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This work contributes to the overall goal of identifying and reducing noise sources and propagation in hydraulic systems. This is a general problem and a primary design concern for all fluid power applications. The need for new methods for identification of noise sources and transmission is evident in order to direct future modeling and experimental efforts aimed at reducing noise emissions of current fluid power machines. In this paper, this goal is accomplished through the formulation of noise functions used to identify contributions and transfer paths from different components of the system. An experimental method for noise transfer path analysis was developed and tested on a simple hydraulic system composed of a reference external gear pump, attached lines, and loading valve. Pressure oscillations in the working fluid are measured at the outlet of the pump. Surface vibrations are measured at multiple locations on the pump and connected system.

This work introduces a new design algorithm to optimize progressively folding thin-walled structures and in order to improve automotive crashworthiness. The proposed design algorithm is composed of three stages: conceptual thickness distribution, design parameterization, and multi-objective design optimization. The conceptual thickness distribution stage generates an innovative design using a novel one-iteration compliant mechanism approach that triggers progressive folding even on irregular structures under oblique impact. The design parameterization stage optimally segments the conceptual design into a reduced number of clusters using a machine learning K-means algorithm. Finally, the multi-objective design optimization stage finds non-dominated designs of maximum specific energy absorption and minimum peak crushing force.

Accurate modeling of the transient structure of reacting diesel jets is important as transient features like autoignition, flame propagation, and flame stabilization have been shown to correlate with combustion efficiency and pollutant formation. In this work, results from Reynolds-averaged Navier-Stokes (RANS) simulations of flame lift-off in diesel jets are examined to provide insight into the lift-off physics. The large eddy simulation (LES) technique is also used to computationally model a lifted jet flame at conditions representative of those encountered in diesel engines. An unsteady flamelet progress variable (UFPV) model is used as the turbulent combustion model in both RANS simulations and LES. In the model, a look-up table of reaction source terms is generated as a function of mixture fraction Z, stoichiometric scalar dissipation rate Xst, and progress variable Cst by solving the unsteady flamelet equations.

Fatigue damage prediction approaches in both time and frequency domains have been developed to simulate the operational life of mechanical structures under random loads. Fatigue assessment of mechanical structures and components subjected to those random loads is increasingly being addressed by frequency domain approaches because of time and cost savings. Current frequency-based fatigue prediction methods focus on stationary random loadings (stationary Power Spectral Density), but many machine components, such as jet engines, rotating machines, and tracked vehicles are subjected to non-stationary PSD conditions under real service loadings. This paper describes a new fatigue damage modeling approach capable of predicting fatigue damage for structures exposed to non-stationary (evolutionary) PSD loading conditions where the PSD frequency content is time-varying.

Electrical connectors and terminals are widely used in the automotive industry. It is desirable to mate the electrical connections using materials or coatings with low friction force to improve the ergonomics of the assembly process while maintaining good electrical conduction over the lifetime of the vehicle. We have previously shown that plasma-enhanced chemical vapor deposition (PECVD) of graphene on gold (Au) and silver (Ag) terminals can significantly reduce the insertion force (friction force during the terminal insertion process). However, the cost of this deposition method is rather high, and its high temperature process (> 400 oC) makes it impractical for materials with low melting temperatures. For example, tin (Sn) coating with a melting temperature of 232 oC is commonly used in electrical connectors, which cannot sustain the high temperature process. In this study, reduced graphene oxide was prepared using a low-cost solution process and applied onto metallic terminals.

The design of better active materials for lithium-ion batteries (LIBs) is crucial to satisfy the increasing demand of high performance batteries for portable electronics and electric vehicles. Currently, the development of new active materials is driven by physical experimentation and the designer’s intuition and expertise. During the development process, the designer interprets the experimental data to decide the next composition of the active material to be tested. After several trial-and-error iterations of data analysis and testing, promising active materials are discovered but after long development times (months or even years) and the evaluation of a large number of experiments. Bayesian global optimization (BGO) is an appealing alternative for the design of active materials for LIBs. BGO is a gradient-free optimization methodology to solve design problems that involve expensive black-box functions. An example of a black-box function is the prediction of the cycle life of LIBs.

This paper explains the basic principles behind two platform technologies that my research team has developed in the field of optical metrology and optical information processing: 1) high-speed 3D optical sensing; and 2) real-time 3D video compression and streaming. This paper will discuss how such platform technologies could benefit the automotive industry including in-situ quality control for additive manufacturing and autonomous vehicle systems. We will also discuss some of other applications that we have been working on such as crime scene capture in forensics.

Inside an automobile, hundreds of connectors and electrical terminals in various locations experience different corrosive environments. These connectors and electrical terminals need to be corrosion-proof and provide a good electrical contact for a vehicle’s lifetime. Saltwater and sulfuric acid are some of the main corrosion concerns for these electrical terminals. Currently, various thin metallic layers such as gold (Au), silver (Ag), or tin (Sn) are plated with a nickel (Ni) layer on copper alloy (Cu) terminals to ensure reliable electrical conduction during service. Graphene due to its excellent chemical stability can serve as a corrosion protective layer and prevent electrochemical oxidation of metallic terminals. In this work, effects of thin graphene layers grown by plasma-enhanced chemical vapor deposition (PECVD) on Au and Ag terminals and thin-film devices were investigated. Various mechanical, thermal/humidity, and electrical tests were performed.

Nested planetary gear trains, which consist of two integrated co-axial single-stage planetary gearsets, have recently been widely implemented in automobile transmissions and various other applications. In the current study, a non-destructive vibrational and acoustical monitoring technique is developed to detect a common type of gear grinding defect for a complex nested gear train structure. A nested gear train which has an unground pinion with unpolished teeth profile is used to exemplify the developed methodology. An experimental test stand with an open and vertical mounting configuration has been designed to acquire both vibrational and acoustical data. The measured data are investigated using several signal processing techniques to identify unground pinions in the gear system. A general frequency spectrum analysis is performed initially, which is then followed by a peak finding algorithm to identify the peaks in the spectrum.

Gas engines often utilize a small-volume pre-chamber in which fuel is injected at near stoichiometric condition to produce a hot turbulent jet which then ignites the lean mixture in the main chamber. Hot jet ignition has several advantages over traditional spark ignition, e.g., more reliable ignition of extra-lean mixtures and more surface area for ignition resulting in faster burning and improved combustion burn time. Our previous experimental results show that supersonic jets could extend the lean flammability limit of fuel/air mixtures in the main chamber in comparison to subsonic jets. The present paper investigated the characteristics of supersonic hot jets generated by combustion of stoichiometric H2/air in a pre-chamber to understand the ignition mechanism of ultra-lean mixtures by supersonic hot jets.

Noise generation in axial piston machines can be attributed to two main sources; fluid borne and structure borne. Any attempt towards noise reduction in axial piston machines should focus on simultaneous reduction of these two sources. A multi-parameter multi-objective optimization approach to design valve plates to reduce both sources of noise for pumps which operate in a wide range of operating conditions has been detailed in a previous work (Seeniraj and Ivantysynova, 2008). The focus of this paper is to explain the background and to demonstrate the functionality and usefulness of the methodology for pump design.

The cost and complexity of aircraft power systems limit the number of integrated system evaluations that can be performed in hardware. As a result, evaluations are often performed using emulators to mimic components or subsystems. As an example, aircraft generation systems are often tested using an emulator that consists of a bank of resistors that are switched to represent the power draw of one or more actuators. In this research, consideration is given to modern wide bandwidth emulators (WBEs) that use power electronics and digital controls to obtain wide bandwidth control of power, current, or voltage. Specifically, this paper first looks at how well a WBE can emulate the impedance of a load when coupled to a real-time model. Capturing the impedance of loads and sources is important for accurately assessing the small-signal stability of a system.

A common scenario in engineering design is the evaluation of expensive black-box functions: simulation codes or physical experiments that require long evaluation times and/or significant resources, which results in lengthy and costly design cycles. In the last years, Bayesian optimization has emerged as an efficient alternative to solve expensive black-box function design problems. Bayesian optimization has two main components: a probabilistic surrogate model of the black-box function and an acquisition functions that drives the design process. Successful Bayesian optimization strategies are characterized by accurate surrogate models and well-balanced acquisition functions. The Gaussian process (GP) regression model is arguably the most popular surrogate model in Bayesian optimization due to its flexibility and mathematical tractability. GP regression models are defined by two elements: the mean and covariance functions.

Shell-and-tube heat exchangers, commonly referred to as radiators, are the most prevalent type of heat exchanger within the automotive industry. A pivotal goal for automotive designers is to increase their thermal effectiveness while mitigating pressure drop effects and minimizing the associated costs of design and operation. Their design is a lengthy and intricate process involving the manual creation and refinement of computer-aided design (CAD) models coupled with iterative multi-physics simulations. Consequently, there is a pressing demand for an integrated tool that can automate these discrete steps, yielding a significant enhancement in overall design efficiency. This work aims to introduce an innovative automation tool to streamline the design process, spanning from CAD model generation to identifying optimal design configurations. The proposed methodology is applied explicitly to the context of shell-and-tube heat exchangers, showcasing the tool's efficacy.