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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

A Cloud Mask for AIRS

With the preliminary release of AIRS Level 1 and 2 data to the scientific community, there is a growing need for an accurate AIRS cloud mask for data assimilation studies and in producing products derived from cloud free radiances. Current cloud information provided with the AIRS data are limited or based on simplified threshold tests. A multispectral cloud detection approach has been developed for AIRS that utilizes the hyper-spectral capabilities to detect clouds based on specific cloud signatures across the short wave and long wave infrared window regions. This new AIRS cloud mask has been validated against the existing AIRS Level 2 cloud product and cloud information derived from MODIS. Preliminary results for both day and night applications over the continental U.S. are encouraging. Details of the cloud detection approach and validation results will be presented at the conference.

Brubaker, N.↗
DOE OSTI2024

A Deep Learning Approach for Detection and Localization of Leaf Anomalies

The detection and localization of possible diseases in crops are usually automated by resorting to supervised deep learning approaches. In this work, we tackle these goals with unsupervised models, by applying three different types of autoencoders to a specific open-source dataset of healthy and unhealthy pepper and cherry leaf images. CAE, CVAE and VQ-VAE autoencoders are deployed to screen unlabeled images of such a dataset, and compared in terms of image reconstruction, anomaly removal, detection and localization. The vector-quantized variational architecture turns out to be the best performing one with respect to all these targets.

Calabro', Davide↗
NASA NTRS2015

Investigation of Asymmetric Thrust Detection with Demonstration in a Real-Time Simulation Testbed

The purpose of this effort is to develop, demonstrate, and evaluate three asymmetric thrust detection approaches to aid in the reduction of asymmetric thrust-induced aviation accidents. This paper presents the results from that effort and their evaluation in simulation studies, including those from a real-time flight simulation testbed. Asymmetric thrust is recognized as a contributing factor in several Propulsion System Malfunction plus Inappropriate Crew Response (PSM+ICR) aviation accidents. As an improvement over the state-of-the-art, providing annunciation of asymmetric thrust to alert the crew may hold safety benefits. For this, the reliable detection and confirmation of asymmetric thrust conditions is required. For this work, three asymmetric thrust detection methods are presented along with their results obtained through simulation studies. Representative asymmetric thrust conditions are modeled in simulation based on failure scenarios similar to those reported in aviation incident and accident descriptions. These simulated asymmetric thrust scenarios, combined with actual aircraft operational flight data, are then used to conduct a sensitivity study regarding the detection capabilities of the three methods. Additional evaluation results are presented based on pilot-in-the-loop simulation studies conducted in the NASA Glenn Research Center (GRC) flight simulation testbed. Data obtained from this flight simulation facility are used to further evaluate the effectiveness and accuracy of the asymmetric thrust detection approaches. Generally, the asymmetric thrust conditions are correctly detected and confirmed.

aircraft safety↗
NASA NTRS2015

Investigation of Asymmetric Thrust Detection with Demonstration in a Real-Time Simulation Testbed

The purpose of this effort is to develop, demonstrate, and evaluate three asymmetric thrust detection approaches to aid in the reduction of asymmetric thrust-induced aviation accidents. This paper presents the results from that effort and their evaluation in simulation studies, including those from a real-time flight simulation testbed. Asymmetric thrust is recognized as a contributing factor in several Propulsion System Malfunction plus Inappropriate Crew Response (PSM+ICR) aviation accidents. As an improvement over the state-of-the-art, providing annunciation of asymmetric thrust to alert the crew may hold safety benefits. For this, the reliable detection and confirmation of asymmetric thrust conditions is required. For this work, three asymmetric thrust detection methods are presented along with their results obtained through simulation studies. Representative asymmetric thrust conditions are modeled in simulation based on failure scenarios similar to those reported in aviation incident and accident descriptions. These simulated asymmetric thrust scenarios, combined with actual aircraft operational flight data, are then used to conduct a sensitivity study regarding the detection capabilities of the three methods. Additional evaluation results are presented based on pilot-in-the-loop simulation studies conducted in the NASA Glenn Research Center (GRC) flight simulation testbed. Data obtained from this flight simulation facility are used to further evaluate the effectiveness and accuracy of the asymmetric thrust detection approaches. Generally, the asymmetric thrust conditions are correctly detected and confirmed.

gas turbine engines↗
NASA NTRS2016

Investigation of Asymmetric Thrust Detection with Demonstration in a Real-Time Simulation Testbed

The purpose of this effort is to develop, demonstrate, and evaluate three asymmetric thrust detection approaches to aid in the reduction of asymmetric thrust-induced aviation accidents. This paper presents the results from that effort and their evaluation in simulation studies, including those from a real-time flight simulation testbed. Asymmetric thrust is recognized as a contributing factor in several Propulsion System Malfunction plus Inappropriate Crew Response (PSM+ICR) aviation accidents. As an improvement over the state-of-the-art, providing annunciation of asymmetric thrust to alert the crew may hold safety benefits. For this, the reliable detection and confirmation of asymmetric thrust conditions is required. For this work, three asymmetric thrust detection methods are presented along with their results obtained through simulation studies. Representative asymmetric thrust conditions are modeled in simulation based on failure scenarios similar to those reported in aviation incident and accident descriptions. These simulated asymmetric thrust scenarios, combined with actual aircraft operational flight data, are then used to conduct a sensitivity study regarding the detection capabilities of the three methods. Additional evaluation results are presented based on pilot-in-the-loop simulation studies conducted in the NASA Glenn Research Center (GRC) flight simulation testbed. Data obtained from this flight simulation facility are used to further evaluate the effectiveness and accuracy of the asymmetric thrust detection approaches. Generally, the asymmetric thrust conditions are correctly detected and confirmed.

propulsion system performance↗
DOE OSTI2025

Optimization-Based Data-Driven Approach for Detecting Fault Location in Power Systems

In grids with large penetration of converterinterfaced resources (CIRs), measurements of voltage, current, and line parameters can fluctuate significantly during fault conditions. These fluctuations, combined with complex network topologies and extensive system branching, make accurate fault location challenging. Faults, such as short circuits, can cause prolonged outages with serious socio-economic impacts, highlighting the need for rapid fault identification to minimize downtime. However, current fault detection methods—such as relays and digital fault recorders—often relay information too slowly, impeding swift corrective action. Given the limited availability of high-resolution phasor measurement units, this paper introduces an optimization-based observer to estimate fault locations, grid line parameters, and voltages using local CIR measurements. To preserve the confidentiality of CIRs and enhance estimation accuracy, this study uses a black-box model of CIRs. This bottom-up, event-driven approach can enhances protection and control systems through optimized and real-time fault detection. Simulation results show that the optimization-based data-driven observer can accurately detect fault locations and estimate grid states and parameters, providing valuable insights for utilities and operators in grid applications.

Subedi, Sunil [ORNL] (ORCID:000000034069090X)↗
DOE OSTI2022

Random Forest Regressor-Based Approach for Detecting Fault Location and Duration in Power Systems

Power system failures or outages due to short-circuits or “faults” can result in long service interruptions leading to significant socio-economic consequences. It is critical for electrical utilities to quickly ascertain fault characteristics, including location, type, and duration, to reduce the service time of an outage. Existing fault detection mechanisms (relays and digital fault recorders) are slow to communicate the fault characteristics upstream to the substations and control centers for action to be taken quickly. Fortunately, due to availability of high-resolution phasor measurement units (PMUs), more event-driven solutions can be captured in real time. In this paper, we propose a data-driven approach for determining fault characteristics using samples of fault trajectories. A random forest regressor (RFR)-based model is used to detect real-time fault location and its duration simultaneously. This model is based on combining multiple uncorrelated trees with state-of-the-art boosting and aggregating techniques in order to obtain robust generalizations and greater accuracy without overfitting or underfitting. Four cases were studied to evaluate the performance of RFR: 1. Detecting fault location (case 1), 2. Predicting fault duration (case 2), 3. Handling missing data (case 3), and 4. Identifying fault location and length in a real-time streaming environment (case 4). A comparative analysis was conducted between the RFR algorithm and state-of-the-art models, including deep neural network, Hoeffding tree, neural network, support vector machine, decision tree, naive Bayesian, and K-nearest neighborhood. Experiments revealed that RFR consistently outperformed the other models in detection accuracy, prediction error, and processing time.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample

Accurate detection of somatic mutations is challenging but critical in understanding cancer formation, progression, and treatment. We recently proposed NeuSomatic, the first deep convolutional neural network-based somatic mutation detection approach, and demonstrated performance advantages on in silico data. In this study, we use the first comprehensive and well-characterized somatic reference data sets from the SEQC2 consortium to investigate best practices for using a deep learning framework in cancer mutation detection. Using the high-confidence somatic mutations established for a cancer cell line by the consortium, we identify the best strategy for building robust models on multiple data sets derived from samples representing real scenarios, for example, a model trained on a combination of real and spike-in mutations had the highest average performance. The strategy identified in our study achieved high robustness across multiple sequencing technologies for fresh and FFPE DNA input, varying tumor/normal purities, and different coverages, with significant superiority over conventional detection approaches in general, as well as in challenging situations such as low coverage, low variant allele frequency, DNA damage, and difficult genomic regions.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2013

A Signal Detection Theory Approach to Evaluating Oculometer Data Quality

Currently, data quality is described in terms of spatial and temporal accuracy and precision [Holmqvist et al. in press]. While this approach provides precise errors in pixels, or visual angle, often experiments are more concerned with whether subjects'points of gaze can be said to be reliable with respect to experimentally-relevant areas of interest. This paper proposes a method to characterize oculometer data quality using Signal Detection Theory (SDT) [Marcum 1947]. SDT classification results in four cases: Hit (correct report of a signal), Miss (failure to report a ), False Alarm (a signal falsely reported), Correct Reject (absence of a signal correctly reported). A technique is proposed where subjects' are directed to look at points in and outside of an AOI, and the resulting Points of Gaze (POG) are classified as Hits (points known to be internal to an AOI are classified as such), Misses (AOI points are not indicated as such), False Alarms (points external to AOIs are indicated as in the AOI), or Correct Rejects (points external to the AOI are indicated as such). SDT metrics describe performance in terms of discriminability, sensitivity, and specificity. This paper presentation will provide the procedure for conducting this assessment and an example of data collected for AOIs in a simulated flightdeck environment.

Latorella, Kara↗
DOE OSTI2021

Hidden Markov Model: a shortest unique representative approach to detect the protein toxins, virulence factors and antibiotic resistance genes

Objective: Currently, next generation sequencing (NGS) is widely used to decode potential novel or variant pathogens both in emergent outbreaks and in routine clinical practice. However, the efficient identification of novel or diverged pathogenomic compositions remains a big challenge. It is especially true for short DNA sequence fragments from NGS, since sequence similarity searching is vulnerable to false negatives or false positives, as is mismatching or matching with unrelated proteins. Therefore, this study aimed to establish a bioinformatics approach that can generate unique motif sequences for profiling searching, resulting in high specificity and sensitivity. Results: In this study, we introduced a Shortest Unique Representative Hidden Markov Model (HMM) approach to identify bacterial toxin, virulence factor (VF), and antimicrobial resistance (AR) in short sequence reads. We first construct unique representative domain sequences of toxin genes, VFs, and ARs to avoid potential false positives, and then to use HMM models to accurately identify potential toxin, VF, and AR fragments. The benchmark shows this approach can achieve relatively high specificity and sensitivity if the appropriate cutoff value is applied. Our approach can be used to recognize the protein sequences of known toxins and pathogens, identifies their common characteristics and then searches for similar sequences in other organisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A numerical approach for detecting switch-like bistability in mass action chemical reaction networks with conservation laws

Theoretical analysis of signaling pathways can provide a substantial amount of insight into their function. One particular area of research considers signaling pathways capable of assuming two or more stable states given the same amount of signaling ligand. This phenomenon of bistability can give rise to switch-like behavior, a mechanism that governs cellular decision making. Investigation of whether or not a signaling pathway can confer bistability and switch-like behavior, without knowledge of specific kinetic rate constant values, is a mathematically challenging problem. Recently a technique based on optimization has been introduced, which is capable of finding example parameter values that confer switch-like behavior for a given pathway. Although this approach has made it possible to analyze moderately sized pathways, it is limited to reaction networks that presume a uniterminal structure. It is this limited structure we address by developing a general technique that applies to any mass action reaction network with conservation laws. In this paper we developed a generalized method for detecting switch-like bistable behavior in any mass action reaction network with conservation laws. The method involves (1) construction of a constrained optimization problem using the determinant of the Jacobian of the underlying rate equations, (2) minimization of the objective function to search for conditions resulting in a zero eigenvalue, (3) computation of a confidence level that describes if the global minimum has been found and (4) evaluation of optimization values, using either numerical continuation or directly simulating the ODE system, to verify that a bistability region exists. The generalized method has been tested on three motifs known to be capable of bistability. We have developed a variation of an optimization-based method for the discovery of bistability, which is not limited to uniterminal chemical reaction networks. Successful completion of the method provides an S-shaped bifurcation diagram, which indicates that the network acts as a bistable switch for the given optimization parameters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1988

Direct and interferometric imaging approaches for detecting earth-like extrasolar planets

This paper discusses functional requirements of space-based observational systems with sufficient sensitivity, resolution, and dynamic range to image earth-like extrasolar planets within a search radius of 10 parsecs from the sun. Both direct and interferometric systems operating at visible and infrared wavelengths are evaluated, and the methods used to establish the system tolerances are presented. Due to the more favorable star/planet contrast ratio in the infrared, optical tolerance requirements are less stringent than in the visible. However, reduction of thermal radiation from the telescope requires cooling of the primary optics. Other tradeoffs between various approaches are enumerated.

Diner, D. J.↗
NASA NTRS2012

Quantum Limits of Space-to-Ground Optical Communications

For a pure loss channel, the ultimate capacity can be achieved with classical coherent states (i.e., ideal laser light): (1) Capacity-achieving receiver (measurement) is yet to be determined. (2) Heterodyne detection approaches the ultimate capacity at high mean photon numbers. (3) Photon-counting approaches the ultimate capacity at low mean photon numbers. A number of current technology limits drive the achievable performance of free-space communication links. Approaching fundamental limits in the bandwidth-limited regime: (1) Heterodyne detection with high-order coherent-state modulation approaches ultimate limits. SOA improvements to laser phase noise, adaptive optics systems for atmospheric transmission would help. (2) High-order intensity modulation and photon-counting can approach heterodyne detection within approximately a factor of 2. This may have advantages over coherent detection in the presence of turbulence. Approaching fundamental limits in the photon-limited regime (1) Low-duty cycle binary coherent-state modulation (OOK, PPM) approaches ultimate limits. SOA improvements to laser extinction ratio, receiver dark noise, jitter, and blocking would help. (2) In some link geometries (near field links) number-state transmission could improve over coherent-state transmission

laser communications↗
NASA NTRS2010

A Unified Nonlinear Adaptive Approach for Detection and Isolation of Engine Faults

A challenging problem in aircraft engine health management (EHM) system development is to detect and isolate faults in system components (i.e., compressor, turbine), actuators, and sensors. Existing nonlinear EHM methods often deal with component faults, actuator faults, and sensor faults separately, which may potentially lead to incorrect diagnostic decisions and unnecessary maintenance. Therefore, it would be ideal to address sensor faults, actuator faults, and component faults under one unified framework. This paper presents a systematic and unified nonlinear adaptive framework for detecting and isolating sensor faults, actuator faults, and component faults for aircraft engines. The fault detection and isolation (FDI) architecture consists of a parallel bank of nonlinear adaptive estimators. Adaptive thresholds are appropriately designed such that, in the presence of a particular fault, all components of the residual generated by the adaptive estimator corresponding to the actual fault type remain below their thresholds. If the faults are sufficiently different, then at least one component of the residual generated by each remaining adaptive estimator should exceed its threshold. Therefore, based on the specific response of the residuals, sensor faults, actuator faults, and component faults can be isolated. The effectiveness of the approach was evaluated using the NASA C-MAPSS turbofan engine model, and simulation results are presented.

Tang, Liang↗
DOE OSTI2024

A data-driven sensor placement approach for detecting voltage violations in distribution systems

Stochastic fluctuations in power injections from distributed energy resources (DERs) combined with load variability can cause constraint violations (e.g., exceeded voltage limits) in electric distribution systems. To monitor grid operations, sensors are placed to measure important quantities such as the voltage magnitudes. Here, in this paper, we consider a sensor placement problem which seeks to identify locations for installing sensors that can capture all possible violations of voltage magnitude limits. We formulate a bilevel optimization problem that minimizes the number of sensors and avoids false sensor alarms in the upper level while ensuring detection of any voltage violations in the lower level. This problem is challenging due to the nonlinearity of the power flow equations and the presence of binary variables. Accordingly, we employ recently developed conservative linear approximations of the power flow equations that overestimate or underestimate the voltage magnitudes. By replacing the nonlinear power flow equations with conservative linear approximations, we can ensure that the resulting sensor locations and thresholds are sufficient to identify any constraint violations. Additionally, we apply various problem reformulations to significantly improve computational tractability while simultaneously ensuring an appropriate placement of sensors. Lastly, we improve the quality of the results via an approximate gradient descent method that adjusts the sensor thresholds. We demonstrate the effectiveness of our proposed method for several test cases, including a system with multiple switching configurations.

24 POWER TRANSMISSION AND DISTRIBUTION↗