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Intrinsic Design of Experiments for Modeling of Internal Combustion Engines
Technical Paper
2018-01-1156
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
In engine research and development there are often different engine parameters that produce similar effects on the end-point results. When calibrating modern engines, a huge number of parameters needs to be set, which also includes compensation parameters for model imperfections. In this context, simpler, more robust, and physically based models should be beneficial both for calibration work load and powertrain performance. In this study, we present an experimental methodology that uses intermediate (“intrinsic”) variables instead of engine parameters. By using simple thermodynamic models, the engine parameters EGR, IVC, and PBoost could be translated into oxygen concentration, temperature and gas density at the start of injection. The reason for this transformation of data is to “move” the Design of Experiment (DoE) closer to the situation of interest (i.e. the combustion) and to be able to construct simpler and more physically based models. In this example, the system was a diesel engine. However, the method can be applied to any experimental system that shares the non-intrinsic nature (e.g. the internal combustion engine), which makes this methodology general. The approach was demonstrated for a heavy-duty diesel engine and five design variables were investigated. Regression models were made using either the engine variables or the intrinsic variables and the resulting regression coefficients were compared and contrasted. By using exactly the same experiments but described in a different way (using the intrinsic variables), the optimization task becomes facilitated. Furthermore, by using physical properties instead of engine settings, these models should be more general and more robust during powertrain optimization.
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Sjoblom, J., Andric, J., and Faghani, E., "Intrinsic Design of Experiments for Modeling of Internal Combustion Engines," SAE Technical Paper 2018-01-1156, 2018, https://doi.org/10.4271/2018-01-1156.Data Sets - Support Documents
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