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NASA NTRS2016

Microgravity-Induced Physiological Fluid Redistribution: Computational Analysis to Assess Influence of Physiological Parameters

Space flight impacts human physiology in many ways, the most immediate being the marked cephalad (headward) shift of fluid upon introduction into the microgravity environment. This physiological response to microgravity points to the redistribution of blood and interstitial fluid as a major factor in the loss of venous tone and reduction in heart muscle efficiency which impact astronaut performance. In addition, researchers have hypothesized that a reduction in astronaut visual acuity, part of the Visual Impairment and Intracranial Pressure (VIIP) syndrome, is associated with this redistribution of fluid. VIIP arises within several months of beginning space flight and includes a variety of ophthalmic changes including posterior globe flattening, distension of the optic nerve sheath, and kinking of the optic nerve. We utilize a suite of lumped parameter models to simulate microgravity-induced fluid redistribution in the cardiovascular, central nervous and ocular systems to provide initial and boundary data to a 3D finite element simulation of ocular biomechanics in VIIP. Specifically, the lumped parameter cardiovascular model acts as the primary means of establishing how microgravity, and the associated lack of hydrostatic gradient, impacts fluid redistribution. The cardiovascular model consists of 16 compartments, including three cerebrospinal fluid (CSF) compartments, three cranial blood compartments, and 10 thoracic and lower limb blood compartments. To assess the models capability to address variations in physiological parameters, we completed a formal uncertainty and sensitivity analysis that evaluated the relative importance of 42 input parameters required in the model on relative compartment flows and compartment pressures. Utilizing the model in a pulsatile flow configuration, the sensitivity analysis identified the ten parameters that most influenced each compartment pressure. Generally, each compartment responded appropriately to parameter variations associated with itself and adjacent compartments. However, several unexpected interactions between components, such as between the choroid plexus and the lower capillaries, were found, and are due to simplifications in the formulation of the model. The analysis illustrates that highly influential parameters and those that have unique influences within the model formulation must be tightly controlled for successful model application.

gravitational physiology↗

NASA NTRSDate not supplied

Construction of a Fluid Flowfield from Discrete Point Data using Machine Learning

Many verification and validation procedures in aerospace engineering involve the comparison of computational fluid dynamics (CFD) data to experimental results from sources like wind tunnel tests. However, an incongruity exists between the data available from these sources: flow visualization is available by default in computational data, whereas in most experimental setups the available data is far more discrete and far more limited: integrated forces and moments, discrete pressure and temperature probes, etc. When differences exist between quantities of interest like lift and drag coefficients, the lack of full-field flow data from the experiments complicates most attempts to reconcile why the different data sources disagree. To this end, a shallow neural network, constrained by certain fluid flow properties, was trained to approximate flow field snapshots given only discrete data like that available in a wind tunnel test. The constructed snapshots, even for complex incompressible fluid flows, were found to agree at the large scales with the true flow fields. With this tool, researchers can more readily and easily understand why quantities of interest differ between their experimental and computational datasets. This in turn improves the resulting data's uncertainty measures.

Yury Lebedev↗

NASA NTRSDate not supplied

Construction of a Fluid Flowfield from Discrete Point Data using Machine Learning

Yury Lebedev↗

NASA NTRS2025

Mission-Maps For Outbound Cislunar Transfer Trajectories

This study quantifies the robustness and sensitivity of an outbound cislunar trajectory for a lunar lander in the form of mission-maps, or topological maps that allows either a computer program or mission designer to intuitively optimize the placement of critical outbound correction burns from the derived sensitivity data. The non-linear multi-body dynamics are applied to generate an outbound cislunar reference profile used by a linear covariance analysis (LinCov) tool to compute the expected Δv and trajectory dispersions due to the initial state uncertainty, sensor errors, maneuver execution errors, and disturbance accelerations along the outbound cislunar profile. The rapid performance analysis capabilities of LinCov are complimented with parallel processing techniques to evaluate hundreds and thousands of different translational burn locations, placements, and targeting constraints to identify the combination that minimizes the total Δv usage (nominal plus 3σ Δv) and trajectory dispersions at lunar orbit insertion. This study utilizes a generalized reference targeting algorithm to quickly assess the integrated closed-loop GN&C system performance due to different targeting configurations and constraints. The resulting mission maps provide an intuitive insight to ascertain each trajectory correction maneuver’s (TCM) sensitivity to different burn times along an outbound cislunar trajectory and quickly identify desirable engineering tradeoffs when performing analysis on the number and placement of these burns that nominally zero. Multiple mission maps are generated for a variety of different performance parameters that allow engineers to visually identify optimal solutions for trajectory correction maneuver placements, the number of correction burns, and the targeting constraints for each burn.

GN&C↗

NASA NTRS2016

Probabilistic Modeling Of Ocular Biomechanics In VIIP: Risk Stratification

Visual Impairment and Intracranial Pressure (VIIP) syndrome is a major health concern for long-duration space missions. Currently, it is thought that a cephalad fluid shift in microgravity causes elevated intracranial pressure (ICP) that is transmitted along the optic nerve sheath (ONS). We hypothesize that this in turn leads to alteration and remodeling of connective tissue in the posterior eye which impacts vision. Finite element (FE) analysis is a powerful tool for examining the effects of mechanical loads in complex geometries. Our goal is to build a FE analysis framework to understand the response of the lamina cribrosa and optic nerve head to elevations in ICP in VIIP. To simulate the effects of different pressures on tissues in the posterior eye, we developed a geometric model of the posterior eye and optic nerve sheath and used a Latin hypercubepartial rank correlation coef-ficient (LHSPRCC) approach to assess the influence of uncertainty in our input parameters (i.e. pressures and material properties) on the peak strains within the retina, lamina cribrosa and optic nerve. The LHSPRCC approach was repeated for three relevant ICP ranges, corresponding to upright and supine posture on earth, and microgravity [1]. At each ICP condition we used intraocular pressure (IOP) and mean arterial pressure (MAP) measurements of in-flight astronauts provided by Lifetime Surveillance of Astronaut Health Program, NASA Johnson Space Center. The lamina cribrosa, optic nerve, retinal vessel and retina were modeled as linear-elastic materials, while other tissues were modeled as a Mooney-Rivlin solid (representing ground substance, stiffness parameter c1) with embedded collagen fibers (stiffness parameters c3, c4 and c5). Geometry creationmesh generation was done in Gmsh [2], while FEBio was used for all FE simulations [3]. The LHSPRCC approach resulted in correlation coefficients in the range of 1. To assess the relative influence of the uncertainty in an input parameter on the peak strains, we ranked and then normalized these coefficients, considering that normalized values 0.5 implied a substantial influence on the range of the peak strains in the optic nerve head (ONH). IOP and ICP were found to have a major influence on the peak strains in the ONH, as did optic nerve and LC stiffness. Interestingly, the stiffness of the sclera far from the scleral canal did not have a large influence on peak strains in ONH tissues; however, the collagen fiber stiffness in the peripapillary sclera and annular ring both influenced the peak strains within the ONH. We have created a physiologically relevant model that incorporated collagen fibers to study the effects of elevated ICP. Elevated ICP resulted in strains in the optic nerve that are not predicted to occur on earth: the upright or supine conditions. We found that IOP, ICP, lamina cribrosa stiffness and optic nerve stiffness had the highest association with these extreme strains in the ONH. These extreme strains may activate mechanosensitive cells that induce tissue remodeling and are a risk factor for the development of VIIP.

biomechanics↗

DOE OSTI2026

Principles and Options for Designing Battery Energy Storage Zoning Ordinances

Deployment of battery energy storage (BESS) systems, both standalone and as part of hybrid systems paired with generation, has rapidly increased in the United States in recent years as utilities and communities have deployed storage to improve electric grid reliability and act as a cost-effective alternative to larger grid infrastructure. The modular nature of BESS technologies means systems may be built near other existing land uses, creating the potential for conflicts with neighboring landowners that can be managed and mitigated through zoning and permitting requirements established by local jurisdictions. While many cities and counties have adopted ordinances specific to BESS into their local zoning codes, these ordinances vary significantly in their requirements and level of detail. Meanwhile, many other jurisdictions, including those home to proposed or existing BESS projects, lack any specific language related to BESS in their zoning codes. Local planning and zoning officials have limited capacity and may lack the familiarity with BESS technologies needed to develop ordinances or otherwise make reasonable zoning decisions that balance safety, community impacts, and other goals. The resulting uncertainty at the local zoning level has led developers to withdraw projects in some areas and has spurred moratoria or bans on energy storage projects in others. This report intends to provide practical resources for practitioners interested in reasonable and effective local regulation of battery energy storage. It does not present a model zoning ordinance, but rather is intended to complement model ordinances developed by others by providing additional context and analysis regarding the structure of energy storage zoning ordinances and the decision points for local officials. Zoning ordinances at the city, town, and county level across the U.S. were surveyed alongside two template model ordinances to identify common elements and options for regulating the zoning and siting of BESS. Common elements identified and analyzed include definitions and general requirements, including cutoffs or tiers used to apply regulations to different system sizes and the permitted zones where jurisdictions allow BESS to be sited; visual, noise, and aesthetic requirements, including property line setbacks, fencing and visual screening, noise, and lighting requirements; and safety and planning requirements, such as site plans, decommissioning plans or funds, and requirements for access by emergency services. The report also summarizes some of the more unique regulations, including those that place additional restrictions on BESS at the local level.

25 ENERGY STORAGE↗

NASA NTRS1990

Quantitative computer representation of propellant processing

With the technology currently available for the manufacture of propellants, it is possible to control the variance of the total specific impulse obtained from the rocket boosters to within approximately five percent. Though at first inspection this may appear to be a reasonable amount of control, when it is considered that any uncertainty in the total kinetic energy delivered to the spacecraft translates into a design with less total usable payload, even this degree of uncertainty becomes unacceptable. There is strong motivation to control the variance in the specific impulse of the shuttle's solid boosters. Any small gains in the predictability and reliability of the booster would lead to a very substantial payoff in earth-to-orbit payload. The purpose of this study is to examine one aspect of the manufacture of solid propellants, namely, the mixing process. The traditional approach of computational fluid mechanics is notoriously complex and time consuming. Certain simplifications are made, yet certain fundamental aspects of the mixing process are investigated as a whole. It is possible to consider a mixing process in a mathematical sense as an operator, F, which maps a domain back upon itself. An operator which demonstrates good mixing should be able to spread any subset of the domain completely and evenly throughout the whole domain by successive applications of the mixing operator, F. Two and three dimensional models are developed and graphical visualization two and three dimensional mixing processes are presented.

Hicks, M. D.↗

DOE OSTI2024

Impurity gas detection for SNF canisters using probabilistic deep learning and acoustic sensing *

Abstract Monitoring impurity gases in spent nuclear fuel (SNF) canisters is a novel structural health monitoring approach for SNF in dry storage. The SNF canisters are sealed containers that do not facilitate visual access to the inside. Acoustic sensing can be deployed by taking advantage of the pathways unobstructed by internal hardware. Although the ultrasonic time-of-flight measurement can provide valuable information, it is limited in its ability to discern the concentration of only one impurity gas. As such, deep learning algorithms, particularly convolutional neural networks (CNNs), offer a promising solution. In this study, CNN-based probabilistic deep learning models were implemented to detect and quantify multiple impurity gases in helium. An experimental platform was established to simulate canister conditions, and ultrasonic test data were collected. The presence of argon and air in helium at concentrations ranging from 0% to 1.2% at increments of 0.05% was considered. The multi-layer perceptron, decision tree, and logistic regression classifiers achieved high accuracies when distinguishing pure helium from helium with impurities. CNN with dropout layers and CNN using maximum likelihood estimation showed a similar performance, indicating their ability to capture uncertainties. The ensemble CNN model exhibited improved predictions and the ability to balance individual gas concentration by integrating 1D- and 2D-CNN models. These findings contribute probabilistic deep learning solutions for impurity gas detection and analysis within SNF canisters, thus ensuring safe storage and management of SNFs.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Embracing Uncertainty and Perseverance. A Brief Perspective on Conducting On-Site NDT Research

Dr. Judi E. See, a Systems Analyst and Human Factors Engineer at Sandia National Laboratories, reflects on her experience conducting NDT research in a male-dominated environment. She emphasizes the importance of persistence, flexibility, and persuasive skills in overcoming challenges, ranging from gaining access to test sites and equipment to building trust with inspectors. She shares her personal experience of navigating professional situations where gender disparities were evident, highlighting the need for women to adapt and overcome obstacles in traditionally male-dominated settings. See's journey demonstrates that perseverance and ingenuity can lead to significant contributions, process improvements, and recognition in the NDT field.

42 ENGINEERING↗

DOE OSTI2025

Decayheatml

This code is designed to predict and analyze the decay heat generated in molten salt reactors (MSRs) using a hybrid approach that combines machine learning and segmented polynomial fitting. The accurate prediction of decay heat is essential for reactor safety and the optimization of spent fuel storage. The code operates through several key components: 1) Data Architecture: It incorporates a modular data architecture that handles various MSR-specific operational parameters such as power density, humidity content, and air ingress. These parameters are sampled using Sobol sequences to ensure comprehensive coverage of operational uncertainties. 2) Machine Learning Framework: The code employs a diverse set of machine learning models, including polynomial regression, decision trees, random forests, gradient boosting, support vector regression, k-nearest neighbors, multi-layer perceptrons, and symbolic regression. These models are trained to predict decay heat over a wide temporal range, from immediate shutdown up to 10,000 years. 3) Region-Optimized Training: The temporal domain is divided into multiple regions, each modeled separately to capture distinct decay heat characteristics across different time scales. This approach significantly improves the accuracy and interpretability of predictions. 4) Segmented Polynomial Interpretation (SPI): The SPI method translates machine learning predictions into piecewise polynomial equations. These equations are physically interpretable and can be directly integrated into existing engineering workflows and safety analyses. 5) Front-End Interfaces: The code includes both a Jupyter notebook interface for research development and a Streamlit web application for operational deployment. These interfaces allow users to interactively explore decay heat predictions, adjust operational parameters, and visualize results in real-time. 6) Applications: The framework supports various applications, including safety system validation and spent fuel container optimization. It enables real-time evaluation of worst-case decay heat scenarios, informing the design of passive safety systems and optimizing container designs for long-term storage. Overall, this code provides a robust, accurate, and user-friendly tool for predicting decay heat in MSRs, enhancing reactor safety, and optimizing spent fuel management.

Retamales, Mauricio Eduardo Tano [Idaho National L↗

NASA NTRSDate not supplied

Olivine Dissolution and Formation of Secondary phases in Ultramafic Soils

Introduction: Olivine has been proposed as an indicator for the duration of water-rock interaction within Martian rocks and sediments [1-3]. The use of olivine as a mineralogical indicator for past aqueous alteration on Mars requires interpretation of a complex combination of factors including pH, temperature, and composition [5,6]. Here, we examine the persistence of natural olivine within terrestrial ultramafic soils (Fe/Mg-rich, Al-poor) developing under different climatic conditions and the incipient dissolution of emplaced forsterite (Fo) and fayalite (Fa) surfaces to investigate environmental effects on incipient olivine dissolution, olivine persistence in soils, and formation of secondary phases. Methods: Field Sites. We examined olivine weathering and secondary material formation in ultramafic soils at 6 sites in the Klamath Mountains (KM) of northern California with a mean annual temperature of ~12.8℃ and precipitation of ~55.7-95.4 cm/year [7], and soil pH of ~6.5-7.3; 4 sites in the Tablelands (TB) of Newfoundland, Canada with a mean annual temperature of <3.9℃ and precipitation of ~120.0 cm/year [8], and soil pH of ~7.7; and at 3 sites at Pickhandle Gulch (PG), Nevada with a mean annual temperature of ~14.4℃ and precipitation of ~14.1 cm/year [7], and soil pH of ~8.5. Sampling sites span an age range of ~12.1-50+ kya in the Klamath Mountains [9,10] and ~13-30 kya in the Tablelands [11]. Pickhandle Gulch sites are undated. Parent Material and Soil Analyses. Polished thin sections of bulk soil prepared by Wagner Petrographic, Inc were carbon-coated and analyzed on a JEOL 2100 SEM in back-scattered electron mode in the EMIL lab at UNLV and at the 13-ID-E synchrotron beamline at Argonne National Laboratory using µXRF, µXRD, and XAS. Soil and parent material samples were powdered in a Fritsch pulverisette and analyzed by XRD and soil by VNIR. Soil preparation is further described in [12]. Disk Preparation, Burial, and Collection. Fo disks were cut from a column prepared via hot-pressing and Fa disks by sintering synthetic fayalite powder, see [13] for detail. Disks were polished to a 0.25-micron level with diamond grit. Disks were buried in 3 KM soils, 4 TB soils, and 3 PG soils, collected after exactly 365 days, and washed gently with 100% reagent grade ethanol to remove potential adhered soil material. Weathered disks and soil samples were stored in a -20℃ freezer until analysis. Unaltered control disks prepared identically to the buried disks were stored at -20℃ for the duration of the experiment. Disk Analyses. One Fo and Fa disk from each climate zone was analyzed on a variable pressure Zeiss Supra 40VP SEM at Northern Arizona University. A separate Fo and Fa disk from each climate zone was analyzed by XPS using a Physical Electronics VersaProbe II at the Penn State Univ. Materials Characterization Lab after a Na-dodecyl sulfate wash and ozonation to remove carbon contamination as in [14]. VNIR measurements were conducted at Johnson Space Center using an ASD FieldSpec3 under ambient lab conditions on a separate Fo and Fa disk from each climate zone. One separate Fo and Fa control sample was analyzed for each technique for comparison with weathered samples. XPS uncertainty was determined from 5 repeat measurements on controls. Results: Bedrock and Soil Results Olivine is present in the parent material in the KM and TB. Olivine is found in ~12.1 ka KM soils but is absent from all older soils, while persisting into the oldest (>20 ka) TB soil (Figure 1). In both locations, olivine is found as cores surrounded by a serpentine rind (Figure 2). VNIR spectra from the analyzed soils possess strong OH-associated spectral features at ~2.33 µm indicating the presence of Mg-rich phyllosilicates as well as ferric-oxide features at ~0.92 µm in the KM (Figure 3). Primary crystalline silicate grains mostly incorporate Fe2+, while poorly crystalline weathering rinds are best fit by ferric oxide XAS standards (Figure 4). µXRF also shows that Fe and Ni concentrate in weathering rinds and Cr remains within interior silicate grains (Figure 4). Buried Sample Results All Fo surfaces exhibited formation of dissolution features including shallow pitting not observed on controls. Dissolution features were most visually widespread on the KM disk (Figure 5). Leaching of Mg from KM and TB Fo disks was evident from <1.6 Mg/Si ratios measured by XPS (Figure 6). Fe-rich precipitates in SEM (Figure 5) and Fe presence in XPS scans (Figure 6) indicate Fe deposition onto KM and TB Fo disk surfaces. The appearance of a spectral feature at 0.55 µm in the VNIR spectra from the TB Fo suggests this Fe is ferric (Figure 7). The PG Fo appears least altered, with minimal formation of dissolution features in SEM (Figure 5), a Mg/Si ratio inconsistent with leaching (~2) (Figure 6), and VNIR spectra almost identical to the control sample. Analysis of Fa surfaces is ongoing. The higher temperatures and more acidic pH in the KM soils likely drive the faster dissolution of the Fo disks described above. While the TB soils experience greater precipitation than in the KM, the cooler temperatures and more basic soil pH facilitate observable but more limited alteration. The dry climate and basic soil pH at PG lead to minimal dissolution of the PG disk surfaces.

A D Feldman↗

NASA NTRSDate not supplied

Status of Mars Retropropulsion Testing in the Langley Unitary Plan Wind Tunnel

Future Mars human landings will be enabled by a powered descent phase starting at supersonic conditions, something which has never been done before on a Mars mission. Significant aerosciences challenges exist due to jet interactions between the retrorocket engine plumes, freestream flow, and vehicle that will affect the aerodynamic behavior during powered descent. Historically, wind tunnel tests have been used to study the interactions with inert gas exhaust simulants in place of rocket engines. On the computational side, flowfield simulations have been completed at full-scale conditions, but the available ground and flight data are not appropriate for calibrating computational uncertainties for aerodynamic interference on proposed Mars descent vehicles, due to insufficient data, dissimilar vehicle geometries, and disparate operating conditions. A wind tunnel test has been designed to begin addressing powered descent aerodynamics risks for large-scale human Mars entry concepts and to identify gaps in computational predictive capabilities. The test will be conducted in the NASA Langley Unitary Plan Wind Tunnel and is designed with improvements in model design and data products over past tests. The test campaign will be run using sub-scale model geometries derived from NASA powered descent reference vehicles: a blunt low lift-to-drag vehicle and a more slender geometry that generates higher unpowered lift. Both models have been fabricated and are ready for testing. The blunt model is equipped with the flexibility to examine the effects of nozzle pointing direction, number, location, size, and area ratio. The main measurements are heatshield aerodynamic interference forces and moments with a custom flow-through balance, discrete and distributed heatshield pressure, and high-speed flowfield visualization. This paper covers the test objectives, facility, models and instrumentation, and planned test matrix.

Mars↗

NASA NTRSDate not supplied

Status of Mars Retropropulsion Testing in the Langley Unitary Plan Wind Tunnel

Supersonic Retropropulsion↗

NASA NTRS2022

The Radiative Effect on Cloud Microphysics from the Arctic to the Tropics

Cloud representation is one of the largest uncertainties in the current weather and climate models. In this article, the observations and modeling of the radiative effect on (cloud) microphysics (REM) from the Arctic to the Tropics are overviewed, providing a new direction to meet the challenge of cloud representation. REM deals with the radiation-induced temperature difference between cloud particles and air. It leads to two common phenomena observed at the surface—dew and frost—and impacts clouds aloft significantly, which is noticed via the wide occurrence of horizontally oriented ice crystals (HOICs). However, REM has been overlooked by all of the operational weather and climate models. Based on the bin model of REM and the global distribution of radiative cooling/warming, the observations of REM from several platforms (e.g., aircrafts, field campaigns, and satellites) are coordinated in this article, yielding a global picture on REM. As a result, the picture is compatible with the global distribution of HOICs and other ice crystal characteristics obtained from various clouds on the globe, such as diamond dust (or clear-sky precipitation) in the Arctic, sub-visual cirrus clouds in the tropical tropopause layer, and other cirrus clouds from the low to high latitudes. In addition, ice crystals possess relatively strong REM compared to liquid drops because their aspect ratio is usually not one. The global picture on REM can be used by the weather and climate modelers to diagnose their cloud representation biases. It can also be used to improve the atmospheric ice retrieval algorithm from satellite observations.

cloud microphysics↗

DOE OSTI2024

Tau Positron Emission Tomography for Predicting Dementia in Individuals With Mild Cognitive Impairment

An accurate prognosis is especially pertinent in mild cognitive impairment (MCI), when individuals experience considerable uncertainty about future progression. To evaluate the prognostic value of tau positron emission tomography (PET) to predict clinical progression from MCI to dementia. This was a multicenter cohort study with external validation and a mean (SD) follow-up of 2.0 (1.1) years. Data were collected from centers in South Korea, Sweden, the US, and Switzerland from June 2014 to January 2024. Participant data were retrospectively collected and inclusion criteria were a baseline clinical diagnosis of MCI; longitudinal clinical follow-up; a Mini-Mental State Examination (MMSE) score greater than 22; and available tau PET, amyloid-β (Aβ) PET, and magnetic resonance imaging (MRI) scan less than 1 year from diagnosis. A total of 448 eligible individuals with MCI were included (331 in the discovery cohort and 117 in the validation cohort). None of these participants were excluded over the course of the study. Exposures included Tau PET, Aβ PET, and MRI. Positive results on tau PET (temporal meta–region of interest), Aβ PET (global; expressed in the standardized metric Centiloids), and MRI (Alzheimer disease [AD] signature region) was assessed using quantitative thresholds and visual reads. Clinical progression from MCI to all-cause dementia (regardless of suspected etiology) or to AD dementia (AD as suspected etiology) served as the primary outcomes. The primary analyses were receiver operating characteristics. In the discovery cohort, the mean (SD) age was 70.9 (8.5) years, 191 (58%) were male, the mean (SD) MMSE score was 27.1 (1.9), and 110 individuals with MCI (33%) converted to dementia (71 to AD dementia). Only the model with tau PET predicted all-cause dementia (area under the receiver operating characteristic curve [AUC], 0.75; 95% CI, 0.70-0.80) better than a base model including age, sex, education, and MMSE score (AUC, 0.71; 95% CI, 0.65-0.77; P = .02), while the models assessing the other neuroimaging markers did not improve prediction. In the validation cohort, tau PET replicated in predicting all-cause dementia. Compared to the base model (AUC, 0.75; 95% CI, 0.69-0.82), prediction of AD dementia in the discovery cohort was significantly improved by including tau PET (AUC, 0.84; 95% CI, 0.79-0.89; P < .001), tau PET visual read (AUC, 0.83; 95% CI, 0.78-0.88; P = .001), and Aβ PET Centiloids (AUC, 0.83; 95% CI, 0.78-0.88; P = .03). In the validation cohort, only the tau PET and the tau PET visual reads replicated in predicting AD dementia. In this study, tau-PET showed the best performance as a stand-alone marker to predict progression to dementia among individuals with MCI. This suggests that, for prognostic purposes in MCI, a tau PET scan may be the best currently available neuroimaging marker.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2015

Lunar Impact Flash Locations from NASA's Lunar Impact Monitoring Program

Meteoroids are small, natural bodies traveling through space, fragments from comets, asteroids, and impact debris from planets. Unlike the Earth, which has an atmosphere that slows, ablates, and disintegrates most meteoroids before they reach the ground, the Moon has little-to-no atmosphere to prevent meteoroids from impacting the lunar surface. Upon impact, the meteoroid's kinetic energy is partitioned into crater excavation, seismic wave production, and the generation of a debris plume. A flash of light associated with the plume is detectable by instruments on Earth. Following the initial observation of a probable Taurid impact flash on the Moon in November 2005,1 the NASA Meteoroid Environment Office (MEO) began a routine monitoring program to observe the Moon for meteoroid impact flashes in early 2006, resulting in the observation of over 330 impacts to date. The main objective of the MEO is to characterize the meteoroid environment for application to spacecraft engineering and operations. The Lunar Impact Monitoring Program provides information about the meteoroid flux in near-Earth space in a size range-tens of grams to a few kilograms-difficult to measure with statistical significance by other means. A bright impact flash detected by the program in March 2013 brought into focus the importance of determining the impact flash location. Prior to this time, the location was estimated to the nearest half-degree by visually comparing the impact imagery to maps of the Moon. Better accuracy was not needed because meteoroid flux calculations did not require high-accuracy impact locations. But such a bright event was thought to have produced a fresh crater detectable from lunar orbit by the NASA spacecraft Lunar Reconnaissance Orbiter (LRO). The idea of linking the observation of an impact flash with its crater was an appealing one, as it would validate NASA photometric calculations and crater scaling laws developed from hypervelocity gun testing. This idea was dependent upon LRO finding a fresh impact crater associated with one of the impact flashes recorded by Earth-based instruments, either the bright event of March 2013 or any other in the database of impact observations. To find the crater, LRO needed an accurate area to search. This Technical Memorandum (TM) describes the geolocation technique developed to accurately determine the impact flash location, and by association, the location of the crater, thought to lie directly beneath the brightest portion of the flash. The workflow and software tools used to geolocate the impact flashes are described in detail, along with sources of error and uncertainty and a case study applying the workflow to the bright impact flash in March 2013. Following the successful geolocation of the March 2013 flash, the technique was applied to all impact flashes detected by the MEO between November 7, 2005, and January 3, 2014.

Moser, D. E.↗

NASA NTRS2018

Analysis of the Honeywell Uncertified Research Engine (HURE) with Ice Crystal Cloud Ingestion at Simulated Altitudes: Public Version

The Honeywell Uncertified Research Engine (HURE), a research version of a turbofan engine that never entered production, was tested in the NASA Propulsion System Laboratory (PSL), an altitude test facility at the NASA Glenn Research Center. The PSL is a facility that is equipped with water spray bars capable of producing an ice cloud consisting of ice particles, having a controlled particle diameter and concentration in the air flow. In preparation for testing of the HURE, numerical analysis of flow and ice particle thermodynamics was performed on the compression system of the turbofan engine to predict operating conditions that could potentially result in a risk of ice accretion due to ice crystal ingestion. The results of those analyses formed the basis of the test matrix. The goal of the test matrix was to have ice accrete in two regions of the compression system: region one, which consists of the fan-stator through the inlet guide vane (IGV), and region two which is the first stator within the high pressure compressor. The predictive analyses were performed with the mean line compressor flow modeling code (COMDES-MELT) which includes an ice particle model. Together these comprise a one-dimensional icing tool. The HURE engine was tested in PSL with the ice cloud over the range of operating conditions of altitude, ambient temperature, simulated flight Mach number, and fan speed with guidance from the analytical predictions. The engine was fitted with video cameras at strategic locations within the engine compression system flow path where ice was predicted to accrete, in order to visually confirm ice accretion when it occurred. In addition, traditional compressor instrumentation such as total pressure and temperature probes, static pressure taps, and metal temperature thermocouples were installed in targeted areas where the risk of ice accretion was expected. The current research focuses on the analysis of the data that was obtained after testing the HURE engine in PSL with ice crystal ingestion. The computational method was enhanced by computing key parameters through the fan-stator at multiple spanwise locations, in order to increase the fidelity with the current mean-line method. In addition, other sources of heat (non-adiabatic walls) were suspected to be the cause of accretion near the splitter-lip and shroud. Since there were no thermocouples near the splitter, a simple order of magnitude heat transfer model was implemented to estimate the wall temperature. Future analyses will require a higher fidelity thermal analysis of the compression system metal walls to accurately calculate the total heat flux to the ice particle. For many data points analyzed, there were differences between the thermodynamic system model and the measured test data that may partially be responsible for uncertainties with the results of the current analyses.

Turbomachinery↗

NASA NTRS1998

Detecting a Terrestrial Biosphere Sink for Carbon Dioxide: Interannual Ecosystem Modeling for the Mid-1980s

There is considerable uncertainty as to whether interannual variability in climate and terrestrial ecosystem production is sufficient to explain observed variation in atmospheric carbon content over the past 20-30 years. In this paper, we investigated the response of net CO2 exchange in terrestrial ecosystems to interannual climate variability (1983 to 1988) using global satellite observations as drivers for the NASA-CASA (Carnegie-Ames-Stanford Approach) simulation model. This computer model of net ecosystem production (NEP) is calibrated for interannual simulations driven by monthly satellite vegetation index data (NDVI) from the NOAA Advanced Very High Resolution Radiometer (AVHRR) at 1 degree spatial resolution. Major results from NASA-CASA simulations suggest that from 1985 to 1988, the northern middle-latitude zone (between 30 and 60 degrees N) was the principal region driving progressive annual increases in global net primary production (NPP; i.e., the terrestrial biosphere sink for carbon). The average annual increase in NPP over this predominantly northern forest zone was on the order of +0.4 Pg (10 (exp 15) g) C per year. This increase resulted mainly from notable expansion of the growing season for plant carbon fixation toward the zonal latitude extremes, a pattern uniquely demonstrated in our regional visualization results. A net biosphere source flux of CO2 in 1983-1984, coinciding with an El Nino event, was followed by a major recovery of global NEP in 1985 which lasted through 1987 as a net carbon sink of between 0.4 and 2.6 Avg C per year. Analysis of model controls on NPP and soil heterotrophic CO2 fluxes (Rh) suggests that regional warming in northern forests can enhance ecosystem production significantly. In seasonally dry tropical zones, periodic drought and temperature drying effects may carry over with at least a two-year lag time to adversely impact ecosystem production. These yearly patterns in our model-predicted NEP are consistent in magnitude with the estimated exchange of CO2 by the terrestrial biosphere with the atmosphere, as determined by previous isotopic (delta (sup 13 C) convolution analysis. Ecosystem simulation results can help further target locations where net carbon sink fluxes have occurred in the past or may be verified in subsequent field studies.

Potter, Christopher S.↗