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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.

Nowadays, the increasing number of structural high pressure die casting (HPDC) aluminum parts need to be joined with high strength steel (HSS) parts in order to reduce the weight of vehicle for fuel-economy considerations. Self-Pierce Riveting (SPR) has become one of the strongest mechanical joining solutions used in automotive industry in the past several decades. Joining HPDC parts with HSS parts can potentially cause joint quality issues, such as joint button cracks, low corrosion resistance and low joint strength. The appropriate heat treatment will be suggested to improve SPR joint quality in terms of cracks reduction. But the heat treatment can also result in the blister issue and extra time and cost consumption for HPDC parts. The relationship between the microstructure of HPDC material before and after heat treatment with the joint quality is going to be investigated and discussed for interpretation of cracks initiation and propagation during riveting.

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.

The formation of ice on aircraft is a highly dynamic process during which ice will expand and contract upon freezing and undergoing changes in temperature. Finite element analysis (FEA) simulations were performed investigating the stress/strain response of an idealized ice sample bonded to an acrylic substrate subjected to a uniform temperature change. The FEA predictions were used to guide the placement of strain gages on custom-built acrylic and aluminum specimens. Tee rosettes were placed in two configurations adjacent to thermocouple sensors. The specimens were then placed in icing conditions such that ice was grown on top of the specimen. It was hypothesized that the ice would expand on freezing and contract as the temperature of the interface returned to the equilibrium conditions.

Near-Field Acoustical Holography (NAH) is an inverse process in which sound pressure measurements made in the near-field of an unknown sound source are used to reconstruct the sound field so that source distributions can be clearly identified. NAH was originally based on performing spatial transforms of arrays of measured pressures and then processing the data in the wavenumber domain, a procedure that entailed the use of very large microphone arrays to avoid spatial truncation effects. Over the last twenty years, a number of different NAH methods have been proposed that can reduce or avoid spatial truncation issues: for example, Statistically Optimized Near-Field Acoustical Holography (SONAH), various Equivalent Source Methods (ESM), etc.

The design optimization of thin-walled tubular structures is of relevance in the automotive industry due to their low cost, ease of manufacturing and installation, and high-energy absorption efficiency. This study presents a methodology to design thin-walled tubular structures for crashworthiness applications. During an impact, thin-walled tubular structures may exhibit progressive collapse/buckling, global collapse/buckling, or mixed collapse/buckling. From a crashworthiness standpoint, the most desirable collapse mode is progressive collapse due to its high-energy absorption efficiency, stable deformation, and low peak crush force (PCF). In the automotive industry, thin-walled components have complex structural geometries. These complexities and the several loading conditions present in a crash reduce the possibility of progressive collapse. The Hybrid Cellular Automata (HCA) method has shown to be an efficient continuum-based approach in crashworthiness design.

Structures with multiple materials have now become one of the perceived necessities for automotive industry to address vehicle design requirements such as light-weight, safety, and cost. The objective of this study is to develop a design methodology for multi-material structures accountable for vehicle crash durability. The heuristic topology synthesis approach of Hybrid Cellular Automaton (HCA) framework is implemented to generate multi-material structures with the constraint on the volume fraction of the final design. The HCA framework is integrated with ordered-SIMP (solid isotropic material with penalization) interpolation, artificial material library, as well as statistical analysis of material distribution data to ensure a smooth transition between multiple practical materials during the topology synthesis.

The kinematic flow ripple for gerotor pumps is often used as a metric for comparison among different gearsets. However, compressibility, internal leakages, and throttling effects have an impact on the performance of the pump and cause the real flow ripple to deviate from the kinematic flow ripple. To counter this phenomenon, the ports can be designed to account for fluid effects to reduce the outlet flow ripple, internal pressure peaks, and localized cavitation due to throttling while simultaneously improving the volumetric efficiency. The design of the ports is typically heuristic, but a more advanced approach can be to use a numerical fluid model for virtual prototyping. In this work, a multi-objective optimization by genetic algorithm using an experimentally validated, lumped parameter, fluid-dynamic model is used to design the port geometry.

Sandwich structures with honeycomb core are widely used in the lightweight design and impact energy absorption applications in automotive, sporting, and aerospace industries. Recently, the auxetic honeycombs with negative Poisson's ratio attract substantial attention for different engineering products. In this study, we implement Additive Manufacturing technology, experimental testing, and Finite Element Analysis (FEA) to design and investigate the mechanical behavior of a novel unit cell for sandwich structure core. The new core model contains the conventional and auxetic honeycomb cells beside each other to create a Hybrid Honeycomb (HHC) for the sandwich structure. The different designs of unit cells with the same volume fraction of 15% are 3D-printed using Fused Deposition Modeling technique, and the comparative study on the mechanical behavior of conventional honeycomb, auxetic honeycomb, and HHC structures is conducted.

This paper introduces a design methodology to tailor the acceleration and displacement responses of a vehicle structure subjected to a dynamic crushing load. The proposed approach is an extension of the hybrid cellular automaton (HCA) method, through which the internal energy density is uniformly distributed within the structure. The proposed approach, referred here to as an extended HCA (xHCA) method, receives the suitable combinations of volume fraction and a finite element meta-parameter for which the algorithm synthesizes the load paths that allow the desired crash response. Lower meta-parameter values lead designs obtained by traditional optimizers, while larger values lead to designs obtained by the HCA method. Simultaneous implementation of multiple values of meta-parameters is presented here as a further development of xHCA method.

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.

Pumped two-phase systems using mini or microchannel heat sink evaporators are prime candidates for high heat flux applications due to relatively low pumping power requirements and efficient heat removal in compact designs. A number of challenges exist in the implementation of these systems including: ensuring subcooled liquid to the pump to avoid cavitation, avoiding dry out conditions in heat exchangers that can lead to failures of the components under cooling, and avoiding flow instabilities that can damage components in an integrated system. To reduce risk and cost, modeling and simulation can be employed in the design and development of these complex systems, but such modeling must include the relevant behavior necessary to capture the above dynamic effects.

Diesel engine cylinder deactivation (CDA) can be used to reduce petroleum consumption and greenhouse gas (GHG) emissions of the global freight transportation system. Heavy duty trucks require complex exhaust aftertreatment (A/T) in order to meet stringent emission regulations. Efficient reduction of engine-out emissions require a certain A/T system temperature range, which is achieved by thermal management via control of engine exhaust flow and temperature. Fuel efficient thermal management is a significant challenge, particularly during cold start, extended idle, urban driving, and vehicle operation in cold ambient conditions. CDA results in airflow reductions at low loads. Airflow reductions generally result in higher exhaust gas temperatures and lower exhaust flow rates, which are beneficial for maintaining already elevated component temperatures. Airflow reductions also reduce pumping work, which improves fuel efficiency.

This paper describes a simulation approach for the modeling of tandem external gear pumps. A tandem gear pump is the combination of two pumps with a common drive shaft. Such design architecture finds application in certain automotive transmission systems. The model presented in this work is applicable for pumps with both helical and spur gears. The simulation model is built on the HYGESim (HYdraulic GEars machines Simulator) previously developed by the authors for external spur gear units. In this work, the model formulation is properly extended to the capabilities of simulating helical gears. Starting directly from the CAD drawings of the unit, the fluid-dynamic model solves the internal instantaneous tooth space volume pressures and the internal flows following a lumped parameter approach. The simulation tool considers also the radial micro-motion of the gears, which influences the internal leakages and the features of the meshing process.

An increase in the number of vehicles per capita coupled with stricter emission regulations have made the development of newer and better hybrid vehicle architectures indispensable. Although electric hybrids have more visibility and are now commercially available, hydraulic hybrids, with their higher power densities and cheaper components, have been rigorously explored as the alternative. Several architectures have been proposed and implemented for both on and off highway applications. The most commonly used architecture is the series hybrid, which requires an energy conversion from the primary source (engine) to the secondary domain. From he re, the power flows either into the secondary source (high-pressure accumulator) or to the wheels depending upon the state of charge of the accumulator. A mode-switching hydraulic hybrid, which is a combination of a hydrostatic transmission and a series hybrid, was recently developed in the author’s research group.

This work presents the implementation of the Efficient Global Optimization (EGO) approach for the design of composite materials under dynamic loading conditions. The optimization algorithm is based on design and analysis of computer experiments (DACE) in which smart sampling and continuous metamodel enhancement drive the design towards a global optimum. An expected improvement function is maximized during each iteration to locate the designs that update the metamodel until convergence. The algorithm solves single and multi-objective optimization problems. In the first case, the penetration of an armor plate is minimized by finding the optimal fiber orientations. Multi-objective formulation is used to minimize the intrusion and impact acceleration of a composite tube. The design variables include the fiber orientations and the size of zones that control the tube collapse.

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.

Wideband Acoustical Holography (WBH), which is a monopole-based, equivalent source procedure (J. Hald, “Wideband Acoustical Holography,” INTER-NOISE 2014), has proven to offer accurate noise source visualization results in experiments with a simple noise source: e.g., a loudspeaker (T. Shi, Y. Liu, J.S. Bolton, “The Use of Wideband Holography for Noise Source Visualization”, NOISE-CON 2016). From a previous study, it was found that the advantage of this procedure is the ability to optimize the solution in the case of an under-determined system: i.e., when the number of measurements is much smaller than the number of parameters that must be estimated in the model. In the present work, a diesel engine noise source was measured by using one set of measurements from a thirty-five channel combo-array placed in front of the engine.

Compared to moving coil loudspeakers, carbon nanotube (CNT) loudspeakers are extremely lightweight and are capable of creating sound over a broad frequency range (1 Hz to 100 kHz). The thermoacoustic effect that allows for this non-vibrating sound source is naturally inefficient and nonlinear. Signal processing techniques are one option that may help counteract these concerns. Previous studies have evaluated a hybrid efficiency metric, the ratio of the sound pressure level at a single point to the input electrical power. True efficiency is the ratio of output acoustic power to the input electrical power. True efficiency data are presented for two new drive signal processing techniques borrowed from the hearing aid industry. Spectral envelope decimation of an AC signal operates in the frequency domain (FCAC) and dynamic linear frequency compression of an AC signal operates in the time domain (TCAC). Each type of processing affects the true efficiency differently.

In modern engine design, downsizing and reducing weight while still providing an increased amount of power has been a general trend in recent decades. Traditionally, an engine design with superior NVH performance usually comes with a heavier, thus sturdier structure. Therefore, modern engine design requires that NVH be considered in the very early design stage to avoid modifications of engine structure at the last minute, when very few changes can be made. NVH design optimization of engine components has become more practical due to the development of computer software and hardware. However, there is still a need for smarter algorithms to draw a direct relationship between the design and the radiated sound power. At the moment, techniques based on modal acoustic transfer vectors (MATVs) have gained popularity in design optimization for their good performance in sound pressure prediction.