A novel heat recovery strategy for internal combustion engines is proposed. The aim is to recover thermal energy from the engine exhaust gases and through cylinder walls to heat pressurized water to supercritical conditions and to directly inject such supercritical water into the combustion chamber. Herein, this strategy is applied to a spark ignition, four-stroke, port fuel injection engine. An in-house solver, which includes a heat exchanger model, has been developed to conduct numerical simulations of the apparatus. At first, the engine model has been validated by comparing the results with experimental measurements. Then, a parametric analysis has been conducted to maximize the engine efficiency by varying the injection start timing, the injector diameter, and the water/fuel ratio. A single-objective genetic algorithm has been used to select the optimal set of such parameters. Finally, a multiobjective genetic algorithm has been used to maximize the engine efficiency and minimize the exhaust gas–water heat exchanger size. The results show that the proposed approach may lead to a significant increase in the engine efficiency, up to about 12%.
On Direct Injection of Supercritical Water into Spark Ignition Engines as a Strategy for Heat Recovery
Antonio Cantiani;Annarita Viggiano;Vinicio Magi
2021-01-01
Abstract
A novel heat recovery strategy for internal combustion engines is proposed. The aim is to recover thermal energy from the engine exhaust gases and through cylinder walls to heat pressurized water to supercritical conditions and to directly inject such supercritical water into the combustion chamber. Herein, this strategy is applied to a spark ignition, four-stroke, port fuel injection engine. An in-house solver, which includes a heat exchanger model, has been developed to conduct numerical simulations of the apparatus. At first, the engine model has been validated by comparing the results with experimental measurements. Then, a parametric analysis has been conducted to maximize the engine efficiency by varying the injection start timing, the injector diameter, and the water/fuel ratio. A single-objective genetic algorithm has been used to select the optimal set of such parameters. Finally, a multiobjective genetic algorithm has been used to maximize the engine efficiency and minimize the exhaust gas–water heat exchanger size. The results show that the proposed approach may lead to a significant increase in the engine efficiency, up to about 12%.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.