A computationally efficient but accurate dynamic modeling approach for vapor compression systems is important for many applications. Nonlinear model order reduction techniques which generate reduced order models based on high fidelity vapor compression cycle (VCC) models are attractive for the purposes. In this paper, a number of technical challenges of applying model order reduction methods to VCCs are described and corresponding solution approaches are presented. It starts with a reformulation of a standard finite volume heat exchanger model for matching the baseline model reduction structure. Reduced order models for evaporator and condenser are constructed from numerical snapshots of the high fidelity models using Proper Orthogonal Decomposition (POD). Methodologies for system stability and numerical efficiency of POD reduced order models are described. The reduced order heat exchanger models are then coupled with quasi-static models of other components to form a reduced order cycle model. Transient simulations were conducted over a wide range of operating conditions and results were compared with the full order model as well as measurements. The validation results indicate that the reduced order model can execute much faster than a high-fidelity finite volume model with negligible prediction errors.

蒸汽压缩系统的计算高效但准确的动态建模方法对于许多应用都很重要。基于高保真蒸汽压缩循环 (VCC) 模型生成降阶模型的非线性模型降阶技术对这些目的很有吸引力。在本文中，描述了将模型降阶方法应用于 VCC 的一些技术挑战，并提出了相应的解决方法。它首先重新制定标准有限体积换热器模型，以匹配基线模型缩减结构。蒸发器和冷凝器的降阶模型是使用适当的正交分解 (POD) 从高保真模型的数值快照构建的。描述了 POD 降阶模型的系统稳定性和数值效率的方法。然后将降阶换热器模型与其他组件的准静态模型耦合以形成降阶循环模型。瞬态模拟是在广泛的操作条件下进行的，并将结果与​​全阶模型和测量结果进行比较。验证结果表明，在预测误差可以忽略的情况下，降阶模型的执行速度比高保真有限体积模型快得多。瞬态模拟是在广泛的操作条件下进行的，并将结果与​​全阶模型和测量结果进行比较。验证结果表明，在预测误差可以忽略的情况下，降阶模型的执行速度比高保真有限体积模型快得多。瞬态模拟是在广泛的操作条件下进行的，并将结果与​​全阶模型和测量结果进行比较。验证结果表明，在预测误差可以忽略的情况下，降阶模型的执行速度比高保真有限体积模型快得多。