Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “ML”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Interpretable ML Approaches for Novel Solid State Electrolyte Design

All-solid-state batteries with Li metal anode can address the safety issues surrounding traditional Li-ion batteries as well as the demand for higher energy densities. However, the development of solid electrolytes simultaneously possessing high ionic conductivity and good chemical and electrochemical stabilities has proven to be a challenge. I will present our informatics approach to explore the Li compound space for promising solid electrolytes using high-throughput multi-property screening and interpretable machine learning. This is accomplished through the generation of a large database of battery-related materials properties of Li compounds. We use tree-based ensemble learning methods and graph neural network approaches to accurately learn relationships between crystal structures and corresponding thermodynamic and kinetic properties, with interpretability being a major focus. Our models give us the ability to enable rapid discovery and design of novel solid-state battery chemistries.

Shreyas J Honrao↗
NASA NTRSDate not supplied

Using Artificial Intelligence (AI) and Machine Learning (ML) to conduct Space Missions Solid Waste Management Survey

The National Aeronautics and Space Administration (NASA) Solid Waste Management team has been focusing on technologies that can operate in microgravity. NASA aims to conduct both short and long-term transit and planetary missions on the lunar and Mars surfaces. Therefore, an updated waste survey is needed to explore technologies for operation in microgravity for transit missions and partial gravity for planetary missions. This paper will utilize Artificial Intelligence and Machine Learning techniques to conduct the survey and generate knowledge graphs for Spacecraft Waste Management.

Artificial Intelligence↗
NASA NTRSDate not supplied

Advancing Wildfire Monitoring with TEMPO and ML tools: Hourly Smoke and Fire‑Front Mapping and Near‑Surface NO₂ Predictions

Wildfires impose substantial impacts on communities and regions downwind of wildfire smoke. We present a TEMPO‑enabled workflow that generates value‑added Level‑3 smoke‑plume masks and fire‑front maps for large wildfires, such as 2024 Park Fire, using the self‑supervised deep learning system SIT‑FUSE, along with near‑surface NO₂ predictions produced by a foundation model (Microsoft Aurora). We conclude by outlining a roadmap for expanding these capabilities to additional Western U.S. wildfire events and for delivering actionable tools to stakeholders. This open-source, reproducible workflow provides a scalable framework for cross-agency wildfire monitoring to overcome traditional limitations in smoke-cloud discrimination and air-quality forecasting by incorporating TEMPO data and beyond.

Xiaohua Pan↗
NASA NTRS2024

Calculating the High-Latitude Ionospheric Electrodynamics Using A Machine Learning-Based Field-Aligned Current Model

We introduce a new framework called Machine Learning (ML) based Auroral Ionospheric electrodynamics Model (ML-AIM). ML-AIM solves a current continuity equation by utilizing the ML model of Field Aligned Currents of Kunduri et al. (2020, https://doi.org/10.1029/2020JA027908), the FAC-derived auroral conductance model of Robinson et al. (2020, https://doi.org/10.1029/2020JA028008), and the solar irradiance conductance model of Moen and Brekke (1993, https://doi.org/10.1029/92gl02109). The ML-AIM inputs are 60-min time histories of solar wind plasma, interplanetary magnetic fields (IMF), and geomagnetic indices, and its outputs are ionospheric electric potential, electric fields, Pedersen/Hall currents, and Joule Heating. We conduct two ML-AIM simulations for a weak geomagnetic activity interval on 14 May 2013 and a geomagnetic storm on 7–8 September 2017. ML-AIM produces physically accurate ionospheric potential patterns such as the two-cell convection pattern and the enhancement of electric potentials during active times. The cross polar cap potentials (ΦPC) from ML-AIM, the Weimer (2005, https://doi.org/10.1029/2004ja010884) model, and the Super Dual Auroral Radar Network (SuperDARN) data-assimilated potentials, are compared to the ones from 3204 polar crossings of the Defense Meteorological Satellite Program F17 satellite, showing better performance of ML-AIM than others. ML-AIM is unique and innovative because it predicts ionospheric responses to the time-varying solar wind and geomagnetic conditions, while the other traditional empirical models like Weimer (2005, https://doi.org/10.1029/2004ja010884) designed to provide a quasi-static ionospheric condition under quasi-steady solar wind/IMF conditions. Plans are underway to improve ML-AIM performance by including a fully ML network of models of aurora precipitation and ionospheric conductance, targeting its characterization of geomagnetically active times.

auroral electrodynamics↗
NASA NTRSDate not supplied

Prediction of High-Latitude Ionospheric Electrodynamics Using the Machine Learning Based Auroral Ionospheric Electrodynamics Model

We introduce a new framework for Machine-Learning (ML) based Auroral Ionosphere Model (ML-AIM). ML-AIM solves a current continuity equation by utilizing the ML model of Field Aligned Currents (FACs) of Kunduri et al., 2020 (https://doi.org/10.1029/2020JA027908), the FAC-derived aurora conductance model of Robinson et al., 2020 (https://doi.org/10.1029/2020JA028008), and the solar irradiance conductance model of Moen & Brekke (1993). The ML-AIM inputs are 60min time histories of solar wind plasma, interplanetary magnetic fields (IMF), and geomagnetic indices, and its outputs are ionospheric electric potential, electric fields, Pederson/Hall currents, and Joule Heating. We conduct two ML-AIM simulations for a weak geomagnetic activity on 14 May 2013 and a geomagnetic storm on 7-8 September 2017. ML-AIM produces reasonable ionospheric potential patterns such as two cell convection patterns and the enhancement of electric potentials during active times. The cross polar cap potential drop from ML-AIM is also comparable to the ones from the Weimer 2005 model, Super Dual Auroral Radar Network (SuperDARN), and Defense Meteorological Satellite Program (DMSP) F17 observations. ML-AIM is unique in a sense that it predicts ionospheric responses to the time-varying solar wind and geomagnetic conditions, while other traditional empirical model like Weimer 2005 is designed to provide static ionospheric conditions under steady solar wind/IMF conditions. In future, ML-AIM will include ML-based models of aurora precipitation and ionospheric conductance, improving its performance during active times.

H. K. Connor↗
NASA NTRS2019

Understanding Machine Learning in Earth Science: A Natural Language Processing Approach

Machine learning (ML) is being increasingly utilized in Earth science research. Benefits of ML include efficiency, reduction of human error, and ability to extract hidden patterns within data. However, the mutual lack of each other’s domain knowledge by ML and Earth science stands as a barrier to timely and effective implementation. Earth science, in particular, faces challenges in generating sample data, compared to those of traditional ML problems such as face recognition or stock predictions, where data is abundant and not lacking in ground truth, which is necessary for labeling. Earth science data are more varying in formats, such as HDF5 and image resolutions, and are not standardized across instruments, even within a given Earth science discipline. Previous studies have been done to outline the specific challenges that Earth science faces with ML, while others have focused on using existing publications to mine information efficiently. Other resources such as Scikit-Learn have developed decision trees for choosing appropriate machine learning algorithms, but application within Earth science subjects becomes much more complex. For the current study, we propose a methodology and tool that aids in implementation of ML in Earth science using natural language processing (NLP). Our work comprises three main parts: (1) analyzing existing publications related to ML and Earth science, using natural language processing: (2) extracting from the publications information on ML models subjects in Earth Science: and (3) visualizing the extracted relationships as a network graph. The resulting network graph should aid the Earth science communities in applying optimal ML algorithms and guiding data preparation through visualization of similar studies. The network graph and analysis of document similarity will be the basis of our next step, which is to develop a decision tree for selecting optimal machine learning methodologies for specified Earth science applications.

Zheng, Laura↗
NASA NTRS2022

Can Machine Learning Reveal Precursors of Reversals of the Geomagnetic Axial Dipole Field?

It is well known that the axial dipole part of Earth’s magnetic field reverses polarity, so that the magnetic North Pole becomes the South Pole and vice versa. The timing of reversals is well documented for the past 160 Myr, but the conditions that lead to a reversal are still not well understood. It is not known if there are reliable ‘precursors’ of reversals (events that indicate that a reversal is upcoming) or what they might be. We investigate if machine learning (ML) techniques can reliably identify precursors of reversals based on time-series of the axial magnetic dipole field. The basic idea is to train a classifier using segments of time-series of the axial magnetic dipole. This training step requires modification of standard ML techniques to account for the fact that we are interested in rare events—a reversal is unusual, while a non-reversing field is the norm. Without our tweak, the ML classifiers lead to useless predictions. Perhaps even more importantly, the usable observational record is limited to 0–2 Ma and contains only five reversals, necessitating that we determine if the data are even sufficient to reliably train and validate an ML algorithm. To answer these questions we use several ML classifiers (linear/non-linear support vector machines and long short-term memory networks), invoke a hierarchy of numerical models (from simplified models to 3-D geodynamo simulations), and two palaeomagnetic reconstructions (PADM2M and Sint-2000). The performance of the ML classifiers varies across the models and the observational record and we provide evidence that this is not an artefact of the numerics, but rather reflects how ‘predictable’ a model or observational record is. Studying models of Earth’s magnetic field via ML classifiers thus can help with identifying shortcomings or advantages of the various models. For Earth’s magnetic field, we conclude that the ability of ML to identify precursors of reversals is limited, largely due to the small amount and low frequency resolution of data, which makes training and subsequent validation nearly impossible. Put simply: the ML techniques we tried are not currently capable of reliably identifying an axial dipole moment (ADM) precursor for geomagnetic reversals. This does not necessarily imply that such a precursor does not exist, and improvements in temporal resolution and length of ADM records may well offer better prospects in the future.

Kyle Gwirtz↗
NASA NTRS2000

Vasoactive neuroendocrine responses associated with tolerance to lower body negative pressure in humans

The purpose of this investigation was to test the hypothesis that peripheral vasoconstriction and orthostatic tolerance are associated with increased circulating plasma concentrations of noradrenaline, vasopressin and renin-angiotensin. Sixteen men were categorized as having high (HT, n=9) or low (LT, n=7) tolerance to lower body negative pressure (LBNP) based on whether the endpoint of their pre-syncopal-limited LBNP (peak LBNP) exposure exceeded -60 mmHg. The two groups were matched for age, height, weight, leg volume, blood volume and maximal oxygen uptake, as well as baseline blood volume and plasma concentrations of vasoactive hormones. Peak LBNP induced similar reductions in mean arterial pressure in both groups. The reduction in leg arterial pulse volume (measured by impedance rheography), an index of peripheral vascular constriction, from baseline to peak LBNP was greater (P<0.05) in the HT group (-0.041 +/- 0.005 ml 100 ml-1) compared to the reduction in the LT group (-0. 025 +/- 0.003 ml 100 ml-1). Greater peak LBNP in the HT group was associated with higher (P<0.05) average elevations in plasma concentrations of vasopressin (pVP, Delta=+7.2 +/- 2.0 pg ml-1) and plasma renin-angiotensin (PRA, Delta=+2.9 +/- 1.3 ng Ang II ml-1 h-1) compared to average elevations of pVP (+2.2 +/- 1.0 pg ml-1) and PRA (+0.1 +/- 0.1 ng Ang II ml-1 h-1) in the LT group. Plasma noradrenaline concentrations were increased (P<0.05) from baseline to peak LBNP in both HT and LT groups, with no statistically distinguishable difference between groups. These data suggest that the renin-angiotensin and vasopressin systems may contribute to sustaining arterial pressure and orthostatic tolerance by their vasoconstrictive actions.

NASA Discipline Cardiopulmonary↗
NASA NTRSDate not supplied

Lightning Instrumentation System

A new comprehensive lightning instrumentation system has been designed for the Mobile Launcher 1 (ML-1) at the Kennedy Space Center, Florida. This new instrumentation system includes the synchronized recording of three B-dot, 3-axis measurement stations, one D-dot sensor and eighteen vehicle measurement channels. Each vehicle measurement channel is comprised of two currents and one voltage measurements. The instrumentation system is composed of centralized transient recorders and digitizers, connected to the transient recorders via fiber optic cables. The transient recorders are triggered by the B-dot or D-dot sensors. When the Space Launch System (SLS) vehicle is present at the ML-1, the transient recorders record data on a dual sampling rate mode, continuous slow 5 kilo-samples per second (per channel) and event driven fast 100 mega-samples per second (per channel). Without the presence of the vehicle at the ML-1, the instrumentation system operates only as an event driven fast 100 mega-samples per second (per channel). In the absence of the vehicle, the only measurements recorded are the B-dot and D-dot stations. Additionally, a portable Lightning Monitoring System (LMS) is temporarily installed inside the Orion Crew Capsule module monitoring one portable B-dot, 3-axis measurement station, and 2 Crew Capsule BUS voltages. The portable LMS has a transient recorder independent of the ML-1 transient recorders, that is triggered by the portable B-dot sensor or transients on the vehicle BUS voltages. This portable instrumentation is removed while performing close out operations before the vehicle launch. For the ML-1 lightning instrumentation system, new custom B-dot and D-dot sensors were designed and prototypes were tested at the International Center for Lightning Research and Testing (ICLRT) at Camp Blanding, Florida. The Ground Special Power (GSP) vehicle measurement channels monitoring on the ML-1 is done via 1) Commercial off-the-shelf (COTS) current shunts and 2) custom Voltage Dividers. The new ML-1 lightning instrumentation system was designed, fabricated, deployed, and tested prior to the summer of 2019, in preparation for the first NASA's SLS mission to be launched from the Launch Complex 39B (LC-39B). The ML-1 lightning instrumentation was designed to complement the LC-39B lightning instrumentation system providing electromagnetic measurements closer to the vehicle, at different heights and inside the Crew capsule module.

Angel G Mata↗
NASA NTRSDate not supplied

A Quantitative Analysis on the Use of Supervised Machine Learning in Earth Science

Recent review papers (Ball et al., 2017; Reichstein et al., 2019) have investigated the opportunities and challenges in applying supervised machine learning (ML) techniques to Earth science problems. A common challenge is the lack of training (or labeled) data. Supervised ML, and especially deep learning (DL), require large training datasets. While there are large, open access Earth science archives, the data typically require preprocessing in preparation for supervised ML, frequently including manual labeling. Our objective is to understand the landscape of supervised ML in the Earth sciences, including which research communities have most rapidly adopted supervised ML, which algorithms are applied, and what data are used to train these algorithms. We conducted a literature survey of Earth science papers published during the last 10 years in journals from the American Geophysical Union (AGU), American Meteorological Society (AMS), the Institute of Electrical and Electronics Engineers(IEEE), and the Society of Photo-Optical Instrumentation Engineers (SPIE). We identified papers containing the terms ML, DL, or the names of individual supervised ML algorithms. "Earth science" is an additional required search term for IEEE and SPIE. We investigate trends in supervised ML usage during the 10-year study period, and manually analyzed AGU papers from 2018-2019 to enable deep-dive statistics.

Katrina S Virts↗
NASA NTRS2010

Automated Desalting Apparatus

Because salt and metals can mask the signature of a variety of organic molecules (like amino acids) in any given sample, an automated system to purify complex field samples has been created for the analytical techniques of electrospray ionization/ mass spectroscopy (ESI/MS), capillary electrophoresis (CE), and biological assays where unique identification requires at least some processing of complex samples. This development allows for automated sample preparation in the laboratory and analysis of complex samples in the field with multiple types of analytical instruments. Rather than using tedious, exacting protocols for desalting samples by hand, this innovation, called the Automated Sample Processing System (ASPS), takes analytes that have been extracted through high-temperature solvent extraction and introduces them into the desalting column. After 20 minutes, the eluent is produced. This clear liquid can then be directly analyzed by the techniques listed above. The current apparatus including the computer and power supplies is sturdy, has an approximate mass of 10 kg, and a volume of about 20 20 20 cm, and is undergoing further miniaturization. This system currently targets amino acids. For these molecules, a slurry of 1 g cation exchange resin in deionized water is packed into a column of the apparatus. Initial generation of the resin is done by flowing sequentially 2.3 bed volumes of 2N NaOH and 2N HCl (1 mL each) to rinse the resin, followed by .5 mL of deionized water. This makes the pH of the resin near neutral, and eliminates cross sample contamination. Afterward, 2.3 mL of extracted sample is then loaded into the column onto the top of the resin bed. Because the column is packed tightly, the sample can be applied without disturbing the resin bed. This is a vital step needed to ensure that the analytes adhere to the resin. After the sample is drained, oxalic acid (1 mL, pH 1.6-1.8, adjusted with NH4OH) is pumped into the column. Oxalic acid works as a chelating reagent to bring out metal ions, such as calcium and iron, which would otherwise interfere with amino acid analysis. After oxalic acid, 1 mL 0.01 N HCl and 1 mL deionized water is used to sequentially rinse the resin. Finally, the amino acids attached to the resin, and the analytes are eluted using 2.5 M NH4OH (1 mL), and the NH4OH eluent is collected in a vial for analysis.

Spencer, Maegan K.↗