• Title/Summary/Keyword: CV valve

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The Development of Pressure Regulator of Propellant Tank for KSR-III (KSR-III 추진제 탱크 압력 조절용 레귤레이터 개발)

  • 정영석;조기주;조인현;김용욱;오승협
    • Journal of the Korean Society of Propulsion Engineers
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    • v.6 no.4
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    • pp.47-58
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    • 2002
  • The pressure regulator has been developed as a pressure-control device of propellant tank in KSR-III. The pressurization system of KSR-III is a basic pressurization system composed of pressurant, He tank and propellant tank. The pressure-control regulator is the most important part of gas-pressurized feed system along with He tank, pyrovalve and He fill valve. The first model of the regulator is tested to satisfy in leakage, strength and basic performance. The second model is tested in the overall test of the KSR-III propulsion system using water. From the test result of the second model, we conclude that the capacity of valve(Cv) must be increased in real system. The third model is modified and tested in the overall test of KSR-III propulsion system using propellant. Finally, the pressure-control regulator is qualified from firing test.

Data-driven Modeling for Valve Size and Type Prediction Using Machine Learning (머신 러닝을 이용한 밸브 사이즈 및 종류 예측 모델 개발)

  • Chanho Kim;Minshick Choi;Chonghyo Joo;A-Reum Lee;Yun Gun;Sungho Cho;Junghwan Kim
    • Korean Chemical Engineering Research
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    • v.62 no.3
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    • pp.214-224
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    • 2024
  • Valves play an essential role in a chemical plant such as regulating fluid flow and pressure. Therefore, optimal selection of the valve size and type is essential task. Valve size and type have been selected based on theoretical formulas about calculating valve sizing coefficient (Cv). However, this approach has limitations such as requiring expert knowledge and consuming substantial time and costs. Herein, this study developed a model for predicting valve sizes and types using machine learning. We developed models using four algorithms: ANN, Random Forest, XGBoost, and Catboost and model performances were evaluated using NRMSE & R2 score for size prediction and F1 score for type prediction. Additionally, a case study was conducted to explore the impact of phases on valve selection, using four datasets: total fluids, liquids, gases, and steam. As a result of the study, for valve size prediction, total fluid, liquid, and gas dataset demonstrated the best performance with Catboost (Based on R2, total: 0.99216, liquid: 0.98602, gas: 0.99300. Based on NRMSE, total: 0.04072, liquid: 0.04886, gas: 0.03619) and steam dataset showed the best performance with RandomForest (R2: 0.99028, NRMSE: 0.03493). For valve type prediction, Catboost outperformed all datasets with the highest F1 scores (total: 0.95766, liquids: 0.96264, gases: 0.95770, steam: 1.0000). In Engineering Procurement Construction industry, the proposed fluid-specific machine learning-based model is expected to guide the selection of suitable valves based on given process conditions and facilitate faster decision-making.

A Study on the Exploration of Treatment Area of Visceral Chuna Manual Therapy Using Ultrasound Image Data (초음파 영상 데이터를 활용한 내장기 추나요법 시술 부위 탐색 연구)

  • Lee, Sang-Jin;Ki, Sung-Hoon;Koh, Dong-Kyun;Lee, Sang-Hun;Lim, Hyoung-Ho;Song, Yun-Kyung
    • Journal of Korean Medicine Rehabilitation
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    • v.32 no.2
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    • pp.139-154
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    • 2022
  • Objectives This study was conducted to confirm anatomical information near the treatment areas of visceral chuna manual therapy and to secure stability and effectiveness during the treatment. Methods For 50 healthy adult men and women, a total of 13 ultrasound images were taken of the 7 treatment areas which are the representative treatment areas of visceral Chuna manual therapy. Results The treatment area of the bottom of the liver can be palpated around the right side ST19 and CV14. The treatment area of the gallbladder and the common bile duct can be palpated around the right side SP16 and ST20. The treatment area of the cardia and the pylorus can be palpated around the left side KI21, right side ST20, ST21, KI19, KI20, CV12, and CV13. The origin point of the mesentery root can be moved to the left and lower left from CV12 and can be palpated. The treatment area of the ileocecal valve and the cecum can be palpated around the right side SP14. The treatment area for the colic flexure can be palpated around the both side LR13. The treatment support point for the kidneys can be palpated around both side BL51. Conclusions It is thought that if the ultrasound image data from the meridians around the treatment site is used as an auxiliary, it will be more effective in terms of safety and effectiveness during the treatment of visceral Chuna manual therapy.

Quality Changes of Commercial Kimchi Products by Different Packaging Methods (소포장 김치의 포장방법별 품질특성 변화)

  • Hong, Seok-In;Park, Jin-Sook;Park, Noh-Hyun
    • Korean Journal of Food Science and Technology
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    • v.27 no.1
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    • pp.112-118
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    • 1995
  • The effect of various packaging methods on kimchi quality was investigated in order to develop the packaging techniques for preventing commercial kimchi products from inflation and explosion, due to fermentative gas evolved during storage and distribution. Kimchi was packaged in different methods; 1) atmospheric packaging(AP), 2) check valve packaging(CV), 3) double packaging(DP), and 4) vacuum packaging(VP). The quality of kimchi during storage at $10^{\circ}C$ was evaluated in terms of gas composition, free volume, pH, titratable acidity color index and sensory properties. The gas composition inside packages showed different curves according to the packaging methods. Due to fermentative gas accumulation in both AP and CV, $CO_2$ concentration increased by 2 stepwise pattern, while $O_2$, concentration decreased exponentially. In DP, $O_2$ concentration remained constant, but $CO_2$ concentration increased by 2 stepwise pattern and then decreased. In contrast, VP produced low $O_2$ and high $CO_2$ concentrations only at the end of storage. The free volume in both AP and CV showed typical sigmoidal curves similar to $CO_2$ concentration changes. It remained constant in DP, but started to increase at the late stage of storage in VP. There was no significant effect of packaging methods on pH changes of kimchi. In titratable acidity, DP maintained relatively higher than others. Regarding to the color change of crushed kimchi juice in all packages, L and b values decreased exponentially but a value remained constant during storage. Color index(L b/a) of crushed kimchi juice decreased exponentially and remained constant at the end of storage. The growth of lactic acid bacteria was VP, CV, AP, DP in increasing order. In sensory test, the sourness scores of DP were fairly higher than those of others, but the texture was not significantly affected by the packaging methods. The preference for kimchi showed VP>AP, CV>DP in order of score. In this study, it could be proposed to employ DP and VP method as the effective packaging techniques for preventing commercial kimchi products from inflation.

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Ground Speed Control of a Direct Injection Sprayer

  • Koo, T.M.;Sumner, H.R.
    • Proceedings of the Korean Society for Agricultural Machinery Conference
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    • 1996.06c
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    • pp.500-510
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    • 1996
  • A Direct injection -mixing total -flow -control sprayer was developed and evaluated . The system provided precise application rates and minimized operator exposure to chemicals as well as providing a possibility for recycling container so f unused chemicals that can causes environmental contamination. Chemicals were metered and injected proportionally to the diluent flow rate to provide constant concentrations. The main diluent flow was varied in response to changes in travel speed. Experimental variables of the sprayer were the control interval, the sensitivity of flow regulating valve, the tolerance of control object and the sensitivity of the injection pump system. The optimal performance of the flow control system was with an average response time of 8.5 sec at an absolute steady state of error of 0.067 L/min (0.8% of flow rate). The average response time of the injection rate was -0.53 sec and the coefficient of variation (CV) of concentration was 3.2%.

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