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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.

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.

The need for greater capacity in automotive transportation (in the midst of constrained resources) and the convergence of key technologies from multiple domains may eventually produce the emergence of a “swarm” concept of operations. The swarm, a collection of vehicles traveling at high speeds and in close proximity, will require management techniques to ensure safe, efficient, and reliable vehicle interactions. We propose a shared-autonomy approach in which the strengths of both human drivers and machines are employed in concert for this management. A fuzzy logic-based control implementation is combined with a genetic algorithm to select the shared-autonomy architecture and sensor capabilities that optimize swarm operations.

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.

This paper presents the continuation of the modeling work described in a companion paper “Modeling of Nonlinear Elastomeric Mounts. Part 1: Dynamic Testing and Parameter Identification” by the same authors. That paper discussed a dynamic test procedure and an optimization methodology to identify and model an elastomeric mount as a non-linear lumped parameter structure. This paper discusses a numerical modeling methodology to confirm or improve the agreement between the dynamic test results and the input-output relationship of the analytical model generated in the companion paper. In this paper, the model developed in the companion paper and the model parameters are input into a dynamic simulation model using a commercial simulation package. The model is then run to produce the numerical force-versus-displacement (F-x) curves of the mount. The numerical F-x curves are compared with the F-x curves obtained from the experiments.

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.

Design optimization for functionality, and manufacturability was virtually impossible in the past. However, recent standardization of file storing formats resulted in seamless data transfer from one software package to another; thus, allowing integration of all facets of product design optimization. This paper describes a metalcasting design optimization process. It focuses on the design of cast parts according to functional requirements while optimizing shape with respect to structural integrity, while ascertaining that the part can be manufactured (cast) without defects.

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.

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.

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.

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.

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.

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.

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.