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The paper presents a method for the indoor testing of road vehicle suspension systems. A suspension is positioned on a rotating drum which is located in the Laboratory for the Safety of Transport at Politecnico di Milano. Special six-axis load cells have been designed and used for measuring the forces/moments acting at each suspension-chassis joints. The forces/moments, wheel accelerations, displacements are measured up to 100 Hz. Two different types of test can be performed. The tire/wheel unbalance effect on the suspension system behavior (Vibration and Harshness, VH) has been analyzed by testing the suspension system from zero to the vehicle maximum speed on a flat surface and by monitoring the forces transmitted to the chassis. In the second kind of test, the suspension system has been excited as the wheel passes over different cleats fixed on the drum.

A comprehensive research is presented aiming at assessing the ride comfort of subjects seated into road or off-road vehicles. Although many papers and books have appeared in the literature, many issues on ride comfort are still to be understood, in particular, the paper investigates the mutual effects of the posture and the vibration caused mostly from road unevenness. The paper is divided into two parts. In the first part, a mathematical model of a seated subject is validated by means of actual measurements on human subjects riding on a car. Such measurements refer to the accelerations acting at the subject/seat interface (vertical acceleration at the seat cushion and horizontal acceleration at the seat back). A proper dummy is used to derive the seat stiffness and damping.

Since transient vehicle HVAC computational fluids (CFD) simulations take too long to solve in a production environment, the goal of this project is to automatically create a lumped-parameter flow network from a steady-state CFD that solves nearly instantaneously. The data mining algorithm k-means is implemented to automatically discover flow features and form the network (a reduced order model). The lumped-parameter network is implemented in the commercial thermal solver MuSES to then run as a fully transient simulation. Using this network a “localized heat transfer coefficient” is shown to be an improvement over existing techniques. Also, it was found that the use of the clustering created a new flow visualization technique. Finally, fixing clusters near equipment newly demonstrates a capability to track localized temperatures near specific objects (such as equipment in vehicles).

This paper presents a nonlinear control approach to achieve good performances in vehicle path following and collision avoidance when the vehicle is driving under cruise highway conditions. Nonlinear model predictive control (NLMPC) is adopted to achieve online trajectory control based on a simplified vehicle model. GMRES/Continuation algorithm is used to solve the online optimization problem. Simulations show that the proposed controller is capable of tracking the desired path as well as avoiding the obstacles.

This paper presents a combined numerical and experimental investigation of the characteristics of spark discharge in a spark-ignition engine. The main objective of this work is to gain insights into the spark discharge process and early flame kernel development. Experiments were conducted in an inert medium within an optically accessible constant-volume combustion vessel. The cross-flow motion in the vessel was generated using a previously developed shrouded fan. Numerical modeling was based on an existing discharge model in the literature developed by Kim and Anderson. However, this model is applicable to a limited range of gas pressures and flow fields. Therefore, the original model was evaluated and improved to predict the behavior of spark discharge at pressurized conditions up to 45 bar and high-speed cross-flows up to 32 m/s. To accomplish this goal, a parametric study on the spark channel resistance was conducted.

Despite numerous research efforts, there is no reliable and widely accepted tool for the prediction of erosion prone material surfaces due to collapse of cavitation bubbles. In the present paper an Erosion Aggressiveness Index (EAI) is proposed, based on the pressure loads which develop on the material surface and the material yield stress. EAI depends on parameters of the liquid quality and includes the fourth power of the maximum bubble radius and the bubble size number density distribution. Both the newly proposed EAI and the Cavitation Aggressiveness Index (CAI), which has been previously proposed by the authors based on the total derivative of pressure at locations of bubble collapse (DP/Dt>0, Dα/Dt<0), are computed for a cavitating flow orifice, for which experimental and numerical results on material erosion have been published. The predicted surface area prone to cavitation damage, as shown by the CAI and EAI indexes, is correlated with the experiments.

Nowadays quasi-3D approaches are included in many commercial and research 1D numerical codes, in order to increase their simulation accuracy in presence of complex shape 3D volumes, e.g. plenums and silencers. In particular, these are regarded as valuable approaches for application during the design phase of an engine, for their capability of predicting non-planar waves motion and, on the other hand, for their low requirements in terms of computational runtime. However, the generation of a high-quality quasi-3D computational grid is not always straightforward, especially in case of complex elements, and can be a time-consuming operation, making the quasi-3D tool a less attractive option. In this work, a quasi-3D module has been implemented on the basis of the open-source CFD code OpenFOAM and coupled with the 1D code GASDYN.

Computational fluid dynamics of gas-fueled large-bore spark ignition engines with pre-chamber ignition can speed up the design process of these engines provided that 1) the reliability of the results is not affected by poor meshing and 2) the time cost of the meshing process does not negatively compensate for the advantages of running a computer simulation. In this work a flame propagation model that runs with arbitrary hybrid meshes was developed and coupled with the KIVA4-MHI CFD solver, in order to address these aims. The solver follows the G-Equation level-set method for turbulent flame propagation by Tan and Reitz, and employs improved numerics to handle meshes featuring different cell types such as hexahedra, tetrahedra, square pyramids and triangular prisms. Detailed reaction kinetics from the SpeedCHEM solver are used to compute the non-equilibrium composition evolution downstream and upstream of the flame surface, where chemical equilibrium is instead assumed.

Metallic foams or sponges are materials with a cell structure suitable for many industrial applications, such as reformers, heat catalytic converters, etc. The success of these materials is due to the combination of various characteristics such as mechanical strength, low density, high specific surface, good thermal exchange properties, low flow resistance and sound absorption. Different materials and manufacturing processes produce different type of structure and properties for various applications. In this work a genetic algorithm has been developed and applied to support the design of catalytic devices. In particular, two substrates were considered, namely the traditional honeycomb and an alternative open-cell foam type. CFD simulations of pressure losses and literature based correlations for the heat and mass transfer were used to support the genetic algorithm in finding the best compromise between flow resistance and pollutant abatement.

In the last few years, the effect of diabatic test conditions on compressor performance maps has been widely investigated, leading some Authors to propose different correction models. The accuracy of turbocharger performance map constitute the basis for the tuning and validation of a numerical method, usually adopted for the prediction of engine-turbocharger matching. Actually, it is common practice in automotive applications to use simulation codes, which can either require measured compression ratio and efficiency maps as input values or calculate them “on the fly” throughout specific sub-models integrated in the numerical procedures. Therefore, the ability to correct the measured performance maps taking into account internal heat transfer would allow the implementation of commercial simulation codes used for engine-turbocharger matching calculations.

Autonomous vehicle operation is dependent upon accurate position estimation and thus a major concern of implementing the autonomous navigation is obtaining robust and accurate data from sensors. This is especially true, in case of Inertial Measurement Unit (IMU) sensor data. The IMU consists of a 3-axis gyro, 3-axis accelerometer, and 3-axis magnetometer. The IMU provides vehicle orientation in 3D space in terms of yaw, roll and pitch. Out of which, yaw is a major parameter to control the ground vehicle’s lateral position during navigation. The accelerometer is responsible for attitude (roll-pitch) estimates and magnetometer is responsible for yaw estimates. However, the magnetometer is prone to environmental magnetic disturbances which induce errors in the measurement.

Multi-dimensional models represent today consolidated tools to simulate the combustion process in HCCI and diesel engines. Various approaches are available for this purpose, it is however widely accepted that detailed chemistry represents a fundamental prerequisite to obtain satisfactory results when the engine runs with complex injection strategies or advanced combustion modes. Yet, integrating such mechanisms generally results in prohibitive computational cost. This paper presents a comprehensive methodology for fast and efficient simulations of combustion in internal combustion engines using detailed chemistry. For this purpose, techniques to tabulate the species reaction rates and to reduce the chemical mechanisms on the fly have been coupled.

The present paper investigates possible enhancement of ESP performance associated with the use of smart tires. In particular a novel control logic based on a direct feedback on the longitudinal forces developed by the four tires is considered. The control logic was developed using a simulation tool including a 14 dofs vehicle model and a smart tires emulator. Performance of the control strategy was evaluated in a series of handling maneuvers. The same maneuvers were performed on a HiL test bench interfacing the same vehicle model with a production ESP ECU. Results of the two logics were analyzed and compared.

The implementation and the combination of advanced boundary conditions and subgrid scale models for Large Eddy Simulation (LES) in the multi-dimensional open-source CFD code OpenFOAM® are presented. The goal is to perform reliable cold flow LES simulations in complex geometries, such as in the cylinders of internal combustion engines. The implementation of a boundary condition for synthetic turbulence generation upstream of the valve port and of the compressible formulation of the Wall-Adapting Local Eddy-viscosity sgs model (WALE) is described. The WALE model is based on the square of the velocity gradient tensor and it accounts for the effects of both the strain and the rotation rate of the smallest resolved turbulent fluctuations and it recovers the proper y₃ near-wall scaling for the eddy viscosity without requiring dynamic procedure; hence, it is supposed to be a very reliable model for ICE simulation.

The Time Variant Discrete Fourier Transform was implemented as a real-time order tracking method using developed software and commercially available hardware. The time variant discrete Fourier transform (TVDFT) with the application of the orthogonality compensation matrix allows multiple tachometers to be tracked with close and/or crossing orders to be separated in real-time. Signal generators were used to create controlled experimental data sets to simulate tachometers and response channels. Computation timing was evaluated for the data collection procedure and each of the data processing steps to determine how each part of the process affects overall performance. Many difficulties are associated with a real-time data collection and analysis tool and it becomes apparent that an understanding of each component in the system is required to determine where time consuming computation is located.

In the paper some methods are presented, with the corresponding practical examples, related to MonteCarlo (MC) techniques for thermal model/test correlation purposes. The MonteCarlo techniques applied to model correlation are intended to be used as an alternative to empirical ‘manual’ correlation techniques, gradients methods, matrix methods based on least square fit minimization. First of all, Design Of Experiments (DoE) tools are used to determine the model response to uncertain parameters and the confidence level of such a response. A sensitivity map is built, allowing the design of the test to maximize the response of the system to the uncertain parameters. Techniques derived from the extreme statistics are used to extrapolate data beyond test limits, with a sufficient confidence in the queue behaviour.

Safety control and protection strategy of high-voltage system of electric vehicles include analysis of circuit condition before connection to high voltage terminal, transient current prevention for capacitive load, real-time monitoring and analysis of high-voltage system during operation, disconnecting strategy of high voltage terminals, vehicle dynamic safety and cooperative management of electrical systems, etc. Monitoring and analysis of some critical parameters of high voltage system such as insulation, electrical harness and connector condition are the basis and difficulties in high-voltage safety and protection. This paper presents several mathematical models of monitoring critical parameters, and experiments were also done to evaluate the model. Disadvantages of the commonly used calculation method are discussed. Single point insulation defect model is introduced and diagnosis method of multiple points defect is also discussed.

A global optimization method has been developed for an engine simulation code and utilized in the search of optimal fuel injection strategies. This method uses a Lagrange interpolation function which interpolates engine output data generated at the vertices and the intermediate points of the input parameters. This interpolation function is then used to find a global minimum over the entire parameter set, which in turn becomes the starting point of a CFD-based optimization. The CFD optimization is based on a steepest descent method with an adaptive cost function, where the line searches are performed with a fast-converging backtracking algorithm. The adaptive cost function is based on the penalty method, where the penalty coefficient is increased after every line search. The parameter space is normalized and, thus, the optimization occurs over the unit cube in higher-dimensional space.

The potentialities shown by controlled differentials is making the automotive industry to explore this field. While VDC systems can only guarantee a safe behaviour at limit, a controlled differential can also increase the handling performance. The system derives from a RWD driveline with a semi-active differential, to which has been added a controlled wet clutch that directly connects the engine to the front axle. This device allows to distribute the drive torque between the two axles. It can be easily understood that in this device the torque distribution doesn't depend only from the central clutch action, but also from the engaged gear. Because of this particular layout this system can't work in the whole gear because thermal problems due to kinematical reasons. So the central clutch controller has to consider the gear position too.

Optimal fuel injection strategies are obtained with a micro-genetic algorithm and an adaptive gradient method for a nonroad, medium-speed DI diesel engine equipped with a multi-orifice, asynchronous fuel injection system. The gradient optimization utilizes a fast-converging backtracking algorithm and an adaptive cost function which is based on the penalty method, where the penalty coefficient is increased after every line search. The micro-genetic algorithm uses parameter combinations of the best two individuals in each generation until a local convergence is achieved, and then generates a random population to continue the global search. The optimizations have been performed for a two pulse fuel injection strategy where the optimization parameters are the injection timings and the nozzle orifice diameters.