• Journal of the Korean Institute of Telematics and Electronics
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• v.16 no.5
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• pp.12-17
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• 1979

This paper presents the investigation of the theoretical symbol error performances of an M-ary differential phase-shift-keyed(DPSK) system in an interference environment. A simple method is transmitted over a nondistroting channel, but additively corrupted by Gaussian noise and cochannel interference. Computed DPSK symbol error performance results for M=2, 4, and 8 are compared with the corresponding curves for coherent phase-shift-Keyed(CPSK) system as a function of carrier-to-noise power ratio(CNR) with carrier-to-interferer power ratio(CIR) as a parameter. Comparisons between DPSK systems reveal, as we might expect, that DPSK system suffers more degradation.

• Geotechnical Engineering
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• v.4 no.3
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• pp.5-18
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• 1988

This study aims at scrutinizing the relationships among the frequency distribution of voids the relative density and the shear strength for the three types of different granular materials. The main results summarized are as follows: (1) The frequency distribution of void ratio of glass beads can be approximated by the negative exponential distribution proposed by Shahinpoor (1981), while as the particle shape changes from spherical to angular the frequency of the denser voids decreases and the distributions are well presented by the beta distribution. (2) For all materials, the standard deviation of void ratio increases with decreasing both the relative density of the material and sphericity of particle. (3) It was found that shear strength of the material was a function of not only the mean void ratio and its standard deviation, but also of the shape of the probability density function of the distribution of void ratio. The more the frequency distribution of void inclines towards the denser voids, the higher the shear strength of the material and vice versa.

• 한국신재생에너지학회:학술대회논문집
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• 2007.11a
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• pp.417-423
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• 2007

Oxy-gasification or oxygen-blown gasification, enables a clean and efficient use of coal and opens a promising way to CO2 capture. The coal gasification process of a slurry feed type, entrained-flow coal gasifier was numerically predicted in this paper. The purposes of this study are to develop an evaluation technique for design and performance optimization of coal gasifiers using a numerical simulation technique, and to confirm the validity of the model. By dividing the complicated coal gasification process into several simplified stages such as slurry evaporation, coal devolatilization, mixture fraction model and two-phase reactions coupled with turbulent flow and two-phase heat transfer, a comprehensive numerical model was constructed to simulate the coal gasification process. The influence of turbulence on the gas properties was taken into account by the PDF (Probability Density Function) model. A numerical simulation with the coal gasification model is performed on the Conoco-Philips type gasifier for IGCC plant. Gas temperature distribution and product gas composition are also presented. Numerical computations were performed to assess the effect of variation in oxygen to coal ratio and steam to coal ratio on reactive flow field. The concentration of major products, CO and H2 were calculated with varying oxygen to coal ratio (0.2-1.5) and steam to coal ratio(0.3-0.7). To verify the validity of predictions, predicted values of CO and H2 concentrations at the exit of the gasifier were compared with previous work of the same geometry and operating points. Predictions showed that the CO and H2 concentration increased gradually to its maximum value with increasing oxygen-coal and hydrogen-coal ratio and decreased. When the oxygen-coal ratio was between 0.8 and 1.2, and the steam-coal ratio was between 0.4 and 0.5, high values of CO and H2 were obtained. This study also deals with the comparison of CFD (Computational Flow Dynamics) and STATNJAN results which consider the objective gasifier as chemical equilibrium to know the effect of flow on objective gasifier compared to equilibrium. This study makes objective gasifier divided into a few ranges to study the evolution of the gasification locally. By this method, we can find that there are characteristics in the each scope divided.

• Economic and Environmental Geology
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• v.29 no.6
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• pp.713-724
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• 1996

The time-series resident solute concentrations, monitored at two field plots using the automated 144-channel TDR system by Kim (this issue), are used to investigate the dominant transport mechanism at field scale. Two models, based on contradictory assumptions for describing the solute transport in the vadose zone, are fitted to the measured mean breakthrough curves (BTCs): the deterministic one-dimensional convection-dispersion model (CDE) and the stochastic-convective lognormal transfer function model (CLT). In addition, moment analysis has been performed using the probability density functions (pdfs) of the travel time of resident concentration. Results of moment analysis have shown that the first and second time moments of resident pdf are larger than those of flux pdf. Based on the time moments, expressed in function of model parameters, variance and dispersion of resident solute travel times are derived. The relationship between variance or dispersion of solute travel time and depth has been found to be identical for both the time-series flux and resident concentrations. Based on these relationships, the two models have been tested. However, due to the significant variations of transport properties across depth, the test has led to unreliable results. Consequently, the model performance has been evaluated based on predictability of the time-series resident BTCs at other depths after calibration at the first depth. The evaluation of model predictability has resulted in a clear conclusion that for both experimental sites the CLT model gives more accurate prediction than the CDE model. This suggests that solute transport at natural field soils is more likely governed by a stream tube model concept with correlated flow than a complete mixing model. Poor prediction of CDE model is attributed to the underestimation of solute spreading and thus resulting in an overprediction of peak concentration.