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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 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Automating Mid- and Long-Range Scheduling for NASA's Deep Space Network

NASA has recently deployed a new mid-range scheduling system for the antennas of the Deep Space Network (DSN), called Service Scheduling Software, or S(sup 3). This system is architected as a modern web application containing a central scheduling database integrated with a collaborative environment, exploiting the same technologies as social web applications but applied to a space operations context. This is highly relevant to the DSN domain since the network schedule of operations is developed in a peer-to-peer negotiation process among all users who utilize the DSN (representing 37 projects including international partners and ground-based science and calibration users). The initial implementation of S(sup 3) is complete and the system has been operational since July 2011. S(sup 3) has been used for negotiating schedules since April 2011, including the baseline schedules for three launching missions in late 2011. S(sup 3) supports a distributed scheduling model, in which changes can potentially be made by multiple users based on multiple schedule "workspaces" or versions of the schedule. This has led to several challenges in the design of the scheduling database, and of a change proposal workflow that allows users to concur with or to reject proposed schedule changes, and then counter-propose with alternative or additional suggested changes. This paper describes some key aspects of the S(sup 3) system and lessons learned from its operational deployment to date, focusing on the challenges of multi-user collaborative scheduling in a practical and mission-critical setting. We will also describe the ongoing project to extend S(sup 3) to encompass long-range planning, downtime analysis, and forecasting, as the next step in developing a single integrated DSN scheduling tool suite to cover all time ranges.

scheduling↗
DOE OSTI2021

The Applicability of Unit Systems to High-Performance Computing Applications

Dimensional analysis is a key technique used to verify the soundness of scientific models. Most experts agree that engineering and scientific software would be made more reliable by integrating dimensional analysis in their type system. We explored how High Performance Computing (HPC) applications could integrate compile-time dimensional analysis. We started by investigating various implementation of unit systems for C++. Eventually, selecting the latest (and most advanced) one to apply to our test codes. We worked with code of increasing complexity, from a projectile trajectory calculation to the proxy-application Lulesh. This included our code, Springs-3D, which focuses on demonstrating language features while performing simple physic computations. Finally, our main contribution is a source-code analysis which extracts constraints on the dimension of all variables, functions, and constants in an application. This resulting system of equations is solved using the dimensions of a few of these objects. This analysis has the potential to greatly reduce the time spent performing dimensional analysis when refactoring application to use a representation of units.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2009

Synthesis Methods, Microscopy Characterization and Device Integration of Nanoscale Metal Oxide Semiconductors for Gas Sensing in Aerospace Applications

A comparison is made between SnO2, ZnO, and TiO2 single-crystal nanowires and SnO2 polycrystalline nanofibers for gas sensing. Both nanostructures possess a one-dimensional morphology. Different synthesis methods are used to produce these materials: thermal evaporation-condensation (TEC), controlled oxidation, and electrospinning. Advantages and limitations of each technique are listed. Practical issues associated with harvesting, purification, and integration of these materials into sensing devices are detailed. For comparison to the nascent form, these sensing materials are surface coated with Pd and Pt nanoparticles. Gas sensing tests, with respect to H2, are conducted at ambient and elevated temperatures. Comparative normalized responses and time constants for the catalyst and noncatalyst systems provide a basis for identification of the superior metal-oxide nanostructure and catalyst combination. With temperature-dependent data, Arrhenius analyses are made to determine an activation energy for the catalyst-assisted systems.

VanderWal, Randy L.↗
DOE OSTI2025

High Mobility and Electrostatics in GeSn Quantum Wells With SiGeSn Barriers

GeSn is an emerging material with potential applications in next‐generation integrated optoelectronics and quantum information processing. While GeSn/SiGeSn quantum wells exhibit promising optical properties, their electrical transport characteristics and governing electrostatics in gated structures remain unexplored. Heterostructure field‐effect transistors are fabricated using GeSn/SiGeSn quantum wells and electronic transport properties of 2D holes are characterized. At 2 K, heterostructure field‐effect transistors with well/barrier compositions of Ge 0.945 Sn 0.055 /Si 0.03 Ge 0.93 Sn 0.04 and Ge 0.9 Sn 0.1 /Si 0.017 Ge 0.927 Sn 0.056 , show peak mobilities of 9000 and 19 000 cm 2 /Vs, respectively, the latter setting a record for the highest mobility reported for GeSn quantum wells with a Sn concentration around 6 % or greater. Remarkably, at low carrier densities, devices with a SiGeSn barrier exhibit mobilities several times higher than previously reported for GeSn quantum wells with a Ge barrier. This higher mobility contrasts with the expectation that alloy scattering from the barrier would reduce carrier mobility. Two mechanisms based on atom probe tomography data analyses are proposed: i) unintentionally improved SiGeSn/GeSn interface and/or ii) reduced alloy scattering from short‐range order. Significant current–voltage hysteresis is observed, with the effective threshold gate voltage shifting by more than 5 V, attributed to non‐equilibrium trapped charge at various interfaces within the SiGeSn heterostructure.

GeSn↗
DOE OSTI2023

Sustainable Supply Chains by Integrating Life Cycle Modeling and Techno-Ecological Synergy with Application to Mitigation of Harmful Algal Blooms

Harmful algal blooms (HABs) are a challenge in water bodies across the planet and are often caused by fertilizer runoff from intensively farmed lands. This chapter describes a framework by which producers of biomass-based products could manage their supply chain to encourage mitigation of HABs and their life cycle environmental impacts, while also considering economic implications. The emphasis is on corn-based products such as ethanol in the region around Lake Erie in Northwest Ohio. It shows how wetland ecosystems could be designed synergistically with farming practices, transportation of the corn, and conversion and use of ethanol. The resulting techno-ecologically synergistic supply chains can be environmentally and economically superior to conventional techno-centric supply chains. The resulting designs can help ethanol manufacturers choose between farms, encourage farmers to adopt wetland ecosystems for intercepting and mitigating nutrient runoff, and determine the location of biorefineries for optimizing environmental and economic objectives.

air quality regulation↗
DOE OSTI2023

Embedding ceramic components in metal structures with hybrid directed energy deposition

The combined benefit of both additive and subtractive manufacturing within the same gantry system enables hybrid directed energy deposition to create complex geometries with smooth surface finish and superior dimensional accuracy. Moreover, with layer-by-layer access to the structure during both the addition and subtraction of material, the insertion of components is now possible, assuming the components can survive the high temperatures associated with the subsequent metal deposition. Ceramic inserts are of interest for a variety of reasons including (1) to create complex interwoven ductile/brittle composites for ballistics or high-temperature applications or (2) to integrate high-temperature strain or temperature sensors protected within ceramic substrate subsumed into a larger metal structure. Here, in this work, stainless steel substrates were machined to create an internal cavity for the insertion of a ceramic component. During the investigation of several different over-the-ceramic deposition strategies, components were inserted, and different process sequences were allowed to continue to envelop the inserted ceramic with varying success. Unmelted powder was used to serve both as a thermal buffer and to provide a flush surface upon which the laser cladding could continue. Subsequent depositions were attempted with both dry and wet powder (addition of machining coolant to wet). The wet powder has previously been demonstrated to not significantly impact the mechanical properties of a final structure and provided a thermal barrier to protect the ceramic piece from the extreme temperatures of the final metal deposition. The wetting of the powder provided stability and minimized displacement caused by the powder flow from the laser cladding head. Finally, the use of an oblique angle for laser cladding allowed for the redirection of some fraction of the introduced thermal energy away from the ceramic component and, consequently, improved the survival of the ceramic inserts. With this combination of techniques, ceramic inserts survived full embedding within a 3D-printed stainless steel structure.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Effective data sampling strategies and boundary condition constraints of physics-informed neural networks for identifying material properties in solid mechanics

Material identification is critical for understanding the relationship between mechanical properties and the associated mechanical functions. However, material identification is a challenging task, especially when the characteristic of the material is highly nonlinear in nature, as is common in biological tissue. In this work, we identify unknown material properties in continuum solid mechanics via physics-informed neural networks (PINNs). To improve the accuracy and efficiency of PINNs, we develop efficient strategies to nonuniformly sample observational data. We also investigate different approaches to enforce Dirichlet-type boundary conditions (BCs) as soft or hard constraints. Finally, we apply the proposed methods to a diverse set of time-dependent and time-independent solid mechanic examples that span linear elastic and hyperelastic material space. The estimated material parameters achieve relative errors of less than 1%. As such, this work is relevant to diverse applications, including optimizing structural integrity and developing novel materials.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Crystal Plasticity Model Validation Using Combined High-Energy Diffraction Microscopy Data for a Ti-7Al Specimen

High-Energy Diffraction Microscopy (HEDM) is a 3-d x-ray characterization method that is uniquely suited to measuring the evolving micromechanical state and microstructure of polycrystalline materials during in situ processing. The near-field and far-field configurations provide complementary information; orientation maps computed from the near-field measurements provide grain morphologies, while the high angular resolution of the far-field measurements provide intergranular strain tensors. The ability to measure these data during deformation in situ makes HEDM an ideal tool for validating micro-mechanical deformation models that make their predictions at the scale of individual grains. Crystal Plasticity Finite Element Models (CPFEM) are one such class of micro-mechanical models. While there have been extensive studies validating homogenized CPFEM response at a macroscopic level, a lack of detailed data measured at the level of the microstructure has hindered more stringent model validation efforts. Here, we utilize an HEDM dataset from an alphatitanium alloy (Ti-7Al), collected at the Advanced Photon Source, Argonne National Laboratory, under in situ tensile deformation. The initial microstructure of the central slab of the gage section, measured via near-field HEDM, is used to inform a CPFEM model. The predicted intergranular stresses for 39 internal grains are then directly compared to data from 4 far-field measurements taken between ~4% and ~80% of the macroscopic yield strength. In conclusion, the intergranular stresses from the CPFEM model and far-field HEDM measurements up to incipient yield are shown to be in good agreement, and implications for application of such an integrated computational/experimental approach to phenomena such as fatigue and crack propagation is discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2019

A mini review on two-dimensional nanomaterial assembly

Two-dimensional (2D) nanomaterials have attracted a great deal of attention since the discovery of graphene in 2004, due to their intriguing physicochemical properties and wide-ranging applications in catalysis, energy-related devices, electronics and optoelectronics. To maximize the potential of 2D nanomaterials for their technological applications, controlled assembly of 2D nanobulding blocks into integrated systems is critically needed. This mini review summarizes the reported strategies of 2D materials-based assembly into integrated functional nanostructures, from in-situ assembly method to post-synthesis assembly. Here, the applications of 2D assembled integrated structures are also covered, especially in the areas of energy, electronics and sensing, and we conclude with discussion on the remaining challenges and potential directions in this emerging field.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2018

The growth and assembly of organic molecules and inorganic 2D materials on graphene for van der Waals heterostructures

The unique properties of graphene have made it a promising material for integration in future electronic applications. The idealized surface of graphene, atomically-flat and without dangling bonds, offers the opportunity to understand the assembly of organic and inorganic molecules to form a wide range of ordered architectures and functional graphene-based heterostructures. In this review, we summarize recent progress in the growth of hierarchical nanostructures on graphene. The self-assembly of organic molecules and inorganic two-dimensional (2D) layers on graphene for the construction of various types of heterostructures are highlighted. Van der Waals interactions between the assembled molecules and graphene are shown to allow the formation of highly-ordered structures with preferred molecular orientations and stacking configurations that circumvent the strict lattice-matching requirements in traditional epitaxial growth. Lastly, we briefly discuss representative applications of graphene-based heterostructures in electronic and optoelectronics.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Multi-channel low-cost light spectrum measurement using a multilayer perceptron

Light is one of the most important elements for residential and work spaces, which affects visual performance, comfort, productivity and well-being. The measures that quantify the characteristics of a light source are derived directly from the spectral power distribution (SPD). In addition the SPD is an important factor influencing the quality of a light source. However, measuring light source spectrum with traditional spectrometers is expensive, difficult to adapt to normal spaces, and hard to integrate with other systems. To address these challenges, a low-cost spectrometer was developed using an artificial neural network, with a resolution of 5 nm in the visible spectrum. The reconstructed SPD has an error lower than 2% and allows the derivation of measurements to characterize the colour quality of light sources. Additionally, the device has wireless communication (Bluetooth and Wi-Fi) in real-time, which allows integration into lighting control applications and other Internet of things (IoT) applications.

Botero-Valencia, Juan Sebastián↗
DOE OSTI2023

In-depth analysis on parallel processing patterns for high-performance Dataframes

The Data Science domain has expanded monumentally in both research and industry communities during the past decade, predominantly owing to the Big Data revolution. Artificial Intelligence (AI) and Machine Learning (ML) are bringing more complexities to data engineering applications, which are now integrated into data processing pipelines to process terabytes of data. Typically, a significant amount of time is spent on data preprocessing in these pipelines, and hence improving its efficiency directly impacts the overall pipeline performance. The community has recently embraced the concept of Dataframes as the de-facto data structure for data representation and manipulation. However, the most widely used serial Dataframes today (R, pandas) experience performance limitations while working on even moderately large data sets. We believe that there is plenty of room for improvement by taking a look at this problem from a high-performance computing point of view. In a prior publication, we presented a set of parallel processing patterns for distributed dataframe operators and the reference runtime implementation, Cylon. In this paper, we are expanding on the initial concept by introducing a cost model for evaluating the said patterns. Furthermore, we evaluate the performance of Cylon on the ORNL Summit supercomputer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Microstructures of the silicon carbide nanowires obtained by annealing the mechanically-alloyed amorphous powders

Silicon, graphite and boron nitride powders were mechanically alloyed for 40 h in argon. The as-milled powders were annealed at 1700 °C in nitrogen for 30 min. The annealed powders are covered by a thick layer of gray–green SiC nanowires, which are 300 nm to 1000 nm in diameter and several hundred microns in length. Trace iron in the raw powders acts as a catalyst, promoting the V–L–S process. It follows that the actual substances contributing to the growth of the SiC nanowires may be silicon, graphite and the metal impurities in the raw powders. The results from HRTEM and XRD reveal that the products contain both straight α/β-SiC nanowires and nodular α/β-SiC nanochains. It is interestingly found that 6H–SiC coexists with 3C–SiC in one nodular nanowire. This novel structure may introduce periodic potential field along the longitudinal direction of the nanowires, and may find applications in the highly integrated optoelectronic devices. - Graphical abstract: Display Omitted - Highlights: • SiC nanowires were prepared by annealing the mechanically alloyed amorphous powders. • SiC nanowires are 300 nm to 1000 nm in diameter and several hundred microns in length. • The products contain both straight α/β-SiC nanowires and nodular α/β-SiC nanochains. • Trace Fe in the raw powders acts as a catalyst, promoting the V–L–S process. • 6H–SiC coexists with 3C–SiC in one nodular SiC nanowire.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2017

A programmable time alignment scheme for detector signals from the upgraded muon spectrometer at the ATLAS experiment

We present a programmable time alignment scheme used in an ASIC for the ATLAS forward muon trigger development. The scheme utilizes regenerated clocks with programmable phases to compensate for the timing offsets introduced by different detector trace lengths. Each ASIC used in the design has 104 input channels with delay compensation circuitry providing steps of ~ 3 ns and a full range of 25 ns for each channel. Detailed implementation of the scheme including majority logic to suppress single-event effects is presented. The scheme is flexible and fully synthesizable. Here, the approach is adaptable to other applications with similar phase shifting requirements. In addition, the design is resource efficient and is suitable for cost-effective digital implementation with a large number of channels.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Chemoresponsive Nanofluidic Pump That Turns Off in the Presence of Lead Ion

There are many applications that require the integration of a pump and a chemical sensor so that the solution being pumped can be analyzed in real time for a specific chemical species and the flow adjusted according to the measured concentration of that species. In this paper, we describe an alternative strategy: a chemoresponsive pump where a single device acts as both the sensor and pump simultaneously. We demonstrate this concept with a nanofluidic Pb 2+ -responsive pump that uses electroosmotic flow as the pumping technology, and a Pb 2+ -binding ionophore that allows the device to selectively respond to Pb 2+ . The pump yields high flow rates at low Pb 2+ concentrations (<1 μM), but flow rate decreases with concentrations above this threshold and ultimately goes to zero at concentrations above 100 μM.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Automated generation and ensemble-learned matching of X-ray absorption spectra

X-ray absorption spectroscopy (XAS) is a widely used materials characterization technique to determine oxidation states, coordination environment, and other local atomic structure information. Analysis of XAS relies on comparison of measured spectra to reliable reference spectra. However, existing databases of XAS spectra are highly limited both in terms of the number of reference spectra available as well as the breadth of chemistry coverage. In this work, we report the development of XASdb, a large database of computed reference XAS, and an Ensemble-Learned Spectra IdEntification (ELSIE) algorithm for the matching of spectra. XASdb currently hosts more than 800,000 K-edge X-ray absorption near-edge spectra (XANES) for over 40,000 materials from the open-science Materials Project database. We discuss a high-throughput automation framework for FEFF calculations, built on robust, rigorously benchmarked parameters. FEFF is a computer program uses a real-space Green’s function approach to calculate X-ray absorption spectra. Here, we will demonstrate that the ELSIE algorithm, which combines 33 weak “learners” comprising a set of preprocessing steps and a similarity metric, can achieve up to 84.2% accuracy in identifying the correct oxidation state and coordination environment of a test set of 19 K-edge XANES spectra encompassing a diverse range of chemistries and crystal structures. The XASdb with the ELSIE algorithm has been integrated into a web application in the Materials Project, providing an important new public resource for the analysis of XAS to all materials researchers. Finally, the ELSIE algorithm itself has been made available as part of veidt, an open source machine-learning library for materials science.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Highly Plasmonic Titanium Nitride by Room-Temperature Sputtering

Titanium nitride (TiN) has recently emerged as an attractive alternative material for plasmonics. However, the typical high-temperature deposition of plasmonic TiN using either sputtering or atomic layer deposition has greatly limited its potential applications and prevented its integration into existing CMOS device architectures. Here, we demonstrate highly plasmonic TiN thin films and nanostructures by a room-temperature, low-power, and bias-free reactive sputtering process. We investigate the optical properties of the TiN films and their dependence on the sputtering conditions and substrate materials. We find that our TiN possesses one of the largest negative values of the real part of the dielectric function as compared to all other plasmonic TiN films reported to date. Two-dimensional periodic arrays of TiN nanodisks are then fabricated, from which we validate that strong plasmonic resonances are supported. Our room-temperature deposition process can allow for fabricating complex plasmonic TiN nanostructures and be integrated into the fabrication of existing CMOS-based photonic devices to enhance their performance and functionalities.

36 MATERIALS SCIENCE↗