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This paper presents a computational investigation of the validity of eddy current testing (ECT) for defects embedded in steel using parametrically designed defects. Of particular focus is the depths at which defects can be detected through ECT. Building on this we characterize interior defects by parametrically describing them and then examining the response fields through measurement. Thereby we seek to establish the depth and direction of detectable cracks. As a second step, we match measurements from eddy current excitations to computed fields through finite element optimization. This develops further our previously presented methods of defect characterization. Here rough contours of synthesized shapes are avoided by a novel scheme of averaging neighbor heights rather than using complex Bézier curves, constraints and such like. This avoids the jagged shapes corresponding to mathematically correct but unrealistic synthesized shapes in design and nondestructive evaluation.

Turbulent jet ignition is a pre-chamber ignition enhancement method that produces a distributed ignition source through the use of a chemically active turbulent jet which can replace the spark plug in a conventional spark ignition engine. In this paper combustion visualization and characterization was performed for the combustion of a premixed propane/air mixture initiated by a pre-chamber turbulent jet ignition system with no auxiliary fuel injection, in a rapid compression machine. Three different single orifice nozzles with orifice diameters of 1.5 mm, 2 mm, and 3 mm were tested for the turbulent jet igniter pre-chamber over a range of air to fuel ratios. The performance of the turbulent jet ignition system based on nozzle orifice diameter was characterized by considering both the 0-10 % and the 10-90 % burn durations of the pressure rise due to combustion.

Deep learning based approaches for object detection are heavily dependent on the nature of data used for training, especially for vehicles driving in cluttered urban environments. Consequently, the performance of Convolutional Neural Network (CNN) architectures designed and trained using data captured under clear weather and favorable conditions, could degrade rather significantly when tested under cloudy and rainy conditions. This naturally becomes a major safety issue for emerging autonomous vehicle platforms relying on CNN based object detection methods. Furthermore, despite a noticeable progress in the development of advanced visual deraining algorithms, they still have inherent limitations for improving the performance of state-of-the-art object detection. In this paper, we address this problem area by make the following contributions.

The automotive industry is subject to stringent regulations in emissions and growing customer demands for better fuel consumption and vehicle performance. Engine calibration, a process that optimizes engine performance by tuning engine controls (actuators), becomes challenging nowadays due to significant increase of complexity of modern engines. The traditional sweep-based engine calibration method is no longer sustainable. To tackle the challenge, this work considers two powerful global optimization methods: genetic algorithm (GA) and Bayesian optimization for steady-state engine calibration for single speed-load point. GA is a branch of meta-heuristic methods that has shown a great potential on solving difficult problems in automotive engineering. Bayesian optimization is an efficient global optimization method that solves problems with computationally expensive testing such as hyperparameter tuning in deep neural network (DNN), engine testing, etc.

The primary function of an airbag control module is to detect crashes, discriminate and predict if a deployment is necessary, then deploy the restraint systems including airbags and where applicable, pretensioners. At General Motors (GM), the internal term for airbag control module is Sensing and Diagnostic Module (SDM). In the 1994 model year, GM introduced its SDM on some of its North American airbag-equipped vehicles. A secondary function of that SDM and all subsequent SDMs is to record crash related data. This data can include data regarding impact severity from internal accelerometers and pre-crash vehicle data from various chassis and powertrain modules. Previous researchers have addressed the accuracy of both the velocity change data, recorded by the SDM, and the pre-crash data, but the assessment of the timing of the pre-crash data has been limited to a single family of modules (Delphi SDM-G).

The paper describes a new strategy for real-time sensor diagnostics that is based on the statistical correlation of various sensor signal pairs. During normal fault-free operation there is a certain correlation between the sensor signals which is lost in the event of a fault. The proposed algorithm quantifies the correlation between sensor signal pairs using real-time scalar metrics based on the Mahalanobis-distance concept. During normal operation all metrics follow a similar pattern, however in the event of a fault; metrics involving the faulty sensor would increase in proportion to the magnitude of the fault. Thus, by monitoring this pattern and using a suitable fault-signature table it is possible to isolate the faulty sensor in real-time. Preliminary simulation results suggest that the strategy can mitigate the false-alarms experienced by most model-based diagnostic algorithms due to an intrinsic ability to distinguish nonlinear vehicle behavior from actual sensor faults.

Air-to-fuel (A/F) ratio is the mass ratio of the air-to-fuel mixture trapped inside a cylinder before combustion begins, and it affects engine emissions, fuel economy, and other performances. Using an A/F ratio and dual-fuel ratio control oriented engine model, a multi-input-multi-output (MIMO) sliding mode control scheme is used to simultaneously control the mass flow rate of both port fuel injection (PFI) and direct injection (DI) systems. The control target is to regulate the A/F ratio at a desired level (e.g., at stoichiometric) and fuel ratio (ratio of PFI fueling vs. total fueling) to any desired level between zero and one. A MIMO sliding mode controller was designed with guaranteed stability to drive the system A/F and fuel ratios to the desired target under various air flow disturbances.

Injury to the lower extremity during an automotive crash is a significant problem. While the introduction of safety features (i.e. seat belts, air bags) has significantly reduced fatalities, lower extremity injury now occurs more frequently, probably for a variety of reasons. Lower extremity trauma is currently based on a bone fracture criterion derived from human cadaver impact experiments. These impact experiments, conducted in the 1960's and 70's, typically used a rigid impact interface to deliver a blunt insult to the 90° flexed knee. The resulting criterion states that 10 kN is the maximum load allowed at the knee during an automotive crash when certifying new automobiles using anthropomorphic dummies. However, clinical studies suggest that subfracture loading can cause osteochondral microdamage which can progress to a chronic and debilitating joint disease.

This paper includes a numerical and experimental study of fluid flow in automotive catalytic converters. The numerical work involves using computational fluid dynamics (CFD) to perform three-dimensional calculations of turbulent flow in an inlet pipe, inlet cone, catalyst substrate (porous medium), outlet cone, and outlet pipe. The experimental work includes using hot-wire anemometry to measure the velocity profile at the outlet of the catalyst substrate, and pressure drop measurements across the system. Very often, the designer may have to resort to offset inlet and outlet cones, or angled inlet pipes due to space limitations. Hence, it is very difficult to achieve a good flow distribution at the inlet cross section of the catalyst substrate. Therefore, it is important to study the effect of the geometry of the catalytic converter on flow uniformity in the substrate.

Human intraspecific variation is a complex problem, but may be better understood by using computational models in tandem with knowledge about the genetic bases of phenotypic traits. These results can be used in a multitude of settings. To move closer to this goal, biologically-realistic mappings between genotype and phenotype are constructed using genetic algorithm and neural network-like models. These models allow for gene-gene and gene-environment interactions to be characterized in the resulting phenotype. Two types of model are introduced: a simple, two-layer model, and a more complex model. The final section will focus on trends of growth and development in relation to relationship to modeling anthropometric traits and other morphological phenomena.

This work discusses the development of SAE procedure J2747, “Hydraulic Pump Airborne Noise Bench Test”. This is a test procedure describing a standard method for measuring radiated sound power levels from hydraulic pumps of the type typically used in automotive power steering systems, though it can be extended for use with other types of pumps. This standard was developed by a committee of industry representatives from OEM's, suppliers and NVH testing firms familiar with NVH measurement requirements for automotive hydraulic pumps. Details of the test standard are discussed. The hardware configuration of the test bench and the configuration of the test article are described. Test conditions, data acquisition and post-processing specifics are also included. Contextual information regarding the reasoning and priorities applied by the development committee is provided to further explain the strengths, limitations and intended usage of the test procedure.

Lower extremity (knee) injury prediction resulting from impact trauma is currently based on a bone fracture criterion derived from experiments on predominantly aged cadavers. Subsequent experimental and theoretical studies indicate that more aged, pathological specimens require higher, not lower, loads to initiate bone fracture. This suggests that a bone fracture criterion based solely on aged specimens may not be representative of the current driving population. In the current study, we sought to determine if cadaver age, physical size, sex, baseline joint pathology, or patellar geometry correlated with fracture load. An analysis was made of data from previous impact experiments conducted on fifteen isolated cadaver knees using a consistent impact protocol. The protocol consisted of sequentially increasing the impact energy with a rigid interface until gross fracture. Gross bone fractures occurred at loads of 6.9±2.0 kN (range 3.2 to 10.6 kN) using this protocol.

Computer simulation was used as a complement to crash and injury field data analysis and physical sled and barrier tests to investigate and predict the spinal injuries of a rear impact in an IndyCar. The model was expected to relate the spinal loads to the observed injuries, thereby predicting the probability and location of spinal fractures. The final goal is to help reduce the fracture risk by optimizing the seat and restraint system design and the driver's position using computer modeling and sled testing. MADYMO Full FE Human Body Model (HBM) was selected for use because of its full spinal structural details and its compatibility with the vehicle and restraint system models. However, the IndyCar application imposed unique challenges to the HBM. First, the driver position in a race car is very different from that in a typical passenger car.

Production software validation is critical during software development, allowing potential quality issues that could occur in the field to be minimized. By developing automated and repeatable software test methods, test cases can be created to validate targeted areas of the control software for confirmation of the expected results from software release to release. This is especially important when algorithm/software development timing is aggressive and the management of development activities in a global work environment requires high quality, and timely test results. This paper presents a hardware-in-the-loop (HIL) test bench for the validation of production transmission controls software. The powertrain model used within the HIL consists of an engine model and a detailed automatic transmission dynamics model. The model runs in an OPAL-RT TestDrive based HIL system.

This paper describes a new tool to capture cylinder pressure information, calculate combustion parameters, and implement control algorithms. There are numerous instrumentation and prototyping systems which can provide some or all of this capability. The Cylinder Pressure Development Controller (CPDC) is unique in that it uses advanced high volume automotive grade circuitry, packaging, and software methodologies. This approach provides insight regarding the implementation of cylinder pressure based controls in a production engine management system. A high performance data acquisition system is described along with a data reduction technique to minimize data processing requirements. The CPDC software architecture is discussed along with model-based algorithm development and autocoding. Finally, CPDC calculated combustion parameters are compared with those from a well established combustion analysis system and thermodynamic simulations.

Advanced chassis control systems enable a vehicle to achieve new levels of performance in handling stability and responsiveness. In recent work by NHTSA and others, the performance of Electronic Stability Control (ESC) systems has been studied with focus on yaw stability and roll stability of vehicles on high friction surfaces. However, it is recognized that vehicle handling responsiveness is also an important aspect that should be maintained. This paper explores the trade-offs between yaw rate, side slip, and roll motions of a vehicle, and their relationships to handling stability and handling responsiveness. This paper further describes how various control systems are able to manage these motions. The paper also discusses methods to assess vehicle stability and responsiveness using specific maneuvers and measurements, and it includes data from vehicle tests on a slippery surface.

Electro-pneumatic valve actuators are used to eliminate the cam shaft of a traditional internal combustion engine. They are used to control the opening timing, duration, and lift of both intake and exhaust valves. A physics based nonlinear mathematical model called the level one model was built using Newton's law, mass conservation and thermodynamic principles. A control oriented model, the level two model, was created by partially linearizing the level one model for model reference parameter identification. This model reduces computational throughput and enables real-time implementation. A model reference adaptive control system was used to identify the nonlinear parameters that were needed for generating a feedforward control signal. The closed-loop valve lift tracking, valve opening and closing timing control strategies were proposed.

The paper describes the development of a vehicle stability indicator based on the correlation between various current vehicle chassis sensors such as hand wheel angle, yaw rate and lateral acceleration. In general, there is a correlation between various pairs of sensor signals when the vehicle operation is linear and stable and a lack of correlation when the vehicle is becoming unstable or operating in a nonlinear region. The paper outlines one potential embodiment of the technology that makes use of the Mahalanobis distance metric to assess the degree of correlation among the sensor signals. With this approach a single scalar metric provides an accurate indication of vehicle stability.

An analytical study of spray from an outwardly opening pressure swirl injector has been presented in this paper. A number of model injectors with varying design configurations have been used in this study. The outwardly opening injection process has been modeled using a modified spray breakup model presented in an earlier study. It has been observed that simulation results from the study clearly capture the mechanism by which an outwardly opening conical spray interacts with the downstream flow field. Velocity field near the tip of the injector shows that the conical streams emanating from an outwardly opening injector have the tendency to entrap air into the flow stream which is responsible for finer spray. A deviation from the optimum set of physical parameters showed a high propensity to produce large spray droplets. This study also emphasizes the importance of computational fluid dynamics (CFD) as an engineering tool to understand the complex physical processes.

During the production controller and software development process, one critical step is the controller and software verification. There are various ways to perform this verification. One of the commonly used methods is to utilize an HIL (hardware-in-the-loop) test bench to emulate powertrain hardware for development and validation of powertrain controllers and software. A key piece of an HIL bench is the plant dynamics model used to emulate the external environment of a modern controller, such as engine (ECM), transmission (TCM) or powertrain controller (PCM), so that the algorithms and their software implementation can be exercised to confirm the desired results. This paper presents a 6-speed automatic transmission plant dynamics model development for hardware-in-the-loop (HIL) test bench for the validation of production transmission controls software. The modeling method, model validation, and application in an HIL test environment are described in details.