ANALYSIS AND COMPARISON OF DIFFERENT MODELING APPROACHES FOR THE SIMULATION OF A MICRO-SCALE ORGANIC RANKINE CYCLE POWER PLANT

asme-orc2015 Tracking Number 180

Simulations of organic Rankine cycles and their components can be performed by many types of modeling tools which are characterized by their own level of details. From empirical laws to deterministic methods via semi-empirical models, different modeling complexities can be chosen to simulate a given system. Beside of the predicted performance, the level of details impacts the simulation speed and its robustness. The more deterministic the method, the more complicated the model development and the more computationally intensive the simulation. There are many applications for which deterministic and complex models cannot be implemented. For example, if the ORC is integrated in a larger system, such as a solar power plant [1], the simulation speed and robustness can be critical if all the components of the system, including the ORC, are modeled in details. Therefore it is substantial to evaluate the prediction error committed when using simplified approaches to evaluate the system performance in off-design operating conditions. In this work, simulation results of an ORC unit predicted by models of different levels of complexity (i.e. deterministic, semi-empirical and empirical approaches) are analyzed. A 3kWe ORC test bench is chosen as study case and experimental data are used for reference. The system consists of two plate heat exchangers, a scroll expander, a volumetric pump and an air-cooled condenser. In this paper, the components of the test bench are modeled using different approaches of increasing complexity and each model is calibrated using experimental data from the test rig. The goodness of fit as well as the benefits and limitations of each modeling methods are analyzed and discussed.