Search Results

Optimizing the trade-off between reliability and cost of operating a microgrid, including vehicles as both loads and sources, can be a challenge. Optimal energy management is crucial to develop strategies to improve the efficiency and reliability of microgrids, as well as new communication networks to support optimal and reliable operation. Prior approaches modeled the grid using MATLAB, but did not include the detailed physics of loads and sources, and therefore missed the transient effects that are present in real-time operation of a microgrid. This article discusses the implementation of a physics-based detailed microgrid model including a diesel generator, wind turbine, photovoltaic array, and utility. All elements are modeled as sources in Simulink. Various loads are also implemented including an asynchronous motor. We show how a central control algorithm optimizes the microgrid by trying to maximize reliability while reducing operational cost.

Weight reduction is very important in automotive design because of stringent demand on fuel economy. Structural optimization of dynamic systems using finite element (FE) analysis plays an important role in reducing weight while simultaneously delivering a product that meets all functional requirements for durability, crash and NVH. With advancing computer technology, the demand for solving large FE models has grown. Optimization is however costly due to repeated full-order analyses. Reanalysis methods can be used in structural vibrations to reduce the analysis cost from repeated eigenvalue analyses for both deterministic and probabilistic problems. Several reanalysis techniques have been introduced over the years including Parametric Reduced Order Modeling (PROM), Combined Approximations (CA) and the Epsilon algorithm, among others.

It is challenging to perform probabilistic analysis and design of large-scale structures because probabilistic analysis requires repeated finite element analyses of large models and each analysis is expensive. This paper presents a methodology for probabilistic analysis and reliability based design optimization of large scale structures that consists of two re-analysis methods; one for estimating the deterministic vibratory response and another for estimating the probability of the response exceeding a certain level. The deterministic re-analysis method can analyze efficiently large-scale finite element models consisting of tens or hundreds of thousand degrees of freedom and large numbers of design variables that vary in a wide range. The probabilistic re-analysis method calculates very efficiently the system reliability for many probability distributions of the design variables by performing a single Monte Carlo simulation.

Scuffing is a failure mechanism which can occur in various engineering components, such as engine cylinder kits, gears and cam/followers. In this research, the scuffing behavior of 4140 steel and ductile iron was investigated and compared through ball-on-disk scuffing tests. A step load of 22.2 N every two minutes was applied with a light mineral oil as lubricant to determine the scuffing load. Both materials were heat treated to various hardness and tests were conducted to compare the scuffing behavior of the materials when the tempered hardness of each material was the same. Ductile iron was found to have a consistently high scuffing resistance before tempering and at tempering temperatures lower than 427°C (HRC ≻45). Above 427°C the scuffing resistance decreases. 4140 steel was found to have low scuffing resistance at low tempering temperatures, but as the tempering temperature increases, the scuffing resistance increased.

The classical definition of reliability may not be readily applicable for repairable systems. Commonly used concepts such as the Mean Time Between Failures (MTBF) and availability can be misleading because they only report limited information about the system functionality. In this paper, we discuss a set of metrics that can help with the design of repairable systems. Based on a set of desirable properties for these metrics, we select a minimal set of metrics (MSOM) which provides the most information about a system, with the smallest number of metrics. The metric of Minimum Failure Free Period (MFFP) with a given probability generalizes MTBF because the latter is simply the MFFP with a 0.5 probability. It also generalizes availability because coupled with repair times it provides a clearer picture of the length of the expected uninterrupted service. Two forms of MFFP are used: transient and steady state.

In this paper, the development of random vibration testing schedules for durability design verification of engine mounted products is presented, based on the equivalent fatigue damage concept and the 95th-percentile customer engine usage data for 150,000 miles. Development of the 95th-percentile customer usage profile is first discussed. Following that, the field engine excitation and engine duty cycle definition is introduced. By using a simplified transfer function of a single degree-of-freedom (SDOF) system subjected to a base excitation, the response acceleration and stress PSDs are related to the input excitation in PSD, which is the equivalent fatigue damage concept. Also, the narrow-band fatigue damage spectrum (FDS) is calculated in terms of the input excitation PSD based on the Miner linear damage rule, the Rayleigh statistical distribution for stress amplitude, a material's S-N curve, and the Miles approximate solution.

An existing challenge in current state-of-the-art autonomous vehicles is the process of safely transferring control from autonomous driving mode to manual mode because the driver may be distracted with secondary tasks. Such distractions may impair a driver’s situational awareness of the driving environment which will lead to fatal outcomes during a handover. Current state-of-the-art vehicles notify a user of an imminent handover via auditory, visual, and physical alerts but are unable to improve a driver’s situational awareness before a handover is executed. The overall goal of our research team is to address the challenge of providing a driver with relevant information to regain situational awareness of the driving task. In this paper, we introduce a novel approach to estimating a driver’s visual focus of attention using a 2D RGB camera as input to a Multi-Input Convolutional Neural Network with shared weights. The system was validated in a realistic driving scenario.

High-fidelity finite element (FE) tire-snow interaction models have the advantage of better understanding the physics of the tire-snow system. They can be used to develop semi-analytical models for vehicle design as well as to design and interpret field test results. For off-terrain conditions, there is a high level of uncertainties inherent in the system. The FE models are computationally intensive even when uncertainties of the system are not taken into account. On the other hand, field tests of tire-snow interaction are very costly. In this paper, dynamic metamodels are established to interpret interaction forces from FE simulation and to predict those forces by using part of the FE data as training data and part as validation data. Two metamodels are built based upon the Krieging principle: one has principal component analysis (PCA) taken into account and the other does not.

The Intelligent Ground Vehicle Competition (IGVC) is a multidisciplinary exercise in product realization for college engineering students. They design, build, and compete with autonomous vehicles in events ranging from lane following, obstacle avoidance, platooning, to Global Positioning System (GPS) navigation. Technologies involved include electronic controls, computer-based vision systems, object detection, rangefinding, and global positioning. The real world applications are in intelligent transportation systems, the military, and manufacturing automation. Students have been creative and have learned a great deal. Industry recruiters have been highly supportive.

The hole stretchability of two Aluminum Alloys (AA6111 and AA6022) are studied by using a two stages integrated finite element framework where the edge geometry and edge damages from the hole piercing processes were considered in the subsequent hole expansion processes. Experimentally it has been found that AA6022 has higher hole expansion ratios than those of AA6111. This observation has been nicely captured by finite element simulations. The main cause of differences have been identified to the volume fractions of the random distributed second phase hard particles which play a critical role in determining the fracture strains of the materials.

Reliability and resiliency (R&R) definitions differ depending on the system under consideration. Generally, each engineering sector defines relevant R&R metrics pertinent to their system. While this can impede cross-disciplinary engineering projects as well as research, it is a necessary strategy to capture all the relevant system characteristics. This paper highlights the difficulties associated with defining performance of such systems while using smart microgrids as an example. Further, it develops metrics and definitions that are useful in assessing their performance, based on utility theory. A microgrid must not only anticipate load conditions but also tolerate partial failures and remain optimally operating. Many of these failures happen infrequently but unexpectedly and therefore are hard to plan for. We discuss real life failure scenarios and show how the proposed definitions and metrics are beneficial.

Vehicle weight reduction is a significant challenge for the modern automotive industry. In recent years, the amount of vehicular components constructed from aluminum alloy has increased due to its light weighting capabilities. Automotive manufacturing processes, predominantly those utilizing various stamping applications, require a thorough understanding of aluminum fracture predictions methods, in order to accurately simulate the process using Finite Element Method (FEM) software or use it in automotive engineering manufacture. This paper presents the strain distribution of A5182 aluminum samples after punch impact under various conditions by Digital Image Correlation (DIC) system, its software also measured the complete strain history, in addition to sample curvature after it was impacted; therefore obtaining the data required to determine the amount of side-wall-curl (Aluminum sheet springback) present after formation.

An application of variation simulation for predicting vehicle interior driveline vibration is presented. The model, based on a “Monte Carlo”-style approach, predicts the noise, vibration and harshness (NVH) response of the vehicle driveline based on distributions of imbalance and runout derived from manufacturing production variation (the forcing function) and the vehicle's sensitivity to the forcing function. The model is used to illustrate the change in vehicle interior vibration that results when changes are made to production variation for runout and imbalance of driveline components, and how those same changes result in different responses based on vehicle sensitivity.

Selecting the right transportation platform is challenging, whether it is at a personal level or at an organizational level. In settings where predominantly the functional aspects rule the decision making process, defining the mobility of a vehicle is critical for comparing different offerings and making acquisition decisions. With the advent of intelligent vehicles, exhibiting partial to full autonomy, this challenge is exacerbated. The same vehicle may traverse independently and with greater tolerance for acceleration than human occupied vehicles, while, at the same time struggle with obstacle avoidance. The problem presents itself at the individual vehicle sensing level and also at the vehicle/fleet level. At the sensing and information level, one can be looking at issues of latency, bandwidth and optimal information fusion from multiple sources including privileged sensing. At the overall vehicle level, one focuses more on the ability to complete missions.

There is a continual need to apply heat treatment processes in innovative ways to optimize material performance. One such application studied in this research is carburizing followed by austempering of low carbon alloy steels, AISI 8620, AISI 8822 and AISI 4320, to produce components with high strength and toughness. This heat treatment process was applied in two steps; first, carburization of the surface of the parts, second, the samples were quenched from austenitic temperature at a rate fast enough to avoid the formation of ferrite or pearlite and then held at a temperature just above the martensite starting temperature to partially or fully form bainite. Any austenite which was not transformed during austempering, upon further cooling formed martensite or was present as retained austenite.

Durability of automotive structures is a primary engineering consideration that is evaluated during a vehicle's design and development. In addition, it is a basic expectation of consumers, who demand ever-increasing levels of quality and dependability. Automakers have developed corporate requirements for vehicle system durability which must be met before a products is delivered to the customer. To provide early predictions of vehicle durability, prior to the construction and testing of prototypes, it is necessary to predict the forces generated in the vehicle structure due to road inputs. This paper describes an application of the “virtual proving ground” approach for vehicle durability load prediction for a vehicle on proving ground road surfaces. Correlation of the results of such a series of simulations will be described, and the modeling and simulation requirements to provide accurate simulations will be presented.

Light-weighting vehicles cause an increase in Aluminum Alloy stamping processes in the Automotive Industry. Surface finish and lubricants of aluminum alloy (AA) sheet play an important role in the deep drawing processes as they can affect the friction condition between the die and the sheet. This paper aims to develop a reliable and practical laboratory test method to experimentally investigate the influence of surface finish, lubricant conditions, draw-bead clearances and pulling speed on the frictional sliding behavior of AA 6XXX sheet metal. A new double-beads draw-bead-simulator (DBS) system was used to conduct the simulated test to determine the frictional behavior of an aluminium alloy with three surface lubricant conditions: mill finish (MF) with oil lube, electric discharge texture (EDT) finish with oil lube and mill finish (MF) with dry lube (DL).

Countless articles and publications3,4,5 have documented and proven the efficacy, benefits and value of operating within a lean system. Furthermore, there exists common agreement amongst leading organizations successfully implementing a lean system that in order to do so it must take into consideration the entire enterprise, that is, from supplier to customer and everything in between6. One of the core issues this paper addresses is when the optimal time is to train and educate the people who currently have, or will have, influence over the ‘enterprise’.

Motivated by experiences in the Formula SAE® competition, an engine control unit (ECU) was designed, developed and tested at Oakland University. A systems approach was taken in which the designs of the electronic architecture and software were driven by the mechanical requirements and operational needs of the engine, and by the need for dynamometer testing and tuning functions. An ECU, powered by a 68HC12 microcontroller was developed, including a four-layer circuit board designed for EMC. A GUI was written with Visual C++® for communication with a personal computer (PC). The ECU was systematically tested with an engine simulator, a 2L Ford engine and a 600cc Honda engine, and finally in Oakland's 2004 FSAE vehicle.

Off-road vehicle operation requires constant decision-making under great uncertainty. Such decisions are multi-faceted and range from acquisition decisions to operational decisions. A major input to these decisions is terrain information in the form of soil properties. This information needs to be propagated to path planning algorithms that augment them with other inputs such as visual terrain assessment and other sensors. In this sequence of steps, many resources are needed, and it is not often clear how best to utilize them. We present an integrated approach where a mission’s overall performance is measured using a multiattribute utility function. This framework allows us to evaluate the value of acquiring terrain information and then its use in path planning. The computational effort of optimizing the vehicle path is also considered and optimized. We present our approach using the data acquired from the Keweenaw Research Center terrains and present some results.