Search Results (7)

The need for a cleaner and more efficient transportation sector emphasizes the development of new technologies aimed at the integrated reduction of pollutant emissions and increases in efficiency. Among these, promising technologies such as low-temperature combustion (LTC) systems operate in the field of the combustion physics, combining the attributes of both spark-ignited (SI) and compression-ignited (CI) engines. In particular, in a gasoline compression ignition (GCI) engine, gasoline is injected in closely spaced multiple pulses near the top dead center (TDC), creating a highly stratified charge which locally auto-ignites based on the thermodynamic conditions. In this work, a sectorial mesh of the combustion chamber was built. Initial and boundary conditions were set according to a one-dimensional model of the engine from a GT-suite platform. Then, a dedicated Matlab R2023b code was used to capture the effect of the pressure wave propagation on the shape of the fuel mass rate in closely spaced multiple injection events. Finally, a 3D-CFD code was validated comparing pressure trace, rate of heat release (RoHR) and emissions with experimental data provided by the test bench. The results highlight the robustness of the tabulated combustion model, which is able to capture the auto-ignition delay with a considerably low amount of computational time compared to common detailed kinetic solvers. Full article

This study is dedicated to improving the efficiency of the flamelet-generated manifold (FGM) tabulated chemistry combustion modeling approach for predicting the combustion process in diesel-ignited internal combustion (IC) engines. The primary focus is on reducing table generation time and memory requirements. To accurately predict dual-fuel combustion processes, it is important to model both premixed and non-premixed combustion regimes. However, attempting to include both regimes in a single FGM lookup table leads to significant increases in the table size and generation time. In response, this work proposes a dual-table configuration, with each table dedicated to a specific regime. The solution is then interpolated from these tables based on the calculated combustion regime indicator during the computational fluid dynamics (CFD) simulation. This approach optimizes computational efficiency while ensuring an accurate representation of dual-fuel combustion. Additionally, to establish a cost-effective and accurate 3D CFD simulation workflow, the dual-table FGM methodology is coupled with the partially averaged Navier–Stokes (PANS) turbulence model. The feasibility of the proposed FGM methodology is tested utilizing six chemical kinetics mechanisms with different levels of detail. The results of this study demonstrated that the dual-table approach significantly accelerates table generation time and reduces memory requirements compared to a single table that includes both combustion regimes. Furthermore, 3D CFD simulation results of the dual-fuel combustion process are validated against available experimental data for three engine operating points. The in-cylinder pressure traces and rate of heat release obtained from the 3D CFD simulations employing the FGM PANS methodology show good agreement with experimental measurements, confirming the accuracy and reliability of this modeling approach. Full article

Nowadays, an accurate and precise description of the combustion phase is essential in spark-ignition (SI) engines to drastically reduce pollutant and greenhouse gas (GHG) emissions and increase thermal efficiency. To this end, computational fluid dynamics (CFD) can be used to study the different phenomena involved, such as the ignition of the charge, combustion development, and pollutant formation. In this work, a validation of a CFD methodology based on the flame area model (FAM) was carried out to model the combustion process in light-duty SI engines fueled with natural gas. A simplified spherical kernel approach was used to model the ignition phase, whereas turbulent flame propagation was described through two variables. A zero-dimensional evolution of the flame kernel radius was used in combination with the Herweg and Maly formulation to take the laminar-to-turbulent flame transition into account. To estimate the chemical composition of burnt gas, two different approaches were considered—one was based on tabulated kinetics, and the other was based on chemical equilibrium. Assessment of the combustion model was first performed by using different operating points of a light-duty SI engine fueled with natural gas and by using the original piston. The results were validated by using experimental data on the in-cylinder pressure, apparent heat release rate, and pollutant emissions. Afterward, two other different piston bowl geometries were investigated to study the main differences between one solution and the others. The results showed that no important improvements in terms of combustion efficiency were obtained by using the new piston bowl shapes, which was mainly due to the very low ($+4\%$) or null increase in turbulent kinetic energy during the compression stroke and due to the higher heat losses ($+20\%$) associated with the increased surface area of the new piston geometries. Full article

Homogeneous charge compression ignition is considered a promising solution to face the increasing regulations imposed by the legislator in the transport sector, thanks to pollutant and CO₂ emissions reduction. In this work, a quasi-dimensional multi-zone HCCI model integrated with 1D commercial software is developed and validated. It is based on the control mass Lagrangian approach and computes the mixture chemistry evolution through offline tabulation of chemical kinetics (tabulated kinetic of ignition). Thus, the simulation can predict mixture auto-ignition with reduced computational effort and high accuracy. Multi-zone schematization mimics the typical thermal stratification of HCCI engines, controlling the combustion evolution. The model is coupled to sub-models for pollutant emissions estimation. Initially, the tabulated chemistry approach is validated against a chemical kinetics solver applied to a constant-volume homogeneous reactor, considering various fuel blends. The model is then used to simulate the operations of four engines using different fuels (hydrogen, methane, n-heptane, and n-heptane/toluene/ethanol blend), under various boundary conditions. The model predictivity is demonstrated against pressure traces, heat release rate, and noxious emissions. The numerical results showed to adequately agree with measured counterparts (average relative error of 1.3% on in-cylinder pressure peak, average absolute error of 0.95 CAD on pressure peak angle, average relative error of 8.4% on uHCs emissions, absolute error below 1 ppm on NOx emissions) only adapting the thermal stratification to the engines under study. The methodology proved to be a reliable tool to investigate the operation of an HCCI engine, applicable in the development of new engine architecture. Full article

In this work, a data-driven methodology for modeling combustion kinetics, Learned Intelligent Tabulation (LIT), is presented. LIT aims to accelerate the tabulation of combustion mechanisms via machine learning algorithms such as Deep Neural Networks (DNNs). The high-dimensional composition space is sampled from high-fidelity simulations covering a wide range of initial conditions to train these DNNs. The input data are clustered into subspaces, while each subspace is trained with a DNN regression model targeted to a particular part of the high-dimensional composition space. This localized approach has proven to be more tractable than having a global ANN regression model, which fails to generalize across various composition spaces. The clustering is performed using an unsupervised method, Self-Organizing Map (SOM), which automatically subdivides the space. A dense network comprised of fully connected layers is considered for the regression model, while the network hyper parameters are optimized using Bayesian optimization. A nonlinear transformation of the parameters is used to improve sensitivity to minor species and enhance the prediction of ignition delay. The LIT method is employed to model the chemistry kinetics of zero-dimensional ${\mathrm{H}}_2–{\mathrm{O}}_2$ and ${\mathrm{CH}}_4$-air combustion. The data-driven method achieves good agreement with the benchmark method while being cheaper in terms of computational cost. LIT is naturally extensible to different combustion models such as flamelet and PDF transport models. Full article

The use of chemical kinetic mechanisms in computer aided engineering tools for internal combustion engine simulations is of high importance for studying and predicting pollutant formation of conventional and alternative fuels. However, usage of complex reaction schemes is accompanied by high computational cost in 0-D, 1-D and 3-D computational fluid dynamics frameworks. The present work aims to address this challenge and allow broader deployment of detailed chemistry-based simulations, such as in multi-objective engine optimization campaigns. A fast-running tabulated chemistry solver coupled to a 0-D probability density function-based approach for the modelling of compression and spark ignition engine combustion is proposed. A stochastic reactor engine model has been extended with a progress variable-based framework, allowing the use of pre-calculated auto-ignition tables instead of solving the chemical reactions on-the-fly. As a first validation step, the tabulated chemistry-based solver is assessed against the online chemistry solver under constant pressure reactor conditions. Secondly, performance and accuracy targets of the progress variable-based solver are verified using stochastic reactor models under compression and spark ignition engine conditions. Detailed multicomponent mechanisms comprising up to 475 species are employed in both the tabulated and online chemistry simulation campaigns. The proposed progress variable-based solver proved to be in good agreement with the detailed online chemistry one in terms of combustion performance as well as engine-out emission predictions (CO, CO₂, NO and unburned hydrocarbons). Concerning computational performances, the newly proposed solver delivers remarkable speed-ups (up to four orders of magnitude) when compared to the online chemistry simulations. In turn, the new solver allows the stochastic reactor model to be computationally competitive with much lower order modeling approaches (i.e., Vibe-based models). It also makes the stochastic reactor model a feasible computer aided engineering framework of choice for multi-objective engine optimization campaigns. Full article

This paper investigates, through computational fluid dynamics (CFD) simulations, the knock resistance improvements that can be obtained in a turbo-charged GDI engine with water injection. In a first step, water and gasoline injector models are validated comparing the results with experimental data from constant volume chamber tests. Then, multi-cycle simulations are performed using the G-equation turbulent combustion model focusing on spray evolution and wall film dynamics. The main intent is analyzing the effectiveness of different water injection timings and injection pressures in a port water injection (PWI) installation. Combustion rates are validated against experimental engine data, with and without water injection. Afterwards, in order to predict autoignition behavior with different spark advance (SA) timings, the extended coherent flamelet model (ECFM) combined with a tabulated kinetic ignition (TKI) dataset is used. End-gas autoignition delays are calculated using a reduced mechanism for toluene primary reference fuel (TPRF), which revealed essential for capturing actual gasoline ignition characteristics. Results indicate that the water atomization quality, i.e., injection pressure, is significant in a PWI installation allowing a reduction of the water wall film formation in the ports. Water injection timing needs also to be carefully chosen for optimized performance. As the injected water allows the SA to be increased, the overall benefits on indicated mean effective pressure and fuel consumption are quantified under the same knock safety margin, matching adequately well the available measurements. Full article