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NASA NTRSDate not supplied

Assessing Risk Due to Small Sample Size in Probability of Detection Analysis Using Tolerance Intervals

Small sample size (e.g.6-30) poses risk in results of probability of detection (POD) analysis using tolerance intervals. This method is also called as the limited sample or LS POD. The analysis is performed either during NDE procedure qualification or for assessment of reliability of an NDE procedure. The risk is primarily due to sampling error. Smaller samples are not likely to be random to the population or representative of the population. The small samples are likely to be biased. Biased samples have smaller standard deviation compared to the population. POD analysis with small biased sample can lead to overestimation of POD. Many sampling schemes are available in statistics to mitigate sampling risk. Primary objective of POD analysis is to determine a decision threshold from signal response measurements of a sample such that it is less than or equal to population decision threshold for 90% POD. Sampling error implies that this NDE reliability condition is violated. One of sampling types is called a representative sample. Representative samples reduce variance in POD estimates but also reduce magnitude of the error. Sampling sensitivity analysis for some sampling types is performed here using repetitive random sampling or Monte Carlo method. Six sampling types are considered for comparison. Some of the sampling types are similar to drawing a representative sample. LS POD model assumes random sampling. Therefore, random sampling is used as a basis for comparison with each sampling type. The sampling types used in the analysis are, A. Nominal and worst-case sampling, B. Worst-case sampling, C. Nominal case sampling, D. Random sampling, E. Random target, and sub-target sampling. F. Nominal target and sub-target sampling. Results of Monte Carlo simulation indicate that type F sampling can mitigate sampling risk and is also more practical to implement. Type A sampling may also mitigate the sampling risk, but it may be less practical to implement.

Ajay M Koshti↗
NASA NTRSDate not supplied

The Kinematic Navigation and Cartography Knapsack (KNaCK) LiDAR System: Overview and Applications.

Improved terrain characterization and navigation sensors and methods are needed to enhance crew safety, ISRU return, and scientific understanding of future landing sites. Specific to the Artemis Program and sustained exploration at the lunar South Pole, extreme low-angle solar illumination conditions pose significant challenges to existing photogrammetry-based robotic navigation. Additionally, a major challenge for navigation on the Moon and other planetary surfaces is the lack of Global Positioning and Navigation Systems (GPS or GNSS). Thus, there is a need for an alternative to image-based navigation that allow for precise and accurate mapping in GPS-denied environments on any planetary body. Here, we describe the Kinematic Navigation and Cartography Knapsack (KNaCK) LiDAR system; a backpack-mounted, mobile navigation and terrain mapping system that uses a velocity-sensing coherent light detection and ranging (LiDAR) system based on a frequency modulated continuous wave (FMCW) technique, contains minimal moving parts, and employs sophisticated positioning algorithms. During a traverse, this instrument emits light pulses to continually scan a scene to build a three-dimensional point cloud representation of topography. A measure of the Doppler-velocity at each of millions of range points sampled per second allows for a 6 degree of freedom (6- DoF) estimate of the sensor’s position and the development of novel position-from-velocity mapping and positioning algorithms for loop-closure in GPS denied environments. Included with paper is the video presentation for the Figure 2: FMCW-LiDAR sensor on Kinematic Navigation and Cartography Knapsack (KNaCK) (Aeva Aeries 1)

M. Zanetti↗
NASA NTRS2021

Communicating Respiratory Health Risk Among Children using a Global Air Quality Index

Air pollution poses a serious threat to children’s respiratory health around the world. Satellite remote-sensing technology and air quality models can provide pollution data on a global scale, necessary for riskcommunication efforts in regions without ground-based monitoring networks. Several large centers, including NASA, produce global pollution forecasts that may be used alongside air quality indices to communicate local, daily risk information to the public. Here we present a health-based, globally applicable air quality index developed specifically to reflect the respiratory health risks among children exposed to elevated outdoor air pollution. Additive, excess-risk air quality indices were developed using 51 different coefficients derived from time-series health studies evaluating the impacts of ambient fine particulate matter, nitrogen dioxide, and ozone on children's respiratory morbidity outcomes. A total of four indices were created which varied based on whether or not the underlying studies controlled for co-pollutants and in the adjustment of excess risks of individual pollutants. Combined with historical estimates of air pollution provided globally at a 25x25 km2 spatial resolution from the NASA's Goddard Earth Observing System composition forecast (GEOS-CF) model, each of these indices were examined in a global sample of 664 small and 140 large cities for study year 2017. Adjusted indices presented the most normal distributions of locally-scaled index values, which has been shown to improve associations with health risks, while indices based on coefficients controlling for co-pollutants had little effect on index performance. We provide the steps and resources need to apply our final adjusted index at the local level using freely-available forecasting data from the GEOS-CF model, which can provide risk communication information for cities around the world to better inform individual behavior modification to best protect children's respiratory health.

Air Qualilty↗
NASA NTRS2015

Multi-Spacecraft Autonomous Positioning System

As the number of spacecraft in simultaneous operation continues to grow, there is an increased dependency on ground-based navigation support. The current baseline system for deep space navigation utilizes Earth-based radiometric tracking, requiring long-duration observations to perform orbit determination and generate a state update. The age, complexity, and high utilization of the ground assets pose a risk to spacecraft navigation performance. In order to perform complex operations at large distances from Earth, such as extraterrestrial landing and proximity operations, autonomous systems are required. With increasingly complex mission operations, the need for frequent and Earth-independent navigation capabilities is further reinforced. The Multi-spacecraft Autonomous Positioning System (MAPS) takes advantage of the growing interspacecraft communication network and infrastructure to allow for Earth-autonomous state measurements to enable network-based space navigation. A notional concept of operations is given in figure 1. This network is already being implemented and routinely used in Martian communications through the use of the Mars Reconnaissance Orbiter and Mars Odyssey spacecraft as relays for surface assets. The growth of this communications architecture is continued through MAVEN, and future potential commercial Mars telecom orbiters. This growing network provides an initial Marslocal capability for inter-spacecraft communication and navigation. These navigation updates are enabled by cross-communication between assets in the network, coupled with onboard navigation estimation routines to integrate packet travel time to generate ranging measurements. Inter-spacecraft communication allows for frequent state broadcasts and time updates from trusted references. The architecture is a software-based solution, enabling its implementation on a wide variety of current assets, with the operational constraints and measurement accuracy determined by onboard systems.

Anzalone, Evan↗
NASA NTRSDate not supplied

Global Carbon Consumption Database for Wildland Fire

Fire plays a significant role on both national and global scales, profoundly impacting landscapes shaped by human activity as well as those left wild. Even though fire can be devastating, wildland fire is a natural and integral force on our landscapes. Fires can also serve to reduce fuels to mitigate wildfire risk and maintain healthy ecosystem functions. However, the smoke produced by fires, regardless of their size or purpose, can pose adverse effects on human health when inhaled downwind. Understanding the influence of smoke on air quality and human well-being necessitates the quantification of emissions that fires release into the atmosphere. In response to this need, we have established a comprehensive global consumption database directly linked to distinct fuels within various fire danger categories. This database, featuring a spatial resolution of approximately 300 meters, builds upon the foundations of the Pettinari, M. Lucrecia (2015) Global Fuelbed database, a global fuel map with standardized Fuel Characteristic Classification System (FCCS) biomass parameters. Consumption is broken down into five Fire Danger categories (Low, Moderate, High, Very High, Extreme), for both ‘new’ and ‘residual’ burning scenarios. We define ‘residual burned area’ as area burning in a region that has burned on a previous day for the same fire season, and ‘new burned area’ as area burning in a region that has not recently burned. This product serves as a valuable tool when used in conjunction with burned area data to rapidly estimate the carbon consumed and released into the atmosphere. Previously, we developed a similar emissions method utilizing satellite information, in conjunction with the FCCS 30-meter United States fuelbed dataset. We implemented this approach on fires, documented during the 2019 Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign to estimate daily carbon emissions. Our emissions estimates were rigorously compared against in-situ measurements of CO2, CO, and black carbon aerosols, revealing a robust agreement between the two datasets.

Emily Gargulinski↗
DOE OSTI2025

Optimal Power Management for Large-Scale Battery Energy Storage Systems via Bayesian Inference

Large-scale battery energy storage systems (BESS) have found ever-increasing use across industry and society to accelerate clean energy transition and improve energy supply reliability and resilience. However, their optimal power management poses significant challenges: the underlying high-dimensional nonlinear nonconvex optimization lacks computational tractability in real-world implementation, and the uncertainty of the exogenous power demand makes exact optimization difficult. This paper presents a new solution framework to address these bottlenecks. The solution pivots on introducing power-sharing ratios to specify each cell’s power quota from the output power demand. To find the optimal power-sharing ratios, we formulate a nonlinear model predictive control (NMPC) problem to achieve power-loss-minimizing BESS operation while complying with safety, cell balancing, and power supply-demand constraints. We then propose a parameterized control policy for the power-sharing ratios, which utilizes only three parameters, to reduce the computational demand in solving the NMPC problem. This policy parameterization allows us to translate the NMPC problem into a Bayesian inference problem for the sake of 1) computational tractability, and 2) overcoming the nonconvexity of the optimization problem. We leverage the ensemble Kalman inversion technique to solve the parameter estimation problem. Concurrently, a low-level control loop is developed to seamlessly integrate our proposed approach with the BESS to ensure practical implementation. This low-level controller receives the optimal power-sharing ratios, generates output power references for the cells, and maintains a balance between power supply and demand despite uncertainty in output power. We conduct extensive simulations and experiments on a 20-cell prototype to validate the proposed approach.

Battery energy storage systems (BESSs)↗
DOE OSTI2021

Short-term apartment-level load forecasting using a modified neural network with selected auto-regressive features

Residential electricity load profiles and their diversity have become increasingly important to realize the benefits of Smart or Transactive Energy Networks (TENs). An important element of TENs will be practical, accurate, and implementable residential load forecasting techniques. While there have been many approaches to short-term load forecasting, few have included forecasting for individual households, partly because the high volatility and idiosyncrasies present in individual household load data can pose significant challenges. In this study, we develop a Convolutional Long Short-Term Memory-based neural network with Selected Autoregressive Features (termed a CLSAF model) to improve short-term household electricity load forecasting accuracy by employing three strategies: autoregressive features selection, exogenous features selection, and a “default” state to avoid overfitting at times of high load volatility. We include aggregations of apartments to floor and building level, because utilities may favor transactive approaches that rely on aggregator models, e.g., a cluster of consumers as opposed to an individual. We demonstrate that the CLSAF model, by virtue of its enhanced feature representation and modest computational resources, can accomplish load forecasting in a multi-family residential building across three spatial granularities (individual apartment/household, floor, and building levels), with an accuracy improvement of up to 25% compared to a persistence model. We propose a data screening technique to characterize time-series electricity-load data. This technique is suitable for integration into a TEN ecosystem and allows one to estimate confidence levels of the load forecasts to optimize computational resources and the risks associated with uncertain forecasts.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2011

In Situ Observations and Sampling of Volcanic Emissions with Unmanned Aircraft: A NASA/UCR Case Study at Turrialba Volcano, Costa Rica

Burgeoning new technology in the design and development of robotic aircraft-unmanned aerial vehicles (UAVs)-presents unprecedented opportunities for the volcanology community to observe, measure, and sample eruption plumes and drifting volcanic clouds in situ. While manned aircraft can sample dilute parts of such emissions, demonstrated hazards to air breathing, and most particularly turbine, engines preclude penetration of the zones of highest ash concentrations. Such areas within plumes are often of highest interest with respect to boundary conditions of applicable mass-loading retrieval models, as well as Lagrangian, Eulerian, and hybrid transport models used by hazard responders to predict plume trajectories, particularly in the context of airborne hazards. Before the 2010 Ejyafyallajokull eruption in Iceland, ICAO zero-ash-tolerance rules were typically followed, particularly for relatively uncrowded Pacific Rim airspace, and over North and South America, where often diversion of aircraft around ash plumes and clouds was practical. The 2010 eruption in Iceland radically changed the paradigm, in that critical airspace over continental Europe and the United Kingdom were summarily shut by local civil aviation authorities and EURO CONTROL. A strong desire emerged for better real-time knowledge of ash cloud characteristics, particularly ash concentrations, and especially for validation of orbital multispectral imaging. UAV platforms appear to provide a viable adjunct, if not a primary source, of such in situ data for volcanic plumes and drifting volcanic clouds from explosive eruptions, with prompt and comprehensive application to aviation safety and to the basic science of volcanology. Current work is underway in Costa Rica at Turrialba volcano by the authors, with the goal of developing and testing new small, economical UAV platforms, with miniaturized instrument payloads, within a volcanic plume. We are underway with bi-monthly deployments of tethered SO2-sondes and are in the planning stages for the deployment of the SIERRA UAV to our site in March 2013. We will be conducting in situ observations simultaneously with ASTER orbital multispectral TIR data acquisitions, in order to compare in situ measurements with estimates of SO2 mass loading and dispersion derived from ASTER data. Though small UAVs are now being considered for use in active volcanic areas for in situ sampling of emissions (e.g., efforts by our group, and by our colleagues at the INGV in Italy and the Applied Science University in Dusseldorf, Germany, and others in the United Kingdom and Iceland), and also for remote sensing, much more needs to be done in the way of instrument development, and in developing small UAVs for both low altitude (tropospheric) and high altitude (stratospheric) applications. In particular, the development of all weather and day/night operational flight capabilities in close proximity to hazardous topography is crucial to a truly responsive volcano in situ measurement system. Finally, it is imperative that national civil aviation authorities recognize the unique benefits of such platforms. It is important that authorities understand that severely restricting or not deploying such tools in airspace over restless volcanoes or within eruption plumes, ostensibly because of the perceived (small) risk that such unmanned aircraft pose to manned air operations, itself poses a bigger transcendental risk to proximal populations and particularly to the aviation community, itself.

Volcanic Ash Advisory Centers (VAACs)↗
DOE OSTI2022

Computational Imaging for Intelligence in Highly Scattering Aerosols (Final Report)

Natural and man-made degraded visual environments pose major threats to national security. The random scattering and absorption of light by tiny particles suspended in the air reduces situational awareness and causes unacceptable down-time for critical systems and operations. To improve the situation, we have developed several approaches to interpret the information contained within scattered light to enhance sensing and imaging in scattering media. These approaches were tested at the Sandia National Laboratory Fog Chamber facility and with tabletop fog chambers. Computationally efficient light transport models were developed and leveraged for computational sensing. The models are based on a weak angular dependence approximation to the Boltzmann or radiative transfer equation that appears to be applicable in both the moderate and highly scattering regimes. After the new model was experimentally validated, statistical approaches for detection, localization, and imaging of objects hidden in fog were developed and demonstrated. A binary hypothesis test and the Neyman-Pearson lemma provided the highest theoretically possible probability of detection for a specified false alarm rate and signal-to-noise ratio. Maximum likelihood estimation allowed estimation of the fog optical properties as well as the position, size, and reflection coefficient of an object in fog. A computational dehazing approach was implemented to reduce the effects of scatter on images, making object features more readily discernible. We have developed, characterized, and deployed a new Tabletop Fog Chamber capable of repeatably generating multiple unique fog-analogues for optical testing in degraded visual environments. We characterized this chamber using both optical and microphysical techniques. In doing so we have explored the ability of droplet nucleation theory to describe the aerosols generated within the chamber, as well as Mie scattering theory to describe the attenuation of light by said aerosols, and correlated the aerosol microphysics to optical properties such as transmission and meteorological optical range (MOR). This chamber has proved highly valuable and has supported multiple efforts inclusive to and exclusive of this LDRD project to test optics in degraded visual environments. Circularly polarized light has been found to maintain its polarization state better than linearly polarized light when propagating through fog. This was demonstrated experimentally in both the visible and short-wave infrared (SWIR) by imaging targets made of different commercially available retroreflective films. It was found that active circularly polarized imaging can increase contrast and range compared to linearly polarized imaging. We have completed an initial investigation of the capability for machine learning methods to reduce the effects of light scattering when imaging through fog. Previously acquired experimental long-wave images were used to train an autoencoder denoising architecture. Overfitting was found to be a problem because of lack of variability in the object type in this data set. The lessons learned were used to collect a well labeled dataset with much more variability using the Tabletop Fog Chamber that will be available for future studies. We have developed several new sensing methods using speckle intensity correlations. First, the ability to image moving objects in fog was shown, establishing that our unique speckle imaging method can be implemented in dynamic scattering media. Second, the speckle decorrelation over time was found to be sensitive to fog composition, implying extensions to fog characterization. Third, the ability to distinguish macroscopically identical objects on a far-subwavelength scale was demonstrated, suggesting numerous applications ranging from nanoscale defect detection to security. Fourth, we have shown the capability to simultaneously image and localize hidden objects, allowing the speckle imaging method to be effective without prior object positional information. Finally, an interferometric effect was presented that illustrates a new approach for analyzing speckle intensity correlations that may lead to more effective ways to localize and image moving objects. All of these results represent significant developments that challenge the limits of the application of speckle imaging and open important application spaces. A theory was developed and simulations were performed to assess the potential transverse resolution benefit of relative motion in structured illumination for radar systems. Results for a simplified radar system model indicate that significant resolution benefits are possible using data from scanning a structured beam over the target, with the use of appropriate signal processing.

58 GEOSCIENCES↗
NASA NTRS2015

Status: Crewmember Noise Exposures on the International Space Station

The International Space Station (ISS) provides a unique environment where crewmembers from the US and our international partners work and live for as long as 6 to 12 consecutive months. During these long-durations ISS missions, noise exposures from onboard equipment are posing concerns for human factors and crewmember health risks, such as possible reductions in hearing sensitivity, disruptions of crew sleep, interference with speech intelligibility and voice communications, interference with crew task performance, and reduced alarm audibility. It is crucial to control acoustical noise aboard ISS to acceptable noise exposure levels during the work-time period, and to also provide a restful sleep environment during the sleep-time period. Acoustic dosimeter measurements, obtained when the crewmember wears the dosimeter for 24-hour periods, are conducted onboard ISS every 60 days and compared to ISS flight rules. NASA personnel then assess the acoustic environment to which the crewmembers are exposed, and provide recommendations for hearing protection device usage. The purpose of this paper is to provide an update on the status of ISS noise exposure monitoring and hearing conservation strategies, as well as to summarize assessments of acoustic dosimeter data collected since the Increment 36 mission (April 2013). A description of the updated noise level constraints flight rule, as well as the Noise Exposure Estimation Tool and the Noise Hazard Inventory implementation for predicting crew noise exposures and recommending to ISS crewmembers when hearing protection devices are required, will be described.

Limardo-Rodriguez, Jose G.↗
NASA NTRSDate not supplied

Landslide Susceptibility and Runout Assessment for the Arun Hydropower Cascade in Nepal Based on Multitemporal Landslide Inventory Developed Using Planet Imagery

The transboundary Pumqu/Arun River basin spreads across Nepal and Tibet. Nearly 95% of the basin lies in Tibet through which the Pumqu River flows forming the Arun River once it enters Nepal. The Arun River valley has about 3,163 MW in five large hydropower projects undergoing construction or planned for the future. Rainfall and earthquake-induced landslides, landslide dammed lakes and landslide-induced glacial lake outburst floods pose major risks to smooth operation of these projects. To safeguard upcoming hydropower projects, areas susceptible to landslides in the Arun River valley must be identified. We generated a multitemporal landslide inventory (2010-2020), modeled landslide susceptibility and runouts around the hydropower sites using Earth Observation (EO) techniques. We used high-resolution satellite imagery from Planet and open-source tool to establish a detailed and comprehensive multitemporal landslide inventory for the basin. The rigorous quality-controlled inventory represents yearly record of landslides from 2010 to 2020. To our knowledge this is one of the most comprehensive landslide inventories for the basin with yearly records of landslides.A data-driven approach was used to map areas susceptible to landslides within the Pumqu/Arun River basin. The multitemporal landslide inventory combined with other readily available EO-based variables were used to create a landslide susceptibility map. The highly susceptible areas delineated from the map was further used to characterize runouts near the hydropower sites. The susceptibility and runout analysis provide a valuable initial estimate of where landslides are likely to trigger and where the mobilized material is likely to end up.

Pukar Amatya↗
DOE OSTI2025

Modeling the impact of extreme weather events and future climate on the radiologically contaminated sites of Enewetak Atoll

Enewetak Atoll underwent 43 historical nuclear tests from 1948 to 1958, including the first hydrogen bomb test, resulting in a substantial nuclear material fallout contaminating the Atoll and the lagoon waters. The radionuclide fallout material deposited in lagoon sediments and land soil will remain for decades to come. With intensifying climate and extreme weather events, the possibility of redistribution of deposited radionuclide material has become a great concern. This study uses a numerical modeling approach to estimate the potential elevated radionuclide concentrations that can be distributed during storm events under current and future climates. We simulated three historical storm scenarios that are most likely to impact Atoll’s environment and remobilize the radionuclide-bound sediments. WRF-ARW was used to reconstruct these storm scenarios under current year (2015) and future year (2090) climates. Storm-induced ocean hydrodynamics conditions were generated using FVCOM. FVCOM-ICM was externally coupled to simulate the fate and transport of radionuclides. Given that the 239 Pu is the largest inventory of the lagoon and Atoll islands, the model results show the highest average 239 Pu concentration that an island may be exposed to is 3.25E-4 Bq/m 3 (becquerel per cubic meters), which is an increase of 84 times the average baseline/existing 239 Pu concentration without the storm conditions. The overall increase in 239 Pu average over all the islands of Atoll is about 20 folds relative to the baseline concentration. Despite the high relative increase ratios, the significantly low activity concentrations may not pose an immediate exposure risk. However, due to the limitations of the study and uncertainties/biases in the historical data used, further research supported by field surveys to better characterize the current contamination level may be needed to make more accurate predictions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2014

Real Time Volcanic Cloud Products and Predictions for Aviation Alerts

Volcanic eruptions can inject significant amounts of sulfur dioxide (SO2) and volcanic ash into the atmosphere, posing a substantial risk to aviation safety. Ingesting near-real time and Direct Readout satellite volcanic cloud data is vital for improving reliability of volcanic ash forecasts and mitigating the effects of volcanic eruptions on aviation and the economy. NASA volcanic products from the Ozone Monitoring Insrument (OMI) aboard the Aura satellite have been incorporated into Decision Support Systems of many operational agencies. With the Aura mission approaching its 10th anniversary, there is an urgent need to replace OMI data with those from the next generation operational NASA/NOAA Suomi National Polar Partnership (SNPP) satellite. The data provided from these instruments are being incorporated into forecasting models to provide quantitative ash forecasts for air traffic management. This study demonstrates the feasibility of the volcanic near-real time and Direct Readout data products from the new Ozone Monitoring and Profiling Suite (OMPS) ultraviolet sensor onboard SNPP for monitoring and forecasting volcanic clouds. The transition of NASA data production to our operational partners is outlined. Satellite observations are used to constrain volcanic cloud simulations and improve estimates of eruption parameters, resulting in more accurate forecasts. This is demonstrated for the 2012 eruption of Copahue. Volcanic eruptions are modeled using the Goddard Earth Observing System, Version 5 (GEOS-5) and the Goddard Chemistry Aerosol and Radiation Transport (GOCART) model. A hindcast of the disruptive eruption from Iceland's Eyjafjallajokull is used to estimate aviation re-routing costs using Metron Aviation's ATM Tools.

Volcanic ash↗
DOE OSTI2021

SDSS-IV MaNGA: Modeling the Spectral Line-spread Function to Subpercent Accuracy

The Sloan Digital Sky Survey IV Mapping Nearby Galaxies at APO (MaNGA) program has been operating from 2014 to 2020, and has now observed a sample of 9269 galaxies in the low redshift universe (z ∼ 0.05) with integral-field spectroscopy. With rest-optical (λλ0.36–1.0 μm) spectral resolution R ∼ 2000 the instrumental spectral line-spread function (LSF) typically has 1σ width of about 70 km s{sup −1}, which poses a challenge for the study of the typically 20–30 km s{sup −1} velocity dispersion of the ionized gas in present-day disk galaxies. In this contribution, we present a major revision of the MaNGA data pipeline architecture, focusing particularly on a variety of factors impacting the effective LSF (e.g., under-sampling, spectral rectification, and data cube construction). Through comparison with external assessments of the MaNGA data provided by substantially higher-resolution R ∼ 10,000 instruments, we demonstrate that the revised MPL-10 pipeline measures the instrumental LSF sufficiently accurately (≤0.6% systematic, 2% random around the wavelength of Hα) that it enables reliable measurements of astrophysical velocity dispersions σ {sub Hα} ∼ 20 km s{sup −1} for spaxels with emission lines detected at signal-to-noise ratio > 50. Velocity dispersions derived from [O II], Hβ, [O III], [N II], and [S II] are consistent with those derived from Hα to within about 2% at σ {sub Hα} > 30 km s{sup −1}. Although the impact of these changes to the estimated LSF will be minimal at velocity dispersions greater than about 100 km s{sup −1}, scientific results from previous data releases that are based on dispersions far below the instrumental resolution should be reevaluated.

47 OTHER INSTRUMENTATION↗
NASA NTRS2016

Solar vs. Fission Surface Power for Mars

A multi-discipline team of experts from the National Aeronautics and Space Administration (NASA) developed Mars surface power system point design solutions for two conceptual missions. The primary goal of this study was to compare the relative merits of solar- versus fission-powered versions of each surface mission. First, the team compared three different solar power options against a fission power system concept for a sub-scale, uncrewed demonstration mission. The 4.5 meter (m) diameter pathfinder lander's primary mission would be to demonstrate Mars entry, descent, and landing techniques. Once on the Martian surface, the lander's In Situ Resource Utilization (ISRU) payload would demonstrate liquid oxygen propellant production using atmospheric resources. For the purpose of this exercise, location was assumed to be at the Martian equator. The three solar concepts considered included a system that only operated during daylight hours (at roughly half the daily propellant production rate of a round-the-clock fission design), a battery-augmented system that operated through the night (matching the fission concept's propellant production rate), and a system that operated only during daylight, but at a higher rate (again, matching the fission concept's propellant production rate). Including 30% mass growth allowance, total payload masses for the three solar concepts ranged from 1,116 to 2,396 kg, versus the 2,686 kg fission power scheme. However, solar power masses are expected to approach or exceed the fission payload mass at landing sites further from the equator, making landing site selection a key driver in the final power system decision. The team also noted that detailed reliability analysis should be performed on daytime-only solar power schemes to assess potential issues with frequent ISRU system on/off cycling. Next, the team developed a solar-powered point design solution for a conceptual four-crew, 500-day surface mission consisting of up to four landers per crewed expedition mission. Unlike the demonstration mission, a lengthy power outage due to the global dust storms that are known to occur on Mars would pose a safety hazard to a crewed mission. A similar fission versus solar power trade study performed by NASA in 2007 concluded that fission power was more reliable-with a much lower mass penalty-than solar power for this application. However, recent advances in solar cell and energy storage technologies and changes in operational assumptions prompted NASA to revisit the analysis. For the purpose of this exercise a particular landing site at Jezero Crater, located at 18o north latitude, was assumed. A fission power system consisting of four each 10 kW Kilopower fission reactors was compared to a distributed network of Orion-derived Ultraflex solar arrays and Lithium ion batteries mounted on every lander. The team found that a solar power system mass of about 9,800 kg would provide the 22 kilowatts (kW) keep-alive power needed to survive a dust storm lasting up to 120-days at average optical depth of 5, and 35 kW peak power for normal operations under clear skies. Although this is less than half the mass estimated during the 2007 work (which assumed latitudes up to 30o) it is still more than the 7,000 kg mass of the fission system which provides full power regardless of dust storm conditions.

Rucker, Michelle A.↗
DOE OSTI2023

An automated and portable method for selecting an optimal GPU frequency

Power consumption poses a significant challenge in current and emerging graphics processing unit (GPU) enabled high-performance computing systems. In modern GPUs, dynamic voltage frequency scaling (DVFS) appears to be a reliable control to regulate power consumption and performance. However, the DVFS design space is large - hence, brute-force approaches are infeasible to select the optimal frequency. Furthermore, no single frequency can be universally optimal for applications with varying computational intensities. Thus, the application's complexity and the availability of a wide range of frequency settings are a challenge in selecting the optimal frequency configuration for a given GPU workload. To that end, this paper proposes a systematic approach that consists of three steps. The feature characterization study identifies the fine-grain GPU utilization metrics that influence the power consumption and execution time of a given workload. To understand the performance, power, and energy consumption behaviors of a workload across GPU's DVFS design space, we derived analytical power and performance models using the identified fine-grain features. Here, it is shown that the same set of GPU utilization metrics can estimate both the power consumption and execution time while being agnostic of changes to frequency and input sizes. Applying a power control with the single objective of reducing power may cause performance degradation, leading to more energy consumption. A multi-objective approach is proposed to select the optimal GPU DVFS configuration for a workload that reduces power consumption with negligible degradation in performance. The evaluation was conducted using SPEC ACCEL benchmarks and three real applications - NAMD LAMMPS, and LSTM on NVIDIA GV100, GA100, and AMD MI210 GPUs. On average, real applications showed 29.6% energy savings with a performance loss of 5.2% on GA100 and 22.6% energy savings with a performance loss of 4.7% on GV100. Moreover, the proposed models are portable to real applications, GPU architectures, and vendors, and require metric collection at only the default frequency rather than all supported DVFS configurations. Additionally, we conducted a comparison between our models and the GPU assembly instructions (PTX)-based static models. The results revealed a significant reduction in the average error rates, with a decrease from 19.7% to 3.1% for power models and from 29.4% to 5.2% for performance models.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2005

Solar Sailing Kinetic Energy Interceptor (KEI) Mission for Impacting/Deflecting Near-Earth Asteroids

A solar sailing mission architecture, which requires a t least ten 160-m, 300-kg solar sail spacecraft with a characteristic acceleration of 0.5 mm/sqs, is proposed as a realistic near- term option for mitigating the threat posed by near-Earth asteroids (NEAs). Its mission feasibility is demonstrated for a fictional asteroid mitigation problem created by AIAA. This problem assumes that a 200-m asteroid, designated 2004WR, was detected on July 4, 2004, and that the expected impact will occur on January 14, 2015. The solar sailing phase of the proposed mission for the AIAA asteroid mitigation problem is comprised of the initial cruise phase from 1 AU t o 0.25 AU (1.5 years), the cranking orbit phase (3.5 years), and the retrograde orbit phase (1 year) prior to impacting the target asteroid at its perihelion (0.75 AU from the sun) on January 1, 2012. The proposed mission will require at least ten kinetic energy interceptor (KEI) solar sail spacecraft. Each KEI sailcraft consists of a 160- m, 150-kg solar sail and a 150-kg microsatellite impactor. The impactor is to be separated from a large solar sail prior to impacting the 200-m target asteroid at its perihelion. Each 150-kg microsatellite impactor, with a relative impact velocity of at least 70 km/s, will cause a conservatively estimated AV of 0.3 cm/s in the trajectory of the 200-m target asteroid, due largely to the impulsive effect of material ejected from the newly-formed crater. The deflection caused by a single impactor will increase the Earth-miss-distance by 0.45Re (where Re denotes the Earth radius of 6,378 km). Therefore, at least ten KEI sailcraft will be required for consecutive impacts, but probably without causing fragmentation, to increase the total Earth-miss-distance by 4.5Re. This miss-distance increase of 29,000 km is outside of a typical uncertainty/error of about 10,000 km in predicting the Earth-miss- distance. A conventional Delta I1 2925 launch vehicle is capable of injecting at least two KEI sailcraft into an Earth escaping orbit. A 40-m solar sail is currently being developed by NASA and industries for a possible flight validation experiment within 10 years, and a 160-m solar sail is expected to be available within 20 years.

Wie, Bong↗
NASA NTRS2015

Use of Proton SEE Data as a Proxy for Bounding Heavy-Ion SEE Susceptibility

Although heavy-ion single-event effects (SEE) pose serious threats to semiconductor devices in space, many missions face difficulties testing such devices at heavy-ion accelerators. Low-cost missions often find such testing too costly. Even well funded missions face issues testing commercial off the shelf (COTS) due to packaging and integration. Some missions wish to fly COTS systems with little insight into their components. Heavy-ion testing such parts and systems requires access to expensive and hard-to-access ultra-high energy ion accelerators, or significant system modification. To avoid these problems, some have proposed using recoil ions from high-energy protons as a proxy to bound heavy-ion SEE rates.

reliability estimation↗