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Autonomous Vehicles (AVs) operate based on image information and 3D maps generated by sensors like cameras, LIDARs and RADARs. This information is processed by the on-board processing units to provide the right actuation signals to drive the vehicle. For safe operation, these sensors should provide continuous high quality data to the processing units without interruption in all driving conditions like dust, rain, snow and any other adverse driving conditions. Any contamination on the sensor surface/lens due to rain droplets, snow, and other debris would result in adverse impact to the quality of data provided for sensor fusion and this could result in error states for autonomous driving. In particular, snow is a common contamination condition during driving that might block a sensor surface or camera lens. Predicting and preventing snow accumulation over the sensor surface of an AV is important to overcome this challenge.

This article presents an approach for comparing alternative repairable systems and calculating the value of information obtained by testing a specified number of such systems. More specifically, an approach is presented to determine the value of information that comes from field testing a specified number of systems in order to appropriately estimate the reliability metric associated with each of the respective repairable systems. Here the reliability of a repairable system will be measured by its failure rate. In support of the decision-making effort, the failure rate is translated into an expected utility based on a utility curve that represents the risk tolerance of the decision-maker. The algorithm calculates the change of the expected value of the decision with the sample size. The change in the value of the decision represents the value of information obtained from testing.

Reliability analysis of engineering structures such as bridges, airplanes, and cars require calculation of small failure probabilities. These probabilities can be calculated using standard Monte Carlo simulation, but this method is impractical for most real-life systems because of its high computational cost. Many studies have focused on reducing the computational cost of a reliability assessment. These include bootstrapping, Separable Monte Carlo, Importance Sampling, and the Combined Approximations. The computational cost can also be reduced using an efficient method for deterministic analysis such as the mode superposition, mode acceleration, and the combined acceleration method. This paper presents and demonstrates a method that uses a combination of Sobol quasi-random sequences and bootstrapping to reduce the number of function calls. The study demonstrates that the use of quasi-random numbers in conjunction bootstrapping reduces dramatically computational cost.

Design optimization occurs through a series of decisions that are a standard part of the product development process. Decisions are made anywhere from concept selection to the design of the assembly and manufacturing processes. The effectiveness of these decisions is based on the information available to the decision maker. Decision analysis provides a structured approach for quantifying the value of information that may be provided to the decision maker. This paper presents a process for determining the value of information that can be gained by evaluating linearly correlated design alternatives. A unique approach to the application of Bayesian Inference is used to provide simulated estimates in the expected utility with increasing observations sizes. The results provide insight into the optimum observation size that maximizes the expected utility when assessing correlated decision alternatives.

There is an increasing interest in transient thermal simulations of automotive brake systems. This paper presents a high-fidelity CFD tool for modeling complete braking cycles including both the deceleration and acceleration phases. During braking, this model applies the frictional heat at the interface on the contacting rotor and pad surfaces. Based on the conductive heat fluxes within the surrounding parts, the solver divides the frictional heat into energy fluxes entering the solid volumes of the rotor and the pad. The convective heat transfer between the surfaces of solid parts and the cooling airflow is simulated through conjugate heat transfer, and the discrete ordinates model captures the radiative heat exchange between solid surfaces. It is found that modeling the rotor rotation using the sliding mesh approach provides more realistic results than those obtained with the Multiple Reference Frames method.

Over the last two decades inverse problems have become increasingly popular due to their widespread applications. This popularity continuously demands designers to find alternative methods, to solve the inverse problems, which are efficient and accurate. It is important to use effective techniques that are both accurate and computationally efficient. This paper presents a method for solving inverse problems through Artificial Neural Network (ANN) theory. The paper also presents a method to apply Grey Wolf optimizer (GWO) algorithm to inverse problems. GWO is a recent optimization method producing superior results. Both methods are then compared to traditional methods such as Particle Swarm Optimization (PSO) and Markov Chain Monte Carlo (MCMC). Four typical engineering design problems are used to compare the four methods. The results show that the GWO outperforms other methods both in terms of efficiency and accuracy.

There is randomness in both the applied loads and the strength of systems. Therefore, to account for the uncertainty, the safety of the system must be quantified using its reliability. Monte Carlo Simulation (MCS) is widely used for probabilistic analysis because of its robustness. However, the high computational cost limits the accuracy of MCS. Smarslok et al. [2010] developed an improved sampling technique for reliability assessment called Separable Monte Carlo (SMC) that can significantly increase the accuracy of estimation without increasing the cost of sampling. However, this method was applied to time-invariant problems involving two random variables. This paper extends SMC to problems with multiple random variables and develops a novel method for estimation of the standard deviation of the probability of failure of a structure. The method is demonstrated and validated on reliability assessment of an offshore wind turbine under turbulent wind loads.

The Combined Approximation (CA) method is an efficient reanalysis method that aims at reducing the cost of optimization problems. The CA uses results of a single exact analysis, and it is suitable for different types of structures and design variables. The second author utilized CA to calculate the frequency response function of a system at a frequency of interest by using the results at a frequency in the vicinity of that frequency. He showed that the CA yields accurate results for small frequency perturbations. This work demonstrates a methodology that utilizes CA to reduce the cost of Monte Carlo simulation (MCs) of linear systems under random dynamic loads. The main idea is to divide the power spectral density function (PSD) of the input load into several frequency bins before calculating the load realizations.

We propose a composite thermal model of the vehicle passenger compartment that can be used to predict and analyze thermal comfort of the occupants of a vehicle. Physical model is developed using heat flow in and out of the passenger compartment space, comprised of glasses, roof, seats, dashboard, etc. Use of a model under a wide variety of test conditions have shown high sensitivity of compartment air temperature to changes in the outside air temperature, solar heat load, temperature and mass flow of duct outlet air from the climate control system of a vehicle. Use of this model has subsequently reduced empiricism and extensive experimental tests for design and tuning of the automatic climate control system. Simulation of the model allowed several changes to the designs well before the prototype hardware is available.

Importance Sampling is a popular method for reliability assessment. Although it is significantly more efficient than standard Monte Carlo simulation if a suitable sampling distribution is used, in many design problems it is too expensive. The authors have previously proposed a method to manage the computational cost in standard Monte Carlo simulation that views design as a choice among alternatives with uncertain reliabilities. Information from simulation has value only if it helps the designer make a better choice among the alternatives. This paper extends their method to Importance Sampling. First, the designer estimates the prior probability density functions of the reliabilities of the alternative designs and calculates the expected utility of the choice of the best design. Subsequently, the designer estimates the likelihood function of the probability of failure by performing an initial simulation with Importance Sampling.

Misfire diagnostics are required to detect missed combustion events which may cause an increase in emissions and a reduction in performance and fuel economy. If the misfire detection system is based on crankshaft speed measurement, driveline torque variations due to rough road can hinder the diagnosis of misfire. A common method of rough road detection uses the ABS (Anti-Lock Braking System) module to process wheel speed sensor data. This leads to multiple integration issues including complexities in interacting with multiple suppliers, inapplicability in certain markets and lower reliability of wheel speed sensors. This paper describes novel rough road detection concepts based on signal processing and statistical analysis without using wheel speed sensors. These include engine crankshaft and Transmission Output Speed (TOS) sensing information. Algorithms that combine adaptive signal processing and specific statistical analysis of this information are presented.

A complete probabilistic model of uncertainty in probabilistic analysis and design problems is the joint probability distribution of the random variables. Often, it is impractical to estimate this joint probability distribution because the mechanism of the dependence of the variables is not completely understood. This paper proposes modeling dependence by using copulas and demonstrates their representational power. It also compares this representation with a Monte-Carlo simulation using dispersive sampling.

General Motors introduced a family of small front wheel drive six speed automatic transmissions for the 2008 model year. The family currently has two variants: 6T40 and 6T45, which cover a range of vehicles from small & compact cars to small SUVs and handle engines torque capacities up to 240 Nm Gas(280 Nm Diesel) & 315 Nm Gas (380 Nm Diesel) respectively. The 6T40/45 transmissions replace GM traditional four speed automatic wrap around transmissions 4T40/45. The wrap around transmissions have Torque Converter, Pump & Controls on the engine axis and the rest of the transmission content on the output axis. The 6T40/45 have an on-axis architecture with majority of the transmission content on the engine axis and final drive & differential on the output axis. The 4T40/45 have input chain transfer whereas the 6T40/45 have an output chain transfer.

Homogenous Charge Compression Ignition (HCCI) combustion offers significant efficiency improvements compared to conventional gasoline engines. However, due to the nature of HCCI combustion, traditional HCCI engines show some degree of sensitivity to in-cylinder thermal conditions; thus higher cylinder-to-cylinder variation was observed especially at low load and high load operating conditions due to different injector characteristics, different amount of reforming as well as non-uniform EGR distribution. To address these issues, a cylinder balancing control strategy was developed for a multi-cylinder engine. In particular, the cylinder balancing control strategy balances CA50 and AF ratio at high load and low load conditions, respectively. Combustion noise was significantly reduced at high load while combustion stability was improved at low load with the cylinder balancing control.

Magnesium alloy extrusions offer potentially more mass saving compared to magnesium castings. One of the tasks in the United States Automotive Materials Partnership (USAMP) ?Magnesium Front End Research and Development? (MFERD) project is to evaluate magnesium extrusion alloys AM30, AZ31 and AZ61 for automotive body applications. Solid and hollow sections were made by lowcost direct extrusion process. Mechanical properties in tension and compression were tested in extrusion, transverse and 45 degree directions. The tensile properties of the extrusion alloys in the extrusion direction are generally higher than those of conventional die cast alloys. However, significant tension-compression asymmetry and plastic anisotropy need to be understood and captured in the component design.

Magnesium alloys are the lightest structural metal and recently attention has been focused on using them for structural automotive components. Fatigue and durability studies are essential in the design of these load-bearing components. In 2006, a large multinational research effort, Magnesium Front End Research & Development (MFERD), was launched involving researchers from Canada, China and the US. The MFERD project is intended to investigate the applicability of Mg alloys as lightweight materials for automotive body structures. The participating institutions in fatigue and durability studies were the University of Waterloo and Ryerson University from Canada, Institute of Metal Research (IMR) from China, and Mississippi State University, Westmorland, General Motors Corporation, Ford Motor Company and Chrysler Group LLC from the United States.

Fully Flexible Valve Actuation (FFVA) systems provide maximum flexibility to adjust lift profiles of engine intake and exhaust valves. A research grade electro-hydraulic servo valve based FFVA system was designed to be used with an engine in a test cell to precisely follow desired lift profiles. Repetitive control was chosen as the control strategy. Crank angle instead of time is used to trigger execution to ensure repeatability. A single control is used for different engine speeds even though the period for one revolution changes with engine speeds. The paper also discusses lift profile extension, instantaneous lift profile switching capability and built-in safety features.

Brake caliper and corner behavior in the off-brake condition can lead, at times, to brake system performance issues such as residual drag (and related issues such as pulsation, judder, and loss of fuel economy), and caliper pryback during aggressive driving maneuvers. The dynamics in the brake corner can be strikingly complex, with numerous friction interfaces, rubber component and grease dynamics, deflections of multiple components, and significant dependence on usage conditions. Displacements of moving parts are usually small, and the residual forces in the caliper interfaces involved are also small in comparison with other forces acting on the same components, making direct observation very difficult. The present work attempts to illuminate off-brake behavior in two different conditions - residual drag and pryback - through the use of low-range pressure distribution sensors placed in between the caliper (pistons and fingers) and the brake pad pressure plates.

Casting, machining and structural simulations were completed on a V8 engine block made in Compacted Graphite Iron (CGI) for use in a racing application. The casting and machining simulations generated maps of predicted tensile strength and residual stress in the block. These strength and stress maps were exported to a finite element structural model of the machined part. Assembly and operating loads were applied, and stresses due to these loads were determined. High cycle fatigue analysis was completed, and three sets of safety factors were calculated using the following conditions: uniform properties and no residual stress, predicted properties and no residual stress, and predicted properties plus residual stress.

A Reliability-Based Design Optimization (RBDO) method for multiple failure regions is presented. The method uses a Probabilistic Re-Analysis (PRRA) approach in conjunction with an approximate global metamodel with local refinements. The latter serves as an indicator to determine the failure and safe regions. PRRA calculates very efficiently the system reliability of a design by performing a single Monte Carlo (MC) simulation. Although PRRA is based on MC simulation, it calculates “smooth” sensitivity derivatives, allowing therefore, the use of a gradient-based optimizer. An “accurate-on-demand” metamodel is used in the PRRA that allows us to handle problems with multiple disjoint failure regions and potentially multiple most-probable points (MPP). The multiple failure regions are identified by using a clustering technique. A maximin “space-filling” sampling technique is used to construct the metamodel. A vibration absorber example highlights the potential of the proposed method.