Accuracy assessment of the turbomachinery performance maps correction models used in dynamic characteristics of supercritical CO2 Brayton power cycle

The supercritical CO2 Brayton cycle (SCO2BC) has emerged as a promising next-generation power generation technology due to its potential for high thermal efficiency and compact components design. Dynamic modeling of SCO2BC is crucial for understanding and analyzing its performance under design and off-design conditions. Turbomachines are critical components in SCO2BC dynamic models. While turbomachinery components are often modeled using their design-point turbomachinery performance maps (TPM), these maps become less accurate during off-design operations. Despite the existence of various TPM correction methods that ensure model validity, the implementation of the accurate ones within dynamic SCO2BC models remains scarce in the literature. This is crucial as it potentially compromises the accuracy of the obtained results. Therefore, there is a need to investigate the errors in the existing literature TPM correction models (in dynamic SCO2BC simulation) by comparing them against dynamic SCO2BC models employing a highly accurate correction method. Thus, in the current work, the common TPM correction methods from the literature are implemented in SCO2BC dynamic models and compared against a baseline mode, which uses the highly-accurate Pham method (at both the component and cycle levels). To the authors’ knowledge, this is the first work to address this research gap for the SCO2BC dynamic simulations and on both component and cycle levels. Additionally, another novelty is the usage of Simcenter Amesim software to dynamically model the SCO2BC aided with Pham model. Multible dynamic models for both the turbomachines of SCO2BC and the whole cycle are constructed and equipped with the different TPM correction methods found in literature to compare their dynamic behaviour that of Pham model. The Ideal Gas Compressibility factor (IGZ) method demonstrates a better performance than the other tested methods, as it exhibits the lowest discrepancy compared to the Pham model. Many of the other tested methods show significant errors. Furthermore, a popular hybrid correction combining IGZ and Pham (IGZ-Ph) is also investigated. Surprisingly, while seemingly beneficial, this hybrid approach negatively impacts accuracy, even resulting in predictions as if no correction was applied.