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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
NASA NTRS2004

NASA Ground-Truthing Capabilities Demonstrated

NASA Research and Education Network (NREN) ground truthing is a method of verifying the scientific validity of satellite images and clarifying irregularities in the imagery. Ground-truthed imagery can be used to locate geological compositions of interest for a given area. On Mars, astronaut scientists could ground truth satellite imagery from the planet surface and then pinpoint optimum areas to explore. These astronauts would be able to ground truth imagery, get results back, and use the results during extravehicular activity without returning to Earth to process the data from the mission. NASA's first ground-truthing experiment, performed on June 25 in the Utah desert, demonstrated the ability to extend powerful computing resources to remote locations. Designed by Dr. Richard Beck of the Department of Geography at the University of Cincinnati, who is serving as the lead field scientist, and assisted by Dr. Robert Vincent of Bowling Green State University, the demonstration also involved researchers from the NASA Glenn Research Center and the NASA Ames Research Center, who worked with the university field scientists to design, perform, and analyze results of the experiment. As shown real-time Hyperion satellite imagery (data) is sent to a mass storage facility, while scientists at a remote (Utah) site upload ground spectra (data) to a second mass storage facility. The grid pulls data from both mass storage facilities and performs up to 64 simultaneous band ratio conversions on the data. Moments later, the results from the grid are accessed by local scientists and sent directly to the remote science team. The results are used by the remote science team to locate and explore new critical compositions of interest. The process can be repeated as required to continue to validate the data set or to converge on alternate geophysical areas of interest.

Lopez, Isaac↗
NASA NTRS2004

Ocean Surface Topography Data Products and Tools

The Physical Oceanography Distributed Active Archiving Center (PO.DAAC), NASA's primary data center for archiving and distributing oceanographic data, is supporting the Jason and TOPEX/Poseidon satellite tandem missions by providing a variety of data products, tools, and distribution methods to the wider scientific and general community. PO.DAAC has developed several new data products for sea level residual measurements, providing a longterm climate data record from 1992 to the present These products provide compatible measurements of sea level residuals for the entire time series including the tandem TOPEX/Poseidon and Jason mission. Several data distribution tool. are available from NASA PO.DAAC. The Near-Real-Time Image Distribution Server (NEREIDS) provides quicklook browse images and binary data files The PO.DAAC Ocean ESIP Tool (POET) provides interactive, on-tine data subsetting and visualization for several altimetry data products.

Jason 1↗
NASA NTRSDate not supplied

The Comet Astrobiology Exploration Sample Return (CAESAR) Mission

The Comet Astrobiology Exploration Sample Return (CAESAR) mission will acquire and return to Earth for laboratory analysis a minimum of 80 grams of surface material from the nucleus of comet 67P/Chur-yumov-Gerasimenko (67P). CAESAR will characterize the surface region sampled, preserve the collected sample in a pristine state, and return evolved volatiles by capturing them in a separate gas reservoir. NASA Goddard Space Flight Center provides project management, systems engineering, safety and mission assurance, contamination control, mission operations, and many other important functions. Northrop Grumman Space Systems will build the spacecraft, based on Dawn mission heritage, which like CAESAR, uses solar electric propulsion. CAESAR was selected by for Phase A study in the New Frontiers 4 Competition and will be proposed to New Frontiers 5.Collection of a sample from the surface of comet 67P is facilitated by a set of cameras that together provide images to support sample site selection, perform optical navigation, and document the sample before, during, and after col-lection. The sample is collected at the end of an arm during a 5-second touch-and-go (TAG) maneuver with the Sample Acquisition System (SAS)designed by Honeybee Robotics for the surface properties of comet 67P observed by the Rosetta mission. After sample collection, and while the sample is still cold (< -80°C), the TAG Arm inserts the sample container into the Sample Containment System (SCS) mounted inside the Sample Return Capsule (SRC). The SCS is sealed, preventing the sample from escaping into space. The sample is slowly warmed inside the SCS to enable sublimation of volatiles, which are collected in the Gas Containment System (GCS), a passively cooled gas reservoir. Separating the volatiles from the solid sample protects the solid sample from alteration. Once all sublimated H2O is transferred to the GCS, the GCS is sealed to capture the volatile sit contains, and the SCS is vented to space to maintain the solid sample under vacuum. The SCS vent is closed before Earth entry to prevent atmospheric contamination. Detailed laboratory analyses of the sample from 67P will trace the history of volatile reservoirs, delineate the chemical pathways that led from simple interstellar species to complex molecules, constrain the evolution of the comet, and evaluate the role of comets in delivering water and prebiotic organics to the early Earth. CAESAR will achieve these goals by carrying out coordinated sample analyses that will link macroscopic properties of the comet with microscale mineralogy, chemistry, and isotopic studies of volatiles and solids. Most of the sample (≥75%) will be set aside for analyses by generations of scientists using continually advancing tools and methods, yielding an enduring scientific treasure that only sample return can provide. This presentation will review development conducted during NF4 Phase A and discuss the NF5 mission concept.

A G Hayes↗
NASA NTRSDate not supplied

NASA GeneLab: Open Science for Life in Space

The NASA GeneLab project (genelab.nasa.gov) seeks to get the most from space-relevant biology experiments by providing and maintaining a public database consisting of DNA, RNA, protein, and metabolite data from spaceflight experiments. Since these types of data, referred to as omics data, are difficult to understand for non-bioinformaticians, the GeneLab data processing team works with the scientific community to develop methods to process these data. The processed data found on GeneLab reveals information about which genes are turned on and turned off in the space environment, which helps us understand how space changes our biology and how we can best mitigate these effects to travel deeper into space.

Jonathan Oribello↗
DOE OSTI2021

SMART-COM – Scalable Multi-Agent Adaptive Resolution Tools for Collaborative Outage Management

The purpose of this grant was to conduct scientific research and prototype applications to support NPP outage staff in their adaptive decision-making in efficient scheduling and resource allocation while preventing violation of safety technical specifications. The project contributed to scientific knowledge and engineering methods in (1) user interface design, (2) scheduling optimization and risk estimation, and (2) natural language processing that would benefit the nuclear power plants in minimizing schedule overruns and even unexpected shutdowns. The research team conducted site visits at a test reactor facility and an operating nuclear power plant to gather necessary information and inputs for research and development of a software application to support NPP staff in executing their outages. The final software application consisted of three modules. First, the natural language processing module supports interactive processing of technical documentation to build a database for outage staff to query non-permissible actions on system components. This module can alleviate outage staff from reviewing extensive documentation and minimize violation of technical specifications, especially in time-sensitive situations. Second, the schedule optimization module schedules outage activities and compute risk indices that outperform existing software and current practice. This module can reduce completion time of an outage that typically have too many activities for human to optimize based on current practice that does not apply the latest operations research. Finally, the visualization module presents progress and risk information of the overall outage and individual activities, as well as enabling access to the natural language processing and schedule optimization modules. This module can provide outage staff with situation awareness that are necessary to make risk-informed decisions in response to unexpected events during the execution of an outage.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

EERC Final Topical Report – Findings on Subtask 3.2 – Produced Water Management Through Geologic Homogenization, Conditions, and Reuse

The Energy & Environmental Research Center (EERC) was awarded a contract by the North Dakota Industrial Commission (NDIC) Oil and Gas Research Program (NDIC No. G-051-101) to conduct a study on the recycling of water used in oil and gas operations, also known as produced water, from oil- and gas-producing regions of North Dakota as directed by Section 19 of North Dakota House Bill 1014. This final report provides a compilation of results of the study, which include regulatory, scientific, technological, and feasibility methods and considerations associated with North Dakota produced water management. The report provides a synopsis of this project’s previously submitted produced water assessment report entitled “Produced Water Management and Recycling Options in North Dakota” (Energy & Environmental Research Center, 2020), with updated values provided as appropriate. The report provides the results from the investigation of a novel produced water management strategy, referred to as geologic homogenization, conditioning, and reuse (GHCR), which aims to use a subsurface geologic formation as a natural medium for managing produced water recycling and reuse. Water management is a significant technical and economic challenge for sustainable oil and gas production, and water volumes are intrinsically linked to oil production volumes. North Dakota oil production rose to over 1.5 million barrels (MMbbl)/day in 2019, and despite a downturn in oil price in early 2020, North Dakota oil production has recovered to 1.1 MMbbl/day as of August 2021. Bakken petroleum system development between 2008 and 2020 has resulted in a nearly fourfold increase in produced water volumes to 642 MMbbl/yr in 2020 after peaking at 740 MMbbl/yr in 2019 and a fivefold increase in saltwater disposal (SWD) volumes to 565 MMbbl/yr in 2020 after a peak of 682 MMbbl/yr in 2019. Produced water and SWD volumes are forecasted to double by 2030. SWD is the primary method of produced water management used in North Dakota, with approximately 95% of the SWD volume occurring through subsurface injection into sandstones of the Dakota Group (Dakota). Localized pressurization of the Dakota resulting from SWD and projected increases in produced water volumes could impact the economics of North Dakota oil production. As a result, there is an emerging need to pursue alternative produced water management approaches, including recycling and reuse. While produced water recycling is not yet widespread, commercial operators are making strides in overcoming the technical challenges of using high salinity produced water in completion operations (Marathon Oil, 2020). As water management continues to be a key focal point in companies’ environmental, social, and governance (ESG) initiatives, focus on water management, including recycling, will likely continue to increase. Laboratory column testing, field sample collection, geologic modeling and numerical simulation, and techno-economic analysis all indicate that GHCR could feasibly be implemented as a potential water management option. Laboratory column testing and field sample collection indicate that the Inyan Kara sandstone and native formation fluid are capable of homogenizing with the Bakken produced water to a point where the fluid composition appears to stabilize. Extracting that stabilized fluid could be considered homogeneous and capable of providing individual batches of hydraulic fracturing fluid. Numerical simulation results indicate that extraction of fluids from the Inyan Kara in a GHCR implementation scenario is capable of reducing formation pressure, which would help ease localized pressurization of the Inyan Kara and extend the available capacity for nearby existing SWD wells. Economic analysis indicates that there are scenarios where GHCR implementation can be a competitive or even lower-cost option than a conventional water management approach. Site-specific conditions will dictate the economic potential of GHCR, but potentially attractive sites for GHCR implementation will be those that are located above a pressurized zone of the Inyan Kara, need six or more Bakken infill wells, and face high costs for conventional SWD and/or freshwater. Based on the regulatory review, drilling into the Inyan Kara for SWD and to harness as a source water for industrial use have precedent in North Dakota, and a workable regulatory solution for GHCR seems likely. However, restrictions in the state regarding surface storage and transport of produced fluids may limit some activities, which will affect how GHCR could ultimately be implemented. In summary, this study reveals pursuing GHCR can be a viable approach to water management in North Dakota. The GHCR concept addresses some of the challenges that hinder the more traditional approaches to recycling in the industry. Furthermore, an assessment of the current landscape of water management within the state reveals the ongoing trend of increasing volumes of produced water and SWD. Projections reveal that the volumes of produced water that need to be managed are expected to double over the next decade (Energy & Environmental Research Center, 2020). With the continued development of the Bakken and continuing driving factors related to ESG initiatives, implementing a practice such as GHCR is a feasible approach to adding recycling of produced water to industry within the state. This subtask was cofunded through the EERC–U.S. Department of Energy Joint Program on Research and Development for Fossil Energy-Related Resources Cooperative Agreement No. DE-FE0024233. Nonfederal funding was provided by the North Dakota Industrial Commission Oil and Gas Research Program. References Energy & Environmental Research Center, 2020, Produced water management and recycling options in North Dakota: Final Report for North Dakota Legislative Management Energy Development and Transmission Committee and North Dakota Industrial Commission. Marathon Oil, 2020, Sustainability report: https://cdn.sanity.io/files/ghcnw9z2/website/ 91744eb6ef8fbe59505a911c6b8d2e8dd9a537fa.pdf?dl (accessed November 2021).

02 PETROLEUM↗
DOE OSTI2024

Modifying the Asynchronous Jacobi Method for Data Corruption Resilience

Moving scientific computation from high-performance computing (HPC) and cloud computing (CC) environments to devices on the edge, i.e., physically near instruments of interest, has received tremendous interest in recent years. Such edge computing environments can operate on data in situ, offering enticing benefits over data aggregation to HPC and CC facilities that include avoiding costs of transmission, increased data privacy, and real-time data analysis. Because of the inherent unreliability of edge computing environments, new fault-tolerant approaches must be developed before the benefits of edge computing can be realized. Motivated by algorithm-based fault tolerance, a variant of the asynchronous Jacobi (ASJ) method is developed that achieves resilience to data corruption by rejecting solution approximations from neighbor devices according to a bound derived from convergence theory. Numerical results on a two-dimensional Poisson problem show that the new rejection criterion, along with a novel approximation to the shortest path length on which the criterion depends, restores convergence for the ASJ variant in the presence of certain types data corruption. Numerical results are obtained for when the singular values in the analytic bound are approximated. Additional linear systems are also explored, one with a more dense sparsity pattern and one that includes advection. All results indicate that successful resilience to data corruption depends on whether the bound tightens fast enough to reject corrupted data before the iteration evolution deviates significantly from that predicted by the convergence theory defining the bound. This observation generalizes to future work on algorithm-based fault tolerance for other asynchronous algorithms, including upcoming approaches that leverage Krylov subspaces.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Strictly Enforcing Invertibility and Conservation in CNN-Based Super Resolution for Scientific Datasets

Abstract Recently, deep convolutional neural networks (CNNs) have revolutionized image “super resolution” (SR), dramatically outperforming past methods for enhancing image resolution. They could be a boon for the many scientific fields that involve imaging or any regularly gridded datasets: satellite remote sensing, radar meteorology, medical imaging, numerical modeling, and so on. Unfortunately, while SR-CNNs produce visually compelling results, they do not necessarily conserve physical quantities between their low-resolution inputs and high-resolution outputs when applied to scientific datasets. Here, a method for “downsampling enforcement” in SR-CNNs is proposed. A differentiable operator is derived that, when applied as the final transfer function of a CNN, ensures the high-resolution outputs exactly reproduce the low-resolution inputs under 2D-average downsampling while improving performance of the SR schemes. The method is demonstrated across seven modern CNN-based SR schemes on several benchmark image datasets, and applications to weather radar, satellite imager, and climate model data are shown. The approach improves training time and performance while ensuring physical consistency between the super-resolved and low-resolution data. Significance Statement Recent advancements in using deep learning to increase the resolution of images have substantial potential across the many scientific fields that use images and image-like data. Most image super-resolution research has focused on the visual quality of outputs, however, and is not necessarily well suited for use with scientific data where known physics constraints may need to be enforced. Here, we introduce a method to modify existing deep neural network architectures so that they strictly conserve physical quantities in the input field when “super resolving” scientific data and find that the method can improve performance across a wide range of datasets and neural networks. Integration of known physics and adherence to established physical constraints into deep neural networks will be a critical step before their potential can be fully realized in the physical sciences.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1975

Application of information-retrieval methods to the classification of physical data

Scientific data received from satellites are characterized as a multi-dimensional time series, whose terms are vector functions of a vector of measurement conditions. Information retrieval methods are used to construct lower dimensional samples on the basis of the condition vector, in order to obtain these data and to construct partial relations. The methods are applied to the joint Soviet-French Arkad project.

Mamotko, Z. N.↗
NASA NTRS1994

Comparison of methods of predicting community response to impulsive and nonimpulsive noise

Several scientific, regulatory, and policy-coordinating bodies have developed methods for predicting community response to sonic booms. The best known of these is the dosage-response relationship of Working Group 84 of the National Academy of Science's Committee on Hearing, Bioacoustics and Biomechanics. This dosage-response relationship between C-weighted DayNight Average Sound Level and the prevalence of annoyance with high energy impulsive sounds was derived from limited amounts of information about community response to regular, prolonged, and expected exposure to artillery and sonic booms. U.S. Army Regulation 201 adapts this approach to predictions of the acceptability of impulsive noise exposure in communities. This regulation infers equivalent degrees of effect with respect to a well known dosage-response relationship for general (nonimpulsive) transportation noise. Differences in prevalence of annoyance predicted by various relationships lead to different predictions of the compatibility of land uses with sonic boom exposure. An examination of these differences makes apparent several unresolved issues in current practice for predicting and interpreting the prevalence of annoyance due to sonic boom exposure.

Fidell, Sanford↗
NASA NTRS1994

Interactive archives of scientific data

A focus on qualitative methods of presenting data shows that visualization provides a mechanism for browsing independent of the source of data and is an effective alternative to traditional image-based browsing of image data. To be generally applicable, such visualization methods, however, must be based upon an underlying data model with support for a broad class of data types and structures. Interactive, near-real-time browsing for data sets of interesting size today requires a browse server of considerable power. A symmetric multi-processor with very high internal and external bandwidth demonstrates the feasibility of this concept. Although this technology is likely to be available on the desktop within a few years, the increase in the size and complexity of achieved data will continue to exceed the capacity of 'worksation' systems. Hence, a higher class of performance, especially in bandwidth, will generally be required for on-demand browsing. A few experiments with differing digital compression techniques indicates that a MPEG-1 implementation within the context of a high-performance browse server (i.e., parallized) is a practical method of converting a browse product to a form suitable for network or CD-ROM distribution.

Treinish, Lloyd A.↗
DOE OSTI2021

A prediction interval method for uncertainty quantification of regression models

This paper considers calculation of prediction intervals (PIs) by neural networks (NNs) for quantifying uncertainty in regression tasks, so as to provide fast, accurate and robust emulators to accelerate scientific simulations. We propose a novel method to learn lower and upper bounds of the PI using independent NNs without defining an exclusive loss. Our method requires no distributional assumption, does not introduce extra hyper-parameters, and can effectively identify out-of-distribution samples and quantify their uncertainty. We demonstrate advantages of our method using a benchmark problem and two real-world scientific applications.

Zhang, Pei↗
DOE OSTI2024

Exploring Object Detection and Image Classification Tasks for Niche Use Case in Naturalistic Driving Studies

Naturalistic driving studies consist of drivers using their personal vehicles and provide valuable real-world data, but privacy issues must be handled very carefully. Drivers sign a consent form when they elect to participate, but passengers do not for a variety of practical reasons. However, their privacy must still be protected. One large study includes a blurred image of the entire cabin which allows reviewers to find passengers in the vehicle; this protects the privacy but still allows a means of answering questions regarding the impact of passengers on driver behavior. A method for automatically counting the passengers would have scientific value for transportation researchers. We investigated different image analysis methods for automatically locating and counting the non-drivers including simple face detection and fine-tuned methods for image classification and a published object detection method. We also compared the image classification using convolutional neural network and vision transformer backbones. Our studies show the image classification method appears to work the best in terms of absolute performance, although we note the closed nature of our dataset and nature of the imagery makes the application somewhat niche and object detection methods also have advantages. We perform some analysis to support our conclusion.

Peruski, Ryan↗
NASA NTRS2018

Advances in Small Particle Handling of Astromaterials in Preparation for OSIRIS-REx and Hayabusa2: Initial Developments

The Astromaterials Acquisition and Curation office at NASA Johnson Space Center has established an Advanced Curation program that is tasked with developing procedures, technologies, and data sets necessary for the curation of future astromaterials collections as envisioned by NASA exploration goals. One particular objective of the Advanced Curation program is the development of new methods for the collection, storage, handling and characterization of small (less than 100 micrometer) particles. Astromaterials Curation currently maintains four small particle collections: Cosmic Dust that has been collected in Earth's stratosphere by ER2 and WB-57 aircraft, Comet 81P/Wild 2 dust returned by NASA's Stardust spacecraft, interstellar dust that was returned by Stardust, and asteroid Itokawa particles that were returned by the JAXA's Hayabusa spacecraft. NASA Curation is currently preparing for the anticipated return of two new astromaterials collections - asteroid Ryugu regolith to be collected by Hayabusa2 spacecraft in 2021 (samples will be provided by JAXA as part of an international agreement), and asteroid Bennu regolith to be collected by the OSIRIS-REx spacecraft and returned in 2023. A substantial portion of these returned samples are expected to consist of small particle components, and mission requirements necessitate the development of new processing tools and methods in order to maximize the scientific yield from these valuable acquisitions. Here we describe initial progress towards the development of applicable sample handling methods for the successful curation of future small particle collections.

Snead, C. J.↗
DOE OSTI2025

Reproducibility of fixed-node diffusion Monte Carlo across diverse community codes: The case of water–methane dimer

Fixed-node diffusion quantum Monte Carlo (FN-DMC) is a widely trusted many-body method for solving the Schrödinger equation, known for its reliable predictions of material and molecular properties. Furthermore, its excellent scalability with system complexity and near-perfect utilization of computational power make FN-DMC ideally positioned to leverage new advances in computing to address increasingly complex scientific problems. Even though the method is widely used as a computational gold standard, reproducibility across the numerous FN-DMC code implementations has yet to be demonstrated. This difficulty stems from the diverse array of DMC algorithms and trial wave functions, compounded by the method’s inherent stochastic nature. Here, this study represents a community-wide effort to assess the reproducibility of the method, affirming that yes, FN-DMC is reproducible (when handled with care). Using the water–methane dimer as the canonical test case, we compare results from eleven different FN-DMC codes and show that the approximations to treat the non-locality of pseudopotentials are the primary source of the discrepancies between them. In particular, we demonstrate that, for the same choice of determinantal component in the trial wave function, reliable and reproducible predictions can be achieved by employing the T-move, the determinant locality approximation, or the determinant T-move schemes, while the older locality approximation leads to considerable variability in results. These findings demonstrate that, with appropriate choices of algorithmic details, fixed-node DMC is reproducible across diverse community codes—highlighting the maturity and robustness of the method as a tool for open and reliable computational science.

Della Pia, Flaviano [Univ. of Cambridge (United Ki↗
DOE OSTI2024

PyOED: An Extensible Suite for Data Assimilation and Model-Constrained Optimal Design of Experiments

This article describes PyOED, a highly extensible scientific package that enables developing and testing model-constrained optimal experimental design (OED) for inverse problems. Specifically, PyOED aims to be a comprehensive Python toolkit for model-constrained OED. The package targets scientists and researchers interested in understanding the details of OED formulations and approaches. It is also meant to enable researchers to experiment with standard and innovative OED technologies with a wide range of test problems (e.g., simulation models). OED, inverse problems (e.g., Bayesian inversion), and data assimilation (DA) are closely related research fields, and their formulations overlap significantly. Thus, PyOED is continuously being expanded with a plethora of Bayesian inversion, DA, and OED methods as well as new scientific simulation models, observation error models, and observation operators. These pieces are added such that they can be permuted to enable testing OED methods in various settings of varying complexities. The PyOED core is completely written in Python and utilizes the inherent object-oriented capabilities; however, the current version of PyOED is meant to be extensible rather than scalable. Specifically, PyOED is developed to “enable rapid development and benchmarking of OED methods with minimal coding effort and to maximize code reutilization.” This article provides a brief description of the PyOED layout and philosophy and provides a set of exemplary test cases and tutorials to demonstrate the potential of the package.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

The NASA Carbon Monitoring System Phase 2 synthesis: scope, findings, gaps and recommended next steps

Abstract Underlying policy efforts to address global climate change is the scientific need to develop the methods to accurately measure and model carbon stocks and fluxes across the wide range of spatial and temporal scales in the Earth system. Initiated in 2010, the NASA Carbon Monitoring System is one of the most ambitious relevant science initiatives to date, exploiting the satellite remote sensing resources, computational capabilities, scientific knowledge, airborne science capabilities, and end-to-end system expertise that are major strengths of the NASA Earth Science program. Here we provide a synthesis of ‘Phase 2’ activities (2011–2019), encompassing 79 projects, 482 publications, and 136 data products. Our synthesis addresses four key questions: What has been attempted? What major results have been obtained? What major gaps and uncertainties remain? and What are the recommended next steps? Through this review, we take stock of what has been accomplished and identify future priorities toward meeting the nation’s needs for carbon monitoring reporting and verification.

54 ENVIRONMENTAL SCIENCES↗