• Resources Recycling
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• v.28 no.1
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• pp.32-39
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• 2019

The present study investigated hydration of alkali activated slag incorporating sulfate as a form of anhydrite by employing thermodynamic modeling using the Gibbs free energy minimization approach. Various parameters were evaluated in the thermodynamic calculations, such as presence of sulfide, precipitation/dissolution of AFt/AFm phase, and the effect of oxic condition on the predicted reaction. The calculations suggested no significant difference in the void volume and chemical shrinkage, which might influence the performance of the mixtures, in spite of various changes of the parameters. Although the types of hydration products and their amount varied according to the input conditions, their variations were smaller range than that induced by water-to-binder ratio. Moreover, it did not affect the amount of C-(N-)A-S-H which was the most important hydration product.

• Atmosphere
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• v.29 no.1
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• pp.75-86
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• 2019

In this paper, we introduce a novel Climate & Environment Database System (CEDS). The CEDS is developed by the National Institute of Meteorological Sciences (NIMS) to provide easy and efficient user interfaces and storage management of climate model data, so improves work efficiency. In uploading the data/files, the CEDS provides an option to automatically operate the international standard data conversion (CMORization) and the quality assurance (QA) processes for submission of CMIP6 variable data. This option increases the system performance, removes the user mistakes, and increases the level of reliability as it eliminates user operation for the CMORization and QA processes. The uploaded raw files are saved in a NAS storage and the Cassandra database stores the metadata that will be used for efficient data access and storage management. The Metadata is automatically generated when uploading a file, or by the user inputs. With the Metadata, the CEDS supports effective storage management by categorizing data/files. This effective storage management allows easy and fast data access with a higher level of data reliability when requesting with the simple search words by a novice. Moreover, the CEDS supports parallel and distributed computing for increasing overall system performance and balancing the load. This supports the high level of availability as multiple users can use it at the same time with fast system-response. Additionally, it deduplicates redundant data and reduces storage space.

• Atmosphere
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• v.29 no.1
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• pp.1-12
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• 2019

For the allergy patient who needs to know the situation about the extent of pollen risk, the National Institute of Meteorological Sciences developed a pollen forecasting system based on the Community Multiscale Air Quality Modeling (CMAQ). In the old system, pollen emission from the oak was estimated just based on the airborne concentration and meteorology factors, resulted in high uncertainty. For improving the quality of current pollen forecasting system, therefore the estimation of pollen emission is now corrected based on the observation of pollen emission at the oak forest to better reflect the real emission pattern. In this study, the performance of the previous (NIMS2014) and current (NIMS2016) model system was compared using observed oak pollen concentration. Daily pollen concentrations and emissions were simulated in pollen season 2016 and accuracy of onset and end of pollen season were evaluated. In the NIMS2014 model, pollen season was longer than actual pollen season; The simulated pollen season started 6 days earlier and finished 13.25 days later than the actual pollen season. The NIMS2016 model, however, the simulated pollen season started only 1.83 days later, and finished 0.25 days later than the actual pollen season, showing the improvement to predict the temporal range of pollen events. Also, the NIMS2016 model shows better performance for the prediction of pollen concentration, while there is a still large uncertainty to capture the maximum pollen concentration at the target site. Continuous efforts to correct these problems will be required in the future.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.341-343
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• 2019

As the generation of high concentration particulate matter increases, much attention is focused on the prediction of particulate matter. Particulate matter refers to particulate matter less than $10{\mu}m$ diameter in the atmosphere and is affected by weather changes such as temperature, relative humidity and wind speed. Therefore, various studies have been conducted to analyze the correlation with weather information for particulate matter prediction. However, the nonlinear time series distribution of particulate matter increases the complexity of the prediction model and can lead to inaccurate predictions. In this paper, we try to mitigate the nonlinear characteristics of particulate matter by using cluster algorithm and classification algorithm of machine learning. The machine learning algorithms used are agglomerative clustering, density-based spatial clustering of applications with noise(DBSCAN).

• Atmosphere
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• v.32 no.4
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• pp.341-351
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• 2022

Recently, interest in the possibility of a washout effect using artificial rain enhancement technology to reduce high-concentration fine dust is growing. Therefore, in this study, the reduction rate of PM₁₀ concentration according to the amount of artificial rain enhancement was calculated during Asian Dust event which occurred over the Korean Peninsula on March 29, 2021 using air quality model [i.e., Community Multiscale Air Quality (CMAQ)] combined with the mesoscale model for artificial rain enhancement (i.e., WRF-MMS). According to WRF-MMS, the washout effect lasted 5 hours, and the maximum precipitation rate was calculated to be 1.5 mm hr^-1. According the CMAQ results, the PM₁₀ reduction rate was up to 22%, and the affected area was calculated to be 6.4 times greater than that of the artificial rain enhancement area. Even if the maximum amount of precipitation per hour is lowered to 0.8 mm hr^-1 (about 50% level), the PM₁₀ reduction rate appears to be up to 16%. In other words, it is believed that this technique can be used as a direct method for reducing high-concentration fine dust even when the artificial rain enhancement effect is weak.

• Journal of the Korean Geotechnical Society
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• v.37 no.1
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• pp.5-15
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• 2021

The areas consisting of frost susceptible soils in cold regions, such as the Arctic area, have problems of frost heave and thaw settlement due to the seasonal air temperature changes and internal temperature of installed structures. Ground stabilization methods for preventing frost heave and thaw settlement of frost susceptible soils include trenching, backfilling and thermo-syphon. The thermo-syphon is the method in which refrigerant can control the ground temperature by transferring the ground temperature to atmosphere in the from of two-phase flow through the heat circulation of the internal refrigerant. This numerical study applied the function of these thermo-syphon as the boundary condition through user-subroutine coding inside ABAQUS and compared and analyzed the temperature results of laboratory experiments.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.1
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• pp.41-52
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• 2021

During atmospheric ascent of a launch vehicle, airborne acoustic loads act on the vehicle and its effect becomes pronounced at transonic speed. In the present study, acoustic loads acting on a hammerhead launch vehicle at a transonic speed have been analyzed using ��-ω SST based IDDES and the results including mean Cp, Cprms, and PSD are compared to available wind-tunnel test data. Mesh dependency of IDDES results has been investigated and it has been concluded that with an appropriate turbulence scale-resolving computational mesh, the characteristic flow features around a transonic hammerhead launch vehicle such as separated shear flow at fairing shoulder and its reattachment on rear body as well as large pressure fluctuation in the region of separated flow behind the boat-tail can be predicted with reasonable accuracy for engineering purposes.

• Journal of the Korean Institute of Gas
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• v.26 no.1
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• pp.20-26
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• 2022

The results of experimental, theoretical, and numerical analysis were compared regarding the concentrations and velocities of flammable gas jets generated by pressurized leakage of methane gas. The concentration was measured through experiments for the jet dispersion process, and the velocities was calculated by applying the self-similarity theory. And the velocities and concentrations were calculated using CFD tools - FLACS and CFX- compared with the results. The difference between self-similarity model and CFD is due to the buoyancy term, which increases as the distance from a leak source increases. The results are compared with dimensionless parameters using the leak source radius and velocity components along the leak axis.

• Atmosphere
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• v.31 no.5
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• pp.563-576
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• 2021

The performance of East Asian summer monsoon is assessed for GC2 and GC3.1, which are climate change models of the current and next climate prediction system in the Korea Meteorological Administration (KMA), GloSea5 and GloSea6. The most pronounced characteristics of GC models are strong monsoon trough and the weakening of the Western North Pacific Subtropical High (WNPSH). These are related to the weakening of the southwesterly wind and resulting weak monsoon band toward the Korean Peninsula. The GC3.1 is known to have improved the model configuration version compared to GC2, such as cloud physics and ocean parameters. We can confirm that the overall improvements of GC3.1 against GC2, especially in pressure, 850 hPa wind fields, and vertical wind shear. Also, the precipitation band stagnant in the south of 30°N in late spring is improved, therefore the biases of rainy onset and withdrawal on the Korean Peninsula are reduced by 2~4 pentad. We also investigate the impact of initialization in comparison with GloSea5 hindcast. Compared with GCs, hindcast results show better simulation within 1 month lead time, especially in pressure and 850 hPa wind fields, which can be expected to the improvement of WNPSH. Therefore, it is expected that the simulation performance of WNPSH will be improved in the result of applying the initialization of GloSea6.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.4
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• pp.105-112
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• 2022

Recently, many studies using reinforcement learning-based autoscaling have been performed to make autoscaling policies that are adaptive to changes in the environment and meet specific purposes. However, training the reinforcement learning-based Horizontal Pod Autoscaler(HPA) policy in a real environment requires a lot of money and time. And it is not practical to retrain the reinforcement learning-based HPA policy from scratch every time in a real environment. In this paper, we implement a reinforcement learning-based HPA in Kubernetes, and propose a transfer leanring technique using a queuing model-based simulation to accelerate the training of a reinforcement learning-based HPA policy. Pre-training using simulation enabled training the policy through simulation experience without consuming time and resources in the real environment, and by using the transfer learning technique, the cost was reduced by about 42.6% compared to the case without transfer learning technique.