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
저널정보
한국소음진동공학회 한국소음진동공학회 강습회자료집 제 9회 설비진단기술강습회
발행연도
2010.4
수록면
37 - 49 (13page)

이용수

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

초록· 키워드

오류제보하기
Accurate noise predictions are needed to design power plants to meet community noise limits. Noise prediction modeling is used to determine equipment noise limits for procurement, evaluate alternative controls, select controls, and verify that predicted levels will meet the community noise limits with an adequate margin. Modeling can also be used at startup to verify the plant has met its noise limits, and to help resolve any noise problems. Increasingly, modeling utilizes ray-tracing software. However, a familiarity with the software and ISO 9613-2, which is typically used to calculate outdoor sound propagation, is not sufficient to develop accurate models. Approaches typically used to model power plants using ray-tracing software are identified and discussed. The starting point is the type of community noise limit, specific limits that must be met, source identification, and sound power levels of sources, which are often difficult to obtain. Beginning with the simplest point-source model, succeeding stages of modeling sophistication up to complex models with finite-sized sources are described. Experience-based techniques for modeling typical power plant equipment are discussed.

목차

ABSTRACT
1. INTRODUCTION
2. BACKGROUND - HOW MODELING IS USED
3. CONCEPT AND EXTENT OF MODELING
4. EQUIPMENT NOISE LEVELS
5. MODELING SMALL EQUIPMENT
6. MODELING PIPES AND VALVES
7. ATMOSPHERIC AND GROUND EFFECTS
8. MODELING BARRIERS
9. EFFECTS OF BUILDINGS AND REFLECTING SURFACES
10. MODELING LARGE EQUIPMENT AND BUILDINGS
11. MODELING COOLING TOWERS AND AIR COOLERS
12. MODELING STEAM-TURBINE PEDESTALS
13. MODELING STACKS
14. MODELING STEAM AND FAN DUCTS
15. CONCLUSIONS
REFERENCES

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

UCI(KEPA) : I410-ECN-0101-2015-530-001509520