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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Deep learning inversion of gravity data for detection of CO 2 plumes in overlying aquifers

In this work, we developed an effective U-Net based deep learning (DL) model for inversion of surface gravity data on a rectangular grid to predict 2-D high-resolution subsurface CO 2 distribution along a vertical cross-section due to CO 2 leakage through a wellbore within a deep CO 2 storage reservoir. We used synthetic data to model two types of CO 2 leakage scenarios: one CO 2 plume in a shallow aquifer (single plume case), and two plumes present at different depths (double plume case). The 3-D synthetic plume samples were created by sampling among predetermined CO 2 plume depths, saturations, and volumes. The corresponding surface gravity data on a rectangular grid were generated by a 3-D forward model. The U-Net model detected 72% of single-plume samples, and one or both plumes in 75% of double-plume samples. Most of the undetected single plumes have small gravity field strengths below the typical noise level of 5 μGal. This model generated reproducible, reliable predictions with acceptable errors and demonstrated improved spatial resolution over the conventional least-squares inversion. In contrast to the conventional least-squares inversion, which often overestimates the size of its target and underestimates its density, this U-Net model accurately delineated the boundary of a target. Furthermore, this DL inversion detected deep, small, or low saturation CO 2 plumes that are often more difficult to resolve with conventional gravity inversion methods. We note the limitations of this feasibility study, including the use of synthetic data with regular CO 2 plume shapes, and the prediction of a 2-D plume cross-section rather than the full 3-D plume, as well, we recognize the lower detection fraction for double-plume scenarios. Nevertheless, this study demonstrates that DL gravity inversion is a promising and potentially superior method to conventional least-squares inversion. Our U-Net based deep learning inversion approach may be adapted for inversion of other types of geophysical data. DL inversion can facilitate near real-time monitoring of geologic carbon sequestration to provide site operators with prompt information about subsurface CO 2 distribution for risk management and mitigation.

58 GEOSCIENCES↗
DOE OSTI2021

A Cyber-Physical Anomaly Detection for Wide-Area Protection Using Machine Learning

Wide-area protection scheme (WAPS) provides system-wide protection by detecting and mitigating small and large-scale disturbances that are difficult to resolve using local protection schemes. As this protection scheme is evolving from a substation-based distributed remedial action scheme (DRAS) to the control center-based centralized RAS (CRAS), it presents severe challenges to their cybersecurity because of its heavy reliance on an insecure grid communication, and its compromise would lead to system failure. This article presents an architecture and methodology for developing a cyber-physical anomaly detection system (CPADS) that utilizes synchrophasor measurements and properties of network packets to detect data integrity and communication failure attacks on measurement and control signals in CRAS. The proposed machine leaning-based methodology applies a rules-based approach to select relevant input features, utilizes variational mode decomposition (VMD) and decision tree (DT) algorithms to develop multiple classification models, and performs final event identification using a rules-based decision logic. Here, we have evaluated the proposed methodology of CPADS using the IEEE 39 bus system for several performance measures (accuracy, recall, precision, and F-measure) in a cyber-physical testbed environment. Furthermore, our experimental results reveal that the proposed algorithm (VMD-DT) of CPADS outperforms the existing machine learning classifiers during noisy and noise-free measurements while incurring an acceptable processing overhead.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Analysis and mitigation of an oscillating background on hybrid complementary metal-oxide semiconductor (hCMOS) imaging sensors at the National Ignition Facility

Nanosecond-gated hybrid complementary metal-oxide semiconductor imaging sensors are a powerful tool for temporally gated and spatially resolved measurements in high energy density science, including inertial confinement fusion, and in laser diagnostics. However, a significant oscillating background excited by photocurrent has been observed in image sequences during testing and in experiments at the National Ignition Facility (NIF). Characterization measurements and simulation results are used to explain the oscillations as the convolution of the pixel-level sensor response with a sensor-wide RLC circuit ringing. Finally, data correction techniques are discussed for NIF diagnostics, and for diagnostics where these techniques cannot be used, a proof-of-principle image correction algorithm is presented.

42 ENGINEERING↗
DOE OSTI2024

First-order crosstalk mitigation in parallel quantum gates driven with multi-photon transitions

Here, we demonstrate an order of magnitude reduction in the sensitivity to optical crosstalk for neighboring trapped-ion qubits during simultaneous single-qubit gates driven with individual addressing beams. Gates are implemented via two-photon Raman transitions, where crosstalk is mitigated by offsetting the drive frequencies for each qubit to avoid first-order crosstalk effects from inter-beam two-photon resonance. The technique is simple to implement, and we find that phase-dependent crosstalk due to optical interference is reduced on the most impacted neighbor from a maximal fractional rotation error of 0.185(4) without crosstalk mitigation to ≤0.006 with the mitigation strategy. Furthermore, we characterize first-order crosstalk in the two-qubit gate and avoid the resulting rotation errors for the arbitrary-axis Mølmer–Sørensen gate via a phase-agnostic composite gate. Finally, we demonstrate holistic system performance by constructing a composite CNOT gate using the improved single-qubit gates and phase-agnostic two-qubit gate. This work is done on the Quantum Scientific Computing Open User Testbed; however, our methods are widely applicable for individual addressing Raman gates and impose no significant overhead, enabling immediate improvement for quantum processors that incorporate this technique.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Exploring Electrocatalysis of Methane on Transition Metal Surfaces

Methane is a potent greenhouse gas because it has a heat-absorbing ability that is 20 times stronger than the better-known carbon dioxide. It is also the dominant component of nature gas (>70%). Its current greenhouse effect is only second to carbon dioxide largely because of its relatively low atmospheric concentration. Alarmingly, however, the latter has seen a 1.5-time increase since 1750, mainly due to anthropogenic livestock farming, coal and natural gas mining, and organic matter decomposing in waste landfills. Mitigating such a trend effectively and efficiently is therefore highly desirable and intensely sought for. This final technical report describes some exciting research progresses made over the last three years supported financially by the aforementioned award. The research conducted focused on exploring electrochemical direct methane to methanol (DMTM) conversion under mild conditions. The goal of research was to explore the possibility of developing potentially game-changing electrocatalysts and electrocatalysis that can convert methane directly to methanol in a most sustainable way for curbing its effect on global warming. Progress has been made in the following three directions. First, by adapting a hydrogen fuel-cell configuration that enables gaseous methane to be transported directly to the electrocatalyst surface, we have overcome the perennial difficulty in measuring activity of methane electro-oxidation at low temperature and ambient pressure that has prevented any meaningful studies of electrocatalysis of methane conversion thus far. Second, we have developed a novel and operational in situ electrochemical infrared (IR) spectral cell for studying electrocatalysis of direct methane to methanol conversion. This novel cell is also expected to alleviate the measuring difficulty caused by low solubility of methane in aqueous electrolyte and low sticking coefficient for methane adsorption onto transition metal surface. It will improve significantly the IR’s signal to noise ratio. Third, we have successfully synthesized nano-sizing copper (Cu) exchanged zeolite mordenite (Cu-ZMOR) and coated it with carbon that enabled electrochemical access to the encaged Cu ions. This opens a door to unravel the chemistry of Cu ions in catalyzing direct methane to methanol conversion under mild experimental conditions. Altogether, these progresses achieved have laid a solid foundation for further exploring electrocatalysis of direct methane to methanol conversion in general where there exists “a plenty of room” for new discoveries and developments.

03 NATURAL GAS↗
DOE OSTI2024

On the Generalizability of Time-of-Flight Convolutional Neural Networks for Noninvasive Acoustic Measurements

Bulk wave acoustic time-of-flight (ToF) measurements in pipes and closed containers can be hindered by guided waves with similar arrival times propagating in the container wall, especially when a low excitation frequency is used to mitigate sound attenuation from the material. Convolutional neural networks (CNNs) have emerged as a new paradigm for obtaining accurate ToF in non-destructive evaluation (NDE) and have been demonstrated for such complicated conditions. However, the generalizability of ToF-CNNs has not been investigated. In this work, we analyze the generalizability of the ToF-CNN for broader applications, given limited training data. We first investigate the CNN performance with respect to training dataset size and different training data and test data parameters (container dimensions and material properties). Furthermore, we perform a series of tests to understand the distribution of data parameters that need to be incorporated in training for enhanced model generalizability. This is investigated by training the model on a set of small- and large-container datasets regardless of the test data. We observe that the quantity of data partitioned for training must be of a good representation of the entire sets and sufficient to span through the input space. The result of the network also shows that the learning model with the training data on small containers delivers a sufficiently stable result on different feature interactions compared to the learning model with the training data on large containers. To check the robustness of the model, we tested the trained model to predict the ToF of different sound speed mediums, which shows excellent accuracy. Furthermore, to mimic real experimental scenarios, data are augmented by adding noise. We envision that the proposed approach will extend the applications of CNNs for ToF prediction in a broader range.

47 OTHER INSTRUMENTATION↗
NASA NTRS2020

In-flight Testing of the Injection of the LISA Pathfinder Test Mass into a Geodesic

LISA Pathfinder is a technology demonstrator space mission, aimed at testing key technologies for detecting gravitational waves in space. The mission is the precursor of LISA, the first space gravitational waves observatory, whose launch is scheduled for 2034. The LISA Pathfinder scientific payload includes two gravitational reference sensors (GRSs), each one containing a test mass (TM), which is the sensing body of the experiment. A mission critical task is to set each TM into a pure geodesic motion, i.e. guaranteeing an extremely low acceleration noise in the sub-Hertz frequency bandwidth. The grabbing positioning and release mechanism (GPRM), responsible for the injection of the TM into a geodesic trajectory, was widely tested on ground, with the limitations imposed by the 1-g environment. The experiments showed that the mechanism, working in its nominal conditions, is capable of releasing the TM into free-fall fulfilling the very strict constraint imposed on the TM residual velocity, in order to allow its capture on behalf of the electrostatic actuation. However, the first in-flight releases produced unexpected residual velocity components, for both the TMs. Moreover, all the residual velocity components were greater than maximum value set by the requirements. The main suspect is that unexpected contacts took place between the TM and the surroundings bodies. As a consequence, ad hoc manual release procedures had to be adopted for the few following injections performed during the nominal mission. These procedures still resulted in non compliant TM states which were captured only after impacts. However, such procedures seem not practicable for LISA, both for the limited repeatability of the system and for the unmanageable time lag of the telemetry/telecommand signals (about 4400 s). For this reason, at the end of the mission, the GPRM was deeply tested in-flight, performing a large number of releases, according to different strategies. The tests were carried out in order to understand the unexpected dynamics and limit its effects on the final injection. Some risk mitigation maneuvers have been tested aimed at minimizing the vibration of the system at the release and improving the alignment between the mechanism and the TM. However, no overall optimal release strategy to be implemented in LISA could be found, because the two GPRMs behaved differently.

LISA↗
NASA NTRS2019

Mechanical Design and Configuration of Penetrations for the Europa Clipper Avionics Vault Structure

The main purpose of the Avionics Vault is to shield radiation sensitive electronics for the Europa Clipper Spacecraft. The vault is a box structure made out of aluminum panels. The panels are roughly 10 mm thick in order to shield the electronics from the orbital total ionizing radiation around Jupiter. The vault requires an electromagnetic interference (EMI) shielding effectiveness (SE) of at least 70 dB in order to mitigate EMI with the spacecraft radar receiver. Overall, the vault accommodates four main types of penetrations: receptacle connectors, pass-through cables, fluid lines, and vent holes. More than 150 cables penetrate the vault panels to connect to electronic boxes inside. Fluid pipes enter and exit the vault to transfer heat to the rest of the spacecraft. Vent holes provide a path for air to escape from the vault during launch. Several novel penetrations designs were created to meet EMI and radiation shielding requirements. Receptacle connectors interface to the vault panels using 1.3 mm thick Ta10W plates. Pass-through cables penetrate the vault using aluminum clamshells after being wrapped with Teflon cushion tape, Kapton tape, and copper tape. Vent hole penetrations consist of a copper mesh for EMI shielding and an aluminum radiation shield bracket to direct air out of the vault during launch. Fluid lines terminate at the vault wall using mechanical fittings that resemble a nut and bolt interface. In addition, most mechanical seams and penetrations utilize EMI gaskets to ensure proper EMI shielding. To reduce risk and confirm that the vault penetration designs were appropriate for EMI shielding, an EMI chamber at the Jet Propulsion Laboratory (JPL) was used to test a mock-up vault panel with multiple variations of all four main types of vault penetrations. This EMI SE test also incorporated different methods for bundling pass-through cables, and a comparison of flange mounted connectors versus jam nut connectors. A low noise preamplifier and a Rohde & Schwarz spectrum analyzer measured E-field levels transmitting through the mock-up vault panel. The results showed a shielding effectiveness of 77 dB for the mock-up vault panel, which exceeds the 70 dB target for Europa Clipper. Both the flange mounted connectors and jam nut connectors exhibited similar EMI SE results at the measured frequencies, and all variations of vault penetrations showed favorable EMI SE levels. Since the flight panels will be much larger and include many more penetrations, there will be testing of the flight vault to confirm its EMI SE is compliant with environmental requirements.

Giersch, Louis↗
DOE OSTI2026

Principles and Options for Designing Battery Energy Storage Zoning Ordinances

Deployment of battery energy storage (BESS) systems, both standalone and as part of hybrid systems paired with generation, has rapidly increased in the United States in recent years as utilities and communities have deployed storage to improve electric grid reliability and act as a cost-effective alternative to larger grid infrastructure. The modular nature of BESS technologies means systems may be built near other existing land uses, creating the potential for conflicts with neighboring landowners that can be managed and mitigated through zoning and permitting requirements established by local jurisdictions. While many cities and counties have adopted ordinances specific to BESS into their local zoning codes, these ordinances vary significantly in their requirements and level of detail. Meanwhile, many other jurisdictions, including those home to proposed or existing BESS projects, lack any specific language related to BESS in their zoning codes. Local planning and zoning officials have limited capacity and may lack the familiarity with BESS technologies needed to develop ordinances or otherwise make reasonable zoning decisions that balance safety, community impacts, and other goals. The resulting uncertainty at the local zoning level has led developers to withdraw projects in some areas and has spurred moratoria or bans on energy storage projects in others. This report intends to provide practical resources for practitioners interested in reasonable and effective local regulation of battery energy storage. It does not present a model zoning ordinance, but rather is intended to complement model ordinances developed by others by providing additional context and analysis regarding the structure of energy storage zoning ordinances and the decision points for local officials. Zoning ordinances at the city, town, and county level across the U.S. were surveyed alongside two template model ordinances to identify common elements and options for regulating the zoning and siting of BESS. Common elements identified and analyzed include definitions and general requirements, including cutoffs or tiers used to apply regulations to different system sizes and the permitted zones where jurisdictions allow BESS to be sited; visual, noise, and aesthetic requirements, including property line setbacks, fencing and visual screening, noise, and lighting requirements; and safety and planning requirements, such as site plans, decommissioning plans or funds, and requirements for access by emergency services. The report also summarizes some of the more unique regulations, including those that place additional restrictions on BESS at the local level.

25 ENERGY STORAGE↗
DOE OSTI2022

Decoding Golden Eagle Movement Behavior from High-Resolution, Variable-Rate Telemetry Data Through Bayesian Filtering

The recent advances in animal tracking technology have enabled the collection of a vast amount of in situ data regarding the movement of wildlife at high spatiotemporal resolution. These data are usually available at variable time resolutions and contains noise (error) originating from GPS fixes. Decoding movement characteristics, particularly of flying animals, from telemetry data while handling these factors is a challenging yet important task for conservation purposes. Typically, this task is broken into two subtasks: resampling, and model calibration. The resampling subtask converts the variable rate positional data into a constant time interval data, while the model calibration subtask uses the resampled data to tune time-invariant parameters of the proposed models. For telemetry data at high temporal resolutions (order of 1 second), it is very challenging to decouple noise from actual movements using interpolation-based resampling techniques. Any errors introduced during resampling can significantly alter the the calibration and prediction attributes of the movement model. We address this problem through a unified Bayesian state-space framework that can handle both the resampling and calibration tasks in a single step. In addition, we use the speed and heading of the bird from telemetry data to regularize the position information of the bird. We use a Kalman filtering approach to include these nonlinearly related motion parameters within the state space framework. We cross-validated to quantify how this inclusion affects the model performance in estimating true bird movements. The relationship between the true state of the bird and environmental and topographical covariates is then represented parametrically. These parameters are then tuned using stochastic sampling strategies like Markov Chain Monte Carlo (MCMC). We use the telemetry data collected from golden eagles in the western USA to demonstrate the applicability of this approach to build a predictive, probabilistic movement model. Our preliminary results show that this approach provides improved predictive performance in terms of capturing higher-order motion parameters such as angular and horizontal accelerations, which may have simpler and more direct relationships with environmental covariates than corresponding speeds. In this talk, we will demonstrate how this state-space approach benefits the prediction capabilities of a movement model in simulating golden eagle paths through a wind power plant in Wyoming given certain atmospheric conditions. The model outcomes are aimed at informing mitigation strategies that can minimize the potential for collisions of golden eagles with wind turbines.

Bayesian methods↗
NASA NTRS1993

Adaptive digital beamforming for a CDMA mobile communications payload

In recent years, Spread-Spectrum Code Division Multiple Access (CDMA) has become a very popular access scheme for mobile communications due to a variety of reasons: excellent performance in multipath environments, high scope for frequency reuse, graceful degradation near saturation, etc. In this way, a CDMA system can support simultaneous digital communication among a large community of relatively uncoordinated users sharing a given frequency band. Nevertheless, there are also important problems associated with the use of CDMA. First, in a conventional CDMA scheme, the signature sequences of asynchronous users are not orthogonal and, as the number of active users increases, the self-noise generated by the mutual interference between users considerably degrades the performance, particularly in the return link. Furthermore, when there is a large disparity in received powers - due to differences in slant range or atmospheric attenuation - the non-zero cross-correlation between the signals gives rise to the so-called near-far problem. This leads to an inefficient utilization of the satellite resources and, consequently, to a drastic reduction in capacity. Several techniques were proposed to overcome this problem, such as Synchronized CDMA - in which the signature sequences of the different users are quasi-orthogonal - and power control. At the expense of increased network complexity and user coordination, these techniques enable the system capacity to be restored by equitably sharing the satellite resources among the users. An alternative solution is presented based upon the use of time-reference adaptive digital beamforming on board the satellite. This technique enables a high number of independently steered beams to be generated from a single phased array antenna, which automatically track the desired user signal and null the unwanted interference source. In order to use a time-reference adaptive antenna in a communications system, the main challenge is to obtain a reference signal highly correlated with the desired user signal and uncorrelated with the interferences. CDMA lends itself very easily to the generation of such a reference signal, thanks to the a priori knowledge of the user's signature sequence. First, the integration of an adaptive antenna in an asynchronous CDMA system is analyzed. The adaptive antenna system can provide increased interference rejection - much higher than that afforded by the code alone - and, since CDMA is mainly interference limited, any reduction in interference converts directly and linearly into an increase in capacity. Analyses and computer simulations are presented that show how an asynchronous CDMA system incorporating adaptive beamforming can provide at least as much capacity as a synchronous system. More importantly, the proposed concept allows the near-far effect to be mitigated without requiring a tight coordination of the users in terms of transmitted power control or network synchronization. The system is extremely robust to the near-far effect because the signals reaching the satellite from directions other than that of the desired user - which are likely to have different power levels - are adaptively canceled by the antenna. Finally, a payload architecture is presented that illustrates the practical implementation of this concept. This digital payload architecture demonstrates that with the advent of high performance CMOS digital processing, the on-board implementation of complex DSP techniques - in particular Digital Beamforming - has become possible, being most attractive for Mobile Satellite Communications.

Munoz-Garcia, Samuel G.↗
DOE OSTI2020

Use of Unmanned Aerial Systems for Post-closure Waste Site Maintenance and Surveillance - 20143

The P- and R-Area Reactor buildings, located at SRS near Aiken, SC, were in-situ decommissioned by grouting below-grade portions of the buildings and demolishing some above-grade structures to grade level. Other reactor building structures were left above-grade and sealed to prevent human or animal access. Because the above grade-structures are expected to continue in their present state for hundreds of years, the condition of the building roofs is critical to mitigate rainwater intrusion. For this reason, building roof areas were strengthened with high strength concrete to ensure the long-term integrity of the roof structure. Periodic inspections of the roof structures are required to ensure that the roofs are functioning properly and to identify damage areas. The traditional method of inspecting closed reactor buildings requires the use of a helicopter and photographer to generate photographs and videos for review. This method proved sufficient but lacked the resolution and clarity that is required for a thorough inspection of the building structure. Fortunately, the Savannah River National Laboratory (SRNL) has established a Small Unmanned Aircraft System (sUAS) program using commercially available and custom-built remote-controlled aircraft. EC and ACP contacted the SRNL program to evaluate whether the sUAS technology could be employed for periodic reactor building inspections. The sUAS can fly within a few meters of the reactor buildings and hover, allowing for a more thorough aerial inspection. The initial sUAS inspection of the P-Area Reactor building was completed in February 2018 and at the R-Area Reactor building in August 2018 and were successful in providing higher resolution photos and videos. The sUAS inspections also revealed that vegetation had begun to grow on the roofs that could potentially damage the structure integrity. SRNL partnered with Virginia Polytechnic Institute and State University to build a custom heavy lift sUAS that could be equipped with herbicides to remotely treat the vegetative growth. Herbicides were successfully applied to the R-Area Reactor building roof using the custom sUAS in September 2018. The use of unmanned aircraft systems at SRS to perform aerial inspections and herbicide treatment in otherwise inaccessible areas has proven to be an efficient and cost-effective technology that provides high value, increases knowledge of facility conditions, and provides for early detection of damage. Periodic inspections of the reactor building roofs using sUAS technology are performed safely, efficiently, and at a significant cost and schedule savings while reducing emissions, noise, and fossil fuel use. The use of sUAS equipment for building inspections and herbicide application at SRS is unique within the DOE complex. The goal of SRS is to apply this remote technology to other waste unit operations and maintenance activities in the future. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2012

An MSK Radar Waveform

The minimum-shift-keying (MSK) radar waveform is formed by periodically extending a waveform that separately modulates the in-phase and quadrature- phase components of the carrier with offset pulse-shaped pseudo noise (PN) sequences. To generate this waveform, a pair of periodic PN sequences is each passed through a pulse-shaping filter with a half sinusoid impulse response. These shaped PN waveforms are then offset by half a chip time and are separately modulated on the in-phase and quadrature phase components of an RF carrier. This new radar waveform allows an increase in radar resolution without the need for additional spectrum. In addition, it provides self-interference suppression and configurable peak sidelobes. Compared strictly on the basis of the expressions for delay resolution, main-lobe bandwidth, effective Doppler bandwidth, and peak ambiguity sidelobe, it appears that bi-phase coded (BPC) outperforms the new MSK waveform. However, a radar waveform must meet certain constraints imposed by the transmission and reception of the modulation, as well as criteria dictated by the observation. In particular, the phase discontinuity of the BPC waveform presents a significant impediment to the achievement of finer resolutions in radar measurements a limitation that is overcome by using the continuous phase MSK waveform. The phase continuity, and the lower fractional out-of-band power of MSK, increases the allowable bandwidth compared with BPC, resulting in a factor of two increase in the range resolution of the radar. The MSK waveform also has been demonstrated to have an ambiguity sidelobe structure very similar to BPC, where the sidelobe levels can be decreased by increasing the length of the m-sequence used in its generation. This ability to set the peak sidelobe level is advantageous as it allows the system to be configured to a variety of targets, including those with a larger dynamic range. Other conventionally used waveforms that possess an even greater spectral efficiency than the MSK waveform, such as linear frequency modulation (LFM) and Costas frequency hopping, have a fixed peak sidelobe level that is therefore not configurable, and can be exceeded by high contrast targets. Furthermore, in the case of a multistatic experiment observing a target in motion, self-interference from the transmitter to the receiver is mitigated by the MSK waveform. Waveforms that have delay Doppler coupling, such as LFM, provide no such protection.

Quirk, Kevin J.↗
NASA NTRSDate not supplied

Global 3D Data Visualization and Analysis Platform with Advanced Machine Learning Capabilities in Support of Lunar Exploration

Introduction: The science goals for NASA’s Artemis program include: a) Understanding the character and origin of lunar polar volatiles, b) Conducting experimental science in the lunar environment and c) Investigating and mitigating exploration risks. The permanently shadowed regions (PSRs) on the Lunar south pole are expected to host large quantities of water-ice and volatiles that are important for sustainable Lunar exploration. There are several missions such as onboard Korea Pathfinder Lunar Orbiter (KPLO: Korean name Danuri) with onboard ShadowCam camera, Astrobotic Peregrine Mission One [4], and other efforts underway to obtain high resolution topographic, minerals, volatiles and other information on the moon. We envision a need in immediate future for platforms to integrate these data sets, provide rendering and visualization capabilities in the context of a 3D Lunar globe for easier information access and analysis. NASA's Celestial Mapping System (CMS) is developed to address the need for 3D tools for planetary science investigations, mission planning, in-situ operations, in a 3D-first design constructed around a unified view of a planetary globe. At present CMS provides many critical functionalities that include: 1) equipment planning and optimized placement on Lunar surface 2) line-of-sight (LOS) analysis 3) powerful measurement tools based on 3D terrain with realistic 3D models to represent rovers, astronauts and equipment 4) visualization of derived mapping products (e.g. resource maps), and 5) a data engine for hosting new observations that are not available in other contemporary lunar data tools. Planetary Data Ingestion: CMS can consume and analyze data from locally hosted and external third party sources. It is compatible with Open Geospatial Consortium (OGC) data and file standards and currently integrates datasets from the Astrogeology Science Center of USGS. This includes global and local data acquired from NASA (LRO, Clementine, Lunar Orbiter) and JAXA (SELENE/Kaguya), with capability of integrating more datasets. In addition, users can specify other WMS-hosted data endpoints, which CMS can then query and stream data from automatically. To set-up an automated process for ingestion and accurate rendering, visualization and analysis of external 3rd party planetary datasets within CMS, we initiated the process of ingesting unique dataset of super-enhanced images of the permanently shadowed regions (PSRs) at the lunar poles which were produced by the Hyper-effective nOise Removal U-net Software (HORUS) tool. This tool was developed to enhance the extremely low-light images of the interior of PSRs and provide the ability to see within these regions at and discern surface features (i.e. boulders and craters) down to 3 meters in size. We focused on the Nobile region on the Lunar south pole, selected site for VIPER mission and stitched several images to create a high-resolution map within one of the PSR of Nobile crater. Figure 1 shows the dark PSR zone form the original NAC layer of LRO as the base layer (left image) and the illuminated areas within that crater (center) which was created by ingesting and merging several of HORUS generated images. At present we employ a semi-automated process to ensure spatial accuracy and merger of several overlapping zones. However, we are in the process of completely automating this process by employing AI based techniques that would rank, sort, and stack the images based on their information density. The georectification of the images would employ selected features. Analysis on Ingested Planetary Datasets: Once an external planetary data-set is successfully ingested, georectified and merged seamlessly as a data-layer; CMS’ numerous analysis tools can be used on this data. A Line of Sight (LOS) tool has been developed for CMS which analyzes terrain profiles and obstructions to determine visibility for remote observers. Figure 1 (right image) shows the viewshed analysis on the same PSR in the Nobile region. The yellow pin shows the observer location outside the PSR. The yellow area shows the visible part of PSR. The obstructed area with no visibility for the observer is shown in red. The Measurements tool allows the user to take area and distance measurements of features on the terrain using various shapes. Measurement type can be specified in a number of ways: Line, Path, Polygon, Circle, Ellipse, Square, Rectangle or Freehand. Once the shape is specified, elevation information can then be extracted along each of these shapes. Figure 2 (left) shows the measurements performed on a crater n illuminated PSR in Nobile region. The equipment placement tool allows the user to place a 3D equipment model at a desired location and analyze its coverage area. The equipment placement tool is coupled with LOS to determine the coverage. Figure 2 (right) shows an equipment placed on the Lunar terrain and it’s coverage area. The red rays are blocked sight lines and the green rays are non-obstructed sight lines with the cyan lines showing the point of intersection with the terrain. More details are provided in the video demonstrations in Reference 5. Overcoming Polar Distortions: 3D geospatial applications exhibit significant distortions in polar imagery due to several reasons: 1) distortions in the source imagery, 2) incompatible tessellation algorithms at the poles, and 3) map projections. We are leveraging new tessellation algorithms and reprojecting data using projections that are better suited for Lunar poles. The goal is to seamlessly switch to polar projections while maintaining 3D view and navigation.

Maps↗
NASA NTRSDate not supplied

Global 3D Data Visualization and Analysis Platform With Advanced Machine Learning Capabilities in Support of Lunar Exploration

Introduction: The science goals for NASA’s Artemis program include: a) Understanding the character and origin of lunar polar volatiles, b) Conducting experimental science in the lunar environment and c) Investigating and mitigating exploration risks [1]. The permanently shadowed regions (PSRs) on the Lunar south pole are expected to host large quantities of water-ice and volatiles that are important for sustainable Lunar exploration [2]. There are several missions such as onboard Korea Pathfinder Lunar Orbiter (KPLO: Korean name Danuri) with onboard ShadowCam camera [3], Astrobotic Peregrine Mission One [4], and other efforts underway to obtain high resolution topographic, minerals, volatiles and other information on the moon. We envision a need in immediate future for platforms to integrate these data sets, provide rendering and visualization capabilities in the context of a 3D Lunar globe for easier information access and analysis. NASA's Celestial Mapping System (CMS) [5] is developed to address the need for 3D tools for planetary science investigations, mission planning, in-situ operations, in a 3D-first design constructed around a unified view of a planetary globe. At present CMS provides many critical functionalities that include: 1) equipment planning and optimized placement on Lunar surface 2) line-of-sight (LOS) analysis 3) powerful measurement tools based on 3D terrain with realistic 3D models to represent rovers, astronauts and equipment 4) visualization of derived mapping products (e.g. resource maps), and 5) a data engine for hosting new observations that are not available in other contemporary lunar data tools [5, 7]. Planetary Data Ingestion: CMS can consume and analyze data from locally hosted and external third party sources. It is compatible with Open Geospatial Consortium (OGC) data and file standards and currently integrates datasets from the Astrogeology Science Center of USGS. This includes global and local data acquired from NASA (LRO, Clementine, Lunar Orbiter) and JAXA (SELENE/Kaguya), with capability of integrating more datasets. In addition, users can specify other WMS-hosted data endpoints, which CMS can then query and stream data from automatically. To set-up an automated process for ingestion and accurate rendering, visualization and analysis of external 3rd party planetary datasets within CMS, we initiated the process of ingesting unique dataset of super-enhanced images of the permanently shadowed regions (PSRs) at the lunar poles which were produced by the Hyper-effective nOise Removal U-net Software (HORUS) tool [8]. This tool was developed to enhance the extremely low-light images of the interior of PSRs and provide the ability to see within these regions at and discern surface features (i.e. boulders and craters) down to 3 meters in size. We focused on the Nobile region on the Lunar south pole, selected site for VIPER mission and stitched several images to create a high-resolution map within one of the PSR of Nobile crater. Figure 1 shows the dark PSR zone form the original NAC layer of LRO as the base layer (left image) and the illuminated areas within that crater (center) which was created by ingesting and merging several of HORUS generated images. At present we employ a semi-automated process to ensure spatial accuracy and merger of several overlapping zones. However, we are in the process of completely automating this process by employing AI based techniques that would rank, sort, and stack the images based on their information density. The georectification of the images would employ selected features. Analysis on Ingested Planetary Datasets: Once an external planetary data-set is successfully ingested, georectified and merged seamlessly as a data-layer; CMS’ numerous analysis tools can be used on this data. A Line of Sight (LOS) tool has been developed for CMS which analyzes terrain profiles and obstructions to determine visibility for remote observers [5,6]. Figure 1 (right image) shows the viewshed analysis on the same PSR in the Nobile region. The yellow pin shows the observer location outside the PSR. The yellow area shows the visible part of PSR. The obstructed area with no visibility for the observer is shown in red. The Measurements tool allows the user to take area and distance measurements of features on the terrain using various shapes. Measurement type can be specified in a number of ways: Line, Path, Polygon, Circle, Ellipse, Square, Rectangle or Freehand. Once the shape is specified, elevation information can then be extracted along each of these shapes. Figure 2 (left) shows the measurements performed on a crater n illuminated PSR in Nobile region. The equipment placement tool allows the user to place a 3D equipment model at a desired location and analyze its coverage area. The equipment placement tool is coupled with LOS to determine the coverage. Figure 2 (right) shows an equipment placed on the Lunar terrain and it’s coverage area. The red rays are blocked sight lines and the green rays are non-obstructed sight lines with the cyan lines showing the point of intersection with the terrain. More details are provided in the video demonstrations in Reference 5. Overcoming Polar Distortions: 3D geospatial applications exhibit significant distortions in polar imagery due to several reasons: 1) distortions in the source imagery, 2) incompatible tessellation algorithms at the poles, and 3) map projections. We are leveraging new tessellation algorithms and reprojecting data using projections that are better suited for Lunar poles. The goal is to seamlessly switch to polar projections while maintaining 3D view and navigation.

Maps↗
NASA NTRS2004

Strategy Developed for Selecting Optimal Sensors for Monitoring Engine Health

Sensor indications during rocket engine operation are the primary means of assessing engine performance and health. Effective selection and location of sensors in the operating engine environment enables accurate real-time condition monitoring and rapid engine controller response to mitigate critical fault conditions. These capabilities are crucial to ensure crew safety and mission success. Effective sensor selection also facilitates postflight condition assessment, which contributes to efficient engine maintenance and reduced operating costs. Under the Next Generation Launch Technology program, the NASA Glenn Research Center, in partnership with Rocketdyne Propulsion and Power, has developed a model-based procedure for systematically selecting an optimal sensor suite for assessing rocket engine system health. This optimization process is termed the systematic sensor selection strategy. Engine health management (EHM) systems generally employ multiple diagnostic procedures including data validation, anomaly detection, fault-isolation, and information fusion. The effectiveness of each diagnostic component is affected by the quality, availability, and compatibility of sensor data. Therefore systematic sensor selection is an enabling technology for EHM. Information in three categories is required by the systematic sensor selection strategy. The first category consists of targeted engine fault information; including the description and estimated risk-reduction factor for each identified fault. Risk-reduction factors are used to define and rank the potential merit of timely fault diagnoses. The second category is composed of candidate sensor information; including type, location, and estimated variance in normal operation. The final category includes the definition of fault scenarios characteristic of each targeted engine fault. These scenarios are defined in terms of engine model hardware parameters. Values of these parameters define engine simulations that generate expected sensor values for targeted fault scenarios. Taken together, this information provides an efficient condensation of the engineering experience and engine flow physics needed for sensor selection. The systematic sensor selection strategy is composed of three primary algorithms. The core of the selection process is a genetic algorithm that iteratively improves a defined quality measure of selected sensor suites. A merit algorithm is employed to compute the quality measure for each test sensor suite presented by the selection process. The quality measure is based on the fidelity of fault detection and the level of fault source discrimination provided by the test sensor suite. An inverse engine model, whose function is to derive hardware performance parameters from sensor data, is an integral part of the merit algorithm. The final component is a statistical evaluation algorithm that characterizes the impact of interference effects, such as control-induced sensor variation and sensor noise, on the probability of fault detection and isolation for optimal and near-optimal sensor suites.

Source record↗
DOE OSTI2021

Smart Methane Emission Detection System Development (Final Report)

Working with the Department of Energy's National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real-time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI's Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators. The goals for this research were to develop two types of models with the following goals: Run in real-time on the edge (≥ 12 Hz), Classification: Achieve less than 5% false positive detection, Classification: Achieve ≥ 95% methane plume detection rate, Regression: achieve ≤ 10 standard cubic feet per hour (scfh) prediction > 70% of the time. In order to achieve these results, multiple infrared (IR) and other sensors were investigated in tandem with the midwave IR (MWIR) OGI to provide additional information to train the underlying models. Information on atmospheric conditions including humidity, temperature, pressure, and solar radiation was provided by a weather station. Several machine learning and deep learning architectures and methods, including looking at quantized classification networks and regressions networks, were explored. As further data was collected, curated, and labeled, it allowed for more refined regressive networks to be adequately trained, leading to better insight into the true flow rates being observed. An important valuable deliverable of this research effort was the development of an advanced network which underwent multiple iterations capable of giving a continuous output. The current network has a predicted mean average percentage error (MAPE) of 12.3% just outside our target goal of 10.00%, but an accuracy of 97.78% at ±50 scfh, well within the overall goal for the Department of Energy (DOE) program. Upon closer inspection, it was observed that more than 10% of datapoints contributing to the MAPE predictions were the result of low flow rate predictions and are beyond the sensitivity of instrument measurement as a result of normal operational variation and noise.

03 NATURAL GAS↗
DOE OSTI2021

Smart Methane Emission Detection System Development (Final Report)

Working with the Department of Energy’s National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real- time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI’s Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators. The goals for this research were to develop two types of models with the following goals: 1. Run in real-time on the edge (≥ 12 Hz) 2. Classification: Achieve less than 5% false positive detection 3. Classification: Achieve ≥ 95% methane plume detection rate 4. Regression: achieve ≤ 10 standard cubic feet per hour (scfh) prediction > 70% of the time In order to achieve these results, multiple infrared (IR) and other sensors were investigated in tandem with the midwave IR (MWIR) OGI to provide additional information to train the underlying models. Information on atmospheric conditions including humidity, temperature, pressure, and solar radiation was provided by a weather station. Several machine learning and deep learning architectures and methods, including looking at quantized classification networks and regressions networks, were explored. As further data was collected, curated, and labeled, it allowed for more refined regressive networks to be adequately trained, leading to better insight into the true flow rates being observed. An important valuable deliverable of this research effort was the development of an advanced network which underwent multiple iterations capable of giving a continuous output. The current network has a predicted mean average percentage error (MAPE) of 12.3% just outside our target goal of 10.00%, but an accuracy of 97.78% at ±50 scfh, well within the overall goal for the Department of Energy (DOE) program. Upon closer inspection, it was observed that more than 10% of datapoints contributing to the MAPE predictions were the result of low flow rate predictions and are beyond the sensitivity of instrument measurement as a result of normal operational variation and noise.

03 NATURAL GAS↗