1. Introduction
Despite the increasing role of electrification in automotive powertrains, marine and off-road applications are expected to continue relying on internal combustion engines for the foreseeable future. This is due to their unique operational requirements, such as high torque demand, long operating hours, packaging constraints, and limited access to refuelling infrastructure. Primary challenges for powertrain development in the marine and off-road sectors involve flexible inclusion of low-carbon alternative fuels while improving fuel efficiency and reducing atmospheric emissions [
1]. Advanced low-temperature combustion concepts (LTC) are currently at the forefront of developments to meet those challenges. Reactivity-controlled compression ignition (RCCI) in particular is under intense investigation by leading marine engine manufacturers, including Wärtsilä [
2] and MAN Energy Solutions [
3]. RCCI retains the benefits of conventional dual-fuel ignition, resulting in improved fuel efficiency, lower emissions of nitrogen oxides (NOx) and particulate matter (PM), and enhanced engine performance in these demanding applications [
4,
5,
6]. Moreover, RCCI enables gradual integration of green hydrogen or ammonia into existing natural gas infrastructures, combining the simultaneous combustion of different fuels. Keeping the existing infrastructure avoids extensive modifications and reduces environmental impact [
7,
8,
9].
Due to its lower combustion temperature, an RCCI engine produces more carbon moNOxide (CO) and hydrocarbon (HC) emissions than a conventional diesel engine [
10]. For example, Benajes et al. [
11] performed dynamic emission tests to compare emissions from a car engine operated in conventional diesel combustion (CDC) and diesel–gasoline RCCI modes. Despite lower PM and NOX production, switching to RCCI mode increased CO and HC engine-out emissions from 7.5 g/kWh to 14.7 g/kWh and from 4.9 g/kWh to 7.9 g/kWh, respectively. Similar emission levels for diesel–gasoline RCCI were shown by Kokjohn et al. [
12] for steady engine operation at 0.6 MPa of indicated mean effective pressure (IMEP), namely, 14 g/kWh and 6 g/kWh for CO and HC, respectively. It should be noted that emission levels strongly depend on combustion controls such as diesel injection strategy, fuel fractions, boost, and exhaust gas recirculation (EGR). However, a reduction in CO and HC emissions usually is associated with an increase in NOX emissions [
13]. Pedrozo et al. [
14] provided emission results for a diesel–natural gas (NG) RCCI engine. At IMEP 0.6 MPa, HC emission levels were similar to other cited works at approximately 6 g/kWh, notably almost completely comprising methane. This unburned methane emission—methane slip—is a potent greenhouse gas contributing to climate change [
15]; so, its excessive emission can negate all of the other emission benefits of RCCI combustion. Mortensen et al. [
16] calculated that just 3% methane slip completely wastes NG’s advantage over coal in terms of greenhouse gas warming potential over a 20-year term.
CO and HC emissions also directly reduce combustion efficiency, so different control approaches have been proposed to reduce these emissions while keeping LTC’s PM and NOx emission benefits. Beside fuel-mixing strategies, variable valve timing and variable compression ratio are two technologies that can affect internal combustion engine emissions. Variable valve timing optimises the engine’s valve timing, improving combustion efficiency, reducing pumping losses, and enhancing power and torque. This leads to reduced NOx and PM emissions while also enhancing fuel efficiency. A variable compression ratio allows for the adjustment of the engine’s compression ratio during operation, thereby optimising combustion based on load and operating conditions. This can lead to more complete combustion while maintaining low NOx emissions [
17,
18]. Mikulski et al. [
6] proposed to adapt in-cylinder fuel reforming from homogenous charge compression ignition (HCCI) technology for RCCI, aiming to reduce methane slip. Although such strategies are used to improve emission control in RCCI engines, using an exhaust aftertreatment system to convert CO and HC is inevitable if further tightening of emission legislation is considered.
Typically, the mitigation of HC and CO emissions in a lean environment involves the use of an oxidation catalyst. Diesel oxidation catalysts (DOCs) are commonly used for oxidising HC and CO in conventional diesel engines. DOCs also improve the efficiency of the diesel particulate filter and selective catalytic reduction [
19]. First-generation DOCs with copper (Cu), nickel (Ni), or other metals were based on gasoline engine technology, but they were phased out due to susceptibility to catalyst poisoning and poor thermal stability. The second-generation ones used high loading of noble metals such as platinum (Pt), palladium (Pd), and rhodium (Rh). They gave greater conversion efficiency of CO and HC. However, they have been associated with high sulphate emissions. Third-generation DOC catalysts use HC adsorption technology, with strong HC adsorption materials such as molecular sieves. Most of the HC emissions during the cold start and warm-up stages are adsorbed, and then desorbed and completely burned in the heating stage [
20,
21]. This solution reduces the necessity of catalyst heating by combustion delay, which reduces the engine’s thermal efficiency [
22].
LTC’s inherent characteristic of low exhaust gas temperature poses a challenge for existing exhaust aftertreatment solutions. Hasan et al. [
23,
24,
25] studied catalytic efficiency of an HCCI engine aftertreatment system in various studies, reporting indicated low methane conversion efficiency of a standard three-way catalytic converter. The efficiency was as low as 16% at 0.4 MPa IMEP, compared to 92% for spark-ignition combustion. Hunicz and Medina [
26] analysed methane oxidation efficiency and, aside from the temperature effect, they pointed out the poisoning effect of acetylene, which is produced by fuel reforming. Prikhodko et al. [
27] examined different DOC converters with different loadings and precious metal proportions, working with a diesel–gasoline RCCI engine. The conversion efficiency was 100% for CO and 80% for HC as soon as the exhaust temperature reached 190 °C in CDC operation, independent of the catalytic material. In contrast, the same conversion efficiencies were not achieved until 300 °C in RCCI operation. The differences in catalytic efficiency were ascribed to overall higher concentrations of CO and HC and also to different hydrocarbon compositions.
Considering the above, RCCI with NG as a low-reactivity fuel is particularly challenging due to the high dissociation enthalpy of methane. Therefore, it is difficult to activate the catalytic reaction under the low temperatures that are typical of RCCI exhaust. This indicates that dedicated methane oxidation catalysts (MOCs) should be applied in RCCI engines. An MOC’s design, precious metal composition, and sizing play a crucial role in reducing methane emissions. Usually, MOCs are based on Pt and Pd, with Pd showing the highest activity under lean conditions and in a low-temperature regime. Stakheev et al. [
28] tested Pt and Pd on aluminium oxide (Al
2O
3) catalysts in lean conditions and with 5000 ppm methane concentration. For the best performing design, a Pt-based catalyst had a light-off temperature of 510 °C, while a Pd-based one had a 360 °C light-off temperature. The Pt catalysts used a Langmuir–Hinshelwood mechanism on metallic Pt, while the Pd catalysts employed a Mars–Van Krevelen mechanism on Pd oxide particles. Pt and Pd catalysts have a different relationship between their activity and metal particle size. Pd catalysts became more active as particle size increased from 1 to 20 nm, while Pt catalysts were mostly unaffected by particle size. These differences stemmed from distinct reaction mechanisms: weaker Pd–O bonds and reduced support effects enhanced the activity of larger Pd particles. Currently, the most investigated type of MOC for large dual-fuel gas engines is Pd on Al
2O
3. However, the newest investigations show advantages of zeolite support [
14]. Sulphur poisoning is a particular challenge for state-of-the-art MOCs. According to Ottinger et al. [
29], as little as 1 part per million (ppm) of sulphur dioxide (SO
2) in the exhaust inhibits the catalyst. Lehtoranta et al. [
30] considered using an upstream SOx trap to mitigate the problem, but the MOC still required 20 h regeneration intervals to keep the methane (CH
4) conversion efficiency above 70%, even with only 0.5 ppm of SO
2 in the exhaust. Importantly, increased concentrations of H
2 in the exhaust can dramatically accelerate regeneration [
31].
Pd-MOCs have been relatively well researched for conventional dual-fuel marine engines, but that is not the case for NG–diesel RCCI because of the low TRL level of the combustion concept. Fast, one-dimensional (1D) reactive simulation can be coupled with engine and aftertreatment models for system level simulation to evaluate the feasibility of such a paring. This model-based development method has been widely used in global R&D. Tziolas et al. [
32] investigated several close-coupled exhaust aftertreatment system (EATS) layouts aimed at meeting future EURO VII diesel emission limits. The study used a heavily predictive EATS model build in Exothermia Suite but coupled it with a non-predicative, fast-running engine model (GT-Suite) of a diesel engine for fast transient simulations. Such an approach is typical for legacy engines, because neither conventional diesel combustion (CDC) nor spark-ignited (SI) flame propagation is particularly sensitive to intake valve closing (IVC) conditions. Recently, a similar co-simulation approach was used by Leon de Syniawa et al. [
33] to develop a comprehensive, detailed kinetic MOC model for SI compressed natural gas (CNG) engines. The baseline for the MOC model was a platinum group metal (PGM) chemistry containing Pt and rhodium (Rh) with Ceria (CeO
2). Importantly, the authors underscored the value of using a predictive combustion model with detailed chemistry to capture the influence of non-legislative emission components on aftertreatment performance [
32]. The modelling assumed indirect coupling of a zero-dimensional (0D) SI stochastic reactor model (SRM) with a 1D catalyst model. Indirect coupling meant only composition of the exhaust was passed to the catalyst brick, while the boundary conditions of intake and exhaust manifold pressure and temperature were imposed in both models directly from the experimental data. At this point, one should note that unlike CDC and SI combustion, the kinetically controlled nature of RCCI combustion makes it very sensitive to IVC conditions. Even small fluctuations in intake and exhaust path include a direct feedback loop in combustion, producing cycle-to-cycle variations in exhaust composition and indicated efficiency. Consequently, inclusion of the MOC brick’s backpressure will affect engine-out emissions and efficiency, yielding fully dynamic two-way coupling between the predictive combustion, air path, and aftertreatment. This is a significant methodological challenge, considering that commercial 1D solvers do not offer a fully predictive approach to model RCCI combustion.
2. Motivation and Objectives
Summarising the above state of the art, NG–diesel RCCI offers ultra-low emissions and near-zero NOx and PM emissions and is considered the next big thing for marine propulsion. On the other hand, CH4 and CO emission levels, although much lower than those of legacy methane-based combustion concepts, still pose a challenge and will require aftertreatment if future emission legislation becomes more stringent. Coupling RCCI with aftertreatment, particularly state-of-the-art MOC, is uncharted territory and contains several knowledge gaps. Some insights suggest that the two technologies might not be complementary. RCCI implies low exhaust temperature and potentially high formaldehyde and nitrous oxide (N2O) emissions. Sulphur from the diesel fraction used as high-reactivity fuel can still be transferred to the exhaust. These factors inhibit MOC performance. On the other hand, the concept offers potential opportunities. An MOC can reduce RCCI’s extensive calibration burden and aftertreatment regeneration can benefit from future H2-NG mixtures that are expected to gradually enter marine bunkering streams.
A predictive 1D simulation framework can be used to resolve this conflict between the challenges and opportunities at an early stage of concept development. The early stage of development of both RCCI and MOC technology for large-bore engines creates a methodological knowledge gap in this respect. To this end, the present work is a step towards developing an integrated methodology capable of predicting the emission and performance characteristics of cutting-edge marine dual-fuel engines working in low-temperature RCCI mode with MOC aftertreatment. To this end, a fully predictive, in-house University of Vaasa advanced thermokinetic multizone (UVATZ) combustion model [
34,
35] is dynamically coupled with a 1D model (GT-Suite) of a prototype engine built to test RCCI combustion on a representative geometry of Wärtsilä 31DF production engines. The integrated combustion–air-path model has been thoroughly calibrated and further coupled with a representative state-of-the-art MOC catalyst model built in the same GT-Suite environment [
36]. The MOC includes a well-established PGM chemistry model created by Khosravi et al. [
37], tuned to the detailed exhaust species portfolio of the UVATZ code. This study’s primary objective is to determine the feasibility of this unique modelling framework for integrated calibration of RCCI engines with aftertreatment. The objective is achieved by performing steady-state simulations focused on convergence and fundamental cross-interactions of the system’s components. With fundamental feasibility confirmed, this study moves on to geometrical optimisation of the MOC and several case studies to support the applied feasibility of the RCCI-MOC system.
3. Methodology
3.1. The Test Engine and the Engine Model
The air-path and combustion models used in this study were identified based on the Wärtsilä Mono single-cylinder research engine (SCRE) platform. The cylinder geometry of the SCRE was selected from Wärtsilä’s 310 mm bore, dual-fuel production engine specifications.
Table 1 lists the main specifications of the test rig. The same engine, running low-temperature RCCI combustion, has provided data for model validation.
AVL Indicom software (version 2015) and a Kistler 6124A cylinder pressure transducer with a 300-bar range and 30 pC/bar sensitivity were set up for measuring combustion-related specifications and storing them. AVL Indicom enables real-time data analysis from sensors. The Kistler 6124A transducer measures cylinder pressure with high precision. Its 300-bar capacity covers most engine pressures, and its 30 pC/bar sensitivity ensures detailed pressure change detection.
Changes were made to the injector piston alignment to accommodate RCCI-like early injections of the high-reactivity fuel (HRF). A centrally mounted twin-needle injector, enclosed within a high-pressure, common-rail fuel system, was optimized for the light fuel oil (LFO) used as HRF [
38]. For RCCI injections, the smaller of the two nozzles was used to facilitate atomisation of the micro-injected quantities. With early injection timings, the narrow-cone injector tip supported proper reactivity stratification without extensive wall wetting. Natural gas, the low-reactivity fuel (LRF), was injected through a multipoint gas injector located upstream of the intake valve. The SCRE incorporates a partially variable intake/exhaust valvetrain.
Unlike a multicylinder engine, the SCRE did not feature a turbocharger, necessitating specific solutions to regulate charge air temperature and pressure. The complete charge air system comprises two compressors, two buffer tanks, a charge air dryer, and two pressure-regulating valves. This setup serves to control the charge air pressure and temperature while stabilising the airflow, thereby simulating the exhaust system of the actual production engine.
For the purpose of this research study, the detailed engine air path has been modelled in GT-Power software (version 2022) with the following assumptions. The intake and exhaust geometry are modelled in full detail, including the mentioned buffer tanks for exact flow calculations. Instead of modelling the complete gas regulating unit (GRU), a simplified configuration with a single injector component was used to regulate the pressure in the gas supply system before delivering it to the engine through port fuel injection. Other than that, the model included a standard map-based direct injector for the HRF and a four-valve, rotational position-based valvetrain. Discrete variable valve actuation was imposed by predefined valve profiles.
Figure 1 presents the governing model’s subsystems. Note that the actual test setup did not involve the exhaust aftertreatment, which has been separately identified for the purpose of the present study.
Section 3.3 provides the corresponding details of the aftertreatment model.
In the GT-Power simulation, the modelling of turbocharger output conditions incorporated an orifice and an intercooler, as depicted in
Figure 1, positioned between the muffler and the exhaust air path. The orifice’s diameter has been optimised to ensure the desired pressure output. Additionally, the intercooler has been fine-tuned to maintain the turbocharger’s temperature at the desired level.
The baseline air-path model (without aftertreatment) underwent thorough calibration against experimental data spanning 40 RCCI operating cases. Calibration involved conducting a three-pressure analysis (TPA) in GT-Power, as depicted in
Figure 1. The measured in-cylinder pressure was matched to the simulated value by adjusting the flow and friction multipliers in the physical air-path model. More information on the TPA is available in the source document [
39].
The calibration results have been thoroughly discussed in another paper by Kakoee et al. [
34]. For transparency, they are synthetically reproduced in
Figure 2, which illustrates a 0% error line (y = x) representing the simulated output on the
Y-axis and the experimental output on the
X-axis for four governing engine parameters. As the exact values of the data were confidential, only the ratio of simulated and experimental data has been shown as a cross-sign on the graphs. The calibration accuracy targets for the governing model parameters are depicted by dashed lines. The targets were stringent, representing the accuracy of steady-state measurement. Either device uncertainty or standard deviation, whichever was higher, was adopted as the measurement error.
Although calibrating the engine air-path model involved exploring various specifications, the four key parameters shown in
Figure 2 were chosen to highlight the model’s accuracy. Brake-specific fuel consumption stayed within a maximum error of 3 percentage across all 40 operating points, and the BMEP error remained under 3%, ensuring precision in each case. Divergence of the dashed lines in the figures indicates that in the higher value of specifications, deviation from the ideal line was higher. BSFC and CA50 in all operating points approximately had the same amounts of deviation in various loads, where higher BMEP and air mass flow rate show high deviations.
Examining the air-path dynamics, the air mass flow rate, a crucial indicator for flow accuracy, demonstrated deviations below or equal to 3%. Turning to combustion, the CA50 metric was selected to showcase simulation accuracy in predicting combustion phasing. CA50 exhibited an error of approximately 1.7 CAD, below the 2 CAD threshold and within the study’s acceptable error range.
The UVATZ model, introduced by A.Vasudev et al. [
35], simulates in-cylinder combustion. It was capable of simulating various low-temperature combustion concepts driven by chemical kinetics; it was parameterised in this study for natural gas and diesel-fuelled RCCI combustion. The UVATZ model considered the dominant factors influencing combustion, such as fuel and thermal stratification, in-cylinder turbulence, intake valve closure (IVC) temperature, and the composition and quantity of residual burnt gas. The combustor is divided into 12 zones, as depicted in
Figure 3. The last two disc-shaped zones represent the cylinder head and piston boundary layers. The remaining 10 zones are annular, with zone 1 adjoining the liner. This zonal arrangement captures the bulk inhomogeneity resulting from compositional and thermal stratification, as shown schematically in
Figure 3.
Interactions between the zones are modelled through heat, mass, and work transfer. Heat loss to the walls was accounted for using the correlation proposed by Chang et al. [
40]. Transport of heat and mass between zones was modelled using gradient-based methods, while turbulence effects were incorporated following the approach of Yang and Martin [
41]. The turbulence submodel involved a single calibration constant ζ
u. Chemical reactions were modelled using the mechanism developed by Yao et al. [
42], which includes 54 species and 269 reactions. The HRF is represented by n-dodecane (nC
12H
26), while the LRF is defined as a mixture of CH
4 and ethane (C
2H
6).
The stratification of HRF was described by a simplified injection model, where the nC12H26 mass was assumed to be linearly distributed across the zones, with the liner zone having the highest concentration. This distribution was imposed at the moment of injection, and the enthalpy of evaporation was considered proportional to the mass of HRF in each zone. The specific profile gradient, ζ∇, was adjusted to match the case-dependent requirements. The UVATZ model was implemented in C++ and used the thermochemical libraries of Cantera. The simulations were performed using the robust CVODES solver, with each closed-cycle simulation typically taking around three minutes to complete.
3.2. The Aftertreatment Model
As explained in the
Section 2, this study focused on the coupling effects of engine gas exchange, RCCI combustion, and working conditions of the MOC, rather than the optimisation of catalytic efficiency. The primary catalyst materials and design used in diesel oxidation were chosen for current investigations. One should note that this catalyst chemistry has been thoroughly validated [
37].
Figure 4 illustrates the catalytic brick. Its dimensions were carefully chosen for optimal performance, based on engine cylinder size, operating conditions, gas dynamics research data, and general requirements for the RCCI-tailored MOC [
33,
34]. A round-profile substrate brick with a diameter of 300 mm was used, which was close to the engine’s exhaust geometry. The baseline brick length was set to 400 mm with 5 mm discretisation in the longitudinal direction for calculations. Catalyst cells were considered square, with a density of 2/cm
2 and a wall thickness of 0.015 cm. These dimensions provided subsonic gas flow velocity and protected the system against backflows under all conditions. Variable cell densities and brick lengths were examined to study their effects on gas dynamics and catalytic chemistry. The catalyst’s substrate was covered with a 0.01 cm thick washcoat layer. The PGM physical properties have two adjustment factors: loading of the site element, i.e., the mass of the washcoat in the unit of volume, and atomic weight. These two specifications were 97 gr/ft
3 and 167.2 g/mol, respectively. The PGM in question was based on Khosravi et al.’s catalyst selection, which was a monolith commercial one and consists of platinum (Pt) and palladium (Pd) with 4:1 mass ratio [
37].
The initial temperature of the catalyst brick’s wall during the simulations was adjusted to 10% below the last part of the exhaust air path. The substrate material was cordierite, selected from GT-Power’s library, with temperature-dependent specific heat, while the washcoat alumina specific heat was considered constant. This approach also was used in Khosravi et al. in an investigation of a DOC PGM catalyst [
37].
The reaction mechanism used in this study was selected from the same work; however, it needed to be tailored to include specifics of the exhaust compositions of the RCCI engine. The implemented chemistry model is presented in
Table 2 and
Table 3, and detailed considerations regarding its final formulation can be found in
Appendix A.
3.3. Model Coupling Assumptions
The UVATZ model has been coupled with a 1D air-path model in GT-Power for predictive simulations of the RCCI engine. The coupling was achieved by using GT-Power’s external cylinder object. The UVATZ model was integrated into a dynamically linked executable, enabling the division of responsibilities between the two models. The air-path dynamics and gas exchange phase of the four-stroke cycle were handled by GT-Power, while the closed part of the cycle, specifically the combustion phase, was handled by the UVATZ model.
Figure 5 illustrates the exchange of information between the two models. At intake valve closure (IVC), the UVATZ model received an input file detailing the mixture’s thermodynamic state, geometric parameters, and solver settings.
It is important to highlight that the predictive cylinder’s wall temperature has been used, providing boundary conditions for the UVATZ heat loss model (Woshini-Chang [
40]). Specifically, the UVATZ model’s heat loss calculations used surface temperatures of the cylinder head, piston, and liner. Following the combustion phase, the simulation results, such as the cylinder-averaged histories of the thermodynamic state and species concentration (from the chemical kinetic mechanism), were transferred back to GT-Power through an output file.
This coupling procedure allowed for streamlined postprocessing within GT-Post (GT-Suite’s postprocessing tool), simplifying the analysis of the results from both the combustion and aftertreatment models. An “aftertreatment inlet” subassembly was created to pass the output species concentration to the catalyst brick. The cycle-averaged mass flow, temperature, and species concentrations were transferred from the explicit circuit to the quasi-steady (QS) circuit, the recommended flow circuit for the aftertreatment chemistry solver. The transferred species included CO2, CO, H2O, CH4, C2H6, C2H4, N2, O2, H2, and nC12H26, which were supposed to be collected in the QS inlet circuit. The temperature in the QS circuit came as direct result of the physical simulation taking into account the heat losses in the exhaust components. The exhaust wall temperature was calculated dynamically within the cylinder.
One should note that exhaust backpressure in the SCRE was regulated with a backpressure valve to mimic the conditions of the production engine with a turbocharger. Accordingly, an adjustable orifice was added before the aftertreatment block. Furthermore, a charge air cooler with controllable efficiency mimics the temperature drop behind the turbocharger. Both objects are within the “turbocharger conditioning” block visible in
Figure 1. The parameters were tuned case-dependently to represent the pressure/temperature drop in a production version of the Wärtsilä 31 engine. The end environment conditions were set to ambient to prevent backflow through the catalyst.
3.4. The Scope of the Research
The coupled system-level model has been used to perform simulations to determine the feasibility of the RCCI—MOC marine engine.
Figure 6 summarises the creation of this GT-UVATZ aftertreatment modelling framework. The entries in blue are the enabling methods and those in green are the resultant simulations presented in the
Section 4.
The simulation plan for this study involved three dedicated campaigns (
Figure 6). Campaigns 1 and 2 secure that independently validated submodels incorporating the framework were valid for the present study. To this end, campaign 1 evaluated the performance and thermal state of the coupled engine model and validated the simulation result against the corresponding experimental data from the Wärtsilä RCCI test campaign (refer to the
Section 3). Analysis of the simulated exhaust components allowed for the formulation of a proper system of reactions to be embedded into the MOC model. This was carried out in campaign 2 resulted from fundamental considerations supported by preliminary simulations. The completed engine aftertreatment model with proper chemistry was used in campaign 3, where the final solution was assessed involving geometric optimisation of the aftertreatment block. This entailed model-based sensitivity analysis on MOC cell density and brick length.
Three experimental operating points were selected as the baseline for the simulations. All of them represent partial-load RCCI operation, where methane slip has been considered problematic.
Table 4 provides relevant data characterising these operating points. Note that all points have ultra-early diesel start of injection (SOI), characteristic of RCCI. Injection commences close to IVC to assure proper premixing of natural gas and diesel. All test point data in
Table 1 were relativised against reference values (ref) for confidentiality reasons. The reference values correspond to the standard IMO (International Maritime Organization) Tier III low-load calibration points for the commercial version of the Wärtsilä W31DF, a multicylinder, lean-burn, NG–diesel engine.
Testing aftertreatment performance across a variety of RCCI operating conditions entailed significantly different calibrations for each of the three test points. SOI remained fixed, but intake charge pressure (Pint), temperature (Tint), and air–fuel ratio () were all varied. Note that the natural gas/diesel blend ratio (BR) definition used here was based on energy content.
5. Conclusions
This study highlighted the efficacy of advanced kinetic-based combustion models for comprehensive engine and aftertreatment simulations. It demonstrated that a coupled multizone model with detailed reaction kinetics and aftertreatment integration can converge in under 12 s for steady-state simulations. Convergence times can vary based on initial conditions and combustion variability. This research study also found that the model accurately captured the impact of aftertreatment-induced backpressure on combustion. Despite the simulation running at a speed of three minutes per cycle—slightly slower than a standalone combustion model—it achieved high accuracy within a 5% error margin for performance metrics and emissions.
Moreover, this study underscored the critical role of hydrogen in driving the spontaneity of methane reactions and the importance of oxygen in catalytic processes.
The analysis of catalyst brick geometries identified a 400 mm long oxidation reactor with a cell density of 10 (1/cm2) as optimal, achieving significant hydrocarbon conversion rates and maintaining an acceptable pressure drop below 0.1 bar, thus offering flexibility in design. However, the introduction of aftertreatment systems was found to compromise engine performance, with efficiency penalties ranging between 2.45% and 2.65% across different load points; while more than 98% of carbon oxides were converted, unburned hydrocarbon reduction was about 70%.
Moving forward, it has been planned to make the developed UVATZ RCCI model faster and to improve its predictive capability. Our step-by-step approach to adding more details to the model helps to prepare a detailed and accurate engine model which has high accuracy in predicting combustion and emission specifications considering the effectiveness of various engine system parts such as various types of aftertreatments, turbochargers, etc.