Knock: A Century of Research

Knock is one of the main limitations on increasing spark-ignition (SI) engine efficiency. This has been known for at least 100 years, and it is still the case today. Knock occurs when conditions ahead of the flame front in an SI engine result in one or more autoignition events in the end gas. The autoignition reaction rate is typically much higher than that of the flame-front propagation. This may lead to the creation of pressure waves in the combustion chamber and, hence, an undesirable noise that gives knock its name. The resulting increased mechanical and thermal loading on engine components may eventually lead to engine failure. Reducing the compression ratio lowers end-gas temperatures and pressures, reducing end-gas reactivity and, hence, mitigating knock. However, this has a detrimental effect on engine efficiency. Automotive companies must significantly reduce their fleet carbon dioxide (CO2) values in the coming years to meet targets resulting from the 2015 Paris Agreement. One path towards meeting these is through partial or full electrification of the powertrain. However, the vast majority of automobiles in the near future will still feature a gasoline-fueled SI engine; hence, improvements in combustion engine efficiency remain fundamental. As knock has been a key limitation for so long, there is a huge amount of literature on the subject. A number of reviews on knock have already been published, including in recent years. These generally concentrate on current understanding and status. The present work, in contrast, aims to track the progress of research on knock from the 1920s right through to the present day. It is hoped that this can be a useful reference for new and existing researchers of the subject and give further weight to occasionally neglected historical activity, which can still provide important insights today

The application of black box models to combustion processes in the internal combustion engine

The internal combustion engine has been under considerable pressure during the last few years. The publics growing sensitivity for emissions and resource wastage have led to increasingly stringent legislation. Engine manufacturers need to invest significant monetary funds and engineering resources in order to meet the designated regulations. In recent years, reductions in emissions and fuel consumption could be achieved with advanced engine technologies such as exhaust gas recirculation (EGR), variable geometry turbines (VGT), variable valve trains (VVT), variable compression ratios (VCR) or extended aftertreatment systems such as diesel particulate filters (DPF) or NOx traps or selective catalytic reduction (SCR) implementations. These approaches are characterised by a highly non-linear behaviour with an increasing demand for close-loop control. In consequence, successful controller design becomes an important part of meeting legislation requirements and acceptable standards. At the same time, the close-loop control requires additional monitoring information and, especially in the field of combustion control, this is a challenging task. Existing sensors in heavy-duty diesel applications for incylinder pressure detection enable the feedback of combustion conditions. However, high maintenance costs and reliability issues currently cancel this method out for mass-production vehicles. Methods of in-cylinder condition reconstruction for real-time applications have been presented over the last few decades. The methodical restrictions of these approaches are proving problematic. Hence, this work presents a method utilising artificial neural networks for the prediction of combustion-related engine parameters. The application of networks for the prediction of parameters such as emission formations of NOx and Particulate Matters will be shown initially. This thesis shows the importance of correct training and validation data choice together with a comprehensive network input set. In addition, an application of an efficient and accurate plant model as a support tool for an engine fuel-path controller is presented together with an efficient test data generation method. From these findings, an artificial neural network structure is developed for the prediction of in-cylinder combustion conditions. In-cylinder pressure and temperature provide valuable information about the combustion efficiency and quality. This work presents a structure that can predict these parameters from other more simple measurable variables within the engine auxiliaries. The structure is tested on data generated from a GT-Power simulation model and with a Caterpillar C6.6 heavy-duty diesel engine

Diesel Engine

Diesel engines, also known as CI engines, possess a wide field of applications as energy converters because of their higher efficiency. However, diesel engines are a major source of NOX and particulate matter (PM) emissions. Because of its importance, five chapters in this book have been devoted to the formulation and control of these pollutants. The world is currently experiencing an oil crisis. Gaseous fuels like natural gas, pure hydrogen gas, biomass-based and coke-based syngas can be considered as alternative fuels for diesel engines. Their combustion and exhaust emissions characteristics are described in this book. Reliable early detection of malfunction and failure of any parts in diesel engines can save the engine from failing completely and save high repair cost. Tools are discussed in this book to detect common failure modes of diesel engine that can detect early signs of failure

THIESEL 2020.Thermo-and Fluid Dynamic Processes in Direct Injection Engines.8th-11th September

'The THIESEL 2020 Conference on Thermo-and Fluid Dynamic Processes in Direct Injection Engines planned in Valencia (Spain) for 8th to 11th September 2020 has been successfully held in a virtual format, due to the COVID19 pandemic. In spite of the very tough environmental demands, combustion engines will probably remain the main propulsion system in transport for the next 20 to 50 years, at least for as long as alternative solutions cannot provide the flexibility expected by customers of the 21st century. But it needs to adapt to the new times, and so research in combustion engines is nowadays mostly focused on the new challenges posed by hybridization and downsizing. The topics presented in the papers of the conference include traditional ones, such as Injection & Sprays, Combustion, but also Alternative Fuels, as well as papers dedicated specifically to CO2 Reduction and Emissions Abatement.Papers stem from the Academic Research sector as well as from the IndustryXandra Marcelle, M.; Desantes Fernández, JM. (2020). THIESEL 2020.Thermo-and Fluid Dynamic Processes in Direct Injection Engines.8th-11th September. Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/150759EDITORIA

Closed-Loop Control of HCCI Engine Dynamics

The topic of the thesis is control of Homogeneous Charge Compression Ignition (HCCI) engine dynamics. HCCI offers a potential to combine high efficiency with very low emissions. In order to fulfill the potential benefits, closed-loop control is needed. The thesis discusses sensors, feedback signals and actuators for closed-loop control of the HCCI combustion. Closed-loop control of the HCCI combustion using ion current is demonstrated. Models of the HCCI dynamics suitable for purposes of control design are presented. It is shown that low-order models are sufficient to describe the HCCI dynamics. Models of HCCI combustion have been determined both by system identification and by physical modeling. Different methods for characterizing and controlling the HCCI combustion are outlined and demonstrated. In cases where the combustion phasing in a six-cylinder heavy-duty engine was controlled, either by a Variable Valve Actuation system using the inlet valve or a dual-fuel system, results are presented. Combustion phasing is a limiting factor of the load control and emission control performance. A system where control of HCCI on a cycle-to-cycle basis is outlined and cylinder individual cycle-to-cycle control on a six-cylinder heavy duty engine is presented. Various control strategies are compared. Model-based control, such as LQG and Model Predictive Control MPC, and PID control are shown to give satisfactory controller performance. An MPC controller is proposed as a solution to the problem of load-torque control with simultaneous minimization of the fuel consumption and emissions, while satisfying the constraints on cylinder pressure

Engine cylinder pressure reconstruction using crank kinematics, block vibrations, and time-delay neural networks

Time-delay feed-forward Artificial Neural Networks are examined for gasoline engine cylinder pressure reconstruction using both measured crank kinematics obtained from a shaft encoder, and measured engine cylinder block vibrations obtained from a production knock sensor. Initially, the study focuses on the information content associated with measured data, which is considered to be of equal importance to the particular network architecture and the training methodology. Several hypotheses are constructed, which when tested, reveal the influence of the data information content on the reconstruction potential and limitations. These hypotheses are tested on real data from a 3-cylinder (DISI) engine. Three distinct ideas emerge through this testing process, which are combined to produce a single pressure reconstruction methodology. Reconstruction results obtained via this methodology, applied to crank kinematics associated with steady-state engine operation, show a marked improvement over previously published reconstruction accuracy. Moreover, in steady-state engine operation, the application of this methodology to acceleration measurements of cylinder block vibration, obtained from a knock sensor, show very significant improvements over previous attempts. But the direct application of this same reconstruction methodology to transient engine operation, proves to be problematic. However, a novel generalisation of the approach in the form of a time-dependent feed-forward neural network is proposed and the required adaptation made to the use of the Levenberg-Marquardt training algorithm. This time-dependent approach has been tested under limited transient conditions and shown in the thesis to give good results, therefore offering considerable potential for use with real engine operation. Overall, the thesis shows that by careful processing of measured engine data, standard neural network architectures and standard training algorithms can be used to reconstruct engine cylinder pressure

Online adaptive fuzzy neural network automotive engine control

Automotive manufacturers are investing in research and development for hybridization and more modern advanced combustion strategies. These new powertrain systems can offer the higher efficiency required to meet future emission legislation, but come at the cost of significantly increased complexity. The addition of new systems to modernise an engine increases the degrees of freedom of the control problem and the number of control variables. Advanced combustion strategies also display interlinked behaviour between control variables. This type of behaviour requires a more orchestrated multi-input multi-output control approach. Model based control is a common solution, but accurate control models can be difficult to achieve and calibrate due to the nonlinear dynamics of the engines. The modelling problem becomes worse when some advanced combustion systems display nonlinear dynamics that can change with time. Any fixed model control system would suffer from increasing model/system mismatch. Direct feedback would help reduce a degree or error from model/system mismatch, but feedback methods are often limited by cost and are generally indirect and slow response. This research addresses these problems with the development of a mobile ionisation sensor and an online adaptive control architecture for multi-input multi-output engine control. The mobile ionisation system offers a cheap, fast response, direct in-cylinder feedback for combustion control. Feedback from 30 averaged cycles can be related to combustion timing with variance as small as 0.275 crank angle degrees. The control architecture combines neural networks and fuzzy logic for the control and reduced modelling effort for complex nonlinear systems. The combined control architecture allows continuous online control adaption for calibration against model/plant mismatch and time varying dynamics. In simulation, set point tracking could be maintained for combustion timing to 4 CAD and AFR to 4, for significant dynamics shifts in plant dynamics during a transient drive cycle.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Adaptive torque-feedback based engine control

The aim of this study was to develop a self-tuning or adaptive SI engine controller using torque feedback as the main control variable, based on direct/indirect measurement and estimation techniques. The indirect methods include in-cylinder pressure measurement, ion current measurement, and crankshaft rotational frequency variation. It is proposed that torque feedback would not only allow the operating set-points to be monitored and achieved under wider conditions (including the extremes of humidity and throttle transients), but to actively select and optimise the set-points on the basis of both performance and fuel economy. A further application could allow the use of multiple fuel types and/or combustion enhancing methods to best effect. An existing experimental facility which comprised a Jaguar AJ-V8 SI engine coupled to a Heenan-Froude Dynamatic GVAL (Mk 1) dynamometer was adopted for this work, in order to provide a flexible distributed engine test system comprising a combined user interface and cylinder pressure monitoring system, a functional dynamometer controller, and a modular engine controller which is close coupled to an embedded PC has been created. The considerable challenges involved in creating this system have meant that the core research objectives of this project have not been met. Nevertheless, an open-architecture software and hardware engine controller and independent throttle controller have been developed, to the point of testing. For the purposes of optimum ignition timing validation and combustion knock detection, an optical cylinder pressure measurement system with crank angle synchronous sampling has been developed. The departure from the project’s initial aims have also highlighted several important aspects of eddy-current dynamometer control, whose closed-loop behaviour was modelled in Simulink to study its control and dynamic response. The design of the dynamometer real-time controller was successfully implemented and evaluated in a more contemporary context using an embedded digital controller.EThOS - Electronic Theses Online ServiceSchool of Mechanical & Systems EngineeringNewcastle UniversityGBUnited Kingdo

Modelling of an automotive natural gas engine for A/F control investigations

In this thesis, the problem of A/F ratio control in a natural gas, internal combustion engine is addressed, with the global objective of reducing exhaust emission pollutants. A review of some mechanical approaches to exhaust pollutant reduction are assessed. It is found that many techniques aid the reduction of exhaust pollutants, but the most effective is the 3-way catalytic converter. To maintain conversion efficiency, the A/F ratio must be strictly controlled within the catalyst window limits around the stoichiometric operating point. In order to investigate possible control techniques, a mathematical model is developed to simulate the physical behaviour of the engine processes. This approach allows a quick turn-around in terms of cost and time, for control investigations. The model demonstrates close trend-wise approximation of the engine states with previous modelling studies, however, a full validation study was not possible. The model is then used to conduct investigations into A/F ratio control through the process of simulation. Conventional Pl-closed-loop control is assessed for steady-state and transient engine conditions, and for varying microprocessor sampling rates. It is found that Pi-control effectively removes state estimation errors, but is unable to remove A/F ratio excursions under transient operation. An open-loop compensation control structure is then developed as an extension to the IM-controller action. Simulation results show this approach to drastically reduce A/F ratio excursions for a number of typical driving scenarios. Potential problems that could well be encountered in the “real” engine environment are then investigated, and the practicality of the new controller assessed. A new approach to control is simulated that affords the most appropriate state estimation for the modelled system. This is shown to improve A/F ratio control upon that of the conventional approach but cannot match the compensation controller ability