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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Small Autonomous Aircraft Servo Health Monitoring

Small air vehicles offer challenging power, weight, and volume constraints when considering implementation of system health monitoring technologies. In order to develop a testbed for monitoring the health and integrity of control surface servos and linkages, the Autonomous Aircraft Servo Health Monitoring system has been designed for small Uninhabited Aerial Vehicle (UAV) platforms to detect problematic behavior from servos and the air craft structures they control, This system will serve to verify the structural integrity of an aircraft's servos and linkages and thereby, through early detection of a problematic situation, minimize the chances of an aircraft accident. Embry-Riddle Aeronautical University's rotary-winged UAV has an Airborne Power management unit that is responsible for regulating, distributing, and monitoring the power supplied to the UAV's avionics. The current sensing technology utilized by the Airborne Power Management system is also the basis for the Servo Health system. The Servo Health system measures the current draw of the servos while the servos are in Motion in order to quantify the servo health. During a preflight check, deviations from a known baseline behavior can be logged and their causes found upon closer inspection of the aircraft. The erratic behavior nay include binding as a result of dirt buildup or backlash caused by looseness in the mechanical linkages. Moreover, the Servo Health system will allow elusive problems to be identified and preventative measures taken to avoid unnecessary hazardous conditions in small autonomous aircraft.

Quintero, Steven↗
NASA NTRS1987

An evaluation of a real-time fault diagnosis expert system for aircraft applications

A fault monitoring and diagnosis expert system called Faultfinder was conceived and developed to detect and diagnose in-flight failures in an aircraft. Faultfinder is an automated intelligent aid whose purpose is to assist the flight crew in fault monitoring, fault diagnosis, and recovery planning. The present implementation of this concept performs monitoring and diagnosis for a generic aircraft's propulsion and hydraulic subsystems. This implementation is capable of detecting and diagnosing failures of known and unknown (i.e., unforseeable) type in a real-time environment. Faultfinder uses both rule-based and model-based reasoning strategies which operate on causal, temporal, and qualitative information. A preliminary evaluation is made of the diagnostic concepts implemented in Faultfinder. The evaluation used actual aircraft accident and incident cases which were simulated to assess the effectiveness of Faultfinder in detecting and diagnosing failures. Results of this evaluation, together with the description of the current Faultfinder implementation, are presented.

Schutte, Paul C.↗
DOE OSTI2022

Using artificial intelligence to detect human errors in nuclear power plants: A case in operation and maintenance

Human error (HE) is an important concern in safety-critical systems such as nuclear power plants (NPPs). HE has played a role in many accidents and outage incidents in NPPs. Despite the increased automation in NPPs, HE remains unavoidable. Hence, the need for HE detection is as important as HE prevention efforts. In NPPs, HE is rather rare. Hence, anomaly detection, a widely used machine learning technique for detecting rare anomalous instances, can be repurposed to detect potential HE. In this study, we develop an unsupervised anomaly detection technique based on generative adversarial networks (GANs) to detect anomalies in manually collected surveillance data in NPPs. More specifically, our GAN is trained to detect mismatches between automatically recorded sensor data and manually collected surveillance data, and hence, identify anomalous instances that can be attributed to HE. We test our GAN on both a real-world dataset and an external dataset obtained from a testbed, and we benchmark our results against state-of-the-art unsupervised anomaly detection algorithms, including one-class support vector machine and isolation forest. Our results show that the proposed GAN provides improved anomaly detection performance. Our study is promising for the future development of artificial intelligence based HE detection systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2006

Millimeter Wave Detection of Localized Anomalies in the Space Shuttle External Fuel Tank Insulating Foam

The Space Shuttle Columbia's catastrophic accident emphasizes the growing need for developing and applying effective, robust and life-cycle oriented nondestructive testing (NDT) methods for inspecting the shuttle external fuel tank spray on foam insulation (SOFI). Millimeter wave NDT techniques were one of the methods chosen for evaluating their potential for inspecting these structures. Several panels with embedded anomalies (mainly voids) were produced and tested for this purpose. Near-field and far-field millimeter wave NDT methods were used for producing images of the anomalies in these panels. This paper presents the results of an investigation for the purpose of detecting localized anomalies in several SOFI panels. To this end, reflectometers at a relatively wide range of frequencies (Ka-band (26.5 - 40 GHz) to W-band (75 - 110 GHz)) and utilizing different types of radiators were employed. The resulting raw images revealed a significant amount of information about the interior of these panels. However, using simple image processing techniques the results were improved in particular as it relate s to detecting the smaller anomalies. This paper presents the results of this investigation and a discussion of these results.

Kharkovsky, S.↗
NASA NTRS1987

Comments on the problem of turbulence in aviation

The problem of turbulence since the beginning of aviation is traced. The problem was not cured by high altitude flight and was acerbated by the downbursts associated with thunderstorms. The accidents that occurred during the period 1982 to 1984 are listed. From this is extracted the weather related accidents. Turbulence accounts for 24% of the accidents involving large commercial carriers and 54% of the weather related accidents. In spite of all the efforts to improve the forecasting and detection of turbulence, the problem is still a large one.

Mclean, James C., Jr.↗
NASA NTRS2001

Spacecraft Fire Safety and Microgravity Combustion Research

Fire safety is an important concern in our daily lives and it plays a special role in the human presence in space. In a spacecraft, the outside environment is hostile and the opportunity to escape is small. Rescue missions are difficult and time consuming. As a result, we should avoid the occurrence of fires in spacecraft as much as possible. If a fire occurs, we need to keep it small and under control. This implies that the materials used on board the spacecraft should be screened carefully, all the machines and devices need to be operated without accident, and fire detectors have to function properly. Once a fire is detected, it can be extinguished quickly and the cabin can be cleaned up to restore operation and sustain life.

Tien, James S.↗
NASA NTRS1996

Intelligent Highway System

Under contract to the Texas Department of Transportation, AlliedSignal Technical Services developed the Transportation Guidance System (TransGuide) used in San Antonio, Texas. The system monitors the passage of traffic over the sensors embedded in the roadways and detects incidents. Control center operators are alerted to the occurrence of an accident and the area of the occurrence is highlighted on a map display. TransGuide incorporates technology AlliedSignal developed under various contracts to NASA at Goddard Space Flight Center, Johnson Space Center and Jet Propulsion Laboratory, including the design of ground control centers.

Source record↗
NASA NTRS2005

Development of Detectability Limits for On-Orbit Inspection of Space Shuttle Wing Leading Edge

At the conclusion of the Columbia Accident Investigation, one of the recommendations of the Columbia Accident Investigation Board (CAIB) was that NASA develop and implement an inspection plan for the Reinforced Carbon-Carbon (RCC) system components of the Space Shuttle. To address these issues, a group of scientists and engineers at NASA Langley Research Center proposed the use of an IR camera to inspect the RCC. Any crack in an RCC panel changes the thermal resistance of the material in the direction perpendicular to the crack. The change in thermal resistance can be made visible by introducing a heat flow across the crack and using an IR camera to image the resulting surface temperature distribution. The temperature difference across the crack depends on the change in the thermal resistance, the length of the crack, the local thermal gradient, and the rate of radiation exchange with the environment. This paper describes how the authors derived the minimum thermal gradient detectability limits for a through crack in an RCC panel. This paper will also show, through the use of a transient, 3-dimensional, finite element model, that these minimum gradients naturally exist on-orbit. The results from the finite element model confirm that there are sufficient thermal gradient to detect a crack on 96% of the RCC leading edge.

Stephan, Ryan A.↗
DOE OSTI2023

Portable chemical detection platform for on-site monitoring of odorant levels in natural gas

The adequate odorization of natural gas is critical to identify gas leaks and to reduce accidents. To ensure odorization, natural gas utility companies collect samples to be processed at core facilities or a trained human technician smells a diluted natural gas sample. In this work, we report a detection platform that addresses the lack of mobile solutions capable of providing quantitative analysis of mercaptans, a class of compounds used to odorize natural gas. Detailed description of the platform hardware and software components is provided. Designed to be portable, the platform hardware facilitates extraction of mercaptans from natural gas, separation of individual mercaptan species, and quantification of odorant concentration, with results reported at point-of-sampling. The software was developed to accommodate skilled users as well as minimally trained operators. Detection and quantification of six commonly used mercaptan compounds (ethyl mercaptan, dimethyl sulfide, n-propylmercaptan, isopropyl mercaptan, tert–butyl mercaptan, and tetrahydrothiophene) at typical odorizing concentrations of 0.1–5 ppm was performed using the device. We demonstrate the potential of this technology to ensure natural gas odorizing concentrations throughout distribution systems.

03 NATURAL GAS↗
NASA NTRS1986

The use of flight test techniques in aircraft accident investigations

Wind shear is a serious safety hazard to commercial aviation. Low level wind shear (downburst) was the cause of the takeoff accident in New Orleans, July 9, 1982, and the landing accident in Dallas, Aug. 2, 1985. Shear layer instability is a common cause of clear air turbulence (CAT) at cruising altitudes. A number of encounters with severe CAT, in which passengers were injured, have recently occurred (Hannibal, MO, April 1981; Morton, WY, July 1982; etc.). Improved accident investigation techniques can lead to a better understanding of the nature of the wind environment associated with downbursts and CAT and to better detection and avoidance procedures. For the past several years, NASA-Ames has worked closely with the National Transportation Safety Board in the investigation of wind related accidents.

Parks, E. K.↗
NASA NTRSDate not supplied

Capturing Multivariate Time Series Interactions to Detect High‑Risk Instability During Approach

The reduction of aviation safety metrics below target thresholds continue to drive down the number of aviation fatalities and accidents. To meet future safety demands, sustained efforts by aviation agencies promoting safety assurance processes and systems have prompted ongoing research on identifying and mitigating in-flight risks. With the projected increase in passenger load factor and rollout of more autonomous systems into the national airspace, the need to detect high-risk events in-time or ahead-of-time is becoming increasingly crucial. New anomaly detection and precursor identification algorithms will need to scale to different airframes, levels of autonomy, and system complexity. While the pervasiveness of deep learning has resulted in the development of performant anomaly detection methods, these sophisticated models currently suffer from low end-user interpretability. Building off our previous work on identifying adverse events in multivariate flight data during descent, we propose a data-driven approach for detecting in-flight adverse events caused by the complex interplay of flight variables. Our approach utilizes ordinal patterns of important aircraft stability variables (e.g., airspeed and descent rate) to capture multivariate flight dynamics that can be used to predict the onset of unstable approaches, a high-risk adverse event that can occur during approach. Through the use of ordinal patterns, we aim to create more interpretable detection models of in-flight adverse events that can be translated to future autonomous systems without difficulty. Our analysis shows the presence of distinct ordinal pattern distributions that can be used to predict unstable approaches 1 minute ahead of time with an accuracy of 0.69 and a recall of 0.73 and 30 seconds ahead with an accuracy of 0.70 and a recall of 0.86.

Risk detection↗
DOE OSTI2020

AMS / BARC Joint Survey (Summary Report)

Report describes a series of joint aerial radiation survey flights at various locations in Southern Nevada and Colorado performed by AMS and BARC (U.S. and India). The flights were conducted over areas of well-characterized natural background in Nevada, at the Large-Area Calibration Pads at Grand Junction, CO, and over real ground contamination at the Nevada National Security Site. The intention of the joint surveys was to compare the responses, processes, and procedures of each country’s aerial radiation detection system. These systems can detect and map ground contamination that may result from a nuclear or radiological accident or incident, and play a significant role in the national radiological emergency response capabilities of both countries.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
NASA NTRS2016

Psychophysiological Sensing and State Classification for Attention Management in Commercial Aviation

Attention-related human performance limiting states (AHPLS) can cause pilots to lose airplane state awareness (ASA), and their detection is important to improving commercial aviation safety. The Commercial Aviation Safety Team found that the majority of recent international commercial aviation accidents attributable to loss of control inflight involved flight crew loss of airplane state awareness, and that distraction of various forms was involved in all of them. Research on AHPLS, including channelized attention, diverted attention, startle / surprise, and confirmation bias, has been recommended in a Safety Enhancement (SE) entitled "Training for Attention Management." To accomplish the detection of such cognitive and psychophysiological states, a broad suite of sensors has been implemented to simultaneously measure their physiological markers during high fidelity flight simulation human subject studies. Pilot participants were asked to perform benchmark tasks and experimental flight scenarios designed to induce AHPLS. Pattern classification was employed to distinguish the AHPLS induced by the benchmark tasks. Unimodal classification using pre-processed electroencephalography (EEG) signals as input features to extreme gradient boosting, random forest and deep neural network multiclass classifiers was implemented. Multi-modal classification using galvanic skin response (GSR) in addition to the same EEG signals and using the same types of classifiers produced increased accuracy with respect to the unimodal case (90 percent vs. 86 percent), although only via the deep neural network classifier. These initial results are a first step toward the goal of demonstrating simultaneous real time classification of multiple states using multiple sensing modalities in high-fidelity flight simulators. This detection is intended to support and inform training methods under development to mitigate the loss of ASA and thus reduce accidents and incidents.

Harrivel, Angela R.↗
NASA NTRS1976

Wind shear detection using measurement of aircraft total energy change

Encounters with wind shears are of concern and have caused major accidents, particularly during landing approaches. Changes in the longitudinal component of the wind affect the aircraft by changing its kinetic energy with respect to the air. It is shown that an instrument which will measure and display the rate of change of total energy of the aircraft with respect to the air will give a leading indication of wind shear problems. The concept is outlined and some instrumentation and display considerations are discussed.

Joppa, R. G.↗
NASA NTRS2012

Modeling and Detection of Ice Particle Accretion in Aircraft Engine Compression Systems

The accretion of ice particles in the core of commercial aircraft engines has been an ongoing aviation safety challenge. While no accidents have resulted from this phenomenon to date, numerous engine power loss events ranging from uneventful recoveries to forced landings have been recorded. As a first step to enabling mitigation strategies during ice accretion, a detection scheme must be developed that is capable of being implemented on board modern engines. In this paper, a simple detection scheme is developed and tested using a realistic engine simulation with approximate ice accretion models based on data from a compressor design tool. These accretion models are implemented as modified Low Pressure Compressor maps and have the capability to shift engine performance based on a specified level of ice blockage. Based on results from this model, it is possible to detect the accretion of ice in the engine core by observing shifts in the typical sensed engine outputs. Results are presented in which, for a 0.1 percent false positive rate, a true positive detection rate of 98 percent is achieved.

May, Ryan D.↗
NASA NTRSDate not supplied

Capturing Complex Multivariate Time Series Interactions to Detect High-Risk Adverse Events During Flight

The reduction of aviation safety metrics below target thresholds continue to drive down the number of aviation fatalities and accidents. To meet future safety demands, sustained efforts by aviation agencies promoting safety assurance processes and systems have prompted ongoing research on identifying and mitigating in-flight risks. With the projected increase in passenger load factor and rollout of more autonomous systems into the national airspace, the need to detect high-risk events in-time or ahead-of-time is becoming increasingly crucial. New anomaly detection and precursor identification algorithms will need to scale to different airframes, levels of autonomy, and system complexity. While the pervasiveness of deep learning has resulted in the development of performant anomaly detection methods, these sophisticated models currently suffer from low end-user interpretability. Building off our previous work on identifying adverse events in multivariate flight data during descent, we propose a data-driven approach for detecting in-flight adverse events caused by the complex interplay of flight variables. Our approach utilizes ordinal patterns of important aircraft stability variables (e.g., airspeed and descent rate) to capture multivariate flight dynamics that can be used to predict the onset of unstable approaches, a high-risk adverse event that can occur during approach. Through the use of ordinal patterns, we aim to create more interpretable detection models of in-flight adverse events that can be translated to future autonomous systems without difficulty. Our analysis shows the presence of distinct ordinal pattern distributions that can be used to predict unstable approaches 1 minute ahead of time with an accuracy of 0.69 and a recall of 0.73 and 30 seconds ahead with an accuracy of 0.70 and a recall of 0.86.

Risk detection↗
NASA NTRS1995

Simulation of the 1994 Charlotte Microburst with Look-Ahead Windshear Radar

A severe microburst occurred on 2 July 1994 at Charlotte, NC, and was associated with the crash of USAir Flight 1016 (FL-1016) (Salottolo 1994; Phillips 1994). The inbound DC-9 unexpectedly encountered a rapidly intensifying rainshaft just seconds before it was to touchdown on runway 18R. The aircraft crashed after encountering strong windshear, killing 37 of the 57 souls on board. The pilots did not recognize the windshear condition in time to prevent the accident and received no warning from the aircraft's Honeywell in-situ windshear detection system or from ground-based systems (Charlotte maintains both an ASR-9 weather radar and a Phase-2 LLWAS). Also two other aircraft landed ahead of FL-1016 without incident and reported smooth approaches to 18R. Section-2 of this paper reports briefly on the reconstruction of the event based on numerical results generated by the Terminal Area Simulation System (TASS) as presented at the National Transportation Safety Board (NTSB) public hearing (Proctor 1994). Section-3 discusses the simulation of this event with a look-ahead windshear radar.

Proctor, F. H.↗
NASA NTRS2005

Millimeter Wave Detection of Localized Anomalies in the Space Shuttle External Fuel Tank Insulating Foam and Acreage Heat Tiles

The Space Shuttle Columbia's catastrophic accident emphasizes the growing need for developing and applying effective, robust and life-cycle oriented nondestructive testing (NDT) methods for inspecting the shuttle external fuel tank spray on foam insulation (SOFI) and its protective acreage heat tiles. Millimeter wave NDT techniques were one of the methods chosen for evaluating their potential for inspecting these structures. Several panels with embedded anomalies (mainly voids) were produced and tested for this purpose. Near-field and far-field millimeter wave NDT methods were used for producing millimeter wave images of the anomalies in SOFI panel and heat tiles. This paper presents the results of an investigation for the purpose of detecting localized anomalies in two SOFI panels and a set of heat tiles. To this end, reflectometers at a relatively wide range of frequencies (Ka-band (26.5 - 40 GHz) to W-band (75 - 110 GHz)) and utilizing different types of radiators were employed. The results clearly illustrate the utility of these methods for this purpose.

Kharkovsky, S.↗