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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 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Role of PHM in Autonomous Decision-Making: Aerospace applications

There is an increased need for onboard decision-making capabilities in cyber-physical systems be it in energy, automotive, aviation, space, or other industries as they aim for increased efficiency, resiliency, and mission assurance capabilities. Emerging next-gen technologies such as multi-rover planetary missions, distributed satellites, unmanned ground and aerial vehicle operations and smart grid systems rely on in-time risk assessment and autonomous decision-making. One critical piece of the autonomy puzzle is reliable prediction of system behavior under time-varying and potentially uncertain environmental conditions. Further, if agent states change during operation such as initiation of faults or degradation, reliable diagnostic tools need to be investigated. In this tutorial, we will revise approaches that integrates existing physics-based and data-driven models of agents interacting with probability models of the environment and component operation state. Role of existing PHM methodologies as they feed into decision-making under uncertainty will be studied. Balancing critical trade-offs between high-fidelity prognostic models, prediction time-horizons and the computational requirements for in-time cost-effective decision-making will be discussed through the implementation of surrogate models. Finally, the audience will be introduced to a real-time application of in-time trajectory planning of an unmanned aerial system (UAS) based on its PHM assessments under uncertain and varying wind conditions.

decision-making↗
NASA NTRSDate not supplied

Cyber Resiliency and the Implementation of a Host-Based Intrusion Detection System in an Urban Air Mobility Environment

With the growth in Urban Air Mobility systems and the increasing reliance on interconnected technologies, ensuring the security of these complex components has become critical. As cities evolve into smart urban centers, the vulnerability to cyber threats escalates, possibly endangering citizens safety and the efficiency of transportation networks.In response to these challenges, this paper presents a study on the need for cyber resilient techniques within future air traffic environments. It will pay specific attention to the implementation of a Host-Based Intrusion Detection System (HIDS) utilizing Atomic OSSEC software, tailored specifically to a NASA simulation of an UrbanAirMobility environments’ unique demands. Further, this study seeks to outline the rational for NASA’s recommendation for a HIDS in such environments. It explores the design, development, and deployment of the proposed HIDS, focusing on its adaptability to monitor the hybrid nature of the Urban Air Mobility environment. Leveraging machine learning algorithms and anomaly detection techniques, the HIDS is equipped to continuously monitor and analyze the behavior of individual host systems, vehicles, and devices, thereby providing a proactive approach to threat detection. Implementing a HIDS is a pivotal strategy for enhancing cyber resiliency, as it gives an organization granular visibility into internal system activities, enables rapid detection and response to anomalous behavior and cyber threats, and fortifies the organizations overall cybersecurity posture. Finally, this study aims to provide recommendations and include learned takeaways that the Urban Air Mobility industry should consider. In brief, this paper highlights the significance of host-based intrusion detection in UrbanAirMobility environments and underscores the necessity of tailored security solutions to safeguard against emerging cyber threats.

UAM↗
NASA NTRSDate not supplied

Advanced Radiation Detectors and Detector Systems Research

Real‐time awareness of space radiation is critical for missions beyond LEO. Missions to the Moon, Mars, Near‐Earth‐Objects (NEOs), inner and outer planets, as well as other destinations in space, will require embedded instrumentation to provide feedback for “smart”, adaptive control systems. Precision instrumentation will also be needed to provide improved data for space radiation modeling and space weather forecasting and awareness. Compact instrumentation on small platforms such as CubeSats and other small space‐faring vehicles will provide a better understanding of the space radiation environment by supplying data from multiple locations in space where information is yet unknown. Compact integrable solid‐state detectors with low noise and high tolerance of operation in a radiation environment are enabled by the application of Wide Band Gap semiconductors as radiation detectors. NASA Glenn Research Center (GRC) is applying its expertise and facilities in harsh environment instrumentation to develop supporting technologies that enable improved instruments for space science missions. A variety of radiation detectors are under development, such as silicon carbide semiconductor detectors, for applications to space science, lunar prospecting, and fission power systems.

Field and particle detectors↗
NASA NTRS2024

Enhancing Metal Additive Manufacturing Training with the Advanced Vision Language Model: A Pathway to Immersive Augmented Reality Training for Non-Experts

This paper introduces an innovative training system for the Renishaw AM400 metal printer, leveraging the synergy of the advanced Vision Language Model (VLM) with Augmented Reality (AR) within the Digital Twins (DT) framework. Aimed at overcoming the limitations of conventional training methods in metal additive manufacturing (AM), our system integrates AR to provide an immersive learning environment, enhancing the real-world experience with interactive digital overlays. The core of the system lies in its use of VLM, which, pre-trained on diverse datasets, excels in processing multi-modal data, thereby offering nuanced and contextually relevant guidance for trainees. Key experiments demonstrate the system’s effectiveness, particularly highlighting the usage of VLM as an Artificial Intelligence (AI) agent to integrate external tools like YOLO-v7 for valve state classification and CRAFT for control panel text recognition. This approach significantly improves recognition accuracy, operational understanding, and human–machine interaction, especially for non-expert users, making complex metal AM operations more accessible. The research not only showcases the potential of AR and VLM in industrial training but also sets a new standard for smart manufacturing practices, indicating broader applications in various industrial domains.

Metal additive manufacturing↗
NASA NTRS2024

NASA’s Astrophysics Cross-Observatory Science Support (ACROSS) Initiative: Enabling Time-Domain and Multimessenger

The Astro2020 Decadal Survey recommended an investment in Time-Domain and 4 Multimessenger Astrophysics (TDAMM) as the top-priority sustaining activity in space for the 5 coming decade. One aspect of NASA’s response to this recommendation is a pilot project, 6 the Astrophysics Cross-Observatory Science Support (ACROSS) initiative, designed to provide 7 support to both missions and observers as they pursue TDAMM science. Here, we present our 8 observations of needs in the community and initial plans for ACROSS activities, including services 9 to facilitate and improve cross-mission follow-up planning and execution; a multimessenger web portal with links to existing mission resources, community tools, and information targeted for 11 TDAMM general observers; development of “Smart target-of-opportunity submission page” proof-12 of-concepts; and ongoing development of a potential TDAMM general observing competitive 13 grant solicitation. As the ACROSS pilot phase begins, we invite discussion of our plans with both 14 missions and observers to better understand their needs and concerns.

T B Humensky↗
NASA NTRS2025

ACROSS: Enabling Time Domain and Multi-Messenger Astrophysics

The U.S. Astro2020 Decadal Survey recommended an investment in Time Domain and Multi-Messenger Astrophysics (TDAMM) as the top-priority sustaining activity in space for the coming decade. One aspect of NASA’s response to this rec-ommendation is a pilot project, the Astrophysics Cross-Observatory Science Support (ACROSS) Initiative, designed to provide support to both missions and observers as they pursue TDAMM science. In this talk, we present our observations of needs in the community and initial plans for ACROSS activities, including services to facilitate and improve cross-mission follow-up planning and execution; a multi-messenger web portal with links to existing mission resources, community tools, and information tar-geted for TDAMM General Observers; development of "Smart Target of Opportunity submission page" proof-of-concepts; and ongoing development of a potential TDAMM general observing competitive grant solicitation. While the initial focus has been to en-hance coordination between NASA missions, we are eager to work with ground-based and international partners as well. We invite discussion with both missions and ob-servers to better understand their needs and concerns as ACROSS progresses. Here we present our efforts on the web-portal and API, along with our development to support NASA’s BurstCube mission.

T B Humensky↗
NASA NTRS2025

Innovative Drug Selection, Storage, and Shelf-Life Strategies for Exploration Spaceflight

Medications have been a part of space travel dating back to the Apollo missions. A safe and effective medication formulary is essential to maintaining crew health and performance during long-duration spaceflight outside of low Earth orbit (LEO). Distance from Earth creates four key operational changes that increase medical risks, including communication, resupply, crewmember health, and evacuation. The current spaceflight pharmaceutical formulary consists of medications indicated to treat a variety of anticipated medical events and healthcare needs during spaceflight, but depends on a robust consumables resupply chain, which may be strained for a Lunar, and possibly non-existent for a Mars mission. The specific medications selections for the formulary may change to optimally align with the mission, crew compliment, and spacecraft design. Medical support at long-duration exploration missions will differ from LEO missions due to mission duration, lack of consumables resupply, prolonged exposure to space radiation, and the absence of emergency medical return capability. Loss of medication resupply limits or removes the ability to replace medications that have been exhausted or degraded, potentially exacerbating the medical risk posture. To address these anticipated risks, long-duration missions must consider use of novel medical technologies, treatment modalities, and smart medical systems that offer greater crew autonomy, such as physiologically based pharmacokinetic modeling, drug repurposing, on demand drug synthesis, or wearable drug delivery / monitoring devices. Once an ideal formulary for exploration space is determined, it is essential to establish the chemical and physical stability of each medication compound, as well as its safety by identifying its degradation profiles and products. Although few studies have been conducted to provide evidence on the physicochemical stability of pharmaceuticals during space missions, the data suggests that the spaceflight environment may promote degradation in some pharmaceuticals. Formulary drug purity and efficacy should be verified by pharmaceutical stability assessments, and can be realized non-destructively, and accessed in remote environments. Non-destructive pharmaceutical analysis and statistical modelling techniques could optimize exploration spaceflight medical care by enabling early detection of suboptimal therapeutics. Likewise, novel packaging, storage strategies, and dosage form innovations are promising countermeasures to optimize pharmaceutical shelf life, purity, and quality of exploration spaceflight medications. As we prepare for more distant exploration missions, risk management planning for astronaut healthcare should include the assembly of a medication formulary that is comprehensive enough to prevent or treat anticipated medical events, remains safe and chemically stable, and retains sufficient potency to last for the duration of the mission. Following extensive review of the literature, we will present innovative formulary optimization strategies, pharmaceutical stability assessment techniques, and storage and packaging solutions that could enhance drug safety and efficacy for future exploration spaceflight missions.

Vernie R Daniels↗
NASA NTRS2024

Piezoelectric impedance-based high-accuracy damage identification using sparsity conscious multi-objective optimization inverse analysis

Two elements are essential in structural health monitoring utilizing dynamic responses: response measurement with high-frequency contents, i.e., small characteristic wavelengths, that can adequately reflect damage features, and effective inverse identification analysis that is however oftentimes under-determined. The advancement of smart structure integration has led to active interrogation through frequency-sweeping piezoelectric impedance measurement at high frequency range. In this research we develop a multi-objective optimization formulation for the identification of damage location and severity utilizing piezoelectric impedance. While one optimization objective is to match the response measurement with finite element model prediction in the damage parametric space, the other is the number of locations of damage, i.e., the sparsity of damage index as the solution vector, since damage usually occurs within a small number of locations. This multi-objective formulation fits well the under-determined nature of damage identification, as it naturally provides multiple solutions as basis for further elucidation. The challenge remaining is how to find a small solution set that can include the actual damage scenario. Here we develop a novel inverse identification framework utilizing the intelligent swarm optimizer which possesses flexibility for enhancement. We first embed a sparsity enforcement process into the population generation of the optimizer, which yields a solution repository intrinsically possessing sparsity. We then apply reinforcement learning so the agents can adaptively opt for local strategies with the aim of enriching the searching patterns to diversify the solutions. Through the incorporation of a Q-table, searching toward more promising directions will be rewarded. Our case analyses employing experimental data indicate that this sparsity-conscious multi-objective particle swarm optimization technique can lead to a small solution set which generally encompasses the true damage scenario. This effectively solves the structural damage identification problem with piezoelectric impedance measurement.

Yang Zhang↗
NASA NTRSDate not supplied

Physics-Guided Deep Learning for Complex System Health Management and Decision Making

The landscape of complex engineered systems is rapidly evolving, from smart manufacturing facilities to next-generation transportation vehicles. As these systems become increasingly sophisticated and interconnected, the need for advanced health management systems grows ever more critical. These systems must go beyond simple monitoring, actively predicting potential failures before they occur. This paradigm shift from fixed maintenance schedules to condition-based predictions is key to optimizing system performance, enhancing safety, and paving the way for autonomous decision-making across various industries. Whether in industrial processes, energy systems, or advanced transportation, the ability to anticipate and prevent failures is becoming a cornerstone of operational excellence. To accurately predict the future health of any complex system, knowledge of its current health state and future operational conditions is essential. Recent advancements in data-driven algorithms have generated growing interest in artificial intelligence for industrial applications. However, the limitations of pure data-driven methods, particularly in industries where data acquisition is costly and limited, have become apparent. This has led to a focus on blending physics with data-driven algorithms, mitigating the drawbacks of both approaches while emphasizing their respective advantages. This research proposes a novel framework for integrating physics-based performance models with deep learning algorithms for the prognostics of complex safety-critical systems. In this approach, physics-based models serve as a blueprint, capturing fundamental system behaviors, while deep learning algorithms, leveraging real-world sensor data, fill in gaps and identify subtle patterns indicative of potential problems. This hybrid methodology, utilizing techniques such as Physics-Informed Neural Networks (PINNs), offers a powerful solution for predicting system health. By fusing domain knowledge with data-driven insights, this approach promises more accurate, adaptable, and reliable models for health prediction. The resulting framework is versatile, applicable across various sectors including aerospace, manufacturing, and energy systems, ultimately contributing to safer, more efficient operations in our increasingly complex technological landscape.

Diagnostics↗
NASA NTRS2026

Diffusion Quantum Monte Carlo Calculation of the Austenite and Martensite Phases of NiTi

NiTi is a promising material for smart and active technologies due to its exhibition of the shape memory effect, superelasticity, and biocompatibility. The shape memory effect is tied to the reversible transition between the austenite and martensite phases. A major research direction is to alloy NiTi with Zr, Hf, Pd, Pt, etc., in order to tune the martensitic transition temperature (MTT). Modeling the MTT from first principles is challenging because the lattice dynamics is complicated by anharmonicity and various low-energy structures. Using density functional theory, the energy difference between the austenite and martensite phases of NiTi varies by up to 100 meV/atom depending on the choice of density functional, which is of the same order of the energy difference itself. Consequently, free energy calculations with different functionals can result in estimates of the MTT that vary by several hundred K. Using diffusion quantum Monte Carlo, we calculated the energy difference between the B2 and B19' structures of NiTi to be 70.9 +- 2.5 meV/atom.

Kevin K Ly↗