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The aim of this work is to analyze particle number and size distribution from a small displacement Euro 5 common rail automotive diesel engine, equipped with a close coupled aftertreatment system, featuring a DOC and a DPF integrated in a single canning. In particular the effects of different combustion processes on PM characteristics were investigated, by comparing measurements made both under normal operating condition and under DPF regeneration mode. Exhaust gas was sampled at engine outlet, at DOC outlet and at DPF outlet, in order to fully characterize PM emissions through the whole exhaust line. After a two stage dilution system, sampled gas was analyzed by means of a TSI 3080 SMPS, in the range from 6 to 240 nm. Particle number and size distribution were evaluated at part load operating conditions, representative of urban driving.

The aerodynamic properties of a BMW car model, representing a 40%-scaled model of a relevant car configuration, are studied computationally by means of the Unsteady RANS (Reynolds-Averaged Navier-Stokes) and Hybrid RANS/LES (Large-Eddy Simulation) approaches. The reference database (geometry, operating parameters and surface pressure distribution) are adopted from an experimental investigation carried out in the wind tunnel of the BMW Group in Munich (Schrefl, 2008). The present computational study focuses on validation of some recently developed turbulence models for unsteady flow computations in conjunction with the universal wall treatment combining integration up to the wall and high Reynolds number wall functions in such complex flow situations. The turbulence model adopted in both Unsteady RANS and PANS (Partially-Averaged Navier Stokes) frameworks is the four-equation ζ − f formulation of Hanjalic et al. (2004) based on the Elliptic Relaxation Concept (Durbin, 1991).

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.

This paper describes a method for optimization of engine settings in view of best total cost of operation fluids. Under specific legal NOX tailpipe emissions requirements the engine out NOX can be matched to the current achievable SCR NOX conversion efficiency. In view of a heavy duty long haul truck application various specific engine operation modes are defined. A heavy duty diesel engine was calibrated for all operation modes in an engine test cell. The characteristics of engine operation are demonstrated in different transient test cycles. Optimum engine operation mode (EOM) selection strategies between individual engine operation modes are discussed in view of legal test cycles and real world driving cycles which have been derived from on-road tests.

Automotive embedded systems have become very complex, are strongly integrated, and the safety-criticality and real-time constraints of these systems raise new challenges. The OSEK/VDX standard provides an open-ended architecture for distributed real-time capable units in vehicles. This is supported by the OSEK Implementation Language (OIL), a language aiming at specifying the configuration of these real-time operating systems. The challenge, however, is to ensure consistency of the concept constraints and configurations along the entire product development. The contribution of this paper is to bridge the existing gap between model-driven systems engineering and software engineering for automotive real-time operating systems (RTOS). For this purpose a bidirectional tool bridge has been established based on OSEK OIL exchange format files.

The use of Ducted Fuel Injection (DFI) for attenuating soot formation throughout mixing-controlled diesel combustion has been demonstrated impressively effective both experimentally and numerically. However, the last research studies have highlighted the need for tailored engine calibration and duct geometry optimization for the full exploitation of the technology potential. Nevertheless, the research gap on the response of DFI combustion to the main engine operating parameters has still to be fully covered. Previous research analysis has been focused on numerical soot-targeted duct geometry optimization in constant-volume vessel conditions. Starting from the optimized duct design, the herein study aims to analyze the influence of several engine operating parameters (i.e. rail pressure, air density, oxygen concentration) on DFI combustion, having free spray results as a reference.

Current developments in automotive industry such as hybrid powertrains and the continuously increasing demands on emission control systems, are pushing complexity still further. Validation of such systems lead to a huge amount of test cases and hence extreme testing efforts on the road. At the same time the pressure to reduce costs and minimize development time is creating challenging boundaries on development teams. Therefore, it is of utmost importance to utilize testing and validation prototypes in the most efficient way. It is necessary to apply high levels of instrumentation and collect as much data as possible. And a streamlined data pipeline allows the fleet managers to get new insights from the raw data and control the validation vehicles as well as the development team in the most efficient way. In this paper we will demonstrate a data-driven approach for validation testing.

A methodology for an efficient failure prediction of automotive steel wheels during fatigue experimental tests is proposed. The strategy joins the CDTire simulative package effectiveness to a specific wheel finite element model in order to deeply monitor the stress distribution among the component to predict damage. The numerical model acts as a Software-in-the-loop and it is calibrated with experimental data. The developed tool, called VirtualWheel, can be applied for the optimisation of design reducing prototyping and experimental test costs in the development phase. In the first section, the failure criterion is selected. In the second one, the conversion of hardware test-rig into virtual model is described in detail by focusing on critical aspects of finite element modelling. In conclusion, failure prediction is compared with experimental test results.

The paper has the aim of assessing and applying control-oriented models capable of predicting HRR (Heat Release Rate) and MFB50 in DI diesel engines. To accomplish this, an existing combustion model, previously developed by the authors and based on the accumulated fuel mass approach, has been modified to enhance its physical background, and then calibrated and validated on a GM 1.6 L Euro 6 DI diesel engine. It has been verified that the accumulated fuel mass approach is capable of accurately simulating medium-low load operating conditions characterized by a dominant premixed combustion phase, while it resulted to be less accurate at higher loads. In the latter case, the prediction of the heat release has been enhanced by including an additional term, proportional to the fuel injection rate, in the model. The already existing and the enhanced combustion models have been calibrated on the basis of experimental tests carried out on a dynamic test bench at GMPT-E.

A model-based approach to control BMEP (Brake Mean Effective Pressure) and NOx emissions has been developed and assessed on a FPT F1C 3.0L Euro VI diesel engine for heavy-duty applications. The controller is based on a zero-dimensional real-time combustion model, which is capable of simulating the HRR (heat release rate), in-cylinder pressure, BMEP and NOx engine-out levels. The real-time combustion model has been realized by integrating and improving previously developed simulation tools. A new discretization scheme has been developed for the model equations, in order to reduce the accuracy loss when the computational step is increased. This has allowed the required computational time to be reduced to a great extent.

To comply with the stringent fuel consumption requirements, many automobile manufacturers have launched vehicle electrification programs which are representing a paradigm shift in vehicle design. Looking specifically at powertrain calibration, optimization approaches were developed to help the decision-making process in the powertrain control. Due to computational power limitations the most common approach is still the use of powertrain calibration tables in a rule-based controller. This is true despite the fact that the most common manual tuning can be quite long and exhausting, and with the optimal consumption behavior rarely being achieved. The present work proposes a simulation tool that has the objective to automate the process of tuning a calibration table in a powertrain model. To achieve that, it is first necessary to define the optimal reference performance.

As an era of autonomous driving approaches, it is necessary to translate handling comfort - currently a responsibility of human drivers - to a vehicle imbedded algorithm. Therefore, it is imperative to understand the relationship between perceived driving comfort and human driving behaviour. This paper develops a methodology able to generate the information necessary to study how this relationship is expressed in highway overtakes. To achieve this goal, the approach revolved around the implementation of sensor Data Fusion, by processing data from CAN, camera and LIDAR from experimental tests. A myriad of variables was available, requiring individuating the key-information and parameters for recognition, classification and understanding of the manoeuvres. The paper presents the methodology and the role each sensor plays, by expanding on three main steps: Data segregation and parameter selection; Manoeuvre detection and processing; Manoeuvre classification and database generation.

The effects of using a B30 blend of ultra-low sulfur diesel and two different Fatty Acid Methyl Esters (FAME) obtained from both Rapeseed Methyl Ester (RME) and Jatropha Methyl Ester (JME) in a Euro 5 small displacement passenger car diesel engine on both full load performance and part load emissions have been evaluated in this paper. In particular the effects on engine torque were firstly analyzed, for both a standard ECU calibration (i.e., without any special tuning for the different fuel characteristics) and for a specifically adjusted ECU calibration obtained by properly increasing the injected fuel quantities to compensate for the lower LHV of the B30: with the latter, the same torque levels measured under diesel operation could be observed with the B30 blend too, with lower smoke levels, thus highlighting the potential for maintaining the same level of performance while achieving substantial emissions benefits.

The world of automotive engineering shows a clear direction for upcoming development trends. Stringent fleet average fuel consumption targets and CO2 penalties as well as rising fuel prices and the consumer demand to lower operating costs increases the engineering efforts to optimize fuel economy. Passenger car engines have the benefit of higher degree of technology which can be utilized to reach the challenging targets. Variable valve timing, downsizing and turbo charging, direct gasoline injection, highly sophisticated operating strategies and even more electrification are already common technologies in the automotive industry but can not be directly carried over into a motorcycle application. The major differences like very small packaging space, higher rated speeds, higher power density in combination with lower production numbers and product costs do not allow implementation such high of degree of advanced technology into small-engine applications.

Recently, a particle number (PN) limit was introduced in the European light-duty vehicles legislation. The legislation requires measurement of PN, and particulate mass (PM), from the full dilution tunnel with constant volume sampling (CVS). Furthermore, PN measurements will be introduced in the next stage of the European Heavy-Duty regulation. Heavy-duty engine certification can be done either from the CVS or from a partial flow dilution system (PFDS). For research and development purposes, though, measurements are often conducted from the raw exhaust, thereby avoiding the high installation costs of CVS and PFDS. Although for legislative measurements requirements exist regarding sampling and transport of the aerosol sample, such requirements do not necessarily apply for raw exhaust measurements. Thus, measurement differences are often observed depending on where in the experimental set up sampling occurs.

Sustainable and energy-efficient consumption is a main concern in contemporary society. Driven by more stringent international requirements, automobile manufacturers have shifted the focus of development into new technologies such as Hybrid Electric Vehicles (HEVs). These powertrains offer significant improvements in the efficiency of the propulsion system compared to conventional vehicles, but they also lead to higher complexities in the design process and in the control strategy. In order to obtain an optimum powertrain configuration, each component has to be laid out considering the best powertrain efficiency. With such a perspective, a simulation study was performed for the purpose of minimizing well-to-wheel CO2 emissions of a city car through electrification. Three different innovative systems, a Series Hybrid Electric Vehicle (SHEV), a Mixed Hybrid Electric Vehicle (MHEV) and a Battery Electric Vehicle (BEV) were compared to a conventional one.

In mathematical and mechanical modeling terms, automotive seating is characterized by boundary conditions at the nonlinear contact interfaces. These contact interfaces are subjected to vibro-impacts (slaps) and frictional slips. The slaps occur in contact interfaces at high amplitude vibrations, being characterized by very short duration, rapid dissipation of energy and large accelerations and decelerations. By considering friction in contact interface modeling, the simulation of the interaction between the driver and the vehicle seat becomes more realistic. Vibro-impacts and frictional slips can be simultaneously developed in a contact surface. The boundary conditions identification for a seat and a wide range of drivers' body types is performed using the concept of interference distance or penetration. The interference distance is introduced as an optimization problem. It is shown that the optimization problem provides robust solutions to minimum distance and interference problems.

The particulate emissions of two brake systems were characterized in a dilution tunnel optimized for PM10 measurements. The larger of them employed a fixed caliper (FXC) and the smaller one a floating caliper (FLC). Both used ECE brake pads of the same lining formulation. Measured properties included gravimetric PM2.5 and PM10, Particle Number (PN) concentrations of both untreated and thermally treated (according to exhaust PN regulation) particles using Condensation Particle Counters (CPCs) having 23 and 10 nm cut-off sizes, and an Optical Particle Sizer (OPS). The brakes were tested over a section (trip-10) novel test cycle developed from the database of the Worldwide harmonized Light-Duty vehicles Test Procedure (WLTP). A series of trip-10 tests were performed starting from unconditioned pads, to characterize the evolution of emissions until their stabilization. Selected tests were also performed over a short version of the Los Angeles City Cycle.

In automotive market, today more than in the past, it is very important to reduce time to market and, mostly, developing costs before the final production start. Ideally, bench and on-road tests can be replaced by multi-body studies because virtual approach guarantees test conditions very close to reality and it is able to exactly replicate the standard procedures. Therefore, today, it is essential to create very reliable models, able to forecast the vehicle behavior on every road condition (including uneven surfaces). The aim of this study is to build an accurate multi-body model of a heavy-duty truck, check its handling performance, and correlate experimental and numerical data related to comfort tests for model tuning and validation purposes. Experimental results are recorded during tests carried out at different speeds and loading conditions on a Belgian blocks track. Simulation data are obtained reproducing the on-road test conditions in multi-body environment.

In the framework of an increasing demand for a more sustainable mobility, where the fuel consumption reduction is a key driver for the development of innovative internal combustion engines, Turbulent Jet Ignition (TJI) represents one of the most promising solutions to improve the thermal efficiency. However, details concerning turbulent jet assisted combustion are still to be fully captured, and therefore the design and the calibration of efficient TJI systems require the support of reliable simulation tools that can provide additional information not accessible through experiments. To this aim, an experimental investigation combined with a 3D-CFD study was performed to analyze the TJI combustion characteristics in a single-cylinder spark-ignition (SI) engine. Firstly, the model was validated against experiments considering stoichiometric mixture at 3000 rpm, wide open throttle operating conditions.