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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.

Driveline and suspension notched components of off-road ground vehicles often experience multiaxial fatigue failures along notch locations. Large nominal load histories may induce local elasto-plastic stress and strain responses at the critical notch locations. Fatigue life prediction of such notched components requires detailed knowledge of local stresses and strains at notch regions. The notched components that are often subject to multiaxial loadings in services, experience complex stress and strain responses. Fatigue life assessment of the components utilizing non-linear Finite Element Analysis (FEA) require unfeasibly inefficient computation times and large data. The lack of more efficient and effective methods of elasto-plastic stress-strain calculation may lead to the overdesign or earlier failures of the components or costly experiments and inefficient non-linear FEA.

Due to modern trends in the automotive industry, such as vehicle electrification, light-weighting, reduced NVH (Noise, Vibration and Harshness) packaging space, etc., it is desirable to have a low profile and light-weight acoustic material with multi-functionality. If one single layer of a thin acoustic material can provide comparable absorption and transmission loss to a multilayer treatment, it will benefit the industry by saving weight, packaging space and system cost. Acoustic absorption and sound transmission loss performance of a new dissipative material at reduced weight and thickness is introduced in this paper. The acoustic performance of the material was evaluated by using random incidence absorption and transmission loss as well as in-vehicle experiment. Further potential applications for this material have been identified using the Statistical Energy Analysis (SEA) method with panel leakage considered.

In recent years, interest in microperforated panels (MPPs) has been growing in the automotive industry and elsewhere. Acoustic performance prediction is an important step toward understanding and designing MPPs. This paper outlines a start-to-finish procedure to predict the transfer impedance of a particular MPP based on its hole geometry and to further use this information in a simple plane wave application. A computational fluid dynamics (CFD) approach was used to calculate the impedance of the MPP and the results compared to impedance tube and flow resistance measurements. The transfer impedance results were then used to create a computationally efficient acoustic finite element (FE) model. The results of the acoustic FE model were also compared to impedance tube measurements.

As More Electric Aircraft design becomes the preferred system concept for several aerospace platforms, the electro-mechanical actuator (EMA) is emerging as a solution of choice for the primary flight control actuation system. This paper will give a brief history of electric actuation for flight systems, diagnosis and prognosis demonstrations and current state of health management research. AFRL and NASA working with industry and academic partners have been developing health management technologies that will help prevent the occurrence of some inherent EMA failure modes. Advanced fault diagnostics and failure prognostics were applied to the critical failure modes identified in the Failure Mode, Effects, and Criticality Analysis (FMECA). Modeling and simulation of EMA with degraded components were developed to support the design and evaluation of physics-based algorithms. Test data were generated using EMA hardware to validate high-fidelity EMA and physics-of-failure models.

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.

Several methods for evaluating damping material performance are commonly used, such as Oberst beam test, power injection method and the long bar test. Among these test methods, the Oberst beam test method has been widely used in the automotive industry and elsewhere as a standard method, allowing for slight bar dimension differences. However, questions have arisen as to whether Oberst test results reflect real applications. Therefore, the long bar test method has been introduced and used in the aerospace industry for some time. In addition to the larger size bar in the long bar test, there are a few differences between Oberst (cantilever) and long bar test (center-driven) methods. In this paper, the differences between Oberst and long bar test methods were explored both experimentally and numerically using finite element analysis plus an analytical method. Furthermore, guidelines for a long bar test method are provided.

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.

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.

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.

Due to the inherent computational cost of multiphysics topology optimization methods, it is a common practice to implement these methods in two-dimensions. However most real-world multiphysics problems are best optimized in three-dimensions, leading to the necessity for large-scale multiphysics topology optimization codes. To aid in the development of these codes, this paper presents a general thermomechanical topology optimization method and describes how to implement the method into a preexisting large-scale three-dimensional topology optimization code. The weak forms of the Galerkin finite element models are fully derived for mechanical, thermal, and coupled thermomechanical physics models. The objective function for the topology optimization method is defined as the weighted sum of the mechanical and thermal compliance. The corresponding sensitivity coefficients are derived using the direct differentiation method and are verified using the complex-step method.

This paper describes the integrated modeling of a permanent magnet (PM) motor used in an electromechanical actuator (EMA). A nonlinear, lumped-element motor electric model is detailed. The parameters, including nonlinear inductance, rotor flux linkage, and thermal resistances, and capacitances, are tuned using FEM models of a real, commercial motor. The field-oriented control (FOC) scheme and the lumped-element thermal model are also described.

The Verband der Automobilindustrie (VDA) 278 is an industry method widely used to measure volatile organic compounds (VOCs). It is most commonly used in the automobile industry to measure and regulate VOC and FOG levels in automotive parts as a safety regulation. The current VDA 278 method has issues from poor accuracy, precision, and reproducibility. There is variability in data due to differences in sample type and handling as well as instrument model. There is little understanding on the reproducibility of measurements of different sample types analyzed on different makes of instruments using VDA 278 analysis. In this work, a round-robin study is performed on diverse sample types, using different makes of instruments in laboratories across the world. It uses improved method conditions developed internally, for better reproducibility, that reduce sources of error.

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.

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 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 regions at war, the increasing use of improvised explosive devices (IEDs) is the main threat against military vehicles. Large cabin”s penetrations and high gross accelerations are primary threats against the occupants” survivability. The occupants” survivability under an IED event largely depends on the design of the vehicle armor. Under a blast load, a vehicle armor should maintain its structural integrity while providing low cabin penetrations and low gross accelerations. This investigation employs Bayesian global optimization (BGO) and non-uniform rational B-splines (NURBS) to design sandwich composite armors that simultaneously mitigate the cabin”s penetrations and the reaction force at the armor”s supports. The armors are made of four layers: steel, carbon fiber reinforced polymer (CFRP), aluminum honeycomb, and CFRP.

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.

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.

For decades, ceramic fiber mats have been used to mechanically support substrates in catalytic converters. Intumescent mats, those that expand with heat, are composed primarily of ceramic fibers, vermiculite, and organic binder. The binder is required for manufacturing, handling, and installation. Unfortunately, under cool operating conditions, its effects on mat performance are often negative. While residual binder is not an automatic precursor to premature failure, it can amplify the effects of other factors such as gap control and vibration. As the mat mount material is heated, sections can become soft and pliable. In the absence of sufficient heat for complete binder removal, regions of the mat may become rigid during the cooling cycle. This results in a decrease in mat resiliency. Several tests can be used to show the relationship between binder level and material performance. These tests typically characterize expansion properties and pressure performance.