Using state-of-the-art engine technologies, current gasoline internal combustion engines of passenger vehicles convert only 25∼35% of fuel energy into useful power, and 65∼75% of fuel energy are wasted as heat through engine cooling and exhaust gas systems. One of the promising technologies that can dramatically improve fuel economy is the waste heat recovery system using Organic Rankine Cycle (ORC). The working fluid of the ORC, however, undergoes both liquid and gas phases throughout the cycle, and it is a challenge to develop heat exchanger models that can be used in a simple and efficient dynamic ORC model. In this study, a simplified ordinary differential equation (ODE) ORC model is developed for system-level design and control studies. First, the first principles model of heat exchange dynamics is described by two partial differential equations (PDE) and one ODE, and then the moving-boundary approach is used to lump the distributed parameters of the heat exchanger by integrating three governing equations over each length of three phases (gas, two-phase, and liquid). The simulation results demonstrate that the proposed dynamic ORC model provides key transient dynamics of the ORC with much less computational load.
- Dynamic Systems and Control Division
Dynamic Modeling of the Organic Rankine Cycle for the Waste Heat Recovery of Internal Combustion Engines
- Views Icon Views
- Share Icon Share
- Search Site
Kum, D, & Bucknor, NK. "Dynamic Modeling of the Organic Rankine Cycle for the Waste Heat Recovery of Internal Combustion Engines." Proceedings of the ASME 2012 5th Annual Dynamic Systems and Control Conference joint with the JSME 2012 11th Motion and Vibration Conference. Volume 2: Legged Locomotion; Mechatronic Systems; Mechatronics; Mechatronics for Aquatic Environments; MEMS Control; Model Predictive Control; Modeling and Model-Based Control of Advanced IC Engines; Modeling and Simulation; Multi-Agent and Cooperative Systems; Musculoskeletal Dynamic Systems; Nano Systems; Nonlinear Systems; Nonlinear Systems and Control; Optimal Control; Pattern Recognition and Intelligent Systems; Power and Renewable Energy Systems; Powertrain Systems. Fort Lauderdale, Florida, USA. October 17–19, 2012. pp. 793-801. ASME. https://doi.org/10.1115/DSCC2012-MOVIC2012-8787
Download citation file: