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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 NTRS2005

Keeping a Beat on the Heart

Feel the relief of a patient suffering from heart arrhythmia, who is able to return home while having her heart monitored by health professionals 24 hours a day, without the fear that she will miss an important indicator and suffer a fatal heart attack - using technology originally developed to conduct experiments on the Space Shuttle. Approximately 400,000 Americans die every year from sudden heart attacks . Medical research revealed that patterns of electrical activity in the heart can act as predictors of these lethal cardiac events known as arrhythmias. Fortunately, certain arrhythmias such as ventricular fibrillation (loss of regular heartbeat and subsequent loss of function) and ventricular tachycardia (rapid heartbeats), can be detected and appropriately treated. Today, patients at moderate risk of arrhythmias can benefit from technology that would permit long- term continuous monitoring of electrical cardiac rhythms outside the hospital environment in the comfort of their own homes. Medical telemetry systems, also known as telemedicine, are evolving rapidly as wireless communication technology advances, evidenced by the commercial products and research prototypes for remote health monitoring that have appeared in recent years. Wireless systems allow patients to move freely in their home and work environment while being monitored remotely by health care professionals.

Liszka, Kathy J.↗
NASA NTRS2022

Neurosymbolic Hybrid Approach to Driver Collision Warning

There are two main algorithmic approaches to autonomous driving systems: (1) An end-to-end system in which a single deep neural network learns to map sensory input directly into appropriate warning and driving responses. (2) A mediated hybrid recognition system in which a system is created by combining independent modules that detect each semantic feature. While some researchers believe that deep learning can solve any problem, others believe that a more engineered and symbolic approach is needed to cope with complex environments with less data. Deep learning alone has achieved state-of-the-art results in many areas, from complex gameplay to predicting protein structures. In particular, in image classification and recognition, deep learning models have achieved accuracies as high as humans. But sometimes it can be very difficult to debug if the deep learning model doesn't work. Deep learning models can be vulnerable and are very sensitive to changes in data distribution. Generalization can be problematic. It's usually hard to prove why it works or doesn't. Deep learning models can also be vulnerable to adversarial attacks. Here, we combine deep learning-based object recognition and tracking with an adaptive neurosymbolic network agent, called the Non-Axiomatic Reasoning System (NARS), that can adapt to its environment by building concepts based on perceptual sequences. We achieved an improved intersection-over-union (IOU) object recognition performance of 0.65 in the adaptive retraining model compared to IOU 0.31 in the COCO data pre-trained model. We improved the object detection limits using RADAR sensors in a simulated environment, and demonstrated the weaving car detection capability by combining deep learning-based object detection and tracking with a neurosymbolic model.

Wang, Pei↗
NASA NTRS1999

Determination of the Corrosive Conditions Present within Aircraft Lap-Splice Joints

The complexity of airframe structure lends itself to damage resulting from crevice corrosion. Fuselage lap-splice joints are a particularly important structural detail in this regard because of the difficulty associated with detection and measurement of corrosion in these occluded regions. The objective of this work is to develop a laboratory corrosion test protocol to identify the chemistry to which lap joints are exposed and to develop a model of the corrosion within the joints. A protocol for collecting and identifying the chemistry of airframe crevice corrosion has been developed. Capillary electrophoresis (CE) is used to identify the ionic species contained in corrosion product samples removed from fuselage lap splice joints. CE analysis has been performed on over sixty corrosion product samples removed from both civilian and military aircraft. Over twenty different ions have been detected. Measurements of pH of wetted corroded surfaces indicated an alkaline occluded solution. After determining the species present and their relative concentrations, the resultant solution was reproduced in bulk and electrochemical tests were performed to determine the corrosion rate. Electrochemical analyses of the behavior of AA2024-T3 in these solutions gave corrosion rates of up to 250 microns per year (10 mpy). Additional tests have determined the relative importance of each of the detected ions in model solutions used for future predictive tests. The statistically significant ions have been used to create a second generation solution. Laboratory studies have also included exposure tests involving artificial lap joints exposed to various simulated bulk and crevice environments. The extent and morphology of the attack in artificial lap joints has been compared to studies of corroded samples from actual aircraft. Other effects, such as temperature and potential, as well as the impact of the environment on fatigue crack growth have also been studied.

Lewis, Karen S.↗
DOE OSTI2023

A Cybersecurity Testbed for Smart Buildings

Smart buildings are equipped with a plethora of cyber-physical systems, such as Internet of Things (IoT) devices and building automation systems. These devices, especially in commercial buildings, use legacy communications and hardware that were not designed with cybersecurity in mind. With increasing cyber threats in recent years, smart buildings have become an increasing target for attacks, but not enough published data are available from these incidents to study or replicate the scenarios to defend buildings. As part of the U.S. Department of Energy-funded project focusing on developing the Building Intelligence with Layered Defense Using Security-Constrained Optimization and Security Risk Detection (BUILD-SOS) platform, we developed a cybersecurity test bed for smart buildings. This test bed includes a building simulation tool, virtual devices, emulated operational technology networks, and remote hardware-in-the-loop. Using this test bed, we performed different cyberattacks on the smart building model and collected both physical building data, to understand the impacts on the building, and network data, to aid in separating mechanical faults from cyberattacks during the detection. This test bed is a significant tool in protecting smart buildings from cyberattacks because it can aid in both cybersecurity analysis and the evaluation of other cyberattack detection tools by testing the tools in a secure environment without impacting the building operations.

cyber-physical systems↗
DOE OSTI2024

A Cybersecurity Testbed for Smart Buildings

Smart buildings are equipped with a plethora of cyber-physical systems, such as Internet of Things (IoT) devices and building automation systems. These devices, especially in commercial buildings, use legacy communications and hardware that were not designed with cybersecurity in mind. With increasing cyber threats in recent years, smart buildings have become an increasing target for attacks, but not enough published data are available from these incidents to study or replicate the scenarios to defend buildings. As part of the U.S. Department of Energy-funded project focusing on developing the Building Intelligence with Layered Defense Using Security-Constrained Optimization and Security Risk Detection (BUILD-SOS) platform, we developed a cybersecurity test bed for smart buildings. This test bed includes a building simulation tool, virtual devices, emulated operational technology networks, and remote hardware-in-the-loop. Using this test bed, we performed different cyberattacks on the smart building model and collected both physical building data, to understand the impacts on the building, and network data, to aid in separating mechanical faults from cyberattacks during the detection. This test bed is a significant tool in protecting smart buildings from cyberattacks because they can aid in both cybersecurity analysis and the evaluation of cyberattack detection tools by testing the tools in a secure environment without impacting the building operations.

alfalfa↗
NASA NTRS1994

Lessons from an evolving rRNA: 16S and 23S rRNA structures from a comparative perspective

The 16S and 23S rRNA higher-order structures inferred from comparative analysis are now quite refined. The models presented here differ from their immediate predecessors only in minor detail. Thus, it is safe to assert that all of the standard secondary-structure elements in (prokaryotic) rRNAs have been identified, with approximately 90% of the individual base pairs in each molecule having independent comparative support, and that at least some of the tertiary interactions have been revealed. It is interesting to compare the rRNAs in this respect with tRNA, whose higher-order structure is known in detail from its crystal structure (36) (Table 2). It can be seen that rRNAs have as great a fraction of their sequence in established secondary-structure elements as does tRNA. However, the fact that the former show a much lower fraction of identified tertiary interactions and a greater fraction of unpaired nucleotides than the latter implies that many of the rRNA tertiary interactions remain to be located. (Alternatively, the ribosome might involve protein-rRNA rather than intramolecular rRNA interactions to stabilize three-dimensional structure.) Experimental studies on rRNA are consistent to a first approximation with the structures proposed here, confirming the basic assumption of comparative analysis, i.e., that bases whose compositions strictly covary are physically interacting. In the exhaustive study of Moazed et al. (45) on protection of the bases in the small-subunit rRNA against chemical modification, the vast majority of bases inferred to pair by covariation are found to be protected from chemical modification, both in isolated small-subunit rRNA and in the 30S subunit. The majority of the tertiary interactions are reflected in the chemical protection data as well (45). On the other hand, many of the bases not shown as paired in Fig. 1 are accessible to chemical attack (45). However, in this case a sizeable fraction of them are also protected against chemical modification (in the isolated rRNA), which suggests that considerable higher-order structure remains to be found (although all of it may not involve base-base interactions and so may not be detectable by comparative analysis). The agreement between the higher-order structure of the small-subunit rRNA and protection against chemical modification is not perfect, however; some bases shown to covary canonically are accessible to chemical modification (45).(ABSTRACT TRUNCATED AT 400 WORDS).

Review↗
DOE OSTI2022

Speaker-targeted Synthetic Speech Detection

Text-to-speech technologies are evolving quickly towards realistic-sounding human-like voices. As this technology improves, so does the opportunity for malpractice in speaker identification (SID) via spoofing, the process of impersonating a voice biometric via synthesis. More data typically equates to a more realistic voice model, which poses an issue for well-known subjects, such as politicians and celebrities, who have vast amounts of multimedia available online. Detection of synthetic speech has relied on signal processing techniques that focus on the generation of new acoustic features and train deep learning models to detect when an audio file has been manipulated through the characterization of unnatural changes or artifacts. However, these techniques do not use any information from the speaker they are evaluating. This paper proposes to incorporate information from the speaker-of-interest (SoI) into the models to avoid specific spoofing attacks for certain vulnerable people. The wealth of data for well-known people can also be used to train a speaker-specific spoofing detector with a higher level of accuracy than a speaker-independent model. The paper proposes a new xResNet-PLDA system and compares it to three different baseline systems: a state-of-the-art speaker identification system, an xResNet system trained to discriminate between bona fide and fake speech, and a speaker identification system in which the PLDA and calibration models were trained with bona fide and fake speech. We evaluated the systems in two different scenarios — a cross-validation scenario and a hold-out scenario — with three different databases. We show how the proposed system outperforms dramatically the baseline systems in each scenario and for each database. Finally, we show how using a small amount of the SoI’s speech to adapt global calibration parameters improves the performance of the system, especially in unseen conditions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Responsible Artificial Intelligence for Insider Threat Mitigation

This report examines the application of artificial intelligence (AI) technologies for insider threat mitigation (ITM) programs in nuclear security facilities. Insider threat detection presents unique challenges due to the subtle and adaptive nature of these threats, the complex signatures involved, and the scarcity of available data for analysis. Traditional human-centered approaches, while essential, face limitations in processing large amounts of data continuously and detecting subtle patterns across multiple systems. AI technologies can potentially address these limitations by providing 24/7 monitoring capabilities, identifying complex patterns that might escape human observation, and offering consistent application of security criteria. However, the deployment of AI in nuclear security contexts introduces significant new risks, including workflow disruption, expanded attack surfaces, potential for misuse, and ethical concerns regarding privacy, fairness, transparency, safety, and security. The high-consequence nature of nuclear security decisions demands careful consideration of these risks and systematic approaches to their mitigation.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
NASA NTRS1997

Fungal degradation of fiber-reinforced composite materials

As described in a previous report, a fungal consortium isolated from degraded polymeric materials was capable of growth on presterilized coupons of five composites, resulting in deep penetration into the interior of all materials within five weeks. Data describing the utilization of composite constituents as nutrients for the microflora are described in this article. Increased microbial growth was observed when composite extract was incubated with the fungal inoculum at ambient temperatures. Scanning electron microscopic observation of carbon fibers incubated with a naturally developed population of microorganisms showed the formation of bacterial biofilms on the fiber surfaces, suggesting possible utilization of the fiber chemical sizing as carbon and energy sources. Electrochemical impedance spectroscopy was used to monitor the phenomena occurring at the fiber-matrix interfaces. Significant differences were observed between inoculated and sterile panels of the composite materials. A progressive decline in impedance was detected in the inoculated panels. Several reaction steps may be involved in the degradation process. Initial ingress of water into the resin matrix appeared to be followed by degradation of fiber surfaces, and separation of fibers from the resin matrix. This investigation suggested that composite materials are susceptible to microbial attack by providing nutrients for growth.

Environmental Microbiology↗
NASA NTRS2004

Free-to-Roll Investigation of Uncommanded Lateral Motions for an Aircraft With Vented Strakes

A free-to-roll study of the low-speed lateral characteristics of the pre-production F/A-18E was conducted in the NASA Langley 12-Foot Low-Speed Tunnel. In developmental flight tests the F/A-18E unexpectedly experienced uncommanded lateral motions in the power approach configuration. The objective of this study was to determine the feasibility of using the free-to-roll technique for the detection of uncommanded lateral motions for the preproduction F/A-18E in the power approach configuration. The data revealed that this technique in conjunction with static data revealed insight into the cause of the lateral motions. The free-to-roll technique identified uncommanded lateral motions at the same angle-of-attack range as experienced in flight tests. The cause of the uncommanded lateral motions was unsteady asymmetric wing stall. The paper also shows that free-to-roll data or static force and moment data alone are not enough to accurately capture the potential for an aircraft to experience uncommanded lateral motion.

Bryan, Elaine M.↗
NASA NTRS1999

Boundary Layer Transition in the NTF: HSR Experience and Plans

Efforts towards understanding boundary layer transition characteristics on a High Speed Civil Transport (HSCT)-class configuration in the National Transonic Facility (NTF) are ongoing. The majority of the High Speed Research (HSR) data base in the NTF has free transition on the wing, even at low Reynolds numbers (Rn) attainable in conventional facilities. Limited data has been obtained and is described herein showing the effects of a conventional, Braslow method based wing boundary-layer trip on drag. Comparisons are made using force data polars and surface flow visualization at selected angles-of-attack and Mach number. Minimum drag data obtained in this study suggest that boundary layer transition occurred very near the wing leading edge by a chord Rn of 30 million. Sublimating chemicals were used in the air mode of operation only at low Rn and low angles-of-attack with no flap deflections; sublimation results suggest that the forebody and outboard wing panel are the only regions with significant laminar flow. The process and issues related to the sublimating chemical technique as applied in the NTF are discussed. Beyond the existing experience, status of efforts to develop a production transition detection system applicable to both air and cryogenic nitrogen environments is presented.

Owens, Lewis R., Jr.↗
NASA NTRS2005

Free-to-Roll Investigation of Uncommanded Lateral Motions for an Aircraft with Vented Strakes

A free-to-roll study of the low-speed lateral characteristics of the pre-production F/A-l8E was conducted in the NASA Langley 12-Foot Low-Speed Tunnel. In developmental flight tests the F/A-18E unexpectedly experienced uncommanded lateral motions in the power approach configuration. The objective of this study was to determine the feasibility of using the free-to-roll technique for the detection of uncommanded lateral motions for the pre-production F/A-l8E in the power approach configuration. The data revealed that this technique in conjunction with static data revealed insight into the cause of the lateral motions. The free-to-roll technique identified uncommanded lateral motions at the same angle-of-attack range as experienced in flight tests. The cause of the uncommanded lateral motions was unsteady asymmetric wing stall. The paper also shows that free-to-roll data or static force and moment data alone are not enough to accurately capture the potential for an aircraft to experience uncommanded lateral motion.

Owens, Elaine M.↗
NASA NTRS2005

Free-to-Roll Investigation of the Pre-Production F/A-18E Powered Approach Wing Drop

A free-to-roll study of the low-speed lateral characteristics of the pre-production F/A-18E was conducted in the NASA Langley 12-Foot Low-Speed Tunnel. In developmental flight tests the F/A-18E unexpectedly experienced uncommanded lateral motions in the power approach configuration. The objective of this study was to determine the feasibility of using the free-to-roll technique for the detection of uncommanded lateral motions for the pre-production F/A-18E in the power approach configuration. The data revealed that this technique in conjunction with static data revealed insight into the cause of the lateral motions. The free-to-roll technique identified uncommanded lateral motions at the same angle-of-attack range as experienced in flight tests. The cause of the uncommanded lateral motions was unsteady asymmetric wing stall. The paper also shows that free-to-roll data or static force and moment data alone are not enough to accurately capture the potential for an aircraft to experience uncommanded lateral motion.

VERTICAL TAIL↗
DOE OSTI2023

PathTrace and MPVEASI: A Path Analysis Comparative Validation Study

Developed in 2018, PathTrace is a software package built with the intention of making path analysis simple and intuitive. PathTrace is a top-down pathway analysis software where a user is able to explore vulnerable pathways into a facility. The intention of utilizing a software tool like PathTrace is to characterize an existing physical protection system (PPS) and to upgrade the system to achieve a high level of response interruption, or probability of interruption (P I ) of the adversary. There are four steps for conducting path analysis using PathTrace. The first step is to identify an image to use to build the model and scale the model within PathTrace using a section of known distance (wall or fence perimeter, for example). The scaling process will produce a grid of cells through which the user is able to build a model. The second step is to fill out the grid of cells with four categories of materials: Barriers, Detection Areas, Jumps, and Targets. These materials apply associated delay and detection values to the cells in which they are applied. The third step is to represent the adversary and response forces. The adversaries are represented by their capabilities in interacting with the materials identified in step two, and the response is represented by how quickly they will be able to respond to an adversary attack. Finally, the user is able to take all of the information from the previous three steps and perform a Most Vulnerable Path (MVP) analysis. In this stage, the user is able to visualize vulnerable adversary pathways and reason about how to upgrade these pathways to provide a high level of P I .

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Securing The Future: 2026 Manufacturing & Critical Infrastructure Threat Landscape

This report outlines the current state of manufacturing weaknesses introduced by the complexities of modern environments, including cloud services and Internet of Things (IoT) devices, with particular attention paid to the unique vulnerabilities encountered by SMMs. It also highlights CyManII’s strategic initiatives and collaborative solutions to mitigate these risks and strengthen the cybersecurity posture of the manufacturing ecosystem. Utilizing data from 2025 to inform forward-looking mitigation strategies, this report provides manufacturers with a clear understanding of both current and emerging cybersecurity threats, as well as practical opportunities to strengthen their cyber ecosystems. The following sections detail key vulnerabilities and threat vectors, along with actionable mitigation strategies, many of which have been developed or piloted through CyManII-led efforts. A thorough understanding of these risks and mitigation strategies is essential for manufacturers seeking to strengthen the security and resilience of their manufacturing operations.

3D Printing↗
DOE OSTI2021

FedADMP: A Joint Anomaly Detection and Mobility Prediction Framework via Federated Learning

With the proliferation of mobile devices and smart cameras, detecting anomalies and predicting their mobility are critical for enhancing safety in ubiquitous computing systems. Due to data privacy regulations and limited communication bandwidth, it is infeasible to collect, transmit, and store all data from mobile devices at a central location. To overcome this challenge, we propose FedADMP, a federated learning based joint Anomaly Detection and Mobility Prediction framework. FedADMP adaptively splits the training process between the server and clients to reduce computation loads on clients. To protect the privacy of user data, clients in FedADMP upload only intermediate model parameters to the cloud server. We also develop a differential privacy method to prevent the cloud server and external attackers from inferring private information during the model upload procedure. Extensive experiments using real-world datasets show that FedADMP consistently outperforms existing methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Analysis of Organophosphorus-Based Nerve Agent Degradation Products by Gas Chromatography-Mass Spectrometry (GC-MS): Current Derivatization Reactions in the Analytical Chemist’s Toolbox

The field of gas chromatography-mass spectrometry (GC-MS) in the analysis of chemical warfare agents (CWAs), specifically those involving the organophosphorus-based nerve agents (OPNAs), is a continually evolving and dynamic area of research. The ever-present interest in this field within analytical chemistry is driven by the constant threat posed by these lethal CWAs, highlighted by their use during the Tokyo subway attack in 1995, their deliberate use on civilians in Syria in 2013, and their use in the poisoning of Sergei and Yulia Skripal in Great Britain in 2018 and Alexei Navalny in 2020. These events coupled with their potential for mass destruction only serve to stress the importance of developing methods for their rapid and unambiguous detection. Although the direct detection of OPNAs is possible by GC-MS, in most instances, the analytical chemist must rely on the detection of the products arising from their degradation. To this end, derivatization reactions mainly in the form of silylations and alkylations employing a vast array of reagents have played a pivotal role in the efficient detection of these products that can be used retrospectively to identify the original OPNA.

sarin↗
NASA NTRS1991

In-flight flow visualization characteristics of the NASA F-18 high alpha research vehicle at high angles of attack

Surface and off-surface flow visualization techniques were used to visualize the 3-D separated flows on the NASA F-18 high alpha research vehicle at high angles of attack. Results near the alpha = 25 to 26 deg and alpha = 45 to 49 deg are presented. Both the forebody and leading edge extension (LEX) vortex cores and breakdown locations were visualized using smoke. Forebody and LEX vortex separation lines on the surface were defined using an emitted fluid technique. A laminar separation bubble was also detected on the nose cone using the emitted fluid technique and was similar to that observed in the wind tunnel test, but not as extensive. Regions of attached, separated, and vortical flow were noted on the wing and the leading edge flap using tufts and flow cones, and compared well with limited wind tunnel results.

Fisher, David F.↗