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Disc brakes operate with very close proximity of the brake pads and the brake rotor, with as little as a tenth of a millimeter of movement of the pads required to bring them into full contact with the rotor to generate braking torque. It is usual for a disc brake to operate with some amount of residual drag in the fully released state, signifying constant contact between the pads and the rotor. With this contact, every miniscule movement of the rotor pushes against the brake pads and changes the forces between them. Sustained loads on the brake corner, and maneuvers such as cornering, can both produce rotor movement relative to the caliper, which can push it steadily against one or both of the brake pads. This can greatly increase the residual force in the caliper, and increase drag. This dependence of drag behavior on the movement of the brake rotor creates some vehicle-dependent behavior.

The establishment of highly-diluted combustion strategies is one of the major challenges that the next generation of sustainable internal combustion engines must face. The desirable use of high EGR rates and of lean mixtures clashes with the tolerable combustion stability. To this aim, the development of numerical models able to reproduce the degree of combustion variability is crucial to allow the virtual exploration and optimization of a wide number of innovative combustion strategies. In this study ignition experiments using a conventional coil system are carried out in a closed volume combustion vessel with side-oriented flow generated by a speed-controlled fan. Acquisitions for four combinations of premixed propane/air mixture quality (Φ=0.9,1.2), dilution rate (20%-30%) and lateral flow velocity (1-5 m/s) are used to assess the modelling capabilities of a newly developed spark-ignition model for large-eddy simulation (GLIM, GruMo-UniMORE LES Ignition Model).

In the context of vehicle electrification, improving vehicle aerodynamics is not only critical for efficiency and range, but also for driving experience. In order to balance the necessary trade-offs between drag and downforce without significant impact on the vehicle styling, we see an increasing amount of active aerodynamic solutions on high-end passenger vehicles. Active rear spoilers are one of the most common active aerodynamic features. They deploy at high vehicle speed when additional downforce is required [1, 2]. For a vehicle with an active rear spoiler, the aerodynamic performance is typically predicted through simulations or physical testing at different static spoiler positions. These positions range from fully stowed to fully deployed. However, this approach does not provide any information regarding the transient effects during the deployment of the rear spoiler, which can be critical to understanding key performance aspects of the system.

The importance of achieving good (low) assembled lateral runout of the brake disc is well recognized in the industry - it is a critical feature for avoiding issues such as wear-induced disc thickness variation and vibration/shudder during braking. Significant efforts and expense has been invested by the industry into reducing disc brake lateral runout. However, wheel assemblies also have some inherent runout, which in turn cause cyclical forces to act on the brake corner during vehicle movement. Despite the stiffness of the wheel bearing (which aligns the brake disc with the caliper and knuckle), these “tire non-uniformity” forces can be sufficient to promote deflection of the assembly that is appreciable compared to typical disc lateral runout tolerances. This paper covers measurements of this phenomenon on three different vehicles (compact, mid-size, and large cars), under a variety of operating conditions such as speed, wheel assembly runout, and wheel assembly balance.

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.

The high performance brake systems of today are usually in a delicate balance - walking the fine line between being overpowered by some of the most potent powertrains, some of the grippiest tires, and some of the most demanding race tracks that the automotive world has ever seen - and saddling the vehicle with excess kilograms of unsprung mass with oversized brakes, forcing significant compromises in drivability with oversized tires and wheels. Brake system design for high performance vehicles has often relied on a very deep understanding of friction material performance (friction, wear, and compressibility) in race track conditions, with sufficient knowledge to enable this razor’s edge design.

Robust engineering is an integral part of the quality initiative, Design For Six Sigma (DFSS), in most companies to enable good designs and products for reliability and durability. Taguchi’s signal-to-noise ratio has been considered as a good performance index for robustness for many years. An alternate approach that is direct and simple for measuring robustness is proposed. In this approach, robustness is measured in terms of an augmented output response and it is a composite index of variation and efficiency of a system. This formulation represents an engineering design intent of a product in a statistical sense, so engineers can understand, communicate, and resonate at ease. Robust formulations are illustrated and discussed with case studies for smaller-the-better, nominal-the-best, and dynamic responses. Confirmation runs of optimization show good agreement of the augmented response with the additive predictive models.

Use of electronic systems in the vehicles is increasing day by day. As Electronic Control Modules (ECMs) become a large part of the vehicle, automotive designers need to take diligent decision of selecting electrical and electronic components. Selecting these components for ECM depends on four major factors: meeting stringent vehicle requirements, performance over the lifespan, robustness/reliability and cost. There is always an urge of reducing the cost of the ECM, but robustness of the controller module must not be compromised. One electrical or electronic component failure or false fault detection not only increases warranty cost but may also stall the vehicle, and interrupts customer’s daily routine creating dissatisfaction. This paper emphasizes on the importance of understanding worst-case operating scenarios considering component tolerances over the operating range, datasheet, and impact of tolerances on performance and fault detection.

The objective of this project was to establish a process to perform reliability-based design optimization for improved robustness and understanding of variability inside door slam durability performance. The existing analysis process assesses only the nominal door design. The updated process was automated to include a large-scale DOE which defines the range of durability performance. Then a large-scale Monte Carlo simulation is run to provide a probabilistic distribution of the predicted durability performance. Many variables are included in the DOE based on manufacturing build tolerance, material & weld properties, and material thickness variation due stamping/thinning. 300 finite element model runs are done to complete the DOE and define the fatigue performance domain. From these full FEA model runs, Kriging surfaces are created to quickly provide estimated performance for 4096 Monte Carlo simulation data points.

Energy management in electric vehicles plays a significant role in both reducing energy consumption and limiting the rate of battery capacity degradation. It is especially important for systems with multiple energy storage units where optimally arbitrating power demand among the energy storage units is challenging. While many optimal control methods exist for designing a good energy management system, in this work a Reinforcement-Learning (RL) methodology is explored to design an energy management system for an electric vehicle with a Hybrid Energy Storage System (HESS) that included a battery and a supercapacitor. The energy management system is designed to optimally divide the traction power request among a battery and a super-capacitor in real-time; while trying to minimize the overall energy consumption and battery degradation.

The validation of brake discs has remained, to this day, heavily reliant on “Thermal Abuse” or “Thermal Cracking” type testing, with many procedures so dated that most engineers active in the industry today cannot even recall the origin of the test. These procedures - of which there are many variants - all share the trait of greatly accelerating durability testing by performing repeated high power (high speed and high deceleration) brake applies to drive huge temperature gradients and internal stress, and often allowing the disc to get very hot, to where the strength of the material from which the disc is constructed is significantly degraded. There is little debate about whether these procedures work; by and large disc durability issues in the field are extremely rare.

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.

Stamped components play an important role in supporting various sub-systems within a typical engine and transmission assembly. In some cases, the stamped components will not initially meet the design criteria, and material may need to be added to strengthen it. However, in other cases the component may be overdesigned, and there will be opportunities to reduce mass while still meeting all design criteria. In this latter case, multiple CAE simulations are often performed to enhance the component design by varying design parameters such as thickness, bend radius, material, etc., The conventional process will assess changes in one parameter at a time, while holding other parameters constant. Though this helps in meeting the design criteria, it is often very difficult to produce the best optimized design within the limited time span with this approach. With the aid of Altair-HyperMorph techniques, multiple design parameters can be varied simultaneously.

Multi-core systems are promising a cost-effective solution for (1) advanced vehicle features requiring dramatically more software and hence an order of magnitude more processing power, (2) redundancy and mixed-IP, mixed-ASIL isolation required for ISO 26262 functional safety, and (3) integration of previously separate ECUs and evolving embedded software business models requiring separation of different software parts. In this context, designing, optimizing and verifying the mapping and scheduling of software functions onto multiple processing cores becomes key. This paper describes several multi-core task design and scheduling design options, including function-to-task mapping, task-to-core allocation (both static and dynamic), and associated scheduling policies such as rate-monotonic, criticality-aware priority assignment, period transformation, hierarchical partition scheduling, and dynamic global scheduling.

Electric motor whine is one of the main noise sources of hybrid and electric vehicles. Compared with permanent magnetic motors, characterization and prediction of traction induction motor is particularly challenging due to high computational costs to calculate the electro-magnetic (EM) forces as noise source, as well as motor slip and harmonic orders change at different torque/speed operating conditions. Historically, induction motor NVH is designed qualitatively by optimizing motor topology including rotor bar, pole number and slot counts etc. A new integrated electromagnetic and NVH analysis method is developed and successfully validated at all dominant motor orders for an automotive traction motor, which enables quantitative prediction of induction motor N&V performance in early design stage: First, a new equivalent rotor current method is used that significantly reduces the computational time required to calculate the EM force over transient responses.

A multi-stage system level method is used to design, optimize and enhance electric motor NVH performance of General Motors’ Chevrolet Bolt battery electric vehicle (BEV). First, the rotor EM (electromagnetic) design optimizes magnet placement between adjacent poles asymmetrically, along with a pair of small slots stamped near the rotor outer surface to lower torque ripple and radial force. The size and placement of stator slot openings under each pole are optimized to lower torque ripple and radial force. Next, motor stator level FE (Finite Element) analysis and modal test correlation are performed to benchmark the orthotropic stator material properties and accurately predict modal results within 7% error below 2 kHz. Furthermore, tangential and radial EM forces are applied on motor-in-fixture subsystem FE model, which predicts surface vibration and pseudo sound power on the motor housing.

Exterior component integration presents competing performance challenges for balanced exterior styling, safety, ‘structural feel’ [1] and durability. Industry standard practices utilize noise and vibration mode maps and source-path-receiver [2] considerations for component mode frequency placement. This modal frequency placement has an influence on ‘structural feel’ and durability performance. Challenges have increased with additional styling content, geometric overhang from attachment points, component size and mass, and sensor modules. Base excitation at component attachment interfaces are increase due to relative positioning of the suspension and propulsion vehicle source inputs. These components might include headlamps, side mirrors, end gates, bumpers and fascia assemblies. Here, we establish basic expectations for the behavior of these systems, and ultimately consolidate existing rationales that are applied to these systems.

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.

Very few activities the brake engineer engages in can induce as much vexation as trying to find a satisfying routing for the flexible brake components such as hoses, wheel speed sensors, and electric parking brake cables. Ever increasing wheel end content, ever decreasing space, more complex suspensions, and bulkier (but lighter weight) suspension components provide quite the morass through which the components must be routed through. When routing is finalized - and free of any major issues - there frequently remains some combinations of articulation position and component tolerances that allow a light “friendly” touch between components (such as a sensor wire and a surface of a bracket or strut tube), or near misses where clearance exists but raises “what if” questions around what would happen if the tolerances would stack up slightly differently on another vehicle.

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.