One-shot gas detection with transformer paired neural networks in Mako collected longwave infrared hyperspectral imagery
Explore the source record for details and available documents.
Benham, Kevin Andrew↗
SEARCH · Engineering Papers
Results for “gas detection”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Air Monitoring for Hazardous Gas Detection
The Hazardous Gas Detection Lab is involved in the design and development of instrumentation that can detect and quantify various hazardous gases. Traditionally these systems are designed for leak detection of the cryogenic gases used for the propulsion of the Shuttle and other vehicles. Mass spectrometers are the basis of these systems, which provide excellent quantitation, sensitivity, selectivity, response and limits of detection. Unfortunately, these systems are large, heavy and expensive. This feature limits the ability to perform gas analysis in certain applications. Smaller and lighter mass spectrometer systems could be used in many more applications primarily due to the portability of the system. Such applications would include air analysis in confined spaces, in-situ environmental analysis and emergency response. In general, system cost is lowered as size is reduced. With a low cost air analysis system, several systems could be utilized for monitoring large areas. These networked systems could be deployed at job-sites for worker safety, throughout a community for pollution warnings, or dispersed in a battlefield for early warning of chemical or biological threats. Presented will be information on the first prototype of this type of system. Included will be field trial data, with this prototype performing air analysis autonomously from an aircraft.
Arkin, C. Richard↗
Air Monitoring for Hazardous Gas Detection
The Hazardous Gas Detection Lab (HGDL) at Kennedy Space Center is involved in the design and development of instrumentation that can detect and quantify various hazardous gases. Traditionally these systems are designed for leak detection of the cryogenic gases used for the propulsion of the Shuttle and other vehicles. Mass spectrometers are the basis of these systems, which provide excellent quantitation, sensitivity, selectivity, response times and detection limits. A Table lists common gases monitored for aerospace applications. The first five gases, hydrogen, helium, nitrogen, oxygen, and argon are historically the focus of the HGDL.
Arkin, C. Richard↗
History, design and performance of the space shuttle hazardous gas detection system
The Hazardous Gas Detection System (HGDS) is designed to detect leaks hich could result in pre-launch or in-flight fires or explosions resulting from the presence of cryogenic rocket propellants. The historical development, design, and performance of the HGDS is discussed. Data for response time, detection limits, accuracy, and drift are presented. Finally, present and future applications are discussed, and some general conclusions are drawn.
Helms, W. R.↗
An integrated knowledge system for the Space Shuttle hazardous gas detection system
A computer-based integrated Knowledge-Based System, the Intelligent Hypertext Manual (IHM), was developed for the Space Shuttle Hazardous Gas Detection System (HGDS) at NASA Marshall Space Flight Center (MSFC). The IHM stores HGDS related knowledge and presents it in an interactive and intuitive manner. This manual is a combination of hypertext and an expert system which store experts' knowledge and experience in hazardous gas detection and analysis. The IHM's purpose is to provide HGDS personnel with the capabilities of: locating applicable documentation related to procedures, constraints, and previous fault histories; assisting in the training of personnel; enhancing the interpretation of real time data; and recognizing and identifying possible faults in the Space Shuttle sub-systems related to hazardous gas detection.
Lo, Ching F.↗
Hydrocarbon gas detection using a HeNe laser
Hydrocarbon gas detection using He-Ne laser radiation absorption, discussing detector electronics
Beam, B. H.↗
Optically selective, acoustically resonant gas detecting transducer
A gas analyzer is disclosed which responds to the resonant absorption or emission spectrum of a specific gas by producing an acoustic resonance in a chamber containing a sample of that gas, and which measures the amount of that emission or absorption by measuring the strength of that acoustic resonance, e.g., the maximum periodic pressure, velocity or density achieved. In the preferred embodiment, a light beam is modulated periodically at the acoustical resonance frequency of a closed chamber which contains an optically dense sample of the gas of interest. Periodic heating of the absorbing gas by the light beam causes a cyclic expansion, movement, and pressure within the gas. An amplitude is reached where the increased losses were the cyclic radiation energy received. A transducing system is inclined for converting the pressure variations of the resonant gas into electronic readout signals.
Dimeff, J.↗
One-shot gas detection with transformer paired neural networks in Mako collected longwave infrared hyperspectral imagery
To date, careful data treatment workflows and statistical detectors are used to perform hyperspectral image (HSI) detection of any gas contained in a spectral library, which is often expanded with physics models to incorporate different spectral characteristics. In general, surrounding evidence or known gas-release parameters are used to provide confidence in or confirm detection capability, respectively. This makes quantifying detection performance difficult as it is nearly impossible to develop an absolute ground truth for gas target pixel presence in collected HSI. Consequently, developing and comparing new detection methods, especially machine learning (ML)-based methods, is susceptible to subjectivity in derived detection map quality. Here, in this work, we demonstrate the first use of transformer-based paired neural networks (PNNs) for one-shot gas target detection for multiple gases while providing quantitative classification and detection metrics for their use on labeled data. Terabytes of training data are generated from a database of long-wave infrared HSI obtained from historical Mako sensor campaigns over Los Angeles. By incorporating labels, singular signature representations, and a model development pipeline, we can tune and select PNNs to detect multiple gas targets that are not seen in training on a quantitative basis. We additionally assess our test set detections using interpretability techniques widely employed with ML-based predictors, but less common with detection methods relying on learned latent spaces.
Hyperspectral imaging↗
Development of an intelligent hypertext manual for the space shuttle hazardous gas detection system
A computer-based Integrated Knowledge System (IKS), the Intelligent Hypertext Manual (IHM), is being developed for the Space Shuttle Hazardous Gas Detection System (HGDS) at the Huntsville Operations Support Center (HOSC). The IHM stores all HGDS related knowledge and presents them in an interactive and intuitive manner. The IHM's purpose is to provide HGDS personnel with the capabilities of: enhancing the interpretation of real time data; recognizing and identifying possible faults in the Space Shuttle sub-system related to hazardous gas detections; locating applicable documentation related to procedures, constraints, and previous fault histories; and assisting in the training of personnel.
Lo, Ching F.↗
Development of an Intelligent Hypertext Manual for the Space Shuttle Hazardous Gas Detection System
The Intelligent Hypertext Manual (IHM) for the Hazardous Gas Detection System (HGDS) of the Space Shuttle is described as an example of an integrated knowledge system. The IHM is described in terms of its design as a system for facilitating the interpretation of real-time data regarding hazardous gases and their successful and timely detection. Hypermedia technology is employed that incorporates text, video, and sound, and the architecture of the IHM integrated knowledge system is based on knowledge elements, a basic user interface, and integrated application software. Knowledge organization enhances the retrieval of documents for launch commit criteria, operations and maintenance requirements, flight-measurement location, and hypermedia incorporation. The integration of computer and AI technologies for the HGDS IHM demonstrates the potential for enhancing efficiency in aerospace operations with aerospace data that are easy to retrieve.
Lo, Ching F.↗
Intelligent hypertext manual development for the Space Shuttle hazardous gas detection system
This research is designed to utilize artificial intelligence (AI) technology to increase the efficiency of personnel involved with monitoring the space shuttle hazardous gas detection systems at the Marshall Space Flight Center. The objective is to create a computerized service manual in the form of a hypertext and expert system which stores experts' knowledge and experience. The resulting Intelligent Manual will assist the user in interpreting data timely, in identifying possible faults, in locating the applicable documentation efficiently, in training inexperienced personnel effectively, and updating the manual frequently as required.
Lo, Ching F.↗
Miniaturized Hollow-Waveguide Gas Correlation Radiometer (GCR) for Trace Gas Detection in the Martian Atmosphere
Gas correlation radiometry (GCR) has been shown to be a sensitive and versatile method for detecting trace gases in Earth's atmosphere. Here, we present a miniaturized and simplified version of this instrument capable of mapping multiple trace gases and identifying active regions on the Mars surface. Reduction of the size and mass of the GCR instrument has been achieved by implementing a lightweight, 1 mm inner diameter hollow-core optical fiber (hollow waveguide) for the gas correlation cell. Based on a comparison with an Earth orbiting CO2 gas correlation instrument, replacement of the 10 meter mUltipass cell with hollow waveguide of equivalent pathlength reduces the cell mass from approx 150 kg to approx 0.5 kg, and reduces the volume from 1.9 m x 1.3 m x 0.86 m to a small bundle of fiber coils approximately I meter in diameter by 0.05 m in height (mass and volume reductions of >99%). This modular instrument technique can be expanded to include measurements of additional species of interest including nitrous oxide (N2O), hydrogen sulfide (H2S), methanol (CH3OH), and sulfur dioxide (SO2), as well as carbon dioxide (CO2) for a simultaneous measure of mass balance.
Wilson, Emily L.↗
Quartz crystals detect gas contaminants during vacuum chamber evacuation
Piezoelectric quartz crystals detect condensable gas contaminants backstreaming into a vacuum chamber when a pump is evacuating the chamber. One crystal acts as a thermometer, the other detects mass change. They are energized by electronic equipment which records frequency changes.
Stephens, J. B.↗
Mid-infrared trace gas detection using continuous-wave difference frequency generation in periodically poled RbTiOAsO4
A tunable mid-infrared continuous-wave (cw) spectroscopic source in the 3.4-4.5 micrometers region is reported, based on difference frequency generation (DFG) in a quasi-phase-matched periodically poled RbTiOAsO4 (PPRTA) crystal. DFG power levels of 10 microW were generated at approximately 4 micrometers in a 20-mm long PPRTA crystal by mixing two cw single-frequency Ti:Al2O3 lasers operating near 713 nm and 871 nm, respectively, using a laser pump power of 300 mW. A quasi-phase-matched infrared wavelength-tuning bandwidth (FWHM) of ~ 12 cm-1 and a temperature tuning rate of 1.02 cm-1/degree C were achieved. Experimental details regarding the feasibility of trace gas detection based on absorption spectroscopy of CO2 in ambient air using this DFG radiation source are also described.
NASA Discipline Life Sciences Technologies↗
Acoustic sensing and autoencoder approach for abnormal gas detection in a spent nuclear fuel canister mock-up
Currently, spent nuclear fuel (SNF) from commercial nuclear power plants is stored in stainless-steel canisters for interim dry storage. To provide an inert environment, these canisters are backfilled with helium after vacuum drying. However, the helium environment may be contaminated during extended storage because of the material degradation. For example, the heavier fission gas xenon may be released from the fuel rods into the canister cavity should the fuel cladding be breached. Other gases such as air and water vapor may also be present as a result of leakage caused by chloride-induced stress corrosion cracking on the canister walls or by insufficient vacuum drying. Therefore, monitoring the gas composition can provide critical information about the health of SNF canisters. In this study, noninvasive testing was conducted on a 2/3-scaled SNF canister mock-up using acoustic sensing. Ultrasonic transducers were placed on the exterior surface of the canister to probe the gas composition. A dataset was collected by sealing the canister mock-up and introducing up to 1.53% argon or 1.29% air into the helium background gas. Three methods were used to detect changes in the gas composition: the time-of-flight (TOF) method, the differential method, and the autoencoder method. Results showed that the TOF method had sufficient resolution to detect abnormal gas concentrations of less than 1.0%. The differential method demonstrated a periodic in-phase and out-of-phase behavior between the benchmark (i.e., pure helium) and abnormal (i.e., with argon or air) state signals. The variational autoencoder (VAE) and the Wasserstein autoencoder (WAE) were trained on the benchmark data and were applied directly to the abnormal state data. It was found that both the unsupervised VAE and the WAE were able to distinguish the benchmark and abnormal states of the canister mock-up based on the reconstruction error.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
High Speed and Low-Noise Open-Path Gas Detection System
Explore the source record for details and available documents.
07 ISOTOPE AND RADIATION SOURCES↗
Impurity gas detection for SNF canisters using probabilistic deep learning and acoustic sensing *
Abstract Monitoring impurity gases in spent nuclear fuel (SNF) canisters is a novel structural health monitoring approach for SNF in dry storage. The SNF canisters are sealed containers that do not facilitate visual access to the inside. Acoustic sensing can be deployed by taking advantage of the pathways unobstructed by internal hardware. Although the ultrasonic time-of-flight measurement can provide valuable information, it is limited in its ability to discern the concentration of only one impurity gas. As such, deep learning algorithms, particularly convolutional neural networks (CNNs), offer a promising solution. In this study, CNN-based probabilistic deep learning models were implemented to detect and quantify multiple impurity gases in helium. An experimental platform was established to simulate canister conditions, and ultrasonic test data were collected. The presence of argon and air in helium at concentrations ranging from 0% to 1.2% at increments of 0.05% was considered. The multi-layer perceptron, decision tree, and logistic regression classifiers achieved high accuracies when distinguishing pure helium from helium with impurities. CNN with dropout layers and CNN using maximum likelihood estimation showed a similar performance, indicating their ability to capture uncertainties. The ensemble CNN model exhibited improved predictions and the ability to balance individual gas concentration by integrating 1D- and 2D-CNN models. These findings contribute probabilistic deep learning solutions for impurity gas detection and analysis within SNF canisters, thus ensuring safe storage and management of SNFs.
47 OTHER INSTRUMENTATION↗
Slow-Light Mid-IR Silicon Photonic Chips for NO 2 and CH 4 Gas Detection
A compact, chip-scale mid-infrared gas sensor is demonstrated, leveraging a two-dimensional photonic crystal waveguide (PCW) fabricated on a silicon-on-insulator (SOI) platform. The PCW comprises a hexagonal lattice with lattice constant a = 860 nm and hole radius r = 0.22a, incorporating a central line defect of reduced-radius holes (r s = 0.7r) to induce slow-light propagation near the photonic band edge with a group index of approximately 73, thereby enhancing light-matter interaction. The sensor operates at fundamental absorption wavelengths of 3.42 μm for nitrogen dioxide (NO 2 ) and 3.40 μm for methane (CH 4 ), utilizing the strongest molecular vibrational transitions for maximum sensitivity. Experimental validation was conducted using dynamically diluted gas mixtures generated by mass flow controllers, with signal acquisition performed by a liquid nitrogen-cooled InSb detector. For NO 2 , the sensor exhibited excellent linear response over 5–25 ppm (part per million) with coefficient of determination R 2 = 0.9934, achieving a detection limit of 210 ppb (part per billion)─representing the first reported silicon photonic-based NO 2 detection. For CH 4 , exposure to 25 ppm resulted in a 6.4% decrease in transmitted intensity, demonstrating multigas sensing capability. The CMOS-compatible fabrication process and compact 3 mm device footprint establish this SOI-PCW platform as a scalable, low-power solution for integrated mid-infrared gas sensing, with significant potential for environmental monitoring and industrial safety applications.
Crystals↗