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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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NASA NTRS1994

Computational predictive methods for fracture and fatigue

The damage-tolerant design philosophy as used by aircraft industries enables aircraft components and aircraft structures to operate safely with minor damage, small cracks, and flaws. Maintenance and inspection procedures insure that damages developed during service remain below design values. When damage is found, repairs or design modifications are implemented and flight is resumed. Design and redesign guidelines, such as military specifications MIL-A-83444, have successfully reduced the incidence of damage and cracks. However, fatigue cracks continue to appear in aircraft well before the design life has expired. The F16 airplane, for instance, developed small cracks in the engine mount, wing support, bulk heads, the fuselage upper skin, the fuel shelf joints, and along the upper wings. Some cracks were found after 600 hours of the 8000 hour design service life and design modifications were required. Tests on the F16 plane showed that the design loading conditions were close to the predicted loading conditions. Improvements to analytic methods for predicting fatigue crack growth adjacent to holes, when multiple damage sites are present, and in corrosive environments would result in more cost-effective designs, fewer repairs, and fewer redesigns. The overall objective of the research described in this paper is to develop, verify, and extend the computational efficiency of analysis procedures necessary for damage tolerant design. This paper describes an elastic/plastic fracture method and an associated fatigue analysis method for damage tolerant design. Both methods are unique in that material parameters such as fracture toughness, R-curve data, and fatigue constants are not required. The methods are implemented with a general-purpose finite element package. Several proof-of-concept examples are given. With further development, the methods could be extended for analysis of multi-site damage, creep-fatigue, and corrosion fatigue problems.

Cordes, J.↗
NASA NTRSDate not supplied

Towards Integrated Computational Materials Engineering for Quantifying Performance Impacts of Microstructure and Defect Interactions in Powder Bed Fusion Parts

Powder bed fusion (PBF) additive manufacturing (AM) enables the creation of parts with complexity and functionality levels that were previously impossible with traditional manufacturing methods. By modifying the laser power, hatch spacing, or the numerous other processing parameters, the PBF process supports the production of a wide set of materials and geometries. However, that same process parameter design flexibility causes the process-design space of PBF to be massive and expensive to explore experimentally. Another challenge is quality variation across a build. As a part is being built, geometric variance between locations, such as at a thin-wall section vs. the bulk material, may cause the specified processing parameters to no longer be acceptable for producing defect-free printing. Furthermore, if the processing parameters deviate during the print process, it is difficult to assess if the part will still perform satisfactorily. Integrated Computational Materials Engineering (ICME) provides a way to understand and address these various challenges. This talk will present advancements in process-structure simulations of PBF at NASA Langley Research Center. The ability to simulate grain-scale PBF microstructures using the Physically Based Monte Carlo method will be demonstrated and compared to experimental measurements. Techniques for simulating three-dimensional lack-of-fusion and keyhole porosity defects based on the specific processing conditions and approaches for integrating the two porosity prediction techniques alongside the computational microstructure evolution models will be shown. Finally, the integration of simulated PBF microstructures, embedded process defects, and crystal plasticity finite element models to elucidate the interaction of porosity and microstructure on micromechanical fields will be demonstrated. These integrated techniques demonstrate an example of using ICME to relate processing to performance for PBF AM materials. With continued maturity, it is hoped that such ICME approaches will lead to next-generation computational-materials supported qualification and certification of AM parts.

Additive manufacturing↗
NASA NTRS1990

A synopsis of Langley Research Center's lidar effort for the 1986 FIRE IFO

The lidar data obtained by the Langley Aircraft Lidar in October 1986 in Wisconsin is being reduced in a transparent, simple fashion and will be published in its reduced form in a NASA Reference Publication (RP). This reduced data will also be submitted to the FIRE data archives. Some of this reduced data will be presented at the FIRE FSET Workshop to acquaint the science team with the data format to be used in the archive and the upcoming catalog contained in the RP. A new method was utilized in Wisconsin for obtain the depolarization ratio of aerosols. This method involves using a half-wave plate to calibrate the lidar under field conditions. The theory behind this technique will be presented at this workshop as well as some of the lidar calibration results. The lidar calibration will be utilized in interpreting some of the dual polarization lidar data obtained during the IFO in Wisconsin. Some of these data are also discussed. A continuous wave laser lab-type lidar simulator was constructed during the previous year. One of the primary reasons for the construction of the simulator was to attempt dual-polarization lidar-like calibrations under laboratory, rather than field conditions. The data collected by this system was used to experimentally check and thus, inspire confidence in the algorithms being used to interpret the lidar data obtained in the field. A computer program which simulates noisy lidar data was used as a part of this effort in order to obtain some feel for the noise in the inversion parameters as a function of noise in the actual measurements. The lidar simulation will be described in addition to presenting some of the lab-generated calibration data.

Alvarez, Jose M.↗
NASA NTRS2001

On the Accuracy of Probabilistic Bucking Load Prediction

The buckling strength of thin-walled stiffened or unstiffened, metallic or composite shells is of major concern in aeronautical and space applications. The difficulty to predict the behavior of axially compressed thin-walled cylindrical shells continues to worry design engineers as we enter the third millennium. Thanks to extensive research programs in the late sixties and early seventies and the contributions of many eminent scientists, it is known that buckling strength calculations are affected by the uncertainties in the definition of the parameters of the problem such as definition of loads, material properties, geometric variables, edge support conditions, and the accuracy of the engineering models and analysis tools used in the design phase. The NASA design criteria monographs from the late sixties account for these design uncertainties by the use of a lump sum safety factor. This so-called 'empirical knockdown factor gamma' usually results in overly conservative design. Recently new reliability based probabilistic design procedure for buckling critical imperfect shells have been proposed. It essentially consists of a stochastic approach which introduces an improved 'scientific knockdown factor lambda(sub a)', that is not as conservative as the traditional empirical one. In order to incorporate probabilistic methods into a High Fidelity Analysis Approach one must be able to assess the accuracy of the various steps that must be executed to complete a reliability calculation. In the present paper the effect of size of the experimental input sample on the predicted value of the scientific knockdown factor lambda(sub a) calculated by the First-Order, Second-Moment Method is investigated.

Arbocz, Johann↗
NASA NTRS1997

Doping Scheme in Atomic Chain Electronics

Due to the dramatic reduction in MOS size, there appear many unwanted effects. In these small devices, the number of dopant atoms in the channel is not macroscopic and electrons may suffer significantly different scattering from device to device since the spatial distribution of dopant atoms is no longer regarded as continuous. This prohibits integration, while it is impossible to control such dopant positions within atomic scale. A fundamental solution is to create electronics with simple but atomically precise structures, which could be fabricated with recent atom manipulation technology. All the constituent atoms are placed as planned, and then the device characteristics are deviation-free, which is mandatory for integration. Atomic chain electronics belongs to this category. Foreign atom chains or arrays form devices, and they are placed on the atomically flat substrate surface. We can design the band structure and the resultant Fermi energy of these structures by manipulating the lattice constant. Using the tight-binding theory with universal parameters, it has been predicted that isolated Si chains and arrays are metallic, Mg chains are insulating, and Mg arrays have metallic and insulating phases [1]. The transport properties along a metallic chain have been studied, emphasizing the role of the contact to electrodes [2]. For electronic applications, it is essential to establish a method to dope a semiconducting chain, which is to control the Fermi energy position without altering the original band structure. If we replace some of the chain atoms with dopant atoms randomly, the electrons will see random potential along die chain and will be localized strongly in space (Anderson localization). However, if we replace periodically, although the electrons can spread over the chain, there will generally appear new bands and band gaps reflecting the new periodicity of dopant atoms. This will change the original band structure significantly. In order to overcome this dilemma, we may place a dopant atom beside the chain at every N lattice periods (N > 1). Because of the periodic arrangement of pant atoms, we can avoid the unwanted Anderson localization. Moreover, since the dopant atoms do not constitute the chain, the overlap interaction between them is minimized, and the band structure modification can be made smallest. Some tight-binding results will be discussed to demonstrate the present idea.

Toshishige, Yamada↗
NASA NTRSDate not supplied

Biological Data for Deep Space Mission Support

Increased biomedical risks and challenges associated with deep space missions (cis-Lunar, Mars transit, Mars surface) require new knowledge discovery and development of novel ecosystem and biomedical support capabilities. This paradigm shift supporting distant and long-duration missions requires biological data to be findable, accessible, interoperable, reusable (FAIR), and maximally open-access (i.e., there is a data governance continuum from closed to mediated to embargoed to open). The NASA “Open Science Data Repositories” (OSDR) aims to meet scientific, technical, and operational spaceflight needs, and offers the ability to upload, download, search, share, analyze, and visualize data across physiological, behavioral, ‘omics, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive (ALSDA), and NASA Biological Institutional Scientific Collection (NBISC). In the past year, ALSDA has undergone a transformation in its data collection, curation, and architecture methods. Standardizing non-genomic (phenotypic) datasets was, and will continue to be, a challenge because of their diverse nature (e.g., molecular, cellular, tissue, whole organism behavior; micro-computed tomography, intraocular pressure, fluorescence microscopy, western blot, ultrasonography; tabular, images, video). This year ALSDA, alongside GeneLab, introduced the Biological Data Management Environment (BDME) with the purpose to accept submission of data from space relevant experiments including spaceflight, radiation, simulated gravity, gravitropism, isolation and confinement, hostile closed environments and/or distance from Earth. In addition to bringing together omics, phenotypic, physiological, bioimaging, and behavioral data into one repository. By integrating with GeneLab a multi-project submission portal aims to reduce the burden on PIs submitting data and enabling the discovery of both omics and phenotypic data. The purpose of ALSDA is to collect, curate, and make all non-human space-relevant biological data maximally findable, accessible, interoperable, and reusable (FAIR). These scope of ALSDA data collected and submitted by PIs include study design metadata, subject metadata, assay metadata (parameters), raw and processed assay data, assay imagery/video, and subject-experienced mission data telemetry (radiation, temperature, humidity, acoustics, vibrations, etc.). In 2021, a community of researchers rallied to form the ALSDA Analysis Working Group (AWG) and provided scientific consensus on dataset sample and assay metadata. The community and excitement around the ALSDA/OSDR system has already led to several data reuse studies, demonstrating value using machine learning (ML), knowledge graphs, and meta-analysis approaches.

space biology↗
NASA NTRSDate not supplied

Biological Data for Deep Space Mission Support

Increased biomedical risks and challenges associated with deep space missions (cis-Lunar, Mars transit, Mars surface) require new knowledge discovery and development of novel ecosystem and biomedical support capabilities. This paradigm shift supporting distant and long-duration missions requires biological data to be findable, accessible, interoperable, reusable (FAIR), and maximally open-access (i.e., there is a data governance continuum from closed to mediated to embargoed to open). The NASA “Open Science Data Repositories” (OSDR) aims to meet scientific, technical, and operational spaceflight needs, and offers the ability to upload, download, search, share, analyze, and visualize data across physiological, behavioral, ‘omics, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive (ALSDA), and NASA Biological Institutional Scientific Collection (NBISC). In the past year, ALSDA has undergone a transformation in its data collection, curation, and architecture methods. Standardizing non-genomic (phenotypic) datasets was, and will continue to be, a challenge because of their diverse nature (e.g., molecular, cellular, tissue, whole organism, behavior; micro-computed tomography, intraocular pressure, fluorescence microscopy, western blot, ultrasonography; tabular, images, video). This year ALSDA, alongside GeneLab, introduced the Biological Data Management Environment (BDME) with the purpose to accept submission of data from space relevant experiments including spaceflight, radiation, simulated gravity, gravitropism, isolation and confinement, hostile closed environments and/or distance from Earth. In addition to bringing together omics, phenotypic, physiological, bioimaging, and behavioral data into one repository. By integrating with GeneLab a multi-project submission portal aims to reduce the burden on PIs submitting data and enabling the discovery of both omics and phenotypic data. The purpose of ALSDA is to collect, curate, and make all non-human space-relevant biological data maximally findable, accessible, interoperable, and reusable (FAIR). These scope of ALSDA data collected and submitted by PIs include study design metadata, subject metadata, assay metadata (parameters), raw and processed assay data, assay imagery/video, and subject-experienced mission data telemetry (radiation, temperature, humidity, acoustics, vibrations, etc.). In 2021, a community of researchers rallied to form the ALSDA Analysis Working Group (AWG) and provided scientific consensus on dataset sample and assay metadata. The community and excitement around the ALSDA/OSDR system has already led to several data reuse studies, demonstrating value using machine learning (ML), knowledge graphs, and meta-analysis approaches.

space biology↗
NASA NTRS2026

Performance Optimization for Lunar Extravehicular Activity Readiness (POLAR) Study: Methods Paper

To better understand which aspects of physical fitness may be most related to performance during Lunar surface operations and thereby help to inform the current NASA fitness standards, much can be learned from fields encompassing the “tactical athlete.” Other physically demanding professions such as law enforcement, military, or rescue professionals often require candidates to meet occupationally-relevant fitness standards. The determination of such standards is a multistep process, including both objective and subjective measures, to determine tasks essential to occupational performance and identify the minimal fitness profile needed to meet physical demands of the job. Notably, fitness is only one component which may contribute to the demands of astronaut selection, flight assignment, and occupational performance. Utilizing a framework to systematically determine which domains of fitness most contribute to relevant job tasks can aid in the refinement of current NASA-STD-3001 fitness standards. Therefore, NASA’s Exercise Physiology & Countermeasures Laboratory conducted the Performance Optimization for Lunar Extravehicular Activity Readiness (POLAR) study to identify and examine a comprehensive list of fitness tests (including NASA-STD-3001 assessments: 1-Repetition Maximum [1-RM] bench press and deadlift) and determine preliminary relationships between identified fitness parameters and novel Artemis-relevant tasks to help inform future investigations for the continued development of aerobic and muscular fitness standards for surface EVAs. This was accomplished through 1) a review of the literature relating fitness assessments to simulated or real EVA performance to identify fitness tests that are most correlated with simulated EVA task performance; 2) a task analysis following a modified framework for physical employment standards development to down select mission critical tasks; and 3) development and pilot testing of a novel, portable Artemis-relevant EVA task circuit to relate to a battery of fitness assessments. The current report describes the methodology used to complete the task analysis, EVA task circuit development, and the pilot study.

Nicole C Strock↗
NASA NTRS2010

Multiple Kernel Learning for Heterogeneous Anomaly Detection: Algorithm and Aviation Safety Case Study

The world-wide aviation system is one of the most complex dynamical systems ever developed and is generating data at an extremely rapid rate. Most modern commercial aircraft record several hundred flight parameters including information from the guidance, navigation, and control systems, the avionics and propulsion systems, and the pilot inputs into the aircraft. These parameters may be continuous measurements or binary or categorical measurements recorded in one second intervals for the duration of the flight. Currently, most approaches to aviation safety are reactive, meaning that they are designed to react to an aviation safety incident or accident. In this paper, we discuss a novel approach based on the theory of multiple kernel learning to detect potential safety anomalies in very large data bases of discrete and continuous data from world-wide operations of commercial fleets. We pose a general anomaly detection problem which includes both discrete and continuous data streams, where we assume that the discrete streams have a causal influence on the continuous streams. We also assume that atypical sequence of events in the discrete streams can lead to off-nominal system performance. We discuss the application domain, novel algorithms, and also discuss results on real-world data sets. Our algorithm uncovers operationally significant events in high dimensional data streams in the aviation industry which are not detectable using state of the art methods

Das, Santanu↗
NASA NTRS2017

Sonora: A New Generation Model Atmosphere Grid for Brown Dwarfs and Young Extrasolar Giant Planets

Brown dwarf and giant planet atmospheric structure and composition has been studied both by forward models and, increasingly so, by retrieval methods. While indisputably informative, retrieval methods are of greatest value when judged in the context of grid model predictions. Meanwhile retrieval models can test the assumptions inherent in the forward modeling procedure. In order to provide a new, systematic survey of brown dwarf atmospheric structure, emergent spectra, and evolution, we have constructed a new grid of brown dwarf model atmospheres. We ultimately aim for our grid to span substantial ranges of atmospheric metallilcity, C/O ratios, cloud properties, atmospheric mixing, and other parameters. Spectra predicted by our modeling grid can be compared to both observations and retrieval results to aid in the interpretation and planning of future telescopic observations. We thus present Sonora, a new generation of substellar atmosphere models, appropriate for application to studies of L, T, and Y-type brown dwarfs and young extrasolar giant planets. The models describe the expected temperature-pressure profile and emergent spectra of an atmosphere in radiative-convective equilibrium for ranges of effective temperatures and gravities encompassing 200 less than or equal to T(sub eff) less than or equal to 2400 K and 2.5 less than or equal to log g less than or equal to 5.5. In our poster we briefly describe our modeling methodology, enumerate various updates since our group's previous models, and present our initial tranche of models for cloudless, solar metallicity, and solar carbon-to-oxygen ratio, chemical equilibrium atmospheres. These models will be available online and will be updated as opacities and cloud modeling methods continue to improve.

emergent spectra↗
NASA NTRS2003

Potential Refinement of the ICRF

The analysis and data used for the ICRF represented the state of the art in global, extragalactic, X/S band microwave astrometry in 1995. The same general analysis method was used to extend the ICRF with subsequent VLBI data in a manner consistent with the original catalog. Since 1995 there have been considerable advances in the geodetic/astrometric VLBI data set and in the analysis that would significantly improve the systematic errors, stability, and density of the next realization of the ICRS when the decision is made to take this step. In particular, data acquired since 1990, including extensive use of the VLBA, are of higher quality and astrometric utility because of changes in instrumentation, schedule design, and networks as well as specifically astrometric intent. The IVS (International VLBI Service for Geodesy and Astrometry) continues to devote a portion of its observing capability to systematic extension of the astrometric data set. Sufficient data distribution exists to select a better set of defining sources. Improvements in troposphere modeling will minimize known systematic astrometric errors while accurate modeling and estimation of station effects from loading and nonlinear motions will permit the reintegration of the celestial reference frame, terrestrial reference frame and Earth orientation parameters though a single VLBI solution. The differences between the current ICRF and the potential next realization will be described.

Ma, Chopo↗
NASA NTRS2003

Review of Laser Ablation Process for Single Wall Carbon Nanotube Production

Different types of lasers are now routinely used to prepare single wall carbon nanotubes (SWCNTs). The original method developed by researchers at Rice University utilized a "double pulse laser oven" process. A graphite target containing about 1 atomic percent of metal catalysts is ablated inside a 1473K oven using laser pulses (10 ns pulse width) in slow flowing argon. Two YAG lasers with a green pulse (532 nm) followed by an IR pulse (1064 nm) with a 50 ns delay are used for ablation. This set up produced single wall carbon nanotube material with about 70% purity having a diameter distribution peaked around 1.4 nm. The impurities consist of fullerenes, metal catalyst clusters (10 to 100 nm diameter) and amorphous carbon. The rate of production with the initial set up was about 60 mg per hour with 10Hz laser systems. Several researchers have used variations of the lasers to improve the rate, consistency and study effects of different process parameters on the quality and quantity of SWCNTs. These variations include one to three YAG laser systems (Green, Green and IR), different pulse widths (nano to microseconds as well as continuous) and different laser wavelengths (Alexandrite, CO, CO2, free electron lasers in the near to far infrared). It is noted that yield from the single laser (Green or IR) systems is only a fraction of the two laser systems. The yield seemed to scale up with the repetition rate of the laser systems (10 to 60 Hz) and depended on the beam uniformity and quality of the laser pulses. The shift to longer wavelength lasers (free electron, CO and CO2) did not improve the quality, but increased the rate of production because these lasers are either continuous (CW) or high repetition rate pulses (kHz to MHz). The average power and the peak power of the lasers seem to influence the yields. Very high peak powers (MegaWatts per square centimeter) are noted to increase ablation of bigger particles with reduced yields of SWCNTs. Increased average powers seem to help the conversion of the carbon from target into vapor phase to improve formation of nanotubes. The use of CW far infrared lasers reduced the need for the oven, at the expense of controlled ablation. Some of these variations are tried with different combinations and concentrations of metal catalysts (Nickel with Cobalt, Iron, Palladium and Platinum) different buffer gases (e.g. Helium); with different oven temperatures (Room temperature to 1473K); under different flow conditions (1 to 1000 kPa) and even different porosities of the graphite targets. It is to be noted that the original Cobalt and Nickel combination worked best, possibly because of improved carbonization with stable crystalline phases. The mean diameter and yield seemed to increase with increasing oven temperatures. Thermal conductivity of the buffer gas and flow conditions dictate the quality as well as quantity of the SWCNTs. Faster flows, lower pressures and heavier gases seem to increase the yields. This review will attempt to cover all these variations and their relative merits. Possible growth mechanisms under these different conditions will also be discussed.

Arepalli, Sivaram↗
DOE OSTI2025

Design Report on the MiniFuel Instrumented Test Apparatus for Understanding Radiation Effects

Most nuclear fuels irradiations at Oak Ridge National Laboratory (ORNL) over the past decade have been conducted using MiniFuel—a static capsule design employing subscale fuel specimens to collect separate-effects irradiated fuel performance data. Irradiation conditions for MiniFuel experiments are predicted pre-test using reactor physics, and thermal models are verified post-irradiation via SiC dilatometry and various spectrometry methods. Relevant fuel performance parameters are also observed post-irradiation in a hot cell, thereby providing a single data point for each parameter representing the cumulative effects of the irradiation conditions. Substantially more data can be harvested from a single test and within a shorter duration by instrumenting irradiation vehicles and measuring desired quantities in situ. This report presents the design and analysis of the MiniFuel INstrumented Irradiation Test Apparatus for Understanding Radiation Effects (MINITAURE)—an instrumented test rig based on the separate-effects MiniFuel concept that aims to capture fission gas release (FGR) and thermal conductivity degradation of fuel specimens during irradiation in the High Flux Isotope Reactor (HFIR). MINITAURE will be integrated with the Materials Irradiation Facility (MIF) located in the HFIR building outside the reactor containment. The MIF will act as the instrumentation and control center for the experiment, enabling real-time feedback from in situ sensors and control of irradiation temperatures via a gas delivery system. Two unique capsule designs were developed to capture each phenomenon: the thermal conductivity capsule, which uses a thermopile method to estimate fuel specimen thermal conductivity, and the fission gas release capsule, which will have continuous flowing gas communication to high-purity germanium detectors that are housed in the MIF for monitoring FGR. This report details the reactor physics and heat transfer modeling activities that were used to inform the experiment design and predict capsule performance. It also describes out-of-pile activities conducted to stand up this new capability and verify the measurement techniques. Modeling efforts to date have demonstrated the feasibility of the in situ measurement techniques and supported the development of the MINITAURE assembly configuration. Out-of-pile testing of the thermal conductivity measurement shows promise in capturing relative changes in thermal conductivity. However, significant errors exist in the measured absolute value, posing a need for further refinement.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRSDate not supplied

AM Powder Flowability Capabilities at NASA Marshall Space Flight Center (MSFC)

Additive Manufacturing (AM) powder flowability is critical to metal 3D printing, because the more fluid the powder is, the better powder spreads. The NASA MSFC’s Contamination Control Team (CCT) studies density, flow, particle size distribution, and morphology for programs such as Moon-to-Mars Planetary Autonomous Construction Technology (MMPACT), ASTM Proficiency Testing for AM and Powder Metallurgy, and MSFC’s AM team. Examining and measuring powder characteristics is essential for improving flowability of AM powder and ensuring lot-to-lot consistency, which will help to prevent defects in manufactured parts. The CCT uses several methods to characterize AM powder. Optical particle size distribution and morphology analyses determine particle parameters including size, circularity, convexity, and dimensions. The Carney and Hall Flowmeter Funnel measures time it takes powder to flow through a funnel to compare relative flowability (free-flowing and non-free-flowing). In addition to this, the device utilizes a density cup which determines apparent density. Lastly, the Revolution Powder Analyzer measures dynamic powder flowability and behavior over time via digital imaging. Using the CCT’s lab, the team has produced ASTM Powder Proficiency Testing results with flow rates, apparent density, particle size distribution, and tap density on titanium-based and nickel-based powder and compared data with other companies/labs. Morphology data was also collected for various regolith simulant powder for the MMPACT program. Lastly, the CCT used morphology data to examine the flowability of Inconel 718 powder for Selective Laser Melting (SLM) and Directed Energy Deposition (DED) printers at MSFC. The CCT’s essential work in studying and enabling AM powder characterization has helped and will continue to help study new and refined powders in the AM industry.

Additive Manufacturing↗
NASA NTRSDate not supplied

AM Powder Flowability Capabilities at NASA Marshall Space Flight Center (MSFC)

Additive Manufacturing (AM) powder flowability is critical to metal 3D printing, because the more fluid the powder is, the better powder spreads. AM Powder Flowability Capabilities at NASA MSFC studies density, flow, particle size distribution, and morphology for programs such as Moon-to-Mars Planetary Autonomous Construction Technology (MMPACT), ASTM Proficiency Testing for AM and Powder Metallurgy, and MSFC’s AM team. Examining and measuring powder characteristics is essential for improving flowability of AM powder and ensuring lot-to-lot consistency, which will help to prevent defects in manufactured parts. The Contamination Control Team (CCT) uses several methods to characterize AM powder. Optical particle size distribution and morphology analyses determine particle parameters including size, circularity, convexity, and dimensions. The Carney and Hall Flowmeter Funnel measures time it takes powder to flow through a funnel to compare relative flowability (free-flowing and non-free-flowing). In addition to this, the device utilizes a density cup which determines apparent density. Lastly, the Revolution Powder Analyzer measures dynamic powder flowability and behavior over time via digital imaging. Using the CCT’s lab, the team has produced ASTM Powder Proficiency Testing results with flow rates, apparent density, particle size distribution, and tap density on titanium-based and nickel-based powder and compared data with other companies/labs. Morphology data was also collected for various regolith simulant powder for the MMPACT program. Lastly, the CCT used morphology data to examine the flowability of Inconel 718 powder for Selective Laser Melting (SLM) and Directed Energy Deposition (DED) printers at MSFC. The CCT’s essential work in studying and enabling AM powder characterization has helped and will continue to help study new and refined powders in the AM industry.

Additive Manufacturing↗
NASA NTRSDate not supplied

Machine Learning Approaches for Rare-Earth Silicate Environmental Barrier Coating Thermochemical and Thermomechanical Property Predictions

Environmental barrier coatings (EBCs) are a necessary enabling technology for the transition from superalloys to silicon carbide (SiC) ceramic matrix composites (CMCs) in gas turbine engines for increased efficiency and decreased fuel costs. SiC-based CMCs are prone to oxidation-based degradation in the engine hot section, and rare-earth (RE) silicates are promising candidates for EBCs due to their close thermal expansion match to the composite substrate and oxidation resistance. However, the design of EBCs is hindered by the large chemical space of candidate materials and the difficulty in obtaining material properties for engineering optimization. This is especially difficult as research continues into mixed-cation or “high-entropy” RE silicates. First-principles computational methods such as density functional theory (DFT) are highly effective at calculating material properties to guide coating design but are limited by their computational cost. Atomistic simulations have the potential to both accelerate property calculations and expand the properties able to be calculated due to their lower computational compared to DFT. However, they require interatomic potentials (IAPs) specific to the material system of interest, and, to our knowledge, there are no suitable IAPs for RE silicates. Machine learning (ML) is a promising technique to accelerate material property predictions indirectly by generating IAPs for atomistic simulations or via direct prediction. In this work, we present two ML approaches to accelerate the calculation of RE silicate properties relevant to EBC design: 1) a ML-derived interatomic potential (IAP) for atomistic simulations of yttrium disilicate (Y2Si2O7) from DFT training data, and 2) a neural network (NN) model to directly predict thermochemical properties of RE silicates and oxides directly from easily obtainable unit cell parameters. Classical MD simulations using the IAP yield lattice properties and bond lengths in good agreement with both DFT and experimental results from x-ray diffraction. Thermodynamic properties calculated using the finite-displacement phonon method and quasi-harmonic approximation were orders of magnitude faster than DFT with good agreement to the DFT results. The IAP was also used to calculate properties such as coefficient of thermal expansion (CTE) that require large simulation supercells and are therefore difficult with DFT. The IAP correctly predicted the anisotropic nature of the CTE in three different phases of Y2Si2O7. The NN model predicts constant pressure heat capacity, Cp, orders of magnitude faster than DFT calculations, which can enable its use as a surrogate model for multiscale simulations. The two methods presented in this work demonstrate the utility of ML for accelerating the prediction of RE silicate properties, which can in turn accelerate EBC design and optimization.

machine learning↗
NASA NTRS2015

Probabilistic Risk Assessment for Astronaut Post Flight Bone Fracture

Introduction: Space flight potentially reduces the loading that bone can resist before fracture. This reduction in bone integrity may result from a combination of factors, the most common reported as reduction in astronaut BMD. Although evaluating the condition of bones continues to be a critical aspect of understanding space flight fracture risk, defining the loading regime, whether on earth, in microgravity, or in reduced gravity on a planetary surface, remains a significant component of estimating the fracture risks to astronauts. This presentation summarizes the concepts, development, and application of NASA's Bone Fracture Risk Module (BFxRM) to understanding pre-, post, and in mission astronaut bone fracture risk. The overview includes an assessment of contributing factors utilized in the BFxRM and illustrates how new information, such as biomechanics of space suit design or better understanding of post flight activities may influence astronaut fracture risk. Opportunities for the bone mineral research community to contribute to future model development are also discussed. Methods: To investigate the conditions in which spaceflight induced changes to bone plays a critical role in post-flight fracture probability, we implement a modified version of the NASA Bone Fracture Risk Model (BFxRM). Modifications included incorporation of variations in physiological characteristics, post-flight recovery rate, and variations in lateral fall conditions within the probabilistic simulation parameter space. The modeled fracture probability estimates for different loading scenarios at preflight and at 0 and 365 days post-flight time periods are compared. Results: For simple lateral side falls, mean post-flight fracture probability is elevated over mean preflight fracture probability due to spaceflight induced BMD loss and is not fully recovered at 365 days post-flight. In the case of more energetic falls, such as from elevated heights or with the addition of lateral movement, the contribution of space flight quality changes is much less clear, indicating more granular assessments, such as Finite Element modeling, may be needed to further assess the risks in these scenarios.

Risk Analysis↗
NASA NTRS1985

Middle atmosphere (60-110 km) tidal oscillations at Saskatoon, Canada (52 deg N, 107 deg W) during 1983/84

Since mid-1978, the medium frequency (2.2 MHz; M.F.) radar at Saskatoon has run continuously, measuring winds by the spaced antenna method in the upper middle atmosphere. As there is so much dynamics activity during the years of MAP it seems very important to establish the features of the tidal field at Saskatoon during 1983 to 1984, and compare these with the climatology from the four years 1978 to 1979 and 1981 to 1982. Wind profiles with 60 to 110 km (80 to 110 km at night) with 3 km resolution are obtained every 5 minutes by means of a full-correlation analysis. Hourly means are formed and harmonic analysis applied to sets of data to obtain the mean winds and tidal oscillation amplitudes and phases. The zonal wind field at Saskatoon is structurally similar to CIRA-72; however, the wind eastward cell is weaker and much more structured, and the summer eastward cell above 85 km is also weaker. For the semidiurnal tide, a strong seasonal change exists, with larger amplitudes and shorter wavelengths, in winter-like months and the reverse in summer-like months. A summary of the tidal parameters at 90 km is given, where mean values from 1978 to 1982 are also shown. This seasonal variation of tides is a midlatitude climatology, as comparisons with Monpazier, France and Christchurch, New Zealand show very similar behavior, as does Durham; and requires antisymmetric tidal modes such as 2,3 and 2,5. Considering the diurnal tide, for most months centered on summer there are long wavelength modes, or superposition of several models. However, some winter months show a short wavelength mode. A summary of the 90 km characteristics is presented.

Manson, A. H.↗