인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Flow losses in centrifugal pumps handling non-Newtonian fluids are of great importance for design optimization, performance prediction, and energy savings. Traditional methods are very limited in determining energy losses due to the complex rheological behavior of such fluids. This study aims to investigate the hydraulic losses and performance degradation mechanism of centrifugal volute pumps handling non-Newtonian emulsions using the entropy production method, focusing on the influence of emulsion type on the loss mechanism. The influence of pump size on fluid’s non-Newtonian behavior and energy loss in a centrifugal pump is also investigated by comparing the entropy distribution in two geometrically similar pumps operating with different emulsions exhibiting shear-thinning behavior. The flow field and entropy production are predicted by computational fluid dynamics (CFD) based on the Reynolds-averaged Navier-Stokes (RANS) equations coupled with the k-epsilon turbulence model. The latter is used to acquire the dissipative entropic components of the flow. The results showed that for a non-Newtonian fluid, energy loss occurs primarily in the impeller, regardless of pump size and flow rate. In addition, the shear-thinning behavior of concentrated emulsions significantly affects hydraulic losses, especially in small-size pumps. Most importantly, small-size pumps generate relatively the highest entropy loss over the entire flow range and the entropy loss increases with the lower limit of the non-Newtonian plateau. This approach showed that the predominance of losses in centrifugal volute pumps operating with non-Newtonian fluids depends on the pump size. Thus, indicating that the hydrodynamic characteristics of two geometrically similar pumps do not scale when the liquid has non-Newtonian rheology.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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