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The Lockheed Martin Low-Speed Wind Tunnel (LSWT) is a closed-return wind tunnel with two solid-wall test sections. This facility originally entered into service in 1967 for aerodynamic research of aircraft in low-speed and vertical/short take-off and landing (V/STOL) flight. Since this time, the client base has evolved to include a significant level of automotive aerodynamic testing, and the needs of the automotive clientele have progressed to include acoustic testing capability. The LSWT was therefore acoustically upgraded in 2016 to reduce background noise levels and to minimize acoustic reflections within the low-speed test section (LSTS). The acoustic upgrade involved detailed analysis, design, specification, and installation of acoustically treated wall surfaces and turning vanes in the circuit as well as low self-noise acoustic wall and ceiling treatment in the solid-wall LSTS.

This study evaluated the interactions between sheet steel, lubricant and measurement system under typical sheet forming conditions using a fixed draw bead simulator (DBS). Deep drawing quality mild steel substrates with bare (CR), electrogalvanized (EG) and hot dip galvanized (HDG) coatings were tested using a fixed DBS. Various lubricant conditions were targeted to evaluate the coefficient of friction (COF) of the substrate and lubricant combinations, with only rust preventative mill oil (dry-0 g/m2 and 1 g/m2), only forming pre-lube (dry-0 g/m2, 1 g/m2, and >6 g/m2), and a combination of two, where mixed lubrication cases, with incremental amounts of a pre-lube applied (0.5, 1.0, 1.5 and 2.0 g/m2) over an existing base of 1 g/m2 mill oil, were analyzed. The results showed some similarities as well as distinctive differences in the friction behavior between the bare material and the coatings.

A Machine Learning (ML) approach is presented to correlate in-cylinder images of early flame kernel development within a spark-ignited (SI) gasoline engine to early-, mid-, and late-stage flame propagation. The objective of this study was to train machine learning models to analyze the relevance of flame surface features on subsequent burn rates. Ultimately, an approach of this nature can be generalized to flame images from a variety of sources. The prediction of combustion phasing was formulated as a regression problem to train predictive models to supplement observations of early flame kernel growth. High-speed images were captured from an optically accessible SI engine for 357 cycles under pre-mixed operation. A subset of these images was used to train three models: a linear regression model, a deep Convolutional Neural Network (CNN) based on the InceptionV3 architecture and a CNN built with assisted learning on the VGG19 architecture.

More than four decades ago, the concept of zero defects was coined by Phillip Crosby. It was only a vision at the time, but the introduction of Artificial Intelligence (AI) in manufacturing has since enabled it to become attainable. Since most mature manufacturing organizations have merged traditional quality philosophies and techniques, their processes generate only a few Defects Per Million of Opportunities (DPMO). Detecting these rare quality events is one of the modern intellectual challenges posed to this industry. Process Monitoring for Quality (PMQ) is an AI and big data-driven quality philosophy aimed at defect detection and empirical knowledge discovery. Detection is formulated as a binary classification problem, where the right Machine Learning (ML), optimization, and statistics techniques are applied to develop an effective predictive system.

This report describes a new driving simulator capability at General Motors (GM) Research and Development's (R&D) Vehicle Development Research (VDR) Laboratory and its application in an iterative HMI development process. The paper also provides an overview of three recent simulator usability tests supporting HMI development.

Automotive technology is rapidly changing with electrification of vehicles, driver assistance systems, advanced safety systems etc. This advancement in technology is making the task of validation and verification of embedded software complex and challenging. In addition to the component testing, integration testing imposes even tougher requirements for software testing. To meet these challenges dSPACE is continuously evolving the Hardware-In-the-Loop (HIL) technology to provide a systematic way to manage this task. The paper presents developments in the HIL hardware technology with latest quad-core processors, FPGA based I/O technology and communication bus systems such as Flexray. Also presented are developments of the software components such as advanced user interfaces, GPS information integration, real-time testing and simulation models. This paper provides a real-world example of implication of integration testing on HIL environment for Chassis Controls.

Styrene-Butadiene Rubber (SBR), a copolymer of butadiene and styrene, is widely used in the automotive industry due to its high durability and resistance to abrasion, oils and oxidation. Some of the common applications include tires, vibration isolators, and gaskets, among others. This paper characterizes the dynamic behavior of SBR and discusses the suitability of a visco-elastic model of elastomers, known as the Kelvin model, from a mathematical and physical point of view. An optimization algorithm is used to estimate the parameters of the Kelvin model. The resulting model was shown to produce reasonable approximations of measured dynamic stiffness. The model was also used to calculate the self heating of the elastomer due to energy dissipation by the viscous damping components in the model. Developing such a predictive capability is essential in understanding the dynamic behavior of elastomers considering that their dynamic stiffness can in general depend on temperature.

The maximum equivalent plastic strain (EPSmax), which can be achieved in the gauge region of a cruciform specimen during in-plane biaxial tensile tests, is limited due to early fracture on the cruciform specimen arm. In this paper, a theoretical model was proposed to determine the factors related to the EPSmax of a cruciform specimen following ISO 16842: 2014. Biaxial tensile tests were carried out to verify the theoretical analyses. Results show that the material strength coefficient (k) has no effect on the EPSmax, and EPSmax increases with the increase of the material hardening exponent (n) and the cross-sectional-area ratio (c) of the arm region to the gauge region. It is found that the applied load ratio (α) has an effect on EPSmax, which decreases as the load ratio increases from 0:1 (i.e. uniaxial tension) to 1:2 (i.e. plane strain state) and then increases as the load ratio increases to 1:1 (i.e. balanced biaxial tension).

The H-point is a critical part of vehicle design as it is the basis for many engineering dimensions within the vehicle interior. A complete design process includes comparisons of the design to competitive benchmark vehicles. However, the competitive design considerations needed to determine the common standard H-point reference are often unknown. The SAE Human Accommodation and Design Devices (HADD) technical committee recently published a new standard benchmark SgRP procedure [2]. This new standard practice needed to be tested with respect to the accuracy and repeatability for determining the unknown h-point design parameters within industry benchmarking tolerances. In 2019, the SAE HADD committee conducted a study to evaluate the reproducibility of the new procedure. This paper presents detailed results of that study and discusses opportunities for applying the new benchmark practice.

A computational study based on unsteady Reynolds-Averaged-Navier-Stokes that resolves the gas-liquid interface was performed to examine the unsteady multiphase flow in a 4 cylinder Inline (i-4) engine. In this study, the rotating motion of the crankshaft and reciprocating motion of the pistons were accounted for to accurately predict the oil distribution in various parts of the engine. Three rotational speeds of the crankshaft have been examined: 1000, 2800, and 4000 rpm. Of particular interest is to examine the mechanisms governing the process of oil drawdown from the engine head into the case. The oil distributions in other parts of the engine have also been investigated to understand the overall crankcase breathing process. Results obtained show the drawdown of oil from the head into the case to be strongly dependent on the venting strategy for the foul air going out of the engine through the PCV system.

Zinc-based electrogalvanized (EG) and hot-dip galvanized (HDGI) coatings have been widely used in automotive body-in-white components for corrosion protection. The formability of zinc coated sheet steels depends on the properties of the sheet and the interactions at the interface between the sheet and the tooling. The frictional behavior of zinc coated sheet steels is influenced by the interfacial conditions present during the forming operation. Friction behavior has also been found to deviate from test method to test method. In this study, various lubrication conditions were applied to both bending under tension (BUT) test and a draw bead simulator (DBS) test for friction evaluations. Two different zinc coated steels; electrogalvanized (EG) and hot-dip galvanized (HDGI) were included in the study. In addition to the coated steels, a non-coated cold roll steel was also included for comparison purpose.

As passenger vehicle design evolves and accelerates, the use of multi-body dynamics solvers has proven to be invaluable in the engineering workflow. MBD solvers allow engineers to build virtual vehicle models that can accurately simulate vehicle responses and calculate internal forces, which previously could only be assessed using physical prototype builds with hundreds of measurement transducers. Evaluation and selection of solvers within an engineering environment is inherently a multi-dimensional activity that can include ease of use, retention of previously developed expertise, accuracy, speed, and integration with existing analysis processes. We discuss here some of the challenges present in developing capability and accumulating data to support each of these criteria. Developing a pilot model that is capable of being applied to a comprehensive set of use cases, and then verifying those use cases, required significant project management activity.

Fretting is an important phenomenon that happens in many mechanical parts. It is the main reason in deadly failures in automobiles, airliners, and turbine engines. The damage is noticed between two surfaces clamped together by bolts or rivets that are nominally at rest, but have a small amplitude oscillation because of vibration or local cyclic loading. Fretting damage can be divided into two types. The first type is the fretting fatigue damage where a crack would initiate and propagate at specific location at the interface of the mating surfaces. Cracks usually initiate in the material with lower strength because of the local cyclic loading conditions which eventually lead to full failure. The second type is the fretting wear damage because of external vibration. Researchers have investigated this phenomenon by theoretical modeling and experimental approaches. Although a lot of research has been done on fretting damage, some of the parameters have not been well studied.

The engine operating point (EOP), which is determined by the engine speed and torque, is an important part of a vehicle's powertrain performance and it impacts FC, available propulsion power, and emissions. Predicting instantaneous EOP in real-time subject to dynamic driver behaviour and environmental conditions is a challenging problem, and in existing literature, engine performance is predicted based on internal powertrain parameters. However, a driver cannot directly influence these internal parameters in real-time and can only accommodate changes in driving behaviour and cabin temperature. It would be beneficial to develop a direct relationship between the vehicle-level parameters that a driver could influence in real-time, and the instantaneous EOP. Such a relationship can be exploited to dynamically optimize engine performance.

Seat lateral support is often talked about as a design parameter, but usually in terms of psychological perception. There are many difficulties in quantifying lateral support mechanically to the engineering teams: Anthropometric variation causes different people to interact with the seat in different places and at different angles, BPD studies are usually planar and don’t distinguish between horizontal support and vertical resistance to sinking in, most mechanical test systems are typically single-DOF and can’t apply vertical and horizontal loads concurrently, and there is scant literature describing the actual lateral loads of occupants. In this study, we characterize the actual lateral loading on example seating from various sized/shaped occupants according to dynamic pressure distribution. From this information, a six-DOF load and position control test robot (KUKA OccuBot) is used to replicate that pressure distribution.

Thermoplastics find application in many automotive components. Off late, hardware testing is supplemented by analysis using finite element (FE) codes. One of the factors determining the analysis accuracy is the representation of the components with suitable material models. While a uniaxial tensile test on the specimens typically provides engineering stress-strain data, material plasticity models in commercial FE solvers, such as LS-DYNA and ABAQUS, require equivalent plastic strain versus true stress as input. Engineering stress and strain can be converted to the corresponding true stress and true strain using equations based on the constant volume assumption; however, these equations are valid only up to the point of necking.

Given continued industry focus on reducing parasitic losses, the ability to accurately measure the magnitude of losses on all driveline components is required. A standardized test procedure enables manufacturers and suppliers to measure component losses consistently, in addition to offering a reliable process to assess enablers for efficiency improvements. This paper reviews the development of SAE draft standard J3218, which is a comprehensive test procedure to break-in and characterize the efficiency of beam axles. Focus areas of the study included ensuring the axle’s efficiency does not change as it is being characterized, building a detailed map of efficiency at a wide range of operating points, and minimizing test time. The resulting break-in procedure uses an asymptotic regression approach to predict fully broken in efficiency of the axle and determine how much the efficiency of the axle changes during the characterization phase.

Electric Vehicles are typically thought of as being quiet and refined, but they do come with some unique N&V challenges. Some of these challenges include a natural sound that can be undesirable due to its tonal nature, presence of high frequency, discontinuities in sound, and characteristics and levels that do not always naturally increase with motor torque and vehicle speed. One approach to address those challenges is Electric Vehicle Sound Enhancement (EVSE) which is a software feature embedded within the infotainment system. EVSE can be used to improve the perception of the vehicle by enhancing the preferred natural sounds of the vehicle, masking unusual and annoying components of the sound and aurally conveying information related to the vehicle performance. A jury study was conducted to better understand how EVSE can be used to accomplish this.

Electrical connections have a normal operational temperature range. A high resistance, such as a poor connection, in an electrical circuit has been reported to cause a temperature increase exceeding normal operational range at the connection. This study measures the temperature increase in a typical automotive bolted battery cable connection with low to zero torque values and simulated high resistance under different load conditions. The torque is changed from maximum design value to 0 Nm and the temperature increase at the connection is measured. The high resistance connections, manually created by adjusting the contacts, are tested for several power loss values at the connection. The temperature rise under these conditions at the connection is measured and subsequently recorded. The maximum temperature increase at the bolted cable connection recorded at low torque values including 0 Nm torque compared to the maximum typical design value of 17 Nm is 10.5 °C.

Third generation advanced high strength steels (AHSS) that rely on the transformation of austenite to martensite have gained growing interest for implementation into vehicle architectures. Previous studies have identified a dependency of the rate of austenite decomposition on the amount of strain and the associated strain path imposed on the sheet. The rate and amount of austenite transformation can impact the work hardening behavior and tensile properties. However, a deeper understanding of the impact on toughness, and thus crash performance, is not fully developed. In this study, the strain path and strain amounts were systematically controlled to understand the associated correlation to impact toughness in the end application condition (strained and baked). Impact toughness was evaluated using an instrumented Charpy machine with a single sheet v-notch sample configuration.