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Ammonia is a promising carbon-free alternative fuel for use in combustion systems. The main associated challenges are its relatively low reactivity and high NOx emissions compared to conventional fuels. Therefore, the combustion behaviour of ammonia and ammonia blends still needs to be better understood over a wide range of conditions. To this end, a comprehensive chemical kinetic mechanism C3MechV3.4, which is an update of C3MechV3.3, has been developed for improved predictions of the combustion of ammonia and ammonia blends. C3MechV3.4 has been validated using a wide range of experimental results for pure ammonia and ammonia/hydrogen, ammonia/methanol and ammonia/n-heptane blends. These validations target different data sets including ignition delay times, species profiles measured as a function of time, and/or temperature and laminar flame speeds over a wide range of conditions.

Combustion in any engine starts with the injection of fuel into the combustion chamber. Atomization of fuel and its mixing plays a vital role in determining the suitable air-fuel (A/F) ratio. Appropriate A/F ratio determines the amount of energy release and pollutant formation for standard engines. Thus an accurate prediction of these processes is required to perform reliable combustion and pollutant formation simulations. In this study, the Eulerian-Lagrangian Spray Atomization (ELSA) model is implemented as a Computational Fluid Dynamics (CFD) tool for the prediction of spray behavior. Past studies performed on diesel fuel suggest good agreement between experiment and simulation indicating the model’s capability. The study aims to validate the ELSA model for gasoline fuel against the test results obtained from Renault and against the pure Lagrangian spray model. The simulations have been performed using CONVERGE CFD v2.4.18.

This paper touches on the mass transport phenomenon in the exhaust gas recirculation (EGR) of a gasoline engine air path. It presents the control-oriented model and control design of the burned gas ratio (BGR) transport phenomenon, witnessed in the intake path of an internal combustion engine (ICE), due to the redirection of burned gases to the intake path by the low-pressure EGR (LP-EGR). Based on a nonlinear AMESim® model of the engine, the BGR in the intake manifold is modeled as a state-space (SS) output time-delay model, or alternatively as an ODE-PDE coupled system, that take into account the time delay between the moment at which the combusted gases leave the exhaust manifold and that at which they are readmitted in the intake manifold. In addition to their mass transport delay, the BGRs in the intake path are also subject to state and input inequality constraints.

The Reynolds stress turbulence model (RSM) has been developed to go beyond the Boussinesq hypothesis and to improve turbulence modeling of flows with significant mean streamline curvature and secondary flow. In this paper the RSM in commercial CFD software CONVERGE is tested for its performance and robustness when applying to complex flows. Several validation cases including flow over flat plate, vortex combustor, diesel engine spray and combustion were selected to test the RSM. The swirling flow in vortex combustor, non-reacting but vaporizing ECN Spray A (free jet) and Sandia small bore diesel engine case are used to demonstrate the benefits of the RSM over the widely used RNG k-epsilon model without model tuning. The vortex combustor case shows the RSM can provide good prediction for strong swirling flow. ECN spray A case was used to demonstrate that the RSM can accurately predict the liquid and vapor penetration lengths of a free jet under diesel engine conditions.

The Extended Coherent Flamelet Model (ECFM) is limited to lower order upwinding schemes to minimize the numerical discrepancy between species and tracers, which can lead to inaccurate estimates of the progress variable and consequently negative conditional mass fractions in the burned gases after ignition. The recently developed Species-Based ECFM (SB-ECFM) removes the species tracers from the definition of the progress variable, and allows the use of higher order schemes. In this study, SB-ECFM is coupled with the Imposed Stretch Spark Ignition Model (ISSIM) to simulate a spark-ignition engine, the transparent combustion chamber (TCC) engine. To examine the spatial discretization effect and demonstrate the improvement due to using higher order schemes, Reynolds-Averaged-Navier-Stokes (RANS) simulations performed with a first-order upwinding scheme and a second-order central differencing scheme are compared.

Fluid-Structure Interaction (FSI) simulation approach can be used to simulate a turbocharger. However, this predictive 3D simulation encounters the challenge of a long computational time. The impeller speed can be above 100,000 rpm, and generally a CFD solver limits the maximum movement of the impeller surface per time step. The maximum movement must be a fraction (~0.3) of the cell length, thus the time step will be very small. A Multiple Reference Frame (MRF) approach can reduce computational time by eliminating the need to regenerate the mesh at each time-step to accommodate the moving geometry. A static local reference zone encompassing the impeller is created and the impact of the impeller movement is modeled via a momentum source. However, the MRF approach is not a predictive simulation because the impeller speed must be given by the User. A new simulation approach was introduced that coupled the FSI and MRF approach.

A computational fluid dynamics (CFD) guided combustion system optimization was conducted for a heavy-duty diesel engine running with a gasoline fuel that has a research octane number (RON) of 80. The goal was to optimize the gasoline compression ignition (GCI) combustion recipe (piston bowl geometry, injector spray pattern, in-cylinder swirl motion, and thermal boundary conditions) for improved fuel efficiency while maintaining engine-out NOx within a 1-1.5 g/kW-hr window. The numerical model was developed using the multi-dimensional CFD software CONVERGE. A two-stage design of experiments (DoE) approach was employed with the first stage focusing on the piston bowl shape optimization and the second addressing refinement of the combustion recipe. For optimizing the piston bowl geometry, a software tool, CAESES, was utilized to automatically perturb key bowl design parameters. This led to the generation of 256 combustion chamber designs evaluated at several engine operating conditions.

In order to design vehicles with diminished gCO2/km emissions level, car manufacturers aim at reducing the weight of their vehicles. One of the solutions advocated by the automotive industry consists in the replacement of metallic parts by lighter systems made of polymer reinforced composites. Unfortunately, the numerical simulations set to evaluate the vibratory and acoustic performances of systems made of this kind of materials are often not sufficiently effective and robust so that convincing test/simulation correlations are rarely met. Indeed, for polymer-based materials, numerous parameters affect the vibroacoustic behavior. On the one hand, it is well known that the viscoelastic properties (Storage -Young- and dissipative moduli) of polymers depend on the temperature, loading frequency and sometimes the humidity content.

This paper is a case study from the TESSA project (French funded research program “Transfert des Efforts des Sources Solidiennes Actives”). The general frame of the work was to assess a collaborative design process between a car manufacturer and a major supplier using FE modelling and condensation of structure borne noise sources as an alternative to classic specification method for structure borne sources. Super elements from different FE commercial softwares have been used to assess the reliability of the method, the compatibility of the softwares and, most important, the relevance of applying a blocked force tensor to the component super element to predict the interior contribution of a component which is the originality of this work. The case study is an internal combustion engine cooling module (fan + shroud + exchangers) from VALEO including all assembly details (clips, decoupling elements) modelled under ABAQUS and its integration in a RENAULT Espace under NASTRAN.

A Machine Learning-Genetic Algorithm (ML-GA) approach was developed to virtually discover optimum designs using training data generated from multi-dimensional simulations. Machine learning (ML) presents a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. In the present work, a total of over 2000 sector-mesh computational fluid dynamics (CFD) simulations of a heavy-duty engine were performed. These were run concurrently on a supercomputer to reduce overall turnaround time. The engine being optimized was run on a low-octane (RON70) gasoline fuel under partially premixed compression ignition (PPCI) mode. A total of nine input parameters were varied, and the CFD simulation cases were generated by randomly sampling points from this nine-dimensional input space. These input parameters included fuel injection strategy, injector design, and various in-cylinder flow and thermodynamic conditions at intake valve closure (IVC).

In order to simulate the working process, an accurate description of heat transfer occurring between in-cylinder gases and combustion chamber walls is required, especially regarding thermal efficiency, combustion and emissions, or cooling strategies. Combustion chamber wall heat transfer models are dominated by zero-dimensional semi-empirical models due to their good compromise between accuracy, complexity and computational efficiency. Classic models such as those from Woschni, Annand or Hohenberg are still widely used, despite having been developed on rather ancient engines. While numerous authors have worked on this topic in the past decades, little information can be found concerning the systematic calibration process of heat transfer models. In this paper, a systematic calibration method based on experimental data processing is tested on the complete operating map of a turbocharged GDI engine.

The present paper is concerned with part of the work performed by Renault, IFPEN and Politecnico di Torino within a research project founded by the European Commission. The project has been focused on the development of a dedicated CNG engine featuring a 25% decrease in fuel consumption with respect to an equivalent Diesel engine with the same performance targets. To that end, different technologies were implemented and optimized in the engine, namely, direct injection, variable valve timing, LP EGR with advanced turbocharging, and diluted combustion. With specific reference to diluted combustion, it is rather well established for gasoline engines whereas it still poses several critical issues for CNG ones, mainly due to the lower exhaust temperatures. Moreover, dilution is accompanied by a decrease in the laminar burning speed of the unburned mixture and this generally leads to a detriment in combustion efficiency and stability.

A computational fluid dynamics (CFD) guided combustion system optimization was conducted for a heavy-duty compression-ignition engine with a gasoline-like fuel that has an anti-knock index (AKI) of 58. The primary goal was to design an optimized combustion system utilizing the high volatility and low sooting tendency of the fuel for improved fuel efficiency with minimal hardware modifications to the engine. The CFD model predictions were first validated against experimental results generated using the stock engine hardware. A comprehensive design of experiments (DoE) study was performed at different operating conditions on a world-leading supercomputer, MIRA at Argonne National Laboratory, to accelerate the development of an optimized fuel-efficiency focused design while maintaining the engine-out NOx and soot emissions levels of the baseline production engine.

Fuels in the gasoline auto-ignition range (Research Octane Number (RON) > 60) have been demonstrated to be effective alternatives to diesel fuel in compression ignition engines. Such fuels allow more time for mixing with oxygen before combustion starts, owing to longer ignition delay. Moreover, by controlling fuel injection timing, it can be ensured that the in-cylinder mixture is “premixed enough” before combustion occurs to prevent soot formation while remaining “sufficiently inhomogeneous” in order to avoid excessive heat release rates. Gasoline compression ignition (GCI) has the potential to offer diesel-like efficiency at a lower cost and can be achieved with fuels such as low-octane straight run gasoline which require significantly less processing in the refinery compared to today’s fuels.

Nowadays Spark Ignition (SI) engine developments focus on downsizing, in order to increase the engine load level and consequently its efficiency. As a side effect, knock occurrence is strongly increased. The current strategy to avoid knock is to reduce the spark advance which limits the potential of downsizing in terms of consumption reduction. Reducing the engine propensity to knock is therefore a first order subject for car manufacturers. Engineers need competitive tools to tackle such a complex phenomenon. In this paper the 3D RANS simulations ability to satisfactorily represent knock tendencies is demonstrated. ECFM (Extended Coherent Flame Model) has been recently implemented by IFPEN in CONVERGE and coupled with TKI (Tabulated Kinetics Ignition) to represent Auto-Ignition in SI engine. These models have been applied on a single cylinder engine configuration dedicated to abnormal combustion study.

Engine knock is an important phenomenon that needs consideration in the development of gasoline fueled engines. In our days, this development is supported by the use of numerical simulation tools to further understand and subsequently predict in-cylinder processes. In this work, a model tool chain based on detailed chemical and physical models is proposed to predict the auto-ignition behavior of fuels with different octane ratings and to evaluate the transition from harmless auto-ignitive deflagration to knocking combustion. In our method, the auto-ignition and emissions are calculated based on a new reaction scheme for mixtures of iso-octane, n-heptane, toluene and ethanol (Ethanol consisting Toluene Reference Fuel, ETRF). The reaction scheme is validated for a wide range of mixtures and every desired mixture of the four fuel components can be applied in the engine simulation.

The current trend of downsizing used in gasoline engines, while reducing fuel consumption and CO2 emissions, imposes severe thermal loads inside the combustion chamber. These critical thermodynamic conditions lead to the possible auto-ignition (AI) of fresh gases hot-spots around Top-Dead-Center (TDC). At this very moment where the surface to volume ratio is high, wall heat transfer influences the temperature field inside the combustion chamber. The use of a realistic wall temperature distribution becomes important in the case of a downsized engine where fresh gases hot spots found near high temperature walls can initiate auto-ignition. This paper presents a comprehensive numerical methodology for an accurately prediction of thermodynamic conditions inside the combustion chamber based on Conjugate Heat Transfer (CHT).

An extensive numerical study of two-phase flow inside the nozzle holes and the issuing jets for a multi-hole direct injection gasoline injector is presented. The injector geometry is representative of the Spray G nozzle, an eight-hole counter-bored injector, from the Engine Combustion Network (ECN). Homogeneous Relaxation Model (HRM) coupled with the mixture multiphase approach in the Eulerian framework has been utilized to capture the phase change phenomena inside the nozzle holes. Our previous studies have demonstrated that this approach is capable of capturing the effect of injection transients and thermodynamic conditions in the combustion chamber, by predicting phenomenon such as flash boiling. However, these simulations were expensive, especially if there is significant interest in predicting the spray behavior as well.

Engine downsizing is potentially one of the most effective strategies being explored to improve fuel economy. A main problem of downsizing using a turbocharger is the small range of stable functioning of the turbocharger centrifugal compressor at high boost pressures, and hence the measurement of the performance maps of both compressor and turbine. Automotive manufacturers use mainly numerical simulations for internal combustion engines simulations, hence the need of an accurate extrapolation model to get a complete turbine performance map. These complete maps are then used for internal combustion engines calibration. Automotive manufacturers use commercial softwares to extrapolate the turbine narrow performance maps, both mass flow characteristics and the efficiency curve.

Due to its harmful effect on both human health and environment, soot emission is considered as one of the most important diesel engine pollutants. In the last decades, the industrial engine manufacturers have been able to strongly reduce its engine-out value by many different techniques, in order to respect the stricter emission norms. Simulation modeling has played and continues to play a key role for this purpose in the engine control system development. In this context, this paper proposes a new soot emission model for a direct injection diesel engine. This soot model is based on a zero-dimensional semi-physical approach coupled with a crank-angle resolved combustion model and a thermodynamic calculation of the burned gas products temperature. Furthermore, a multi linear regression model has been used to estimate the soot emissions as function of significant physical combustion parameters.