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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Prediction of Tensile Strength of Friction Stir Weld Joints with Adaptive Neuro-Fuzzy Inference System (ANFIS) and Neural Network

Friction-stir-welding (FSW) is a solid-state joining process where joint properties are dependent on welding process parameters. In the current study three critical process parameters including spindle speed (𝑁), plunge force (𝐹𝑧), and welding speed (𝑉) are considered key factors in the determination of ultimate tensile strength (UTS) of welded aluminum alloy joints. A total of 73 weld schedules were welded and tensile properties were subsequently obtained experimentally. It is observed that all three process parameters have direct influence on UTS of the welded joints. Utilizing experimental data, an optimized adaptive neuro-fuzzy inference system (ANFIS) model has been developed to predict UTS of FSW joints. A total of 1200 models were developed by varying the number of membership functions (MFs), type of MFs, and combination of four input variables (𝑁,𝑉,𝐹𝑧,𝐸𝐹𝐼) utilizing a MATLAB platform. Note EFI denotes an empirical force index derived from the three process parameters. For comparison, optimized artificial neural network (ANN) models were also developed to predict UTS from FSW process parameters. By comparing ANFIS and ANN predicted results, it was found that optimized ANFIS models provide better results than ANN. This newly developed best ANFIS model could be utilized for prediction of UTS of FSW joints.

Mohammad W Dewan↗
NASA NTRS2015

Exercise Sensing and Pose Recovery Inference Tool (ESPRIT) - A Compact Stereo-based Motion Capture Solution For Exercise Monitoring

Crew exercise is important during long-duration space flight not only for maintaining health and fitness but also for preventing adverse health problems, such as losses in muscle strength and bone density. Monitoring crew exercise via motion capture and kinematic analysis aids understanding of the effects of microgravity on exercise and helps ensure that exercise prescriptions are effective. Intelligent Automation, Inc., has developed ESPRIT to monitor exercise activities, detect body markers, extract image features, and recover three-dimensional (3D) kinematic body poses. The system relies on prior knowledge and modeling of the human body and on advanced statistical inference techniques to achieve robust and accurate motion capture. In Phase I, the company demonstrated motion capture of several exercises, including walking, curling, and dead lifting. Phase II efforts focused on enhancing algorithms and delivering an ESPRIT prototype for testing and demonstration.

Lee, Mun Wai↗
NASA NTRS2016

Supplement: The Rate of Binary Black Hole Mergers Inferred from Advanced LIGO Observations Surrounding GW150914

This article provides supplemental information for a Letter reporting the rate of (BBH) coalescences inferred from 16 days of coincident Advanced LIGO observations surrounding the transient (GW) signal GW150914. In that work wereported various rate estimates whose 90% confidence intervals fell in the range 2600 Gpc(exp -3) yr(exp -1). Here we givedetails on our method and computations, including information about our search pipelines, a derivation of ourlikelihood function for the analysis, a description of the astrophysical search trigger distribution expected frommerging BBHs, details on our computational methods, a description of the effects and our model for calibrationuncertainty, and an analytic method for estimating our detector sensitivity, which is calibrated to our measurements.

gravitational waves – stars: black holes↗
NASA NTRS2018

Atmospheric Energy Deposition Modeling and Inference for Varied Meteoroid Structures

Asteroids populations are highly diverse, ranging from coherent monoliths to loosely-bound rubble piles with a broad range of material and compositional properties. These different structures and properties could significantly affect how an asteroid breaks up and deposits energy in the atmosphere, and how much ground damage may occur from resulting blast waves. We have previously developed a fragment-cloud model (FCM) for assessing the atmospheric breakup and energy deposition of asteroids striking Earth. The approach represents ranges of breakup characteristics by combining progressive fragmentation with releases of variable fractions of debris and larger discrete fragments. In this work, we have extended the FCM to also represent asteroids with varied initial structures, such as rubble piles or fractured bodies. We have used the extended FCM to model the Chelyabinsk, Benesov, Kosice, and Tagish Lake meteors, and have obtained excellent matches to energy deposition profiles derived from their light curves. These matches provide validation for the FCM approach, help guide further model refinements, and enable inferences about pre-entry structure and breakup behavior. Results highlight differences in the amount of small debris vs. discrete fragments in matching the various flare characteristics of each meteor. The Chelyabinsk flares were best represented using relatively high debris fractions, while Kosice and Benesov cases were more notably driven by their discrete fragmentation characteristics, perhaps indicating more cohesive initial structures. Tagish Lake exhibited a combination of these characteristics, with lower-debris fragmentation at high altitudes followed by sudden disintegration into small debris in the lower flares. Results from all cases also suggest that lower ablation coefficients and debris spread rates may be more appropriate for the way in which debris clouds are represented in FCM, offering an avenue for future model refinement.

Deposition↗
NASA NTRS2018

An Artificial Neural Network for Inferring Solar Wind Proxies at Mars

We present a novel method to determine solar wind proxies from sheath measurements at Mars. Specifically, we develop an artificial neural network (ANN) to simultaneously infer seven solar wind proxies: ion density, ion speed, ion temperature, and interplanetary magnetic field magnitude and its vector components, using spacecraft measurements of ion moments, magnetic field magnitude, magnetic field components in the sheath, and the solar extreme ultraviolet flux. The ANN was trained and tested using3 years of data from the Mars Atmosphere and Volatile EvolutioN (MAVEN) spacecraft. When compared with MAVEN spacecraft's in situ measured values of the solar wind parameters, we find that the ANN proxies for the solar wind ion density, ion speed, ion temperature, and interplanetary magnetic field magnitude havepercentage differences of 50% or less for 84.4%, 99.9%, 86.8%, and 79.8% of the instances, respectively. Forthe cone angle and clock angle proxies, 69.1% and 53.3% of instances, respectively, have angle differences of 30* or less.

Neural Network↗
NASA NTRS2019

Parameters Inference and Model Reduction for the Single-Particle Model of Li Ion Cells

The Single-Particle Model (SPM) of Li ion cell is a computationally efficient model for simulating Li ion cell for weak to moderate currents. The model depends n a number of parameters describing the geometry and material properties of a cell components. In order to apply the model to simulating a cell, the best-fit parametric values have to be inferred from a constant discharge data. We report our efforts to determine the best-fit set for 18650 LP batteries. We found that rather than being best-fit by a particular point in the parametric space the data is fit equally well by an ensemble of points clustering about an effective multidimensional manifold in the parametric space. This property of the SPM is known to be shared by a multitude of the so-called "sloppy models" of complex systems, characterized by a few stiff directions in the parametric space, in which the predicted behavior varies significantly, and a number of sloppy directions in which the behavior doesn't change appreciably. Only the stiff parameters combinations are identifiable. Geometrical features of the BFM give insights to possible reduction of the SPM to a model having fewer sloppy parameters. We have constructed a hierarchy of such models. The fully reduced model depends on only stiff effective parameters which are identifiable and can be used for characterization of the battery's state of health.

Khasin, Michael↗
NASA NTRS2020

Dynamic Medical Risk Assessment Supported by Inference Networks

The Human Research Program's next generation risk model, the Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT), provides researchers with estimates of astronaut medical and health risk. MEDPRATs Susceptibility Inference Network (SIN) facilitates the dynamic component of this tool. The SIN provides MEDPRAT with a "memory" that allows the system to use knowledge of what simulation events have occurred to alter the representative probability that future simulation events will occur during a given trial. The SIN allows for the medical events being simulated to be related to, and influence the likelihood of one another, providing a more robust risk estimate. We present initial work of our efforts to mathematically quantify and represent these dependent relationships between medical events.

McIntyre, L.↗
NASA NTRS2020

Spatial Variation in Erosion Rates in Mars Equatorial Regions Inferred from Ejecta Retention of 1-3 Km Diameter Craters

The modification of impact craters has long been used to infer the geomorphic forcing on Mars [1], as well as estimate the spatial and temporal variability of this erosion and gradation [e.g., 2]. Here, we studied the population of small primary craters (1-3 km) to understand differences in ejecta retention across equatorial Mars. Specifically, we evaluated whether craters in our study population had observable ejecta deposits (defined on the basis of distinct tone or texture with respect to their surroundings).This is a proxy for the resurfacing rate because only relatively fresh craters retain their ejecta deposits. More broadly, this is part of a larger project we are undertaking [3] to examine crater morphometry and other characteristics from CTX-derived digital terrain models (DTMs), augmented by qualitative observations.

Fassett, C. I.↗
NASA NTRS2019

Midlatitude Lightning NOx Production Efficiency Inferred From OMI and WWLLN Data

Oxides of nitrogen are critical trace gases in the troposphere and are precursors for nitrate aerosol and ozone, which is an important pollutant and greenhouse gas. Lightning is the major source of NOx(NO + NO2) in the mid-to upper troposphere. We estimate the production efficiency (PE) of lightning NOx (LNOx) using satellite data from the Ozone Monitoring Instrument (OMI) and the ground-based WorldWide Lightning Location Network (WWLLN) in three northern midlatitude, primarily continental regions that include much of North America, Europe and East Asia. Data were obtained over 5 boreal summers, 2007 –2011 and comprise the largest number of midlatitude convective events to date for estimating the LNOx PE with satellite NO2and ground-based lightning measurements. In contrast to some previous studies, the algorithm assumes no minimum flash-rate threshold and estimates freshly produced LNOxby subtracting a background of aged NOx estimated from the OMI dataset itself. We infer an average value of 180 ± 100 moles LNOx produced per lightning flash. We also show evidence of a dependence of PE on lightning flash rate and find an approximate empirical power function relating moles LNOxto flashes. PE decreases by an order of magnitude for a 2-order of magnitude increase in flash rate. This phenomenon has not been reported in previous satellite LNOxstudies but is consistent with ground-based observations suggesting an inverse relationship between flash rate and size.

LNOx↗
NASA NTRSDate not supplied

MEDLI2: MISP Inferred Aerothermal Environment and Flow Transition Assessment

The Mars Entry, Descent, and Landing Instrumentation 2 (MEDLI2) sensor suite on the Mars2020 mission contained multiple sensors on the aeroshell to measure the aerothermal environment during entry into the Martian atmosphere. These sensors performed superbly and successfully returned forebody and aftbody heating measurements. Analysis of MEDLI2 data indicated flow transitioning from a laminar to turbulent state on the heatshield. No evidence of flow transition was observed on the backshell. Two methods were used to estimate flow transition times on the heatshield: (1) temperature gradient of near-surface thermocouple data and (2) heat flux gradient from an inverse reconstruction approach using thermocouple data and material response modeling. Both methods produced similar transition times with an estimated accuracy of ±1 s. To assess various transition criteria, transition parameters were evaluated at each sensor location using flow field solutions from computational fluid dynamics (CFD) simulations. The idea was to use conservative values inferred from MEDLI2 data as transition criteria for other Mars missions. To test this hypothesis, MEDLI data from the Mars Science Laboratory (MSL) mission was used to compare predicted vs. actual flow transition times. The comparisons suggest smooth wall transition criteria are not well-suited in modeling the rapid progression of a turbulent transition front. Transition criteria containing a roughness element parameter agreed better with the flight data. In summary, critical transition values derived from MEDLI2 data may be used as a starting point in constructing a flow transition model for future Mars missions.

Chun Y Tang↗
NASA NTRS2020

Conditions of Loss Cone Filling by Scattering on the Curved Field Lines for 30 keV Protons During Geomagnetic Storm as Inferred From Numerical Trajectory Tracing

The rate of pitch angle scattering on the curved magnetic field lines is well parameterized by the ratio of the minimum field line curvature radius to the maximum effective particle gyroradius (K = RC/rg). The critical value of this ratio (Kcr) corresponding to the loss cone filling is of special interest since it corresponds to the low altitude isotropic boundaries (IBs). The early theoretical estimates gave Kcr = 8, whereas recent estimations of the K parameter on the field lines corresponding to the observed IBs during the geomagnetic storms revealed KIB values in the range of 3–30. We numerically trace the trajectories of the 30 keV protons in the magnetic field of the global magnetohydrodynamic simulation of the intense storm in order to infer statistical distribution of Kcr. The electric field and effects of nonstationarity are neglected in this study. It is found that although the Kcr values do show some variations during the course of the storm, its range is rather narrow 4 < Kcr < 8. The result suggests that higher KIB values found in the observational studies, if not caused by the magnetosphere-ionosphere mapping error, should be attributed to some other mechanism of pitch angle scattering. The Kcr values tend to be lower (4–6) during the main phase because the region of low K values approaches the Earth and the equatorial loss cone size becomes larger due to a larger equatorial magnetic field in the near earth region. The remaining variation of Kcr is explained by the presence of the guide component of the magnetic field.

S Dubyagin↗
NASA NTRS2021

Bias Correction of Hydrologic Projections Strongly Impacts Inferred Climate Vulnerabilities in Institutionally Complex Water Systems

Water-resources planners use regional water management models (WMMs) to identify vulnerabilities to climate change. Frequently, dynamically downscaled climate inputs are used in conjunction with land-surface models (LSMs) to provide hydrologic streamflow projections, which serve as critical inputs for WMMs. Here, we show how even modest projection errors can strongly affect assessments of water availability and financial stability for irrigation districts in California. Specifically, our results highlight that LSM errors in projections of flood and drought extremes are highly interactive across timescales, path-dependent, and can be amplified when modeling infrastructure systems (e.g., misrepresenting banked groundwater). Common strategies for reducing errors in deterministic LSM hydrologic projections (e.g., bias correction) can themselves strongly distort projected climate vulnerabilities and misrepresent their inferred financial consequences. Overall, our results indicate a need to move beyond standard deterministic climate projection and error management frameworks that are dependent on single simulated climate change scenario outcomes.

Keyvan Malek↗
NASA NTRS2022

GEDI Launches a New Era of Biomass Inference from Space

Accurate estimation of aboveground forest biomass stocks is required to assess the impacts of land use changes such as deforestation and subsequent regrowth on concentrations of atmospheric CO 2 . The Global Ecosystem Dynamics Investigation (GEDI) is a lidar mission launched by NASA to the International Space Station in 2018. GEDI was specifically designed to retrieve vegetation structure within a novel, theoretical sampling design that explicitly quantifies biomass and its uncertainty across a variety of spatial scales. In this paper we provide the estimates of pan-tropical and temperate biomass derived from two years of GEDI observations. We present estimates of mean biomass densities at 1 km resolution, as well as estimates aggregated to the national level for every country GEDI observes, and at the sub-national level for the United States. For all estimates we provide the standard error of the mean biomass. These data serve as a baseline for current biomass stocks and their future changes, and the mission’s integrated use of formal statistical inference points the way towards the possibility of a new generation of powerful monitoring tools from space.

Ralph Dubayah↗
NASA NTRS2024

Emplacement of Shergottites in the Martian Crust Inferred From 3D Petrofabric and Crystal Size Distribution Analyses

Shergottites are mafic to ultramafic igneous rocks that represent a majority of known martian meteorites. They are subdivided into gabbroic, poikilitic, basaltic, and olivine-phyric categories based on differences in mineralogy and textures. Their geologic contexts are unknown so analyses of crystal sizes and preferred orientations have commonly been used to infer where shergottites solidified. Such environments range from subsurface cumulates to shallow intrusives to extrusive lava flows, which all have contrasting implications for interactions with crustal material, cooling histories, and potential in situ exposure at the surface. In this study, we present a novel three-dimensional (3D) approach to better understand the solidification environments of these samples and improve our knowledge of shergottites’ geologic contexts. Shape preferred orientations of most phases and crystal size distributions of late-forming minerals were measured in 3D using X-ray computed tomography (CT) on eight shergottites representing the gabbroic, poikilitic, basaltic, and olivine-phyric categories. Our analyses show that highly anisotropic, rod-like pyroxene crystals are strongly foliated in the gabbroic samples but have a weaker foliation and a mild lineation in the basaltic sample, indicating a directional flow component in the latter. Star volume distribution analyses revealed that most phases (maskelynite, pyroxene, olivine, and oxides/sulfides) preserve a foliated texture with variable strengths, and that the phases within individual samples are strongly to moderately aligned with respect to one another. In combination with relative cooling rates during the final stages of crystallization determined from interstitial oxide/sulfide crystal size distribution analyses, these results indicate that the olivine-phyric samples were emplaced as shallow intrusives (e.g., dikes/sills) and that the gabbroic, poikilitic, and basaltic samples were emplaced in deeper subsurface environments.

Martian meteorites↗
NASA NTRSDate not supplied

PM 2.5 Concentrations over Major Metropolitan Regions Inferred from Airborne High Spectral Resolution Lidar Measurements Using Machine Learning Regression

We use measurements of near-surface aerosol backscatter, extinction, and depolarization acquired by four NASA Langley Research Center airborne High Spectral Resolution Lidars (HSRLs) to develop a machine learning regression methodology to infer PM2.5 concentrations at the surface and aloft. These airborne HSRL measurements were acquired over major metropolitan regions in the United States and Asia during more than 170 flights since 2010. Hourly surface PM2.5 measurements from the EPA air quality system and similar networks in other countries acquired within 10 km and 15 minutes of these near-surface HSRL measurements are used to train models that compute PM2.5 concentrations from the HSRL measurements. We examine several regression methods and find that exponential Gaussian Process algorithms consistently give the best performance in terms of the lowest root-mean-square (RMS) errors and the highest correlations. Model performance varies significantly depending on various combinations of HSRL aerosol measurements (e.g., aerosol backscatter, extinction, depolarization, backscatter color ratios, lidar ratios, aerosol optical thickness) and retrievals (e.g., mixed layer height, aerosol type) used in the regressions. Models that use near-surface measurements of aerosol backscatter and aerosol intensive properties such as depolarization, backscatter color ratio, and lidar ratio typically give the best performance with RMS errors around 4 mg/m3 and correlation coefficients above 0.9. HSRL measurements were often acquired when the aircraft flew systematic “raster-scan” patterns for several hours over these cities. These flight patterns enabled measurements of the spatial, temporal, and vertical variabilities in the distributions of aerosol backscatter and aerosol intensive properties and allowed us to derive the corresponding variabilities in PM2.5 concentrations. We present examples of such variabilities over urban areas in the United States as well as Asia. We describe also how the distribution of surface PM2.5 varies with aerosol type and use these retrievals to examine model simulations of surface PM2.5 in these metropolitan regions. We also discuss how this methodology may be applied to measurements from satellite lidars such as CALIOP on CALIPSO and ATLID on EarthCARE.

lidar↗
DOE OSTI2024

Leveraging Hydropower Multi-Sensor Data for Inference and Age-Informed Modeling

Increased demand of operational flexibility such as faster ramp up/down in generation, and more frequent start/stops are putting hydropower plants and their associated components in unprecedented stress. Consequently, these plants are at the high risk of extended and more frequent outage to accommodate unscheduled, and unexpected maintenance. Therefore, hydropower plants are in critical need of data driven and age-informed analysis for their regular and unscheduled operation. Yet not all hydropower plants are exhaustively equipped with sensors and/or measurement streams for their respective components – demanding solutions on how to detect, identify, and locate the cause of any event from the unobservable. Idaho National Laboratory (INL) analyzed the anonymized measurements and event records from the Hydropower Research Institute (HRI) to address this issue, as part of the Water Power Technologies Office (WPTO) funded one year multi-lab project. First, we investigated how time series of multiple sensor measurements can be leveraged to identify an event “root cause” as well as to develop an inference (i.e., estimate the unobservable) problem. INL also investigated how individual hydropower components’ reaction or response times vary across the pre-event, during event, and post-event conditions – enabling the hydropower dynamic models to be age-informed. Finally, the impact of clustering multi-sensor time series on short-term vibration prediction is analyzed. INL will present key findings from these analyses and recommend next steps for stakeholder adoption.

13 HYDRO ENERGY↗
DOE OSTI2026

Automated Membership Inference Attacks: Discovering MIA Signal Computations using LLM Agents

Membership inference attacks (MIAs), which enable adversaries to determine whether specific data points were part of a model's training dataset, have emerged as an important framework to understand, assess, and quantify the potential information leakage associated with machine learning systems. Designing effective MIAs is a challenging task that usually requires extensive manual exploration of model behaviors to identify potential vulnerabilities. In this paper, we introduce AutoMIA -- a novel framework that leverages large language model (LLM) agents to automate the design and implementation of new MIA signal computations. By utilizing LLM agents, we can systematically explore a vast space of potential attack strategies, enabling the discovery of novel strategies. Our experiments demonstrate AutoMIA can successfully discover new MIAs that are specifically tailored to user-configured target model and dataset, resulting in improvements of up to 0.18 in absolute AUC over existing MIAs. This work provides the first demonstration that LLM agents can serve as an effective and scalable paradigm for designing and implementing MIAs with SOTA performance, opening up new avenues for future exploration.

Tran, Toan Viet [Emory University]↗