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NASA NTRS2019

Comparing Flood-Producing Catastrophic Hurricanes in GEOS: A Study of Juan 1985 and Harvey 2017

Hurricanes Juan (1985) and Harvey (2017) bear remarkable similarities because of the exceptional amount of accumulated precipitation over coastal regions in the Gulf, consequent to the peculiar and rare situation in which a hurricane stalls for several days on a coastal area, displaying minimal progress. This is particularly dangerous because: a) terrestrial drag affect only part of the circulation so that weakening is not as fast as when there is rapid motion inland; b) a part of the circulation remains over water and as such low-level moisture flux convergence is maintained; c) precipitation lasts for a very long time over the same area; d) moisture recycling from flooded areas further contributes to maintaining flood conditions. From the forecast perspective, hurricanes with track singularities such as stalling points, loops, or multiple landfalls over the same region are exceptionally difficult to predict because they result from small variations of opposing mechanisms and are therefore intrinsically unstable. Intensity fluctuations and track singularities are strongly coupled. The representation of fluxes undergoing differential drag (ocean vs. land) inside the same circulation is critically dependent upon resolution. Recent modeling development efforts within the NASA Global Modeling and Assimilation Office (GMAO) have brought the horizontal resolution of the global cubed-sphere Goddard Earth Observing System (GEOS) atmospheric model down to 12 km. This has led to innovative findings in the field of tropical cyclone (TC) modeling within a global framework. The GEOS atmospheric model and data assimilation system (DAS), used to produce the MERRA-2 (Modern Era Retrospective analysis for Research and Applications) reanalysis, has a "Replay" capability that adds a forcing term to the model equations to constrain it to follow a specified trajectory. This capability is used to produce global, downscaled versions of the 50 km MERRA-2 reanalysis at a 12.5 km resolution. The full DAS and replay modes both use the Incremental Analysis Update or IAU for applying an analysis increment to the model's background state four times each day at 00Z, 06Z, 12Z and 18Z. In a full DAS mode, a six-hour free run of the model is made (Predictor Step), writing states that will be used as backgrounds or first-guesses for the analysis centered on 00Z. The Analysis system then produces its best estimate of the 00Z state by blending backgrounds with observations. The difference between this best estimate and the model's original first guess at 00Z is treated as an analysis increment and added uniformly as a forcing term to the model during a second, now forced, run of the 12Z to 03Z window, which produces the assimilated results. Replay mode is very similar, except only the 00Z background is needed from the predictor segment, which is thus half as long, and instead of doing an analysis, a previously analyzed state at 00Z is read in. The increment is then computed and applied to the corrector step in the same way as in the original DAS. When downscaling horizontal spatial filters can be applied to the "analysis" increment used to force the Corrector step, so as to reproduce the full meteorological state or selectively choose or discard certain forcings. In this presentation, full global hindcast simulations of Juan and operational forecasts of Harvey are compared.

Flooding↗
NASA NTRS2021

Probabilistic Machine Learning Estimation of Ocean Mixed Layer Depth from Dense Satellite and Sparse In-Situ Observations

The ocean mixed layer plays an important role in the coupling between the upper ocean and atmosphere across a wide range of time scales. Estimation of the variability of the ocean mixed layer is therefore important for atmosphere-ocean prediction and analysis. The increasing coverage of in situ Argo profile data allows for an increasingly accurate analysis of the mixed layer depth (MLD) variability associated with deviations from the seasonal climatology. However, sampling rates are not sufficient to fully resolve subseasonal (<90 day) MLD variability. Yet, many multivariate observations-based analyses include implicit modeled subseasonal MLD variability. One analysis method is optimal interpolation of in situ data, but the interior analysis can be improved by leveraging surface data with regression or variational approaches. Here, we demonstrate how machine learning methods and satellite sea surface temperature, salinity, and height facilitate MLD estimation in a pilot study of two regions: the mid-latitude southern Indian and the eastern equatorial Pacific Oceans. We construct multiple machine learning architectures to produce weekly 1/2° gridded MLD anomaly fields (relative to a monthly climatology) with uncertainty estimates. We test multiple traditional and probabilistic machine learning techniques to compare both accuracy and probabilistic calibration. We validate our methodology by applying it to ocean model simulations. We find that incorporating sea surface data through a machine learning model improves the performance of spatiotemporal MLD variability estimation compared to optimal interpolation of Argo observations alone. These preliminary results are a promising first step for the application of machine learning to MLD prediction.

Machine Learning↗
NASA NTRS1996

SSME Condition Monitoring Using Neural Networks and Plume Spectral Signatures

For a variety of reasons, condition monitoring of the Space Shuttle Main Engine (SSME) has become an important concern for both ground tests and in-flight operation. The complexities of the SSME suggest that active, real-time condition monitoring should be performed to avoid large-scale or catastrophic failure of the engine. In 1986, the SSME became the subject of a plume emission spectroscopy project at NASA's Marshall Space Flight Center (MSFC). Since then, plume emission spectroscopy has recorded many nominal tests and the qualitative spectral features of the SSME plume are now well established. Significant discoveries made with both wide-band and narrow-band plume emission spectroscopy systems led MSFC to develop the Optical Plume Anomaly Detection (OPAD) system. The OPAD system is designed to provide condition monitoring of the SSME during ground-level testing. The operational health of the engine is achieved through the acquisition of spectrally resolved plume emissions and the subsequent identification of abnormal emission levels in the plume indicative of engine erosion or component failure. Eventually, OPAD, or a derivative of the technology, could find its way on to an actual space vehicle and provide in-flight engine condition monitoring. This technology step, however, will require miniaturized hardware capable of processing plume spectral data in real-time. An objective of OPAD condition monitoring is to determine how much of an element is present in the SSME plume. The basic premise is that by knowing the element and its concentration, this could be related back to the health of components within the engine. For example, an abnormal amount of silver in the plume might signify increased wear or deterioration of a particular bearing in the engine. Once an anomaly is identified, the engine could be shut down before catastrophic failure occurs. Currently, element concentrations in the plume are determined iteratively with the help of a non-linear computer code called SPECTRA, developed at the USAF Arnold Engineering Development Center. Ostensibly, the code produces intensity versus wavelength plots (i.e., spectra) when inputs such as element concentrations, reaction temperature, and reaction pressure are provided. However, in order to provide a higher-level analysis, element concentration is not specified explicitly as an input. Instead, two quantum variables, number density and broadening parameter, are used. Past experience with OPAD data analysis has revealed that the region of primary interest in any SSME plume spectrum lies in the wavelength band of 3300 A to 4330 A. Experience has also revealed that some elements, such as iron, cobalt and nickel, cause multiple peaks over the chosen wavelength range whereas other elements (magnesium, for example) have a few, relatively isolated peaks in the chosen wavelength range. Iteration with SPECTRA as a part of OPAD data analysis is an incredibly labor intensive task and not one to be performed by hand. What is really needed is the "inverse" of the computer code but the mathematical model for the inverse mapping is tenuous at best. However, building generalized models based upon known input/output mappings while ignoring details of the governing physical model is possible using neural networks. Thus the objective of the research project described herein was to quickly and accurately predict combustion temperature and element concentrations (i.e., number density and broadening parameter) from a given spectrum using a neural network. In other words, a neural network had to be developed that would provide a generalized "inverse" of the computer code SPECTRA.

Hopkins, Randall↗
NASA NTRS2022

Mission Planning for Trident: Discovery proposal to Neptune’s moon, Triton

Trident was one of the four Discovery-class Step-1 mission proposals selected by NASA in 2020 for further development and study; however, in 2021, the Step-2 proposal was not down-selected to transition into the next phase of mission development, i.e., a mission for flight.Neptune’s largest moon, Triton, was the primary focus of study for Trident. Triton’s physical and orbital characteristics make it a unique planetary target for scientific exploration, providing opportunities for investigations in a wide variety of scientific fields, including geomorphological, atmospheric, geophysical, magnetospheric, and ionospheric studies. The science objectives of the Trident mission encompassed an in-depth interior-to-exterior set of objectives, focused on multiple outstanding questions resulting from the 1989 encounter of Voyager 2, and subsequent analysis.Ball Aerospace Corp. was tasked with building the Trident spacecraft, with JPL responsible for providing Engineering Support (Mission Design & Navigation, Mission Planning, Flight Operations, Ground Data Systems, Systems Engineering) and leading Project Management. The observatory would carry a wide-ranging suite of scientific instruments onboard, including an Infrared Spectrometer (IRS) and Narrow Angle Camera (NAC) to be provided by Ball Aerospace Corp., a Wide Angle Camera (WAC) from JPL, a Magnetometer from UCLA, a contributed Plasma Science Suite from IRF (Sweden), and a contributed Radio Science instrument from ASI (Italy). All of these instruments would be used to collect unique datasets during the Triton encounter. Trident would have taken advantage of an ~13-yr, nearly-ballistic trajectory to Triton, utilizing a timely Jupiter Gravity Assist, to execute a 10-day long encounter in the Neptunian system. Launch was planned for October 2025, with Triton arrival scheduled for December 2038. The timeline for this mission would have been sub-divided into seven major phases: Launch, Commissioning, Inner Planet Cruise, Outer Planet Cruise, Approach, Encounter, and Science Data Return. Multiple planetary flybys were planned to be performed during the cruise, including three Earth flybys and one Venus flyby in the Inner Planet Cruise phase, and one Jupiter flyby in the Outer Planet Cruise phase. Along with conventional (Range and Doppler) tracking data, Delta-DOR and Optical Navigation data were also to be acquired to assist with spacecraft navigation during the Approach and Encounter phases. A 3 meter X-Band High Gain Antenna would allow playback of all science data at 1 kbps within 1 year after the Triton Encounter. The Mission Planning element on Trident encompassed and informed multiple aspects of this proposal, ranging from science observation planning during the Triton Encounter phase, to generation of activity timelines for all mission phases; performing ground coverage analysis for science observations to be acquired by all instruments and tracing them to science requirements; evaluation of spacecraft resources including data volume stored onboard, power/energy consumption, telecom (commanding/telemetry) requirements, and overall, working at the interface of science and engineering teams on the mission. All of these functions that were performed by the Mission Planning team on this proposal are discussed in this paper.

Prockter, Louise↗
NASA NTRS1997

Impact Crises, Mass Extinctions, and Galactic Dynamics: A Unified Theory

A general hypothesis linking mass extinctions of life with impacts of large asteroids and comets is based on astronomical data, impact dynamics, and geological information. The waiting times of large-body impacts on the Earth, derived from the flux of Earth-crossing asteroids and comets, and the estimated size of impacts capable of causing large-scale environmental disasters predict that impacts of objects (sup 3)5 km in diameter ((sup 3)10(exp 7) Mt TNT equivalent) could be sufficient to explain the record of about 25 extinction pulses in the last 540 m.y., with the five recorded major mass extinctions related to the impacts of the largest objects of (sup 3)10 km in diameter ( (sup 3)10(exp 8) Mt events). Smaller impacts (about 10(exp 6)-10(exp 7) Mt), with significant regional and even global environmental effects, could be responsible for the lesser boundaries in the geologic record. Tests of the "kill curve" relationship for impact-induced extinctions based on new data on extinction intensities and several well-dated large impact craters suggest that major mass extinctions require large impacts, and that a step in the kill curve may exist at impacts that produce craters of -100 km diameter, with smaller impacts capable of only relatively weak extinction pulses. Single impact craters < about 60 km in diameter should not be associated with global extinction pulses detectable in the Sepkoski database (although they may explain stage and zone boundaries marked by lesser faunal turnover), but multiple impacts in that size range may produce significant stepped extinction pulses. Statistical tests of the last occurrences of species at mass-extinction boundaries are generally consistent with predictions for abrupt or stepped extinctions, and several boundaries are known to show "catastrophic" signatures of environmental disasters and biomass crash, impoverished postextinction fauna and flora dominated by stress-tolerant and opportunistic species, and gradual ecological recovery and radiation of new taxa. Isotopic and other geochemical signatures are also generally consistent with the expected after-effects of catastrophic impacts. Seven of the recognized extinction pulses are associated with concurrent (in some cases multiple) stratigraphic impact markers (e.g., layers with high Ir, shocked minerals, microtektites), and/or large, dated impact craters. Other less-well-studied crisis intervals show elevated Ir, still well below that of the K/T spike, which might be explained by low-Ir impactors, ejecta blowoff, or the sedimentary reworking and dilution of impact signatures. The best explanation for a possible periodic component of about 30 m.y. in mass extinctions and clusters of impacts is the modulation of the comet flux associated with the solar system's periodic passage through the plane of the Milky Way Galaxy. The quantitative agreement among paleontological, geological, and astronomical data suggests an important underlying unification of the processes involved.

Rampino, M.R.↗
NASA NTRSDate not supplied

In-Space Inspection Needs: Opportunities for advanced NDE tools such as x-ray CT for additively manufactured parts, in-situ resource utilization, geological applications, and more

It is now 50 years since the first human presence on the surface of the Moon and as we strive to return with women and men in the next few years, we embrace new technical challenges, goals, and innovative solutions to address 21st century objectives. These new ambitions carry fresh challenges and risks, with the field of NDE playing an increasingly more relevant role towards meeting these essential goals. In recent years, more advanced NDE tools have triggered a rapid expansion of applications for the space industry. In particular, x-ray Computed Tomography (CT) has proven to be a trusted and powerful asset for spaceflight hardware inspection, as well as applied geotechnical analysis for natural materials (e.g., rocks, soils) for NASA and across industry. However, such methods have yet to be extended to “deep space” applications such as those that are now part of the US National Space Policy Directive (SPD-1) and the accelerated push to return humans to the Moon (i.e., Artemis). For this reason, advancing these powerful Earth-based laboratory methods via new technologies, integrated computational solutions, and creative engineering approaches is directly aligned with national space policies, as well as with multiple NASA Strategic Plan priorities. The use of x-ray CT at scales as fine as a few microns or smaller can identify spacecraft part failure modes relevant to quality assurance for flight hardware and AM parts such as those recently developed for ISS. This technology could also identify valuable metallic phases within geological materials (i.e., rocks or drill cores), enabling resource-relevant triage of samples for In-Situ Resource Utilization (ISRU) and high science value sample return to Earth laboratories. There is also significant application for 3D imaging tools for medical use such as inspecting protective gear as well as bone density degradation studies which are critical in establishing a sustained presence in space. Timing for development of these tools for space use is advantageous as we prepare for new opportunities in the next few years and recognize recent commercial technology advancements which make it feasible. Moreover, as NASA strives to take full advantage of developments in AM technologies, including In-Space Manufacturing (ISM), it is widely recognized that NDE tools such as CT will play an essential role in acceptance of these parts for widespread use. New in-space 3D inspection tools with complimentary technology such as AI-based automated feature recognition (accelerated by machine learning), rapid compositional analysis, and advanced sample manipulation, would be a game-changing step toward a new class of crew-based laboratory sensors once human outposts on the Moon are established.

In-Space Non-Destructive Evaluation↗
DOE OSTI2023

Circular dichroism in hard X-ray photoelectron diffraction observed by time-of-flight momentum microscopy

X-ray photoelectron diffraction (XPD) is a powerful technique that yields detailed structural information of solids and thin films that complements electronic structure measurements. Among the strongholds of XPD we can identify dopant sites, track structural phase transitions, and perform holographic reconstruction. High-resolution imaging of k ll -distributions (momentum microscopy) presents a new approach to core-level photoemission. It yields full-field k x -k y XPD patterns with unprecedented acquisition speed and richness in details. Here, we show that beyond the pure diffraction information, XPD patterns exhibit pronounced circular dichroism in the angular distribution (CDAD) with asymmetries up to 80%, alongside with rapid variations on a small k ll -scale (0.1 Å -1 ). Measurements with circularly-polarized hard X-rays (hν = 6 keV) for a number of core levels, including Si, Ge, Mo and W, prove that core-level CDAD is a general phenomenon that is independent of atomic number. The fine structure in CDAD is more pronounced compared to the corresponding intensity patterns. Additionally, they obey the same symmetry rules as found for atomic and molecular species, and valence bands. Further, the CD is antisymmetric with respect to the mirror planes of the crystal, whose signatures are sharp zero lines. Calculations using both the Bloch-wave approach and one-step photoemission reveal the origin of the fine structure that represents the signature of Kikuchi diffraction. To disentangle the roles of photoexcitation and diffraction, XPD has been implemented into the Munich SPRKKR package to unify the one-step model of photoemission and multiple scattering theory.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

An extended focused assessment with sonography in trauma ultrasound tissue-mimicking phantom for developing automated diagnostic technologies

Medical imaging-based triage is critical for ensuring medical treatment is timely and prioritized. However, without proper image collection and interpretation, triage decisions can be hard to make. While automation approaches can enhance these triage applications, tissue phantoms must be developed to train and mature these novel technologies. Here, we have developed a tissue phantom modeling the ultrasound views imaged during the enhanced focused assessment with sonography in trauma exam (eFAST). The tissue phantom utilized synthetic clear ballistic gel with carveouts in the abdomen and rib cage corresponding to the various eFAST scan points. Various approaches were taken to simulate proper physiology without injuries present or to mimic pneumothorax, hemothorax, or abdominal hemorrhage at multiple locations in the torso. Multiple ultrasound imaging systems were used to acquire ultrasound scans with or without injury present and were used to train deep learning image classification predictive models. Performance of the artificial intelligent (AI) models trained in this study achieved over 97% accuracy for each eFAST scan site. We used a previously trained AI model for pneumothorax which achieved 74% accuracy in blind predictions for images collected with the novel eFAST tissue phantom. Grad-CAM heat map overlays for the predictions identified that the AI models were tracking the area of interest for each scan point in the tissue phantom. Overall, the eFAST tissue phantom ultrasound scans resembled human images and were successful in training AI models. Tissue phantoms are critical first steps in troubleshooting and developing medical imaging automation technologies for this application that can accelerate the widespread use of ultrasound imaging for emergency triage.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2000

Recent Trends in the Analysis of Quasioptical Systems

The recent trend in microwave instruments is the use of multiple millimeter and submillimeter wavelength bands. These systems are typically analyzed by using physical optics, Gaussian beams or ray tracing techniques. Physical optics offers high accuracy at the expense of computation time. This trade-off becomes particularly apparent in the analysis of multiple reflector antennas, such as beam waveguide antennas, where physical optics is used to compute the current on each reflector from the current on the previous reflector. At the other end of the spectrum is ray tracing approaches that ignore diffraction effects entirely. These methods are fast but sacrifice the ability to predict some effects accurately. An intermediate approach is to use an appropriate set of expansion functions to model the field between the reflectors. If the set is chosen wisely only a few coefficients need to be determined from each reflector current. The field is then computed at the next reflector through the use of the expansion functions and their coefficients rather than by using the previous reflector current. For a beam waveguide system with no enclosing tubes an excellent set of expansion functions is the Gaussian beam mode set. In many cases a preliminary design which includes the effects on diffraction may be obtained by considering only the fundamental mode and a thin lens model for the reflectors. Higher-order modes are included to model the effects of the curved reflector, which include asymmetric distortion of the beam, cross polarization, and beam truncation. This paper describes a computer code implementing higher-order Gaussian beam scattering by multiple reflector systems. There are four essential steps in the algorithm. (1) Compute the current on the first reflector using physical optics using either a feed model or by an incident set of Gaussian beam modes. (2) Find the direction of propagation for the reflected Gaussian beam-set using ray tracing. (3) Determine the waist size and location for the output beam set by examining the amplitude and phase distribution of the current on the reflector. (4) Compute the amplitudes of the individual modes in the output mode set. These steps are then repeated for each addition reflector in the chain. In each of these cases the previous Gaussian beam set provides the input field for the current calculation. Details of the four steps discussed above will be discussed. Examples will compare results from the Gaussian beam approach to pure physical optics, illustrating both its merits and limitations. Hybrid approaches capable of eliminating some of the limitations will also be discussed.

Hoppe, D. J.↗
DOE OSTI2023

20 kV Gallium Nitride pn Diode Electro-Magnetic Pulse Arrestor for Grid Reliability (Final Scientific/Technical Report)

This project developed vertical Gallium Nitride (GaN) pn diodes under two main thrusts: (1) A focus on relatively higher-voltage devices for use as fast EMP arrestors to protect the electric grid; and (2) A focus on a Foundry effort to establish the manufacturability of relatively lower voltage devices. For the first thrust, the aim was to develop devices that go into avalanche breakdown to clamp the voltage across sensitive grid equipment subject to voltage transients induced by electromagnetic pulses (EMPs). Devices with breakdown voltages exceeding 6.5 kV were achieved, and breakdown times shorter than 1 ns were demonstrated, which is sufficiently fast to protect against the fast component of an EMP-induced signal. Key challenges included the epitaxial growth of thick (50 µm or more), low-doped (low 10 15 cm -3 range) GaN layers comprising the drift regions of the diodes, as well as the design and fabrication of edge termination structures (step-etched junction termination extensions) to prevent premature breakdown. Midway through the project, an additional emphasis was put on large-area, high-current devices, and forward currents of approximately 400 A were achieved in composite devices towards the end of the project. Experimental and theoretical studies of impact ionization and avalanche ruggedness were also conducted. For the second thrust (the Foundry), the focus was primarily on 1.2-kV-class devices, although towards the end of the project outstanding results on 3.3-kV class devices were also achieved. The aim of the Foundry was to develop a high-yield, reliable, and economic vertical GaN pn diode process. The Foundry conducted characterization of incoming epitaxial material and correlated this information with the yield and performance of fully processed devices. Wafer maps of diode characteristics such as forward and reverse current as well as capacitance-voltage curves were measured using auto-probing. Several mask sets comprising different-area devices as well as multiple edge termination designs (implanted junction termination extensions, guard rings, and combinations thereof) were studied, and machine-learning-based approaches were utilized to analyze the data. Packaging and reliability efforts were also undertaken for the Foundry diodes, which are necessary for a viable commercial process.

20KV↗
DOE OSTI2023

20 kV Gallium Nitride pn Diode Electro-Magnetic Pulse Arrestor for Grid Reliability (Final Report)

This project developed vertical Gallium Nitride (GaN) pn diodes under two main thrusts: (1) A focus on relatively higher-voltage devices for use as fast EMP arrestors to protect the electric grid; and (2) A focus on a Foundry effort to establish the manufacturability of relatively lower-voltage devices. For the first thrust, the aim was to develop devices that go into avalanche breakdown to clamp the voltage across sensitive grid equipment subject to voltage transients induced by electromagnetic pulses (EMPs). Devices with breakdown voltages exceeding 6.5 kV were achieved, and breakdown times shorter than 1 ns were demonstrated, which is sufficiently fast to protect against the fast component of an EMP-induced signal. Key challenges included the epitaxial growth of thick (50 um or more), low-doped (low 10 15 cm -3 range) GaN layers comprising the drift regions of the diodes, as well as the design and fabrication of edge termination structures (step-etched junction termination extensions) to prevent premature breakdown. Midway through the project, an additional emphasis was put on large-area, high-current devices, and forward currents of approximately 400 A were achieved in composite devices towards the end of the project. Experimental and theoretical studies of impact ionization and avalanche ruggedness were also conducted. For the second thrust (the Foundry), the focus was primarily on 1.2-kV-class devices, although towards the end of the project outstanding results on 3.3-kV-class devices were also achieved. The aim of the Foundry was to develop a high-yield, reliable, and economic vertical GaN pn diode process. The Foundry conducted characterization of incoming epitaxial material and correlated this information with the yield and performance of fully processed devices. Wafer maps of diode characteristics such as forward and reverse current as well as capacitance-voltage curves were measured using auto-probing. Several mask sets comprising different-area devices as well as multiple edge termination designs (implanted junction termination extensions, guard rings, and combinations thereof) were studied, and machine-learning-based approaches were utilized to analyze the data. Packaging and reliability efforts were also undertaken for the Foundry diodes, which are necessary for a viable commercial process.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2002

Initial Assessment of the Effects of Nonmetallic Inclusions on Fatigue Life of Powder-Metallurgy-Processed Udimet(TM) 720

The fatigue lives of modern powder metallurgy (PM) disk alloys are influenced by variabilities in alloy microstructure and mechanical properties. These properties can vary due to the different steps of materials/component processing and machining. One of these variables, the presence of nonmetallic inclusions, has been shown to significantly degrade low-cycle fatigue (LCF) life. Nonmetallic inclusions are inherent defects in powder alloys that are a by-product of powder-processing techniques. Contamination of the powder can occur in the melt, during powder atomization, or during any of the various handling processes through consolidation. In modern nickel disk powder processing facilities, the levels of inclusion contamination have been reduced to less than 1 part per million by weight. Despite the efforts of manufacturers to ensure the cleanliness of their powder production processes, the presence of inclusions remains a source of great concern for the designer. the objective of this study was to investigate the effects on fatigue life of these inclusions. Since natural inclusions occur so infrequently, elevated levels of inclusions were carefully introduced in a nickel-based disk superalloy, Udimet 720 (registered trademark of Special Metals Corporation), produced using PM processing. Multiple strain-controlled fatigue tests were then performed on this material at 650 C. Analyses were performed to compare the LCF lives and failure initiation sites as functions of inclusion content and fatigue conditions. A large majority of the failures in specimens with introduced inclusions occurred at cracks initiating from inclusions at the specimen surface. The inclusions could reduce fatigue life by up to 100 times. These effects were found to be dependent on strain range and strain ratio. Tests at lower strain ranges and higher strain ratios produced larger effects of inclusions on life.

Gabb, T. P.↗
NASA NTRS2021

Integration of an Arm Kinematics Hot Patch onboard the Curiosity Rover

NASA's Mars Science Laboratory (MSL) mission has updated the Curiosity rover's flight software multiple times since landing on Mars on August 6, 2012. The most common patching method has been a hot patch, in which running flight software is modified after being copied into RAM from its persistent storage. The latest hot patch to be installed on Curiosity fixed an issue in the robotic arm software that computes generalized inverse kinematics. Additional unit testing performed since the start of the surface mission revealed that this software can sometimes produce erroneous solutions.The cause was identified as numerical instability in a quartic root finder. When the inputs to that solver are not well conditioned, floating-point numerical issuescan cause erroneous roots to be reported. In theory, this could result in the robotic arm turret instruments being commanded to unintended positions, for example, below the terrain surface. Out of approximately 3.7 million unit test cases, 97.2\% of the position errors were below 5 mm. However, there were 16 test cases where theposition error was greater than 20 cm, and the maximum position error was 1.2 meters.The patch was uploaded to Curiosity on sol 2642 (January 11, 2020) after the solution was developed, re-implemented as a hot patch, and validated and verified using Earth-based Curiosity testbeds. A checkout test of the patch was performed on Curiosity on sol 2657, and nominal use of the patch began on sol 2658. In this paper, we describe the steps that led to integrating the arm kinematic hot patch into Curiosity's flight software, from the discovery of the bug to the nominal use of the patch in flight.

Maimone, Mark↗
NASA NTRSDate not supplied

Application of Analytical Hierarchy Process for Narrowing Down Nep Candidate Reactor Designs

Nuclear electric propulsion (NEP)-powered vehicles have been contemplated for human Mars missions. The nuclear power system contemplates using a high temperature light-weight nuclear reactor for the production of electrical power in the range of 2-5 MWe with a 3-10 year service life. A myriad of technology options exist for achieving these mission objectives. The analytic hierarchy process (AHP), a multi-attribute decision method, is being used to narrow down the candidate designs. The AHP is a structured decision process that fuses model-supplied quantitative data with subjective assessments to facilitate decisions that involve multiple competing criteria. Proven end-to-end nuclear design and systems analysis tools will be used to provide quantitative performance data such as end-to-end system reliability, system robustness to recover from off-normal conditions, system specific weight (α in kg/kWe), and the ability to meet the service life-time and power level requirements. Three high assay low enriched uranium (HALEU) reactor concepts, namely, gas-, heat pipe- and pumped liquid-cooled nuclear cores coupled to a He/Xe gas or supercritical CO2- Brayton power conversion system are being modeled using the AHP to understand the trade-offs associated with these design combinations. In addition to the reactor and power conversion options, there are a myriad of additional components and subsystems – e.g., radiators, heat exchangers, and recuperators – that also figure into the evaluation process. The choice of the right overall system is a multidimensional problem that has to include not only quantitative data, but also the so called “external factors”, examples of which include the component and subsystem Technology Readiness Levels (TRLs), the associated Advancement Degrees of Difficulty (AD2), the cost and schedule required to achieve a technical maturity consistent with mission infusion, alignment with the priorities of NASA’s Space Nuclear Propulsion program, and alignment with other ongoing government and commercial investments in micro reactors. Focused expert elicitations form the basis for qualitative data set. As a final step, decision-makers individually express their opinions regarding the relative importance of the criteria and preferences among the alternatives through pairwise comparisons. The paper will describe the AHP approach, progress to-date applying it to the NEP human Mars mission problem, and preliminary results. Use of AHP provides sufficient flexibility for incorporating industry input at different stages as the technologies evolve through additional research and development. It is expected that the decision process will be ongoing and expanded to examine additional options and technology choices, culminating in a defensible set of candidate reactor concepts that will form the basis for developing a multi-year NEP technology maturation strategy. .

Dasari V Rao↗
NASA NTRS2024

Automation of the ICME Workflow Incorporating Material Digital Twins at Different Length Scales Within a Robust Information Management System

Recent successes in Integrated Computational Materials Engineering (ICME) have demonstrated the potential in designing ‘fit-for-purpose’ materials for a given application in a cost and time efficient manner. However, the material design process must contain a level of judicious automation in the material decision process; that is implementing some optimization algorithms to truly enable the benefits of ICME, particularly when considering materials at multiple length and time scales. Furthermore, the ability to effectively store developed material models, experimental data used for validation, and link models at multiple length and time scales must be implemented to ensure traceability across the material design process, such that the data gathered can be leveraged towards efficient material design. To enable such an optimization scheme a robust framework must exist: (1) that can capture changes made at a given length scale, (2) automatically propagate changes upstream to the highest scale, and (3) evaluate the material’s performance at the structural level. In this work, a developed framework for tracking material changes, automatically running the necessary simulations to determine the properties at the next highest scale, and saving each iteration of the design process to maintain the application’s digital thread is presented for polymer matrix composites (PMCs). The Automated Information Management Across Organizations and Scales (AIMAOS) program offers users an interactive graphical user interface (GUI) for defining constituent materials, building lamina and laminates, and applying effective laminate properties to finite element and composite optimization third party software. At each length scale, the necessary input files are automatically written, and subsequent analysis tools are called to solve for effective properties at the next scale, which are then read by the AIMAOS tool and displayed to the user. As changes are made to the material at lower length scales, information is automatically propagated upstream to higher length scales, and changes made are automatically tracked and versioned to maintain traceability during the design process. The AIMAOS tools serves as the first step in enabling optimized design of composites from the nano to the macroscale for a given application.

Brandon L Hearley↗
NASA NTRS2026

Microstructural Evolution and Mechanical Properties of LP-DED NASA HR-1 – A Hydrogen Resistant AM Superalloy for Space Propulsion Applications

The National Aeronautics and Space Administration (NASA) has actively pursued metal additive manufacturing (AM) technologies for spaceflight applications since the late 2000s. AM offers transformative advantages in cost, schedule, part consolidation, and design flexibility. Among the various AM techniques, laser powder directed energy deposition (LP-DED) is particularly well suited for fabricating complex geometries with fine feature resolution. In propulsion systems that utilize high-pressure gaseous hydrogen—such as liquid hydrogen rocket engines—hydrogen environment embrittlement (HEE) presents a serious threat to material performance 1,2 . Mechanical property degradation under these conditions can compromise component reliability, especially under cyclic loading. To address this challenge, NASA developed NASA HR-1 (Hydrogen Resistant-1) as a solution for liquid rocket engine components operating in hydrogen-rich environments, using the LP-DED technique 3-9 . A key component in a liquid rocket engine is the exhaust nozzle, which is typically regeneratively cooled (regen) due to the high heat flux. NASA HR-1 was specifically developed for regen nozzle applications using hydrogen as a propellant, providing resistance to HEE, a critical issue for many materials. The AM version of NASA HR-1 was also formulated to achieve high ultimate tensile strength, along with high yield strength and ductility in this environment 5,6 . Low-cycle fatigue (LCF) is another important consideration in nozzle design, as components are expected to endure multiple starts and missions. Additionally, the LP-DED version of the alloy exhibits improved thermal conductivity compared to its wrought counterpart, which benefits nozzle cooling. Overall, NASA HR-1 offers an excellent balance of high strength, HEE resistance, LCF performance, thermal conductivity, and ductility to meet the demanding requirements of channel-cooled nozzles and other components used with hydrogen and other propellants. The LP-DED–processed NASA HR-1 requires several post-processing heat treatment steps to achieve the material properties desirable for its intended application 6 . These steps include stress relief, homogenization, solution annealing, and aging for precipitation hardening. The stress relief treatment mitigates residual stresses accumulated during the LP-DED process and minimizes the potential for distortion. Homogenization, a common step for AM materials, reduces elemental segregation and promotes recrystallization to develop a more equiaxed grain structure. The subsequent solution annealing treatment heats the part to a solid solution temperature to dissolve the undesirable η-phase that forms during cooling from homogenization, followed by rapid cooling to retain an η-phase–free microstructure. Finally, aging promotes precipitation of the strengthening γ′ phase in the alloy. The integration of compositional design and optimized thermal processing enables high-quality LP-DED NASA HR-1 components with excellent microstructural and mechanical stability. Improved chemical and microstructure homogeneity enhances ductility and fatigue resistance—both critical for safe and reliable operation in high-pressure hydrogen environments. NASA has successfully fabricated and hot-fire tested multiple subscale and full-scale channel wall nozzles using LP-DED NASA HR-1 5,6, 9-14 . These efforts included process refinements to support thin-wall construction and various channel geometries. Throughout development, several key observations emerged. After homogenization, the as-built columnar grain structure transforms into a fully equiaxed microstructure. However, subsequent treatments—such as solution annealing and aging—result in changes that are more difficult to track. The grain structure remains largely unchanged, and the γ′ precipitates, typically 5–10 nm in diameter, are beyond the resolution of scanning electron microscopy (SEM). While transmission electron microscopy (TEM) can resolve these fine precipitates, TEM sample preparation is time-consuming and difficult for LP-DED material. As an alternative, differential scanning calorimetry (DSC) offers a useful, qualitative approach to monitor precipitate evolution throughout different stages of heat treatment. The overall goal is to improve the understanding of how heat treatment affects the microstructure and mechanical performance of LP-DED NASA HR-1. This paper presents heat treatment design considerations, microstructural characterization, mechanical testing – including tensile and LCF testing in both air and hydrogen environments.

Superalloy↗
NASA NTRS2016

Utilizing the ISS Mission as a Testbed to Develop Cognitive Communications Systems

The ISS provides an excellent opportunity for pioneering artificial intelligence software to meet the challenges of real-time communications (comm) link management. This opportunity empowers the ISS Program to forge a testbed for developing cognitive communications systems for the benefit of the ISS mission, manned Low Earth Orbit (LEO) science programs and future planetary exploration programs. In November, 1998, the Flight Operations Directorate (FOD) started the ISS Antenna Manager (IAM) project to develop a single processor supporting multiple comm satellite tracking for two different antenna systems. Further, the processor was developed to be highly adaptable as it supported the ISS mission through all assembly stages. The ISS mission mandated communications specialists with complete knowledge of when the ISS was about to lose or gain comm link service. The current specialty mandated cognizance of large sun-tracking solar arrays and thermal management panels in addition to the highly-dynamic satellite service schedules and rise/set tables. This mission requirement makes the ISS the ideal communications management analogue for future LEO space station and long-duration planetary exploration missions. Future missions, with their precision-pointed, dynamic, laser-based comm links, require complete autonomy for managing high-data rate communications systems. Development of cognitive communications management systems that permit any crew member or payload science specialist, regardless of experience level, to control communications is one of the greater benefits the ISS can offer new space exploration programs. The IAM project met a new mission requirement never previously levied against US space-born communications systems management: process and display the orientation of large solar arrays and thermal control panels based on real-time joint angle telemetry. However, IAM leaves the actual communications availability assessment to human judgment, which introduces unwanted variability because each specialist has a different core of experience with comm link performance. Because the ISS utilizes two different frequency bands, dynamic structure can be occasionally translucent at one frequency while it can completely interdict service at the other frequency. The impact of articulating structure on the comm link can depend on its orientation at the time it impinges on the link. It can become easy for a human specialist to cross-associate experience at one frequency with experience at the other frequency. Additionally, the specialist's experience is incremental, occurring one nine-hour shift at a time. Only the IAM processor experiences the complete 24x7x365 communications link performance for both communications links but, it has no "learning capability." If the IAM processor could be endowed with a cognitive ability to remember past structure-induced comm link outages, based on its knowledge of the ISS position, attitude, communications gear, array joint angles and tracking accuracy, it could convey such experience to the human operator. It could also use its learned communications link behaviors to accurately convey the availability of future communications sessions. Further, the tool could remember how accurately or inaccurately it predicted availability and correct future predictions based on past performance. The IAM tool could learn frequency-specific impacts due to spacecraft structures and pass that information along as "experience." Such development would provide a single artificial intelligence processor that could provide two different experience bases. If it also "knew" the satellite service schedule, it could distinguish structure blockage from schedule or planet blockage and then quickly switch to another satellite. Alternatively, just as a human operator could judge, a cognizant comm system based on the IAM model could "know" that the blockage is not going to last very long and continue tracking a comm satellite, waiting for it to track away from structure. Ultimately, once this capability was fully developed and tested in the Mission Control Center, it could be transferred on-orbit to support development of operations concepts that include more advanced cognitive communications systems. Future applications of this capability are easily foreseen because even more dynamic satellite constellations with more nodes and greater capability are coming. Currently, the ISS fully employs a 300 million bit-per-second (Mbps) return link for harvesting payload science. In the coming eighteen months, it will step up to 600 Mbps. Already there is talk of a 1.2 billion bit-per-second (Gbps) upgrade for the ISS and laser comm links have already been tested from the ISS. Every data rate upgrade mandates more complicated and sensitive communications equipment which implies greater expertise invested in the human operator. Future on-orbit cognizant comm systems will be needed to meet greater performance demands aboard larger, far more complicated spacecraft. In the LEO environment, the old-style one-satellite-per-spacecraft operations concept will give way to a new concept of a single customer spacecraft simultaneously using multiple comm satellites. Much more highly-dynamic manned LEO missions with decades of crew members potentially increase the demand for communications link performance. A cognizant on-board communications system will meet advanced communications demands from future LEO missions and future planetary missions. The ISS has fledgling components of future exploration programs, both LEO and planetary. Further, the Flight Operations Directorate, through the IAM project, has already begun to develop a communications management system that attempts to solve advanced problems ideally represented by dynamic structure impacting scheduled satellite service. With an earnest project to integrate artificial intelligence into the IAM processor, the ISS Program could develop a cognizant communications system that could be adapted and transferred to future on-orbit avionics designs.

Jackson, Dan↗
DOE OSTI2021

Fault Detection Utilizing Convolution Neural Network on Timeseries Synchrophasor Data From Phasor Measurement Units

An end-to-end supervised learning method is proposed for fault detection in the electric grid using Big Data from multiple Phasor Measurement Units (PMUs). The approach consists of preprocessing steps aimed at reducing data noise and dimensionality, followed by utilization of six classification models considered for detecting faults. Three of the models were variants of Convolutional Neural Network (CNN) architectures that consider a single type of measurement (voltage, current or frequency) at all PMUs or all types together also at all PMUs. CNN based models were compared to traditional methods of Logistic Regression (LR), Multi-layer Perceptron (MLP) and Support Vector Machine (SVM). Evaluation was conducted on two-year data measured by PMUs at 37 locations in a large electric grid. Here, the response variable for classification were extracted from the grid-wide outage event log. Experiments show that CNN-based models outperformed traditional methods on one year out-of-sample outage detection over the entire grid.

42 ENGINEERING↗