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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Surrogate multi-fidelity data and model fusion for scientific discovery and uncertainty quantification in Earth System Models

This whitepaper addresses the Earth and Environmental Systems Sciences Division (EESSD)’s predictability challenges in modeling the integrated water cycle and data-model integration. Specifically, it focuses on reducing and characterizing the uncertainty in the representation of process models for unresolved physics, either due to model resolution or limited by the physical under standing or computational efficiency, and the use of observational data for in-situ process parameter optimization within ESM. The described methods may also be used to determine the nature of responses (e.g. strength and direction), and hence to identify critical processes that drive the overall ESM responses to perturbation in the forcing

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The Application of Convolutional Neural Networks (CNNs) to Recognize Defects in 3D-Printed Parts

Cracks and pores are two common defects in metallic additive manufacturing (AM) parts. In this paper, deep learning-based image analysis is performed for defect (cracks and pores) classification/detection based on SEM images of metallic AM parts. Three different levels of complexities, namely, defect classification, defect detection and defect image segmentation, are successfully achieved using a simple CNN model, the YOLOv4 model and the Detectron2 object detection library, respectively. The tuned CNN model can classify any single defect as either a crack or pore at almost 100% accuracy. The other two models can identify more than 90% of the cracks and pores in the testing images. In addition to the application of static image analysis, defect detection is also successfully applied on a video which mimics the AM process control images. The trained Detectron2 model can identify almost all the pores and cracks that exist in the original video. This study lays a foundation for future in situ process monitoring of the 3D printing process.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Direct mechanistic connection between acoustic signals and melt pool morphology during laser powder bed fusion

Various nondestructive diagnostic techniques have been proposed for in situ process monitoring of laser powder bed fusion (LPBF), including melt pool pyrometry, whole-layer optical imaging, acoustic emission, atomic emission spectroscopy, high speed melt pool imaging, and thermionic emission. Correlations between these in situ monitoring signals and defect formation have been demonstrated with acoustic signals having been shown to predict pore formation with especially high confidence in recent machine learning studies. Here, in this work, time-resolved acoustic data are collected in both the conduction and keyhole welding regimes of LPBF-processed Ti-6Al-4V alloy. A non-dimensionalized Strouhal number analysis, used in whistle aeroacoustics, is applied to demonstrate that the acoustic signals recorded in the keyhole regimes can be directly associated with the vapor depression morphology. This mechanistic understanding developed from whistle aeroacoustics shows that acoustic monitoring during the LPBF process can provide a direct probe into the vapor depression dynamics and defect occurrence, especially in the keyhole regimes relevant to printing and defect formation.

36 MATERIALS SCIENCE↗
NASA NTRS2011

Microwave Permittivity and Permeability Measurement on Lunar Soils

There has been interest in finding ways to process the lunar regolith since the early analyses of lunar samples returned from the Apollo moon missions. This fact has led to proposals for using microwaves to perform in-situ processing of the lunar soil to support future colonization of the moon. More recently, there has been speculation that the excellent microwave absorption of lunar soil came from the nanophase iron content in the regolith. The motivation for the present study was to begin obtaining a more fundamental understanding of the dielectric and magnetic properties of the regolith at microwave frequencies. A major objective of this study was to obtain information that would help answer the question about whether nanophase iron plays a major role in heating lunar soils. These new measurements over a wide frequency range can also determine the magnitude of the dielectric and magnetic absorption and if there are any resonant features that could be used to enhance processing of the regolith in the future. In addition, these microwave measurements would be useful in confirming that new simulants being developed, particularly those containing nanophase iron, would have the correct composition to simulate the lunar regolith. The results of this study suggest that nanophase iron does not play a major role in heating lunar regolith.

Barmatz, Martin↗
NASA NTRS2015

Relativistic Electron Response to the Combined Magnetospheric Impact of a Coronal Mass Ejection Overlapping with a High-Speed Stream: Van Allen Probes Observations

During early November 2013, the magnetosphere experienced concurrent driving by a coronal mass ejection (CME) during an ongoing high-speed stream (HSS) event. The relativistic electron response to these two kinds of drivers, i.e., HSS and CME, is typically different, with the former often leading to a slower buildup of electrons at larger radial distances, while the latter energizing electrons rapidly with flux enhancements occurring closer to the Earth. We present a detailed analysis of the relativistic electron response including radial profiles of phase space density as observed by both Magnetic Electron and Ion Sensor (MagEIS) and Relativistic Electron Proton Telescope instruments on the Van Allen Probes mission. Data from the MagEIS instrument establish the behavior of lower energy (<1 MeV) electrons which span both intermediary and seed populations during electron energization. Measurements characterizing the plasma waves and magnetospheric electric and magnetic fields during this period are obtained by the Electric and Magnetic Field Instrument Suite and Integrated Science instrument on board Van Allen Probes, Search Coil Magnetometer and Flux Gate Magnetometer instruments on board Time History of Events and Macroscale Interactions during Substorms, and the low-altitude Polar-orbiting Operational Environmental Satellites. These observations suggest that during this time period, both radial transport and local in situ processes are involved in the energization of electrons. The energization attributable to radial diffusion is most clearly evident for the lower energy (<1 MeV) electrons, while the effects of in situ energization by interaction of chorus waves are prominent in the higher-energy electrons.

Magnetosphere↗
DOE OSTI2015

Rapid thermal processing chamber for in-situ x-ray diffraction

Rapid thermal processing (RTP) is widely used for processing a variety of materials, including electronics and photovoltaics. Presently, optimization of RTP is done primarily based on ex-situ studies. As a consequence, the precise reaction pathways and phase progression during the RTP remain unclear. More awareness of the reaction pathways would better enable process optimization and foster increased adoption of RTP, which offers numerous advantages for synthesis of a broad range of materials systems. To achieve this, we have designed and developed a RTP instrument that enables real-time collection of X-ray diffraction data with intervals as short as 100 ms, while heating with ramp rates up to 100 °Cs{sup −1}, and with a maximum operating temperature of 1200 °C. The system is portable and can be installed on a synchrotron beamline. The unique capabilities of this instrument are demonstrated with in-situ characterization of a Bi{sub 2}O{sub 3}-SiO{sub 2} glass frit obtained during heating with ramp rates 5 °C s{sup −1} and 100 °C s{sup −1}, revealing numerous phase changes.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Relativistic electron response to the combined magnetospheric impact of a coronal mass ejection overlapping with a high-speed stream: Van Allen Probes observations

During early November 2013, the magnetosphere experienced concurrent driving by a coronal mass ejection (CME) during an ongoing high-speed stream (HSS) event. The relativistic electron response to these two kinds of drivers, i.e., HSS and CME, is typically different, with the former often leading to a slower buildup of electrons at larger radial distances, while the latter energizing electrons rapidly with flux enhancements occurring closer to the Earth. In this paper, we present a detailed analysis of the relativistic electron response including radial profiles of phase space density as observed by both Magnetic Electron and Ion Sensor (MagEIS) and Relativistic Electron Proton Telescope instruments on the Van Allen Probes mission. Data from the MagEIS instrument establish the behavior of lower energy (<1 MeV) electrons which span both intermediary and seed populations during electron energization. Measurements characterizing the plasma waves and magnetospheric electric and magnetic fields during this period are obtained by the Electric and Magnetic Field Instrument Suite and Integrated Science instrument on board Van Allen Probes, Search Coil Magnetometer and Flux Gate Magnetometer instruments on board Time History of Events and Macroscale Interactions during Substorms, and the low-altitude Polar-orbiting Operational Environmental Satellites. These observations suggest that during this time period, both radial transport and local in situ processes are involved in the energization of electrons. Finally, the energization attributable to radial diffusion is most clearly evident for the lower energy (<1 MeV) electrons, while the effects of in situ energization by interaction of chorus waves are prominent in the higher-energy electrons.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Additive Manufacturing of Nickel Superalloys: Opportunities for Innovation and Challenges Related to Qualification

Innovative designs for turbines can be achieved by advances in nickel-based superalloys and manufacturing methods, including the adoption of additive manufacturing. In this regard, selective electron beam melting (SEBM) and selective laser melting (SLM) of nickel-based superalloys do provide distinct advantages. Furthermore, the direct energy deposition (DED) processes can be used for repair and reclamation of nickel alloy components. The current paper explores opportunities for innovation and qualification challenges with respect to deployment of AM as a disruptive manufacturing technology. Here in the first part of the paper, fundamental correlations of processing parameters to defect tendency and microstructure evolution will be explored using DED process. In the second part of the paper, opportunities for innovation in terms of site-specific control of microstructure during processing will be discussed. In the third part of the paper, challenges in qualification of AM parts for service will be discussed and potential methods to alleviate these issues through in situ process monitoring, and big data analytics are proposed.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Evaluation of In-Situ Analysis Strategies at Scale for Power Efficiency and Scalability

The increasing gap between available compute power and I/O capabilities is resulting in simulation pipelines running on leadership computing facilities being reformulated. In particular, in-situ processing is complementing conventional post-process analysis, however, it can be performed by using the same compute resources as the simulation or using secondary dedicated resources. In this paper, we focus on three different in-situ analysis strategies, which use the same compute resources as the ongoing simulation but different data movement strategies. We evaluate the costs incurred by these strategies in terms of run time, scalability and power/energy consumption. Furthermore, we extrapolate power behavior to peta-scale and investigate different design choices through projections. Experimental evaluation at full machine scale on Titan supports that using fewer cores per node for in-situ analysis is the optimum choice in terms of scalability. Hence, further research effort should be devoted towards developing in-situ analysis techniques following this strategy in future high-end systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Demonstrating the viability of Lagrangian in situ reduction on supercomputers

Performing exploratory analysis and visualization of large-scale time-varying computational science applications is challenging due to inaccuracies that arise from under-resolved data. In recent years, Lagrangian representations of the vector field computed using in situ processing are being increasingly researched and have emerged as a potential solution to enable exploration. However, prior works have offered limited estimates of the encumbrance on the simulation code as they consider “theoretical” in situ environments. Further, the effectiveness of this approach varies based on the nature of the vector field, benefitting from an in-depth investigation for each application area. With this study, an extended version of Sane et al. (2021), we contribute an evaluation of Lagrangian analysis viability and efficacy for simulation codes executing at scale on a supercomputer. We investigated previously unexplored cosmology and seismology applications as well as conducted a performance benchmarking study by using a hydrodynamics mini-application targeting exascale computing. Here, to inform encumbrance, we integrated in situ infrastructure with simulation codes, and evaluated Lagrangian in situ reduction in representative homogeneous and heterogeneous HPC environments. To inform post hoc accuracy, we conducted a statistical analysis across a range of spatiotemporal configurations as well as a qualitative evaluation. Additionally, our study contributes cost estimates for distributed-memory post hoc reconstruction. In all, we demonstrate viability for each application — data reduction to less than 1% of the total data via Lagrangian representations, while maintaining accurate reconstruction and requiring under 10% of total execution time in over 90% of our experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Diagnostic and predictive capabilities of the TCR digital platform

The Transformational Challenge Reactor program is leveraging additive manufacturing technologies to fabricate the nuclear components required to assemble a microreactor core. Compared with traditional manufacturing processes, additive manufacturing allows for direct observation of the interior of the component during manufacturing. This unique capability promises significant possibilities for creating a new paradigm for nuclear component qualification by leveraging in-situ process data. This report describes FY21 efforts to predict material tensile properties based on data collected during the laser powder bed fusion printing process. The primary focus of this report is the test campaign designed to generate the large quantities of training data required to implement artificial intelligence algorithms that can predict these material properties. Preliminary prediction results and a demonstration of the overall data collection, analysis, and visualization pipeline are also provided.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

ThermoPore: Predicting part porosity based on thermal images using deep learning

Part qualification is often a critical and labor-intensive process in additive manufacturing, particularly in the detection of defects such as porosity, which stands to benefit significantly from advancements in machine learning. We present a deep learning approach for quantifying and localizing ex-situ porosity within Laser Powder Bed Fusion fabricated samples utilizing in-situ thermal image monitoring data. Our goal is to build the real time porosity map of parts based on thermal images acquired during the build. The quantification task builds upon the established Convolutional Neural Network model architecture to predict pore count and the localization task leverages the spatial and temporal attention mechanisms of the novel Video Vision Transformer model to indicate areas of expected porosity. Our model for porosity quantification achieved a R 2 score of 0.57 and our model for porosity localization produced an average Intersection over Union (IoU) score of 0.32 and a maximum of 1.0. This work is setting the foundations of part porosity “Digital Twins” based on additive manufacturing monitoring data and can be applied downstream to reduce time-intensive post-inspection and testing activities during part qualification and certification. In addition, we seek to accelerate the acquisition of crucial insights normally only available through ex-situ part evaluation by means of machine learning analysis of in-situ process monitoring data.

Deep learning↗
DOE OSTI2024

Achieving environmental stability in an atomically thin quantum spin Hall insulator via graphene intercalation

Atomic monolayers on semiconductor surfaces represent an emerging class of functional quantum materials in the two-dimensional limit — ranging from superconductors and Mott insulators to ferroelectrics and quantum spin Hall insulators. Indenene, a triangular monolayer of indium with a gap of ~ 120 meV is a quantum spin Hall insulator whose micron-scale epitaxial growth on SiC(0001) makes it technologically relevant. However, its suitability for room-temperature spintronics is challenged by the instability of its topological character in air. It is imperative to develop a strategy to protect the topological nature of indenene during ex situ processing and device fabrication. Here we show that intercalation of indenene into epitaxial graphene provides effective protection from the oxidising environment, while preserving an intact topological character. Our approach opens a rich realm of ex situ experimental opportunities, priming monolayer quantum spin Hall insulators for realistic device fabrication and access to topologically protected edge channels.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Facilitating Staging-based Unstructured Mesh Processing to Support Hybrid In-Situ Workflows

In-situ and in-transit processing alleviate the gap between the computing and I/O capabilities by scheduling data analytics close to the data source. Hybrid in-situ processing splits data analytics into two stages: the data processing that runs in-situ aims to extract regions of interest, which are then transferred to staging services for further in-transit analytics. To facilitate this type of hybrid in-situ processing, the data staging service needs to support complex intermediate data representations generated/consumed by the in-situ tasks. Unstructured (or irregular) mesh is one such derived data representation that is typically used and bridges simulation data and analytics. However, how staging services efficiently support unstructured mesh transfer and processing remains to be explored. This paper investigates design options for transferring and processing unstructured mesh data using staging services. Using polygonal mesh data as an example, we show that hybrid in-situ workflows with staging-based unstructured mesh processing can effectively support hybrid in-situ workflows, and can significantly decrease data movement overheads.

data-driven↗
DOE OSTI2015

Metrology for Fuel Cell Manufacturing

The project was divided into three subprojects. The first subproject is Fuel Cell Manufacturing Variability and Its Impact on Performance. The objective was to determine if flow field channel dimensional variability has an impact on fuel cell performance. The second subproject is Non-contact Sensor Evaluation for Bipolar Plate Manufacturing Process Control and Smart Assembly of Fuel Cell Stacks. The objective was to enable cost reduction in the manufacture of fuel cell plates by providing a rapid non-contact measurement system for in-line process control. The third subproject is Optical Scatterfield Metrology for Online Catalyst Coating Inspection of PEM Soft Goods. The objective was to evaluate the suitability of Optical Scatterfield Microscopy as a viable measurement tool for in situ process control of catalyst coatings.

08 HYDROGEN↗
DOE OSTI2025

Nickel Sulfide-Nanowire-Filled Carbon Nanotubes as an Efficient Overall Water Splitting Electrocatalyst

Pursuing stable, efficient, and cost-effective nanostructured bifunctional electrocatalysts is crucial for advancing the electrochemical water splitting process and enabling clean hydrogen energy production. In recent years, considerable efforts have focused on developing highly efficient and durable commercial electrocatalysts for the oxygen evolution reaction (OER) and overall water splitting (OWS). This research introduces an OWS electrocatalyst-nickel sulfide-filled carbon nanotubes grown on a carbon cloth substrate (Ni 3 S 2 @CNTs/CC), synthesized via a one-step in-situ process. The synergistic integration of metal sulfide (Ni 3 S 2 ) and carbon nanotubes provides abundant active sites for catalytic reactions, ensuring a robust composite nanostructure with enhanced durability. Furthermore, the electrocatalytic performance for OER and OWS has been significantly improved by a simple acid treatment to the electrocatalysts, which introduces physical and chemical defects, particularly oxygen functional groups (the acid-treated sample is termed as Ni 3 S 2 @CNTs/CC-AT). As OER electrocatalysts, Ni 3 S 2 @CNTs/CC and Ni 3 S 2 @CNTs/CC-AT present overpotentials of 304 and 200 mV, respectively, for achieving a current density of 10 mA/cm 2 in the OER process. Furthermore, for complete water splitting in 1.0 M KOH electrolyte, Ni 3 S 2 @CNTs/CC and Ni 3 S 2 @CNTs/CC-AT exhibit potentials of 1.63 and 1.44 V, respectively, to achieve a current density of 10 mA/cm 2 when employed as both anode and cathode. Moreover, Ni 3 S 2 @CNTs/CC and Ni 3 S 2 @CNTs/CC-AT demonstrate durable nature for 22 and 20 h durability in the OER and OWS processes, respectively, offering a promising alternative to ruthenium- and iridium-based electrocatalysts for electrochemical hydrogen production through water splitting. In conclusion, the in-situ synthesis method and acid treatment strategy described in this research are promising approaches to fabricating high-performance encapsulated carbon-nanotube-based electrocatalysts.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Update on Plasma Processing R&amp;D for LCLS-II

SRF cavities performance preservation is crucial, from vertical test to accelerator operation. Field emission is still one of the main problems to overcome and plasma cleaning has been proven successful by SNS, in cleaning field emitters and increasing the work function of Nb. A collaboration has been established between FNAL, SLAC and ORNL with the purpose of applying plasma processing to LCLS-II cavities, in order to minimize and overcome field emission without affecting the high Q of N-doped cavities. The recipe will follow the neon-oxygen active plasma adopted at SNS, allowing in-situ processing of cavities and cryomodules from hydrocarbon contaminants. A novel method for plasma ignition has been developed at FNAL: a plasma glow discharge is ignited using high order modes to overcome limitations imposed by the fundamental power coupler. The results of experiments on 9-cell LCLS-II cavity are presented, along with plasma ignition studies. In addition the RF system is shown and N-doped Nb samples studies are discussed.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Effect of threshold parameters on infrared segmentation methods for porosity detection in electron beam powder bed fusion

In-situ process monitoring has seen significant interest in additive manufacturing to address qualification and certification goals. This is especially prevalent in metal powder bed fusion processes such as electron beam powder bed fusion (PBF-EB), with layer-wise infrared imaging being commonly used to detect defects. Here, this work compares two different segmentation methods (static thresholding and statistical thresholding) used for detecting porosity from in-situ infrared imaging data for PBF-EB. Samples were manufactured at a variety of focus offset values to induce porosity. Then, the segmented infrared images were compared to ex-situ X-ray computed tomography scans, which served as a ground-truth reference for objective evaluation. Through this analysis framework, the influential parameters, static threshold and N-value (number of standard deviations above the mean pixel value), respectively, for both image segmentation methods were analyzed and compared for their effects on porosity detection. With optimal parameter settings, the two methods had similar porosity detection performance, but the statistical method performed better under a larger variety of parameter settings.

Infrared imaging↗