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

논문 기본 정보

자료유형
학술저널
저자정보
Pang, Yeoun Gyu (Department of Biosystems Engineering, Kangwon National University) Kim, Sang Hun (Department of Biosystems Engineering, Kangwon National University)
저널정보
한국농업기계학회 바이오시스템공학(구 한국농업기계학회지) 바이오시스템공학 제43권 제3호
발행연도
2018.1
수록면
194 - 201 (8page)

이용수

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

초록· 키워드

오류제보하기
Purpose: The objective of this study was to propose a formula for the theoretical grain mean transport velocities of an elliptically moving oscillator by modifying the grain mean transport velocity formula applied to linear motion and to compare the calculated values with the experimental values of grain mean transport velocity. Methods: The values of the throwing index ($K_v$) and the maximum horizontal velocities for various positions on the elliptical oscillator were obtained using kinematic analysis. To obtain the actual grain transport velocity, the mean transport velocities of perilla grains at six positions on the sieve surface were measured using a high-speed camera and compared with the theoretical values. The cam with an eccentric bearing on the oscillator was designed to be eccentric by 1.6 cm so that the lengths of the major axis of the elliptical motion were 3.2-3.6 cm. The material used in the experiments was perilla grain. Results: The experimental result was consistent with the theoretical value calculated using the proposed formula ($R^2$ is 0.80). It is considered that the angle difference between the maximum accelerations in the directions vertical and horizontal to the sieve has as much influence on the grain mean transport velocity as the value of Kv itself. Conclusions: It was possible to theoretically obtain the grain mean transport velocities through a screening device in elliptical motion by modifying the formula of the grain mean transport velocities used in linear motion.

목차

등록된 정보가 없습니다.

참고문헌 (0)

참고문헌 신청

함께 읽어보면 좋을 논문

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

이 논문의 저자 정보

최근 본 자료

전체보기

댓글(0)

0