Derivation and validation of a heat transfer model in a hydrogen combustion engine. (5th April 2016)

• Record Type: Journal Article
• Authors: Michl, Johannes; Neumann, Jens; Rottengruber, Hermann; Wensing, Michael
• Is Part Of: Applied thermal engineering. Volume 98(2016:Apr.)
• Page Start: 502
• Page End: 512
• Publication Date: 2016-04-05
• Subjects: Heat transfer -- Internal combustion engine -- Simulation -- Hydrogen; Heat engineering -- Periodicals; Heating -- Equipment and supplies -- Periodicals; Periodicals; 621.40205
• DOI: 10.1016/j.applthermaleng.2015.12.062
• Languages: English
• ISSNs: 1359-4311

Abstract

Highlights: We propose a phenomenological wall heat transfer model for a hydrogen engine. The model contains assembled parts from literature with refined or new formulations. Measurements with temperature sensors are conducted at the combustion chamber walls. Our model captures effects of highly variable engine calibration in high quality. Abstract: Due to marked differences of material properties and combustion characteristics between hydrogen and carbon-based fuels, an increased heat flux inside the cylinder of hydrogen-fuelled internal combustion engines occurs. The present study aims for a simulation model that describes the wall heat transfer within the entire range of engine calibration possibilities. Conducted experiments on a hydrogen engine equipped with temperature sensors provide the base for the present study. Using the surface temperature method, highly resolved instantaneous surface heat fluxes can be identified. These measurements are compared with results from published heat transfer models that are originally developed for conventional fuels. As no satisfying result with these models can be obtained for the hydrogen engine, a modified heat transfer model is derived and validated based on the experimental data. Necessary developments on characteristic model features lead to a distinctive model for a hydrogen engine. It is demonstrated that the proposed model attains a consistently high prediction quality for a broad range of engine calibration variabilities.