When exposed to mechanical environments such as shock and vibration, electrical connections may experience increased levels of contact resistance associated with the physical characteristics of the electrical interface. A phenomenon known as electrical chatter occurs when these vibrations are large enough to interrupt the electric signals. It is critical to understand the root causes behind these events because electrical chatter may result in unexpected performance or failure of the system. The root causes span a variety of fields, such as structural dynamics, contact mechanics, and tribology. Therefore, a wide range of analyses are required to fully explore the physical phenomenon. This paper intends to provide a better understanding of the relationship between structural dynamics and electrical chatter events. Specifically, electrical contact assembly composed of a cylindrical pin and bifurcated structure were studied using high fidelity simulations. Structural dynamic simulations will be performed with both linear and nonlinear reduced-order models (ROM) to replicate the relevant structural dynamics. Subsequent multi-physics simulations will be discussed to relate the contact mechanics associated with the dynamic interactions between the pin and receptacle to the chatter. Each simulation method was parametrized by data from a variety of dynamic experiments. Both structural dynamics and electrical continuity were observed in both the simulation and experimental approaches, so that the relationship between the two can be established.
Piezoelectric stack actuators can convert an electrical stimulus into a mechanical displacement, which facilitates their use as a vibration-excitation mechanism for modal and vibration testing. Due to their compact nature, they are especially suitable for applications where typical electrodynamic shakers may not be physically feasible, e.g., on small-scale centrifuge/vibration (vibrafuge) testbeds. As such, this work details an approach to extract modal parameters using a distributed set of stack actuators incorporated into a vibrafuge system to provide the mechanical inputs. A derivation that considers a lumped-parameter stack actuator model shows that the transfer functions relating the mechanical responses to the piezoelectric voltages are in a similar form to conventional transfer functions relating the mechanical responses to mechanical forces, which enables typical curve-fitting algorithms to extract the modal parameters. An experimental application consisted of extracting modal parameters from a simple research structure on the centrifuge’s arm excited by the vibrafuge’s stack actuators. A modal test that utilized a modal hammer on the same structure with the centrifuge arm stationary produced similar modal parameters as the modal parameters extracted from the combined-environments testing with low-level inertial loading.
Multiple Input Multiple Output (MIMO) vibration testing provides the capability to expose a system to a field environment in a laboratory setting, saving both time and money by mitigating the need to perform multiple and costly large-scale field tests. However, MIMO vibration test design is not straightforward oftentimes relying on engineering judgment and multiple test iterations to determine the proper selection of response Degree of Freedom (DOF) and input locations that yield a successful test. This work investigates two DOF selection techniques for MIMO vibration testing to assist with test design, an iterative algorithm introduced in previous work and an Optimal Experiment Design (OED) approach. The iterative-based approach downselects the control set by removing DOF that have the smallest impact on overall error given a target Cross Power Spectral Density matrix and laboratory Frequency Response Function (FRF) matrix. The Optimal Experiment Design (OED) approach is formulated with the laboratory FRF matrix as a convex optimization problem and solved with a gradient-based optimization algorithm that seeks a set of weighted measurement DOF that minimize a measure of model prediction uncertainty. The DOF selection approaches are used to design MIMO vibration tests using candidate finite element models and simulated target environments. The results are generalized and compared to exemplify the quality of the MIMO test using the selected DOF.
Aerospace structures are often subjected to combined inertial acceleration and vibration environments during operation. Traditional qualification approaches independently assess a system under inertial and vibration environments but are incapable of addressing couplings in system response under combined environments. Considering combined environments throughout the design and qualification of a system requires development of both analytical and experimental capabilities. Recent ground testing efforts have improved the ability to replicate flight conditions and aid qualification by incorporating combined centrifuge acceleration and vibration environments in a “vibrafuge” test. Modeling these loading conditions involves the coupling of multiple physical phenomena to accurately capture dynamic behavior. In this work, finite element analysis and model validation of a simple research structure was conducted using Sandia’s SIERRA analysis suite. Geometric preloading effects due to an applied inertial load were modeled using SIERRA coupled analysis capability, and structural dynamics analysis was performed to evaluate the updated structural response compared to responses under vibration environments alone. Results were validated with vibrafuge testing, using a test setup of amplified piezoelectric actuators on a centrifuge arm.
Physics-Based Reduced Order Models (ROMs) tend to rely on projection-based reduction. This family of approaches utilizes a series of responses of the full-order model to assemble a suitable basis, subsequently employed to formulate a set of equivalent, low-order equations through projection. However, in a nonlinear setting, physics-based ROMs require an additional approximation to circumvent the bottleneck of projecting and evaluating the nonlinear contributions on the reduced space. This scheme is termed hyper-reduction and enables substantial computational time reduction. The aforementioned hyper-reduction scheme implies a trade-off, relying on a necessary sacrifice on the accuracy of the nonlinear terms’ mapping to achieve rapid or even real-time evaluations of the ROM framework. Since time is essential, especially for digital twins representations in structural health monitoring applications, the hyper-reduction approximation serves as both a blessing and a curse. Our work scrutinizes the possibility of exploiting machine learning (ML) tools in place of hyper-reduction to derive more accurate surrogates of the nonlinear mapping. By retaining the POD-based reduction and introducing the machine learning-boosted surrogate(s) directly on the reduced coordinates, we aim to substitute the projection and update process of the nonlinear terms when integrating forward in time on the low-order dimension. Our approach explores a proof-of-concept case study based on a Nonlinear Auto-regressive neural network with eXogenous Inputs (NARX-NN), trying to potentially derive a superior physics-based ROM in terms of efficiency, suitable for (near) real-time evaluations. The proposed ML-boosted ROM (N3-pROM) is validated in a multi-degree of freedom shear frame under ground motion excitation featuring hysteretic nonlinearities.
The mechanical properties of additive and traditionally manufactured alloys are largely dependent on the characteristics and distribution of dislocation cell networks that develop during the fabrication process. This work demonstrates the ability to quantitatively characterize these dislocation structures by high angular resolution electron backscatter diffraction analysis using a direct electron detector. The defect structures are characterized in terms of the geometrically necessary dislocation density and the associated Burgers vector and line direction. The results are discussed in terms of potential defect formation mechanisms.
A numerical and experimental investigation about the chemical kinetic interactions between 2-ethylhexylnitrate (EHN) and PRF91 was performed in this study. Rapid compression machine experiments were conducted to investigate the effect of EHN on the autoignition reactivity of the fuel, and a reduced chemical kinetic mechanism was developed including an EHN sub-model. Experiments showed that the ignition delay decreases as the fuel is doped with EHN, but the effect of the doping level of EHN on the ignition is highly non-linear. Moreover, experiments showed that the EHN effectiveness is lowest during the transition between the low-temperature regime and the negative temperature coefficient (NTC) regime, and it rapidly increases as the temperature increases. Both detailed and (developed) reduced mechanisms were validated against the experimental results, allowing a more in-depth EHN-fuel chemistry study. Additionally, ignition delay sensitivity and EHN effectiveness sensitivity analyses were performed with the reduced mechanism to identify the reactions that control the interaction between EHN and the fuel. As the result, EHN thermal decomposition is only relevant for very low temperatures. The chemistry of EHN-doped fuel is more sensitive to intermediate temperature reactions than that of straight fuel, especially at lower temperatures, due to the effect of EHN on the NTC behavior of the fuel. Finally, the chemistry of EHN-doped fuel is very sensitive to NO2-to-NO reactions, especially at high temperatures, because these reactions transform the NO2 generated by EHN into NO, which is a very effective fuel reactivity enhancer.
Wear and degradation of interfaces remain a significant roadblock in commonly used mechanical assemblies across various industries, resulting in loss of their efficiency and ultimately functionality. To advance the state-of-the-art in tribological applications, new materials must be developed not only to resist degradation, wear, and higher frictional losses in extreme environments (i.e. high temperatures, contact/shear forces, etc.), but also to benefit from them. By adopting hard, catalytic interfaces, a wide range of contact conditions can emerge where mechanochemical interactions lead to the formation of protective or self-healing lubricious films. Here, we demonstrate the mechanochemically driven formation of protective carbon films on Pt-Au alloys during sliding in an ethanol environment. We demonstrate the effect of temperature and contact pressure on film formation. The films formed on Pt-Au alloys exhibit a highly graphitic structure as indicated by Raman and Transmission Electron Microscopy (TEM) analyses. The observed results are further supported by molecular dynamics simulations that show the changes in the dissociation and transformation of ethanol molecules with applied pressure and temperature. The results create a new understanding of transformations in the contact and suggest a solution for addressing tribological challenges in the mechanical systems operated in low viscosity fuels.
Slip blockage during slip band intersections impacts mechanical properties of crystalline materials. Molecular dynamics simulations of eight band intersections in Fe70Ni10Cr20 alloys revealed that secondary bands always transmit into ε bands more easily than into twin bands. While this is surprising because the ε-phase has a different crystal structure from the matrix, our finding that twins do not possess easy crystallographic pathways for transmission explains this phenomenon. We also found that the band intersection regions preferentially nucleate voids. These findings provide understanding of the deformation and damage behavior of FCC metals. For example, since hydrogen promotes ε-bands, our findings can help understand hydrogen compatibility.
Developing reliable interatomic potential models with quantified predictive accuracy is crucial for atomistic simulations. Commonly used potentials, such as those constructed through the embedded atom method (EAM), are derived from semi-empirical considerations and contain unknown parameters that must be fitted based on training data. In the present work, we investigate Bayesian calibration as a means of fitting EAM potentials for binary alloys. The Bayesian setting naturally assimilates probabilistic assertions about uncertain quantities. In this way, uncertainties about model parameters and model errors can be updated by conditioning on the training data and then carried through to prediction. We apply these techniques to investigate an EAM potential for a family of gold–copper systems in which the training data correspond to density-functional theory values for lattice parameters, mixing enthalpies, and various elastic constants. Through the use of predictive distributions, we demonstrate the limitations of the potential and highlight the importance of statistical formulations for model error.
Electric Vehicles (EV) present a unique challenge to electric power system (EPS) operations because of the potential magnitude and timing of load increases due to EV charging. Time-of-Use (TOU) electricity pricing is an established way to reduce peak system loads. It is effective at shifting the timing of some customer-activated residential loads – such as dishwashers, washing machines, or HVAC systems – to off-peak periods. EV charging, though, can be larger than typical residential loads (up to 19.2 kW) and may have on-board controls that automatically begin charging according to a pre-set schedule, such as when off-peak periods begin. To understand and quantify the potential impact of EV charging’s response to TOU pricing, this paper simulates 10 distribution feeders with predicted 2030 EV adoption levels. The simulation results show that distribution EPS experience an increase in peak demand as high as 20% when a majority of the charging begins immediately after on-peak times end, as might occur if EV charging is automatically scheduled. However, if charging start times are randomized within the off-peak period, EV charging is spread out and the simulations showed a decrease in the peak load to be 5% lower than results from simulations that did not implement TOU rates.
The value of long-term wave hindcasts for investigating wave climates, wave energy resources, and extreme wave conditions has motivated research developing, calibrating and validating wave hindcast models. Past hindcast model validation studies examined the accuracy in modeling bulk wave parameters of overall sea states without considering the dependency of the model's skill within different sea states. In the present study, a framework for wave hindcast model validation is developed by examining the model accuracy for the most frequently occurring sea states, sea states contributing the most energy to total wave power, sea states associated with hurricane events, and those with the largest model error. Validations using bulk wave parameters and frequency-directional spectra at these key sea states and extreme wave conditions based on univariate and bivariate-contour methods provide insights to improve model accuracy, identifying the model's strong and weak points, and pathways for improvement, e.g., modeling wave-current interactions and adjusting wind data. This study adds to a growing body of research demonstrating that a carefully calibrated and verified spectral wave hindcast model can be used to estimate key wave energy parameters over a wide range of wave energy climates, as well as their spatial, temporal, frequency, directional, and probabilistic distributions.
Switchgrass is a promising feedstock for cellulosic biorefineries, due to its ability to maintain comparatively high biomass yields across a wide range of soil and climatic conditions. However, there is an incomplete understanding of the economic and environmental tradeoffs associated with cultivating switchgrass on low-productivity land for conversion to biofuels. This study surveys prior literature and demonstrates a new integrated assessment framework, including agroecosystem, ecosystem services valuation, technoeconomic, and life-cycle assessment models, to quantify and contextualize the economic and environmental impacts of switchgrass cultivation on marginal land with downstream conversion to biofuels. Monetizing and incorporating the value of ecosystem services, such as improved water quality benefits from nitrate and sediment reductions, climate change mitigation benefits from CO2 emission reduction, and recreational and pollination benefits from increased biodiversity, the modeled multifunctional landscape reduces the ethanol production cost by 33.3–58.9 cents/l-gasoline-equivalent ($1.3–2.2/gge). Planting switchgrass in low productivity land improves soil health, resulting in the carbon footprint reduction credit of 12.8–20.2 gCO2e/MJ. For an improved switchgrass-to-ethanol conversion pathway with the maximum benefits from ecosystem services, the minimum ethanol selling price and carbon footprint of ethanol, respectively, could reach to 31 cents/l-gasoline-equivalent (47% reduction relative to average gasoline price) and 3 gCO2e/MJ (97% reduction relative to gasoline). This low carbon renewable ethanol leads to substantial State and/or Federal policy incentives (∼$1/l-gasoline-equivalent) providing a large benefit to biorefinery operators, farmers, and the public as a whole.
We develop a methodology for comparing agent-based models that are developed for the same domain, but may differ in the data sets (e.g., geographical regions) to which they are applied, and in the structure of the model. Our approach is to learn a response surface in the common parameter space of the models and compare the regions corresponding to qualitatively different behaviors in the models. As an example, we develop an active learning algorithm to learn phase shift boundaries in contagion processes in order to compare two agent-based models of rooftop solar panel adoption developed for different regions. We present results for 2D and 3D subspaces of the parameter space, though the approach scales to higher dimensions as well.
Interfaces between cementitious materials and host-rock are often found. Such interfaces might lead to chemical and structural alteration of both materials. In the case of carbonate rocks, cement carbonation can lead to structural failure. This study characterized the long-term performance of interfaces between Ordinary Portland Cement (OPC) and carbonate rocks (limestone and marl). Rocks and cement were studied for their solid phase characterization, pH dependence leaching test, and semi-dynamic mass transport tests. Experimental results were used in the development of a geochemical reactive transport model to simulate the evolution of OPC-rock interfaces. Simulations show the propagation of a carbonation front from the interface into the cementitious materials and the redistribution of phases accompanied with volume changes, with the rate and extent dependent on the rock type.
Conversion cathode materials are gaining interest for secondary batteries due to their high theoretical energy and power density. However, practical application as a secondary battery material is currently limited by practical issues such as poor cyclability. To better understand these materials, we have developed a pseudo-two-dimensional model for conversion cathodes. We apply this model to FeS2 – a material that undergoes intercalation followed by conversion during discharge. The model is derived from the half-cell Doyle–Fuller–Newman model with additional loss terms added to reflect the converted shell resistance as the reaction progresses. We also account for polydisperse active material particles by incorporating a variable active surface area and effective particle radius. Using the model, we show that the leading loss mechanisms for FeS2 are associated with solid-state diffusion and electrical transport limitations through the converted shell material. The polydisperse simulations are also compared to a monodisperse system, and we show that polydispersity has very little effect on the intercalation behavior yet leads to capacity loss during the conversion reaction. We provide the code as an open-source Python Battery Mathematical Modeling (PyBaMM) model that can be used to identify performance limitations for other conversion cathode materials.
Metallic additive manufacturing (AM) provides a customizable and tailorable manufacturing process for new engineering designs and technologies. The greatest challenge currently facing metallic AM is maintaining control of microstructural evolution during solidification and any solid state phase transformations during the build process. Ti-6Al-4V has been extensively surveyed in this regard, with the potential solid state and solidification microstructures explored at length. This work evaluates the applicability of previously determined crystallographic markers of microstructural condition observed in electron beam melting powder bed fusion (PBF-EB) builds of Ti-6Al-4V in a directed energy deposition (DED) build process. The aim of this effort is to elucidate whether or not these specific crystallographic textures are useful tools for indicating microstructural conditions in AM variants beyond PBF-EB. Parent β-Ti grain size was determined to be directly related to α-Ti textures in the DED build process, and the solid state microstructural condition could be inferred from the intensity of specific α-Ti orientations. Qualitative trends on the as-solidified β-Ti grain size were also determined to be related to the presence of a fiber texture, and proposed as a marker for as-solidified grain size in any cubic metal melted by AM. Analysis of the DED Ti-6Al-4V build also demonstrated a near complete fracture of the build volume, suspected to originate from accumulated thermal stresses in the solid state. Crack propagation was found to only appreciably occur in regions of slow cooling with large α+β colonies. Schmid factors for the basal and prismatic α-Ti systems explained the observed crack pathway, including slower bifurcation in colonies with lower Schmid factors of both slip systems. Colony morphologies and localized equiaxed β-Ti solidification were also found to originate from build pauses during production and uneven heating of the build edges during deposition. Tailoring of DED Ti-6Al-4V microstructures with the insight gained here is proposed, along with cautionary insight on preventing unplanned build pauses to maintain an informed and controlled thermal environment for microstructural control.
Reaching astrophysically relevant high energy density (HED) material states in the laboratory is an ongoing effort at multiple experimental facilities. We have developed a new dynamic compression platform for the Z Pulsed Power Facility that allows for sample sizes 100s of [Formula: see text]m in thickness that accommodate multiple grains in order to fully capture bulk properties, such as material strength. A pair of experiments compressed platinum (Pt) to HED conditions and conventional inverse Lagrangian analysis as well as a recent Bayesian calibration technique were used to determine the principal isentrope to 650 GPa with density uncertainties of <2%. These low uncertainties are calculated for single sample experiments, presenting the possibility of even smaller experimental uncertainties with multiple samples the platform allows. Our new platform extends the accessible Pt ramp pressures achievable on the Z machine to over 80% of the pressure recently achieved using the National Ignition Facility planar Hohlraum platform. This new capability, the next generation evolution of the stripline platform, was made possible by advancements in both our understanding of the Z pulsed power driver and our overall magnetohydrodynamic modeling capabilities.
LixCoO2 (LCO) is a common battery cathode material that has recently emerged as a promising material for other applications including electrocatalysis and as electrochemical random access memory (ECRAM). During charge-discharge cycling LCO exhibits phase transformations that are significantly complicated by electron correlation. While the bulk phase diagram for an ensemble of battery particles has been studied extensively, it remains unclear how these phases scale to nanometer dimensions and the effects of strain and diffusional anisotropy at the single-particle scale. Understanding these effects is critical to modeling battery performance and for predicting the scalability and performance of electrocatalysts and ECRAM. Here we investigate isolated, epitaxial LiCoO2 islands grown by pulsed laser deposition. After electrochemical cycling of the islands, conductive atomic force microscopy (c-AFM) is used to image the spatial distribution of conductive and insulating phases. Above 20 nm island thicknesses, we observe a kinetically arrested state in which the phase boundary is perpendicular to the Li-planes; we propose a model and present image analysis results that show smaller LCO islands have a higher conductive fraction than larger area islands, and the overall conductive fraction is consistent with the lithiation state. Thinner islands (14 nm), with a larger surface to volume ratio, are found to exhibit a striping pattern, which suggests surface energy can dominate below a critical dimension. When increasing force is applied through the AFM tip to strain the LCO islands, significant shifts in current flow are observed, and underlying mechanisms for this behavior are discussed. The c-AFM images are compared with photoemission electron microscopy images, which are used to acquire statistics across hundreds of particles. The results indicate that strain and morphology become more critical to electrochemical performance as particles approach nanometer dimensions.
Short-pulse, laser-solid interactions provide a unique platform for studying complex high-energy-density matter. We present the first demonstration of solid-density, micron-scale keV plasmas uniformly heated by a high-contrast, 400 nm wavelength laser at intensities up to 2×1021 W/cm2. High-resolution spectral analysis of x-ray emission reveals uniform heating up to 3.0 keV over 1 μm depths. Particle-in-cell simulations indicate the production of a uniformly heated keV plasma to depths of 2 μm. The significant bulk heating and presence of highly ionized ions deep within the target are attributed to the few MeV hot electrons that become trapped and undergo refluxing within the target sheath fields. These conditions enabled the differentiation of atomic physics models of ionization potential depression in high-energy-density environments.
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