation were determined. Implementation of these optimal molding conditions led to a reduction of approximately 5.3% in deformation compared to the conditions before optimization. It is noteworthy that all injection molding outcomes presented in this paper were obtained through robust injection molding simulations, ensuring both research objectivity and speed.

• Journal of Pharmacopuncture
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• v.4 no.1
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• pp.49-53
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• 2001

This experiment has investigated the influence of Yamen (Du. 15) point injection on learning and memory dysfunction caused by cerebral ischemia and reprofusion in bilateral cervical general artery combined with bleeding on mouse tail to mimic vascular dementia in human beings. By dividing 40 mice into 4 groups (group1false operation group, group2model group, group3point injection with Cerebrolysin group4point injection with saline.) According to random dividing principles, we observed the influence of Yamen(Du. 15) point injection on the time of swimming the whole course used by model mice which had received treatment for different days in different groups, and the influence of those mice on wrong times they entered blind end. The result showed that point injection with Cerebrolysin and saline could improve learning and memory dysfunction of the mice caused by cerebral ischemia.

• Transactions of Materials Processing
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• v.9 no.5
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• pp.512-525
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• 2000

Designers are frequently faced with multiple quality issues in injection molded parts. These issues are usually In conflict with each other, and thus tradeoff needs to be made to reach a final compromised solutions. The objective of this study is to develop an automated injection molding design methodology, whereby part defects such as warpage and weld line are optimized. The features of the proposed methodology are as follows: first, Utility Function approach is applied to transform the original multiple objective problem into single objective problem. Second is an implementation of a direct search-based Injection molding optimization procedure with automated consideration of process variation. The Space Reduction Method based on Taguchi's DOE(Design Of Experiment) is used as a general optimization tool in this study. The computational experimental verification of the methodology was partially carried out for a can model of Cavallero Plastics Incorporation, U. S. A. Applied to production, this study will be of immense value to companies in reducing the product development time and enhancing the product quality.

• Proceedings of the KSME Conference
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• 2000.04b
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• pp.919-924
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• 2000

Spray impingement model and fuel film formation model were developed and incorporated into the computational fluid dynamics code, STAR-CD. The spray/wall interaction process were modelled by considering the change of behaviour with surface temperature condition and fuel film formation. We divided behaviour of fuel droplets after impingement into stick, rebound and splash using Weber number and parameter K. Spray impingement model accounts for mass conservation, energy conservation and heat transfer to the impinging droplets. A fuel film formation model was developed by Integrating the continuity, the Navier-Stokes and the energy equations along the direction of fuel film thickness. The validation of the model was conducted using diesel spray experimental data and gasoline spray impingement experiment. In all cases, the prediction compared reasonably well with experimental results. Spray impingement model and fuel film formation model have been applied to a direct injection diesel engine combustion chamber.

• International Journal of Railway
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• v.6 no.2
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• pp.69-77
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• 2013

In this study, numerical analysis and model experiments were conducted to analyze behavioral characteristics acting on the track roadbed with excavation through steel pipe injection, a non-exclusive method of crossing construction under railroad as primary target. In model experiments that simulate injection excavation behaviors with an increase in the depth of soil cover, the upper displacement was measured by construction of the first and the second pipes in order to predict actual behaviors, and the behavior characteristics were verified through numerical analysis. The investigation results showed that surface displacement was smaller under the condition of higher soil cover. In the case of injecting two pipes, when the first pipe was injected, deformation of the surface increased linearly in both settlement and uplift experiments. However, when the second pipe was injected, the amount of change was found to be very small due to the relaxation and plastic zones around the first pipe. In addition, the results of numerical analysis on the same cross section with the model experiment found that the results of investigation into settlement ratio and volume loss were in very good agreement with those obtained by the model experiment.