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In order to ensure the safety of a structure, adequate strength for structural elements must be provided. Moreover, catastrophic deformations such as buckling must be prevented. Using the linear finite element method, deterministic buckling analysis is completed in two main steps. First, a static analysis is performed using an arbitrary ordinate applied loading pattern. Using the obtained element axial forces, the geometric stiffness of the structure is assembled. Second, an eigenvalue problem is performed between structure's elastic and geometric stiffness matrices, yielding the structure's critical buckling loads. However, these deterministic approaches do not consider uncertainty the structure's material and geometric properties. In this work, a new method for finite element based buckling analysis of a structure with uncertainty is developed. An imprecise probability formulation is used to quantify the uncertainty present in the mechanical characteristics of the structure.

Due to the low cost of the battery cells and excellent performance at ambient temperature, Lithium-ion (Li-ion) battery is a promising technology for propulsion applications. However, the performance of Li-ion batteries erodes drastically at extreme temperatures (above 65 °C or below 0 °C). Therefore, in order to maintain battery life and performance, it is crucial to keep the batteries within the temperature range where their operating characteristics are optimal. The need for expensive and complex thermal management systems has in fact kept the Li-ion technology from becoming the first choice for Hybrid Electric Vehicle (HEV) applications. In this paper, we propose a Phase Change Material (PCM) for the temperature control. Due to its high heat capacity, PCM absorbs the heat dissipated by the battery. As long as the heat emitted by the battery does not melt the PCM completely, the system is stable.

New tire model is under development in European Commission research project called VERT (Vehicle Road Tire Interaction, BRPR-CT97-0461). The objective of the project is to create a physical model for tire/surface contact simulation. One of the subtasks has been to develop a method for snow surface characterization. The aim is simulate winter tire on snow surface with FEM software. This kind of simulation has been earlier done with snow model parameters from laboratory experiments. A snow shear box device has been developed in Helsinki University of Technology to measure mechanical properties of snow in field conditions. Both shear and compression properties can be measured with the device. With the device, a large number of snow measurements have been done at the same time with VERT winter tire testing in Nokian Tyres'' test track in Ivalo Finland. Measurement data have been postprocessed afterwards and parameters for material models have been evaluated.

High interest is expressed in using analytical models to eliminate costly driveline tests used to determine the stresses produced in the driveshaft and driveline during resonant operating conditions. This paper discusses an analytical model to simulate the driveline-bending integrity, test procedure. Three major subsystems are modeled in this analytical approach, namely powertrain, rear axle, and driveshaft. Imbalance masses were added on the driveshaft to induce the whirl motion of the driveshaft. The combination of nonlinear Multi-body System Simulation (MSS) and linear Finite Element Analysis (FEA) in the time domain was employed for the evaluation of the dynamic interaction between several parts.

Reducing the high cost of hardware testing with analytical methods has been highly accelerated in the automotive industry. This paper discusses an analytical model to simulate the driveline bending integrity test for the longitudinal 4WD-driveline configuration. The dynamic stresses produced in the adapter/transfer case and propeller shaft can be predicted analytically using this model. Particularly, when the 4WD powertrain experiences its structural bending during the operation speed and the propeller shaft experiences the critical whirl motion and its structural bending due to the inherent imbalance. For a 4WD-Powertrain application, the dynamic coupling effect of a flexible powertrain with a flexible propeller shaft is significant and demonstrated in this paper. Three major subsystems are modeled in this analytical model, namely the powertrain, the final rear drive, and the propeller shafts.

This paper describes how distributive computing along with statistical subsystem simulation can be applied to produce near production ready embedded vehicle software and calibrations. Coupling distributive computing and statistical simulation was first employed over a decade ago at General Motors to design and analyze propulsion subsystem hardware. Recently this method of simulation has been enhanced extending its capabilities to both test embedded vehicle code as well as develop calibrations. A primary advantage of this simulation technique is its ability to generate data from a statistically significant population of subsystems. The result is the acquisition of an optimal data set enabling the development of a robust design now including both embedded code and calibrations. Additionally it has been shown that there are significant economic advantages in terms of time and cost associated with this type of development when compared to traditional method.

The development of diesel engines is constantly leading to greater increases in the power density. The heat load into the combustion chamber walls increases with the increased power density. Estimating correct local heat fluxes inside the combustion chamber is one of the most challenging tasks in engine simulation. In this study, the heat load of the piston was estimated with the help of the modern simulation tools CFD and FEM. The objective of the work was to evaluate the thermal stress of a research engine designed for an exceptionally high maximum and mean pressure. The local heat transfer coefficient and gas temperature were simulated with a CFD code with the standard and modified wall functions and used as boundary values for the FEM analysis. As a reference case, a model of a production engine with measured piston surface temperatures was used to validate the combined CFD and FEM analysis.

Performance data offers a powerful tool for system condition assessment and health monitoring. In most applications, a host of various types of sensors is employed and data on key parameters (describing the system performance) is compiled for further analysis and evaluation. In ensuring the adequacy of the data acquisition process, two important questions arise: (1) is the complied data robust and reasonable in representing the system parameters; and (2) is the duration of data acquisition adequate to capture a favorable percentage (say for example 90%) of the critical values of a given system parameter? The issue related to the robustness and reasonableness of data can be addressed through known values for key parameters of the system. This is the information that is not often available. And as such, methods based on trends in a given system parameter, expected norms, the parameter's relation with other known parameters, and simulations can be used to assure the quality of the data.

Perception in adverse weather conditions is one of the most prominent challenges for automated driving features. The sensors used for mid-to-long range perception most impacted by weather (i.e., camera and LiDAR) are susceptible to data degradation, causing potential system failures. This research series aims to better understand sensor data degradation characteristics in real-world, dynamic environmental conditions, focusing on adverse weather. To achieve this, a dataset containing LiDAR (Velodyne VLP-16) and camera (Mako G-507) data was gathered under static scenarios using a single vehicle target to quantify the sensor detection performance. The relative position between the sensors and the target vehicle varied longitudinally and laterally. The longitudinal position was varied from 10m to 175m at 25m increments and the lateral position was adjusted by moving the sensor set angle between 0 degrees (left position), 4.5 degrees (center position), and 9 degrees (right position).