In this paper we present a dynamic model formulation for thermal-fluid systems that enables direct minimization of dissipative and frictional losses, otherwise known as entropy generation. While analysis based upon the second law of thermodynamics has long been used for optimizing steady-state design of thermodynamic systems, transient performance and efficiency have largely been overlooked. Across a range of sectors, from power generation to microelectronics, answering the question of how to control a system to minimize these transient losses is becoming increasingly important. Unfortunately, thermodynamic expressions for entropy generation are typically highly nonlinear and not easily amenable to control synthesis and design. Here we derive a control-oriented dynamic model of a notional thermal-fluid system with entropy generation rate as part of the dynamic state vector. The proposed model formulation technique is generalizable to a wide range of energy conversion systems and, importantly, enables the synthesis of model-based state feedback controllers which can in turn optimize transient system performance to minimize irreversibilities due to energy conversion and transport in real-time. To illustrate the utility of the formulation, we design a state feedback H∞ controller to minimize the total entropy generation rate of the notional system in the presence of pulsed, episodic load disturbances.
- Dynamic Systems and Control Division
Second Law Modeling and Robust Control for Thermal-Fluid Systems
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Nash, AL, & Jain, N. "Second Law Modeling and Robust Control for Thermal-Fluid Systems." Proceedings of the ASME 2018 Dynamic Systems and Control Conference. Volume 2: Control and Optimization of Connected and Automated Ground Vehicles; Dynamic Systems and Control Education; Dynamics and Control of Renewable Energy Systems; Energy Harvesting; Energy Systems; Estimation and Identification; Intelligent Transportation and Vehicles; Manufacturing; Mechatronics; Modeling and Control of IC Engines and Aftertreatment Systems; Modeling and Control of IC Engines and Powertrain Systems; Modeling and Management of Power Systems. Atlanta, Georgia, USA. September 30–October 3, 2018. V002T28A002. ASME. https://doi.org/10.1115/DSCC2018-9056
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