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The aim of this work is to apply an innovative adaptive ℒ1 techniques to control flutter phenomena affecting highly flexible wings and to evaluate the efficiency of this control algorithm and architecture by performing the following tasks: i) adaptation and analysis of an existing simplified nonlinear plunging/pitching 2D aeroelastic model accounting for structural nonlinearities and a quasi-steady aerodynamics capable of describing flutter and post-flutter limit cycle oscillations, ii) implement the ℒ1 adaptive control on the developed aeroelastic system to perform initial control testing and evaluate the sensitivity to system parameters, and iii) perform model validation and calibration by comparing the performance of the proposed control strategy with an adaptive back-stepping algorithm. The effectiveness and robustness of the ℒ1 adaptive control in flutter and post-flutter suppression is demonstrated.

TFC/IW, total fuel consumption divided by inertia weight is reported with other engineering variables for recent EPA data for industry passenger cars and truck. TFC/IW is used in comparisons between gasoline and diesel engines, 49 States and California, passenger cars and trucks. The California fuel economy penalty due to more stringent emissions standards is discussed. The relationship between TFC/IW and ton miles per gallon is shown. Special attention is focused on 4 cylinder gasoline powered vehicles in 49 States passenger car fleet. The use of TFC/IW to answer the question, ‘What Changed?’ when comparing the fuel economies of two fleets is described.

The onboard “SODART” telescope silicon detector cooling system of the “Spectrum-X-Gamma” observatory, which is designed for the space objects X-ray radiation study, is described. The scale-down model of the passive cooling system description and thermal vacuum test results of this model are given. In the real cooling system the minimal detector temperature at 300 mW heat release is expected about 107 K.

A survey of the in-service fuel consumption of passenger vehicles and derivatives in the Australian fleet was carried out in 1984-85. Seven hundred and four owners across Australia took part in the survey. Vehicle owners reported by questionnaire the amount of fuel used during four tank fills of normal operation, the distance travelled, and other details of the operating circumstances. The survey shows a clear downward trend in the fuel consumption of the Australian passenger fleet. The data also provides comparisons of actual fuel consumption obtained on the road, with laboratory derived values for fuel consumption. Vehicles in a sub-set of 40 were fitted with fuel flow meters during the survey and tested to Australian Standard 2077 for fuel consumption. The questionnaire method is shown to be a valid and accurate technique for determining in-service fuel consumption.

The current research was aimed at determining the most effective way to use alternative renewable feedstock to power a diesel engine. Mimusops elengi, a new and novel biofuel was recognized for this current study, which is widely available in the south of India. The investigation was conducted on B20 volume basis (20% Mimusops elengi methyl ester blended with 80% diesel). Furthermore, it was recognized that when the performance characteristics were traded off, the emission magnitude has slightly higher. To address the diesel engine pollution, an oxygenated nano additive like titanium oxide was dissipated only with the fuel blend at distinct mass fractions of 25 parts per million (ppm) with differing injection pressures of 180 bar, 200 bar, 220 bar, and 240 bar. The tests were created using a statistical programme known as design of experiments, which is purely based on Taguchi and response surface methodology.

Mathematical modeling and control of artificial ecosystems, such as MELISSA, require first the study of physical and biological characteristics in optimal and limiting conditions. Following the previous determination of the stoichiometric equations (Spirulina compartment) and regarding the two phototrophic compartments of MELISSA (Rhodospirillaceae and Spirulina), we have first to focus our control study on the growth kinetics for the light source. In this paper, we recall the theoretical equations of microbial growth kinetics and emphasise the problem of the light transfer in a photobioreactor. We present their adaptations to our pilot plant taking into account technological and biological specifics (lamp spectrum, working illuminated volume, growth rate,…). We then develop the principles and structure of the control system and describe tests of both the hardware and software for several steady state configurations.

This paper describes the application of statistical techniques known as Design of Experiments (D.O.E.) to efficiently use the results of numerical analysis data in order to improve the configuration of automotive systems. The general framework of these techniques is presented in a format aiming at the design engineer as their end user. Besides, a case study is presented with the purpose of illustrating their practical use. The first step of the case study is to build predictive models for the behaviour of the automotive system being developed by means of the Response Surface Method (RSM), using the proper D.O.E. options. Once these predictive models are available, automatic numerical optimization algorithms are used to improve the responses of interest for given operating conditions. Finally, the automotive systems are robust designed taking into account that the operating conditions vary randomly.

Plug-in Hybrid Electric Vehicles (PHEVs) use electric energy from the grid rather than fuel energy for most short trips, therefore drastically reducing fuel consumption. Different configurations can be used for PHEVs. In this study, the parallel pre-transmission, series, and power-split configurations were compared by using global optimization. The latter allows a fair comparison among different powertrains. Each vehicle was operated optimally to ensure that the results would not be biased by non-optimally tuned or designed controllers. All vehicles were sized to have a similar all-electric range (AER), performance, and towing capacity. Several driving cycles and distances were used. The advantages of each powertrain are discussed.

Driver state monitoring is a crucial technology for enhancing road safety and preventing human error-caused accidents in the era of autonomous vehicles. This paper presents CogniSafe, a comprehensive driver monitoring system that uses deep learning and computer vision methods to detect various types of driver distractions and fatigue. CogniSafe consists of four modules: Driver anomaly detection and classification: A novel two-phase network that proposes and recognizes driver anomalies, such as texting, drinking, and adjusting radios, using multimodal and multiview input. Gaze estimation: A video-based neural network that jointly learns head pose and gaze dynamics, achieving robust and efficient gaze estimation across different head poses. Eye state analysis: A multi-tasking CNN that encodes features from both eye and mouth regions, predicting the percentage of eye closure (PERCLOS) and the frequency of mouth opening (FOM).

This paper is an extension of our previous work on the CHASE (Classification by Holistic Analysis of Scene Environment) algorithm, that automatically classifies the driving complexity of a road scene image during day-time conditions and assigns it an ‘Ease of Driving’ (EoD) score. At night, apart from traffic variations and road type conditions, illumination changes are a major predominant factor that affect the road visibility and the driving easiness. In order to resolve the problem of analyzing the driving complexity of roads at night, a brightness detection module is incorporated in our end-to-end nighttime EoD system, which computes the ‘brightness factor’ (bright or dark) for that given night-time road scene. The brightness factor along with a multi-level machine learning classifier is then used to classify the EoD score for a night-time road scene. Our end-to-end ‘Night-time EoD system’ is a real-time onboard system implemented and tested on road scene data collected in Japan.

Due to the rising costs of fuel and increasingly stringent regulations, auto makers are in need of technology to enable more fuel-efficient powertrain technologies to be introduced to the marketplace. Such powertrains must not sacrifice performance, safety or driver comfort. Today's engine and powertrain manufacturers must, therefore, do more with less by achieving acceptable vehicle performance while reducing fuel consumption. One effective method to achieve this is the extreme downsizing of current direct injection spark ignited (DISI) engines through the use of high levels of boosting and cooled exhaust gas recirculation (EGR). Key challenges to highly downsized gasoline engines are retarded combustion to prevent engine knocking and the necessity to operate at air/fuel ratios that are significantly richer than the stoichiometric ratio.

The objective of the development of the aerodynamic drag predictive tool CDaero was for use as a module for the Automobile Design Support System (AutoDSS). CDaero is an empirically based drag coefficient predictive tool based initially on the MIRA (Motor Industry Research Association) algorithm. The development philosophy was to be able to predict the aerodynamic drag coefficient of an automobile with knowledge of the features of the surface geometry control curves. These are the curves that control the 3-dimensional geometry as seen in the profile, plan and front and rear views. CDaero has been developed in a computing environment using the equation solver TKSolver™. Fifty-one input feature values are first determined from the automobile geometry and then entered into the program. CDaero models the drag coefficient with thirteen different components covering the basic body, as well as additional components such as the wheels, mud flaps, etc.

Powertrain electrification is becoming increasingly common in the transportation sector to address the challenges of global warming and deteriorating air quality. This paper introduces a novel “Build Your Hybrid” approach to experience and test various hybrid powertrain concepts. This approach is applied to the light commercial vehicles (LCV) segment due to the attractive combination of a Diesel engine and a partly electrified powertrain. For this purpose, a demonstrator vehicle has been set up with a flexible P02 hybrid topology and a prototype Hybrid Control Unit (HCU). Based on user input, the HCU software modifies the control functions and simulation models to emulate different sub-topologies and levels of hybridization in the demonstrator vehicle. Three powertrain concepts are considered for LCVs: HV P2, 48V P2 and 48V P0 hybrid. Dedicated hybrid control strategies are developed to take full advantage of the synergies of the electrical system and reduce CO2 and NOx emissions.

Consideration of the approaching ‘energy crisis’ reveals two requirements for future light duty automotive engines. 1) maximum economy and 2) the ability (perhaps with detail design re-optimisation) to accept a range of fuels of petroleum or other extract, of differing ignition characteristics. One combustion system which meets these requirements is the MAN ‘FM’, the potential of which has already been demonstrated in truck-size engines but on which little information has been published in light-duty engine bore sizes. The paper describes both design and experimental work carried out to evaluate the application of the FM combustion system to a light duty passenger car engine. Consideration is given to the critical design parameters associated with the application of the FM system to a multi-cylinder gasoline based engine and how the criteria can be met. Details of the design and construction of a single cylinder derivative of the multi-cylinder engine are given.

Dynamic skip fire (DSF) has shown significant fuel economy improvement potential via reduction of pumping losses that generally affect throttled spark-ignition (SI) engines. In DSF operation, individual cylinders are fired on-demand near peak efficiency to satisfy driver torque demand. For vehicles with a downsized-boosted 4-cylinder engine, DSF can reduce fuel consumption by 8% in the WLTC (Class 3) drive cycle. The relatively low cost of cylinder deactivation hardware further improves the production value of DSF. Lean burn strategies in gasoline engines have also demonstrated significant fuel efficiency gains resulting from reduced pumping losses and improved thermodynamic characteristics, such as higher specific heat ratio and lower heat losses. Fuel-air mixture stratification is generally required to achieve stable combustion at low loads.

This study proposes a novel biofuel for spark ignition (SI) engine, α-pinene (C10H16), which is non-oxygenated and thus has a gravimetric energy density comparable to that of hydrocarbon fuels. The ignition characteristics of α-pinene were evaluated in an ignition quality tester (IQT) under standard temperature and pressure conditions. The measured ignition delay time (IDT) of α-pinene is 10.5 ms, which is lower than that of iso-octane, 17.9 ms. The estimated research octane number (RON) for pinene from IQT is 85. A temperature sweep in IQT showed that that α-pinene is less reactive at low temperatures, but more reactive at high temperatures when compared to isooctane. These results suggest that α-pinene has high octane sensitivity (OS) and is suitable for operation in turbocharged SI engines. With these considerations, α-pinene was operated in a single cylinder SI engine.

The Swedish Biomimetics 3000's μMist® platform technology has been used to develop a radically new injection system. This prototype system, developed and characterized with support from Lotus, as part of Swedish Biomimetics 3000®'s V₂IO innovation accelerating model, delivers improved combustion efficiency through achieving exceptionally small droplets, at fuel rail pressures far less than conventional GDI systems and as low as PFI systems. The system gives the opportunity to prepare and deliver all of the fuel load for the engine while the intake valves are open and after the exhaust valves have closed, thereby offering the potential to use advanced charge scavenging techniques in PFI engines which have hitherto been restricted to direct-injection engines, and at a lower system cost than a GDI injection system.

Propulsion system control algorithms covering the functional needs of xEV propulsion (‘x’ donates P0-P4 configurations) systems are presented in this paper. The scope and foundation are based on generic well-established HEV controller architectures. However, unlike conventional HEV (series, parallel and power split) powertrains, the next generation of integrated electric propulsion configurations will utilize a single micro controller that supports multiple control functions ranging from the electric machines, inverters, actuators, clutch solenoids, coolant pumps, etc. This presents a unique challenge to architect control algorithms within the AUTOSAR framework while satisfying the complex timing requirements of motor/generator-inverter (MGi) control and increased interface definitions between software components to realize functional integration between the higher level propulsion system and its sub-systems.

mDSF is a novel cylinder deactivation technology developed at Tula Technology, which combines the torque control of Dynamic Skip Fire (DSF) with Miller cycle engines to optimize fuel efficiency at minimal cost. mDSF employs a valvetrain with variable valve lift plus deactivation and novel control algorithms founded on Tula’s proven DSF technology. This allows cylinders to dynamically alternate among 3 potential states: high-charge fire, low-charge fire, and skip (deactivation). The low-charge fire state is achieved through an aggressive Miller cycle with Early Intake Valve Closing (EIVC). The three operating states in mDSF can be used to simultaneously optimize engine efficiency and driveline vibrations. Acceleration performance is retained using the all-cylinder, high-charge firing mode.

eROSITA (extended ROentgen Survey with an Imaging Telescope Array) is a powerful X-ray telescope under development by the Max-Planck-Institut für extraterrestrische Physik (MPE) in Garching, Germany. eROSITA is the core instrument on the Russian SRG1 mission which is planned for launch in 2011. It comprises seven nested Wolter-I grazing incidence telescopes, each equipped with its own CCD camera. The mirror modules have to be maintained at 20°C while the cameras are operated at -80°C. Both, mirrors and CCDs have to be kept within tight limits. The CCD cooling system consists of passive thermal control components only: two radiators, variable conductance heat pipes (VCHP) and two special thermal storage units. The orbit scenario imposes severe challenges on the thermal control system and also on the attitude control system.