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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 NTRS2010

Evaluation of the Air Quality Monitor's Performance on the International Space Station

The Air Quality Monitor (AQM) was flown to the International Space Station (ISS) as an experiment to evaluate its potential to replace the aging Volatile Organic Analyzer (VOA), which ceased operations in August 2009. The AQM (Figure 1) is a small gas chromatography/differential mobility spectrometer (GC/DMS) manufactured by Sionex. Data was presented at last year s ISIMS conference that detailed the preparation of the AQM for flight, including instrument calibration. Furthermore, initial AQM data was compared to VOA results from simultaneous runs of the two instruments. Although comparison with VOA data provided a measure of confidence in the AQM performance, it is the comparison with results from simultaneously acquired air samples (grab sample containers-GSCs) that will define the success (or failure) of the AQM performance. This paper will update the progress in the AQM investigation by comparing AQM data to results from the analyses of GSC samples, returned from ISS. Additionally, a couple of example will illustrate the AQM s ability to detect disruptions in the spacecraft s air quality. Discussion will also focus upon a few unexpected issues that have arisen and how these will be a addressed in the final operational unit now being built.

Limero, Thomas↗
DOE OSTI2022

A Novel Approach for Real-Time Quality Monitoring in Machining of Aerospace Alloy through Acoustic Emission Signal Transformation for DNN

Gamma titanium aluminide (γ-TiAl) is considered a high-performance, low-density replacement for nickel-based superalloys in the aerospace industry due to its high specific strength, which is retained at temperatures above 800 °C. However, low damage tolerance, i.e., brittle material behavior with a propensity to rapid crack propagation, has limited the application of γ-TiAl. Any cracks introduced during manufacturing would dramatically lower the useful (fatigue) life of γ-TiAl components, making the workpiece surface’s quality from finish machining a critical component to product quality and performance. To address this issue and enable more widespread use of γ-TiAl, this research aims to develop a real-time non-destructive evaluation (NDE) quality monitoring technique based on acoustic emission (AE) signals, wavelet transform, and deep neural networks (DNN). Previous efforts have opted for traditional approaches to AE signal analysis, using statistical feature extraction and classification, which face challenges such as the extraction of good/relevant features and low classification accuracy. Hence, this work proposes a novel AI-enabled method that uses a convolutional neural network (CNN) to extract rich and relevant features from a two-dimensional image representation of 1D time-domain AE signals (known as scalograms), subsequently classifying the AE signature based on pedigreed experimental data and finally predicting the process-induced surface quality. The results of the present work show good classification accuracy of 80.83% using scalogram images, in-situ experimental data, and a VGG-19 pre-trained neural network, establishing the significant potential for real-time quality monitoring in manufacturing processes.

36 MATERIALS SCIENCE↗
NASA NTRS2017

Preparation of the NASA Air Quality Monitor for a U.S. Navy Submarine Sea Trial

For the past 4 years, the Air Quality Monitor (AQM) has been the operational instrument for measuring trace volatile organic compounds on the International Space Station (ISS). The key components of the AQM are the inlet preconcentrator, the gas chromatograph (GC), and the differential mobility spectrometer. Onboard the ISS are two AQMs with different GC columns that detect and quantify 22 compounds. The AQM data contributes valuable information to the assessment of air quality aboard ISS for each crew increment. The US Navy is looking to update its submarine air monitoring suite of instruments and the success of the AQM on ISS has led to a jointly planned submarine sea trial of a NASA AQM. In addition to the AQM, the Navy is also interested in the Multi-Gas Monitor (MGM), which measures major constituent gases (oxygen, carbon dioxide, water vapor, and ammonia). A separate paper will present the MGM sea trial preparation and the analysis of most recent ISS data. A prototype AQM, which is virtually identical to the operational AQM, has been readied for the sea trial. Only one AQM will be deployed during the sea trial, but this is sufficient for NASA purposes and to detect the compounds of interest to the US Navy for this trial. The data from the sea trial will be compared to data from archival samples collected before, during, and after the trial period. This paper will start with a brief history of past collaborations between NASA and the U.S. and U.K. navies for trials of air monitoring equipment. An overview of the AQM technology and protocols for the submarine trial will be presented. The majority of the presentation will focus on the AQM preparation and a summary of available data from the trial.

Limero, Thomas↗
DOE OSTI2024

Hydra: Computer Vision for Online Data Quality Monitoring

Hydra is a system utilizing computer vision for near real-time data quality monitoring. Currently operational across all of Jefferson Lab’s experimental halls, it reduces the workload of shift takers by autonomously monitoring diagnostic plots during experiments. Hydra uses "off-the-shelf" supervised learning technologies and is supported by a comprehensive MySQL database. To simplify access, web apps have been developed to facilitate both labeling and monitoring of Hydra’s inferences. Hydra can connect with the alarm system and incorporates complete historical tracking, enabling it to identify issues that shift takers could miss. When issues are detected, a natural first question is: "Why does Hydra think there is a problem?" To answer, Hydra employs Gradient-weighted Class Activation Maps (GradCAM) to identify regions of the image that are important for the specific classification. This interpretive layer enhances transparency and trustworthiness, which is essential for integration with experiment workflows and operation. The Hydra system, results, and sociological considerations for deployment will be discussed.

Jeske, Torri↗
DOE OSTI2024

Enhancing Data Quality Monitoring at CMS with Interactive Visualization Tools and Automated Reference Run Selection

Current data quality monitoring (DQM) tools at CMS offer granularity limited to per-run analysis. Consequently, issues manifesting at the per-lumisection level can go unnoticed or, even if detectable, often lead to the classification of the whole run as bad, resulting in unnecessary data loss. Additionally, shifters have to evaluate a large set of monitoring elements during their long shifts, increasing the probability of human errors or overlooked problems. In this contribution, we present ongoing work on the development of tools that will provide shifters with an accessible, granularity-enhanced view of DQM data through interactive and dynamic visualizations. Furthermore, we introduce a reference run selection tool currently under development, which will automate the selection based on data-taking conditions and will offer a curated set of training data for machine learning models that will be used for the partial automation of the offline data certification process. These endeavors will be integrated into the DIALS website, enabling enhancements in data certification accuracy and improving the accessibility of DQM at CMS.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1987

Recent developments in water quality monitoring for Space Station reclaimed wastewaters

This paper discusses the recent developments in water quality monitoring for Space Station reclaimed wastewaters. A preprototype unit that contains an ultraviolet absorbance organic carbon monitor integrated with pH and conductivity sensors is presented. The preprototype has provisions for automated operation and is a reagentless flow-through system without any gas/liquid interfaces. The organic carbon monitor detects by utraviolet absorbance the organic impurities in reclaimed wastewater which may be correlated to the organic carbon content of the water. A comparison of the preprototype organic carbon detection values with actual total organic carbon measurements is presented. The electrolyte double junction concept for the pH sensor and fixed electrodes for both the pH and conductivity sensors are discussed. In addition, the development of a reagentless organic carbon analyzer that incorporates ultraviolet oxidation and infrared detection is presented. Detection sensitivities, hardware development, and operation are included.

Small, John W.↗
DOE OSTI2022

AI Data Quality Monitoring with Hydra

Hydra is an extensible framework for training and managing AI for near real time monitoring that aims to replace the tedious and repetitive data quality monitoring activities the shift crew and online monitoring coordinator typically perform. It continuously scans incoming data in the form of monitoring plots for signs of problems, flagging them for human review. A web app was developed such that experts can efficiently label images for training. Labels are stored in a database for use in training and model validation. Backed up by a comprehensive database, it utilizes an additional web based front-end for viewing the current monitoring status from anywhere in the world. The system has been in production use for the GlueX experiment at Jefferson Lab for more than 2 years with new features still under active development.

Britton, Thomas↗
NASA NTRS2009

Air Quality Monitoring: Risk-Based Choices

Air monitoring is secondary to rigid control of risks to air quality. Air quality monitoring requires us to target the credible residual risks. Constraints on monitoring devices are severe. Must transition from archival to real-time, on-board monitoring. Must provide data to crew in a way that they can interpret findings. Dust management and monitoring may be a major concern for exploration class missions.

James, John T.↗
NASA NTRS1973

Development of a laboratory prototype water quality monitoring system suitable for use in zero gravity

The development of a laboratory prototype water quality monitoring system for use in the evaluation of candidate water recovery systems and for study of techniques for measuring potability parameters is reported. Sensing techniques for monitoring of the most desirable parameters are reviewed in terms of their sensitivities and complexities, and their recommendations for sensing techniques are presented. Rationale for selection of those parameters to be monitored (pH, specific conductivity, Cr(+6), I2, total carbon, and bacteria) in a next generation water monitor is presented along with an estimate of flight system specifications. A master water monitor development schedule is included.

Misselhorn, J. E.↗
DOE OSTI2022

Deep Learning for In-Situ Layer Quality Monitoring during Laser-Based Directed Energy Deposition (LB-DED) Additive Manufacturing Process

Defects are a leading issue for the rejection of parts manufactured through the Directed Energy Deposition (DED) Additive Manufacturing (AM) process. In an attempt to illuminate and advance in situ quality monitoring and control of workpieces, we present an innovative data-driven method that synchronously collects sensing data and AM process parameters with a low sampling rate during the DED process. The proposed data-driven technique determines the important influences that individual printing parameters and sensing features have on prediction at the inter-layer qualification to perform feature selection. Three Machine Learning (ML) algorithms including Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN) are used. During post-production, a threshold is applied to detect low-density occurrences such as porosity sizes and quantities from CT scans that render individual layers acceptable or unacceptable. This information is fed to the ML models for training. Training/testing are completed offline on samples deemed “high-quality” and “low-quality”, utilizing only features recorded from the build process. CNN results show that the classification of acceptable/unacceptable layers can reach between 90% accuracy while training/testing on a “high-quality” sample and dip to 65% accuracy when trained/tested on “low-quality”/“high-quality” (respectively), indicating over-fitting but showing CNN as a promising inter-layer classifier.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

The Air Quality Monitor “Benzene” Anomaly: Ground Testing and On-going Effects

The International Space Station (ISS) Air Quality Monitors (AQMs) have provided targeted in-flight analysis of volatile organic compounds (VOCs) in the ISS atmosphere since early 2013. During their initial half decade of use covering multiple sets of units, the AQMs performed well, meeting their validation criteria and showing excellent accuracy compared to archival samples. In addition to routine environmental monitoring, the AQMs have also been used during a number of contingency situations and investigations related to Environmental Control and Life Support Systems (ECLSS). These include a potential ammonia leak, increases in atmospheric ethanol, and efforts to locate potential sources of polydimethylsiloxanes that lead to the production of dimethylsilanediol (DMSD) in the US Water Processor Assembly (WPA). As the fleet of AQMs has aged, several issues have arisen. These have ranged from pervasive problems on electronics boards to loss of sensitivity due to operating in an elevated CO2 environment. The most notable issue encountered during on-orbit operations was incorrect identification of compounds. This initially occurred in mid-2020, when AQM1 reported the presence of benzene. While the AQM team questioned the validity of these results, the concentration of the “benzene” continued to increase and eventually exceeded the 30- and 180-day Spacecraft Maximum Allowable Concentration (SMAC). This led to wide-ranging efforts by a number of groups aimed at understanding the situation and identifying the source of the “benzene.” AQM1 failed after being relocated to the Russian Segment as part of the investigation, and the unit was returned for evaluation. When archive samples collected while the AQM was measuring elevated benzene showed no detectable benzene, the focus of the investigation shifted to determining the cause of the false positive readings. Here, we will discuss the results of this investigation by the AQM team, potential causes of the interference, and subsequent reporting of AQM1 results.

W T Wallace↗
NASA NTRS2024

The Air Quality Monitor “Benzene” Anomaly: Ground Testing and On-going Effects

The International Space Station (ISS) Air Quality Monitors (AQMs) have provided targeted in-flight analysis of volatile organic compounds (VOCs) in the ISS atmosphere since early 2013. During their initial half decade of use covering multiple sets of units, the AQMs performed well, meeting their validation criteria and showing excellent accuracy compared to archival samples. In addition to routine environmental monitoring, the AQMs have also been used during a number of contingency situations and investigations related to Environmental Control and Life Support Systems (ECLSS). These include a potential ammonia leak, increases in atmospheric ethanol, and efforts to locate potential sources of polydimethylsiloxanes that lead to the production of dimethylsilanediol (DMSD) in the US Water Processor Assembly (WPA). As the fleet of AQMs has aged, several issues have arisen. These have ranged from pervasive problems on electronics boards to loss of sensitivity due to operating in an elevated CO2 environment. The most notable issue encountered during on-orbit operations was incorrect identification of compounds. This initially occurred in mid-2020, when AQM1 reported the presence of benzene. While the AQM team questioned the validity of these results, the concentration of the “benzene” continued to increase and eventually exceeded the 30- and 180-day Spacecraft Maximum Allowable Concentration (SMAC). This led to wide-ranging efforts by a number of groups aimed at understanding the situation and identifying the source of the “benzene.” AQM1 failed after being relocated to the Russian Segment as part of the investigation, and the unit was returned for evaluation. When archive samples collected while the AQM was measuring elevated benzene showed no detectable benzene, the focus of the investigation shifted to determining the cause of the false positive readings. Here, we will discuss the results of this investigation by the AQM team, potential causes of the interference, and subsequent reporting of AQM1 results.

W T Wallace↗
NASA NTRSDate not supplied

The Air Quality Monitor “Benzene” Anomaly: Ground Testing and On-going Effects

The International Space Station (ISS) Air Quality Monitors (AQMs) have provided targeted in-flight analysis of volatile organic compounds (VOCs) in the ISS atmosphere since early 2013. During their initial half decade of use covering multiple sets of units, the AQMs performed well, meeting their validation criteria and showing excellent accuracy compared to archival samples. In addition to routine environmental monitoring, the AQMs have also been used during a number of contingency situations and investigations related to Environmental Control and Life Support Systems (ECLSS). These include a potential ammonia leak, increases in atmospheric ethanol, and efforts to locate potential sources of polydimethylsiloxanes that lead to the production of dimethylsilanediol (DMSD) in the US Water Processor Assembly (WPA). As the fleet of AQMs has aged, several issues have arisen. These have ranged from pervasive problems on electronics boards to loss of sensitivity due to operating in an elevated CO2 environment. The most notable issue encountered during on-orbit operations was incorrect identification of compounds. This initially occurred in mid-2020, when AQM1 reported the presence of benzene. While the AQM team questioned the validity of these results, the concentration of the “benzene” continued to increase and eventually exceeded the 30- and 180-day Spacecraft Maximum Allowable Concentration (SMAC). This led to wide-ranging efforts by a number of groups aimed at understanding the situation and identifying the source of the “benzene.” AQM1 failed after being relocated to the Russian Segment as part of the investigation, and the unit was returned for evaluation. When archive samples collected while the AQM was measuring elevated benzene showed no detectable benzene, the focus of the investigation shifted to determining the cause of the false positive readings. Here, we will discuss the results of this investigation by the AQM team, potential causes of the interference, and subsequent reporting of AQM1 results.

William T Wallace↗
DOE OSTI2025

Online and Offline Data Quality Monitoring for the Mu2e Calorimeter

This thesis presents the design, implementation, and validation of a calorimeter Data Quality Monitoring (DQM) toolchain for the Mu2e experiment at Fermilab. Mu2e searches for charged lepton flavor violation via coherent muon-to-electron conversion in the field of an aluminum nucleus, $\mu^- Al \rightarrow e^-Al$, a process whose observation would constitute clear evidence of physics beyond the Standard Model. Achieving target sensitivity requires stringent control of detector performance and data integrity during acquisition, as subtle issues in readout configuration, data formatting, or electronics behavior can compromise reconstruction and bias downstream analyzes. To address these challenges, this work develops a multi-layer DQM approach spanning both raw data validation and reconstructed digi-level diagnostics. At the low level, a fragment analysis component performs word- and bit-field decoding of calorimeter readout blocks, enabling sanity checks of the expected structure and producing detailed error and integrity statistics useful for commissioning and troubleshooting. At the digi level, the CaloDigiDQM analyzer is implemented within the art framework and transforms each CaloDigiCollection into a structured hierarchy of ROOT histograms designed for fast drill-down diagnostics. The module generates coherent monitoring views at global, disk, board, and channel granularity, including occupancy, waveform-derived features (baseline, RMS, peak amplitude and position), and left-right sensor consistency metrics. Detector-aware channel-to-electronics mapping is performed through the conditions system (CaloDAQMap), ensuring that diagnostics remain aligned with hardware identifiers used in operations. For end-to-end testing without reliance on live DAQ data, a synthetic CaloDigi producer is developed to generate realistic waveforms with controlled noise and pulse shapes. The resulting system supports both offline ROOT-file production and online operation, including optional histogram streaming through otsdaq via ots::HistoSender. This toolchain provides a practical and scalable foundation for calorimeter commissioning and stable data collection, enabling early detection of anomalies and reducing operational risk for Mu2e.

Vakulenko, Mark [Drew U.] (ORCID:0009000276197818)↗
NASA NTRS2018

The Dawn of Geostationary Air Quality Monitoring: Case Studies from Seoul and Los Angeles

With the near-future launch of geostationary pollution monitoring satellite instruments over North America, East Asia, and Europe, the air quality community is preparing for an integrated global atmospheric composition observing system at unprecedented spatial and temporal resolutions. One of the ways that NASA has supported this community preparation is through demonstration of future space-borne capabilities using the Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) airborne instrument. This paper integrates repeated high-resolution maps from GeoTASO, ground-based Pandora spectrometers, and low Earth orbit measurements from the Ozone Mapping and Profiler Suite (OMPS), for case studies over two metropolitan areas: Seoul, South Korea on June 9, 2016 and Los Angeles, California on June 27, 2017. This dataset provides a unique opportunity to illustrate how geostationary air quality monitoring platforms and ground-based remote sensing networks will close the current spatiotemporal observation gap. GeoTASO observes large differences in diurnal behavior between these urban areas, with NO2 accumulating within the Seoul Metropolitan Area through the day but NO2 peaking in the morning and decreasing throughout the afternoon in the Los Angeles Basin. In both areas, the earliest morning maps exhibit spatial patterns similar to emission source areas (e.g., urbanized valleys, roadways, major airports). These spatial patterns change later in the day due to boundary layer dynamics, horizontal transport, and chemistry. The nominal resolution of GeoTASO is finer than will be obtained from geostationary platforms, but when NO2 data over Los Angeles are up-scaled to the expected resolution of TEMPO, spatial features discussed are conserved. Pandora instruments installed in both metropolitan areas capture the diurnal patterns observed by GeoTASO, continuously and over longer time periods, and will play a critical role in validation of the next generation of satellite measurement. These case studies demonstrate that different regions can have diverse diurnal patterns and that day-to-day variability due to meteorology or anthropogenic patterns such as weekday/weekend variations in emissions is large. Low Earth orbit measurements, despite their inability to capture the diurnal patterns at fine spatial resolution, will be essential for intercalibrating the geostationary radiances and cross-validating the geostationary retrievals in an integrated global observing system.

Geostationary↗