Meramveliotakis George, Kosmadakis George, Karellas Sotirios
Thermal Hydraulics and Multiphase Flow Laboratory, Institute of Nuclear & Radiology Sciences & Technology, Energy & Safety, National Center for Scientific Research Demokritos, Agia Paraskevi, 15341, Greece.
Laboratory of Steam Boilers and Thermal Plants, School of Mechanical Engineering, National Technical University of Athens, Athens, 15780, Greece.
Open Res Eur. 2022 Jul 7;1:148. doi: 10.12688/openreseurope.14313.3. eCollection 2021.
The aim of this work is to evaluate three methodologies regarding semi-empirical scroll compressor modeling for different refrigerants and conduct a comparative analysis of their results and accuracy. The first step is to improve a semi-empirical model for scroll compressors based on established techniques, and further enhance the physical background of some of its sub-processes leading to more accurate predictions. Focus is then given on the compressor operation when changing the refrigerant, proposing three methods in total. The first method refers to the standard model, requiring an optimization process for the calibration of all the model parameters. The second method relies on a reference refrigerant, and also uses optimization procedures, but for the fine-tuning of a small subset of the parameters. The third method is more generalized, without the need of any optimization process for the parameters identification, when fluid change occurs, leading to a very fast approach. Το evaluate the accuracy and verify the applicability of each method also related to the necessary computational time, two scroll compressors each with three different refrigerants are considered (HFCs and HFOs and their blends). The model is evaluated with the available manufacturer data, using R134a as reference refrigerant. The results show that the first method predicts the key indicators with a very high accuracy, with the maximum discrepancy of 2.06%, 4.17% and 3.18 K for the mass flow rate, electric power and discharge temperature respectively. The accuracy of the other two methods is dropping, but within acceptable levels in most of the cases. Therefore, in cases that reduced accuracy can be accepted, the third method is preferred for compressor performance prediction when changing the refrigerant, which provides results at a small fraction of time compared with the other two methods, once the parameters are calibrated for a reference case.
这项工作的目的是评估针对不同制冷剂的涡旋压缩机半经验建模的三种方法，并对其结果和准确性进行对比分析。第一步是在现有技术基础上改进涡旋压缩机的半经验模型，并进一步强化其一些子过程的物理背景，从而实现更精确的预测。然后重点关注更换制冷剂时压缩机的运行情况，总共提出了三种方法。第一种方法是标准模型，需要对所有模型参数进行校准的优化过程。第二种方法依赖于一种参考制冷剂，也使用优化程序，但仅对一小部分参数进行微调。第三种方法更具通用性，在发生流体变化时无需进行任何参数识别的优化过程，从而实现非常快速的建模。为了评估每种方法的准确性并验证其与所需计算时间相关的适用性，考虑了两台分别使用三种不同制冷剂（氢氟碳化物、氢氟烯烃及其混合物）的涡旋压缩机。使用R134a作为参考制冷剂，根据制造商提供的可用数据对模型进行评估。结果表明，第一种方法预测关键指标的准确性非常高，质量流量、电功率和排气温度的最大偏差分别为2.06%、4.17%和3.18K。其他两种方法的准确性有所下降，但在大多数情况下仍处于可接受水平。因此，在可以接受较低准确性的情况下，更换制冷剂时预测压缩机性能首选第三种方法，一旦针对参考工况校准了参数，该方法与其他两种方法相比能在极短时间内得出结果。
