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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.

The interaction of fuel sprays and in-cylinder flow in direct-injection engines is expected to alter kinetic energy and integral length scales at least during some portions of the engine cycle. High-speed particle image velocimetry was implemented in an optical four-valve, pent-roof spark-ignition direct-injection single-cylinder engine to quantify this effect. Non-firing motored engine tests were performed at 1300 RPM with and without fuel injection. Two fuel injection timings were investigated: injection in early intake stroke represents quasi-homogenous engine condition; and injection in mid compression stroke mimics the stratified combustion strategy. Two-dimensional crank angle resolved velocity fields were measured to examine the kinetic energy and integral length scale through critical portions of the engine cycle. Reynolds decomposition was applied on the obtained engine flow fields to extract the fluctuations as an indicator for the turbulent flow.

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.

This paper proposes a multi-level decoupled method for optimizing the structural design of a wind turbine blade. The proposed method reduces the design space by employing a two-level optimization process. At the high-level, the structural properties of each section are approximated by an exponential function of the distance of that section from the blade root. High-level design variables are the coefficients of this approximating function. Target values for the structural properties of the blade are determined at that level. At the low-level, sections are divided into small decoupled groups. For each section, the low-level optimizer finds the thickness of laminate layers with a minimum mass, whose structural properties meet the targets determined by the high-level optimizer. In the proposed method, each low-level optimizer only considers a small number of design variables for a particular section, while traditional, single-level methods consider all design variables simultaneously.

Inflatable space structures can have lower launching cost and larger habitat volume than their conventional rigid counterparts. These structures are made of composite laminates, and they are flexible when folded and partially inflated. They contain light-activated resins, and can be cured with the sun light after being inflated in space. A spacecraft can burst due to cracks caused by meteor showers or debris. Therefore, it is critical to identify the important fracture failure modes, and assess their probability. This information will help a designer minimize the risk of failure and keep the mass and cost low. This paper presents a probabilistic approach for finding the required thickness of an inflatable habitat shell for a prescribed reliability level, and demonstrates the superiority of probabilistic design to its deterministic counterpart.

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.

This paper presents the characterization of the random noise in driveline output shaft torque measurements that is commonly induced by road disturbances. To investigate the interaction between the shaft torque and road side excitation, torque signals are measured using a magnetoelastic torque sensor, as well as a conventional strain gauge sensor, under various types of road surfaces and conditions such as unevenness. A generalized de-trending method for producing a stationary random signal is first conducted. Statistical methods, in particular the probability density function and transform technique, are utilized to provide an evident signature for identifying the road excitation effect on the vehicle output shaft torque. Analysis results show how the road surface can act as a disturbance input to the vehicle shaft torque.

The chassis are subject to both road profile and engine or pump/motor vibration when a vehicle is moving on the road. The suspension is developed to reduce the effect of the road conditions to the chassis. The vibration from engine or pump/motor of hydraulic hybrid vehicles (HHV) will be also transmitted to the chassis and needs to be isolated. A mixed mode magnetorheological (MR) fluid mount is presented to isolate force vibration for a two degree of freedom (DOF) model based on quarter car concept. The MR fluid mount is designed to work in flow mode and squeeze mode separately and simultaneously. The skyhook control for the MR fluid mount is also been designed and simulated. Both displacement transmissibility and force transmissibility for each mode and for combined modes have been obtained. These simulation results present a basis for designing a more effective controller to control both the displacement transmissibility and force transmissibility.

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.

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.

A summary of the design and development process for a Formula SAE engine is described. The focus is on three fundamental elements on which the entire engine package is based. The first is engine layout and displacement, second is the fuel type, and third is the air induction method. These decisions lead to a design around a 4-cylinder 600cc motorcycle engine, utilizing a turbocharger and ethanol E-85 fuel. Concerns and constraints involved with vehicle integration are also highlighted. The final design was then tested on an engine dynamometer, and finally in the 2007 M-Racing FSAE racecar.

The problem of predicting the quality of weld is critical to manufacturing. A great deal of data is collected under multiple conditions to predict the quality. The data generated at Daimler Chrysler has been used to develop a model based on grammatical evolution. Grammatical Evolution Technique is based on Genetic Algorithms and generates rules from the data which fit the data. This paper describes the development of a software tool that enables the user to choose input variables such as the metal types of top and bottom layers and their thickness, intensity and speed of laser beam, to generate a three dimensional map showing weld quality. A 3D weld quality surface can be generated in response to any of the two input variables picked from the set of defining input parameters. This tool will enable the user to pick the right set of input conditions to get an optimal weld quality. The tool is developed in Matlab with Graphical User Interface for the ease of operation.

Fatigue behaviors of aluminum 5754-O spot friction welds made by a concave tool in lap-shear specimens are investigated based on experimental observations and a fatigue life estimation model. Optical micrographs of the welds before and after failure under quasi-static and cyclic loading conditions are examined. The micrographs indicate that the failure modes of the 5754 spot friction welds under quasi-static and cyclic loading conditions are quite different. The dominant kinked fatigue cracks for the final failures of the welds under cyclic loading conditions are identified. Based on the experimental observations of the paths of the dominant kinked fatigue cracks, a fatigue life estimation model based on the stress intensity factor solutions for finite kinked cracks is adopted to estimate the fatigue lives of the welds.

In this investigation, dissimilar 5754/7075 and 7075/5754 spot friction welds were first made under different processing conditions. The spot friction welds in lap-shear specimens were tested under quasi-static loading conditions. The optimal processing times to maximize the failure loads of the 5754/7075 and 7075/5754 welds under lap-shear loading conditions are identified. The maximum failure load of the 7075/5754 welds is about 40% larger than that of the 5754/7075 welds. Optical micrographs of both types of spot friction welds made at different processing times before and after failure are examined. The micrographs show different weld geometries and different failure modes of spot friction welds made at different processing times. The failure modes of the 5754/7075 and 7075/5754 spot friction welds appear to be quite complex and strongly depend on the geometry and the strength of the interfacial surface between the two deformed sheet materials.

This paper presents a computer generated model of a hydraulic circuit that is typically seen in hybrid hydraulic vehicles (HHV's). HHV's have shown considerable potential for increasing fuel economy and decreasing emissions for mid-size and commercial trucks that exhibit urban driving habits. The aim of the present model is to aid in further optimizing the performance of the hybrid powertrain and serve as a baseline for future studies into the noise and vibration (NV) associated with the system. The model simulates the regenerative braking process of a parallel type hybrid hydraulic propulsion system. Regenerative braking captures and stores otherwise lost energy into an accumulator. This stored energy can then later be used to propel the vehicle, thus reducing the vehicle's reliance on a conventional internal combustion engine (ICE). This model will serve as a baseline for future developments and will be expanded and validated experimentally in ensuing research.

The greatest demand facing the automotive industry has been to provide safer vehicles with high fuel efficiency at minimum cost. Current automotive vehicle structures have one fundamental handicap: a short crumple zone for crash energy absorption. This leaves limited room for further safety improvement, especially for high-speed crashes. Breakthrough technologies are needed. One potential breakthrough is to use active devices instead of conventional passive devices. An innovative inflatable bumper concept [1], called the “I-bumper,” is being developed by the authors for crashworthiness and safety of military and commercial vehicles. The proposed I-bumper has several active structural components, including a morphing mechanism, a movable bumper, two explosive airbags, and a morphing lattice structure with a locking mechanism that provides desired rigidity and energy absorption capability during a vehicular crash.