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The automotive industry continues to increase the utilization of computer-aided engineering. This put demands on finding reliable objective measures that correlate to subjective driver assessments on driving stability performance. However, the drivers’ subjective perception of driving stability can be difficult to quantify objectively, especially on test tracks where the wind conditions cannot be controlled. The advancement in driving simulator technology may enable evaluation of driving stability with high repeatability. The purpose of this study is to correlate the subjective assessment of driving stability to reliable objective measures and to evaluate the usefulness of a driving simulator for the subjective assessment. Two different driver clinic studies were performed in a state-of-the-art driving simulator. The first study included 38 drivers (professional, experienced and common drivers) and focused on crosswind gust sensitivity.

In vehicle development, a subjective evaluation of the vehicle’s behavior at high speeds is usually conducted by experienced drivers with the objective of assessing driving stability. To avoid late design changes, it is desirable to predict and resolve perceived instabilities early in the development phase. In this study, a mathematical model is developed from measurements during on-road tests to predict the driver’s ability to identify vehicle instabilities under excitations such as aerodynamic excitations. A vehicle is fitted with add-ons to create aerodynamic excitations and is driven by multiple drivers on a high-speed track. Drivers’ evaluation, responses, cabin motion, and crosswind conditions are recorded. The influence of yaw and roll rates, lateral acceleration, and steering angle at various frequency ranges when predicting the drivers’ evaluation of induced excitation is demonstrated. The drivers’ evaluation of vehicle behavior is influenced by driver-vehicle interactions.

The present paper aims at developing an innovative procedure to create a one-dimensional (1D) real-time capable simulation model for a heavy-duty diesel engine. The novelty of this approach is the use of the top-level engine configuration, test cell measurement data, and manufacturer maps as opposite to common practice of utilizing a detailed 1D engine model. The objective is to facilitate effective model adjustments and hence further increase the application of Hardware-in-the-Loop (HiL) simulations in powertrain development. This work describes the development of Fast Running Model (FRM) in GT-SUITE simulation software. The cylinder and gas-path modeling and calibration are described in detail. The results for engine performance and exhaust emissions produced satisfactory agreement with both steady-state and transient experimental data.

In order to meet emissions and power requirements, modern engine design has evolved in complexity and control. The cost and time restraints of calibration and testing of various control strategies have made virtual testing environments increasingly popular. Using Hardware-in-the-Loop (HiL), Volvo Penta has built a virtual test rig named VIRTEC for efficient engine testing, using a model simulating a fully instrumented engine. This paper presents an innovative Artificial Neural Network (ANN) based model for engine simulations in HiL environment. The engine model, herein called Artificial Neural Network Engine (ANN-E), was built for D8-600 hp Volvo Penta engine, and directly implemented in the VIRTEC system. ANN-E uses a combination of feedforward and recursive ANNs, processing 7 actuator signals from the engine management system (EMS) to provide 30 output signals.

The current work investigates a method in 1D modeling of cooling systems including discretized cooling package with non-uniform boundary conditions. In a stacked cooling package the heat transfer through each heat exchanger depends on the mass flows and temperature fields. These are a result of complex three-dimensional phenomena, which take place in the under-hood and are highly non-uniform. A typical approach in 1D simulations is to assume these to be uniform, which reduces the authenticity of the simulation and calls for additional calibrations, normally done with input from test measurements. The presented work employs 3D CFD simulations of complete vehicle in STAR-CCM+ to perform a comprehensive study of mass-flow and thermal distribution over the inlet of the cooling package of a Volvo FM commercial vehicle in several steady-state operating points.

Underhood Thermal Management has become an important topic for the majority of automotive OEM's. To keep combustion engines cool and manage waste heat efficiently is an important part in the design of vehicles with low fuel consumption. To be able to predict cooling performance and underhood airflow with good precision within a virtual design process, it is of utmost importance to model and simulate the cooling fan efficiently and accurately, and this has turned out to be challenging. Simulating the cooling fan in a vehicle installation involves capturing complex fluid dynamic interaction between rotating blades and stationary objects in the vicinity of the fan. This interaction is a function of fan rotation rate, fan blade profile, upstream and downstream installation components. The flow is usually highly turbulent and small geometry details, like the distance between the blade tip and the fan shroud, have strong impact on the fan performance characteristics.

A common fan model to use in automotive under hood simulations is the Multiple Reference Frame (MRF) model and within the industry, for this specific application, this model is well known to under predict performance. In this paper we have examined the possibilities of correcting this deficiency with a simple “speed correction”. This is done by testing and simulating a production fan in the Volvo Fan Test Rig for two operating speeds, 1200 rpm and 2400 rpm. Pressure rise, fan power and static efficiency are presented as functions of volumetric flow rate. The simulations verify that using the MRF model the common behavior of under predicting pressure rise and performance of the fan occur. In addition, this work shows that; although the MRF is not predicting fan performance correctly it constitutes a reliable fan modeling strategy.

This paper describes an analysis of the Diesel Oil Surrogate (DOS) model used at Chalmers University (Sweden), including 70 species participating in 310 reactions, and subsequent improvements prompted by the model's systematic tendency to under-predict the combustion intensity in simulations of kinetically-driven combustion modes, e.g. Homogeneous Charged Compression Ignition (HCCI). Key bases of the model are the properties of a model Diesel fuel with the molecular formula C14H28. In the vapor phase, a global reaction decomposes the starting fuel, C14H28, into its constituent components; n-heptane (C7H16) and toluene (C7H8). This global reaction was modified to yield a higher n-heptane:toluene ratio, due to the importance of preserving an n-heptane-like cetane number.

The aim of this study was to examine the influence of computer manikin users' background and knowledge for the results of a computer manikin simulation. Subjects taking part in the study were either production engineers or ergonomists. A manual task that presented production and ergonomics problems was used. The task was simulated prior to the subjects' sessions, using the computer manikin software Jack. During the sessions, the animated simulation was shown to the test subject. Results show that there are differences in how production engineers and ergonomists interpret results from a manikin simulation. Depending on the user's background, certain aspects that are difficult to visualise with the computer manikin were interpreted differently, regarding e.g. detected problems and holistic perspectives.