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The present work represents the continuation of the introductory study presented in part I [11] where the experimental plan, the measurement system and the tools developed for the testing of a modern Wankel engine were illustrated. In this paper the motored data coming from the subsequent stage of the testing are presented. The AIE 225CS Wankel rotary engine produced by Advanced Innovative Engineering UK, installed in the test cell of the University of Bath and equipped with pressure transducers selected for the particular application, has been preliminarily tested under motored conditions in order to validate the data acquisition software on the real application and the correct determination of the Top Dead Centre (TDC) location which is of foremost importance in the computation of parameters such as the indicated work and the combustion heat release when the engine is tested later under fired conditions.

Particulate emissions from gasoline direct injection (GDI) engines continue to be a topic of substantial research interest. Forthcoming regulation both in the USA and the EU will further reduce their emission and drive innovation. Substantial research effort is spent undertaking experiments to understand, characterize, and research particle number (PN) emissions from engines and vehicles. Recent advances in computing power, data storage, and understanding of artificial intelligence algorithms now mean that these are becoming an important tool in engine research. In this work a random forest (RF) algorithm is used for the prediction of PN emissions from a highly boosted (up to 32 bar BMEP) GDI engine. Particle size, concentration, and the accumulation mode geometric standard deviation (GSD) are all predicted by the model. The results are analysed and an in depth study on parameter importance is carried out.

Wankel rotary engines are becoming an increasingly popular area of research with regard to their use as a range extender in the next generation of Hybrid Electric Vehicle (HEV). Due to their simple design, lightness, compactness and very favourable power-to-weight ratio, they represent one of the best alternative solutions to classic reciprocating piston engines. On the other hand, current Wankel engines still need improvements in terms of specific fuel consumption and emissions. This paper describes an innovative approach for the assessment of the performance of a modern rotary engine. All the experimental activities will be carried out within the Innovate UK funded ADAPT Intelligent Powertrain project led by Westfield Sportscars Limited.

The major contribution of this paper is the general description of a complete integrating procedure of autonomous vehicle system. Using Robot Operating System (ROS) as the framework, process from senor integration to path planning and path tracking were performed. Based on an off-road All-Terrain Vehicle, an Extended Kalman filter based autonomous control strategy was developed on the ROS. Both the position estimation and autonomous control were performed on the ROS platform. For the position estimation phase, sensory measurements from GPS, IMU and wheel odometry were acquired and processed on ROS. In accordance with the ROS architecture, separate packages were developed for each sensor to gather and publish corresponding measurements. Furthermore, Extended Kalman filtering was performed to fuse all sensory measurements to achieve an optimizing accuracy.

Current turbocharger models are based on characteristic maps derived from experimental measurements taken under steady conditions on dedicated gas stand facility. Under these conditions heat transfer is ignored and consequently the predictive performances of the models are compromised, particularly under the part load and dynamic operating conditions that are representative of real powertrain operations. This paper proposes to apply a dynamic mathematical model that uses a polynomial structure, the Volterra Series, for the modelling of the turbocharger system. The model is calculated directly from measured performance data using an extended least squares regression. In this way, both compressor and turbine are modelled together based on data from dynamic experiments rather than steady flow data from a gas stand. The modelling approach has been applied to dynamic data taken from a physics based model, acting as a virtual test cell.

Many More-Electric Aircraft (MEA) Electric Power System (EPS) architecture paradigms are being studied in order to provide the on-board electrical loads with high-quality supply and to ensure safe operation. EPS with dc distribution appears to be more promising due to higher efficiency, higher reliability, easier integration and lower overall weight. Another advantage of dc systems is the ease of sources paralleling, together with the optimization of load power sharing, this can lead to further EPS weight reduction. The DC bus can be fed by multiple sources such as generators, batteries and other energy storage devices. Many loads in MEA EPS are tightly controlled by power electronic converters and often behave as constant power loads (CPL). These are known as main contributors to the degradation of EPS stability margins. Therefore, stability study is one of the key topics in the assessment of potential EPS architecture candidates.

It has been reported that lower extremity injuries represent a measurable portion of all moderate-to-severe automobile crash- related injuries. Thus, a simple tool to assist with the design of leg and foot injury countermeasures is desirable. The objective of this study is to develop a mathematical model which can predict load propagation and kinematics of the foot and leg in frontal automotive impacts. A multi-body model developed at the University of Virginia and validated for blunt impact to the whole foot has been used as basis for the current work. This model includes representations of the tibia, fibula, talus, hindfoot, midfoot and forefoot bones. Additionally, the model provides a means for tensioning the Achilles tendon. In the current study, the simulations conducted correspond to tests performed by the Transport Research Laboratory and the University of Nottingham on knee-amputated cadaver specimens.

In both the marine and power industries there are now a choice of off-the-shelf condition monitoring systems available that utilise artificial intelligence techniques to analyse engine performance data. These systems are proving to be a valuable aid in optimising performance and reducing down-time by assisting with maintenance planning. These systems rely on careful monitoring of an engine's performance, for instance engine speed, fuelling, boost pressure, turbine inlet pressure, turbocharger speed, and exhaust temperature. With this data, they utilise a variety of interpolation and pattern recognition algorithms to compare it with previously recorded data stored in lookup tables. This paper describes how a neural network approach can be used as a cheap alternative for the analysis of this data, greatly reducing the need for such large lookup tables and complex pattern recognition programs.