• Tribology and Lubricants
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• v.37 no.3
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• pp.91-98
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• 2021

This paper presents a hydrostatic bearing design and rotordynamic analysis of a turbo expander for a hydrogen liquefaction plant. Th~e turbo expander includes the turbine and compressor wheel assembled to a shaft supported by two hydrostatic radial and thrust bearings. The rated speed is 75,000 rpm and the rated power is 6 kW. For the bearing operation, we use pressurized air at 8.5 bar as the lubricant that is supplied to the bearing through the orifice restrictor. We calculate the bearing stiffness and flow rate for various gauge pressure ratios and select the orifice diameter providing the maximum bearing stiffness. Additionally, we conduct a rotordynamic analysis based on the calculated bearing stiffness and damping considering design parameters of the turbo expander. The predicted Cambell diagram indicates that there are two critical speeds under the rated speed and there exists a sufficient separation margin for the rated speed. In addition, the predicted rotor vibration is under 1 ㎛ at the rated speed. We conduct the operating test of the turbo expander in the test rig. For the operation, we supply pressurized air to the turbine and monitor the shaft vibration during the test. The test results show that there are two critical speeds under the rated speed, and the shaft vibration is controlled under 2.5 ㎛.

• Journal of Microbiology and Biotechnology
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• v.31 no.12
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• pp.1624-1631
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• 2021

Prodigiosin as a high-valued compound, which is a microbial secondary metabolite, has the potential for antioxidant and anticancer effects. However, the large-scale production of functionally active Hahella chejuensis-derived prodigiosin by fermentation in a cost-effective manner has yet to be achieved. In the present study, we established carbon source-optimized medium conditions, as well as a procedure for producing prodigiosin by fermentation by culturing H. chejuensis using 10 L and 200 L bioreactors. Our results showed that prodigiosin productivity using 250 ml flasks was higher in the presence of glucose than other carbon sources, including mannose, sucrose, galactose, and fructose, and could be scaled up to 10 L and 200 L batches. Productivity in the glucose (2.5 g/l) culture while maintaining the medium at pH 6.89 during 10 days of cultivation in the 200 L bioreactor was measured and increased more than productivity in the basal culture medium in the absence of glucose. Prodigiosin production from 10 L and 200 L fermentation cultures of H. chejuensis was confirmed by high-performance liquid chromatography (HPLC) and liquid chromatography-mass spectrometry (LC-MS) analyses for more accurate identification. Finally, the anticancer activity of crude extracted prodigiosin against human cancerous leukemia THP-1 cells was evaluated and confirmed at various concentrations. Conclusively, we demonstrate that culture conditions for H. chejuensis using a bioreactor with various parameters and ethanol-based extraction procedures were optimized to mass-produce the marine bacterium-derived high purity prodigiosin associated with anti-cancer activity.

• CELLMED
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• v.11 no.4
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• pp.18.1-18.8
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• 2021

Jawarishe Jalinoos (JJ) is an orally used formulation available in semisolid dosage form, prepared with powdered plant materials mixed in honey or sugar syrup. It has many admirable pharmacological effects and used in Unani medicine to treat various acute and chronic disorders since ancient times. The ICH Harmonised Tripartite Guideline stated that photostability testing should be an essential part of stability testing to confirm that light exposure does not result in an unacceptable change in drugs substance and finished products. To date, the effect of light on JJ is not studied, in this study photostability evaluation of JJ was carried out. The test sample was manufactured with genuine ingredients in the in-door pharmacy of the National Institute of Unani Medicine. JJ was packed in two transparent polyethylene terephthalate airtight containers. The first sample was analysed at zero-day and the second sample was placed in a stability chamber subjected to light challenge with an overall illumination of 1.2 million lux hours combined with near ultraviolet energy of 200-watt hours per square meter by using option 2, along with 30±2℃ temperature and relative humidity 70±5%. Analysis of both finished products showed no considerable changes in organoleptic characters. Less than 5% variation was observed in physicochemical parameters. HPTLC fingerprinting showed justifiable variation. Microbial load and specific counts were within the limit prescribed by WHO. As no unacceptable changes were noted in JJ subjecting to light challenge, it is concluded that JJ is a photostable Unani compound formulation.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.27 no.3
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• pp.181-186
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• 2021

The research was aimed to improve pre-harvest methods to maintain marketability in postharvest coniferous lettuce. Non-woven pots or plug plates were used to generate seedlings. No difference was found in growth characteristics of lettuce grown in non-woven pots vs plug plates. Since, seedling method with non-woven pots was convenient, lettuce harvested from non-woven pots used for water dipping treatment. The quality parameters, such as fresh weight loss, SPAD value, and appearance of lettuce were investigated after harvest. The lettuce with dipping treatment inside the plastic box container showed lower weight loss, higher SPAD value and better appearance compared to those exposed to the control (non-packaging) during the storage at 2℃. The treated plant showed higher SPAD and hue angle values 21.9 and 113.8°, respectively, compared to that of 18.8 and 108.3°, in non-treated plants. Therefore, it seems that the water dipping treatment is effective for storage method to maintain freshness of the lettuce. We showed the non-woven pot growing as a convenient seedling method for packaging treatment. Further studies will be continued to improve freshness postharvest of other horticultural crops.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.75-93
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• 2022

Machine learning (ML) data-driven meta-model is proposed as a surrogate model to reduce the excessive computational cost of the physics-based model and facilitate the real-time prediction of a nuclear power plant's transient response. To forecast the transient response three machine learning (ML) meta-models based on recurrent neural networks (RNNs); specifically, Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and a sequence combination of Convolutional Neural Network (CNN) and LSTM are developed. The chosen accident scenario is a control element assembly withdrawal at power concurrent with the Loss Of Offsite Power (LOOP). The transient response was obtained using the best estimate thermal hydraulics code, MARS-KS, and cross-validated against the Design and control document (DCD). DAKOTA software is loosely coupled with MARS-KS code via a python interface to perform the Best Estimate Plus Uncertainty Quantification (BEPU) analysis and generate a time series database of the system response to train, test and validate the ML meta-models. Key uncertain parameters identified as required by the CASU methodology were propagated using the non-parametric Monte-Carlo (MC) random propagation and Latin Hypercube Sampling technique until a statistically significant database (181 samples) as required by Wilk's fifth order is achieved with 95% probability and 95% confidence level. The three ML RNN models were built and optimized with the help of the Talos tool and demonstrated excellent performance in forecasting the most probable NPP transient response. This research was guided by the Systems Engineering (SE) approach for the systematic and efficient planning and execution of the research.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.94-107
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• 2022

Accidents prevention and mitigation is the highest priority of nuclear power plant (NPP) operation, particularly in the aftermath of the Fukushima Daiichi accident, which has reignited public anxieties and skepticism regarding nuclear energy usage. To deal with accident scenarios more effectively, operators must have ample and precise information about key safety parameters as well as their future trajectories. This work investigates the potential of machine learning in forecasting NPP response in real-time to provide an additional validation method and help reduce human error, especially in accident situations where operators are under a lot of stress. First, a base-case SGTR simulation is carried out by the best-estimate code RELAP5/MOD3.4 to confirm the validity of the model against results reported in the APR1400 Design Control Document (DCD). Then, uncertainty quantification is performed by coupling RELAP5/MOD3.4 and the statistical tool DAKOTA to generate a large enough dataset for the construction and training of neural-based machine learning (ML) models, namely LSTM, GRU, and hybrid CNN-LSTM. Finally, the accuracy and reliability of these models in forecasting system response are tested by their performance on fresh data. To facilitate and oversee the process of developing the ML models, a Systems Engineering (SE) methodology is used to ensure that the work is consistently in line with the originating mission statement and that the findings obtained at each subsequent phase are valid.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.28 no.3
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• pp.231-236
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• 2022

The research was aimed to improve pre-harvest methods to maintain marketability in postharvest leaf lettuce. In this study, the effect of hydroponics on the growth and post-harvest storage characteristics of 'Chongchima' lettuce grown in peat mass medium hydroponic system using a non-powered culture device or deep water culture (DWC) was evaluated. There was no difference in fresh weight, leaf number, SPAD, moisture content, and C/N ratio between peat moss growing medium hydroponic and DWC methods except plant height. It was found that lettuce cultivation by a nonpowered hydroponics method is easier than the existing DWC. The quality parameters, such as fresh weight loss, SPAD value, and general appearance of lettuce were investigated after harvest. There was no significant difference in fresh weight loss and general appearance during storage of lettuce by the hydroponics methods. However, with the increased storage time of SPAD, which is related to chlorophyll content, was slightly higher in peat moss medium hydroponic was than DWC. When crops such as lettuce are grown under favorable conditions without any agronomic abnormalities, it is suggested that post-harvest storage is not significantly affected by peat moss growing medium hydroponic and DWC.

• Structural Engineering and Mechanics
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• v.81 no.5
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• pp.565-574
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• 2022

The infrastructures such as offshore, bridges, power plant, oil and gas piping and aircraft operate in a harsh environment during their service life. Structural integrity of engineering components used in these industries is paramount for the reliability and economics of operation. Two regression models based on the concept of Gaussian process regression (GPR) and Minimax probability machine regression (MPMR) were developed to predict stress intensity factor range (𝚫K). Both GPR and MPMR are in the frame work of probability distribution. Models were developed by using the fatigue crack growth data in MATLAB by appropriately modifying the tools. Fatigue crack growth experiments were carried out on Eccentrically-loaded Single Edge notch Tension (ESE(T)) specimens made of API 5L X65 Grade steel in inert and corrosive environments (2.0% and 3.5% NaCl). The experiments were carried out under constant amplitude cyclic loading with a stress ratio of 0.1 and 5.0 Hz frequency (inert environment), 0.5 Hz frequency (corrosive environment). Crack growth rate (da/dN) and stress intensity factor range (𝚫K) values were evaluated at incremental values of loading cycle and crack length. About 70 to 75% of the data has been used for training and the remaining for validation of the models. It is observed that the predicted SIF range is in good agreement with the corresponding experimental observations. Further, the performance of the models was assessed with several statistical parameters, namely, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Efficiency (E), Root Mean Square Error to Observation's Standard Deviation Ratio (RSR), Normalized Mean Bias Error (NMBE), Performance Index (ρ) and Variance Account Factor (VAF).

• Journal of the Korean Society of Safety
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• v.37 no.5
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• pp.80-88
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• 2022

The Korean nuclear industry had developed the SPACE (Safety and Performance Analysis Code for nuclear power plants) code, which adopts a two-fluid, three-field model that is comprised of gas, continuous liquid and droplet fields and has the capability to simulate three-dimensional models. According to the revised law by the Nuclear Safety and Security Commission (NSSC) in Korea, the multiple failure accidents that must be considered for the accident management plan of a nuclear power plant was determined based on the lessons learned from the Fukushima accident. Generally, to improve the reliability of the calculation results of a safety analysis code, verification is required for the separate and integral effect experiments. Therefore, the goal of this work is to verify the calculation capability of the SPACE code for multiple failure accidents. For this purpose, an experiment was conducted to simulate a Control Element Drive Mechanism (CEDM) break with a safety injection failure using the ATLAS test facility, which is operated by Korea Atomic Energy Research Institute (KAERI). This experiment focused on the comparison between the experiment results and code calculation results to verify the performance of the SPACE code. The results of the overall system transient response using the SPACE code showed similar trends with the experimental results for parameters such as the system pressure, mass flow rate, and collapsed water level in component. In conclusion, it can be concluded that the SPACE code has sufficient capability to simulate a CEDM break with a safety injection failure accident.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.68 no.1
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• pp.8-19
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• 2023

Adzuki beans have gained popularity in recent years due to their health benefits. Breeding of Adzuki beans is less favorable than with other legumes due to low genetic diversity. This study aimed to evaluate the genetic diversity of 252 adzuki bean germplasms from China, Japan, and Korea using 18 agro-morphological parameters and comparing their performance to three prominent Korean cultivars: Geomguseul, Arari, and Chungjupat. Leaf shape, pod color, and seed coat color were among the qualitative traits that showed wide variations. The quantitative variables also showed wide variations among adzuki bean germplasms. Although there was no significant difference (p < 0.05), the average rate of germination declined in the order of Korean (91.44%) > Chinese (91.31%) > Japanese (87.47%) adzuki beans. Chinese adzuki beans needed fewer days to flower (DF, 58.22 days) and days to mature (DM, 107.13 days), which varied significantly compared to the Korean and Japanese adzuki beans (p < 0.05). The average number of pods per plant (PPP) and one-hundred seeds weight (HSW) were higher in Japanese adzuki beans compared to the Korean and Chinese adzuki beans although the variation of each was not significant. Almost 29.76% of the accessions had early-blooming flowers, 3.97% were premature, 21.43% produced more PPP, and 3.97% yielded more SPP compared to control cultivars. Results of hierarchical cluster and principal component analyses revealed three clusters with significant variation in all quantitative variables except for RG (p < 0.05). The key factors in multivariate analyses were DF, DM, and HSW. Our study investigated the genetic diversity of adzuki bean accessions and identified ten early maturing and ten high PPP-yielding accessions. Our findings would help farmers and breeders to select the top-performing accessions that can provide them with various options.