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About this book

Sebastian Hann describes the development of a quasi-dimensional burn rate model that enables the prediction of a fuel variation, without the need for a recalibration of the model. The model is valid for spark-ignition combustion engines powered by conventional and carbon-neutral fuels. Its high predictive ability was achieved by modeling the fuel-dependent laminar flame speed based on reaction kinetics calculations. In addition, the author discards a fuel influence on flame wrinkling by performing an engine measurement data analysis. He investigates the fuel influence on engine knock and models it via ignition delay times obtained from reaction kinetics calculations.

Table of Contents

Frontmatter

Chapter 1. Introduction

Abstract
Climate change demands extensive measures on a global scale to limit its impact on the environment to a tolerable level. In [4], for example, a necessary CO2 emission reduction by 60% compared to 1990 in the European Union was estimated to keep the global warming below 2 °C. Assuming an equal share of the necessary CO2 reduction among all CO2 emitters, the transport sector faces serious challenges.
Sebastian Hann

Chapter 2. Fundamentals

Zusammenfassung
In the context of reducing CO2 emissions, alternative or synthetic fuels can significantly improve the ecological footprint of internal combustion engines. To adapt an engine to changing fuel characteristics, 0D/1D simulations offer an efficient way to streamline the engine development process. A prerequisite for this is the reliable modeling of the fuel influence on combustion.
Sebastian Hann

Chapter 3. Measurement Data Analysis

Zusammenfassung
In order to evaluate the relevance of the Darrieus-Landau instability and the fuel-dependent flame wrinkling for engine combustion, as described in Chapter 2.3.2 and Chapter 2.5, a measurement data analysis was carried out on two engines. Their technical data and available operating conditions are listed in Table 3.1 and Table 3.2 for engine A and B, respectively.
Sebastian Hann

Chapter 4. Burn Rate Model Improvement

Abstract
In its original specification, the burn rate model described in Chapter 2.2 only allows for a prediction of moderate fuel variations, like adding hydrogen to methane (see [74]). For a more significant fuel variation like methane, gasoline and ethanol, a recalibration is necessary (see Figures 5.2, 5.3 and 5.4). The reason for the needed recalibration is either the burn rate model itself or a missing, fuel-dependent influence.
Sebastian Hann

Chapter 5. Burn Rate Model Validation

Abstract
The improved burn rate model was validated using engines A and B. Their data are provided in Table 3.1 and Table 3.2. The PTA settings were given in Chapter 3. Additionally, measurements of engines C, D, E and F were available for model validation. Engine C is a high-turbulence long-stroke engine with different pistons to change the compression ratio.
Sebastian Hann

Chapter 6. Engine Knock Investigation and Modeling

Abstract
As validated in Chapter 5, the influence of a fuel variation on engine combustion characteristics can be predicted by only using fuel-specific models for the laminar flame speed and thickness. These characteristics represent, to a certain extent, the chemical effect of the fuel. To test a similar possibility for engine knock prediction, the fuel influence on engine knock is investigated in the following.
Sebastian Hann

Chapter 7. Conclusion and Outlook

Abstract
In this thesis, a quasi-dimensional spark-ignition burn rate model for predicting the effects of changing fuel, air-fuel ratio λ, exhaust gas recirculation (EGR) and water injection was developed and validated for a wide range of boundary conditions. In the validation process, a high predictive ability was proven, especially concerning the prediction of fuel influence on engine performance. The investigated fuels were methane, CNG substitutes, methanol, ethanol, gasoline, hydrogen, methyl formate and DMC+ (65 vol−% dimethyl carbonate, 35 vol−% methyl formate). Based on the model calibration for one fuel at one λ, the change of heat release rate (HRR) with a variation of fuel and λ could be predicted, without recalibration.
Sebastian Hann

Backmatter

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