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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,081 records · Page 60

Observing crystal nucleation in four dimensions using atomic electron tomography

Nucleation plays a critical role in many physical and biological phenomena that range from crystallization, melting and evaporation to the formation of clouds and the initiation of neurodegenerative diseases. However, nucleation is a challenging process to study in experiments, especially in the early stage when several atoms or molecules start to form a new phase from a parent phase. A number of experimental and computational methods have been used to investigate nucleation processes, but experimental determination of the three-dimensional atomic structure and the dynamics of early stage nuclei has been unachievable. Here we use atomic electron tomography to study early stage nucleation in four dimensions (4D: that is, including time) at atomic resolution. Using FePt nanoparticles as a model system, we find that early stage nuclei are irregularly shaped, each has a core of one to a few atoms with the maximum order parameter, and the order parameter gradient points from the core to the boundary of the nucleus. We capture the structure and dynamics of the same nuclei undergoing growth, fluctuation, dissolution, merging and/or division, which are regulated by the order parameter distribution and its gradient. These experimental observations are corroborated by molecular dynamics simulations of heterogeneous and homogeneous nucleation in liquid–solid phase transitions of Pt. Our experimental and molecular dynamics results differ from classical nucleation theory, indicating that a theory beyond this is needed to describe early stage nucleation at the atomic scale. Looking forward, we anticipate that the reported approach will open the door to the study of many fundamental problems in materials science, nanoscience, condensed matter physics and chemistry, such as phase transition, atomic diffusion, grain boundary dynamics, interface motion, defect dynamics and surface reconstruction with 4D atomic resolution.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Structural Design Considerations for a 50 kW-Class Solar Array for NASA's Asteroid Redirect Mission

NASA is planning an Asteroid Redirect Mission (ARM) to take place in the 2020s. To enable this multi-year mission, a 40 kW class solar electric propulsion (SEP) system powered by an advanced 50 kW class solar array will be required. Powered by the SEP module (SEPM), the ARM vehicle will travel to a large near-Earth asteroid, descend to its surface, capture a multi-metric ton (t) asteroid boulder, ascend from the surface and return to the Earth-moon system to ultimately place the ARM vehicle and its captured asteroid boulder into a stable distant orbit. During the years that follow, astronauts flying in the Orion multipurpose crew vehicle (MPCV) will dock with the ARM vehicle and conduct extra-vehicular activity (EVA) operations to explore and sample the asteroid boulder. This paper will review the top structural design considerations to successfully implement this 50 kW class solar array that must meet unprecedented performance levels. These considerations include beyond state-of-the-art metrics for specific mass, specific volume, deployed area, deployed solar array wing (SAW) keep in zone (KIZ), deployed strength and deployed frequency. Analytical and design results are presented that support definition of stowed KIZ and launch restraint interface definition. An offset boom is defined to meet the deployed SAW KIZ. The resulting parametric impact of the offset boom length on spacecraft moment of inertias and deployed SAW quasistatic and dynamic load cases are also presented. Load cases include ARM spacecraft thruster plume impingement, asteroid surface operations and Orion docking operations which drive the required SAW deployed strength and damping. The authors conclude that to support NASA's ARM power needs, an advanced SAW is required with mass performance better than 125 W/kg, stowed volume better than 40 kW/cu m, a deployed area of 200 sq m (100 sq m for each of two SAWs), a deployed SAW offset distance of nominally 3-4 m, a deployed SAW quasistatic strength of nominally 0.1 g in any direction, a deployed loading displacement under 2 m, a deployed fundamental frequency above 0.1 Hz and deployed damping of at least 1%. These parameters must be met on top of challenging mission environments and ground testing requirements unique to the ARM project.

solar arrays↗

Radioisotope Power System Dose Estimation Tool

The Radioisotope Power System Dose Estimation Tool (RPS-DET) is used to create, perform and analyze particle-transport simulations of radioisotope power systems (RPS). RPS-DET consists of a Matlab graphical user interface (GUI) to generate and analyze Monte Carlo N-Particle (MCNP) 6 input and output files. Simulation geometries are created using a library-based method of combining multiple RPS units and environmental scenarios while source terms are produced from databases of time-dependent photon and neutron spectra generated by the software packages IsoChain and ORIGEN. Simulation results are saved in a three-dimensional format for neutron and photon flux and dose in tissue or silicon and may be interactively inspected via the GUI. RPS-DET allows flexibility in the simulation-space by providing the ability to mix-and-match RPS units with relevant environments along with capturing the effects of fuel-aging for the particle-energy-spectra and intensity. This work has been benchmarked against measurements of existing RPS units and found to comparably predict neutron and gamma flux, absorbed dose, and dose equivalent in humans and electronics (silicon).

Smith, Michael↗

A level set approach for the computational study of a yield stress fluid filling a thin mold

Many important engineering and scientific applications such as cement slurries, foams, crude oil, and granular avalanches involve the concept of yield stress. Therefore, modeling yield stress fluids in different flow configurations, including the accurate prediction of the yield surface, is important. In this paper, we present a computational model based on the finite element method to study the flow of yield stress fluids in a thin mold and compare the results with data from flow visualization experiments. Here, we use the level set method to describe the interface between the filling fluid and air. We use polypropylene glycol as a model Newtonian fluid and Carbopol for the model yield stress fluid, as the Carbopol solution demonstrates yielding without thixotropy. To describe the yielding and shear-thinning behavior, we use a generalized Newtonian constitutive equation with a Bingham–Carreau–Yasuda form. We compare the results obtained from the mold filling experiments with the results from the three-dimensional (3D) model and from a reduced-order Hele-Shaw (HS) model that is two-dimensional, including the effect of shear-thinning along the thin direction only approximately. We show that both the 3D and the HS model can capture the experimental meniscus shape reasonably well for all the fluids considered at three different flow rates. This indicates that the shape evolution is insensitive to the dimensionality of the model. However, the viscosity and yield surfaces predicted by the 3D and HS models are different. The HS model underestimates the high viscosity and unyielded regions compared to the estimation by the 3D model.

42 ENGINEERING↗

Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials

There is a need to characterize complex materials and their dynamics under reaction conditions to accelerate materials design. Adsorbate vibrational excitations are selective to adsorbate/surface interactions and infrared (IR) spectra associated with activating adsorbate vibrational modes are accurate, capture details of most modes, and can be obtained operando. Current interpretation depends on heuristic peak assignments for simple spectra, precluding the possibility of obtaining detailed structural information. Here, we combine data-based approaches with chemistry-dependent problem formulation to develop physics-driven surrogate models that generate synthetic IR spectra from first-principles calculations. Using synthetic IR spectra of carbon monoxide on platinum, we implement multinomial regression via neural network ensembles to learn probability distributions functions (pdfs) that describe adsorption sites and quantify uncertainty. We use these pdfs to infer detailed surface microstructure from experimental spectra and extend this methodology to other systems as a first step towards characterizing complex interfaces and closing the materials gap.

Science & Technology - Other Topics↗

Modeling Fatigue Damage Onset and Progression in Composites Using an Element-Based Virtual Crack Closure Technique Combined With the Floating Node Method

A new methodology is proposed to model the onset and propagation of matrix cracks and delaminations in carbon-epoxy composites subject to fatigue loading. An extended interface element, based on the Floating Node Method, is developed to represent delaminations and matrix cracks explicitly in a mesh independent fashion. Crack propagation is determined using an element-based Virtual Crack Closure Technique approach to determine mixed-mode energy release rates, and the Paris-Law relationship to obtain crack growth rate. Crack onset is determined using a stressbased onset criterion coupled with a stress vs. cycle curve and Palmgren-Miner rule to account for fatigue damage accumulation. The approach is implemented in Abaqus/Standard® via the user subroutine functionality. Verification exercises are performed to assess the accuracy and correct implementation of the approach. Finally, it was demonstrated that this approach captured the differences in failure morphology in fatigue for two laminates of identical stiffness, but with layups containing θdeg plies that were either stacked in a single group, or distributed through the laminate thickness.

De Carvalho, Nelson V.↗

A hierarchical stochastic modeling approach for representing point bar geometries and petrophysical property variations

The flow of fluids in point bars is affected by the existence of heterogeneities like shale drapes that are found on the surfaces of inclined heterolithic stratifications. In fact, these shale drapes can act as fluid flow baffles; therefore, developing a framework for modeling point bars and their associated heterogeneities is vital. In this study, a stochastic process-based modeling approach is presented for capturing the main point bar heterogeneities: accretion surfaces (i.e., the aerial heterogeneity) and inclined heterolithic stratifications (i.e., the vertical heterogeneity). The former was modeled using a sine-generation function and the latter, with a sigmoidal function after which they were combined into a 3D point bar model. To ensure proper modeling of petrophysical properties, we developed a more representative gridding scheme which generates curvilinear grids representative of the point bar geometry. This grid was then transformed into a rectilinear grid to allow for geostatistical simulation after which all petrophysical properties were mapped back into the original curvilinear grid. An essential element of this modeling approach is the stochastic representation of shale drapes at the interface between successive accretion surfaces. The workflow was tested using a real field dataset for the Cranfield field, Mississippi. The constructed model was then subjected to a flow simulation study mimicking a CO 2 storage scenario. Various sensitivities were simulated to evaluate the effect of heterogeneities on CO 2 flow within the point-bar. Results demonstrate the importance of representing point-bar related heterogeneity and the spatial distribution of shale drapes on CO 2 plume migration and storage.

58 GEOSCIENCES↗

Dynamic Model of Back-to-Back Converter for System Level Phasor Simulation

The power system is expected to evolve rapidly with increasing deployment of power electronic interface and conditioning systems, microgrids, and hybrid AC/DC grids. Among power electronic systems, back-to-back (BTB) converters can be a powerful interface to integrate microgrids and networked microgrids. However, in system level dynamic simulations for synchronous machine dominated system with BTB converters, a detailed electromagnetic sinusoidal models is not warranted as these simulations are focused on electromechanical transients. This paper focus on developing a simple phasor model for back- to-back converters that can be easily integrated into powerflow solvers to facilitate large-scale power system simulations. The model is implemented using C++ language and integrated into GridLAB-D software as a potential new capability. The GridLAB-D phasor domain model is validated against the electromagnetic transient (EMT) simulation of the detailed switching model. Simulation results show that the phasor model successfully captures the dominant dynamics of the converters.

Mahmood, Hisham↗

Alumina – Stabilized SEI and CEI in Potassium Metal Batteries

Aluminum oxide (Al 2 O 3 ) nanopowder is spin-coated onto both sides of commercial polypropene separator to create artificial solid-electrolyte interphase (SEI) and artificial cathode electrolyte interface (CEI) in potassium metal batteries (KMBs). Here, this significantly enhances the stability, including of KMBs with Prussian Blue (PB) cathodes. For example, symmetric cells are stable after 1,000 cycles at 0.5 mA/cm 2 –0.5 mAh/cm 2 and 3.0 mA/cm 2 –0.5 mAh/cm 2 . Alumina modified separators promote electrolyte wetting and increase ionic conductivity (0.59 vs. 0.2 mS/cm) and transference number (0.81 vs. 0.23). Cryo-stage focused ion beam (cryo-FIB) analysis of cycled modified anode demonstrates dense and planar electrodeposits, versus unmodified baseline consisting of metal filaments (dendrites) interspersed with pores and SEI. Alumina-modified CEI also suppresses elemental Fe crossover and reduces cathode cracking. Mesoscale modeling of metal – SEI interactions captures crucial role of intrinsic heterogeneities, illustrating how artificial SEI affects reaction current distribution, conductivity and morphological stability.

25 ENERGY STORAGE↗

A Modular Framework for Integrating and Visualizing Telemetry for Mars 2020 Rover Mechanism Operations

The analysis of mechanism telemetry requires a wide variety of tools to quickly and effectively assess spacecraft state, capture long-term trends in system performance, and identify and track anomalous events. Such analysis often requires spacecraft telemetry to first be transformed into derived fields and aggregated statistics before operators can begin their analysis. In past missions, aspects of this process have been automated, but operators were expected to use their own tools and procedures to understand and visualize the data, which led to redundant and inconsistent tools and processes. The Mech Data Tools Python library (MDT) was developed to provide a flexible, unified tool set for operators to extract and analyze mechanism telemetry over the life of the Mars 2020 surface mission. MDT consists of a set of configurable components that implement standard interfaces for ingesting input and producing output. Components can be chained together to form a data processing pipeline. Data are ingested from several sources within the greater Mars 2020 cloud infrastructure and stored in pandas DataFrames, which allows users to leverage the data manipulation capabilities present within the widely-used pandas library. Visualization capabilities are provided through the Plotly library, which generates interactive plots for users to interpret. Following the beginning of Mars 2020 surface operations, usage of MDT has spread to all mechanism-focused subsystems and has demonstrated great utility in analyzing early surface activities. This paper describes MDT’s evolution from heritage mechanism telemetry tools, the critical architecture decisions and challenges faced over MDT’s two years of development, and current applications of MDT in support of mechanism operations.

Wolsieffer, Ben↗

Effects of G-Jitter on Interfacial Dynamics of Two Miscible Liquids: Application of MIM

We designed an experiment to examine the effects of g-jitter on mixing of two miscible liquids using the Microgravity Vibration Isolation Mount (MIM). The global bifurcation of the interface was observed with the MIM operating alternatively to either transmit the g-jitter, isolate from the g-jitter or to provide controlled vibration levels with well defined amplitude and frequency content. With the MIM in isolation mode, the interface remains stationary indicating buoyancy induced convection is negligibly small such that mixing occurs via intrinsic mass diffusion without the masking effect of vibration driven convection. Analytical and computational results are in agreement with the experimental findings. Operation of the MIM in forced mode with conditions typical of g-jitter shows that vibration induced convective flows can excite instability mechanisms such as Kelvin-Helmholtz to generate large amplitude quasi-stationary waves oriented vertically for various cases with Stokes-Reynolds number in the range of 0.003 to 0.5. The two and four mode quasi-stationary waves are also predicted with a mathematical model. Though unplanned, the effect of a primary thruster filing was captured and shown to cause a catastrophic bifurcation, enhancing local mass transport. In light of the findings, experiments planned for the International Space Station should consider the potential effects of g-jitter.

Duval, Walter M. B.↗

Atmosphere-Biosphere Interaction Study (Field Campaign Report)

The objective of this campaign was to collect hyperspectral reflectance measurements and complementary optical and infrared data of land surface (i.e., vegetation and soil) throughout the growing season (i.e., from March to September) at high temporal frequency (e.g., < 30 minutes). To collect the measurements, the EcoSpec system, a tower-based sensor system housing seven optical and infrared sensors that was developed at Argonne National Laboratory, was deployed to the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility Southern Great Plains (SGP) atmospheric observatory (within the crop, south of the eddy correlation flux measurement system [ECOR] tower). The EcoSpec system consists of a spectroradiometer having 2,151 channels (350–2,500 nm), red-greenblue (RGB) camera, diffuse radiometer, thermal infrared (TIR) sensors, albedometers, Photochemical Reflectance Index (PRI) sensors, Normalized Difference Vegetation Index (NDVI) sensors, and wetness sensor. The system is designed to collect an array of land surface measurements, including hyperspectral reflectance of plants and soils, RGB photos capturing contextual information of land surface, radiant temperature of sky and land surface, incoming and reflected shortwave radiation, and incoming radiation components (direct and diffuse) as frequent as every minute from up to 12 discrete positions around the tower. This would allow us to capture inherent heterogeneity of the terrestrial ecosystem and to obtain representative spectral signatures of the plant and soil surfaces via averaging those measurements. The SGP site provides advanced atmospheric and other physical measurements that are essential for our investigation of ecosystem functions using hyperspectral reflectance measurements of land surfaces. Located in the mid-latitudes, the SGP site exhibits dynamic seasonality and a wide range of meteorological variability and flux properties that make it ideal for studying interactions between near-surface atmosphere and biosphere across a range of environmental conditions. The atmosphere, plants, and soil control terrestrial carbon and water cycles. To make more accurate climate forecasts, researchers need to understand ecosystem dynamics at the biosphere–atmosphere interface. Even though our ability to forecast ecosystem dynamics and climate at both the regional and global scales has advanced significantly, we still do not know how local phenomena affecting water and carbon fluxes—such as diurnal variations—interrelate with large-scale atmospheric/climate phenomena, and vice versa. The lack of a more thorough understanding of ecosystem dynamics and climate interactions is a key knowledge gap for the improvement of earth system models (ESMs).

54 ENVIRONMENTAL SCIENCES↗

Phase-field modeling of diffusion bonding in 316H stainless steel: Impact of processing conditions on grain morphology and bonding quality

A novel multi-phase, multi-component phase‐field model is presented to study the diffusion bonding of 316H stainless steel. Combined with targeted experimental investigations, this model simulates the bond-growth process and predicts the bonding quality. Unlike previous models, our approach captures the simultaneous evolution of voids and grain structures, while quantifying bonding quality using defined bonding ratio. A comprehensive analysis of bond process control is performed by changing temperature, pressure and surface roughness observing the resulting bond structure, which is consistent with experimental observations and analytical predictions. Temperature is determined to be the dominant factor, with the transition from a flat to a robust bond occurring between 1000 °C and 1050 °C. At the ideal bonding temperature of 1050 °C, a surface roughness exceeding 0.6 μm or an applied stress below 4 MPa results in poor bonding quality. Beyond this, higher pressures and smoother surfaces reduce void size, accelerate void shrinkage, and lead to improved bond integrity. This diffuse-interface model can be extended to other material systems if supplied with appropriate thermodynamic and kinetic data. In conclusion, this makes it an effective modeling platform for optimizing high-temperature diffusion bonding and developing reliable bonded components such as compact heat exchangers.

Diffusion bonding↗

Sub-Grid Modeling of Electrokinetic Effects in Micro Flows

Advances in micro-fabrication processes have generated tremendous interests in miniaturizing chemical and biomedical analyses into integrated microsystems (Lab-on-Chip devices). To successfully design and operate the micro fluidics system, it is essential to understand the fundamental fluid flow phenomena when channel sizes are shrink to micron or even nano dimensions. One important phenomenon is the electro kinetic effect in micro/nano channels due to the existence of the electrical double layer (EDL) near a solid-liquid interface. Not only EDL is responsible for electro-osmosis pumping when an electric field parallel to the surface is imposed, EDL also causes extra flow resistance (the electro-viscous effect) and flow anomaly (such as early transition from laminar to turbulent flow) observed in pressure-driven microchannel flows. Modeling and simulation of electro-kinetic effects on micro flows poses significant numerical challenge due to the fact that the sizes of the double layer (10 nm up to microns) are very thin compared to channel width (can be up to 100 s of m). Since the typical thickness of the double layer is extremely small compared to the channel width, it would be computationally very costly to capture the velocity profile inside the double layer by placing sufficient number of grid cells in the layer to resolve the velocity changes, especially in complex, 3-d geometries. Existing approaches using "slip" wall velocity and augmented double layer are difficult to use when the flow geometry is complicated, e.g. flow in a T-junction, X-junction, etc. In order to overcome the difficulties arising from those two approaches, we have developed a sub-grid integration method to properly account for the physics of the double layer. The integration approach can be used on simple or complicated flow geometries. Resolution of the double layer is not needed in this approach, and the effects of the double layer can be accounted for at the same time. With this approach, the numeric grid size can be much larger than the thickness of double layer. Presented in this report are a description of the approach, methodology for implementation and several validation simulations for micro flows.

Chen, C. P.↗

Ultrafast Exciton Dissociation at the 2D-WS 2 Monolayer/Perovskite Interface

For an excitonic photovoltaic (PV) device to perform efficiently, photogenerated excitons in the charge donor need to be dissociated through charge transfer (CT) to the acceptor rapidly after their photogeneration and remain separated for a longer time to allow the collection of charges. To improve the efficiency of these steps, several combinations of materials have been examined. Due to their excellent optical properties, two-dimensional transition-metal dichalcogenides (2D-TMDs) have recently been explored. Another promising class of materials to platform efficient PVs is organic–inorganic perovskites. Here, we report on the ultrafast exciton dissociation through electron transfer from a 2D tungsten disulfide (WS 2 ) monolayer to a thin layer of methylammonium lead iodide (CH 3 NH 3 PbI 3 ) perovskites. Photoluminescence measurements showed that when the 2D-WS 2 monolayer was covered with perovskites, its emission completely quenched, suggesting that the CT process is highly efficient. Despite that pump–probe spectroscopy measurements were carried out with a ~45 fs temporal resolution, the CT dynamics were not captured. A comparison of the ultrafast dynamics of the two band-edge excitons of the charge donor (2D-WS 2 ) suggested that electron transfer is the dominant pathway of CT. Furthermore, these pump–probe measurements indicated that a small fraction of transferred electrons remained in the perovskites up to almost 2 ns. These findings may open a new horizon for understanding the dissociation of photogenerated excitons in 2D-TMDs through hybridization with another class of nanomaterials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

SAM-ML: Integrating data-driven closure with nuclear system code SAM for improved modeling capability

Advanced reactors often involve complicated thermal-fluid (T-F) phenomena. Modeling such phenomena with the traditional one-dimensional (1-D) system code is a challenging task. The System Analysis Module (SAM), a modern nuclear system code, has developed a coarse mesh multi-dimensional (multi-D) flow model to capture the spatial effect of T-F phenomena in advanced reactors. As a coarse mesh solver, constitutive relations are required for SAM's multi-D model for unresolved fine-scale physics, such as turbulence. Here this work presents a novel approach that integrates neural networks as data-driven closure for SAM's multi-D flow model. The data-driven closure is trained with fine-resolution data to ensure its accuracy while maintaining a coarse mesh setup to ensure its efficiency and consistency with SAM. We demonstrate the applicability of this SAM-ML capability in an open volume thermal stratification problem, where a neural network model serves as the eddy viscosity closure. A customized interface between the neural network and SAM is developed to ensure flexible and efficient data exchange. The SAM-ML results demonstrate superior performance compared to SAM's built-in zero-equation eddy viscosity closure. The case study shows that although the generalization capability of the data-driven closure still needs to be improved for different transient case or different geometric setup, SAM -ML demonstrates good potential for challenging simulation problems with improved accuracy and computational efficiency.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

para -Xylene Ultra-selective Zeolite MFI Membranes Fabricated from Nanosheet Monolayers at the Air-Water Interface

The control of membrane morphology and microstructure is crucial to improve the separation performance of molecular-sieve membranes. This can be enabled by making thin, dense, and uniform seed-crystal coatings, which are then intergrown into continuous membranes. Herein, we show a novel and simple floating particle coating method can give closely packed monolayers of zeolite nanosheets on nonporous or porous supports. The zeolite nanosheet monolayer is formed at the air–water interface in a conical Teflon trough. As the water in the trough is drained, the monolayer is deposited on a support placed below. Membranes prepared by gel-free secondary growth of the nanosheets deposited by this method show unprecedented ultra-selective performance for separation of para- from ortho-xylene (separation factor >10,000).

36 MATERIALS SCIENCE↗

Where to look? Automating attending behaviors of virtual human characters

This research proposes a computational framework for generating visual attending behavior in an embodied simulated human agent. Such behaviors directly control eye and head motions, and guide other actions such as locomotion and reach. The implementation of these concepts, referred to as the AVA, draws on empirical and qualitative observations known from psychology, human factors and computer vision. Deliberate behaviors, the analogs of scanpaths in visual psychology, compete with involuntary attention capture and lapses into idling or free viewing. Insights provided by implementing this framework are: a defined set of parameters that impact the observable effects of attention, a defined vocabulary of looking behaviors for certain motor and cognitive activity, a defined hierarchy of three levels of eye behavior (endogenous, exogenous and idling) and a proposed method of how these types interact.

NASA Discipline Space Human Factors↗