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NASA NTRS1992

Microgravity accelerometer characterization on Columbia STS-32 mission

The Honeywell In-Space Accelerometer (HISA) is a three-axis microgravity accelerometer instrument package recently developed by Honeywell Systems and Research Center (SRC) to monitor oscillatory and transient accelerations onboard spacecraft and spaceborne structures. The HISA was designed to be co-located with materials and life sciences experiments to record real-time accelerometer event data, sampling time, and temperature. The HISA was originally developed to monitor the microgravity disturbances associated with a polymer morphology experiment developed by 3M Company in Minneapolis, Minnesota. The HISA was first flight tested with the 3M experiment on the Space Shuttle Atlantis STS-34 in October 1989. The HISA was successfully flown on a second shuttle mission (Columbia STS-32 in January 1990) in support of the NASA JSC-sponsored Microgravity Disturbances Experiment (MDE), which focused on the effects of microgravity disturbances on the growth of high-quality Indium crystals. The primary objective of the STS-32 MDE experiment was to investigate the effects of crew-induced gravity disturbances on the microstructure (crystal defects and uniformity of impurity distribution) of float-zone-grown crystals. The float-zone technique involves establishing a suspended molten zone between two cylindrical samples a pure, single-crystal sample and an impure, polycrystalline sample. Microgravity disturbances due to crew treadmill activity and orbiter maneuvering system thruster firings were sensed and recorded by the HISA to understand their effects on the stability of the float zone. The principle of operation of the HISA, the flight configuration of the HISA supporting the MDE experiment, and the characterization of STS-32 treadmill disturbance data are summarized.

Schoess, Jeff↗

NASA NTRS2014

Applications of Satellite Remote Sensing for Response to and Recovery from Meteorological Disasters

Numerous on‐orbit satellites provide a wide range of spatial, spectral, and temporal resolutions supporting the use of their resulting imagery in assessments of disasters that are meteorological in nature. This presentation will provide an overview of recent use of Earth remote sensing by NASA's Short‐term Prediction Research and Transition (SPoRT) Center in response to disaster activities in 2012 and 2013, along with case studies supporting ongoing research and development. The SPoRT Center, with support from NASA's Applied Sciences Program, has explored a variety of new applications of Earth‐observing sensors to support disaster response. In May 2013, the SPoRT Center developed unique power outage composites representing the first clear sky view of damage inflicted upon Moore and Oklahoma City, Oklahoma following the devastating EF‐5 tornado that occurred on May 20. Subsequent ASTER, MODIS, Landsat‐7 and Landsat‐8 imagery help to identify the damaged areas. Higher resolution imagery of Moore, Oklahoma were provided by commercial satellites and the recently available International Space Station (ISS) SERVIR Environmental Research and Visualization System (ISERV) instrument. New techniques are being explored by the SPoRT team in order to better identify damage visible in high resolution imagery, and to monitor ongoing recovery for Moore, Oklahoma. This presentation will provide an overview of near real‐time data products developed for dissemination to SPoRT's partners in NOAA's National Weather Service, through collaboration with the USGS and other federal agencies. Specifically, it will focus on integration of various data sets within the NOAA National Weather Service Damage Assessment Toolkit, which allows meteorologists in the field to consult available satellite imagery while performing their damage assessment.

Molthan, Andrew I.↗

NASA NTRS2014

Applications of Satellite Remote Sensing for Response to and Recovery from Meteorological Disasters

Molthan, Andrew L.↗

NASA NTRSDate not supplied

Novel Hourly-Resolved Global Cloud Property Composite from Operational Satellites Imagers

Numerous applications in satellite remote sensing of surface properties, atmospheric state, composition, and radiation require accurate knowledge of the location and characteristics of clouds. Operational satellite imager radiances are valuable for cloud detection and for deriving many different physical parameters that can be used for a variety of weather, aviation, and energy applications. The NASA Satellite ClOud and Radiation Property retrieval System (SatCORPS) applies a suite of algorithms to meteorological satellite data to provide cloud properties, radiative fluxes and other parameters on a global scale. This paper describes a new global high-resolution dataset of cloud properties made available for community use that is constructed from analyses of a constellation of meteorological satellite imagers. Data taken from Meteosat-8/9 and -11, Himawari-8/9, GOES-16, and -17/-18, Aqua, Terra, Suomi-NPP and NOAA-20 are processed and composited on a 3-km grid to provide hourly global coverage. A historical multi-year dataset is available to serve various needs including modeling challenges related to cloud evaluations and parameterizations. Near real-time data products are also currently available between 60N and 60S. Efforts are underway to operationalize polar orbiting satellite cloud detection methods for low-latency applications over polar regions. The cloud detection and retrieval algorithms have been developed over many years to support NASA weather and climate programs such as the Clouds and Earth’s Radiant Energy System (CERES). To improve the utility of the data products, machine learning and other innovative methods are applied in various ways to help minimize data product uncertainties under the most challenging conditions and to improve their consistency at all times of day. A brief description of the methods highlighting the unique aspects of the SatCORPS data products will be presented along with information on their status and availability.

SatCORPS↗

DOE OSTI2026

Model-based, in-situ, non-destructive qualification and certification of parts made by autonomous additive manufacturing

To address the significant productivity challenges associated with the qualification and certification (Q&C) tasks of additively manufactured (AM) parts, which have traditionally relied on rigorous post‐build inspection and testing, we propose an integrated framework that combines model‐based qualification and certification (MBQ&C) with autonomous additive manufacturing (AAM). MBQ&C employs high‐fidelity predictive models, developed within the Integrated Computational Materials Engineering (ICME) paradigm, to simulate process–structure–property–performance relationships for assessing a part’s fitness for use. Since predictive models are commonly machine learning (ML)-based or reduced-order surrogates of validated physics models, they run efficiently, enabling timely inference. In parallel, the self-driving AAM utilises ML-based adaptive, closed‐loop control strategies to avoid, mitigate, or repair defects and anomalies during fabrication, thereby increasing the likelihood of producing acceptable parts. A key feature of the combined AAM-MBQ&C framework is that predictive models explicitly incorporate defects or anomalies that persist after the build, using instance-specific data captured via in-situ sensing. This customisation enables a build‐specific assessment of fitness for use, rather than relying on nominal or generic parameters. Such individualised evaluation provides a robust basis for Q&C-related acceptance decisions relating to each build. Additionally, the rapid solution capabilities of ML or reduced-order models enable the determination of a part’s suitability for service shortly after build completion. As the framework matures, it has the potential to substantially reduce reliance on conventional point‐design approaches—such as time‐consuming post‐build computed tomography scanning and costly destructive testing. Thus, the AAM-MBQ&C framework represents a transformative, scalable strategy for quality assurance of AM components, as parts produced within a stable, validated, and certified envelope can be certified with reduced testing. Key benefits include: (1) significant gains in Q&C productivity through efficient, model-centric assessment; (2) performance-based classification of defects into critical and non-critical categories; (3) the ability to predict potential deviations in the performance of parts affected by real-time, adaptive process control interventions relative to those produced under a certified process, and (4) the enabling of virtual Q&C for service environments that are difficult, hazardous, or impractical to access or reproduce experimentally. Collectively, these capabilities strengthen the business case for AM, particularly for high‐consequence and mission‐critical applications. Finally, although this work focuses on powder-based AM, the proposed techniques could be extended to AM processes employing alternative feedstock forms.

Gunasegaram, Dayalan↗

NASA NTRS2010

SPOT Program

A Spacecraft Position Optimal Tracking (SPOT) program was developed to process Global Positioning System (GPS) data, sent via telemetry from a spacecraft, to generate accurate navigation estimates of the vehicle position and velocity (state vector) using a Kalman filter. This program uses the GPS onboard receiver measurements to sequentially calculate the vehicle state vectors and provide this information to ground flight controllers. It is the first real-time ground-based shuttle navigation application using onboard sensors. The program is compact, portable, self-contained, and can run on a variety of UNIX or Linux computers. The program has a modular objec-toriented design that supports application-specific plugins such as data corruption remediation pre-processing and remote graphics display. The Kalman filter is extensible to additional sensor types or force models. The Kalman filter design is also strong against data dropouts because it uses physical models from state and covariance propagation in the absence of data. The design of this program separates the functionalities of SPOT into six different executable processes. This allows for the individual processes to be connected in an a la carte manner, making the feature set and executable complexity of SPOT adaptable to the needs of the user. Also, these processes need not be executed on the same workstation. This allows for communications between SPOT processes executing on the same Local Area Network (LAN). Thus, SPOT can be executed in a distributed sense with the capability for a team of flight controllers to efficiently share the same trajectory information currently being computed by the program. SPOT is used in the Mission Control Center (MCC) for Space Shuttle Program (SSP) and International Space Station Program (ISSP) operations, and can also be used as a post -flight analysis tool. It is primarily used for situational awareness, and for contingency situations.

Smith, Jason T.↗

DOE OSTI2026

A non-intrusive framework using acoustic signals and deep learning for boiling diagnostics in visual-limited environments

Accurate monitoring of boiling heat transfer is critical for safeguarding high-power systems operating in environments where conventional optical diagnostics are hindered by radiation fields or restricted visual accessibility. This study presents a non-intrusive framework that integrates hydroacoustic sensing with deep learning to infer near-wall boiling characteristics and enable predictive thermal assessment without visual access. In a prototypical subcooled flow-boiling facility representative of the Isotope Production Facility (IPF) at Los Alamos, hydrophones capture boiling-induced acoustic emissions that are transformed into background-removed Short-Time Fourier Transform (STFT) spectrograms. A convolutional neural network (CNN) then regresses heat flux, wall superheat, and key bubble parameters directly from these spectrograms. The CNN achieved predictive accuracy under nominal conditions and demonstrated robustness and generalization under acoustic noise for Signal-to-Noise Ratios (SNRs) down to approximately 0 dB. When integrated into an ANSYS CFX wall-boiling model, the acoustically inferred parameters reproduced boiling curve and critical heat flux (CHF) values consistent with image-based benchmarks. Furthermore, the model retained reliable performance under moderate variations in bulk temperature, flow rate, and hydrophone placement, confirming its generalizability across practical boundary conditions. These results demonstrate the feasibility of hydroacoustic-based deep learning as a viable path toward real-time, radiation-tolerant boiling diagnostics and predictive thermal safety assessment in inaccessible systems such as the IPF.

42 ENGINEERING↗

NASA NTRS1992

Gravity and the geoid in the Nepal Himalaya

Materials within the Himalaya are rising due to convergence between India and Asia. If the rate of erosion is comparable to the rate of uplift the mean surface elevation will remain constant. Any slight imbalance in these two processes will lead to growth or attrition of the Himalaya. The process of uplift of materials within the Himalaya coupled with surface erosion is similar to the advance of a glacier into a region of melting. If the melting rate exceeds the rate of downhill motion of the glacier then the terminus of the glacier will receed up-valley despite the downhill motion of the bulk of the glacier. Thus although buried rocks, minerals and surface control points in the Himalaya are undoubtably rising, the growth or collapse of the Himalaya depends on the erosion rate which is invisible to geodetic measurements. Erosion rates are currently estimated from suspended sediment loads in rivers in the Himalaya. These typically underestimate the real erosion rate since bed-load is not measured during times of heavy flood, and it is difficult to integrate widely varying suspended load measurements over many years. An alternative way to measure erosion rate is to measure the rate of change of gravity in a region of uplift. If a control point moves vertically it should be accompanied by a reduction in gravity as the point moves away from the Earth's center of mass. There is a difference in the change of gravity between uplift with and without erosion corresponding to the difference between the free-air gradient and the gradient in the acceleration due to gravity caused by a corresponding thickness of rock. Essentially gravity should change precisely in accord with a change in elevation of the point in a free-air gradient if erosion equals uplift rate. We were funded by NASA to undertake a measurement of absolute gravity simultaneously with measurements of GPS height within the Himalaya. Since both absolute gravity and time are known in an absolute sense to 1 part in 10(exp 10) it is possible to estimate gravity with a precision of 0.1 mu gal. Known systematic errors reduce the measurement to an absolute uncertainty of 6 mu gal. The free air gradient at the point of measurement is typically about 3 mu gals/cm. At Simikot where our experiment was conducted we determined a vertical gravity gradient of 4.4 mu gals/cm.

Bilham, Roger↗

NASA NTRS2013

Modeling the Skills and Practices of Scientists through an “All-Inclusive” Comparative Planetology Student Research Investigation

To effectively prepare the nation's future Science, Technology, Engineering, and Mathematics (STEM) workforce, students in today's classrooms need opportunities to engage in authentic experiences that model skills and practices used by STEM professionals. Relevant, real-world authentic research experiences allow students to behave as scientists as they model the process of science. This enables students to get a true sense of STEM-related professions and also allows them to develop the requisite knowledge, skills, curiosity, and creativity necessary for success in STEM careers. Providing professional development and opportunities to help teachers infuse research in the classroom is one of the primary goals of the Expedition Earth and Beyond (EEAB) program. EEAB, facilitated by the Astromaterials Research and Exploration Science (ARES) Directorate at the NASA Johnson Space Center, is an Earth and planetary science education program designed to inspire, engage, and educate teachers and students in grades 5-12 by getting them actively involved with exploration, discovery, and the process of science. The program combines the expertise of scientists and educators to ensure the professional development provided to classroom teachers is scientifically valid and also recognizes classroom constraints. For many teachers, facilitating research in the classroom can be challenging. In addition to addressing required academic standards and dealing with time constraints, challenges include structuring a research investigation the entire class can successfully complete. To build educator confidence, foster positive classroom research experiences, and enable teachers to help students model the skills and practices of scientists, EEAB has created an "allinclusive" comparative planetology research investigation activity. This activity addresses academic standards while recognizing students (and teachers) potentially lack experience with scientific practices involved in conducting research. Designed as an entry level research engagement investigation, the activity introduces, illustrates, and teaches the skills involved in each step of the research process. Students use astronaut photos, provided through the ARES Crew Earth Observations (CEO) payload on the International Space Station (ISS) as well as remote sensing imagery of other planetary worlds. By including all the necessary tools to complete the investigation, students can focus on gaining experience in the process of science. Additionally, students are able to extend their experience of modeling the skills and practices of scientists through the opportunity to request new data of Earth from the ISS. Professional development offered through in-person and webinar trainings, along with the resources provided, enable educators to gain first-hand experience implementing a structured research investigation in the classroom. Through data and feedback collected from teachers, this type of "all-inclusive" investigation activity aims to become a model that can be utilized for other research topics and STEM disciplines.

Graff, Paige↗

NASA NTRSDate not supplied

Engineered Yeast to Test Risks for Human Exploration of the Lunar Surface

Jessica W. Chau, Natalie N. Ball, Aditya Hindupur, Sandra T. Vu, Jennifer Gil Acevedo, Lauren C. Liddell, Chinmayee Govinda Raj, Gentry, Sergio R. Santa Maria, A. Mark Settles Crewed exploration of the Moon carries risks of long duration exposure to reduced gravity and to deep space radiation. The Lunar Explorer Instrument for space biology Applications (LEIA) investigates the effects of increased radiation and reduced gravity on yeast viability and growth in a Commercial Lunar Payload Services (CLPS) surface mission to the south polar region. LEIA conducts yeast genetics experiments to quantify growth, metabolism, and synthetic biology-enabled production of human nutrients, while taking real time measurements of biologically relevant radiation exposure on the lunar surface. We have engineered beta-carotene producing yeast strains to test the importance of selected DNA damage repair and reactive oxygen species (ROS) defense pathways in mitigating cellular damage from lunar surface radiation. Carotenoids are important dietary antioxidants, and beta-carotene is pro-vitamin A, which is needed for vision and immune function. Carotenoids are sensitive to ROS produced by ionizing radiation and NASA is testing on-demand production of carotenoids from yeast in the BioNutrients space flight experiments. In LEIA, we test the effects of deep space on carotenoid yield in engineered yeast strains. The LEIA team uses CRISPR-Cas9 to engineer yeast to express carotenoids as well as to generate loss-of-function mutations. We are generating mutations in the RAD51 DNA damage repair locus and three genes that function to reduce oxidative damage to the cell: SOD1, SOD2, and TSA1. These strains are tested for carotenoid production using microfluidics and LED spectroscopy to allow remote sensing of cellular growth and carotenoid levels. Keywords: synthetic biology, oxidative stress tolerance, biosensors, space radiation, beyond low Earth orbit, lunar surface, CRISPR/Cas9, gene editing, desiccation, carotenoids.

synthetic biology↗

NASA NTRS2010

Operation of a Third Generation JPL Electronic Nose in the Regenerative ECLSS Module Simulator at MSFC

An electronic nose has been developed at the Jet Propulsion Laboratory (JPL) to monitor spacecraft cabin air for anomalous events such as leaks and spills of solvents, coolants or other fluids with near-real-time analysis. It is designed to operate in the environment of the US Lab on ISS and was deployed on the International Space Station for a seven-month experiment in 2008-2009. In order improve understanding of ENose response to crew activities, an ENose was installed in the Regenerative ECLSS Module Simulator (REMS) at Marshall Space Flight Center (MSFC) for several months. The REMS chamber is operated with continuous analysis of the air for presence and concentration of CO, CO2, ethane, ethanol and methane. ENose responses were analyzed and correlated with logged activities and air analyses in the REMS.

Electronic Nose (ENose)↗

DOE OSTI2023

Enhancing photonic systems using topology and non-Hermiticity

The broad goal of this project was to develop new analytical and numerical insights for how important photonic processes can be improved using recently discovered principles in topological and non-Hermitian physics. In particular, there are two recent discoveries that we aimed to harness to achieve this goal. First, it was discovered in condensed matter physics that crystalline symmetries can protect low-dimensional topologically protected states in lattices without the need for breaking time-reversal symmetry. These so-called ‘higher-order’ topological systems represent an important development for photonic systems, where it is very difficult to break time-reversal symmetry, and which had been previously thought necessary to realize topological phenomena. Second, the last decade has seen a significant amount of interest in phenomena which are unique to non-Hermitian systems, i.e., systems which do not conserve energy. For example, spatially patterned gain and loss can be used to realize exceptional points, which are degeneracies in a system’s spectrum where the system becomes defective, while the existence of radiative losses also enables a new route to confinement through bound states in the continuum. For such non-Hermitian phenomena, photonics again represents a critical platform, as photonic systems naturally lose light to their radiative environments, making them generally non-Hermitian, and it is also possible to incorporate additional gain or loss. Based on these broad principles, we pursued a range of projects to harness bound states in the continuum in a variety of different systems and architectures, develop real-space methods for classifying topological systems to yield better photonic design principles, and a novel Brillouin-based fiber laser for sensing strain.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

NASA NTRS2014

A Framework for Land Cover Classification Using Discrete Return LiDAR Data: Adopting Pseudo-Waveform and Hierarchical Segmentation

Acquiring current, accurate land-use information is critical for monitoring and understanding the impact of anthropogenic activities on natural environments.Remote sensing technologies are of increasing importance because of their capability to acquire information for large areas in a timely manner, enabling decision makers to be more effective in complex environments. Although optical imagery has demonstrated to be successful for land cover classification, active sensors, such as light detection and ranging (LiDAR), have distinct capabilities that can be exploited to improve classification results. However, utilization of LiDAR data for land cover classification has not been fully exploited. Moreover, spatial-spectral classification has recently gained significant attention since classification accuracy can be improved by extracting additional information from the neighboring pixels. Although spatial information has been widely used for spectral data, less attention has been given to LiDARdata. In this work, a new framework for land cover classification using discrete return LiDAR data is proposed. Pseudo-waveforms are generated from the LiDAR data and processed by hierarchical segmentation. Spatial featuresare extracted in a region-based way using a new unsupervised strategy for multiple pruning of the segmentation hierarchy. The proposed framework is validated experimentally on a real dataset acquired in an urban area. Better classification results are exhibited by the proposed framework compared to the cases in which basic LiDAR products such as digital surface model and intensity image are used. Moreover, the proposed region-based feature extraction strategy results in improved classification accuracies in comparison with a more traditional window-based approach.

Light Detection & Ranging (LIDAR)↗

DOE OSTI2021

Change-point Detection and Image Segmentation for Time Series of Astrophysical Images

Many astrophysical phenomena are time-varying, in the sense that their intensity, energy spectrum, and/or the spatial distribution of the emission suddenly change. This paper develops a method for modeling a time series of images. Under the assumption that the arrival times of the photons follow a Poisson process, the data are binned into 4D grids of voxels (time, energy band, and x-y coordinates), and viewed as a time series of non-homogeneous Poisson images. The method assumes that at each time point, the corresponding multiband image stack is an unknown 3D piecewise constant function including Poisson noise. It also assumes that all image stacks between any two adjacent change points (in time domain) share the same unknown piecewise constant function. The proposed method is designed to estimate the number and the locations of all of the change points (in time domain), as well as all of the unknown piecewise constant functions between any pairs of the change points. The method applies the minimum description length principle to perform this task. A practical algorithm is also developed to solve the corresponding complicated optimization problem. Simulation experiments and applications to real data sets show that the proposed method enjoys very promising empirical properties. Applications to two real data sets, the XMM observation of a flaring star and an emerging solar coronal loop, illustrate the usage of the proposed method and the scientific insight gained from it.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1994

A note on two-dimensional asymptotic magnetotail equilibria

In order to understand, on the fluid level, the structure, the time evolution, and the stability of current sheets, such as the magnetotail plasma sheet in Earth's magnetosphere, one has to consider magnetic field configurations that are in magnetohydrodynamic (MHD) force equilibrium. Any reasonable MHD current sheet model has to be two-dimensional, at least in an asymptotic sense (B(sub z)/B (sub x)) = epsilon much less than 1. The necessary two-dimensionality is described by a rather arbitrary function f(x). We utilize the free function f(x) to construct two-dimensional magnetotail equilibria are 'equivalent' to current sheets in empirical three-dimensional models. We obtain a class of asymptotic magnetotail equilibria ordered with respect to the magnetic disturbance index Kp. For low Kp values the two-dimensional MHD equilibria reflect some of the realistic, observation-based, aspects of three-dimensional models. For high Kp values the three-dimensional models do not fit the asymptotic MHD equlibria, which is indicative of their inconsistency with the assumed pressure function. This, in turn, implies that high magnetic activity levels of the real magnetosphere might be ruled by thermodynamic conditions different from local thermodynamic equilibrium.

Voigt, Gerd-Hannes↗

NASA NTRS2008

Control Architecture for Robotic Agent Command and Sensing

Control Architecture for Robotic Agent Command and Sensing (CARACaS) is a recent product of a continuing effort to develop architectures for controlling either a single autonomous robotic vehicle or multiple cooperating but otherwise autonomous robotic vehicles. CARACaS is potentially applicable to diverse robotic systems that could include aircraft, spacecraft, ground vehicles, surface water vessels, and/or underwater vessels. CARACaS incudes an integral combination of three coupled agents: a dynamic planning engine, a behavior engine, and a perception engine. The perception and dynamic planning en - gines are also coupled with a memory in the form of a world model. CARACaS is intended to satisfy the need for two major capabilities essential for proper functioning of an autonomous robotic system: a capability for deterministic reaction to unanticipated occurrences and a capability for re-planning in the face of changing goals, conditions, or resources. The behavior engine incorporates the multi-agent control architecture, called CAMPOUT, described in An Architecture for Controlling Multiple Robots (NPO-30345), NASA Tech Briefs, Vol. 28, No. 11 (November 2004), page 65. CAMPOUT is used to develop behavior-composition and -coordination mechanisms. Real-time process algebra operators are used to compose a behavior network for any given mission scenario. These operators afford a capability for producing a formally correct kernel of behaviors that guarantee predictable performance. By use of a method based on multi-objective decision theory (MODT), recommendations from multiple behaviors are combined to form a set of control actions that represents their consensus. In this approach, all behaviors contribute simultaneously to the control of the robotic system in a cooperative rather than a competitive manner. This approach guarantees a solution that is good enough with respect to resolution of complex, possibly conflicting goals within the constraints of the mission to be accomplished by the vehicle(s).

Huntsberger, Terrance↗

DOE OSTI2021

Development of a Framework for Data Integration, Assimilation, and Learning for Geological Carbon Sequestration (DIAL-GCS) (Final Report)

This project aimed to develop and demonstrate a Data Integration, Assimilation, and Learning framework for geologic carbon sequestration projects (DIAL-GCS). DIAL-GCS is an intelligence monitoring system (IMS) for automating GCS closed-loop management by leveraging recent developments in machine learning technologies, complex event processing (CEP), and reduced-order modeling. The safe and efficient operation of GCS repositories requires integrated monitoring to track the injected CO¬2 as it moves within a storage reservoir. GCS projects are data intensive, as a result of proliferation of digital instrumentation and smart-sensing technologies. GCS projects are also resource intensive, often requiring multidisciplinary teams performing different monitoring, verification, accounting (MVA) tasks throughout the lifecycle of a project to ensure secure containment of injected CO2. The success of GCS thus depends in a large part on our ability to access, assimilate, and analyze heterogeneous data and information sources in a timely manner. This project included a number of meaningful and necessary tasks to transform the human domain knowledge into machine-interpretable rules for automating knowledge extraction and discovery in GCS. The specific technical objectives of the proposed DIAL-GCS project were to develop an ontology-driven GCS data management module for storing, querying, and exchanging GCS data (both historic and live sensor data) from multiple sources and in heterogeneous formats. Incorporate a CEP engine for detecting abnormal situations by seamlessly combining expert knowledge, rule-based reasoning, and machine learning. Enable uncertainty quantification and predictive analytics using a combination of coupled-process modeling, AI/ML methods, and reduced-order modeling, and integrate and demonstrate the system’s capabilities with both real and simulated data. As far as we know, this is one of the first projects aimed to develop intelligent monitoring systems (IMS) targeting the GCS. Under this project, the team had developed a large number of web applications and scientific algorithms that contribute the main theme of intelligent monitoring. The team has published more than a dozen peer reviewed papers and disseminated the research results at multiple technical meetings.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Fe-Coated Optical Fiber for Distributed Corrosion Monitoring in Soil and Aqueous Environments

Natural gas pipeline corrosion represents a substantial cost and safety concern during normal operations. The effective and real-time monitoring of corrosion is important to detect and mitigate pipeline risks before corrosion-related catastrophic events happen. Here, we describe the use of iron (Fe) coated optical fiber sensors (OFS) for distributed corrosion monitoring where Fe acts as a corrosion proxy. By using an optical backscattering reflectometer (OBR), corrosion was monitored based on the increase in the backscattered intensity amplitude of the light being passed as Fe underwent corrosion. The Fe-coated OFSs were prepared with a film thickness between 25–225 nanometers (nm) by an electroless plating approach and corroded by a carbon dioxide (CO2)-saturated acidic electrolyte. The corrosion rate (CR) was approximately 2.5 millimeters (mm)/year for Fe with a film thickness of 30 nm and increased with film thickness. Backscattering-based CRs were supported by visible light transmission measurements, which have been previously demonstrated. Additionally, a correlation between the intermittent transmission and residual Fe film thickness during the corrosion of Fe was established. The corrosion was measurable out to > 100 meters (m), which is a significant improvement over our previous work, which showed corrosion sensing at < 10 m. The corrosion sensor was also tested in soil at > 1 foot depth.

corrosion↗