Showing papers by "Naval Surface Warfare Center published in 2019"

Lithium-ion Battery Thermal Safety by Early Internal Detection, Prediction and Prevention

Abstract: Temperature rise in Lithium-ion batteries (LIBs) due to solid electrolyte interfaces breakdown, uncontrollable exothermic reactions in electrodes and Joule heating can result in the catastrophic failures such as thermal runaway, which is calling for reliable real-time electrode temperature monitoring. Here, we present a customized LIB setup developed for early detection of electrode temperature rise during simulated thermal runaway tests incorporating a modern additive manufacturing-supported resistance temperature detector (RTD). An advanced RTD is embedded in a 3D printed polymeric substrate and placed behind the electrode current collector of CR2032 coin cells that can sustain harsh electrochemical operational environments (acidic electrolyte without Redox, short-circuiting, leakage etc.) without participating in electrochemical reactions. The internal RTD measured an average 5.8 °C higher temperature inside the cells than the external RTD with almost 10 times faster detection ability, prohibiting thermal runaway events without interfering in the LIBs’ operation. A temperature prediction model is developed to forecast battery surface temperature rise stemming from measured internal and external RTD temperature signatures.

Methods of AI for Multimodal Sensing and Action for Complex Situations

Abstract: Artificial intelligence (AI) seeks to emulate human reasoning, but is still far from achieving such results for actionable sensing in complex situations. Instead of emulating human situation understanding, machines can amplify intelligence by accessing large amounts of data, filtering unimportant information, computing relevant context, and prioritizing results (for example, answers to human queries) to provide human–machine shared context. Intelligence support can come from many contextual sources that augment data reasoning through physical, environmental, and social knowledge. We propose a decisions-to-data multimodal sensor and action through contextual agents (human or machine) that seek, combine, and make sense of relevant data. Decisions-to-data combines AI computational capabilities with human reasoning to manage data collections, perform data fusion, and assess complex situations (that is, context reasoning). Five areas of AI developments for context-based AI that cover decisions-to-data include: (1) situation modeling (data at rest), (2) measurement control (data in motion), (3) statistical algorithms (data in collect), (4) software computing (data in transit), and (5) human–machine AI (data in use). A decisions-to-data example is presented of a command-guided swarm requiring contextual data analysis, systems-level design, and user interaction for effective and efficient multimodal sensing and action.

Thermomagnetic properties and magnetocaloric effect of FeCoNiCrAl-type high-entropy alloys

Abstract: In this work, we investigate the effects of substituting Ni/Al for Cr on the thermomagnetic and magnetocaloric properties of FeCoNiCrAl-type high entropy alloys (HEAs). Ni and Al appear to prefer the BCC phase, and increases in the Al composition appear to stabilize the BCC phase. In contrast to Al, Ni content yields an increase in the FCC phase fraction, resulting in a drop off in magnetization. The phase transformation from BCC to FCC was intensified at annealing temperatures of 800 °C and higher due to increased diffusion rates and the resulting spinodal decomposition. A magnetic phase transition around 150 K was found in the FeCoNi1.5Cr0.5Al annealed alloy potentially corresponding to the FCC phase, and a very broad magnetic phase transition was observed in the annealed FeCoNiCrAl alloy, resulting in a high refrigerant capacity of RCFWHM = 242.6 J⋅kg-1 near room temperature. A peak magnetic entropy change of −ΔSM = 0.674 J⋅kg-1⋅K-1 was also obtained at applied fields of ∼70 kOe at 290 K in the FeCoNiCrAl HEA. These magnetocaloric values are comparable to Fe-based metallic glasses such as Fe-Tm-B-Nb and Fe-Zr-B-Co alloys, with a similar transition near room temperature.

Bayesian Estimation of Three-Dimensional Chromosomal Structure from Single-Cell Hi-C Data.

Abstract: The problem of three-dimensional (3D) chromosome structure inference from Hi-C data sets is important and challenging. While bulk Hi-C data sets contain contact information derived from millions of cells and can capture major structural features shared by the majority of cells in the sample, they do not provide information about local variability between cells. Single-cell Hi-C can overcome this problem, but contact matrices are generally very sparse, making structural inference more problematic. We have developed a Bayesian multiscale approach, named Structural Inference via Multiscale Bayesian Approach, to infer 3D structures of chromosomes from single-cell Hi-C while including the bulk Hi-C data and some regularization terms as a prior. We study the landscape of solutions for each single-cell Hi-C data set as a function of prior strength and demonstrate clustering of solutions using data from the same cell.

Stamped multilayer graphene laminates for disposable in-field electrodes: application to electrochemical sensing of hydrogen peroxide and glucose

Abstract: A multi-step approach is described for the fabrication of multi-layer graphene-based electrodes without the need for ink binders or post-print annealing. Graphite and nanoplatelet graphene were chemically exfoliated using a modified Hummers’ method and the dried material was thermally expanded. Expanded materials were used in a 3D printed mold and stamp to create laminate electrodes on various substrates. The laminates were examined for potential sensing applications using model systems of peroxide (H2O2) and enzymatic glucose detection. Within the context of these two assay systems, platinum nanoparticle electrodeposition and oxygen plasma treatment were examined as methods for improving sensitivity. Electrodes made from both materials displayed excellent H2O2 sensing capability compared to screen-printed carbon electrodes. Laminates made from expanded graphite and treated with platinum, detected H2O2 at a working potential of 0.3 V (vs. Ag/AgCl [0.1 M KCl]) with a 1.91 μM detection limit and sensitivity of 64 nA·μM−1·cm−2. Electrodes made from platinum treated nanoplatelet graphene had a H2O2 detection limit of 1.98 μM (at 0.3 V), and a sensitivity of 16.5 nA·μM−1·cm−2. Both types of laminate electrodes were also tested as glucose sensors via immobilization of the enzyme glucose oxidase. The expanded nanographene material exhibited a wide analytical range for glucose (3.7 μM to 9.9 mM) and a detection limit of 1.2 μM. The sensing range of laminates made from expanded graphite was slightly reduced (9.8 μM to 9.9 mM) and the detection limit for glucose was higher (18.5 μM). When tested on flexible substrates, the expanded graphite laminates demonstrated excellent adhesion and durability during testing. These properties make the electrodes adaptable to a variety of tests for field-based or wearable sensing applications.

Assessment of Computational Fluid Dynamic for Surface Combatant 5415 at Straight Ahead and Static Drift β = 20 deg

Abstract: Collaboration is described on assessment of computational fluid dynamics (CFD) predictions for surface combatant model 5415 at static drift β = 0 deg and 20 deg using recent tomographic particle image velocimetry (TPIV) experiments. Assessment includes N-version verification and validation to determine the confidence intervals for CFD solutions/codes, and vortex onset, progression, instability, and turbulent kinetic energy (TKE) budget analysis. The increase in β shows the following trends. Forces and moment increase quadratically/cubically, and become unsteady due to shear layer, Karman and flapping instabilities on the bow. Wave elevation becomes asymmetric; its amplitude increases, but the total wave elevation angle remains same. The vortex strength and TKE increase by about two orders of magnitude, and for large β, the primary vortices exhibit helical mode instability similar to those for delta wings. Forces and moment for both β and wave elevation for β = 0 deg are compared within 4% of the data, and are validated at 7% interval. Wave elevation for β = 20 deg, and vortex core location and velocities for both β are compared within 9% of the data, and are validated at 12% interval. The vortex strength and TKE predictions show large 70% errors and equally large scatter and are not validated. Thus, both errors and scatter need reduction. TKE budgets show transport of turbulence into the separation bubble similar to canonical cases, but pressure transport is dominant for ship flows. Improved CFD predictions require better grids and/or turbulence models. Investigations of solution-adaptive mesh refinement for better grid design and hybrid Reynolds-averaged Navier-Stokes/large eddy simulation models for improved turbulent flow predictions are highest priority.

Acoustic Supercoupling in a Zero-Compressibility Waveguide.

Abstract: Funneling acoustic waves through largely mismatched channels is of fundamental importance to tailor and transmit sound for a variety of applications. In electromagnetics, zero-permittivity metamaterials have been used to enhance the coupling of energy in and out of ultranarrow channels, based on a phenomenon known as supercoupling. These metamaterial channels can support total transmission and complete phase uniformity, independent of the channel length, despite being geometrically mismatched with their input and output ports. In the field of acoustics, this phenomenon is challenging to achieve, since it requires zero-density metamaterials, typically realized with waveguides periodically loaded with membranes or resonators. Compared to electromagnetics, the additional challenge is due to the fact that conventional acoustic waveguides do not support a cut-off for the dominant mode of propagation, and therefore zero-index can be achieved only based on a collective resonance of the loading elements. Here we propose and experimentally realize acoustic supercoupling in a dual regime, using a compressibility-near-zero acoustic channel. Rather than engineering the channel with subwavelength inclusions, we operate at the cut-off of a higher-order acoustic mode, demonstrating the realization and efficient excitation of a zero-compressibility waveguide with effective soft boundaries. We experimentally verify strong transmission through a largely mismatched channel and uniform phase distribution, independent of the channel length. Our results open interesting pathways towards the realization of extreme acoustic parameters and their implementation in relevant applications, such as ultrasound imaging, acoustic transduction and sensing, nondestructive evaluation, and sound communications.

Interface Defect Engineering for Improved Graphene-Oxide-SemiconductorJunction Photodetectors

Abstract: The deeply depleted graphene-oxide-semiconductor (D2GOS) junction detector provides an effective architecture for photodetection, enabling direct readout of photogenerated charge. Because of an inh...

Multistatic Doppler Estimation Using Global Positioning System Passive Coherent Location

Abstract: In previous works, methods were explored for position estimation utilizing satellite-borne signals of opportunity, mainly the global positioning system (GPS). The GPS signal was exploited for use in a multistatic passive coherent location (PCL) system. The GPS signal is especially attractive for PCL applications because of the native capability to produce position and velocity estimation. This paper examines the signal specifications for PCL implementation and explores the potential limitations of the proposed solutions. GPS specific methods are developed for multistatic PCL velocity estimation in a three-dimensional plane. The method developed is combined with previously completed work of GPS PCL position estimation for a complete system design in range and Doppler. The PCL system is evaluated against conventional GPS position estimation and velocity estimation and proves to have comparable metrics of performance. Analysis and simulation are performed for verification and validation of the developed methods.

Radiation-Pressure-Enabled Traceable Laser Sources at CW Powers up to 50 kW

Abstract: Radiation pressure has recently been shown to have practical application for multikilowatt continuous wave (CW) laser power measurement. One key advantage lies in its ability to measure without absorbing the laser beam. This enables a new measurement paradigm where laser power can be measured traceable to the SI without perturbing the beam. Combining this measurement scheme with a laser constitutes a “traceable source” where laser output power is traceable to the SI in real time. This greatly simplifies the calibration process for multikilowatt laser power meters and yields a path to high-accuracy laser-based material processing. Here, we discuss the state of the art of this approach by describing recent results from calibrations of laser power meters performed using a radiation-pressure-enabled traceable source at CW powers from 1 to 50 kW. We describe measurement results and uncertainty contributions with expanded uncertainties at or below 1.7% for powers above 10 kW. We also briefly discuss the status of development of a radiation-pressure-based technology designed to provide source traceability in the laser manufacturing environment.

Influence of Alloy Additions on the Microstructure, Texture, and Hardness of Low-Pressure Cold-Sprayed Al-Cu Alloys

Abstract: This paper examines a series of Al-Cu binary alloy coatings, ranging from 2 to 5 weight percent copper, produced using low-pressure cold spray (CS) deposition with helium as the carrier gas. Binary Al-Cu alloy feedstock powder was produced through inert gas atomization and was sprayed over a variety of temperatures and pressures. Using helium gas, this set of Al-Cu alloys was successfully deposited as high-density coatings. Raising the carrier gas pressure increased the particle velocity and deposition efficiency (DE) in the case of spraying the Al-5 wt.% Cu powders. A clear composite deformation structure was formed in all coatings with clear prior particle centers surrounded by severely deformed regions with ultrafine grains. Microstructural deformation generated by the CS process produced a weak but clear fiber texture for both Al-2 wt.% Cu and Al-5 wt.% Cu coatings. The copper content of the feedstock powder directly influenced the coating hardness and porosity, while having no systematic effect on the DE.

Generalized Extreme Value Distributions of Fields in Nested Electromagnetic Cavities

Abstract: In the framework of immunity testing, there has been recently a great interest to determine the maximum field distribution in a nested reverberation chamber, the inner cavity being considered as the equipment under test (EUT). The generalized extreme value distribution is a generalization of three asymptotic distributions that does not require knowledge of the field sample parent distribution in comparison with traditional method. It can therefore be applied in situations when the parent distribution is unknown, such as when an EUT is reverberant or is operating in the undermoded regime. In this paper, we present an experimental validation of the maximum statistical distribution of the internal field samples inside different nested cavity configurations. The Anderson–Darling test is applied to verify if a dataset is collected from a single statistical population inside the EUT with different stirring scenarios and two different EUT aperture dimensions. Parametric and nonparametric estimation are then used to model and to verify the results. The parametric and nonparametric distributions show good agreement for all test configurations considered, extending earlier works which considered reverberation-chamber fields themselves with no EUT included or did not include EUT internal stirring.

A Mechanochemical Route to Cutting Highly Strain-Hardening Metals

Abstract: Highly strain-hardening metals such as Al, Ni, and stainless steels, although relatively soft, are well known as being difficult to cut, because of an unsteady and highly redundant mode of plastic deformation—sinuous flow—prevailing during chip formation. This difficulty in cutting is greatly ameliorated, if the workpiece surface ahead of the chip formation region is coated with certain chemical media such as glues, inks, and alcohols that are quite benign. High-speed imaging shows that the media effect a change in the local plastic deformation mode, from sinuous flow to one characterized by periodic fracture—segmented flow. This flow transition, due to a mechanochemical effect, results in significant reduction of deformation forces and energy, often > 50%, thus facilitating the cutting. The effect is mostly pronounced at smaller undeformed chip thickness, typical of finish and semi-finish machining regimes. The quality of the cut surface, as measured by defect density and surface roughness, improves by an order of magnitude, when the media are applied. Furthermore, this surface is relatively strain free in contrast to conventionally machined surfaces. The mechanochemical effect, with a strong coupling to the flow mode, is controllable, with the media showing similar efficacy across different metal systems. The results suggest opportunities for improving performance of machining processes for many difficult-to-cut gummy metals.

Simultaneous Dimensionality and Complexity Model Selection for Spectral Graph Clustering

Abstract: Our problem of interest is to cluster vertices of a graph by identifying underlying community structure. Among various vertex clustering approaches, spectral clustering is one of the most popular methods because it is easy to implement while often outperforming more traditional clustering algorithms. However, there are two inherent model selection problems in spectral clustering, namely estimating both the embedding dimension and number of clusters. This paper attempts to address the issue by establishing a novel model selection framework specifically for vertex clustering on graphs under a stochastic block model. The first contribution is a probabilistic model which approximates the distribution of the extended spectral embedding of a graph. The model is constructed based on a theoretical result of asymptotic normality of the informative part of the embedding, and on a simulation result providing a conjecture for the limiting behavior of the redundant part of the embedding. The second contribution is a simultaneous model selection framework. In contrast with the traditional approaches, our model selection procedure estimates embedding dimension and number of clusters simultaneously. Based on our conjectured distributional model, a theorem on the consistency of the estimates of model parameters is presented, providing support for the validity of our method. Algorithms for our simultaneous model selection for vertex clustering are proposed, demonstrating superior performance in simulation experiments. We illustrate our method via application to a collection of brain graphs.

Transferring Random Samples in Actuator Systems for Binary Damage Detection

Abstract: Data-driven models can accurately estimate the condition of systems, for example a hydraulic actuator. However, maintenance on the system can lower the predictive ability of condition models by changing the marginal and conditional distributions of the data. In this study, we propose to use transfer learning to address this issue in the context of a hydraulic actuator. Transfer learning aims to use knowledge from one system to improve modeling in another. This work uses random sampling to transfer samples between actuator rebuilds to predict a binary indicator of system damage in a rebuilt actuator. Features are selected based on distributional differences. We find that successful transfer using random sampling can occur when features are selected appropriately. Also, transferring only the damage data allows the model to improve as more baseline data from the rebuilt actuator becomes available.

Component Model Development for Ship-Level Impact of High Temperature Superconducting Power Cables

Abstract: The need for ship system level models for assessing the benefits of high temperature superconducting (HTS) technology is articulated. The development of ship designs with HTS power cables using Smart Ship Systems Design (S3D) showed the need for new components such as the cable terminations that serve multiple HTS cables at the nodes. Conceptual designs of such multi-cable terminations are discussed. Results of the investigations of several alternative thermal insulation systems for HTS cables are discussed in the context of reducing the mass, size, and complexity as well as increasing the resiliency of the shipboard integrated power system. Design considerations for gaseous helium HTS power devices are discussed.

A Method for Controlled Odor Delivery in Olfactory Field-Testing.

Abstract: A widely recognized limitation in mammalian olfactory research is the lack of current methods for measuring odor availability (i.e., the quantifiable amount of odor presented and thus available for olfaction) of training or testing materials during behavioral or operational testing. This research utilized an existing technology known as Controlled Odor Mimic Permeation Systems (COMPS) to produce a reproducible, field-appropriate odor delivery method that can be analytically validated and quantified, akin to laboratory-based research methods, such as permeation devices that deliver a stable concentration of a specific chemical vapor for instrumental testing purposes. COMPS were created for 12 compounds across a range of carbon chain lengths and functional groups in such a way to produce similar permeation rates for all compounds. Using detection canines as a model, field-testing was performed to assess the efficacy of the method. Additionally headspace concentrations over time were measured as confirmation of odor availability using either externally sampled internal standard-solid phase microextraction-gas chromatography-mass spectrometry (ESIS-SPME-GC-MS) or collection onto a programmable temperature vaporizing (PTV) GC inlet with MS detection. Finally, lifetime usage was considered. An efficient method for producing and measuring reliable odor availabilities across various chemical functional groups was developed, addressing a noted gap in existing literature that will advance canine and other nonhuman mammal research testing.

An acoustic source model for asymmetric intraglottal flow with application to reduced-order models of the vocal folds.

Abstract: The complex three-way interaction between airflow, tissue, and sound, for asymmetric vocal fold vibration, is not well understood. Current modeling efforts are not able to explain clinical observations where drastic differences in sound production are often observed, with no noticeable differences in the vocal fold kinematics. To advance this understanding, an acoustical model for voiced sound generation in the presence of asymmetric intraglottal flows is developed. The source model operates in conjunction with a wave reflection analog propagation scheme and an asymmetric flow description within the glottis. To enable comparison with prior work, the source model is evaluated using a well-studied two-mass vocal fold model. The proposed source model is evaluated through acoustic measures of interest, including radiated sound pressure level, maximum flow declination rate, and spectral tilt, and also via its effects on the vocal fold dynamics. The influence of the model, in comparison to the standard symmetric Bernoulli flow description, results in an increased transfer of energy from the fluid to the vocal folds, increased radiated sound pressure level and maximum flow declination rate, and decreased spectral tilt. These differences are most pronounced for asymmetric vocal fold configurations that mimic unilateral paresis and paralysis, where minor kinematic changes can result in significant acoustic and aerodynamic differences. The results illustrate that fluid effects arising from asymmetric glottal flow can play an important role in the acoustics of pathological voiced speech.

Characterization and Modeling of Low Modulus Composite Patched Aluminum Center Crack Tension Specimen Using DIC Surface Displacements

Abstract: Composite patch repairs of aluminum structures used in marine and aerospace industries are designed using closed form solutions assuming thin, plane stress, linear-elastic structures or numerical methods for repairs of thick aluminum. Both methods are based on linear elastic fracture mechanics and compare crack tip predictions to a critical strain energy release rate or stress intensity. Analytical and numerical predictions are reasonable for linear-elastic behavior, but these methods do not account for elastic-plastic behavior at the crack tip that initiates above the linear-elastic limit and continues until the ultimate load. This research used digital image correlation and finite element analysis to study the full field displacement and J-integral ahead of the crack tip for un-patched and patched center crack tension specimens loaded monotonically to failure. Free surface crack tip strain and J-integral behavior remained an intrinsic property of the aluminum directly related to the crack opening displacement (COD) and were independent of one sided composite patch reinforcement. However, the crack tip bending deformations induced by the patch reinforcement increased the COD by 20% over the un-patched behavior after patch failure, most likely due to observed changes in the formation of the plastic zone ahead of the crack. Comparison of test results and analytical predications indicated a significant difference between linear elastic and elastic plastic predictions beyond the linear-elastic limit highlighting the need to utilize elastic plastic fracture mechanics and the J-integral to optimize composite patched center crack tension specimens for ultimate load.