Reactivity Controlled Compression Ignition (RCCI) is a viable
Low-Temperature Combustion (LTC) regime that can provide
high indicated thermal efficiency and very low nitrogen oxides
(NOx) and particulate matter (PM) emissions compared to the
traditional diesel Compression Ignition (CI) mode. [1] The burn
duration in RCCI engines is generally shorter compared to the
burn duration for compression ignition and spark ignition (SI)
combustion modes.[2, 3] This leads to a high Pressure Rise Rate
(PRR) and limits their operational range. It is important to predict
the Maximum Pressure Rise Rate (MPRR) in RCCI engines
and avoid excessive MPRRs to enable safe RCCI operation over
a wide range of engine conditions. In this paper, two controloriented
models are presented to predict the MPRR in an RCCI
engine. The first approach includes a combined physical and
empirical model that uses the first principle of thermodynamics
to estimate the pressure rise rate inside the cylinder, and
the second approach estimates MPRR through a machine learning
method based on Kernelized Canonical Correlation Analysis
(KCCA) and Linear Parameter Varying (LPV) methods.
The KCCA-LPV approach proved to have higher prediction
accuracy compared to physics-based modeling while requiring
less amount of calibration. The KCCA-LPV approach could
estimate MPRR with an average error of 47 kPa/CAD while
the physics-based approach’s average estimation error was 87
kPa/CAD.
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Design Optimization of an Ethanol Heavy-Duty Engine Using Design of Experiments and Bayesian Optimization
Diesel-fueled engines still hold a large market share in the medium and heavy-duty transportation sector. However, the increase in fossil fuel prices and the strict emission regulations are leading engine manufacturers to seek cleaner alternatives without a compromise in performance. Alcohol-based fuels, such as ethanol, offer a promising alternative to diesel fuel in meeting regulatory demands. Ethanol provides cleaner combustion and lower levels of soot due to its chemical properties, in particular its lower level of carbon content. In addition, the stoichiometric operating conditions of alcohol fueled engines enable the mitigation of NOx emissions in aftertreatment stage. With the promise of retrofitting diesel engines to run on ethanol to reduce emissions, the thermal efficiency of these engines remains the primary optimization target. In order to find the optimal ethanol-fueled engine design that maximizes the thermal efficiency, a large design space needs to be investigated using engineering tools.
In this study, previous research by the authors on optimizing the design of a single-cylinder ethanol-fueled engine was extended to explore the design space for a heavy-duty multi-cylinder engine configuration. A heavy-duty engine setup with multiple operating conditions at different engine speeds and loads were considered. A design optimization analysis was performed to identify the potential designs that maximize the indicated thermal efficiency in an ethanol-fueled compression ignition engine. First, a computational fluid dynamics (CFD) model of the engine was validated using experimental data for four drive cycle points. Using a design of experiments (DoE) approach and a parameterized piston bowl geometry, the model was then exercised to explore the relationship among geometric features of the piston bowl and spray targeting angle and indicated thermal efficiency across all tested operating conditions. After evaluating 165 candidate designs, a piston bowl geometry was identified that yielded an increase between 1.3 to 2.2 percentage points in indicated thermal efficiency for all tested conditions, while satisfying the operational design constraints for peak pressure and maximum pressure rise rate. The increased performance was attributed to enhanced mixing that led to the formation of a more homogeneous distribution of in-cylinder temperature and equivalence ratio, higher combustion temperatures, and shorter combustion duration. Finally, a Bayesian optimization (BOpt) analysis was employed to find the optimal piston bowl geometry with a fixed spray injector angle for one of the operating conditions. Using BOpt, a piston candidate was identified that resulted in a 1.9 percentage point increase in thermal efficiency from the baseline design, yet only required 65% of the design samples investigated using the DoE approach.
more » « less- Award ID(s):
- 1853114
- NSF-PAR ID:
- 10414644
- Date Published:
- Journal Name:
- ASME 2022 ICE Forward Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Reactivity controlled compression ignition (RCCI) engines center on a combustion strategy with higher thermal efficiency, lower particulate matter (PM), and lower oxides of nitrogen (NOx) emissions compared to conventional diesel combustion (CDC) engines. However, real time optimal control of RCCI engines is challenging during transient operation due to the need for high fidelity combustion models. Development of a simple, yet accurate control-oriented RCCI model from physical laws is time consuming and often requires substantial calibrations. To overcome these challenges, data-driven models can be developed. In this paper, a data-driven linear parameter varying (LPV) model for an RCCI engine is developed. An LPV state space model is identified to predict RCCI combustion phasing as a function of multiple RCCI control variables. The results show that the proposed method provides a fast and reliable route to identify an RCCI engine model. The developed model is then used for the design of a model predictive controller (MPC) to control crank angle for 50% fuel burnt (CA50) for varying engine conditions. The experimental results show that the designed MPC with the data-driven LPV model can track desired CA50 with less than 1 crank angle degree (CAD) error against changes in engine load.more » « less
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Reactivity controlled compression ignition (RCCI) engines center on a combustion strategy with higher thermal efficiency, lower particulate matter (PM), and lower oxides of nitrogen (NOx) emissions compared to conventional diesel combustion (CDC) engines. However, real time optimal control of RCCI engines is challenging during transient operation due to the need for high fidelity combustion models. Development of a simple, yet accurate control-oriented RCCI model from physical laws is time consuming and often requires substantial calibrations. To overcome these challenges, data-driven models can be developed. In this paper, a data-driven linear parametervarying (LPV) model for an RCCI engine is developed. An LPV state space model is identified to predict RCCI combustion phasing as a function of multiple RCCI control variables. The results show that the proposed method provides a fast and reliable route to identify an RCCI engine model. The developed model is then used for the design of a model predictive controller (MPC) to control crank angle for 50% fuel burnt (CA50) for varying engine conditions. The experimental results show that the designed MPC with the data-driven LPV model can track desired CA50 with less than 1 crank angle degree (CAD) error against changes in engine load.more » « less
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Reactivity controlled compression ignition (RCCI) engines center on a combustion strategy with higher thermal efficiency, lower particulate matter (PM), and lower oxides of nitrogen (NOx) emissions compared to conventional diesel combustion (CDC) engines. However, real time optimal control of RCCI engines is challenging during transient operation due to the need for high fidelity combustion models. Development of a simple, yet accurate control-oriented RCCI model from physical laws is time consuming and often requires substantial calibrations. To overcome these challenges, data-driven models can be developed. In this paper, a data-driven linear parametervarying (LPV) model for an RCCI engine is developed. An LPV state space model is identified to predict RCCI combustion phasing as a function of multiple RCCI control variables. The results show that the proposed method provides a fast and reliable route to identify an RCCI engine model. The developed model is then used for the design of a model predictive controller (MPC) to control crank angle for 50% fuel burnt (CA50) for varying engine conditions. The experimental results show that the designed MPC with the data-driven LPV model can track desired CA50 with less than 1 crank angle degree (CAD) error against changes in engine load.more » « less
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With the growing trend of decarbonization in ground transportation, low and zero-carbon fuels have attracted extensive research interest. Liquid ammonia is a promising alternative fuel due to its relatively high volumetric energy density, mature production and distribution infrastructure, convenience of storage, and zero carbon emissions. However, ammonia combustion also suffers from low flame speed and weak chemical reactivity. In this work, we computationally investigate the suitable engine-relevant thermochemical conditions for auto-ignition of constant volume ammonia spray, as well as its spray dynamics, vaporization, flash boiling effects, and emissions. The simulation is first validated by comparing it against available experimental data from a vaporizing ammonia spray and is then extended to chemically reactive conditions. Results show that ammonia sprays under engine-relevant conditions (60 bar and 1200 K) can only successfully auto-ignite for cases with ambient hydrogen addition, through enhancement of thermal condition and chemical reactivity. A chemical flux analysis is conducted to further understand the important species and reactions that promote ammonia auto-ignition from hydrogen, which potentially can be introduced via H2solubility, exhaust gas recirculation, and onboard ammonia thermal decomposition. Furthermore, results have indicated that charge cooling effects can further decrease the temperature in the flow field and make auto-ignition more difficult. This study provided useful insights for the application of ammonia as a zero-carbon diesel fuel for ground transportation.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1115/ICEF2022-90257
