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This paper is based on a Research Project of the Department of Mechanical Engineering (DiME) in collaboration with Aprilia, the Italian motorbike manufacturer. In an attempt to simulate the functioning of the cooling plant of the Aprilia RSV-4 motorbike a numerical model was constructed using mono-dimensional and three-dimensional simulation codes. Our ultimate aim was to create a simulation model which could be of assistance to engine designers to improve cooling plant performance, thereby reducing research and development costs. The model allows to simulate the running conditions of the whole cooling circuit upon variations in environmental and running conditions. In particular, the centrifugal pump of the cooling plant was simulated by a 3D commercial software, while the whole circuit was built by a 1D commercial code which allows simulation of all the thermal exchanges and pressure drops in the cooling circuit.

This paper presents the experimental results obtained on Fuel Cell System designed and realized in Istituto Motori and based on 16 kW PEM (Proton Exchange Membrane) stack. This activity is part of the HBUS Project, whose goal is the realization of a midi-bus for public transportation in urban areas based on fuel cell technology. This Project involves several private and public partners, comprising Istituto Motori as research institution, I2T3 (Industrial Innovation Through Technological Transfer) agency for technological innovation and project management, together with public transportation companies and vehicle component makers.

The paper describes the challenges and results achieved in developing a new high-speed Diesel combustion system capable of exceeding the imaginative threshold of 100 kW/l. High-performance, state-of-art prototype components from automotive diesel technology were provided in order to set-up a single-cylinder research engine demonstrator. Key design parameters were identified in terms boost, engine speed, fuel injection pressure and injector nozzle flow rates. In this regard, an advanced piezo injection system capable of 3000 bar of maximum injection pressure was selected, coupled to a robust base engine featuring ω-shaped combustion bowl and low swirl intake ports. The matching among the above-described elements has been thoroughly examined and experimentally parameterized.

The paper describes an experimental activity on the spatial and temporal liquid- and vapor-phase distributions of diesel fuel at engine-like conditions. The influence of the k-factor (0 and 1.5) of a single-hole axial-disposed injector (0.100 mm diameter and 10 L/d ratio) has been studied by spraying fuel in an optically-accessible constant-volume combustion vessel. A high-speed imaging system, capable of acquiring Mie-scattering and Schlieren images in a near simultaneous fashion mode along the same line of sight, has been developed at the Michigan Technological University using a high-speed camera and a pulsed-wave LED system. The time resolved pair of schlieren and Mie-scattering images identifies the instantaneous position of both the vapor and liquid phases of the fuel spray, respectively. The studies have been performed at three injection pressures (70, 120 and 180 MPa), 23.9 kg/m3 ambient gas density and 900 K gas temperature in the vessel.

This study aims at building an efficient and robust radial basis function (RBF) artificial neural network (ANN), to reconstruct the in-cylinder pressure of a diesel engine starting from the signal of a low-cost accelerometer placed on the engine block. The accelerometer is a perfect non-intrusive replacement for expensive probes and is prospectively suitable for production vehicles. The RBF network is trained using measurements from different engine operating conditions. Training data are composed of time series from the accelerometer and corresponding measured in-cylinder pressure signals. The RBF network is then validated using data not included in training and the results show good correspondence between measured and reconstructed pressure signal. Various network parameters are used to optimize the network quality.

This study aims at building efficient and robust artificial neural networks (ANN) able to reconstruct the in-cylinder pressure of Diesel engines and to identify engine conditions starting from the signal of a low-cost accelerometer placed on the engine block. The accelerometer is a perfect non-intrusive replacement for expensive probes and is prospectively suitable for production vehicles. In this view, the artificial neural network is meant to be efficient in terms of response time, i.e. fast enough for on-line use. In addition, robustness is sought in order to provide flexibility in terms of operation parameters. Here we consider a feed-forward neural network based on radial basis functions (RBF) for signal reconstruction, and a feed-forward multi-layer perceptron network with tan-sigmoid transfer function for signal classification. The networks are trained using measurements from a three-cylinder real engine for various operating conditions.