Application of auto-associative neural networks to transient fault detection in an IC engine

The tailpipe emissions from automotive engines have been subject to steadily reducing legislative limits. This reduction has been achieved through the addition of sub-systems to the basic four-stroke engine which thereby increases its complexity. To ensure the entire system functions correctly, each system and / or sub-systems needs to be continuously monitored for the presence of any faults or malfunctions. This is a requirement detailed within the On-Board Diagnostic (OBD) legislation. To date, a physical model approach has been adopted by me automotive industry for the monitoring requirement of OBD legislation. However, this approach has restrictions from the available knowledge base and computational load required. A neural network technique incorporating Multivariant Statistical Process Control (MSPC) has been proposed as an alternative method of building interrelationships between the measured variables and monitoring the correct operation of the engine. Building upon earlier work for steady state fault detection, this paper details the use of non-linear models based on an Auto-associate Neural Network (ANN) for fault detection under transient engine operation. The theory and use of the technique is shown in this paper with the application to the detection of air leaks within the inlet manifold system of a modern gasoline engine whilst operated on a pseudo-drive cycle. Copyright © 2007 by ASME.

Steady-State and Transient Performance of Biodiesel-Fueled Compression-Ignition-Based Electrical Generation

This paper investigates the performance characteristics of rapeseed methyl ester, EN 14214 biodiesel, when used for electrical generation in compression ignition engines. The work was inspired by the need to replace fossil diesel fuel with a sustainable low carbon alternative while maintaining generator performance, power quality, and compliance with ISO 8528-5. A 50-kVA Perkins diesel engine generator was used to assess the impact of biodiesel with particular regard to gen-set fuel consumption, load acceptance, and associated standards. Tests were performed on the diesel gen-set for islanded and grid-connected modes of operation, hence both steady-state and transient performance were fully explored. Performance comparisons were made with conventional fossil diesel fuel, revealing minimal technical barriers for electrical generation from this sustainable, carbon benign fuel. Recommendations for improved transient performance are proposed and validated through tests.

Project Omnivore: A Variable Compression Ratio ATAC 2-Stroke Engine for Ultra-Wide-Range HCCI Operation on a Variety of Fuels

The paper describes the principal features of Omnivore, a spark-ignition-based research engine designed to investigate the possibility of true wide-range HCCI operation on a variety of fossil and renewable liquid fuels. The engine project is part-funded jointly by the United Kingdom's Department for the Environment, Food and Rural Affairs (DEFRA) and the Department of the Environment of Northern Ireland (DoENI). The engineering team includes Lotus Engineering, Jaguar Cars, Orbital Corporation and Queen's University Belfast.

One Dimensional Modeling of a Turbogenerating Spark Ignition Engine Operating on Biogas

Turbocompounding is generally regarded as the process of recovering a proportion of the exhaust gas energy from a reciprocating engine and applying it to the output power of the crankshaft. In conventional turbocompounding, the power turbine has been mechanically connected to the crankshaft but now a new method has emerged. Recent advances in high speed electrical machines have enabled the power turbine to be coupled to an electric generator. Decoupling the power turbine from the crankshaft and coupling it to a generator allows the power electronics to control the turbine speed independently in order to optimize the turbine efficiency for different engine operating conditions.

Comparison of Experimental PIV Data and CFD Simulations for Flow in a Diesel Particulate Filter Inlet Diffuser

Flow maldistribution of the exhaust gas entering a Diesel Particulate Filter (DPF) can cause uneven soot distribution during loading and excessive temperature gradients during the regeneration phase. Minimising the magnitude of this maldistribution is therefore an important consideration in the design of the inlet pipe and diffuser, particularly in situations where packaging constraints dictate bends in the inlet pipe close to the filter, or a sharp diffuser angle. This paper describes the use of Particle Image Velocimetry (PIV) to validate a Computational Fluid Dynamic (CFD) model of the flow within the inlet diffuser of a DPF so that CFD can be used with confidence as a tool to minimise this flow maldistribution. PIV is used to study the flow of gas into a DPF over a range of steady state flow conditions. The distribution of flow approaching the front face of the substrate was of particular interest to this study. Optically clear diffusing cones were designed and placed between pipe and substrate to allow PIV analysis to take place. Stereoscopic PIV was used to eliminate any error produced by the optical aberrations caused by looking through the curved wall of the inlet cone. In parallel to the experiments, numerical analysis was carried out using a CFD program with an incorporated DPF model. Boundary conditions for the CFD simulations were taken from the experimental data, allowing an experimental validation of the numerical results. The CFD model incorporated a DPF model, the cement layers seen in segmented filters and the intumescent matting that is commonly used to pack the filter into a metal casing. The mesh contained approximately 580,000 cells and used the realizable ?-e turbulence model. The CFD simulation predicted both pressure drop across the DPF and the velocity field within the cone and at the DPF face with reasonable accuracy, providing confidence in the use the CFD in future work to design new, more efficient cones.

Quantifying the Energy & carbon emissions implications of a 10% electric vehicles target

EU Directive 2009/28/EC on Renewable Energy requires each Member State to ensure 10% of transport energy (excluding aviation and marine transport) comes from renewable sources by 2020 (10% RES-T target). In addition to the anticipated growth in biofuels, this target is expected to be met by the increased electrification of transport coupled with a growing contribution from renewable energy to electricity generation. Energy use in transport accounted for nearly half of Ireland’s total final energy demand and about a third of energy-related carbon dioxide emissions in 2007. Energy use in transport has grown by 6.3% per annum on average in the period 1990 – 2007. This high share and fast growth relative to other countries highlights the challenges Ireland faces in meeting ambitious renewable energy targets. The Irish Government has set a specific target for Electric Vehicles (EV) as part of its strategy to deliver the 10% RES-T target. By 2020, 10% of all vehicles in its transport fleet are to be powered by electricity. This paper quantifies the impacts on energy and carbon dioxide emissions of this 10% EV target by 2020. In order to do this an ‘EV Car Stock’ model was developed to analyse the historical and future make-up of the passenger car portion of the fleet to 2025. Three scenarios for possible take-up in EVs were examined and the associated energy and emissions impacts are quantified. These impacts are then compared to Ireland’s 10% RES-T target and greenhouse gas (GHG) emissions reduction targets for 2020. Two key findings of the study are that the 10% EV target contributes 1.7% to the 10% RES-T target by 2020 and 1.4% to the 20% reduction in Non-ETS emissions by 2020 relative to 2005.

A Theoretical and Experimental Study of Resonance in a High Performance Engine Intake System: Part 1

The unsteady gas dynamic phenomena in engine intake systems of the type found in racecars have been examined. In particular, the resonant tuning effects, including cylinder-to-cylinder power variations, which can occur as a result of the interaction between an engine and its airbox have been considered. Frequency analysis of the output from a Virtual 4-Stroke 1D engine simulation was used to characterise the forcing function applied by an engine to an airbox. A separate computational frequency sweeping technique, which employed the CFD package FLUENT, was used to determine the natural frequencies of virtual airboxes in isolation from an engine. Using this technique, an airbox with a natural frequency at 75 Hz was designed for a Yamaha R6 4-cylinder motorcycle engine. The existence of an airbox natural frequency at 75 Hz was subsequently confirmed by an experimental frequency sweeping technique carried out on the engine test bed. A coupled 1D/3D analysis which employed the engine simulation package Virtual 4-Stroke and the CFD package FLUENT, was used to model the combined engine and airbox system. The coupled 1D/3D analysis predicted a 75 Hz resonance of the airbox at an engine speed of 9000 rpm. This frequency was the induction frequency for a single cylinder. An airbox was fabricated and tested on the engine. Static pressure was recorded at a grid of points in the airbox as the engine was swept through a speed range of 3000 to 10000 rpm. The measured engine speed corresponding to resonance in the airbox agreed well with the predicted values. There was also good correlation between the amplitude and phase of the pressure traces recorded within the airbox and the 1D/3D predictions.

A Capabilities-Aware Framework for Using Computational Accelerators in Data-Intensive Computing

Multicore computational accelerators such as GPUs are now commodity components for highperformance computing at scale. While such accelerators have been studied in some detail as stand-alone computational engines, their integration in large-scale distributed systems raises new challenges and trade-offs. In this paper, we present an exploration of resource management alternatives for building asymmetric accelerator-based distributed systems. We present these alternatives in the context of a capabilities-aware framework for data-intensive computing, which uses an enhanced implementation of the MapReduce programming model for accelerator-based clusters, compared to the state of the art. The framework can transparently utilize heterogeneous accelerators for deriving high performance with low programming effort. Our work is the first to compare heterogeneous types of accelerators, GPUs and a Cell processors, in the same environment and the first to explore the trade-offs between compute-efficient and control-efficient accelerators on data-intensive systems. Our investigation shows that our framework scales well with the number of different compute nodes. Furthermore, it runs simultaneously on two different types of accelerators, successfully adapts to the resource capabilities, and performs 26.9% better on average than a static execution approach.

Performance and characteristics of platinum, palladium/rhodium and palladium automotive catalysts when subjected to ethanol, acetaldehyde and a synthetic E85 exhaust gas mixture

The performance optimisation of automotive catalysts has been the focus of a great deal of research for many years as the automotive industry has endeavored to reduce the emission of toxic and pollutant gases generated from internal combustion engines. Just as the emissions from diesel and gasoline combustion vary so do the emissions from combustion of alternative fuels such as ethanol; the variation is in both quantity and chemical composition. In particular, when ethanol is contained in the fuel, ethanol and acetaldehyde are present in the exhaust gas stream and these are two compounds which the catalytic converter has not traditionally been designed to manage. The aim of the study outlined in this paper was to assess the performance of various catalyst formulations when subjected to a representative ethanol exhaust gas mixture. Three automotive catalytic converter formulations were tested including a fully Pt sample, a PdRh three-way catalyst sample and a fully Pd sample. Initially the samples were tested using single component hydrocarbon light-off tests followed by a set of tests with carbon monoxide included as an inlet gas to observe its effect on each individual hydrocarbon oxidation. Finally, each formulation was tested using a full E85 exhaust gas mixture. The study was carried out using a synthetic gas reactor along with FTIR and FID exhaust gas analysers. All formulations showed selectivity toward acetaldehyde formation from ethanol dehydrogenation which resulted in negative acetaldehyde conversion across each of the samples during the mixture tests. The fully Pt sample was the most detrimentally affected by the introduction of carbon monoxide into the gas feed. The Pd and PdRh samples exhibited a tendency toward acetaldehyde decomposition resulting in methane and carbon monoxide formation. The Pt sample did not form methane but did form ethylene as a result of ethanol dehydration.