CFD Study of Reactivity Controlled Compression Ignition (RCCI) Combustion in a Heavy-Duty Diesel Engine

In this paper, a numerical study is carried out to investigate the combustion and emission characteristics of reactivity controlled compression ignition (RCCI) combustion mode in a heavy-duty, single-cylinder diesel engine with gasoline and diesel fuels using KIVA-CHEMKIN code with a reduced primary reference fuel (PRF) mechanism. Firstly, a comparison is performed between RCCI and CDC performance and emissions to show the superior characteristics of RCCI combustion. Then, the effect of diesel fuel mass fraction in SOI-1 on combustion and emissions of RCCI engine is studied. It is shown that by increasing the diesel mass fraction in SOI-1, combustion event occurs earlier and PPRR is slightly higher. But this parameter has a trivial influence compared to PRF number and SOI timing

A Numerical Study of Fuel Stratification, Heat Transfer Loss, Combustion, and Emissions Characteristics of a Heavy- Duty RCCI Engine Fueled by E85/Diesel

Reactivity-controlled compression ignition is a new advanced combustion strategy developed to reach cleaner and more efficient combustion by controlling fuel stratification inside the engine cylin-der and reducing heat loss. While its potential to produce high efficiency and low emissions and to reach higher loads than other Low-Temperature Combustion strategies (LTC) has been confirmed numerous times, its operating range is still limited to moderate loads. One potential solution to in-crease the operating range is using E85 fuel as the premixed fuel due to the potential of providing a longer combustion duration. This work will focus on developing a computational fluid dynamics (CFD) model for a reactivity-controlled compression ignition (RCCI) engine fueled by E85/diesel with a double step piston bowl geometry. The model is used to investigate the effects of four differ-ent design parameters, namely injection timing, boost pressure, initial temperature, and spray in-cluded angle, to identify their impact on all crucial parameters describing combustion i.e. the strati-fication level, heat loss, and emissions characteristics. It has been found that the start of injection affects the fuel stratification levels inside the cylinder, with the optimum location for efficiency lo-cated in the moderate stratified region. The boost pressure mainly influences the mean gas tem-perature, the start of combustion, combustion duration, and the recession time of the Heat Release Rate (HRR) curve. It is found that the boost pressure does not have an influence on the heat loss of the engine and the heat loss is more correlated to flame temperature than the average tempera-ture. It is also proven that the boost pressure could assist in the suppression of NOx, but when the intake pressure is too high, the thermal efficiency drops. Furthermore, the results show that the ini-tial temperature is preferred to be as low as possible but sufficiently high enough to burn all the in-troduced fuel. Intake temperature alters the HRR shape and combustion duration significantly. Last-ly, it is found that the combination of the spray included angle and piston bowl geometry can sub-stantially determine the way the flame is formed and its location. The study on the effect of spray angle provides essential insights on the origin of unburned hydrocarbon emission, HRR shape, and heat loss

Reactivity controlled compression ignition engine: Pathways towards commercial viability

© 2020 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/).Reactivity-controlled compression ignition (RCCI) is a promising energy conversion strategy to increase fuel efficiency and reduce nitrogen oxide (NOx) and soot emissions through improved in-cylinder combustion process. Considering the significant amount of conducted research and development on RCCI concept, the majority of the work has been performed under steady-state conditions. However, most thermal propulsion systems in transportation applications require operation under transient conditions. In the RCCI concept, it is crucial to investigate transient behavior over entire load conditions in order to minimize the engine-out emissions and meet new real driving emissions (RDE) legislation. This would help further close the gap between steady-state and transient operation in order to implement the RCCI concept into mass production. This work provides a comprehensive review of the performance and emissions analyses of the RCCI engines with the consideration of transient effects and vehicular applications. For this purpose, various simulation and experimental studies have been reviewed implementing different control strategies like control-oriented models particularly in dual-mode operating conditions. In addition, the application of the RCCI strategy in hybrid electric vehicle platforms using renewable fuels is also discussed. The discussion of the present review paper provides important insights for future research on the RCCI concept as a commercially viable energy conversion strategy for automotive applications.Peer reviewe

Numerical Simulation of a RCCI engine using Flamelet Generated Manifold combustion model and OpenFOAM

Reactivity-controlled compression ignition is an advanced dual-fuel combustion strategy developed to reach cleaner and more efficient combustion by controlling charge stratification. In this strategy, incylinder fuel blending is used to control in-cylinder heat release rate and to optimize combustion phasing and duration. In practice this is mostly implemented by a PFI system for the high-octane and a DI system for the high-cetane fuel.To study this concept numerically is challenging. Detailed chemical models are needed to resolve the combustion details and predict efficiency and emission formation accurately. If the chemistry is solved used direct integration, a transport equation for each chemical species is needed which involve a wide spectrum timescales. The Flamelet Generated Manifold (FGM) combustion model is an effective way to reduce the runtime of detailed kinetic simulations especially for internal combustion engines. In this method, simulation results of pre-computed representative 1D flames are used to store relevant properties in a set of tables. Specifically, in FGM the data is mapped to a new coordinate system (ie. controlling variables). The source terms of the controlling variables data and other relevant thermal and transport data is then retrieved during runtime to solve the transport equations for the controlling variables in the CFD model. In this work the method is applied to RCCI combustion. A special representative 1D-flame is used to capture the pre-mixed and diffusive combustion present in a typical RCCI engine. The model features will be introduced and the results of the developed approach in the OpenFOAM framework will be presented to show the applicability of the model

Combining flamelet-generated manifold and machine learning models in simulation of a non-premixed diffusion flame

Flamelet Generated Manifold (FGM) is an example of a chemistry tabulation or a flamelet method that is under attention because of its accuracy and speed in predicting combustion characteristics. However, the main problem in applying the model is a large amount of memory required. One way to solve this problem is to apply machine learning (ML) to replace the stored tabulated data. Four different machine learning methods, including two Artificial Neural Networks (ANNs), a Random Forest (RF), and a Gradient Boosted Trees (GBT), are trained, validated, and compared in terms of various performance measures. The progress variable source term and transport properties are replaced with the ML models. Particular attention was paid to the progress variable source term due to its high gradient and wide range of its value in the control variables space. Data preprocessing is shown to play an essential role in improving the performance of the models. Two ensemble models, namely RF and GBT, exhibit high training efficiency and acceptable accuracy. On the other hand, the ANN models have lower training errors and take longer to train. The four models are then combined with a one-dimensional combustion code to simulate a counterflow non-premixed diffusion flame in engine-relevant conditions. The predictions of the ML-FGM models are compared with detailed chemical simulations and the original FGM model for key combustion properties and representative species profiles

A chemical kinetic study of low alcohol/iso-octane blends in both premixed and partially premixed combustion

The use of alcoholic fuels in both compression ignition and spark ignition engines has received much attention in recent years. Using these fuels, either in pure form or in a blended form with gasoline, not only reduces engine emissions but also improves engine performance. Therefore, a careful examination of the chemical kinetics of combustion of alcoholic fuels is of great importance. Since the run time of combustion chemistry calculations is proportional to the square of the total number of species in the chemical kinetic mechanism, the use of a detailed chemical kinetic mechanism for engine design and optimization is not practical. In this paper, first, the performance of a reduced mechanism has been investigated in terms of predicting ignition delay time and laminar flame speed. Then, the mechanism is utilized to study and compare three different blends of alcohol fuels (namely methanol and ethanol) and iso-octane with identical stoichiometric air to fuel ratio, volumetric energy content, octane numbers and latent heat. Combustion properties for both premixed and non-premixed diffusion flames, and homogeneous reactor configurations are studied. The results indicate that ignition delay and laminar flame speed are more defined by the iso-octane and methanol content rather than ethanol. Generally, blends with lower iso-octane and higher methanol give a longer ignition delay time and a higher laminar flame speed. The main source of pollutant formation for one of the blends are also discussed.Peer reviewe

Generalizing progress variable definition in CFD simulation of combustion systems using tabulated chemistry models

In the Computational Fluid Dynamics (CFD) simulation of advanced combustion systems, the chemical kinetics must be examined in detail to predict the emissions and performance characteristics accurately. Nevertheless, the combustion simulation with detailed chemical kinetics is complicated because of the number of equations and a broad timescale spectrum. The Flamelet-Generated Manifold (FGM) is one of the examples of tabulation methods that has received much attention in recent years due to its fast and accurate prediction of combustion characteristics. The Progress Variable (PV) definition in FGM and other PV-based tabulated approaches is often selected randomly or depending on the user's experience. When complicated combustion systems are involved, such choices can become extremely difficult. In the current work, a generic approach for formulating a global PV is developed and tested in various operating conditions relevant to combustion engines. The method is based on a genetic algorithm optimization to maximize the monotonicity of PV, ensuring that for each value of PV, the dependent thermophysical properties have unique values. The FGM model's ability to reproduce the detailed kinetics evolution of the essential combustion and emission parameters of a non-premixed diffusion flame in Spray A configuration is evaluated in both one-dimensional counterflow and CFD simulation. It is concluded that with the use of the current approach, important combustion characteristics can be predicted much better compared to non-optimized PV while eliminating the manual selection of PV definition by the user. Since the algorithm needs to be executed before the chemistry tabulation in the pre-processing step, it does not increase the runtime of the FGM simulation. The algorithm only needs a few minutes to be finished on a standard desktop. The improvement in the results and the distribution of the values of important species in the computational domain is examined