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This paper demonstrates how the validation and verification phase of prototype development can be simplified through the application of the Model Development Suite (MoDS) software by integrating advanced statistical and numerical techniques. The authors have developed and present new numerical and software integration methods to support a) automated model parameter estimation (model calibration) with respect to experimental data and, b) automated global sensitivity analysis through using a High Dimensional Model Representation (HDMR). These methods are demonstrated at 1) a component level by performing systematic parameter estimation of various friction models for heavy-duty IC engine applications, 2) at a sub-component level by performing a parameter estimation for an engine performance model, and 3) at a system level for evaluating fuel efficiency losses (and CO2 sources) in a vehicle model over 160 ‘real-world’ and legislated drive cycles.

In recent years, there has been rapid progress in characterizing the detailed chemical kinetics associated with the oxidation of liquid hydrocarbons and their blends. However adding these fuel models to the industrial engineer's toolkit has proven a major challenge due to issues associated with high CPU cost and the poor suitability of many of the most promising and well known fuel models to IC engine applications. This paper demonstrates the state-of-the-art in the analysis and modelling of current and future transportation fuels or fuel blends for internal combustion engine applications. First-of-all, a benchmarking of eleven representative fuel models (39 to 1034 species in size) is carried out at engine/engine-like operating conditions by adopting the standard Research Octane and Cetane Number test data for comparison. Next, methods to construct a fuel model for a commercial fuel are outlined using a simple, yet robust surrogate mapping technique.

This paper builds upon recent publication (SAE Technical Paper 2011-01-1388, 2011, doi:10.4271/2011-01-1388) and outlines the on-going development of an advanced simulator for virtual engine mapping and optimization of engine performance, combustion and emissions characteristics. The model is further advanced through development of new sub-models for turbulent mixing, multiple injection events, variable injection pressures, engine breathing and gas exchange, as well as particulates formation and oxidation. The result is a simulator which offers engine design and performance data typically associated with 1D thermodynamic engine cycle simulations but with the "physics-based" model robustness usually associated with 3D CFD methods. This combination then enables efficient optimization of engine design with respect to engine performance, combustion characteristics and exhaust gas emissions.

This paper describes the development of a novel data model for storing and sharing data obtained from engine experiments, it then outlines a methodology for automatic model development and applies it to a state-of-the-art engine combustion model (including chemical kinetics) to reduce corresponding model parameter uncertainties with respect engine experiments. These challenges are met by adopting the latest developments in the semantic web to create a shared data model resource for the IC engine development community. The relevant data can be extracted and then used to set-up simulations for parameter estimation by passing it to the relevant application models. A methodology for incorporating experimental and model uncertainties into the model optimization procedure is presented.

Employing detailed chemistry into modern engine simulation technologies has potential to enhance the robustness and predictive power of such tools. Specifically this means significant advancements in the ability to compute the onset of ignition, low and high temperature heat release, local extinction, knocking, exhaust gas emissions formation etc. resulting in a set of tools which can be employed to carry out virtual engineering studies and add additional insight into common IC engine development activities such as computing IMEP, identifying safe/feasible operating ranges, minimizing exhaust gas emissions and optimizing operating strategy. However the adoption of detailed chemistry comes at a greater computational cost, this paper investigates the means to retain computational robustness and ease of use whist reducing computational timescales.

Regulations on emissions from diesel and gasoline fuelled engines are becoming more stringent in all parts of the world. Hence there is a great deal of interest in developing advanced combustion systems that offer the efficiency of a diesel engine, but with low PM and NOx. One promising approach is that of Partially-Premixed Compression Ignition (PPCI) or Low Temperature Combustion (LTC). Using this approach, PM can be reduced in compression ignition engines by promoting the mixing of fuel and air prior to combustion. This paper describes the application of an advanced combustion simulator for fuels, combustion and emissions to analyze the key processes which occur in PPCI combustion mode. A detailed chemical kinetic model with advanced PM population balance sub-model is employed in a PPCI engine context to examine the impact of ignition resistance on combustion, mixing, ignition and emissions.

In recent decades, “physics-based” gas-dynamics simulation tools have been employed to reduce development timescales of IC engines by enabling engineers to carry out parametric examinations and optimisation of alternative engine geometry and operating strategy configurations using desktop PCs. However to date, these models have proved inadequate for optimisation of in-cylinder combustion and emissions characteristics thus extending development timescales through additional experimental development efforts. This research paper describes how a Stochastic Reactor Model (SRM) with reduced chemistry can be employed to successfully determine in-cylinder pressure, heat release and emissions trends from a diesel fuelled engine operated in compression ignition direct injection mode using computations which are completed in 147 seconds per cycle.

This research describes the potential to adopt detailed chemical kinetics for practical and potential future fuels using tri-component surrogate mixtures capable of simulating fuel octane “sensitivity” . Since the combustion characteristics of modern fuels are routinely measured using the RON and MON of the fuel, a methodology to generate detailed chemical kinetic mechanisms for these fuels based on these data is presented. Firstly, a novel correlation between various tri-component blends (comprised of i-octane, n-heptane and toluene) and fuel RON and MON was obtained by carrying out standard octane tests. Secondly, a chemical kinetic mechanism for tri-component fuels was validated using a Stochastic Reactor Model (SRM) suite, an in-cylinder engine combustion simulator, and a series of engine experiments conducted in HCCI operating mode.

Analyzing the combustion characteristics, engine performance, and emissions pathways of the internal combustion (IC) engine requires management of complex and an increasing quantity of data. With this in mind, effective management to deliver increased knowledge from these data over shorter timescales is a priority for development engineers. This paper describes how this can be achieved by combining conventional engine research methods with the latest developments in process informatics and statistical analysis. Process informatics enables engineers to combine data, instrumental and application models to carry out automated model development including optimization and validation against large data repositories of experimental data.