• Korean Journal of Optics and Photonics
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• v.35 no.4
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• pp.150-156
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• 2024

One of the key factors in research regarding long-distance laser beam propagation, as in free-space optical communication or laser power transmission, is the transmission efficiency of the laser beam. As a way to improve efficiency, we perform extensive numerical simulations of the effect of modifying the laser beam's profile, especially replacing the fundamental Gaussian beam with a super-Gaussian beam. Numerical simulations of the transmitted power in the ideal diffraction-limited beam diameter determined by the optical system of the transmitter, after about 1-km propagation, reveal that the second-order super-Gaussian beam can yield superior performance to that of the fundamental Gaussian beam, in both single-channel and coherently combined multi-channel laser transmitters. The improvement of the transmission efficiency for a 1-km propagation distance when using a second-order super-Gaussian beam, in comparison with a fundamental Gaussian beam, is estimated at over 1.2% in the singlechannel laser transmitter, and over 4.2% and over 4.6% in coherently combined 3- and 7-channel laser transmitters, respectively. For a range of the propagation distance varying from 750 to 1,250 m, the improvement in transmission efficiency by use of the second-order super-Gaussian beam is estimated at over 1.2% in the single-channel laser transmitter, and over 4.1% and over 4.0% in the coherently combined 3- and 7-channel laser transmitters, respectively. These simulation results will pave the way for future advances in the generation of higher-order super-Gaussian beams and the development of long-distance optical energy-transfer technology.

• Korean Chemical Engineering Research
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• v.45 no.1
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• pp.57-66
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• 2007

Natural gases generally consist of mainly $^{12}C$ and about 1.1% of $^{13}C$. It is well known that a stable carbon isotope, $^{13}C$, has been widely used for the applications of medical, pharmaceutical, and agricultural tracers. As a result, the development of the separation and concentrating technology of $^{13}C$ can cause of high value-added products and the possibility of the generation of new carbon materials, In general, there are two kinds of approaches to obtain a stable $^{13}C$ isotope by the separation of cryogenic distillation. One is to obtain a concentrated $^{13}CH_4$ isotope from natural gas. Another approach is to get concentrated $^{13}CO$ by distillation followed by a chemical reaction of $CH_4$ and $H_2O$. In this study, rigorous process simulations of the cryogenic distillation have been performed and analyzed for the concentrated separation of $^{13}C$ isotopes from LNG and NG by using commercial process simulator. Due to the very small differences of relative volatilities and separabilities of $^{12}C$ and $^{13}C$, the process design and operation of effective separation and concentration of $^{13}C$ need special strategies and feasibility studies. Utilization of vapor pressure data to acentric factor in SRK equation of state and optimized process conditions have been able to predict for the effective of the separation yield and concentration of $^{13}C$ for the cryogenic distillation. The various operation strategies for both batch and continuous cryogenic distillation are also studied and suggested for the basic design of the process. Development of this study can provide a tool for the effective design and operation of the cryogenic separation of $^{13}C$.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.