• Transactions of Materials Processing
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• v.33 no.2
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• pp.103-111
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• 2024

With the increasing global interest in carbon neutrality, the automotive industry is also transitioning to the production of eco-friendly cars, specifically electric vehicles. In order to achieve comparable driving distances to internal combustion engine vehicles, the application of high-capacity battery packs has led to an increase in vehicle weight. To achieve light-weighting and durability requirements of automotive components simultaneously, there is a demand for research on the application of Ultra-High Strength Steel (UHSS). However, when manufacturing chassis components using UHSS, there are challenges related to fracture defects due to lower elongation compared to regular steel sheets, as well as spring-back issues caused by high tensile strength. In this study, a simulated specimen that is not affected by the property changes of four materials was designed to improve formability of the rear cross member, which is the most challenging automotive chassis component. The influence and correlation of material-specific variables were analyzed through finite element analysis (FEA) for each material with tensile strength of 440, 590, 780, and 980 MPa grades, resulting in the development of a predictive equation. To validate the equation, the simulated specimens of 980 MPa grade were produced from the test molds. Then the reliability of the FEA and predictive equation was verified with measured specimen data using a 3D scanner. The results of this study can be proposed to improve the formability of UHSS chassis components in future researches.

• Fire Science and Engineering
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• v.21 no.4
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• pp.32-37
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• 2007

An accurate knowledge of the flash point is important in developing appropriate preventive and control measures in industrial fire protection. The lower flash points for the 2-propanol+acetic acid and 2-propanol+-n-propionic acid systems were measured by Cleveland open cup apparatus. The experimental data were compared with the values calculated by the Raoult's law, the Wilson equation and the NRTL(non random two liquids) equation. The calculated values based on the Wilson and NRTL equations were found to be better than those based on the Raoult's law. And the predictive curve of the flash point prediction model, based on NRTL equation described the experimentally-derived data were more effective than the case of the Wilson equation.

• Progress in Superconductivity and Cryogenics
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• v.22 no.3
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• pp.25-30
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• 2020

The diminishing of heat leak into cryogenic vessels can prolong the storage time of cryogenic liquid. With the storage of cryogenic liquid reducing, the heat leak decreases, while the actual storage time increases. Regarding to the theoretical analysis, the obtained results seems to be constructive for the cryogenic insulation system applications. This study presents a predictive assessment of heat leak occurring in non-vacuum tanks with a single layer of insulation. A Radial steady-state heat transfer, based on heat conduction equation, is taken into consideration. Graphical results show the thermal performance of the insulation used, they also allow us to choose the appropriate insulation thickness according to the shape and diameter of the storage tank.

• Journal of Welding and Joining
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• v.25 no.1
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• pp.37-41
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• 2007

The purpose of this study is to establish the predictive method of angular distortion of the double-sided multi-pass butt weldment achieve it, the behavior of angular distortion in the butt weldment were investigated using comprehensive finite element analyses and experiments. The angular distortion in the multi-pass butt weldment strongly depends on the welding heat input(Q) and the effective bending rigidity of the weld throat. The effective bending rigidity of the first welding pass on the backing side was defined as the function of dimensionless parameter(k) and a bending rigidity of bead-on-plate weldment. Based on the results, the predictive equation for angular distortion of multi-pass butt weldment was proposed and verified by experiments.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.99-104
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• 1991

An adaptive predictive control method for SISO and MIMO plants is proposed. In this method, future predictions of process output based on a bilinear CARIMA model are used to calculate the control input. Also, a classical recursive adaptation algorithm, equation error method, is used to decrease the uncertainty of the process model. As a result of the application on distillation process, the ability of the set-point tracking and the disturbance rejection is acceptable to apply to the industrial distillation processes.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.11
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• pp.58-66
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• 2001

GPC has been reported as a useful self-tuning control algorithm for systems with unknown time-delay and parameters. GPC is easy to understand and implement, and thus has won popularity among many practicing engineers. Despite its success, GPC does not guarantee is nominal stability. So, in this paper, GPC is rederived in frequency domain instead of in the time domain to guarantee its nominal stability. Derivation of GPC in frequency domain involves spectral factorization and Diophantine equation. Frequency domain GPC control law is stable because the zeros of characteristic polynomial are strictly Schur. Recursive least square algorithm is used to identify unknown parameters. To see the effectiveness of the proposed controller, the controller is simulated for a numerical problem that changes in dead-time, in order and in parameters.

• Food Science and Biotechnology
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• v.15 no.5
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• pp.715-720
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• 2006

This study was performed to develop a predictive model for the growth kinetics of Listeria monocytogenes in tryptic soy broth (TSB) using a response surface model with a combination of potassium lactate (PL), temperature, and pH. The growth parameters, specific growth rate (SGR), and lag time (LT) were obtained by fitting the data into the Gompertz equation and showed high fitness with a correlation coefficient of $R^2{\geq}0.9192$. The polynomial model was identified as an appropriate secondary model for SGR and LT based on the coefficient of determination for the developed model ($R^2\;=\;0.97$ for SGR and $R^2\;=\;0.86$ for LT). The induced values that were calculated using the developed secondary model indicated that the growth kinetics of L. monocytogenes were dependent on storage temperature, pH, and PL. Finally, the predicted model was validated using statistical indicators, such as coefficient of determination, mean square error, bias factor, and accuracy factor. Validation of the model demonstrates that the overall prediction agreed well with the observed data. However, the model developed for SGR showed better predictive ability than the model developed for LT, which can be seen from its statistical validation indices, with the exception of the bias factor ($B_f$ was 0.6 for SGR and 0.97 for LT).

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.9
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• pp.900-907
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• 2014

This paper proposes a MPC (Model Predictive Control) method for the torque and flux controls of induction motor. The proposed MPC method selects the optimized voltage vector for the matrix converter control using the predictive modeling equation of the induction motor and cost function. Hence, the reference voltage vector that minimizes the cost function of the torque and flux error within the control period is selected and applied to the actual system. As a result, it is possible to perform the torque and flux control of induction motor using only the MPC controller without a PI (Proportional-Integral) or hysteresis controller. Even though the proposed control algorithm is more complicated and has lots of computations compared with the conventional MPC, it can perform torque ripple reduction by synthesizing voltage vectors of various magnitude. This feature provides the reduction of amount of calculations and the improvement of the control performance through the adjustment of the number of the unit vectors n. The proposed control method is validated through the PSIM simulation.

• International Journal of Highway Engineering
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• v.16 no.3
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• pp.43-50
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• 2014

PURPOSES : The dynamic modulus can be determined by applying the various theories from the Impact Resonance Testing(IRT) Method. The objective of this paper is to determine the best theory to produce the dynamic modulus that has the lowest error as the dynamic modulus data obtained from these theories(Complex Wave equation Resonance Method related to either the transmissibility loss or not, Dynamic Stiffness Resonance Method) compared to the results for dynamic modulus determined by using the Universal Testing Machine. The ultimate object is to develop the predictive model for the dynamic modulus of a Linear Visco-Elastic specimen by using the Complex Wave equation Resonance Method(CWRM) came up for an existing study(S. O. Oyadiji; 1985) and the Optimization. METHODS : At the destructive test which uses the Universal Testing Machine, the dynamic modulus results along with the frequency can be used for determining the sigmoidal master curve function related to the reduced frequency by applying Time-Temperature Superposition Principle. RESULTS : The constant to be solved from Eq. (11) is a value of 14.13. The reduced dynamic modulus obtained from the IRT considering the loss factor related to the impact transmissibility has RMSE of 367.7MPa, MPE of 3.7%. When the predictive dynamic modulus model was applied to determine the master curve, the predictive model has RMSE of 583.5MPa, MPE of 3.5% compared to the destructive test results for the dynamic modulus. CONCLUSIONS : Because we considered that the results obtained from the destructive test had the most highest source credibility in this study, the dynamic modulus data obtained respectively from DSRM, CWRM were compared to the results obtained from the destructive test by using th IRT. At the result, the reduced dynamic modulus derived from DSRM has the most lowest error.

• Korean Journal of Food Science and Technology
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• v.37 no.5
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• pp.850-855
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• 2005

Model was developed to predict the growth of Listeria monocytogenes in raw pork. Experiment condition for model development was full 5-by-7 factorial arrangements of temperature (0, 5, 10, 15, and $20^{\circ}C$) and time (0, 1, 2, 3, 18, 48, and 120 hr). Gompertz values A, C, B, and M, and growth kinetics, exponential growth rate (EGR), generation time (GT), lag phase duration (LPD), and maximum population density (MPD) were calculated based on growth increased data. GT and LPD values gradually decreased, whereas EGR value gradually increased with increasing temperature. Response surface analysis (RSA) was carried out using Gompertz B and M values, to formulate equation with temperature being main control factor. This equation was applied to Gompertz equation. Experimental and predictive values for GT, LPD, and EGR, compared using the model, showed no significant differences (p<0.01). Proposed model could be used to predict growth of microorganisms for exposure assessment of MRA, thereby allowing more informed decision-making on potential regulatory actions of microorganisms in raw pork.