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The Swedish Biomimetics 3000's μMist® platform technology has been used to develop a radically new injection system. This prototype system, developed and characterized with support from Lotus, as part of Swedish Biomimetics 3000®'s V₂IO innovation accelerating model, delivers improved combustion efficiency through achieving exceptionally small droplets, at fuel rail pressures far less than conventional GDI systems and as low as PFI systems. The system gives the opportunity to prepare and deliver all of the fuel load for the engine while the intake valves are open and after the exhaust valves have closed, thereby offering the potential to use advanced charge scavenging techniques in PFI engines which have hitherto been restricted to direct-injection engines, and at a lower system cost than a GDI injection system.

The importance of new technologies to improve the performance and fuel economy of internal combustion engines is now widely recognized and is essential to achieve CO₂ emissions targets and energy security. Increased hybridization, combustion improvements, friction reduction and ancillary developments are all playing an important part in achieving these goals. Turbocharging technology is established in the diesel engine field and will become more prominent as gasoline engine downsizing is more widely introduced to achieve significant fuel economy improvements. The work presented here introduces, for the first time, a new technology that applies conventional turbomachinery hardware to depressurize the exhaust system of almost any internal combustion engine by novel routing of the exhaust gases. The exhaust stroke of the piston is exposed to this low pressure leading to reduced or even reversed pumping losses, offering ≻5% increased engine torque and up to 5% reduced fuel consumption.

New European emissions legislation (Euro5) specifies a limit for Particle Number (PN) emissions and therefore drives measurement of PN during vehicle development and homologation. Concurrently, the use of biofuel is increasing in the marketplace, and Euro5 specifies that reference fuel must contain a bio-derived portion. Work was carried out to test the effect of fuels containing different levels of Fatty Acid Methyl Ester (FAME) on particle number, size, mass and composition. Measurements were conducted with a Cambustion Differential Mobility Spectrometer (DMS) to time-resolve sub-micron particles (5-1000nm), and a Horiba Solid Particle Counting System (SPCS) providing PN data from a Euro5-compliant measurement system. To ensure the findings are relevant to the modern automotive business, testing was carried out on a Euro4 compliant passenger car fitted with a high-pressure common-rail diesel engine and using standard homologation procedures.

The established method of clearing a misted car windshield or of maintaining a clear view under misting conditions is through the application of an air supply via jet outlets in the instrument panel. The ability of such arrangements to perform adequately is a function of the prevailing environmental conditions, the vehicle speed, the condition of the demist air source and the geometry and arrangement of the jet outlets. This paper presents experimental data obtained in a purpose built environmental chamber designed to accommodate simple rectangular jets impinging on a misted glass surface. The facility consists of three conditioned air sources applied to a test chamber designed to represent the external, internal and demist air flows. Mist conditions on the glass surface are determined using a novel technique employing a CCD camera acquiring grey scale images which are digitally analysed to generate mist detection, grading and clearing contour data.

Particle Image Velocimetry has developed over the last decade into a relatively mature flow-field measurement technique, capable of providing insight into time averaged and instantaneous flows that in the past have not been readily accessible. The application of the method in the measurement and analysis of flows around road vehicles has so far been limited to a relatively small number of specialist applications, but its use is expanding. This paper reviews the modern digital PIV technique placing emphasis on the important considerations required to obtain reliable and accurate data. This includes comments on each aspect of the PIV process, including initial setup and image acquisition, processing, validation and analysis. A number of automotive case studies are presented covering different aspects of the method, including a diffuser exit flow, edge radius optimization, ‘A’ pillar flow and aerial wake flows.

In this paper the effect of in-cylinder crevices formed by the piston cylinder clearance, above the first ring, and the spark plug cavity, on the entrapment of unburned fuel air mixture during the late compression, expansion and exhaust phases of a spark ignition engine cycle, have been simulated using the Computational Fluid Dynamic (CFD) code KIVA II. Two methods of fuelling the engine have been considered, the first involving the carburetion of a homogeneous fuel air mixture, and the second an attempt to simulate the effects of manifold injection of fuel droplets into the cylinder. The simulation is operative over the whole four stroke engine cycle, and shows the efflux of trapped hydrocarbon from crevices during the late expansion and exhaust phases of the engine cycle.

Stringent IC engine PM emission regulation requires development of future filter substrate materials to achieve high filtration efficiency, low filter pressure drop, low cost and highly durable solutions. Monolithic wall flow filters perform well as they achieve high filtration efficiency due to the formation of the PM cake structure while maintaining low substrate face velocities due to the large filtration area. Within the process industry, Micropore™ slotted metallic membrane filters offer both large surface areas and low filter pressure drops while maintaining the durability of metal substrates. The pore structure and pore arrangement can be readily tailored to suit specific applications. This paper characterizes a 300 μm thickness Micropore™ metallic membrane with slots of 10 μm by 400 μm in size in the context of application as an engine exhaust particulate filter. The investigation was based on single layer of Micropore™ slotted metallic membrane with size of 52 mm in diameter.

Crankcase emissions are a complex mixture of combustion products and, specifically Particulate Matter (PM) from lubricant oil. Crankcase emissions contribute substantially to the particle mass and particle number (PN) emitted from an internal combustion engine. Environmental legislation demands that the combustion and crankcase emissions are either combined to give a total measurement or the crankcase gases are re-circulated back into the engine, both strategies require particle filtration. There is a lack of understanding regarding the physical processes that generate crankcase emissions of lubricant oil, specifically how the bulk lubricant oil is atomised into droplets. In this paper the crankcase of a motored compression ignition engine, has been optically accessed to visualise the lubricant oil distribution. The oil distribution was analysed in detail using high speed laser diagnostics, at engine speeds up to 2000 rpm and oil temperatures of 90°C.

The intrinsic model accuracy limit of a commonly used Exhaust Gas Recirculation (EGR) mass flow rate model in diesel engine air path control is discussed in this paper. This EGR mass flow rate model is based on the flow of a compressible ideal gas with unchanged specific heat ratio through a restriction cross-area within a duct. A practical identification procedure of the model parameters is proposed based on the analysis of the engine data and model structure. This procedure has several advantages which include simplicity, low computation burden and low engine test cost. It is shown that model tuning requires only an EGR valve sweep test at a few engine steady state operating points.

In modern production diesel engine control systems, fuel path control is still largely conducted through a system of tables that set mode, timing and injection quantity and with common rail systems, rail pressure. In the hands of an experienced team, such systems have proved so far able to meet emissions standards, but they lack the analytical underpinning that lead to systematic solutions. In high degree of freedom systems typified by modern fuel injection, there is substantial scope to deploy optimising closed loop strategies during calibration and potentially in the delivered product. In an optimising controller, a digital algorithm will explicitly trade-off conflicting objectives and follow trajectories during transients that continue to meet a defined set of criteria. Such an optimising controller must be based on a model of the system behaviour which is used in real time to investigate the consequences of proposed control actions.

The recent development of diesel engine fuel injection systems has been dominated by how to manage the degrees of freedom that common rail multi-pulse systems now offer. A number of production engines already use four injection events while in research, work based on up to eight injection events has been reported. It is the degrees of freedom that lead to a novel experimental requirements. There is a potentially complex experimental program needed to simply understand how injection parameters influence the combustion process in steady state. Combustion behavior is not a continuum and as both injection and EGR rates are adjusted, distinct combustion modes emerge. Conventional calibration processes are severely challenged in the face of large number of degrees of freedom and as a consequence new development approaches are needed.

More and more stringent emission regulations require advanced control technologies for combustion engines. This goes along with increased monitoring requirements of engine behaviour. In case of emissions behaviour and fuel consumption the actual combustion efficiency is of highest interest. A key parameter of combustion conditions is the in-cylinder pressure during engine cycle. The measurement and detection is difficult and cost intensive. Hence, modelling of in-cylinder conditions is a promising approach for finding optimum control behaviour. However, on-line controller design requires real-time scenarios which are difficult to model and current modelling approaches are either time consuming or inaccurate. This paper presents a new approach of in-cylinder condition prediction. Rather than reconstructing in-cylinder pressure signals from vibration transferred signals through cylinder heads or rods this approach predicts the conditions.

Low temperature diesel combustion (LTC) exhibits ultra low NOx and smoke emissions, but currently it has the problems of increased CO and THC emissions, and higher combustion instability compared to conventional diesel combustion. This study evaluated the effects of fuel injection parameters on combustion stability in a single cylinder research diesel engine running at low and intermediate speeds and loads under LTC operating conditions. The LTC operation was achieved using high rates of EGR. In this work, the fuel injection timing and injection pressure were varied to investigate their effects on combustion stability at fixed engine speed and total fuel quantity. The cylinder pressure and THC emissions were measured during the tests. The THC emissions and the coefficient of variability of IMEP (CoV(IMEP)) were used to assess combustion stability. The relationship between these two parameters was also evaluated.

The in cylinder air motion, fuel air mixing, evaporation, combustion and exhaust emissions have been simulated for a four stroke direct injection gasoline engine using the KIVA II code. A strong controlled tumbling air motion was created in the cylinder, through a combination of a conventional pentroof four valve cylinder head, in conjunction with a piston having a stepped crown and offset combustion bowl. A range of injection strategies were employed to optimise combustion rate and exhaust emission (NOx and unburned hydrocarbons (fuel)), at two operating conditions - one with a stoichiometric air fuel mixture and the other with a lean mixture of 30:1 air/fuel ratio. Injection directed towards the piston bowl with a hollow cone jet, in a single pulse, has shown the best results regarding burned mass fraction and level of unburned HC. Fuel concentration, air motion, combustion characteristics and pollutants level are presented for lean and stoichiometric cases.

Variable compression ratio in conjunction with a control system is an effective way to improve performance and reduce emissions in a diesel engine. There are various methods that may be employed that include geometry changes and varying valve timing to change the effective compression ratio. In this paper, a simulation study is presented that is based on a modern, multi-cylinder, fixed compression ratio diesel engine equipped with exhaust gas recirculation (EGR) and a variable geometry turbocharger (VGT). The engine is represented using the GT-Power code, and includes a predictive combustion model. The aim of the investigation is to identify the impact of variable compression ratio on fuel economy and emission reduction and whether realistic optimal conditions exist. This paper describes how a formal design of experiments procedure is used to define the simulation conditions. Cost functions are defined with different weights for fuel consumption, NOx and soot emissions.

One of the most critical challenges currently facing the diesel engine industry is how to improve fuel economy under emission regulations. Improvement in fuel economy can be achieved by precisely controlling Air/Fuel ratio and by monitoring fuel consumption in real time. Accurate and repeatable measurements of fuel rate play a critical role in successfully controlling air/fuel ratio and in monitoring fuel consumption. Volumetric and gravimetric measurements are well-known methods for measuring fuel consumption of internal combustion engines. However, these methods are not suitable for obtaining fuel flow rate data used in real-time control/measurement. In this paper, neural networks are used to solve the problem concerning discontinuous data of fuel flow rate measured by using an AVL 733 s fuel meter. The continuous parts of discontinuous fuel flow rate are used to train and validate a neural network, which can then be used to predict the discontinuous parts of the fuel flow rate.

The integration of selective catalytic reduction catalysts (SCR) into diesel particulate filters (DPF) as a way to treat nitrogen oxides (NOx) and particulate matter (PM) emission is an emerging technology in diesel exhaust aftertreatment. This is driven by ever-tightening limits on NOx and PM emission. In an integrated SCR-in-DPF (also known as SCRF®, SCR-on-DPF, SDPF, or SCR coated filter), the SCR catalyst is impregnated within the porous walls of the DPF. The compact, low weight/volume of the integrated unit provides improvement in the diesel engine cold start emission performance. Experimental investigations have shown comparable performance with standard SCR and DPF units for NOx conversion and PM control, respectively. The modelling of the integrated unit is complicated.

In order to identify predictive models for a diesel engine combustion process, combustion cylinder pressure together with other fuel path variables such as rail pressure, injector current and sleeve pressure of 1000 continuous cycles were sampled and collected at high resolution. Using these engine steady state test data, three types of modeling approach have been studied. The first is the Auto-Regressive-Moving-Average (ARMA) model which had limited prediction ability for both peak combustion pressure (Pmax) and Indicated Mean Effective Pressure (IMEP). By applying correlation analysis, proper inputs were found for a linear predictive model of Pmax and IMEP respectively. The prediction performance of this linear model is excellent with a 30% fit number for both Pmax and IMEP. Further nonlinear modeling work shows that even a nonlinear Neural Network (NN) model does not have improved prediction performance compared to the linear predictive model.