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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
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논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2022.11
- 수록면
- 508 - 511 (4page)
이용수
이 논문의 연구 히스토리 (3)
2022
Prediction analysis to the heat transfer coefficient models in two-phase flow boiling inside the multiport mini-channel tubes
Nu
,
laily Agustia
,
ini
외 2명
대한설비공학회 학술발표대회논문집
2022.11
학술대회자료
Prediction of boiling heat transfer coefficient using a data-based model for R290 and R1234yf inside multiport mini-channel tube
Nu
,
laily Agustia
,
ini
외 2명
대한설비공학회 학술발표대회논문집
2022.06
학술대회자료
- 이 논문의 후속연구가 궁금하신가요?
- 연관 학술논문 또는 학술발표를 통해 보다 발전된 연구결과를 확인하실 수 있습니다.
- 이 논문의 연구 히스토리 확인하기
초록· 키워드
오류제보하기
An analysis to the prediction of heat transfer coefficient model is provided in this study. The heat transfer data is collated from an experiment study inside multiport mini-channel tubes with working fluid of R1234yf in two different horizontal multiport tubes. The data ranges with mass flux of 50 to 500 kg/㎡s, heat flux of 3 to 12 kW/㎡, saturation temperature of 6 ℃, and vapor quality up to 1. Evaluation of heat transfer data through analysis of mas flux, heat flux, and vapor quality to the heat transfer coefficient are conducted. Assessment of heat transfer coefficient data through the comparison of empirical correlation modes in superposition, asymptotic, flow pattern, or others existed correlation models with experiment data of heat transfer coefficient are presented. Development of machine learning prediction model to the heat transfer coefficient of R1234yf is one of the other methods beside empirical correlation. It could be used to predict the heat transfer coefficient of R1234yf inside two types of tubes with hydraulic diameter about 0.831 mm and 0.969 mm. The result showed that machine learning prediction models could provide lower mean deviation than empirical correlation. Therefore, the available methods can be used to predict the heat transfer coefficient of R1234yf inside multiport mini-channel tubes.
목차
Abstract
1. Introduction
2. Method of analysis
3. Machine learning models
4. Conclusion
References
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