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At least 163 records · Page 9

All sources NASA NTRS DOE OSTI

DOE OSTI2023

Predicting future well performance for environmental remediation design using deep learning

Here in this study, we developed a deep learning (DL) framework with a multi-channel three-dimensional convolutional neural network (MC3D-CNN) to predict well performance and thereby assist future environmental remediation design. Such prediction of extraction well performance at designated locations is critical for configuring pump-and-treat (P&T) well network design and operation, setting reasonable target closure dates for overall remedying, and estimating remedy costs. The framework is developed with operational and monitoring data routinely collected during P&T remedy operations, including well extraction and injection rates as well as in situ contaminant concentrations. Traditionally, the collected data were rarely used for purposes other than assessing past well performance and the accuracy of the conceptual site model. However, recent advances in data-driven computational approaches enable better use of the large datasets to inform future well performance, enhance site characterization, and improve remediation planning. In this study, we established a DL framework to integrate transient three-dimensional contaminant plumes and multiple aquifer properties (e.g., hydraulic conductivity and hydrostratigraphic maps) to identify characteristic patterns controlling and representing extraction well mass recovery, aiming at providing future mass recovery estimates for existing wells and candidate wells at any proposed locations. We evaluated our framework by using a realistic synthetic dataset generated from a well-calibrated flow and transport model used in the 200 West Area of the U.S. Department of Energy’s Hanford Site in southeastern Washington state. The multi-channel feature in our framework allows integration of various types and temporal densities of training datasets for DL model development. Overall, we found that the trained DL model achieved an accuracy of over 90% in ranking extraction well performance in validation datasets, and over 80% in predicting high-performance-ranking well locations. This data-informed approach provides a flexible tool to support adaptive site management, streamline decision-making, and potentially reduce remediation time and costs. Our DL framework can be used as a filtering tool to improve the current P&T network optimization design by reducing the number of candidate well locations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

CY2019 Annual Closure Monitoring Report for Corrective Action Unit 98, Frenchman Flat, Underground Test Area, Nevada National Security Site, Nevada (January 2019–December 2019), Revision 1

Three types of monitoring are performed for CAU 98: water quality, water level, and institutional control. These are monitored to determine whether the URs remain protective of human health and the environment, and to ensure that the regulatory boundary objectives are being met. Monitoring data will be used in the future, once multiple years of data are available, to evaluate consistency with the groundwater flow and contaminant transport models because the contaminant boundaries calculated with the models are the primary basis of the UR boundaries. Six wells were sampled for water-quality monitoring in 2019. Contaminants of concern were detected only in the two source/plume wells already known to contain contamination as a result of a radionuclide migration experiment. Tritium concentrations in both of these wells, RNM-2S and UE-5n, remain above the Safe Drinking Water Act maximum contaminant level of 20,000 picocuries per liter but declined in 2019 as compared to measurements in 2018. All other contaminants of concern are below the minimum detection level plus analytical error. The water-level monitoring network includes 16 wells. Depth to water measured in 2019 is generally consistent with recent measurements for all wells. Many wells continue to exhibit a long-term downward trend in water level, though changes from 2018 to 2019 are minimal. The sharp 2016 decline in water level in Well ER-5-3-2 remains unexplained, with the lower level persisting through 2019. Rising water-level trends continue to be observed in Well ER-5-3 deep piezometer and former water supply Well WW-5A. Water supply Well WW-5B experienced a rise in water level as a result of an absence of pumping in the first part of the year (due to a mechanical problem), whereas water levels declined in WW-4 and WW-4A in response to greater pumping in 2019. Institutional control monitoring confirmed the URs are recorded in U.S. Department of Energy and U.S. Air Force land management systems, and that no activities within Frenchman Flat basin are occurring that could potentially affect the contaminant boundaries. Survey of groundwater resources in basins surrounding Frenchman Flat similarly identify no current or pending development that would indicate the need to increase monitoring activities or would otherwise cause concern for the closure decision. The URs continue to prevent exposure of the public, workers, and the environment to contaminants of concern by preventing use of potentially contaminated groundwater.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1982

Description of the Deep Space Network

The facilities, functions, and operations capabilities of the Deep Space Network are summarized. Network activities in support of planned future as well as ongoing deep space missions are described as are activities in support of radio astronomy experiments and the Astronomical Radio Interferometric Earth Surveying (ARIES) Network.

Renzetti, N. A.↗

DOE OSTI2022

Tropical Cirrus in Global Storm-Resolving Models: 1. Role of Deep Convection

Pervasive cirrus clouds in the upper troposphere and tropical tropopause layer (TTL) influence the climate by altering the top-of-atmosphere radiation balance and stratospheric water vapor budget. These cirrus are often associated with deep convection, which global climate models must parameterize and struggle to accurately simulate. By comparing high-resolution global storm-resolving models from the Dynamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains (DYAMOND) intercomparison that explicitly simulate deep convection to satellite observations, we assess how well these models simulate deep convection, convectively generated cirrus, and deep convective injection of water into the TTL over representative tropical land and ocean regions. The DYAMOND models simulate deep convective precipitation, organization, and cloud structure fairly well over land and ocean regions, but with clear intermodel differences. All models produce frequent overshooting convection whose strongest updrafts humidify the TTL and are its main source of frozen water. Intermodel differences in cloud properties and convective injection exceed differences between land and ocean regions in each model. We argue that, with further improvements, global storm-resolving models can better represent tropical cirrus and deep convection in present and future climates than coarser-resolution climate models. To realize this potential, they must use available observations to perfect their ice microphysics and dynamical flow solvers.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Advected carbon younger than the sediment fuels microbial metabolism in a pumped deep aquifer

Ever deeper wells are drilled worldwide to pump potable groundwater. Recent studies argue that overpumping compresses clays and releases reactive dissolved organic carbon (DOC), which in turn drives a series of microbial reactions that affect groundwater potability including arsenic concentrations. Here, we use a novel method to measure the radiocarbon ages of microbial RNA to determine the source of reactive or metabolizable DOC and argue against the compression of clays as the sole source of carbon. We show that microbial RNA (5,230; 5,550; 6,250 yr; n = 3 wells), DOC (280-10,800 yr; n = 13), and methane (modern to 6,240 yr; n = 3), from an overpumped deep aquifer in Bangladesh are much younger than the overlying clay layers deposited during the Pleistocene over 12,000 years ago. Mass-balance indicates that at least half of the carbon incorporated into RNA has to come from reactive DOC or methane that is advected downward via vertical recharge. This metabolism of advected organic carbon could have implications for the quality of water pumped from deep aquifers.

Biological and medical sciences↗

DOE OSTI2020

Scaling Global Warming Impacts on Ocean Ecosystems: Lessons From a Suite of Earth System Models

An important technique used by climate modelers to isolate the impacts of increasing greenhouse gasses on Earth System processes is to simulate the impact of an abrupt increase in carbon dioxide. The spatial pattern of change provides a “fingerprint” that is generally much larger than natural variability. Insofar as the response to radiative forcing is linear (the impact of quadrupling CO 2 is twice the impact of doubling CO 2 ) this fingerprint can then be used to estimate the impact of historical greenhouse gas forcing. However, the degree to which biogeochemical cycles respond linearly to radiative forcing has rarely been tested. In this paper, we evaluate which ocean biogeochemical fields are likely to respond linearly to changing radiative forcing, which ones do not, and where linearity breaks down. We also demonstrate that the representation of lateral mixing by mesoscale eddies, which varies significantly across climate models, plays an important role in modulating the breakdown of linearity. Globally integrated surface rates of biogeochemical cycling (primary productivity, particulate export) respond in a relatively linear fashion and are only moderately sensitive to mixing. By contrast, the habitability of the interior ocean (as determined by hypoxia and calcite supersaturation) behaves non-linearly and is very sensitive to mixing. This is because the deep ocean, as well as certain regions in the surface ocean, are very sensitive to the magnitude of deep wintertime convection. The cessation of convection under global warming is strongly modulated by the representation of eddy mixing.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2010

Thermal Performance Expectations of the Advanced Stirling Convertor Over a Range of Operating Scenarios

The Advanced Stirling Radioisotope Generator (ASRG) will enable various missions such as small body sample return, atmospheric missions around Venus, as well as long - duration deep space missions. Analysis of the temperature distributions are performed on an Advanced Stirling Convertor, and the results are compared with available experimental measurements. This analysis includes applied environmental conditions that are similar to those that will be experienced while the convertor is in operation. The applied conditions represent a potential mission profile including pre-takeoff sterilization, launch, transit, and return. The results focus on the anticipated peak temperatures of the magnets in the linear alternator. These results confirm that the ASC can support future missions to deep space targets, extreme environment landers, as well as more conventional goals.

Reid, Terry V.↗

NASA NTRS1976

Computation on collisionless steady-state plasma flow past a charged disk

A computer method is presented using the 'inside-out' approach, for predicting the structure of the disturbed zone near a moving body in space. The approach uses fewer simplifying assumptions than other available methods, and is applicable to large ranges of the values of body and plasma parameters. Two major advances concerning 3-dimensional bodies are that thermal motions of ions as well as of electrons are treated realistically by following their trajectories in the electric field, and the technique for achieving self-consistency is promising for very large bodies. Three sample solutions were obtained for a disk-shaped body, charged negatively to a potential 4kT/e. With ion Mach number 4, and equal ion and electron temperatures, the wakes of a relatively small body (radius 5 Debye lengths) and a relatively large body (radius 100 Debye lengths) both begin to fill up between 2 and 3 body radii downstream. For the large body there is in addition a potential well (about 6kT/e deep) behind the body. Increasing the ion Mach number to 8 for the large body causes the potential well to become wider and longer but not deeper. For the large body, the quasineutrality assumption is validated outside of a cone-shaped region in the very near wake. For the large as well as the small body, the disturbed zone behind the body extends transversely no more than 2 or 3 body radii, a result of significance for the design of spacecraft boom instrumentation.

Parker, L. W.↗

DOE OSTI2020

Deep-Learning-Enabled Fast Optical Identification and Characterization of 2D Materials

Not provided.

Chemistry↗

NASA NTRS2022

Development of a Loop Antenna Deployment System for TEM-Based Subsurface Mars Water Detection

The subsurface of Mars has the potential to harbor existing deposits of liquid water, which are of great interest both scientifically (in the search for life) and as resources for future astronauts living on the red planet. However, the depth to a potential subsurface aquifer may be kilometers deep, which is well beyond what current surface or orbital approaches can survey while also confirming the unique signature for liquid water. Inspired by critical technology for exploring deep aquifers on Earth, we are developing the Transient H2O Reconnaissance (TH2OR) instrument at NASA’s Jet Propulsion Laboratory (JPL). TH2OR operates on the principle of transient electromagnetics (TEM), which leverages induction and electromotive force to induce a current in a subsurface water body using a loop-shaped antenna that provides both transmit and receive functions when placed on the surface. On Mars, TEM may be even more effective given the relative dryness of the subsurface compared to a more conductive, saline, liquid, water body on Earth. However, to probe deep within the subsurface (below 5 km) a large enclosed antenna is needed – specifically, a 100-m diameter effective loop or a shape with roughly equivalent area that can transmit at low frequencies (kHz-Hz). The deployment of a large-scale structure on the surface is complicated by the fact that the transmit wire must be both lightweight and robust to contact with the surface. Further, TH2OR may be delivered to the surface by a non-mobile vehicle, so it is desired that the deployment can be activated from a static location. This paper provides an overview of the deployment trade study, focusing on our current, favored approach, using a projectile wire launcher. Building on past approaches in the literature, we have developed and fielded an Earth-based, gas-projectile prototype for launching a triangle-shaped antenna onto analogue terrain. Our results compare simulated launch performance to actual field tests conducted under Earth gravity and pressure conditions. We discuss how Earth performance maps to a prospective Mars deployment under reduced gravity and pressure. We also provide lessons learned and next steps towards the development of an integrated TH2OR instrument for finding water on Mars.

Stamenkovic, Vlada↗

NASA NTRS2017

NASA's Space Launch System: A Transformative Capability for Deep Space Missions

Already making substantial progress toward its first launches, NASA’s Space Launch System (SLS) exploration-class launch vehicle presents game-changing new opportunities in spaceflight, enabling human exploration of deep space, as well as a variety of missions and mission profiles that are currently impossible. Today, the initial configuration of SLS, able to deliver more than 70 metric tons of payload to low Earth orbit (LEO), is well into final production and testing ahead of its planned first flight, which will send NASA’s new Orion crew vehicle around the moon and will deploy 13 CubeSats, representing multiple disciplines, into deep space. At the same time, production work is already underway toward the more-capable Block 1B configuration, planned to debut on the second flight of SLS, and capable of lofting 105 tons to LEO or of co-manifesting large exploration systems with Orion on launches to the lunar vicinity. Progress being made on the vehicle for that second flight includes initial welding of its core stage and testing of one of its engines, as well as development of new elements such as the powerful Exploration Upper Stage and the Universal Stage Adapter “payload bay.” Ultimately, SLS will evolve to a configuration capable of delivering more than 130 tons to LEO to support humans missions to Mars. In order to enable human deep-space exploration, SLS provides unrivaled mass, volume, and departure energy for payloads, offering numerous benefits for a variety of other missions. For robotic science probes to the outer solar system, for example, SLS can cut transit times to less than half that of currently available vehicles or substantially increased spacecraft mass. In the field of astrophysics, SLS’ high payload volume, in the form of payload fairings with a diameter of up to 10 meters, creates the opportunity for launch of large-aperture telescopes providing an unprecedented look at our universe. This presentation will give an overview of SLS’ capabilities and its current status, and discuss the vehicle’s potential for human exploration of deep space and other game-changing utilization opportunities.

Creech, Stephen D.↗

NASA NTRS2006

Room to Live: the sizing of Lunar and Martian Habitats

In order for man to return to space or extra terrestrial bodies for long duration missions it is important that adequate habitat volume be defined early to avoid costly delays and redesign. To properly define a habitat volume two major factors need to be considered. The first factor is the free or open space. This is the space that allows the crew room to move about the habitat. This space will vary based on crew size and length of the mission. The second major factor is the stowage space required for equipment and supplies. This includes both fixed volumes and consumables. Fixed volumes include items such as tools, communication equipment, Advanced Life Support (ALS) equipment, and support equipment. Consumables include items like filters, food, water and oxygen. This space is also dependent on crew size and mission length. A review of past missions into alien environments, such as deep sea habitats as well as space based habitats will be used to validate the assumption made in this paper. Once these key factors are defined trades must be run to optimize the overall volume of a habitat. This includes trades of disposable vs. reusable for items such as clothing, dishes, and water. Another factor to consider is the availability of in situ resources to aid in the construction of the habitat structure as well as re-supply of consumable items. A review of past missions into alien environments, such as deep sea habitats as well as space based habitats will be used to validate the assumption made in this paper. The result is a habitat sizing tool to provide a first order estimate of habitat volumes for extended mission to the surface of the moon and Mars.

McGregor, Walter L.↗

DOE OSTI2022

Matrix Multiply Performance of GPUs on Exascale-class HPE/Cray Systems

The computation of dense matrix-matrix products (GEMMs) is central to many modeling and simulation workloads as well as AI/ML deep learning campaigns. In fact, millions of dollars are spent annually on computing GEMMs, and large model training demands are increasing exponentially. Specialized processors such as GPUs are designed to perform well for these operations. However, the performance of GEMMs on GPUs can exhibit complex behaviors depending on many factors, making it challenging to optimize the performance of GEMMs on these processors. In this study we undertake an examination of GEMM performance on several leading GPU models taken from product lines of GPUs to be deployed in forthcoming exascale computing systems. We show results to illustrate the many factors that can affect performance of GEMMs on GPUs. We then present data collected from a large number of test runs for an example GEMM operation to show the dependence behaviors of GEMM rate on matrix dimensions. Finally, we show results from machine learning-based performance models using novel feature engineering methods to fit the measured performance, providing a potential basis for GEMM performance tuning and autotuning methods for GPUs. Recommendations are also given for how to achieve high GEMM performance on modern GPUs.

Melesse Vergara, Veronica↗

NASA NTRS1987

Ka-band (32 GHz) benefits to planned missions

The benefits of using 32 GHz downlinks for a set of deep space missions, as well as the implications to radio science and the Deep Space Network (DSN) are documented. The basic comparison is between the use of the current X-band (8.4 GHz) and a 32 GHZ (Ka-band) downlink. There was shown to be approximately an 8 dB (about 600%) link advantage for 32 GHz. This 8 dB advantage would be able to either reduce mission cost or improve mission science return. Included here are studies on how the 8 dB advantage would be used for the Cassini and Mars Sample Return missions. While the work is preliminary, it shows that the 8 dB advantage can be exploited to provide large benefits to future deep space missions. There can be significant mass and/or power savings to the spacecraft, which can translate into cost savings. Alternatively, the increased downlink telecommunications performance can provide a greater science return.

Hansen, D. M.↗

DOE OSTI2026

3D Play Fairway Analysis for Examining of Superhot Reservoir Production Scenarios

The DEEPEN (DE-risking Exploration for geothermal Plays in magmatic ENvironments) project was a multi-laboratory, international effort to reduce uncertainty and improve resource characterization in superhot geothermal systems. Building on this foundation, this work advances open-source tools designed to lower the exploration risk and cost of superhot geothermal projects while promoting transparency, reproducibility, and efficiency in exploration workflows. These tools are being tested at two key sites: (1) the Nesjavellir Geothermal Area in Iceland, where the Icelandic Deep Drilling Project (IDDP) will drill its third well, and (2) Newberry Volcano in Oregon, USA, where Mazama Energy will pilot the first superhot enhanced geothermal system (EGS). A major outcome is the creation of a modular, open-source Python framework for play fairway analysis (PFA) in 2D and 3D, called geoPFA. The PFA workflow has been expanded to produce pseudo conceptual models, and will soon be refined to assess reservoir components through integration with the thermo-hydraulic-mechanical-chemical (THMC) simulator TReactMech, to enable iterative coupling between PFA and THMC models, improving characterization of superhot systems. All three of the Icelandic Deep Drilling Project's production scenarios were analyzed via this framework: (1) a superhot deep injection well paired with conventional production wells at Nesjavellir, (2) a superhot deep production well at Nesjavellir, and (3) superhot enhanced geothermal system at Newberry Volcano. This analysis provides useful insights around conceptual modeling of these production scenarios, helping to inform decisions around which scenario is best suited for which types of environments.

15 GEOTHERMAL ENERGY↗

DOE OSTI2026

3D Play Fairway Analysis for Examining of Superhot Drilling Production Scenarios: Preprint

15 GEOTHERMAL ENERGY↗

DOE OSTI2022

Model Assumptions and Data Characteristics: Impacts on Domain Adaptation in Building Segmentation

Studies on domain adaptation (DA) for remote sensing (RS) imagery analysis lack consistency in selection and description of evaluation scenarios. Without properly characterizing datasets, model assumptions, and evaluation scenarios, it is difficult to objectively compare DA methods and reach conclusions about their suitability across different applications. With this motivation, this work seeks to empirically assess to which extent the interaction between data characteristics and model assumptions influences the effectiveness of DA methods. Using the widely explored task of building footprint segmentation as a case study, we perform a large-scale study across over 200 DA scenarios that include variations across view angles, areas observed, and sensors used for data acquisition. Rather than adopting different model architectures or optimization criteria, we contrast the performances of two DA methods based on adversarial learning that differ only in their assumptions about source and target domains. Informed by metadata and data characteristics unveiled using traditional computer vision (CV) techniques as well as pretrained deep models, we provide a detailed meta-analysis of experiments highlighting the importance of accurately considering data assumptions for DA in RS segmentation tasks. As demonstrated by a “cherry-picking” exercise, different claims regarding which model is best could be made by selecting different subsets of evaluation scenarios. While well-calibrated assumptions can be beneficial, mismatching assumptions can lead to negative biases in DA applications. Furthermore, this study intends to motivate the community toward more consistent evaluation protocols while providing recommendations and insights toward creating novel benchmark datasets, documenting data characteristics, application-specific knowledge, and model assumptions.

42 ENGINEERING↗

NASA NTRSDate not supplied

Deep Neural Network Based Convergence Classification for Computational Fluid Dynamics

A supervised deep learning approach is coupled with heuristic convergence criteria to construct a classification model for detecting the completion (convergence) of computational fluid dynamics (CFD) simulations. Heuristic convergence criteria alone are not always sufficient and more complex decisions are often left to a human analyst. The proposed approach leverages heuristic convergence criteria as well as two deep neural network (DNN) models, one binary and one multi-class, to improve the efficiency and consistency of convergence classification across a wide range of flight regimes. The DNN models presented are each trained on a subset of ascent aerodynamic CFD simulations for NASA’s Space Launch System and were produced using NASA’s unstructured Navier-Stokes solver FUN3D. Individual solutions are analyzed intermittently and are classified as sufficiently converged, further iterations required, or switch from steady Reynolds Averaged Navier-Stokes (RANS) to unsteady RANS CFD based on the iterative histories of four aerodynamic coefficients. The implemented classification model is shown to produce solutions that closely correlate to solutions produced by a human analyst. This work lays groundwork for expanding the capabilities of DNNs for automating and improving more of the CFD process.

SLS↗