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At least 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Interannual Variability in the Thermal Habitat of Alexandrium catenella in the Bay of Fundy and the Implications of Climate Change

Globally, harmful algal blooms (HABs) are an increasing problem. In the Gulf of Maine and Bay of Fundy, blooms of the toxic dinoflagellate Alexandrium catenella are annually recurrent phenomena. As this region is one of the most rapidly warming areas of the global ocean, an improved understanding of the mechanisms driving the initiation of local A. catenella blooms, their interannual variability and the implications of future climate change is critical to local monitoring strategies and marine resources management. A 27-year (1988–2014) time series of weekly A. catenella cell counts from the Bay of Fundy and concurrent satellite-measured sea surface temperature, freshwater discharge from the St. John River and wind-driven turbulence are compared to assess their relationship to variability in bloom phenology metrics. The mean thermal habitat associated with early detection of A. catenella is 6.5 ± 1.6°C, whereas that of bloom initiation averages 9.2 ± 1.5°C. Both thermal habitats for A. catenella are trending earlier over the study period. Bloom initiations that precede the arrival of the thermal habitat mean (occur in colder water) are associated with higher spring freshwater discharge and are generally weaker blooms. Increased spring freshwater discharge is also associated with earlier bloom initiation and earlier maximum concentration dates. No significant relationship was observed with the strength of wind-driven mixing. Removal of the mean thermal seasonal cycle shows that surface temperature anomalies have a strong negative relationship to the bloom phenology metrics and arrival of thermal habitat: warmer years are linked to earlier arrival of thermal habitats (∼12 days °C–1) and earlier detection and bloom initiation dates (∼33 days °C–1). Using these relationships and present trends in Bay of Fundy surface temperature warming over the period 1982–2019, we project the arrival dates of bloom thermal habitat and bloom phenology metrics out to the middle of this century. Based on current rates of sea surface temperature change, bloom phenology metrics (e.g., bloom initiation, early detection), can be expected to shift 1–2 months earlier in the season by mid-century. Such changes in the phenology of A. catenella blooms will need to be incorporated into both monitoring strategies and forecasting models for the region.

Andre F. Bucci↗
NASA NTRS2000

Quantum Decision Maker Theory and Simulation

A quantum device simulating the human decision making process is introduced. It consists of quantum recurrent nets generating stochastic processes which represent the motor dynamics, and of classical neural nets describing the evolution of probabilities of these processes which represent the mental dynamics.

Quantum↗
NASA NTRS1999

Quantum Decision-Maker

A quantum device simulating human decision making process is introduced. It consists of quantum recurrent nets generating stochastic processes which represent the motor dynamics, and a classical neural nets describing evolution of probabilities of these processes which represent the mental dynamics.

quantum↗
NASA NTRS1995

Ulysses Solar Wind Plasma Observations at High Southerly Latitudes

The solar wind is an ionized gas flowing from the solar corona and consisting primarily of free electrons, protons, and doubly-ionized helium. Ulysses solar wind plasma observations through -80.2 degrees solar latitude and continuing equatorward to -40.1 degrees are summarized. Recurrent high-speed streams and corotating interaction regions dominate at mid latitudes. Observation and analysis is given.

Ulysses↗
NASA NTRS2013

Towards a decision support system for space flight operations

The Mission Operations Directorate (MOD) at the Johnson Space Center (JSC) has put in place a Model Based Systems Engineering (MBSE) technological framework for the development and execution of the Flight Production Process (FPP). This framework has provided much added value and return on investment to date. This paper describes a vision for a model based Decision Support System (DSS) for the development and execution of the FPP and its design and development process. The envisioned system extends the existing MBSE methodology and technological framework which is currently in use. The MBSE technological framework currently in place enables the systematic collection and integration of data required for building an FPP model for a diverse set of missions. This framework includes the technology, people and processes required for rapid development of architectural artifacts. It is used to build a feasible FPP model for the first flight of spacecraft and for recurrent flights throughout the life of the program. This model greatly enhances our ability to effectively engage with a new customer. It provides a preliminary work breakdown structure, data flow information and a master schedule based on its existing knowledge base. These artifacts are then refined and iterated upon with the customer for the development of a robust end-to-end, high-level integrated master schedule and its associated dependencies. The vision is to enhance this framework to enable its application for uncertainty management, decision support and optimization of the design and execution of the FPP by the program. Furthermore, this enhanced framework will enable the agile response and redesign of the FPP based on observed system behavior. The differences between the anticipated system behavior and the observed behavior may be due to the processing of tasks internally, or due to external factors such as changes in program requirements or conditions associated with other organizations that are outside of MOD. The paper provides a roadmap for the four increments of this vision. These increments include (1) the existing capabilities (2) hardware and software system components and interfaces with the NASA ground system, (3) uncertainty management and (4) re-planning and automated execution. Each of these increments provides value independently; but some may also enable building of a subsequent increment.

Ruszkowski, James↗
NASA NTRS2018

Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding

As spacecraft send back increasing amounts of telemetry data, improved anomaly detection systems are needed to lessen the monitoring burden placed on operations engineers and reduce operational risk. Current spacecraft monitoring systems only target a subset of anomaly types and often require costly expert knowledge to develop and maintain due to challenges involving scale and complexity. We demonstrate the effectiveness of Long Short-Term Memory (LSTMs) networks, a type of Recurrent Neural Network (RNN), in overcoming these issues using expert-labeled telemetry anomaly data from the Soil Moisture Active Passive (SMAP) satellite and the Mars Science Laboratory (MSL) rover, Curiosity. We also propose a complementary unsupervised and nonparametric anomaly thresholding approach developed during a pilot implementation of an anomaly detection system for SMAP, and offer false positive mitigation strategies along with other key improvements and lessons learned during development.

Soderstrom, Tom↗
NASA NTRS2018

Analytic Expressions for Derivatives from Series Solutions to the Three Body Problem

This paper presents a notation system to facilitate to solution of differential equations via Taylor series expansions and applies it to solve the circular restricted three body problem. Unlike previous Taylor series methods in the astrodynamics literature, computer algebra solvers are not used. Instead the notation system allows one to solve a system of differential equations analytically “by hand” without resorting to computer algebra software. This method produces recurrence relations explicitly in terms of a sequence of derivatives of the state with respect to time for the coefficients of Taylor Series solutions that can be evaluated numerically or manipulated further to investigate properties of the solution. For example, additional derivatives with respect to other parameters may also be found, including those that describe the dependence of the solution on initial conditions.

Strange, Nathan↗
NASA NTRS2021

The RS CVn Type star GT Mus Shows Most Energetic X-ray Flares Throughout the 2010s

We report that the RS CVn-type star GT Mus (HR4492, HD101379+HD101380) was the most active star in the X-ray sky in the last decade in terms of the scale of recurrent energetic flares. We detected eleven flares from GT Mus in eight years of observations with MAXI (Monitor of All-sky X-ray Image)from 2009 August to 2017 August. The detected flare peak luminosities were 1–4×1033erg s−1in the2.0–20.0 keV band for its distance of 109.6 pc. The durations of the flares ranged from 1–4 days. The flare energies are calculated to be 1–8×1038erg, which are at the upper end of the observed stellar flare. We carried out X-ray follow-up observations for one of the eleven flares with NICER (Neutron star Interior Composition Explorer) on 2017 July 18 and found that the flare cooled quasi-statically. On the basis of a quasi-static cooling model, the flare loop length is derived to be 4×1012cm (or60R). The electron density is derived to be1×1010cm−3, which is consistent with the typical value of solar and stellar flares (1010−11cm−3).

Ryo Sasaki↗
NASA NTRS2019

Subsurface Cl-bearing salts as potential contributors to recurring slope lineae (RSL) on Mars

We report laboratory experimental results that support a brine-related hypothesis for the recurring slope lineae (RSL) on Mars in which the subsurface Cl-salts, i.e., hydrous chlorides and oxychlorine salts (HyCOS) are the potential source materials. Our experiments revealed that within the observed RSL temperature window TRSL (250–300 K), the deliquescence of HyCOS could occur in relative humidity ranges (RH≥22%–46%) much lower than those for hydrous (Mg, Fe)-sulfates (RH≥75%–96%). In addition, we demonstrated that the RH values kept by common HyCOS and hydrous sulfates in enclosures have a general trend as RH(sulfates) > RH(perchlorates) > RH(chlorides) (with same type of cation) in wide T range. It means that the required RH range for a Cl-bearing salt to deliquescence can be satisfied by a co-existing salt of different type, e.g., in the subsurface layers of mixed salts on Mars. Furthermore, we found a strong temperature dependence of the deliquescence rates for all tested HyCOS, e.g., a duration of 1–5 sols for all HyCOS at the high end (300 K) of TRSL, and of 20–70 sols for all tested HyCOS (except NaClO4·H2O) at the low end (250 K) of TRSL, which is consistent with the observed seasonal behavior of RSL on Mars. From a mass-balance point of view, the currently observed evidences on Mars do not support a fully-brine-wetted track model, thus we suggest a brine-triggered granular-flow model for the most RSL. Considering the recurrence of RSL in consecutive martian years, our experimental results support the rehydration of remnant HyCOS layers during a martian cold season through H2O vapor-to-salt direct interaction. We found that the evidences of HyCOS rehydration under Mars relevant P-T-RH conditions are detectable in a few minutes by in situ Raman spectroscopy. This rehydration would facilitate the recharge of H2O back into subsurface HyCOS, which could serve as the source material to trigger RSL in a subsequent warm season. The major limiting factor for this rehydration is the H2O supply, i.e., the H2O vapor density carried by current Mars atmospheric circulation and the diffusion rate of H2O vapor into the salt-rich subsurface in a cold season. In a worst-case scenario, these H2O supplies can support a maximum increase of hydration degrees of two for totally dehydrated HyCOS, whereas the full rehydration of subsurface HyCOS layers can be easily reached during a>30° obliquity period that has H2O vapor density 10× to 20× times the value of current obliquity. Overall, our results imply the existence of a large amount of Cl-bearing salts in the subsurface at RSL sites.

Alian Wang↗
NASA NTRS2020

The Fast Radio Burst Luminosity Function and Death Line in the Low-Twist Magnetar Model

We explore the burst energy distribution of fast radio bursts(FRBs)in the low-twist magnetar model ofWadiasingh & Timokhin(WT19). Motivated by the power-lawfluence distributions of FRB121102, we proposean elementary model for the FRB luminosity function of individual repeaters with an inversion protocol thatdirectly relates the power-law distribution index of magnetar short burstfluences to that for FRBs. The protocolindicates that the FRB energy scales virtually linearly with crust/field dislocation amplitude, if magnetar shortbursts prevail in the magnetoelastic regime. Charge starvation in the magnetosphere during bursts(required inWT19)for individual repeaters implies the predicted burstfluence distribution is narrow,3 decades for yieldingstrains and oscillation frequencies feasible in magnetar crusts. Requiring magnetic confinement and chargestarvation, we obtain a death line for FRBs, which segregates magnetars from the normal pulsar population,suggesting only the former will host recurrent FRBs. We convolve the burst energy distribution for individualmagnetars to define the distribution of luminosities in evolved magnetar populations. The broken power-lawluminosity function’s low-energy character depends on the population model, while the high-energy index tracesthat of individual repeaters. Independent of the evolved population, the broken power-law isotropic-equivalentenergy/luminosity function peaks at∼1037–1040erg with a low-energy cutoff at∼1037erg. Lastly, we considerthe localfluence distribution of FRBs andfind that it can constrain the subset of FRB-producing magnetarprogenitors. Our model suggests that improvements in sensitivity may reveal aflattening of the global FRBfluencedistribution and saturation in FRB rates.

Cosmology↗
NASA NTRS2021

Perceive: Proactive Exploration of Risky Concept Emergence for Identifying Vulnerabilities & Exposures

National databases that collect various kinds of textual threat reports such as ASRS, CERT, and NVD manually process their reports individually. They then offer data products to disseminate the aggregate information, like newsletters, alerts or individual report searching. The goal of this research is to connect these individual reports thematically and temporally to identify emerging or recurring threats, by analyzing large collections of text, source code, collaboration and communication patterns. This capability, I argue, enables us to identify the emergence and recurrence of such themes, and the contexts in which they re-occur, facilitating faster and more capable mitigation. I propose two models to shed light on this goal: An empirical model of vulnerabilities as bugs, the commit flow model, and one of the vulnerabilities and aviation safety threats as topics, the topic flow model. I use as gold standard existing manual workflows in both domains, reflected in the existing data products by these organizations, and empirically evaluate if the automated model scan match or outperform existing manual practices.

ASRS↗
NASA NTRSDate not supplied

Li-ion Battery Aging with Hybrid Physics-Informed Neural Networks and Fleet-wide Data

In this work, we propose a hybrid model for Li-ion battery discharge and aging prediction that leverages fleet-wide data to predict future capacity drops.The model is built upon an hybrid approach merging physics-based and empirical equations, as well as neural network models in a recurrent neural network cell. The hybrid physics-informed neural network can predict voltage discharge cycles given the loading profile, and estimate the used capacity of the battery under random-loading conditions by tracking aging parameters connected to the residual capacity of the battery. By merging information on the battery aging parameters with existing fleet-wide aging data, the model can predict the future residual capacity of the battery that is being monitored, and therefore enable predictions of voltage discharge curves far ahead in the battery life cycle. We validated the approach using the NASA Prognostics Data Repository Battery data-set, which contains experimental data on Li-ion batteries discharged at random loading conditions in a controlled environment. The approach also allows the identification of discrepancies between the battery aging trend and the trend observed at the fleet level, so that batteries behaving differently from the rest of the fleet can be subject to closer monitoring and further testing to refine predictions.

PINN↗
NASA NTRS2021

A NICER Look at Thermonuclear X-ray Bursts from Aql X-1

We present spectral and temporal properties of all the thermonuclear X-ray bursts observed from Aql X-1 by the Neutron StarInterior and Composition Explorer (NICER) between 2017 July and 2021 April. This is the first systematic investigation of alarge sample of type I X-ray bursts from Aql X-1 with improved sensitivity at low energies. We detect 22 X-ray bursts includingtwo short recurrence burst events in which the separation was only 451 s and 496 s. We perform time resolved spectroscopy of thebursts using the fixed and scaled background (fa method) approaches. We show that the use of a scaling factor to the pre-burstemission is the statistically preferred model in about 68% of all the spectra compared to the fixed background approach. Typically the fa values are clustered around 1–3, but can reach up to 11 in a burst where photospheric radius expansion is observed. Such fa values indicate a very significant increase in the pre-burst emission especially at around the peak flux moments of the bursts.We show that the use of the fa factor alters the best fit spectral parameters of the burst emission. Finally, we employed a reflectionmodel instead of scaling the pre-burst emission. We show that reflection models also do fit the spectra and improve the goodnessof the fits. In all cases we see that the disc is highly ionized by the burst emission and the fraction of the reprocessed emission tothe incident burst flux is typically clustered around 20%

Tolga Güver↗
NASA NTRS2022

Satellite-derived Cyanobacteria Frequency and Magnitude in Headwaters & Near-dam Reservoir Surface Waters of the Southern U.S.

Reservoirs are dominant features of the modern hydrologic landscape and provide vital services. However, the unique morphology of reservoirs can create suitable conditions for excessive algae growth and associated cyanobacteria blooms in shallow in-flow reservoir locations by providing warm water environments with relatively high nutrient inputs, deposition, and nutrient storage. Cyanobacteria harmful algal blooms (cyanoHAB) are costly water management issues and bloom recurrence is associated with economic costs and negative impacts to human, animal, and environmental health. As cyanoHAB occurrence varies substantially within different regions of a water body, understanding in-lake cyanoHAB spatial dynamics is essential to guide reservoir monitoring and mitigate potential public exposure to cyanotoxins. Cloud-based computational processing power and high temporal frequency of satellites enables advanced pixel-based spatial analysis of cyanoHAB frequency and quantitative assessment of reservoir headwater in-flows compared to near-dam surface waters of individual reservoirs. Additionally, extensive spatial coverage of satellite imagery allows for evaluation of spatial trends across many dozens of reservoir sites. Surface water cyanobacteria concentrations for sixty reservoirs in the southern U.S. were estimated using 300 m resolution European Space Agency (ESA) Ocean and Land Colour Instrument (OLCI) satellite sensor for a five year period (May 2016–April 2021). Of the reservoirs studied, spatial analysis of OLCI data revealed 98% had more frequent cyanoHAB occurrence above the concentration of >100,000 cells/mL in headwaters compared to near-dam surface waters (P < 0.001). Headwaters exhibited greater seasonal variability with more frequent and higher magnitude cyanoHABs occurring mid-summer to fall. Examination of reservoirs identified extremely high concentration cyanobacteria events (>1,000,000 cells/mL) occurring in 70% of headwater locations while only 30% of near-dam locations exceeded this threshold. Wilcoxon signed-rank tests of cyanoHAB magnitudes using paired-observations (dates with observations in both a reservoir's headwater and near-dam locations) confirmed significantly higher concentrations in headwater versus near-dam locations (p < 0.001).

Amber R Ignatius↗
NASA NTRS2022

Satellite-derived cyanobacteria frequency and magnitude in headwaters & near-dam reservoir surface waters of the Southern U.S.

Reservoirs are dominant features of the modern hydrologic landscape and provide vital services. However, the unique morphology of reservoirs can create suitable conditions for excessive algae growth and associated cyanobacteria blooms in shallow in-flow reservoir locations by providing warm water environments with relatively high nutrient inputs, deposition, and nutrient storage. Cyanobacteria harmful algal blooms (cyanoHAB) are costly water management issues and bloom recurrence is associated with economic costs and negative impacts to human, animal, and environmental health. As cyanoHAB occurrence varies substantially within different regions of a water body, understanding in-lake cyanoHAB spatial dynamics is essential to guide reservoir monitoring and mitigate potential public exposure to cyanotoxins. Cloud-based computational processing power and high temporal frequency of satellites enables advanced pixel-based spatial analysis of cyanoHAB frequency and quantitative assessment of reservoir headwater in-flows compared to near-dam surface waters of individual reservoirs. Additionally, extensive spatial coverage of satellite imagery allows for evaluation of spatial trends across many dozens of reservoir sites. Surface water cyanobacteria concentrations for sixty reservoirs in the southern U.S. were estimated using 300m resolution European Space Agency (ESA) Ocean and Land Colour Instrument (OLCI) satellite sensor for a five year period (May 2016–April 2021). Of the reservoirs studied, spatial analysis of OLCI data revealed 98% had more frequent cyanoHAB occurrence above the concentration of >100,000 cells/mL in headwaters compared to near-dam surface waters (P < 0.001). Headwaters exhibited greater seasonal variability with more frequent and higher magnitude cyanoHABs occurring mid-summer to fall. Examination of reservoirs identified extremely high concentration cyanobacteria events (>1,000,000 cells/mL) occurring in 70% of headwater locations while only 30% of near-dam locations exceeded this threshold. Wilcoxon signed-rank tests of cyanoHAB magnitudes using paired-observations (dates with observations in both a reservoir's headwater and near-dam locations) confirmed significantly higher concentrations in headwater versus near-dam locations (p < 0.001).

cyanobacteria↗
NASA NTRSDate not supplied

Dense Feature Tracking of Atmospheric Winds with Deep Optical Flow

Atmospheric winds are a key physical phenomenon impacting natural hazards, energy transport, ocean currents, large-scale circulation, and ecosystem fluxes. Observing winds is a complex process and presents a large gap in NASA’s Earth Observation System. Atmospheric motion vectors (AMVs) aim to fill this gap by making numerical estimates of cloud movement between sequences of multi-spectral satellite images, tracking clouds and water vapor. Recent imaging hardware and software advancements have enabled the use of numerical optical flow techniques to produce accurate and dense vector fields outperforming traditional methods. This work presents WindFlow as the first machine learning based system for feature tracking atmospheric motion using optical flow. Due to the lack of large-scale satellite-based observations, we leverage high-resolution numerical simulations from NASA's GEOS-5 Nature Run to perform supervised learning and transfer to satellite images. We demonstrate that our approach using deep learning based optical flow scales to ultra-high-resolution images of size 2881x5760 with less than 1 m/s bias and 2.5 m/s average error. Four network and learning architectures are compared and it is found that recurrent all-pairs field transforms (RAFT) produces the lowest errors on all metrics for wind speed and direction. Results on held out numerical outputs shows RAFT's good performance in each of the spatial, temporal, and physical dimensions. A comparison between WindFlow and an operational AMV product against rawinsonde observations show that RAFT transfers across simulations and thermal infrared satellite observations. This work shows that machine learning based optical flow is an efficient approach to generating robust feature tracking for AMVs consistently over large regions.

Atmospheric winds↗
NASA NTRS2023

Impact of Pacific Ocean Heatwaves on Phytoplankton Community Composition

Since 2013, marine heatwaves have become recurrent throughout the equatorial and northeastern Pacific Ocean and are expected to increase in intensity relative to historic norms. Among the ecological ramifications associated with these high temperature anomalies are increased mortality of higher trophic organisms such as marine mammals and seabirds, which are likely triggered by changes in the composition of phytoplankton, the base of the marine trophic food web. Here, we assimilated satellite ocean color data into an ocean biogeochemical model to describe changes in the abundance of phytoplankton functional types (PFTs) during the last decade's (2010s) warm anomalies in the equatorial and northeastern Pacific Ocean. We find important changes associated with the "Blob" warm anomaly in the Gulf of Alaska, where reduced silica supply led to a switch in community composition from diatoms to dinoagellates, resulting in an increase in surface ocean chlorophyll during the Summer-Fall of 2014. A more dramatic change was observed in the equatorial Pacific, where the extreme warm conditions of the 2016 El Niño resulted in a major decline of about 40 % in surface chlorophyll, which was associated with a nearly total collapse in diatoms.

Pacific Ocean↗