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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).

Biofuels are a renewable energy source. When used as extenders for transportation fuels, biofuels contribute to the global reduction of Green House Gas and CO2 emissions from the transport sector and to security and independence of energy supply. On a “Well to Wheel” basis they are much more CO2 efficient than conventional fossil fuels. All vehicles currently in circulation in Europe are capable of using 5 % biodiesel. The introduction of higher percentages biodiesel needs new specific standards and vehicle tests validation. The development of vehicles compatible with 30% biodiesel blends in diesel fuel includes the validation of each part of both engine and fuel vehicle systems to guarantee normal operation for the entire life of the vehicle.

Progress in Diesel spray modelling highly depends on a better knowledge of the instantaneous injection velocity and of the hydraulic section at the exit of each injection hole. Additionally a better identification of the mechanisms which cause fragmentation is needed. This necessitates to begin with a precise computation of the two-phase flow which arises due to the presence of cavitation within the injectors. For that aim, a VOF type interface tracking method has been developed and improved (Segment Lagrangian VOF method) which allows to describe numerically the onset and development of cavitation within Diesel injectors. Furthermore, experiments have been performed for validation purpose, on transparent one-hole injectors for high pressure injection conditions. Two different entrance geometries (straight and rounded) and various upstream and downstream pressure levels have been considered.

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.

To reduce greenhouse gas emissions such as CO2, automakers are actively pursuing alternative propulsion systems. Improvements to current engine technology are being investigated along with new power plant technologies. Fuel Cell Vehicles offer an exciting option by producing electric power through a reaction that combines hydrogen and oxygen to make water. However, hydrogen storage onboard vehicles and construction of an expensive hydrogen distribution and fueling infrastructure remain as challenges today. In addition, greenhouse gas emissions from the production of hydrogen must be considered since most hydrogen is currently produced from non-renewable sources. While these issues are being worked on, Renault has chosen to pursue a fuel cell vehicle with a fuel processor that converts gasoline and other liquid fuels to hydrogen onboard the vehicle.

This paper introduces a research work on the air loop system for a downsized two-stroke two-cylinder diesel engine conducted in framework of the European project dealing with the POWERtrain for Future Light-duty vehicles - POWERFUL. The main objective was to determine requirements on the air management including the engine intake and exhaust system, boosting devices and the EGR system and to select the best possible technical solution. With respect to the power target of 45 kW and scavenging demands of the two-cylinder two-stroke engine with a displacement of 0.73 l, a two-stage boosting architecture was required. Further, to allow engine scavenging at any operation, supercharger had to be integrated in the air loop. Various air loop system layouts and concepts were assessed based on the 1-D steady state simulation at full and part load with respect to the fuel consumption.

Studies using a bi-fuelled (autogas/gasoline) Renault Laguna vehicle meeting °the 1996 European exhaust emission legislation has demonstrated that over the European test cycle at 25°C the LPG operated vehicle provides substantial benefits of reduced emissions compared to unleaded reference gasoline. At lower test temperatures (i.e. 5°C) even larger reduction in emissions have been observed. Lower CO (up to 95% at -5°C and 65% at 25°C), HC (90% at -5°C and 40% at 25°C) emissions and lower ozone HC reactivity have been observed and could all offer significant environmental air-quality benefits for LPG. Various autogas mixtures have been tested including 70/30, 30/70 and 49/30/21 (% mass propane / butane / propene). Results show that NOx emissions for this vehicle appear dependent on autogas composition. The two gas mixtures containing only 30% butane gave about 50% more NOx at +25°C than the 70% butane autogas mixture.

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.

Interest in operational cost reduction is driving engine manufacturers to consider low-cost fuel substitution in heavy-duty diesel engines. These dual-fuel (DF) engines could be operated either in diesel-only mode or operated with premixed natural gas (NG) ignited by a pilot flame of compression-ignited direct-injected diesel fuel. Under certain conditions, dual-fuel operation can result in increased cycle-to-cycle variability (CCV) during combustion. CFD can greatly help in understanding and identifying critical parameters influencing CCV. Innovative modelling techniques and large computing resources are needed to investigate the factors affecting CCV in dual-fuel engines. This paper discusses the use of the High Performance Computing resource Titan, at Oak Ridge National Laboratory, to investigate CCV of a dual-fuel engine.

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.

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.

Electrical Low Pressure Impactor (ELPI) is often employed to measure the particle number and size distribution of internal combustion engines exhaust gas. If appropriate values of particle density are available, the particle mass can be estimated by this method. Exhaust particles of three Euro3 passenger cars (one gasoline operating under stoichiometric conditions, one Diesel and one Diesel equipped with Diesel Particulate Filter) are measured using the current European regulations (gravimetric method on the are New European Driving Cycle) and estimated by ELPI particle number and size distribution. Different values for particle density are used to estimate the particle mass using all ELPI stages or only some of them. The results show that the particle mass estimated by ELPI is well correlated with the mass determined by filters for PM emissions higher than 0.025 g/km. This correlation is not very good at lower emissions.

The Computational Chemistry Consortium (C3) is dedicated to leading the advancement of combustion and emissions modeling. The C3 cluster combines the expertise of different groups involved in combustion research aiming to refine existing chemistry models and to develop more efficient tools for the generation of surrogate and multi-fuel mechanisms, and suitable mechanisms for CFD applications. In addition to the development of more accurate kinetic models for different components of interest in real fuel surrogates and for pollutants formation (NOx, PAH, soot), the core activity of C3 is to develop a tool capable of merging high-fidelity kinetics from different partners, resulting in a high-fidelity model for a specific application. A core mechanism forms the basis of a gasoline surrogate model containing larger components including n-heptane, iso-octane, n-dodecane, toluene and other larger hydrocarbons.

Fuel consumption in internal combustion engines and their associated CO2 emissions have become one of the major issues facing car manufacturers everyday for various reasons: the Kyoto protocol, the upcoming European regulation concerning CO2 emissions requiring emissions of less than 130g CO2/km before 2012, and customer demand. One of the most efficient solutions to reduce fuel consumption is to downsize the engine and increase its specific power and torque by using turbochargers. The engine and the turbocharger have to be chosen carefully and be finely tuned. It is essential to understand and characterise the turbocharger's behaviour precisely and on its whole operating range, especially at low engine speeds. The characteristics at low speed are not provided by manufacturers of turbochargers because compressor maps cannot be achieve on usual test bench.

The internal combustion engine (ICE) has long dominated the heavy-duty sector by using liquid fossil fuels such as diesel but global commitments by countries and OEMs to reduce lifecycle carbon dioxide (CO2) emissions has garnered interest in alternative fuels like hydrogen. Hydrogen is a unique gaseous fuel that contains zero carbon atoms and has desired thermodynamic properties of high energy density per unit mass and high flame speeds. However, there are challenges related to its adoption to the heavy-duty sector as a drop-in fuel replacement for compression ignition (CI) diesel combustion given its high autoignition resistance. To overcome this fundamental barrier, engine manufacturers are exploring dual fuel combustion engines by substituting a fraction of the diesel fuel with hydrogen which enables fuel flexibility when there is no infrastructure and retrofittability to existing platforms.

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).

The standard capability of engine experimental studies is that ensemble averaged quantities like in-cylinder pressure from multiple cycles and emissions are reported and the cycle to cycle variation (CCV) of indicated mean effective pressure (IMEP) is captured from many consecutive combustion cycles for each test condition. However, obtaining 3D spatial distribution of all the relevant quantities such as fuel-air mixing, temperature, turbulence levels and emissions from such experiments is a challenging task. Computational Fluid Dynamics (CFD) simulations of engine flow and combustion can be used effectively to visualize such 3D spatial distributions. A dual fuel engine is considered in the current study, with manifold injected natural gas (NG) and direct injected diesel pilot for ignition.

The accurate modeling of fuel spray behavior under diesel engine conditions requires well-characterized boundary conditions. Among those conditions, the spray cone angle is important due to its impact on the spray mixing process, flame lift-off locations and subsequent soot formation. The spray cone angle is a highly dynamic variable, but existing correlations have been developed mainly for diesel fuels at quasi-steady state and relatively low injection pressures. The objective of this study was to develop spray cone angle correlations for both diesel and a light-end gasoline fuel over a wide range of diesel-engine operating conditions that are capable of capturing both the transient and quasi-steady state processes. Two important macroscopic characteristics of solid cone sprays, the spray cone angle and spray penetration, were measured using a single-hole heavy-duty injector using two fuels at diesel engine conditions in an optical constant volume vessel.

The Particulate Measurement Programme (PMP) works on the identification of a method to replace or complete the existing particle mass (PM) measurement method. The French PMP subgroup, composed by IFP, PSA Peugeot-Citroën, Renault and UTAC, proposes an improved gravimetric method for the measurement of emitted particles, and conducted an inter-laboratory test to evaluate its performances. The technical programme is based on tests carried out on a Euro3 Diesel passenger car (PC), tested on the New European Driving Cycle (NEDC). To achieve low particulate matter (PM) emissions, the EGR is disconnected and a paraffinic fuel is used. The regulated pollutants are also measured. It is shown that the multiple filter weighing and a 0.1 μg balance instead of a 1 μg one are not necessary, as the first weighing and the 1 μg balance performances are satisfactory for type-approval purposes.

Reduction of pollutants and greenhouse gas emissions is one of the main objectives of car manufacturers and innovative solutions have to be considered to achieve this goal. Electric vehicles, and in particular Fuel Cell Electric Vehicles, appear to be a promising alternative. Renault is therefore investigating the technical and economic viability of a Fuel Cell Electric Vehicle (FCEV). A basic question of this study is the choice of the fuel that will be used for this kind of vehicle. Liquid fuels such as gasoline, diesel, naphtha, and gas-to-liquid can be a bridge for the introduction of fuel cell technologies while hydrogen infrastructure and storage are investigated. Therefore, multi-fuel Fuel Processor Systems that can convert liquid fuels to hydrogen while meeting automotive constraints are desired. Renault and Nuvera have joined forces to tackle this issue in a 3-year program where the objective is to develop and to integrate a Fuel Processor System (FPS) on a vehicle.