Performance prediction methods for centrifugal compressors： A review

doi:10.19799/j.cnki.2095-4239.2023.0361

Abstract

Abstract:

Compressed-air energy storage is considered the most promising large-scale physical energy-storage technology; in addition, the compressor, as a key component, has an important impact on the overall performance of the system. In this field, centrifugal compressors have advantages over other types of compressors with characteristics of large flow, high pressure ratio, and wide operating conditions. Owing to the influence of the charging characteristics of the fixed-volume gas-storage device, the compressor often functions in the off-design condition. The accurate prediction of the compressor's performance can improve system efficiency and reduce R&D investment. Since the 1950s, considerable amount of research has been conducted on the performance prediction of centrifugal compressors and various performance prediction methods have been established. In this paper, performance prediction methods are categorized into mechanism modeling, similarity conversion, and data-driven models. According to the summarization of the basic principles and research progress of each method, the differences of each method in terms of the modeling cycle, prediction accuracy, portability, and application scenarios are qualitatively analyzed, and the future development trend of performance prediction is prospected. The findings of this study could guide in the research and application of the performance prediction method to centrifugal compressors.

Key words: centrifugal compressors, performance prediction, compressed air energy storage

CLC Number:

TK 02

Kaixuan WANG, Zhitao ZUO, Qi LIANG, Wenbin GUO, Haisheng CHEN. Performance prediction methods for centrifugal compressors： A review[J]. Energy Storage Science and Technology, 2023, 12(11): 3435-3444.

Figures/Tables 8

Table 1

Type of loss models"

流通部件	损失模型	公式
吸气室	吸气室损失	$Δ h I N L = ξ i c C 12 2$ ^[13]
进口导叶	进口导叶损失	$Δ h I G V = e S K E$ ^[13]
进口导叶	叶轮进口冲角损失	$Δ h I N C = W L 2 / 2$ ^[13]
叶轮损失模型	叶片表面摩擦损失	$Δ h S F = 2 C f L B d H B W 2$ ^[14]
	叶片载荷损失	$Δ h B L = 0.05 D f 2 U 22$ ^[13]
	叶片尾迹混合损失	$Δ h R C = 0.02 t a n α 2 - 1 / 2 D f 2 U 22$ ^[13]
扩压器	无叶扩压器	$Δ h d i f = ξ v l c 32 2$ ^[15]
扩压器	有叶扩压器	$Δ h d i f = ξ v C 32 2 + ξ s h u 22 2 D 2 D 3 2 1 - φ r φ r 0 2$ ^[15]
蜗壳	蜗壳损失	$Δ h v o l = ξ v o l C 42 2$ ^[15]
回流器及弯管	混合损失	$Δ h i n c, R C = 0.8 1 - c m 6 c 6 s i n α$ ^[16]
回流器及弯管	冲角损失	$Δ h m i x, R C = c m, w a k e - c m, m i x c 6 2$ ^[16]

Table 1

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Table 2

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性能预测方法		建模周期	预测精度	可移植性	适用类型	应用场景
机理建模类	数值模拟	长	高	较差	单级/多级	设计优化
机理建模类	损失模型	较短	较高	好	单级/多级	设计优化
相似换算类	级性能叠加法	短	较差	较好	多级	工业应用
相似换算类	性能换算法	短	较差	较好	单级	工业应用
数据驱动类	拟合函数法	较短	较差	差	单级/多级	工业应用
数据驱动类	智能算法	较长	较高	差	单级/多级	工业应用

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