인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
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지원사업
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커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
As data centers expand rapidly, cooling systems have become a major contributor to their overall energy consumption, typically accounting for 30-40% of the total facility power usage. This study presents a physics-based white-box thermodynamic model for air-cooled chillers in data center cooling applications. The model, implemented in Python using the CoolProp library, incorporates an Enhanced Power Law compressor efficiency formulation to better capture part-load behavior. Key model parameters were identified through inverse analysis using a Differential Evolution (DE) algorithm, and the calibrated model showed a mean absolute error (MAE) of 0.089, with relative errors within 5%, over an operating range of part-load ratio (PLR) 0.25-1.00 and outdoor air temperatures of 15-35℃. With the R-22 model as the baseline, five alternative refrigerants (R-32, R-134a, R-410a, R-1234yf, and R-1233zd) were compared by means of contour maps of the change in the coefficient of performance (ΔCOP) across the entire operating domain. Annual 8,760-hour simulations were conducted for six climatically distinct U.S. cities representing ASHRAE climate zones 1A, 2A, 4A, 4C, 5A, and 6B. Of these, San Francisco (marine climate, ASHRAE 4C) and Cincinnati, Kentucky (continental climate, ASHRAE 4A) were selected as representative cities for a detailed evaluation of refrigerant-specific annual energy performance. Among the candidate refrigerants, R-1233zd showed the lowest annual energy consumption in both climates, whereas R-410a recorded the lowest COP under most operating conditions.
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