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The structure of HCCI (homogeneous charge compression ignition) combustion flames was quantitatively analyzed by measuring the two-dimensional gas temperature distribution using phosphor thermometry. It was found from the relation between a turbulent Reynolds number and Karlovitz number that, when compared with the flame propagation in an S.I. engine, HCCI combustion has a wider flame structure with respect to the turbulence scale. As a result of our experimentation for the influence of low temperature reaction (LTR) using two types of fuel, it was also confirmed that different types of fuel produce different histories of flame kernel structure.

To date, the universal metric for road safety has been historical crash data, specifically, crash frequency and severity, which are direct measures of safety. However, there are well-recognized shortcomings of the crash-based approach; its greatest drawback being that it is reactive and requires long observational periods. Surrogate measures of safety, which encompass measures of safety that do not rely on crash data, have been proposed as a proactive approach to road safety analysis. This white paper provides an overview of the concept and evolution of surrogate measures of safety, as well as the emerging and future methods and measures. This is followed by the identification of the standards needs in this discipline as well as the scope of SAE’s Surrogate Measures of Safety Committee.

Modern diesel engines are equipped with an increasing number of actuators set to improve human comfort and fuel consumptions while respecting the restricted emissions regulations. In spite of the great progress made in the electronic and data-processing domains, the physical-based emissions models remain time consuming and too complicated to be used in a dynamic calibrating process. Therefore, until these days, the calibration of the engine's cartographies is done manually by experimental experts on dynamic test bed, but the results are not often the best compromise in the consumption-emissions formula due to the increasing number of actuators and to the nonlinear and complex relations between the different variables involved in the combustion process. Recently, neural networks are successfully used to model dynamic multiple inputs - multiple outputs processes by learning from examples and without any additional or detailed information about the process itself.

Current developments in fuels and emissions regulations are resulting in increasingly severe operating environment for the injection system. Formation of deposits within the holes of the injector nozzle or on the outside of the injector tip may have an adverse effect on overall system performance. This paper provides a critical review of the current understanding of the main factors affecting deposit formation. Two main types of engine test cycles, which attempt to simulate field conditions, are described in the literature. The first type involves cycling between high and low load. The second involves steady state operation at constant speed either at medium or high load. A number of influences on the creation of deposits are identified. This includes fouling through thermal condensation and cracking reactions at nozzle temperatures of around 300°C. Also the design of the injector holes is an influence, because it can influence cavitation.

A consortium of interested parties has conducted an experimental characterization of two Tau parallel kinematic machines which were built as a part of the EU-funded project, SMErobot1. Characteristics such as machine stiffness, work envelope, repeatability and accuracy were considered. This paper will present a brief history of the Tau parallel machine, the results of this testing and some comment on prospective application to the aerospace industry.

As the number of actuators and sensors increases in modern combustion engines, the task of optimizing engine performance becomes increasingly complex. Efficient information processing techniques are therefore important, both for off-line calibration of engine maps, and on-line adjustments based on sensor data. In-cylinder pressure sensors are slowly spreading from laboratory use to production engines, thus making data with high temporal resolution of the combustion process available. The standard way of using the cylinder pressure data for control and diagnostics is to focus on a few important physical features extracted from the pressure trace, such as the combustion phasing CA50, the indicated mean effective pressure IMEP, and the ignition delay. These features give important information on the combustion process, but much information is lost as the information from the high-resolution pressure trace is condensed into a few key parameters.

In the truck industry there is a continuous demand to increase the efficiency and to decrease the emissions. To acknowledge both these issues a waste heat recovery system (WHR) is combined with a partially premixed combustion (PPC) engine to deliver an efficient engine system. Over the past decades numerous attempts to increase the thermal efficiency of the diesel engine has been made. One such attempt is the PPC concept that has demonstrated potential for substantially increased thermal efficiency combined with much reduced emission levels. So far most work on increasing engine efficiency has been focused on improving the thermal efficiency of the engine while WHR, which has an excellent potential for another 1-5 % fuel consumption reduction, has not been researched that much yet. In this paper a WHR system using a Rankine cycle has been developed in a modeling environment using IPSEpro.