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A generalized log-linear poisson-modeled correlation to predict the optimal heat rejection pressure of transcritical CO2 systems

机译：广义对数线性泊松模型相关性，以预测跨临界CO2系统的最佳排出压力

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摘要

In this study, a comprehensive literature survey of all the experimental data available in literature regarding optimal heat rejection pressure on transcritical carbon dioxide systems is carried out. Based on the phenomenological analysis and quantitative assessment of the data, a Poisson regression analysis is performed and a new dimensionless correlation to predict the optimal heat rejection pressure is developed. The influences of relevant parameters on heat rejection pressure, such as ambient temperature and temperature at the gas cooler are taken into account in the correlation development. The proposed correlation is tested against experimental measurements and is further validated by comparison to all existing experimental data collected from literature. Additionally, the predictive ability of the newly proposed correlation is discussed, tested against experimental data found in literature and compared with existing correlations. Upon comparison with existing correlations, the new generalized dimensionless correlation proposed in this study predicts the optimal heat rejection pressure with an average deviation of 1.31% and a standard deviation of 4.26 bar. Thus, becoming the most accurate correlation up to date, yet providing a much wider range of temperature applicability than the correlations currently available in literature.
机译：在本研究中，进行了关于关于跨临界二氧化碳系统的最佳排出压力的文献中的所有实验数据的综合文献调查。基于数据的现象学分析和定量评估数据，开发了一种泊松回归分析，并开发了预测最佳排出压力的新无量子的相关性。在相关性显影中，考虑了在相关显影中考虑了相关参数对散热压力的影响，例如气体冷却器处的环境温度和温度。通过与从文献中收集的所有现有的实验数据进行比较，对实验测量进行测试，并进一步验证所提出的相关性。另外，讨论了新提出的相关性的预测能力，针对文献中发现的实验数据和与现有相关性进行了测试。在与现有相关性比较时，本研究中提出的新的广义无量纲相关性预测了最佳排出压力，平均偏差为1.31％，标准偏差为4.26巴。因此，成为最准确的相关性，但提供比目前文献中可用的相关性更广泛的温度适用性。

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