A Bayesian approach to the determination of ignition delay

A novel in-cylinder pressure method for determining ignition delay has been proposed and demonstrated. This method proposes a new Bayesian statistical model to resolve the start of combustion, defined as being the point at which the band-pass in-cylinder pressure deviates from background noise and the combustion resonance begins. Further, it is demonstrated that this method is still accurate in situations where there is noise present. The start of combustion can be resolved for each cycle without the need for ad hoc methods such as cycle averaging. Therefore, this method allows for analysis of consecutive cycles and inter-cycle variability studies. Ignition delay obtained by this method and by the net rate of heat release have been shown to give good agreement. However, the use of combustion resonance to determine the start of combustion is preferable over the net rate of heat release method because it does not rely on knowledge of heat losses and will still function accurately in the presence of noise. Results for a six-cylinder turbo-charged common-rail diesel engine run with neat diesel fuel at full, three quarters and half load have been presented. Under these conditions the ignition delay was shown to increase as the load was decreased with a significant increase in ignition delay at half load, when compared with three quarter and full loads.

Ignition delay in an ethanol fumigated common rail diesel engine

A novel method for determining ignition delay is presented. This method utilises combustion resonance as a means of determining the onset of ignition. Results are shown from an ethanol fumigation study comprising of substitutions up to 50% at full, three-quarter and half load. It has been demonstrated that at full load there is a decrease in ignition delay with increasing ethanol substitutions, whereas at half load there is an increase in ignition delay with increasing ethanol substitutions. It is suggested that this conflicting result is a consequence of the auto ignition of ethanol.

The use of artificial neural networks for identifying sustainable biodiesel feedstocks

Over the past few decades, biodiesel produced from oilseed crops and animal fat is receiving much attention as a renewable and sustainable alternative for automobile engine fuels, and particularly petroleum diesel. However, current biodiesel production is heavily dependent on edible oil feedstocks which are unlikely to be sustainable in the longer term due to the rising food prices and the concerns about automobile engine durability. Therefore, there is an urgent need for researchers to identify and develop sustainable biodiesel feedstocks which overcome the disadvantages of current ones. On the other hand, artificial neural network (ANN) modeling has been successfully used in recent years to gain new knowledge in various disciplines. The main goal of this article is to review recent literatures and assess the state of the art on the use of ANN as a modeling tool for future generation biodiesel feedstocks. Biodiesel feedstocks, production processes, chemical compositions, standards, physio-chemical properties and in-use performance are discussed. Limitations of current biodiesel feedstocks over future generation biodiesel feedstock have been identified. The application of ANN in modeling key biodiesel quality parameters and combustion performance in automobile engines is also discussed. This review has determined that ANN modeling has a high potential to contribute to the development of renewable energy systems by accelerating biodiesel research.

The influence of fatty acid methyl ester profiles on inter-cycle variability in a heavy duty compression ignition engine

With the advent of alternative fuels, such as biodiesels and related blends, it is important to develop an understanding of their effects on inter-cycle variability which, in turn, influences engine performance as well as its emission. Using four methanol trans-esterified biomass fuels of differing carbon chain length and degree of unsaturation, this paper provides insight into the effect that alternative fuels have on inter-cycle variability. The experiments were conducted with a heavy-duty Cummins, turbo-charged, common-rail compression ignition engine. Combustion performance is reported in terms of the following key in-cylinder parameters: indicated mean effective pressure (IMEP), net heat release rate (NHRR), standard deviation of variability (StDev), coefficient of variation (CoV), peak pressure, peak pressure timing and maximum rate of pressure rise. A link is also established between the cyclic variability and oxygen ratio, which is a good indicator of stoichiometry. The results show that the fatty acid structures did not have a significant effect on injection timing, injection duration, injection pressure, StDev of IMEP, or the timing of peak motoring and combustion pressures. However, a significant effect was noted on the premixed and diffusion combustion proportions, combustion peak pressure and maximum rate of pressure rise. Additionally, the boost pressure, IMEP and combustion peak pressure were found to be directly correlated to the oxygen ratio. The emission of particles positively correlates with oxygen content in the fuel as well as in the air-fuel mixture resulting in a higher total number of particles per unit of mass.

Fluid-structure interaction analysis of full scale vehicle-barrier impact using coupled SPH-FEA

Portable water-filled barriers (PWFB) are roadside structures used to separate moving traffic from work-zones. Numerical PWFB modelling is preferred in the design stages prior to actual testing. This paper aims to study the fluid-structure interaction of PWFB under vehicular impact using several methods. The strategy to treat water as non-structural mass was proposed and the errors were investigated. It was found that water can be treated with the FEA-NSM model for velocities higher than 80kmh-1. However, full SPH/FEA model is still the best treatment for water and necessary for lower impact velocities. The findings in this paper can be used as guidelines for modelling and designing PWFB.

Continuous remote emissions monitoring – the lynchpin for air quality management

Diesel particulate matter (DPM), in particular, has been likened in a somewhat inflammatory manner to be the ‘next asbestos’. From the business change perspective, there are three areas holding the industry back from fully engaging with the issue: 1. There is no real feedback loop in any operational sense to assess the impact of investment or application of controls to manage diesel emissions. 2. DPM are getting ever smaller and more numerous, but there is no practical way of measuring them to regulate them in the field. Mass, the current basis of regulation, is becoming less and less relevant. 3. Diesel emissions management is generally wholly viewed as a cost, yet there are significant areas of benefit available from good management. This paper discusses a feedback approach to address these three areas to move the industry forward. The six main areas of benefit from providing a feedback loop by continuously monitoring diesel emissions have been identified: 1. Condition-based maintenance. Emissions change instantaneously if engine condition changes. 2. Operator performance. An operator can use a lot more fuel for little incremental work output through poor technique or discipline. 3. Vehicle utilisation. Operating hours achieved and ratios of idling to under power affect the proportion of emissions produced with no economic value. 4. Fuel efficiency. This allows visibility into other contributing configuration and environmental factors for the vehicle. 5. Emission rates. This allows scope to directly address the required ratio of ventilation to diesel emissions. 6. Total carbon emissions - for NGER-type reporting requirements, calculating the emissions individually from each vehicle rather than just reporting on fuel delivered to a site.

Ignition delay of bio-fuels in a common-rail compression ignition engine

The utility of a novel technique for determining the ignition delay in a compression ignition engine has been shown. This method utilises statistical modelling in the Bayesian paradigm to accurately resolve the start of combustion from a band-pass in-cylinder pressure signal. Applied to neat diesel and six biofuels, including four fractionations of palm oil of varying carbon chain length and degree of unsaturation, the relationships between ignition delay, cetane number and oxygen content have been explored. It is noted that the expected negative relationship between ignition delay and cetane number held, as did the positive relationship between ignition delay and oxygen content. The degree of unsaturation was also identified as a potential factor influencing the ignition delay.

Stiffness-damping matching method of an ECAS system based on LQG control

A novel method of matching stiffness and continuous variable damping of an ECAS (electronically controlled air suspension) based on LQG (linear quadratic Gaussian) control was proposed to simultaneously improve the road-friendliness and ride comfort of a two-axle school bus. Taking account of the suspension nonlinearities and target-height-dependent variation in suspension characteristics, a stiffness model of the ECAS mounted on the drive axle of the bus was developed based on thermodynamics and the key parameters were obtained through field tests. By determining the proper range of the target height for the ECAS of the fully-loaded bus based on the design requirements of vehicle body bounce frequency, the control algorithm of the target suspension height (i.e., stiffness) was derived according to driving speed and road roughness. Taking account of the nonlinearities of a continuous variable semi-active damper, the damping force was obtained through the subtraction of the air spring force from the optimum integrated suspension force, which was calculated based on LQG control. Finally, a GA (genetic algorithm)-based matching method between stepped variable damping and stiffness was employed as a benchmark to evaluate the effectiveness of the LQG-based matching method. Simulation results indicate that compared with the GA-based matching method, both dynamic tire force and vehicle body vertical acceleration responses are markedly reduced around the vehicle body bounce frequency employing the LQG-based matching method, with peak values of the dynamic tire force PSD (power spectral density) decreased by 73.6%, 60.8% and 71.9% in the three cases, and corresponding reduction are 71.3%, 59.4% and 68.2% for the vehicle body vertical acceleration. A strong robustness to variation of driving speed and road roughness is also observed for the LQG-based matching method.

An Artificial Neutral Network (ANN) model for predicting biodiesel kinetic viscosity as a function of temperature and chemical compositions

An Artificial Neural Network (ANN) is a computational modeling tool which has found extensive acceptance in many disciplines for modeling complex real world problems. An ANN can model problems through learning by example, rather than by fully understanding the detailed characteristics and physics of the system. In the present study, the accuracy and predictive power of an ANN was evaluated in predicting kinetic viscosity of biodiesels over a wide range of temperatures typically encountered in diesel engine operation. In this model, temperature and chemical composition of biodiesel were used as input variables. In order to obtain the necessary data for model development, the chemical composition and temperature dependent fuel properties of ten different types of biodiesels were measured experimentally using laboratory standard testing equipments following internationally recognized testing procedures. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture was optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the absolute fraction of variance (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found that ANN is highly accurate in predicting the viscosity of biodiesel and demonstrates the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties at different temperature levels. Therefore the model developed in this study can be a useful tool in accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Effect of joint mechanism on vehicle redirectional capability of water-filled road safety barrier systems

Portable water-filled barriers (PWFBs) are roadside appurtenances that prevent vehicles from penetrating into temporary construction zones on roadways. PWFBs are required to satisfy the strict regulations for vehicle re-direction in tests. However, many of the current PWFBs fail to re-direct the vehicle at high speeds due to the inability of the joints to provide appropriate stiffness. The joint mechanism hence plays a crucial role in the performance of a PWFB system at high speed impacts. This paper investigates the desired features of the joint mechanism in a PWFB system that can re-direct vehicles at high speeds, while limiting the lateral displacement to acceptable limits. A rectangular “wall” representative of a 30 m long barrier system was modeled and a novel method of joining adjacent road barriers was introduced through appropriate pin-joint connections. The impact response of the barrier “wall” and the vehicle was obtained and the results show that a rotational stiffness of 3000 kNm/rad at the joints seems to provide the desired features of the PWFB system to re-direct impacting vehicles and restrict the lateral deflection. These research findings will be useful to safety engineers and road barrier designers in developing a new generation of PWFBs for increased road safety.

Seat Comfort. M4-M6.

Hardness is defined as the resistance and load bearing capability of an item. Seat hardness is an important factor in seat comfort as it impacts on a number of variables including seat postural stability, postural control, pressure comfort as a result of tissue deformation, and occupant vibration. The development of the test rig further on described in this report will enable Futuris Automotive to develop their current comfort testing procedures and thus increase the comfort of their automotive seats. The test rig consists of a buttock indenter, which produces a controlled application of a load to a seat cushion with measured displacement via a linear indenter. In parallel with the physical property presented, an analytic (software) finite element tool was developed to simulate seat pressure in an ANSYS Workbench V13 environment. This report also details the procedure required for Futuris to accurately and precisely measure cushion hardness which will enhance their comfort testing procedures, product development and target settings. The report is divided into three main sections: 1 Test equipment specification (M4) - A detailed description of the process used to build the seat cushion indenter and a description of the indenter mechanical structure and electrical functionality (chapter 2). 2 Analytic tool specification (M5) – A detailed description of the CAE seat and indenter software tool, developed as a finite element model (FEM) under ANSYS Workbench V13 to simulate indentation of a physical seat cushion similar to the hardware tool (chapter 3). 3 Product Development and Comfort Design Procedure (M6) - The cushion hardness testing procedure to be used with the physical indenter. This milestone is partially incomplete, as it covers a description of the test procedure to be applied, however not the operating system (control software) required to operate the physical property (chapter 4). Although outside the scope of this project, this report also details the testing procedures required to measure overall seatback hardness.

Determinants of second row passenger comfort

China is becoming an increasingly important automotive market. Customer’s vehicle usage, preferences and requirements differ from traditional western markets in a number of aspects – rear seat usage rates are higher, vehicles are used for business purposes as well as for private transport and rear seat usage is generally more important to Chinese customers compared to their western counterparts. The purpose of this project is to dimension and investigate these differences from an ergonomics perspective and use these results to guide the design of future products. The focus for this project will be specific to vehicles in the CD segment. More specifically, this project focuses on the second row ‘ambience’. Ambience refers to the global feeling perceived by second row passengers, and the main factors contributing to ambience are: ingress and egress comfort, seat comfort, roominess, and ease of use of the controls. In order to investigate the aforementioned parameters, an experimental study has been conducted in Shanghai, China. This experiment involved 80 healthy Chinese CD- and D-car customers. These subjects were asked to evaluate different features present in the second row environment of three different cars: A Ford Mondeo, Toyota Camry and Mercedes S-class. Various data has been collected during this experiment: First, the anthropometric dimensions of the subjects have been measured. The subjects were also asked to fill a questionnaire about demographics, their own car usage, and their perception of a various number of features present in the three tested cars. A great amount of technical data was also collected. The first part of this report presents the results given by the questionnaires. It includes Chinese demographics, vehicle usage habits, and the subjective perception of the features present in the tested cars. It also presents the results of the anthropometric measurements. This gives a first insight into Chinese customers’ habits and preferences. The second part deals with the technical data recorded during the experiment: second row seat adjustment ranges, roominess, optimal location of controls, and pressure mapping analysis. Analysis of technical data allows a deeper understanding of the factors contributing to comfort and ambience perception. Using the technical data together with the comfort ratings given by the subjects in the questionnaire, recommendations on several design parameters were provided. Finally, an experimental study of car ingress-egress has been conducted in a University laboratory controlled environment. During this study, the ingress and egress motion of 20 customers from Chinese origin was recorded using a motion capture system. The last part of this report presents the protocol and data processing that led to building an ingress-egress motion database that was provided to Ford.

Seat Comfort. M2-M3

This (seat) attribute target list and Design for Comfort taxonomy report is based on the literature review report (C3-21, Milestone 1), which specified different areas (factors) with specific influence on automotive seat comfort. The attribute target list summarizes seat factors established in the literature review (Figure 1) and subsumes detailed attributes derived from the literature findings within these factors/classes. The attribute target list (Milestone 2) then provides the basis for the “Design for Comfort” taxonomy (Milestone 3) and helps the project develop target settings (values) that will be measured during the testing phase of the C3-21 project. The attribute target list will become the core technical description of seat attributes, to be incorporated into the final comfort procedure that will be developed. The Attribute Target List and Design for Comfort Taxonomy complete the target definition process. They specify the context, markets and application (vehicle classes) for seat development. As multiple markets are addressed, the target setting requires flexibility of variables to accommodate the selected customer range. These ranges will be consecutively filled with data in forthcoming studies. The taxonomy includes how and where the targets are derived, reference points and standards, engineering and subjective data from previous studies as well as literature findings. The comfort parameters are ranked to identify which targets, variables or metrics have the biggest influence on comfort. Comfort areas included are seat kinematics (adjustability), seat geometry and pressure distribution (static comfort), seat thermal behavior and noise/vibration transmissibility (cruise comfort) and eventually material properties, design and features (seat harmony). Data from previous studies is fine tuned and will be validated in the nominated contexts and markets in follow-up dedicated studies.

Seat Comfort. M1

This literature review reports on high quality research studies focused on measuring occupant comfort in automotive vehicles. The review covers the most important variables in automotive seating design that impact on occupant comfort. These findings will help Futuris and the University develop the target settings that will be measured during the testing phase of the C3-21 project. The review also provides valuable information that may be incorporated into the final comfort procedure that will be developed.

Predicting reduced driver alertness on monotonous highways : a driving simulator study

Impaired driver alertness increases the likelihood of drivers’ making mistakes and reacting too late to unexpected events while driving. This is particularly a concern on monotonous roads, where a driver’s attention can decrease rapidly. While effective countermeasures do not currently exist, the development of in-vehicle sensors opens avenues for monitoring driving behavior in real-time. The aim of this study is to predict drivers’ level of alertness through surrogate measures collected from in-vehicle sensors. Electroencephalographic activity is used as a reference to evaluate alertness. Based on a sample of 25 drivers, data was collected in a driving simulator instrumented with an eye tracking system, a heart rate monitor and an electrodermal activity device. Various classification models were tested from linear regressions to Bayesians and data mining techniques. Results indicated that Neural Networks were the most efficient model in detecting lapses in alertness. Findings also show that reduced alertness can be predicted up to 5 minutes in advance with 90% accuracy, using surrogate measures such as time to line crossing, blink frequency and skin conductance level. Such a method could be used to warn drivers of their alertness level through the development of an in-vehicle device monitoring, in real-time, drivers' behavior on highways.