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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.

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.

A synchronous machine model is set forth that simultaneously incorporates magnetizing path saturation, leakage saturation, and transfer function representations of the rotor circuits. A parameter identification procedure consisting of voltage step tests as well as standstill frequency response tests is described. The model's predictions are validated using the Naval Combat Survivability Generation and Propulsion test bed.

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.

Typically, earthmoving machines do not have wheel suspensions. This lack of components often causes uncomfortable driving, and in some cases reduces machine productivity and safety. Several solutions to this problem have been proposed in the last decades, and particularly successful is the passive solution based on the introduction of accumulators in the hydraulic circuit connecting the machine boom. The extra capacitance effect created by the accumulator causes a magnification of the boom oscillations, in such a way that these oscillations counter-react the machine oscillation caused by the driving on uneven ground. This principle of counter-reacting machine oscillations through the boom motion can be achieved also with electro-hydraulic solutions, properly actuating the flow supply to the boom actuators on the basis of a feedback sensors and a proper control strategy.

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.

Resource recovery, including that of urine water extraction, is one of the most crucial aspects of long-term life support in interplanetary space travel. This paper will consequently examine an innovative approach to processing raw, undiluted urine based on low-temperature freezing. This strategy is uniquely different from NASA's current emphasis on either ‘integrated’ (co-treatment of mixed urine, grey, and condensate waters) or ‘high-temperature’ (i.e., VCD [vapor compression distillation] or VPCAR [vapor phase catalytic ammonia removal]) processing strategies, whereby this liquid freeze-thaw (LiFT) procedure would avoid both chemical and microbial cross-contamination concerns while at the same time securing highly desirable reductions in likely ESM levels.

As part of the NASA Specialized Center of Research and Training for Advanced Life Support (NSCORT-ALS) at Purdue University, a complementary disinfection process, which uses ultraviolet (UV) radiation as the primary disinfectant and iodine as the secondary, residual disinfectant, is being developed. UV radiation was selected as the primary disinfectant because it is effective at inactivating a broad spectrum of microorganisms and has minimal potential for the formation of disinfection byproducts. Iodine, which is effective at inactivating many microorganisms and is less likely to react and form disinfection byproducts than other halogens, was selected as the residual disinfectant because it has the potential for dual use as an on-line UV monitor, as well as a disinfectant.

Modern on-road vehicles have been making steady strides when it comes to employing technological advances featuring active safety systems. However, off-highway machines are lagging in this area and are in dire need for modernization. One chassis system that has been receiving much attention in the automotive field is the steering system, where several electric and electrohydraulic steering architectures have been implemented and steer-by-wire technologies are under current research and development activities. On the other hand, off-highway articulated steering vehicles have not adequately evolved to meet the needs of Original Equipment Manufacturers (OEM) as well as their end customers. Present-day hydrostatic steering systems are plagued with poor energy efficiency due to valve throttling losses and are considered passive systems relative to safety, adjustability, and comfort.

This paper presents the Statistical Energy Approach (SEA) method for estimating the gross response in complex interconnected structural systems. The method is intended to compensate for the difficulties present in evaluating parameters and excitation needed when attempting to use traditional methods of linear vibration analysis. The amount of information needed to apply the method is modest and the formulas are easy to use. Some limitation on application is demonstrated by a detailed example.

A common scenario in engineering design is the availability of several black-box functions that describe an event with different levels of accuracy and evaluation cost. Solely employing the highest fidelity, often the most expensive, black-box function leads to lengthy and costly design cycles. Multi-fidelity modeling improves the efficiency of the design cycle by combining information from a small set of observations of the high-fidelity function and large sets of observations of the low-fidelity, fast-to-evaluate functions. In the context of Bayesian optimization, the most popular multi-fidelity model is the auto-regressive (AR) model, also known as the co-kriging surrogate. The main building block of the AR model is a weighted sum of two Gaussian processes (GPs). Therefore, the AR model is well suited to exploit information generated by sources that present strong linear correlations.

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.

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.

A novel Blended Hydraulic Hybrid transmission architecture is presented in this paper with benefits over conventional designs. This novel configuration combines elements of a hydrostatic transmission, a parallel hybrid, and a selectively connectable high pressure accumulator using passive and actively controlled logic elements. Losses are reduced compared to existing series hybrid transmissions by enabling the units to operate efficiently at pressures below the current high pressure accumulator's pressure. A selective connection to the high pressure accumulator also allows for higher system precharge which increases regenerative braking torque and energy capture with little determent to system efficiency. Finally operating as a hydrostatic transmission increases transmission stiffness (i.e. driver response) and may improve driver feel in certain situations when compared to a conventional series hybrid transmission.

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.