Earticle

초록

영어

Liquefied hydrogen is attracting attention as an energy source of the future due to its hydrogen storage rate and low risk. However, the disadvantage is that the unit price is high due to technical difficulties in production, transportation, and storage. This study was conducted to improve the design accuracy and development period of needle valves, which are important parts with a wide technical application range among liquefied hydrogen equipment. Since the needle valve must discharge an appropriate flow rate of the liquefied fluid, it is important to determine the needle valve design parameters suitable for the target flow rate. Computational Fluid Dynamics and Artificial Neural Network technology used to determine the design variables of fluid flow were applied to improve the setting and analysis time of the parameter. In addition, procedures and methods for applying the design parameter of needle valves to Convolutional Neural Networks were presented. The procedure and appropriate conditions for selecting parameters and functional conditions of the Convolutional Neural Network were presented, and the accuracy of predicting the flow coefficient according to the design parameter was secured 95%. It is judged that this method can be applied to other structures and machines.

목차

ABSTRACT
1. 서론
2. 해석을 통한 Data 확보
2.1 유량계수(Cv)
2.2 해석방법
2.3 해석 결과 및 도출한 유량계수
3. 합성곱 신경망의 학습
3.1 데이터 확보 및 학습개요
3.2 합성곱 신경망의 함수 조건의 선정
4. 유량계수의 학습 결과
5. 결론
References

키워드

저자

• 황나규미 [ Na-Gyu-Mi Hwang | Department of Mechanical Engineering, Dong A University ]
• 강정호 [ Jung-Ho Kang | Member, Department of Mechanical Engineering, Dong A University ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국기계항공기술학회지(구 한국기계기술학회지) [Journal of the Korean Society of Mechanical and Aviation Technology ]

• 간기

  격월간

• pISSN

  1229-604X

• eISSN

  2508-3805

• 수록기간

  1999~2026

• 등재여부

  KCI 등재

• 십진분류

  KDC 550 DDC 620