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Notched spoilers may be used to suppress flow-induced cavity resonance in vehicles with open sunroofs or side windows. The notches are believed to generate streamwise vortices that break down the structure of the leading edge cross-stream vortices predominantly responsible for the cavity excitation. The objectives of the present study were to gain a better understanding of the buffeting suppression mechanisms associated with notched spoilers, and to gather data for computational model verification. To this end, experiments were performed to characterize the surface pressure field downstream of straight and notched spoilers mounted on a rigid wall to observe the effects of the notches on the static and dynamic wall pressure. Detailed flow velocity measurements were made using hot-wire anemometry. The results indicated that the presence of notches on the spoiler reduces drag, and thus tends to move the flow reattachment location closer to the spoiler.

A methodology for modeling elastomeric mounts as nonlinear lumped parameter models is discussed. A key feature of this methodology is that it integrates dynamic test results under different conditions into the model. The first step is to model the mount as a linear model that is simple but reproduces accurately results from dynamic tests under small excitations. Frequency Response Functions (FRF) enables systematic calculation of the parameters for the model. Under more realistic excitation, the mount exhibits non-linearity, which is investigated in the next step. For nonlinear structures, a simple and intuitive method is to use time-domain force-displacement (F-x) curves. Experiments to obtain the F-x curves involve controlling the displacement excitation and measuring the induced forces. From the F-x curves, stiffness and damping parameters are obtained with an optimization technique.

The damping effect that is observed when a fibrous acoustical treatment is applied to a thin metal panel typical of automotive structures has been modeled by using three independent techniques. In the first two methods the fibrous treatment was modeled by using the limp frame formulation proposed by Bolton et al., while the third method makes use of a general poro-elastic model based on the Biot theory. All three methods have been found to provide consistent predictions that are in excellent agreement with one another. An examination of the numerical results shows that the structural damping effect results primarily from the suppression of the nearfield acoustical motion within the fibrous treatment, that motion being closely coupled with the vibration of the base panel. The observed damping effect is similar in magnitude to that provided by constrained layer dampers having the same mass per unit area as the fibrous layer.

A mathematical model of a gas-charged mono-tube racing damper is presented. The model includes bleed orifice, piston leakage, and shim stack flows. It also includes models of the floating piston and the stiffness characteristics of the shim stacks. The model is validated with experimental tests on an Ohlins WCJ 22/6 damper and shown to be accurate. The model is exercised to show the effects of tuning on damper performance. The important results of the exercise are 1) the pressure variation on the compression side of the piston is insignificant relative to that on the rebound side because of the gas charge, 2) valve shim stiffness can be successfully modeled using stacked thin circular plates, 3) bleed orifice settings dominate the low speed regime, and 4) shim stack stiffness dominates the high speed regime.

One dominant “wind noise” generating mechanism in road vehicles is the interaction between turbulent flows and flexible structures which include side glass windows. In this study, the effects of seal mechanical properties on the sound generated from flow-induced vibration of side glass windows were investigated. The primary goal was to assess the influence of seal support properties on the noise generated from a plate. Two different models to calculate the optimal support stiffness of the seal that minimizes the velocity response are presented. The results show that both the velocity response and the sound radiation are strongly influenced by dissipation of vibration energy at the edges. It is demonstrate that support tuning can yield significant noise and vibration reduction.

This work proposes a new method to design crashworthiness structures that made of functionally graded cellular (porous) material. The proposed method consists of three stages: The first stage is to generate a conceptual design using a topology optimization algorithm so that a variable density is distributed within the structure minimizing its compliance. The second stage is to cluster the variable density using a machine-learning algorithm to reduce the dimension of the design space. The third stage is to maximize structural crashworthiness indicators (e.g., internal energy absorption) and minimize mass using a metamodel-based multi-objective genetic algorithm. The final structure is synthesized by optimally selecting cellular material phases from a predefined material library. In this work, the Hashin-Shtrikman bounds are derived for the two-phase cellular material, and the structure performances are compared to the optimized structures derived by our proposed framework.

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.

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.

A analytical technique for predicting the aerodynamic performance of propellers with tip devices (proplets) using vortex lattice method shows that the ideal efficiency of a fixed diameter propeller can be improved by 1-5%. By suitable orientation and sweep of the proplet, the noise analysis method presented predicts that propellers with tip devices will have approximately the same noise as propellers without tip devices. Therefore proplets can be added to a fixed diameter propeller to improve the efficiency with no increase in noise or the noise may be reduced by decreasing the diameter with no loss in aerodynamic efficiency.

“Truck Ride” in this study refers to some vehicle ride parameters involved in tractor-trailer combinations. For the study, a mathematical model of a tractor-trailer vehicle as a vibrating system was developed. Principles of vibration theory were applied to the model while a digital computer was employed to investigate the complex system. To parallel the analytical investigation of the tractor-trailer vehicle, vehicle studies were conducted using a magnetic tape recorder and associated instrumentation installed in the tractor. Parameters studied included coupler position on the tractor, laden weight of trailer, spring rates of the different axles of the combination, damping capacity associated with each spring rate, vehicle speed, and “tar strip” spacing of the highway and cab mountings. The mathematical results were used as a basis for empirical study. A comparison of calculated and empirical data are reported.

In the past decades, automotive structure design has sought to minimize its mass while maintaining or improving structural performance. As such, topology optimization (TO) has become an increasingly popular tool during the conceptual design stage. While the designs produced by TO methods provide significant performance-to-mass ratio improvements, they require considerable computational resources when solving large-scale problems. An alternative for large-scale problems is to decompose the design domain into multiple scales that are coupled with homogenization. The problem can then be solved with hierarchical multiscale topology optimization (MSTO). The resulting optimal, homogenized macroscales are de-homogenized to obtain a high-fidelity, physically-realizable design. Even so MSTO methods are still computationally expensive due to the combined costs of solving nested optimization problems and performing de-homogenization.

Spherical beamforming was used to visualize sound radiation during a vehicle passby test. Forward and backward propagation procedures are compared in terms of computational expense. A spherical spreading correction factor is described, along with a maximum liklihood procedure for obtaining an optimal array weighting dependent on the relative distance between the microphones and the focus point. The de-Dopplerized microphone outputs are multiplied by the weighting factors and summed to yield the source strengths over a reconstruction plane “attached” to the vehicle. Results obtained using a 16 element sparse array during an actual passby are used to demonstrate the present approach.

An overview of model development for seated occupants is presented. Two approaches have been investigated for modeling the vertical response of a seated dummy: finite element and simplified mass-spring-damper methods. The construction and implementation of these models are described, and the various successes and drawbacks of each modeling approach are discussed. To evaluate the performance of the models, emphasis was also placed on producing accurate, repeatable measurements of the static and dynamic characteristics of a seated dummy.

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.

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.

Tire/road noise has become a significant issue in the automotive industry, especially for electric vehicles. Among the various tire/road noise sources, the air-cavity mode can amplify the forces transmitted from the tire to the suspension system causing noticeable cabin noise near 200 Hz. Furthermore, when the tire is deformed by loading, the fundamental air-cavity mode separates into two acoustic modes, a fore-aft mode and vertical mode due to the break in geometrical symmetry. This is important because the two components of the split mode can increase force levels at the hub by interacting with neighboring structural modes, thus resulting in increased interior noise levels. In this research, finite element simulations of five commercial tires at rated load were performed with a view to identifying the frequency split and its interaction with structural resonances. These results have been compared with previously obtained empirical results.

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.

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.