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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Feasibility Study of Distributed Decision-Making on the Edge for Urban Air Mobility

The Concept of Operations for Urban Air Mobility (UAM) put forward by FAA, NASA, and several industry stakeholders acknowledges the diversity and complexity in UAM operations and, thereby, envisions a federated architecture for UAM management. In this architecture, the decision-making is distributed to a set of service providers who collectively manage the shared airspace usage by different stakeholders. This notionally brings autonomy closer to the UAM businesses and encourages to explore the feasibility of decision making on the very edge, which is the topic of the presented research. This paper reports research conducted on the hypothesis based on which the residual compute capability onboard smart unmanned aerial systems (UASs) is utilized to build situational awareness and resolve conflicts by passive and active coordination among multiple UASs, thereby implementing a layer of distributed autonomy in UAM. Key features of the edge-computing approach involve inter-UAS information exchange, independent assessment of own flight and environmental conditions, and estimation of other UASs’ flight preferences, incorporating machine learning techniques in the last two. Parallel computing on portable graphics processing unit (GPU) enables the machine learning workflow on the edge. A custom-built 3D simulator is used to evaluate the efficacy of the distributed decision-making on the edge. Each edge node, representing a smart UAS, connects to the simulator from a remote location and independently controls the behavior of the corresponding virtual asset in the simulator, analogous to participants in an online multi-player game. The presented edge-computing-based distributed decision-making framework is envisioned to pave the way for collective mobility of autonomous air vehicles in the future shared airspace, while allowing the inclusion of the business preferences of the UAS operators within allowed regulatory limits.

Edge computing↗
NASA NTRSDate not supplied

Feasibility Study of Distributed Decision-Making on the Edge for Urban Air Mobility

The Concept of Operations for Urban Air Mobility (UAM) put forward by FAA, NASA, and several industry stakeholders acknowledges the diversity and complexity in UAM operations and, thereby, envisions a federated architecture for UAM management. In this architecture, the decision-making is distributed to a set of service providers who collectively manage the shared airspace usage by different stakeholders. This notionally brings autonomy closer to the UAM businesses and encourages to explore the feasibility of decision making on the very edge, which is the topic of the presented research. This paper reports research conducted on the hypothesis based on which the residual compute capability onboard smart unmanned aerial systems (UASs) is utilized to build situational awareness and resolve conflicts by passive and active coordination among multiple UASs, thereby implementing a layer of distributed autonomy in UAM. Key features of the edge-computing approach involve inter-UAS information exchange, independent assessment of own flight and environmental conditions, and estimation of other UASs’ flight preferences, incorporating machine learning techniques in the last two. Parallel computing on portable graphics processing unit (GPU) enables the machine learning workflow on the edge. A custom-built 3D simulator is used to evaluate the efficacy of the distributed decision-making on the edge. Each edge node, representing a smart UAS, connects to the simulator from a remote location and independently controls the behavior of the corresponding virtual asset in the simulator, analogous to participants in an online multi-player game. The presented edge-computing-based distributed decision-making framework is envisioned to pave the way for collective mobility of autonomous air vehicles in the future shared airspace, while allowing the inclusion of the business preferences of the UAS operators within allowed regulatory limits.

Edge computing↗
NASA NTRSDate not supplied

Feasibility Study of an All-Electric 150 Passenger Aircraft

Among the potential future technologies for low-carbon transport aircraft, all-electric concepts stand out environmentally, yet their feasibility and cost-effectiveness at scale remain uncertain. This paper examines the conceptual design of an all-electric 150-passenger aircraft with a range of 840 NM based on assumed future battery cell-level specific energy of1620Wh/kg. The tube and wing design is propelled by open rotors driven by four electric motors per nacelle and utilizes advanced structural materials for weight reduction. Powertrain technology gaps are identified. The conceptual design shows that all-electric aviation could supplant about 30% of current fossil fuel use by transport aircraft and potentially more with air-traffic-control modifications.

electric aircraft↗
NASA NTRS2024

Simulated Feasibility of 3D Lightning Mapping from Space

In addition to the awe it inspires, lightning can illuminate the microphysical processes hidden away within deep convection. The current generation of space-based lightning mapping uses mostly 2D optical imaging to connect overall flash characteristics to their parent storm dynamics, but are missing a dimension’s worth of information. With lightning now classified as an essential climate variable, future spaceborne mappers will need improved capabilities to take advantage of the 3D structure of lightning flashes to support meteorological and climate modeling. We report here on a study of the feasibility of implementing a radio frequency (RF)–based network of satellites for 3D lightning mapping in high resolution from low Earth orbit (LEO). Lightning sources are simulated using existing tools of the Lightning Mapping Array (LMA), modified for orbital detection, and spatially reconstructed using a Levenberg-Marquardt geolocation algorithm to assess sources of uncertainty in these solutions. We analyze the benefits and limitations of this approach compared to existing orbital and ground-based methods. Results of this study show that lightning can be mapped in 3D with a vertical location accuracy better than 2 km using as few as five satellites in LEO capable of measuring the time-of-arrival of impulsive RF signals in the very high frequency (VHF) band. The consequence of this study is that high-resolution, spaceborne 3D mapping of lightning is achievable across most of the globe, having crucial implications for our understanding of not only lightning, but also severe weather development, climate science, and more.

CubeSpark↗
NASA NTRSDate not supplied

Quantum Leap: Evaluating the Feasibility of Quantum Machine Learning Using NASA Earth Observational Data

This study explores the feasibility of leveraging quantum machine learning (QML) to analyze NASA Earth Observational (EO) data for climate change research, with a particular focus on the phenomenon of ”crop frosting” which has become more prevalent due to climate change. We implemented and evaluated two QML models, the Variational Quantum Classifier (VQC) and Quantum Support Vector Classifier (QSVC), in both simulated and real quantum computing environments using a 127 qubit IBM quantum processor. Our study emphasizes the scientific rigor in comparing these quantum models with a classical Support Vector Machine (SVM) classifier, highlighting their performance in processing climate data. The results offer valuable insights into the potential scientific advantages, limitations, and scalability of QML for analyzing EO datasets, thus paving the way for more advanced climate modeling and predictive analytics using quantum computing. We showcased how Environmental Interaction Knowledge Graphs (EIKGs) and Digital Twins (DTs) can be integrated into this study. This research underscores the transformative potential of Classical and QML leveraging KGs and DT to address the multifaceted challenges posed by climate change.

Quantum Computing↗
NASA NTRS2025

A Preliminary Study on the Feasibility of Large Language Models for Detecting Micro-Behaviors Among Team Members in Space Missions

Large-language models (LLMs) have been recently used for spoken language understanding (SLU) to infer meaning and semantics from speech in tasks such as speaker intent and sentiment classification. Due to being trained on large amounts of data, and their ability to understand context and relationships between words, LLMs are competent, enabling them to generalize across tasks without requiring many task-specific training samples. This research examines the feasibility of few-shot learning in LLMs for detecting subtle, brief, and possibly unconscious interactions between team members, called ``micro-behaviors," and provides insights into the appropriate design of LLMs for this task. Our data came from 5 teams participating in a 45-day mission at the US National Aeronautics and Space Administration’s (NASA) Human Exploration Research Analog (HERA). More specifically we used data collected from team interaction battery (TIB) tasks teams performed five times in-mission which comprise an average 1.5 hours of conversation data per day. Micro-behaviors were coded according to an adapted version of Smith & Griffins (2022) theoretical framework in terms of Violation (i.e., presence of valenced behavior, uplifting/positive or discouraging/negative), Intensity (i.e., force of behavior in terms of how uplifting or discouraging is the behavior), and Intent (i.e., motive of the behavior in terms of whether it was deliberate or unintentional). We explore the ability of LLMs to detect the presence and intensity of micro-behaviors. We examine employing and fine-tuning readily available LLMs (i.e., RoBERTa, DistilBERT), as well as prompting state-of-the-art sequence classification models (i.e., Llama-2, Llama-3). In a total of 13,058 conversational turns (17.8% uplifting, 3.3% discouraging, 75.76% neutral, 3.14% nulls), we compute the macro F1-score of the 3-way micro-behavior classification task (i.e., classifying among uplifting, discouraging, and neutral; 33% chance). Results indicate that the RoBERTa model achieves a F1-score of 36.2% (uplift: 43.3% precision (P), 15.1% recall (R); discourage: 20% P, 0.5% R). These results significantly improve when we augment the data via paraphrasing in the RoBERTa model, reaching a 41.2% macro F1-score (uplift: 37.7% P, 86.3% R; discourage: 3.5% P, 1.8% R). Finally, the Llama-2 model with 3-shot prompting yields 38% macro F1-score (uplift: 28.7% P, 20% R; discourage: 7.2% P, 18% R), which is slightly better compared to the RoBERTa model without data augmentation, highlighting the effectiveness of sequence classification models in detecting minority classes with a small sample size. Findings indicate that LLMs hold potential to detect subtle behaviors in conversations, which could be valuable in assessing team behavior in space exploration missions. Future studies will evaluate the performance of different LLM prompting strategies or fine-tuning methods.

Ankush Raut↗
NASA NTRSDate not supplied

Assessing the Feasibility of Using Various Earth Observations to Monitor Environmental Trends Associated with River Flow Impediments Near Energy Intake Structures

Grassing events, characterized by the release of river vegetation in large quantities, are often responsible for major flow impediments surrounding industrial water intake structures. These sudden flow impediments halt critical municipal functions, such as cooling at energy generating facilities, leading to widespread energy disruptions for surrounding communities. Because the origin and cause of grassing events in rivers are largely unknown, employees at these facilities can only reactively respond after they occur by pausing energy generation to manually remove the accumulation of aquatic vegetation. In 2023, the Dresden Generating Station, in collaboration with the United States Geological Survey, began looking into using remote sensing methodologies to locate floating aquatic vegetation surrounding their intake structure located along the Kankakee River in northern Illinois. This project contributed to this effort by conducting a case study that compared the performance, practicality, and feasibility of using various Earth observations (Landsat 9 OLI-2, Landsat 8 OLI, Sentinel-2 MSI, and DOVE PlanetScope) and vegetation indices (NDVI, EVI, SAVI, and GCI) to monitor aquatic vegetation in the Kankakee River. Additionally, this study incorporated several environmental metrics to identify potential triggers for grassing events. Results from this project found that while Landsat sensors provide a lower spatial resolution than commercial satellite Earth observations, areas of aquatic vegetation were similarly identified; therefore, it may not be necessary to acquire expensive, high-resolution datasets for continued monitoring. Finally, preliminary correlation analyses showed a potential negative relationship between the river discharge and the presence of aquatic vegetation (-0.875 correlation coefficient), suggesting that periods of low flow could lead to large releases in aquatic vegetation.

Marisa Smedsrud↗
NASA NTRS2003

Distal airways in humans: dynamic hyperpolarized 3He MR imaging--feasibility

Dynamic hyperpolarized helium 3 (3He) magnetic resonance (MR) imaging of the human airways is achieved by using a fast gradient-echo pulse sequence during inhalation. The resulting dynamic images show differential contrast enhancement of both distal airways and the lung periphery, unlike static hyperpolarized 3He MR images on which only the lung periphery is seen. With this technique, up to seventh-generation airway branching can be visualized. Copyright RSNA, 2003.

NASA Discipline Life Sciences Technologies↗
NASA NTRS1998

New Approach to Concept Feasibility and Design Studies for Astrophysics Missions

JPL has assembled a team of multidisciplinary experts with corporate knowledge of space mission and instrument development. The advanced Concept Design Team, known as Team X, provides interactive design trades including cost as a design parameter, and advanced visualization for pre-Phase A Studies.

Concept Feasibility Design Studies Team X Astrophy↗