메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색
질문

논문 기본 정보

자료유형
학술대회자료
저자정보
전승민 (성균관대학교) 이강윤 (성균관대학교)
저널정보
대한전자공학회 대한전자공학회 학술대회 2024년도 대한전자공학회 추계학술대회 논문집
발행연도
2024.11
수록면
176 - 179 (4page)

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색
질문

초록· 키워드

오류제보하기
Efficient cooling systems are essential for maintaining the stable operation of high-performance semiconductor devices such as CPUs. Traditional CPU cooling methods, primarily based on air and liquid cooling systems, face limitations in handling the increased heat generated by highly integrated semiconductor structures. This study investigates the potential for enhanced cooling performance by employing a counterflow cooling system, which combines air and liquid cooling, and compares it with the parallel flow system. Using thermodynamic simulations, we examined the effects of counterflow cooling under varying heat exchanger areas, air inlet temperatures, and water inlet temperatures. The results indicated that larger heat exchanger areas, such as 0.25 m² and 0.3 m², yield higher Log Mean Temperature Difference (LMTD) values in counterflow cooling. In smaller areas (0.03 to 0.2 m²), parallel flow cooling showed slightly better LMTD values; however, in areas between 0.02 and 0.005 m², counterflow cooling demonstrated superior performance again. At extremely small areas (0.001 to 0.0001 m²), both methods exhibited similar results. The patterns in heat transfer quantity followed the LMTD results, with visualizations highlighting the comparative advantages of each cooling method. Limitations include assumptions of ideal fluid flow and constant thermal conductivity, which may not fully align with real-world systems. Future research will address these limitations by examining the effects of multi-layered heat exchangers, various cooling fluids, and complex systems such as data centers and AI accelerators.

목차

Abstract
I. 서론
II. 본론
III. 구현
IV. 결론
참고문헌

참고문헌 (0)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

최근 본 자료

전체보기

댓글(0)

0