• Journal of Korean Society of Steel Construction
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• v.25 no.6
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• pp.587-599
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• 2013

A simple but accurate analytical method to evaluate the fire resistance of unprotected concrete filled tubular (CFT) columns under standard fire condition is proposed based on the fire design framework of EC4. To this end, the accuracy of the current tabulation method for the temperature prediction proposed by Lawson et al. was first critically evaluated, and a new prediction equation for the temperature gradient across the CFT section was then proposed based on available test and finite element analysis results. Overall, the axial strength predicted by using the proposed equation under the general fire design framework of EC4 was more accurate than that based on existing methods and appeared reasonable for design purposes. The results of this study are directly usable for the more rational fire analysis and design of unprotected CFT columns.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.28 no.5
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• pp.526-534
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• 2004

A new second-moment closure model for turbulent heat fluxes is proposed on the basis of the elliptic equation. The new model satisfies the near-wall balance between viscous diffusion, viscous dissipation and temperature-pressure gradient correlation, and also has the characteristics of approaching its respective conventional high Reynolds number model far away from the wall. The predictions of turbulent heat transfer in a channel flow have been carried out with constant wall heat flux and constant wall temperature difference boundary conditions respectively. The velocity field variables are supplied from the DNS data and the differential equations only fur the mean temperature and the scalar flux are solved by the present calculations. The present model is tested by direct comparisons with the DNS to validate the performance of the model predictions. The prediction results show that the behavior of the turbulent heat fluxes in the whole region is well captured by the present model.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.135-135
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• 2017

Greenhouse have been developed to provide the plants with good environmental conditions for cultivation crop, two major factors of which are the inside air temperature and humidity. The inside temperature are influenced by the heating systems, ventilators and for systems among others, which in turn are geverned by some type of controller. Likewise, humidity environment is the result of complex mass exchanges between the inside air and the several elements of the greenhouse and the outside boundaries. Most of the existing models are based on the energy balance method and heat balance equation for modelling the heat and mass fluxes and generating dynamic elements. However, greenhouse are classified as complex system, and need to make a sophisticated modeling. Furthermore, there is a difficulty in using classical control methods for complex process system due to the process are non linear and multi-output(MIMO) systems. In order to predict the time evolution of conditions in certain greenhouse as a function, we present here to use of recurrent neural networks(RNN) which has been used to implement the direct dynamics of the inside temperature and inside humidity of greenhouse. For the training, we used algorithm of a backpropagation Through Time (BPTT). Because the environmental parameters are shared by all time steps in the network, the gradient at each output depends not only on the calculations of the current time step, but also the previous time steps. The training data was emulated to 13 input variables during March 1 to 7, and the model was tested with database file of March 8. The RMSE of results of the temperature modeling was $0.976^{\circ}C$, and the RMSE of humidity simulation was 4.11%, which will be given to prove the performance of RNN in prediction of the greenhouse environment.

• Journal of Advanced Marine Engineering and Technology
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• v.32 no.5
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• pp.684-690
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• 2008

In most engineering applications related with the heat conduction phenomena, a conventional Fourier heat conduction equation has been successfully applied and it has supplied quite reasonable results. However, it is well known that the Fourier heat conduction equation is failed in the application to the extremely small space and short time, in other words, a nano-scale system and a pico-second time. In this study, non-Fourier effect was evaluated in the heat conduction by considering the concept of a phase lag model. The results show the existence of a heat wave, which means that the heat is transferred with a finite speed while an infinite speed of heat transfer is assumed in the conventional Fourier heat conduction. In addition, the copper and the gold are tested to evaluate the phase lag time between the heat flux and the temperature gradient. The results show that the gold has the heat wave speed faster than that of the copper consistent with the prediction based on an actual experiment.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.2
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• pp.78-82
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• 2022

Phenology determines the timing of crop development, and the timing of phenological events is strongly influenced by the temperature during the growing season. In process-based model, leaf area is simulated dynamically by coupling of morphology and phenology module. Therefore, the prediction of leaf appearance rate and final leaf number affects the performance of whole crop model. The dataset for the model equation was collected from SPA R chambers with five different temperature treatments. Beta distribution function (proposed by Yan and Hunt (1999)) was used for describing the leaf appearance rate as a function of temperature. The optimum temperature and the critical value were estimated to be 26.0℃ and 35.3℃, respectively. For evaluation of the model, the accumulated number of onion leaves observed in a temperature gradient chamber was compared with model estimates. The model estimate is the result of accumulating the daily increase in the number of onion leaves obtained by inputting the daily mean temperature during the growing season into the temperature model. In this study, the coefficient of determination (R²) and RMSE value of the model were 0.95 and 0.89, respectively.