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The transportation sector is facing three revolutions: shared mobility, electrification, and autonomous driving. To inform decision making and guide smart transportation system development at the city-level, it is critical to model and evaluate how travelers will behave in these systems. Two key components in such models are (1) individual travel demands with high spatial and temporal resolutions, and (2) travelers’ sociodemographic information and trip purposes. These components impact one’s acceptance of autonomous vehicles, adoption of electric vehicles, and participation in shared mobility. Existing methods of travel demand generation either lack travelers’ demographics and trip purposes, or only generate trips at a zonal level. Higher resolution demand and sociodemographic data can enable analysis of trips’ shareability for car sharing and ride pooling and evaluation of electric vehicles’ charging needs.

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.

This paper reviews some applications of lattice Boltzmann methods (LBM) to compute multiphase flows. The method is based on the solution of a kinetic equation which describes the evolution of the distribution of the population of particles whose collective behavior reproduces fluid behavior. The distribution is modified by particle streaming and collisions on a lattice. Modeling of physics at a mesoscopic level enables LBM to naturally incorporate physical properties needed to compute complex flows. In multiphase flows, the surface tension and phase segregation are incorporated by considering intermolecular attraction forces. Furthermore, the solution of the kinetic equations representing linear advection and collision, in which non-linearity is lumped locally, makes it parallelizable with relative ease. In this paper, a brief review of the lattice Boltzmann method relevant to engine sprays will be presented.

Layered materials are one of the most commonly used acoustical treatments in the automotive industry, and have gained increased attention, especially owing to the popularity of electric vehicles. Here, a method to model and couple layered systems with various layer types (i.e., poro-elastic layers, solid-elastic layers, stiff panels, and fluid layers) is derived that makes it possible to stably predict their acoustical properties. In contrast with most existing methods, in which an equation system is constructed for the whole structure, the present method involves only the topmost layer and its boundary conditions at two interfaces at a time, which are further simplified into an equivalent interface. As a result, for a multi-layered system, the proposed method splits a complicated system into several smaller systems and so becomes computationally less expensive.

In the dense fabric of urban areas, electric scooters have rapidly become a preferred mode of transportation. As they cater to modern mobility demands, they present significant safety challenges, especially when interacting with pedestrians. In general, e-scooters are suggested to be ridden in bike lanes/sidewalks or share the road with cars at the maximum speed of about 15-20 mph, which is more flexible and much faster than pedestrians and bicyclists. Accurate prediction of pedestrian movement, coupled with assistant motion control of scooters, is essential in minimizing collision risks and seamlessly integrating scooters in areas dense with pedestrians. Addressing these safety concerns, our research introduces a novel e-Scooter collision avoidance system (eCAS) with a method for predicting pedestrian trajectories, employing an advanced Long short-term memory (LSTM) network integrated with a state refinement module.

In this paper, a recently-developed algorithmic method of deriving the state equations of power systems containing power electronic components is described. Therein the system is described by the pertinent branch parameters and the circuit topology; however, unlike circuit-based algorithms, the difference equations are not implemented at the branch level. Instead, the composite system state equations are established. A demonstration of the computer implementation of this algorithm to model a variable-speed, constant-frequency aircraft generation system is described. Because of the large number of states and complexity of the system, particular attention is placed on the development of a model structure which provides optimal simulation efficiency.

Models for the components of a long-duration UAV power system are set forth. The models include the solar array, solar array power converter, fuel cell and electrolyzer system and corresponding power converter, and propulsion load. Based on these models, a power management control is derived, which when coupled with the component models, are used to simulate power system performance during start-up, through a day-night cycle, and through a solar eclipse.

This paper describes research to analyze widespread fatigue damage in lap joints. The particular objective is to determine when large numbers of small cracks could degrade the joint strength to an unacceptable level. A deterministic model is described to compute fatigue crack growth and residual strength of riveted panels that contain multiple cracks. Fatigue crack growth tests conducted to evaluate the predictive model are summarized, and indicate good agreement between experimental and numerical results. Monte Carlo simulations are then performed to determine the influence of statistical variability on various analysis parameters.

The basic idea behind large-eddy simulation (LES) is to accurately resolve the large energy-containing scales and to use subgrid-scale (SGS) models for the smaller scales. The accuracy of LES can be significantly impacted by the numerical discretization schemes and the choice of the SGS model. This work investigates the accuracy of low-order LES codes in the simulation of a turbulent round jet which is representative of fuel jets in engines. The turbulent jet studied is isothermal with a Reynolds number of 6800. It is simulated using Converge, which is second-order accurate in space and first-order in time, and FLEDS, developed at Purdue University, which is sixth-order accurate in space and fourth-order in time. The high-order code requires the resolution of acoustic time-scales and hence is approximately 10 times more expensive than the low-order code.

Typically, average-value models of power system components neglect harmonic information. Herein, a systematic method of including harmonic information in average-value models based on the theory of multiple reference frames is set forth. Computer simulation results show that when there is significant harmonic distortion of the ac distribution bus the models presented herein are more accurate than traditional average-value models. Furthermore, much of the computational advantage of average-value techniques over detailed modeling techniques is retained.

There has always been, from the very first UAV, a need for providing cost-effective methods of deploying unmanned aircraft systems at high altitudes. Missions for UAVs at high altitudes are used to conduct atmospheric research, perform global mapping missions, collect remote sensing data, and establish long range communications networks. The team of Gevers Aircraft, Technology Management Group, and Purdue University have designed an innovative balloon launched UAV for these near space applications. A UAV (Payload Return Vehicle) with a nested morphing wing was designed in order to meet the challenges of high altitude flight, and long range and endurance without the need for descent rate control with rockets or a feathering mode.

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.

In order to improve efficiency and increase the operation of electric vehicles, assistive energy regeneration systems can be used. A hydraulic energy recovery system is modeled to be used as a regenerative system for supplementing energy storage for a pure electric articulated passenger bus. In this study a pump/motor machine is modeled to transform kinetic energy into hydraulic energy during braking, to move the hydraulic fluid from the low pressure reservoir to the hydraulic accumulator. The simulation of the proposed system was used to estimate battery savings. It was found that on average, approximately 39% of the battery charge can be saved when using a real bus driving cycle.

A track-type grapple log skidder was dynamically modeled to allow machine modification by computer to determine the effects of these modifications on the operation of the machine in the forest. The model consisted of an undercarriage, power train, log/drag force, and logging equipment (arch and grapple). This skidder had three types of logging attachments: winch, swinging boom (grapple), and single-function arch (grapple). Each was modeled and simulated under various conditions. The dynamic model of the skidder can be used to analyze its drawbar pull capability and lateral stability with various log weights and soil types on steep slopes. Validation of this model is needed later.

This paper discusses the necessity for designing farm tractors which have logical, rather than arbitrary, safety features. The paper is directed primarily to those who buy and use industrial equipment and urges this group to exercise their influence on tractor design by purchasing only those vehicles which meet recommended standards for safety and construction.

Conventional suspension analysis of three-dimensional suspensions typically use two-dimensional analyses. This is done by projecting suspension components onto two-dimensional planes and then performing a two-dimensional analysis in each of these orthogonal planes or neglecting motions in one of the planes entirely. This requires multiple iterations because changes in one plane require a checking of their effects on motion in the other orthogonal planes. In doing so, much of the insight and accuracy gained from a three-dimensional analysis can be lost. A three-dimensional kinematic analysis approach is presented and applied to a dual A-Arm suspension system. All motions are considered instantaneously about a screw axis instead of a point as used by the usual two-dimensional modeling approach. The model predicts deflections of suspension components in response to the three-dimensional forces present at the contact patch.

The most common and lethal weapons against military vehicles are the improvised explosive devices (IEDs). In an explosion, critical cabin’s penetrations and high accelerations can cause serious injuries and death of military personnel. This investigation uses single and multi-fidelity Bayesian optimization (BO) to design sandwich composite armors for blast mitigation. BO is an efficient methodology to solve optimization problems that involve black-box functions. The black-box function of this work is the finite element (FE) simulation of the armor subjected to blast. The main two components of BO are the surrogate model of the black-box function and the acquisition function that guides the optimization. In this investigation, the surrogate models are Gaussian Process (GP) regression models and the acquisition function is the multi-objective expected improvement (MEI) function. Information from low and high fidelity FE models is used to train the GP surrogates.

Due to the instabilities that may occur in power systems with regulated loads such as those used in military aircraft, ships, and terrestrial vehicles, many analysis techniques and design methodologies have been developed to ensure stable operation for expected operating conditions. However, many of these techniques are difficult to apply to complex systems and do not guarantee large-displacement stability following major disturbances such as faults, regenerative operation, large pulsed loads, and/or the loss of generating capacity. In this paper, a design paradigm is set forth guaranteeing large-displacement stability of a power system containing a significant penetration of regulated (constant-power) loads for any value of load power up to and including the steady-state rating of the source. Initial investigations are performed using an idealized model of a dc-source to determine the minimum requirements that ensure large-displacement stability.

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.

The Indiana Space Grant Consortium is one of 52 members of the National Space Grant College and Fellowship Program (“Space Grant”), which was initiated by NASA in 1988. Space Grant is designed to be a source of NASA-related information, awards, and programs to enhance education, outreach, and workforce development for the United States. Based on the land grant model of public university education, Space Grant seeks to spread the vision of NASA to increase science, technology, engineering, and math (STEM) awareness; NASA-related education; workforce development; outreach and research activities. This paper describes the evolution of these activities in Indiana.