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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

VFA Biorefinery Design for Informed Production of Ground and Aviation Fuels

The rising demand for low-carbon intensity fuel options requires evaluation of a system which can support their production economically and sustainably. Volatile fatty acids (VFAs) ranging from C2 to C8 can be derived in high yield from arrested anaerobic digestion of biomass. This talk provides an overview of our work in which we upgrade wet waste-derived VFAs using ketonization to elongate their carbon backbone to reach a range relevant to jet or diesel fuels. Kinetic models were used to predict ketone profiles from varying VFA profiles, thereby informing potential carbon flow to either (a) mixed paraffins for use in diesel or aviation fuel, or (b) ethers for use in diesel fuel. To do this, the anticipated products were screened for critical fuel characteristics using predictive tools. The criteria for neat and blended bioblendstocks were chosen based on conventional petrofuel requirements, and they were applied to inform fuel targets, identify limiting characteristics, and guide conversion development. While paraffins can serve as either diesel or aviation fuels, the latter has stringent criteria which include a precise distillation curve range tied to carbon distribution. Fuels which fall outside this range typically fail to meet other property metrics, and as such flashpoint and viscosity were identified as potentially limiting properties of this VFA aviation fuel. However, paraffin fractions which fall outside aviation criteria may meet diesel fuel requirements, which are looser except for the flashpoint minimum. Flashpoint was also a concern for smaller VFA diesel ethers, which posed an additional oxygenate risk of high water solubility. This fuel-informed conversion design process was demonstrated through production of paraffins with reduced sooting as compared to petrodiesel (~34%), and increased renewable aviation fuel blend levels (>70%). VFA-derived ethers improved petrodiesel by increasing fuel autoignition quality by 56% and reducing sooting by 86%. These VFA aviation and diesel fuels could enable greenhouse gas (GHG) reductions of over 165% and 50%, respectively, relative to their purely fossil-derived counterparts. Collectively, this work provides (i) an overview of how we leveraged the flexibility of VFAs for fuel production; and (ii) insight into the potential of a VFA biorefinery to accommodate varied feed and fuel applications, while also considering the merit of tailored fuel production pathways and GHG reduction potential.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Process Anomaly Detection for Sparsely Labeled Events in Nuclear Power Plants

An essential aspect of online monitoring, subtle anomaly detection increases the detection lead time for equipment failure and enables a nuclear power plant (NPP) to mitigate unexpected partial or full outages, resulting in significant cost saving to the plant. Once an anomaly is detected by plant staff, its cause and severity are investigated. Because the vast majority of anomalies require some level of investigation, including some that require time-consuming examination, before they are passed over to the engineering organization for further analysis, plants are often equipped with tools to assist the staff in performing anomaly detection. Those tools operate as a black box and are often based on statistical methods that establish sensor correlations using preconfigured mathematical models and flag correlation deviations as anomalies. Due to the number of anomalies detected at a given NPP on a daily basis, a significant number of flagged anomalies usually await examination for days or weeks. A primary cause of this backlog is that the methods used by the tools generate many false positives. Though this is usually attributed to oversensitive model settings due to very narrow normal operation bands, it can also be associated with the model development being inadequate for the process being monitored, or with missing model inputs that could have explained misclassified positives. The performance of anomaly detection tools impacts their plant acceptance and utilization, especially when the effort to address false positives generated by the tool depletes the value or cost saved by using that tool. Thus, means to advance anomaly detection performance have been investigated by the Department of Energy’s Light Water Reactor Sustainability program. Previous and ongoing efforts have targeted unsupervised machine-learning (ML) methods, which do not require the labeling of any data fed into the ML model. By contrast, in supervised anomaly detection methods, every data point is labeled as either a normal or abnormal process condition, and the model is trained to replicate the classification process. Supervised methods usually outperform unsupervised methods, due to the added value in differentiating normal from anomalous states of the monitored process. An NPP’s corrective action program requires it to track and document, via a dedicated report, the resolution of any issues that occur within the plant. Once created, each report is reviewed by a plant screening committee, and several classifications and decisions are made. Recently, a collaborating NPP developed an artificial intelligence and ML-based classifier to categorize a condition report (CR) into classes that can serve to label the data as normal or anomalous. Applying CRs as labels represents a semi-supervised use case. Semi-supervised ML assumes that labels exist for some data points (i.e., labeled anomalies, in this case) but not for the rest. In this effort, semi-supervised ML methods were used to fuse data from CRs with anomaly detection methods in order to test the hypothesis that partially labeled anomalies would improve the accuracy of the anomaly detection methods. Specifically, two methods were used. The first is the deep Semi-supervised Anomaly Detection (deep SAD) method, which can handle labels ranging from fully unsupervised to fully supervised cases. The second is a newly designed ML method developed specifically for this effort and referred to as the high-order feature (HOF)-based method. To evaluate these two methods in controlled environments, synthetic data generators were developed and used. The first datasets used a spring-mass-damper (SMD) system simulator commonly found in mechanical engineering references. This was used to create two use cases: a one- and a three-mass system. Anomalies were introduced by changing the spring and damper coefficients while the system was actuated by random forces. The second datasets used the commercial Dymola-Modelica software to build a simplified nuclear reactor model. Anomalies were added in the form of corrupted sensor readings and/or control commands. The deep SAD method was tested using the SMD system, while the HOF method was tested using both datasets. Application of the deep SAD semi-supervised ML method demonstrated that labels can generate increased confidence in detecting true anomalies. This helped increase the number of true positives and decrease the number of false negatives—something that would aid in addressing the backlog of possible anomalies. Application of the HOF method demonstrated that labels can aid in down selecting from a candidate set of features to a more optimal subset in order to better differentiate between normal and anomalous conditions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

NaCl aggregation in water at elevated temperatures and pressures: Comparison of classical force fields

The properties of water vary dramatically with temperature and density. This can be exploited to control its effectiveness as a solvent. Thus, supercritical water is of keen interest as solvent in many extraction processes. The low solubility of salts in lower density supercritical water has even been suggested as a means of desalination. The high temperatures and pressures required to reach supercritical conditions can present experimental challenges during collection of required physical property and phase equilibria data, especially in salt-containing systems. Molecular simulations have the potential to be a valuable tool for examining the behavior of solvated ions at these high temperatures and pressures. However, the accuracy of classical force fields under these conditions is unclear. We have, therefore, undertaken a parametric study of NaCl in water, comparing several salt and water models at 200 bar–600 bar and 450 K–750 K for a range of salt concentrations. We report a comparison of structural properties including ion aggregation, hydrogen bonding, density, and static dielectric constants. All of the force fields qualitatively reproduce the trends in the liquid phase density. An increase in ion aggregation with decreasing density holds true for all of the force fields. The propensity to aggregate is primarily determined by the salt force field rather than the water force field. This coincides with a decrease in the water static dielectric constant and reduced charge screening. While a decrease in the static dielectric constant with increasing NaCl concentration is consistent across all model combinations, the salt force fields that exhibit more ionic aggregation yield a slightly smaller dielectric decrement.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

HEPOM: Using Graph Neural Networks for the Accelerated Predictions of Hydrolysis Free Energies in Different pH Conditions

Hydrolysis is a fundamental family of chemical reactions where water facilitates the cleavage of bonds. The process is ubiquitous in biological and chemical systems, owing to water’s remarkable versatility as a solvent. However, accurately predicting the feasibility of hydrolysis through computational techniques is a difficult task, as subtle changes in reactant structure like heteroatom substitutions or neighboring functional groups can influence the reaction outcome. Furthermore, hydrolysis is sensitive to the pH of the aqueous medium, and the same reaction can have different reaction properties at different pH conditions. In this work, we have combined reaction templates and high-throughput ab initio calculations to construct a diverse data set of hydrolysis free energies. The developed framework automatically identifies reaction centers, generates hydrolysis products, and utilizes a trained graph neural network (GNN) model to predict ΔG values for all potential hydrolysis reactions in a given molecule. The long-term goal of the work is to develop a data-driven, computational tool for high-throughput screening of pH-specific hydrolytic stability and the rapid prediction of reaction products, which can then be applied in a wide array of applications including chemical recycling of polymers and ion-conducting membranes for clean energy generation and storage.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Solvent absorption rate of perfluorosulphonic acid membranes towards understanding direct coating processes

Here we present a method for measurement of the rate of solvent absorption by perfluorosulfonic acid (PFSA) membranes using a force tensiometer. The method presented here can be used as a tool to understand solvent absorption and should provide a rationale for designing catalyst inks for direct coating processes since it is necessary to understand how the absorption rate – on a time scale of seconds to minutes – compares to the time scales of the coating process in order to minimize membrane swelling. This method allows for rapid screening of the absorption and swelling behavior of different solvents and mixtures. Using this method, the absorption of water/1-propanol mixtures were measured for three thicknesses of Nafion PFSA membrane – Nafion 1135, Nafion 115, and Nafion 117. We find that the absorption rate is dependent on the ratio of the solvents as well as the thickness of the membrane. The analysis indicates that the highest absorption rate occurs when the mass percentage of 1-propanol in the mixture is 50%, whereas the lowest rates are for pure water and 1-propanol. In addition, membrane distortion also occurs most quickly for the 50% 1-propanol mixture. Our results suggest that catalyst inks that are highly rich (=90%) in water or 1-propanol are likely best for direct coating as such formulations will minimize absorption and swelling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2017

Using Satellite Remote Sensing to Map Changes in Aquatic Invasive Plant Cover in the Sacramento-San Joaquin River Delta of California

Waterways of the Sacramento San Joaquin Delta have recently become infested with invasive aquatic weeds such as floating water hyacinth (Eichhoria crassipes) and water primrose (Ludwigia peploides). These invasive plants cause many negative impacts, including, but not limited to: the blocking of waterways for commercial shipping and boating; clogging of irrigation screens, pumps and canals; and degradation of biological habitat through shading. Zhang et al. (1997, Ecological Applications, 7(3), 1039-1053) used NASA Landsat satellite imagery together with field calibration measurements to map physical and biological processes within marshlands of the San Francisco Bay. Live green biomass (LGB) and related variables were correlated with a simple vegetation index ratio of red and near infra-red bands from Landsat images. More recently, the percent (water area) cover of water hyacinth plotted against estimated LGB of emergent aquatic vegetation in the Delta from September 2014 Landsat imagery showed a 80% overall accuracy. For the past two years, we have partnered with the U. S. Department of Agriculture (USDA) and the Department of Plant Sciences, University of California at Davis to conduct new validation surveys of water hyacinth and water primrose coverage and LGB in Delta waterways. A plan is underway to transfer decision support tools developed at NASA's Ames Research Center based on Landsat satellite images to improve Delta-wide integrated management of floating aquatic weeds, while reducing chemical control costs. The main end-user for this application project will be the Division of Boating and Waterways (DBW) of the California Department of Parks and Recreation, who has the responsibility for chemical control of water hyacinth in the Delta.

Map Changes↗
NASA NTRS2016

Analysis of Satellite and Airborne Imagery for Detection of Water Hyacinth and Other Invasive Floating Macrophytes and Tracking of Aquatic Weed Control Efficacy

Waterways of the Sacramento San Joaquin Delta have recently become infested with invasive aquatic weeds such as floating water hyacinth (Eichhoria crassipes) and water primrose (Ludwigia peploides). These invasive plants cause many negative impacts, including, but not limited to: the blocking of waterways for commercial shipping and boating; clogging of irrigation screens, pumps and canals; and degradation of biological habitat through shading. Zhang et al. (1997, Ecological Applications, 7(3), 1039-1053) used NASA Landsat satellite imagery together with field calibration measurements to map physical and biological processes within marshlands of the San Francisco Bay. Live green biomass (LGB) and related variables were correlated with a simple vegetation index ratio of red and near infra-red bands from Landsat images. More recently, the percent (water area) cover of water hyacinth plotted against estimated LGB of emergent aquatic vegetation in the Delta from September 2014 Landsat imagery showed an 80 percent overall accuracy. For the past two years, we have partnered with the U. S. Department of Agriculture (USDA) and the Department of Plant Sciences, University of California at Davis to conduct new validation surveys of water hyacinth and water primrose coverage and LGB in Delta waterways. A plan is underway to transfer decision support tools developed at NASA's Ames Research Center based on Landsat satellite images to improve Delta-wide integrated management of floating aquatic weeds, while reducing chemical control costs. The main end-user for this application project will be the Division of Boating and Waterways (DBW) of the California Department of Parks and Recreation, who has the responsibility for chemical control of water hyacinth in the Delta.

Agriculture↗
NASA NTRS2014

Spinoff 2013

Topics covered include: Innovative Software Tools Measure Behavioral Alertness; Miniaturized, Portable Sensors Monitor Metabolic Health; Patient Simulators Train Emergency Caregivers; Solar Refrigerators Store Life-Saving Vaccines; Monitors Enable Medication Management in Patients' Homes; Handheld Diagnostic Device Delivers Quick Medical Readings; Experiments Result in Safer, Spin-Resistant Aircraft; Interfaces Visualize Data for Airline Safety, Efficiency; Data Mining Tools Make Flights Safer, More Efficient; NASA Standards Inform Comfortable Car Seats; Heat Shield Paves the Way for Commercial Space; Air Systems Provide Life Support to Miners; Coatings Preserve Metal, Stone, Tile, and Concrete; Robots Spur Software That Lends a Hand; Cloud-Based Data Sharing Connects Emergency Managers; Catalytic Converters Maintain Air Quality in Mines; NASA-Enhanced Water Bottles Filter Water on the Go; Brainwave Monitoring Software Improves Distracted Minds; Thermal Materials Protect Priceless, Personal Keepsakes; Home Air Purifiers Eradicate Harmful Pathogens; Thermal Materials Drive Professional Apparel Line; Radiant Barriers Save Energy in Buildings; Open Source Initiative Powers Real-Time Data Streams; Shuttle Engine Designs Revolutionize Solar Power; Procedure-Authoring Tool Improves Safety on Oil Rigs; Satellite Data Aid Monitoring of Nation's Forests; Mars Technologies Spawn Durable Wind Turbines; Programs Visualize Earth and Space for Interactive Education; Processor Units Reduce Satellite Construction Costs; Software Accelerates Computing Time for Complex Math; Simulation Tools Prevent Signal Interference on Spacecraft; Software Simplifies the Sharing of Numerical Models; Virtual Machine Language Controls Remote Devices; Micro-Accelerometers Monitor Equipment Health; Reactors Save Energy, Costs for Hydrogen Production; Cameras Monitor Spacecraft Integrity to Prevent Failures; Testing Devices Garner Data on Insulation Performance; Smart Sensors Gather Information for Machine Diagnostics; Oxygen Sensors Monitor Bioreactors and Ensure Health and Safety; Vision Algorithms Catch Defects in Screen Displays; and Deformable Mirrors Capture Exoplanet Data, Reflect Lasers.

Source record↗
NASA NTRS2019

An Integrated Data Analytics Platform

An Integrated Science Data Analytics Platform is an environment that enables the confluence of resources for scientific investigation. It harmonizes data, tools and computational resources which subsequently enable the research community to focus on the investigation rather than spending time on security, data preparation, management, etc. OceanWorks is a NASA technology integration project to establish a cloud-based Integrated Ocean Science Data Analytics Platform at NASA’s Physical Oceanography Distributed Active Archive Center (PO.DAAC) for big ocean science. It focuses on advancement and maturity by bringing together several NASA open-source, big data projects for parallel analytics, anomaly detection, in-situ to satellite data matchup, quality-screened data subsetting, search relevancy, and data discovery. Our communities are relying on data distributed through data centers such as the PO.DAAC, COAPS, NCAR, and many others to conduct their research. In typical investigations, scientists would engage in: search for data, evaluate the relevance of that data, download it, and then apply algorithms to identify trends. Such workflow cannot scale if the research involves a massive amount of data or multi-variate measurements. NASA’s Surface Water and Ocean Topography (SWOT) mission is expected to produce massive amount of observational data during its 3-year nominal mission. Collections like SWOT challenges all existing Earth Science data archival, distribution and analysis paradigms. In this paper, we will discuss how OceanWorks enhances the analysis of physical ocean data where the computation is done on an elastic cloud platform next to the archive to deliver fast, web-accessible services for working with oceanographic measurements.

Yang, Chaowei↗
DOE OSTI2024

Calcium-organic matter fouling in nanofiltration: Synchrotron-based X-ray fluorescence and absorption near-edge structure spectroscopy for speciation

Calcium (Ca)-enhanced organic matter (OM) fouling of nanofiltration (NF) membranes leads to reduced flux during desalination and requires frequent cleaning. Fouling mechanisms are not fully understood, which limits the development of targeted fouling control methods. This study employed synchrotron-based X-ray fluorescence (XRF) and X-ray absorption near-edge structure (XANES) spectroscopy to quantify the spatial distribution and mass of Ca deposition as well as changes in the Ca coordination environment characteristic of specific fouling mechanisms, respectively. Bench-scale filtration experiments were performed using feed solutions containing Ca and ten different types of organic matter (OM), as well as the common scalants, calcium carbonate (CaCO 3 ) and calcium sulfate (CaSO 4 ). Osmotic backwash (OB) was performed at regular intervals for fouling control. Ca-OM aggregation resulted in greater flux decline and lower flux recovery during OB than Ca conditioning of membranes followed by filtration of feed solution with OM. Linear combination fitting (LCF) of XANES absorption spectra from fouled membranes indicated that Ca-OM aggregation preferentially occurred for OM types that exhibited both high carboxylic group and negative charge density. Consequently, these OM types exhibited greater deposition of Ca and TOC on the membrane surface when compared to other OM types. For the coexistence of scalants and OM, Ca speciation within the fouling layer was characteristic of both Ca bound to the membrane (i.e. potential bridging, charge screening) as well as Ca-OM aggregation and deposition mechanisms, while a range of crystal polymorphs were observed to occur simultaneously. XRF and XANES represent powerful tools for the elucidation of NF fouling mechanisms by quantification of Ca deposition as well as Ca speciation. Fouling control methods should target OM types with high carboxyl group density and negative charge to neutralize or eliminate interactions with Ca.

42 ENGINEERING↗
DOE OSTI2021

Interfacial X-Ray Scattering from Small Surfaces: Adapting Mineral-Fluid Structure Methods for Microcrystalline Materials

Crystal truncation rod (CTR) X-ray diffraction is an invaluable tool for measuring mineral surface and adsorbate structures, and has been applied to several environmentally and geochemically important systems. Traditionally, the method has been restricted to single crystals with lateral dimensions >3 mm. Minerals that meet this size criterion represent a minute fraction of those that are relevant to interfacial geochemistry questions, however. Crystal screening, data collection, and CTR measurement methods have been developed for crystals of <0.3 mm in lateral size using the manganese oxide mineral chalcophanite (ZnMn 3 O 7 •3H 2 O) as a case study. Furthermore, this work demonstrates the feasibility of applying the CTR technique to previously inaccessible surfaces, opening up a large suite of candidate substrates for future study.

58 GEOSCIENCES↗
DOE OSTI2020

Updates to a Preliminary Probabilistic Performance Assessment Model for Radiological and Chemical Contamination at the West Valley Site, New York - 20420

The New York State Energy Research and Development Authority (NYSERDA) is the owner of the Western New York Nuclear Service Center (WNYNSC), a 1,351-ha site located approximately 48 km south of Buffalo, New York. In 1962, Nuclear Fuel Services, Inc. (NFS) entered into agreements with the Atomic Energy Commission and New York State to construct the first commercial reprocessing plant of nuclear fuel in the United States at the WNYNSC. NFS built and operated the spent fuel reprocessing plant and waste disposal facilities, processing 640 Mg (640 metric tons) of spent nuclear fuel from 1966 to 1972 under an Atomic Energy Commission license. Nuclear fuel reprocessing operations halted in 1972 and never restarted, leaving behind radioactive and chemical wastes. The U.S. Department of Energy (DOE) was required to complete certain waste management activities under the West Valley Demonstration Project (WVDP) Act of 1980 including decommissioning of WVDP facilities. As collaborating agencies, NYSERDA and the DOE are tasked with making decisions about decommissioning and risk reduction for the West Valley Site. Neptune and Company, Inc. (Neptune) was contracted to develop a probabilistic performance assessment (PPA) model to assist the agencies in their decision making process for decommissioning the WVDP and WNYNSC. One important tool that is needed in order to inform the decision-making process is a science-based model of the West Valley Site that evaluates potential future consequences for human health and the environment. This forms the core of the spatial domain of the West Valley PPA Model. The PPA Model, developed using the GoldSim system modeling software, is a tool intended to provide support for decision making that evaluates uncertainty, in a manner that is transparent, defensible, and robust. The PPA Model includes contaminant transport and health effects components, and is organized around geographically-grouped contaminated facilities. These include the waste disposal areas licensed by the U.S. Nuclear Regulatory Commission and the State of New York, a waste tank farm for storage of high level radioactive waste resulting from reprocessing operations, and several areas contaminated with radioactive and chemical constituents. The PPA Model evaluates contaminant transport from these sources to points of exposure across the site and into receiving surface waters and sediments. Hypothetical people and wildlife could be exposed to contamination at these locations, and the effects of these exposures are evaluated. Contaminant transport processes to be evaluated in the PPA Model include groundwater and surface water transport, contaminant translocation by plants and animals, diffusion, and erosion. The evaluation of exposures to people in this preliminary model is limited to a resident farmer scenario, and ecological assessment is performed at the level of a screening analysis. The results of these preliminary evaluations inform future model developments. PPA Model results are subjected to sensitivity analysis in order to determine those pathways and parameters that are most significant in influencing the results. This information allows analysts and decision makers to focus on those aspects of Site behavior and processes. With this information, the decision makers can drive informed, defensible decisions regarding decommissioning of the Site. This paper includes an update of the information presented at WM2019 [1]. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Deep Learning for Modeling Enhanced Geothermal Systems

Enhanced Geothermal Systems (EGS) offer a vast potential to expand the use of geothermal energy. Heat is extracted from this engineered system by injecting cold water into a subsurface fractures, which are in contact with the hot dry rock, and pulled through the production wells. Creating EGS requires improving the natural permeability of hot crystalline rocks. To develop economically viable EGS reservoirs, significant technical barriers (e.g., better stimulation technologies without adequate water and/or permeability) and non-technical barriers (e.g., land access and permitting) must be overcome. In this short conference paper, we present a workflow to address a part of this challenge – “How to develop economically viable EGS using existing technologies?”. Our workflow called the GeoThermalCloud (GTC) for EGS, leverages recent advances in machine learning, deep learning, and cloud computing. This GTC framework is open-source and available at https://github.com/SmartTensors/GeoThermalCloud.jl. The GTC framework provides trained deep learning (DL) models to estimate the net present value of a given EGS design scenario. The Geothermal Design Tool (https://github.com/GeoDesignTool/GeoDT.git), a fast and simplified multi-physics solver, is used to develop a database for training DL models. The database consists of EGS design parameters (inputs to DL model) and their net present value (output of DL model) in uncertain geologic systems. The EGS design parameters for constructing this training database are based on Utah FORGE but include the options of more wells and deeper depths. The DL models are trained by ingesting the EGS design parameters and estimating the corresponding net present value. Such an emulation allows us to screen various EGS designs quickly and identify good development strategies by coupling them with optimization techniques. Our preliminary results show promise in DL emulation of net present value. However, a lot more work is needed to improve the predictive capability of DL models (i.e., extensive hyperparameter tuning is necessary). This will be the primary focus of our future work.

artificial neural networks, geothermal↗
NASA NTRS1978

Making The Invisible Visible

In public and private archives throughout the world there are many historically important documents that have become illegible with the passage of time. They have faded, been erased, acquired mold, water and dirt stain, suffered blotting or lost readability in other ways. While ultraviolet and infrared photography are widely used to enhance deteriorated legibility, these methods are more limited in their effectiveness than the space-derived image enhancement technique. The aim of the JPL effort with Caltech and others is to better define the requirements for a system to restore illegible information for study at a low page-cost with simple operating procedures. The investigators' principle tools are a vidicon camera and an image processing computer program, the same equipment used to produce sharp space pictures. The camera is the same type as those on NASA's Mariner spacecraft which returned to Earth thousands of images of Mars, Venus and Mercury. Space imagery works something like television. The vidicon camera does not take a photograph in the ordinary sense; rather it "scans" a scene, recording different light and shade values which are reproduced as a pattern of dots, hundreds of dots to a line, hundreds of lines in the total picture. The dots are transmitted to an Earth receiver, where they are assembled line by line to form a picture like that on the home TV screen.

Source record↗
DOE OSTI2024

Predicting Partial Atomic Charges in Metal–Organic Frameworks: An Extension to Ionic MOFs

Molecular simulation is an invaluable tool to predict and understand the usage of metal–organic frameworks (MOFs) for gas storage and separation applications. Accurate partial atomic charges, commonly obtained from density functional theory (DFT) calculations, are often required to model the electrostatic interactions between the MOF and adsorbates, especially when the adsorbates have dipole or quadrupole moments, such as water and CO 2 . Machine learning (ML) models have been previously employed to predict partial charges and avoid the computational cost associated with DFT calculations. However, previous ML models suffer from small training data sets, which limit their scope of application. In this work, we introduce two novel machine learning models, PACMOF2-neutral and PACMOF2-ionic, aimed at predicting the density-derived electrostatic and chemical (DDEC6) partial atomic charges for both neutral and ionic MOFs. These models not only yield DFT-level accuracy at a fraction of the computational cost but also demonstrate a remarkable improvement in prediction of adsorption, as validated with grand canonical Monte Carlo simulations. Furthermore, the robustness and fast computational time of the PACMOF2 models, along with their transferability to other porous materials such as covalent organic frameworks and zeolites, underscores their potential in high-throughput screening of MOFs for diverse applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

US EPA Superfund Radon Vapor Intrusion Screening Level (RVISL) Electronic Calculator - 20340

Currently, there is no U.S. Environmental Protection Agency (EPA) guidance on correlating soil or groundwater levels of radon with indoor radon concentrations at Superfund sites. EPA is developing the Radon Vapor Intrusion Level Calculator (RVISL) Calculator which is an online tool. The RVISL calculator: (1) lists three radon isotopes (Rn-219, Rn-220 and Rn-222) known to pose a potential cancer risk through the inhalation pathway; (2) provides generally recommended risk and Applicable or Relevant and Appropriate Requirements (ARAR) based preliminary remediation goals (PRG) for groundwater, soil gas (exterior to buildings and sub-slab) and indoor air for default target risk and ARAR based levels and exposure scenarios; and (3) allows calculation of site-specific PRGs based on user-defined target risk and ARAR based levels and exposure scenarios. EPA developed the RVISL calculator to help risk assessors, remedial project managers, and others involved with risk assessment and decision making at radioactively contaminated sites. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Implementing Mixed Reality Tools to Support Mission Delivery at Hanford - 20520

Mission Support Alliance's (MSA) Public Works and Information Systems organizations have been working with Microsoft Corporation to develop and implement mixed reality operational solutions using their HoloLens technology. This collaborative effort is intended to drive innovative solutions and significantly improve MSA's efficiency in performing work at the Hanford Site. HoloLens, a mixed reality tool, is a commercial off-the-shelf technology that combines a head-mounted viewing screen with applications to help people and organizations learn, communicate, and collaborate more effectively through the use of mixed reality. https://microsoft.com/en-us/hololens MSA's initial pilot successfully demonstrated its ability to use mixed reality utilizing HoloLens' advanced features in a variety of ways, illustrating the potential efficiency gains previously stated as goals of this pilot. These achievements include the development of proprietary technology that allows synchronization between a HoloLens device and a mobile device or alternate global positioning system (GPS) device. This synchronization enables the HoloLens device to access real time GPS location data anywhere on the Hanford Site. This real time location data, combined with MSA's improved Hanford Geographical Information System (GIS) data, enables the mixed reality pilot application to identify underground utility systems as well as related utility attributes. This ability will aid utility workers in several areas, including excavation activities, the placement of large cranes, identifying interactions and conflicts with future underground utility placements and, eventually, recognizing and de-conflicting tank waste transfer system valve alignments. The HoloLens mixed reality application is also able to provide full or small scale holographic images of a facility's digital twin. This feature will allow MSA's Water and Sewer Utilities team to train and validate procedures associated with the new water treatment facility virtually, before it is constructed. This reduces the amount of time it takes to complete the training and procedure validation, and accelerates the overall construction schedule. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2013

Verification of the H2O Linelists with Theoretically Developed Tools

Two basic rules (i.e., the pair identity and the smooth variation rules) resulting from the properties of the energy levels and wave functions of H2O states govern how the spectroscopic parameters vary with the H2O lines within the individually defined groups of lines. With these rules, for those lines involving high j states in the same groups, variations of all their spectroscopic parameters (i.e., the transition frequency, intensity, pressure broadened half-width, pressure-induced shift, and temperature exponent) can be well monitored. Thus, the rules can serve as simple and effective tools to screen the H2O spectroscopic data listed in the HITRAN database and verify the latter's accuracies. By checking violations of the rules occurring among the data within the individual groups, possible errors can be picked up and also possible missing lines in the linelist whose intensities are above the threshold can be identified. We have used these rules to check the accuracies of the spectroscopic parameters and the completeness of the linelists for several important H2O vibrational bands. Based on our results, the accuracy of the line frequencies in HITRAN 2008 is consistent. For the line intensity, we have found that there are a substantial number of lines whose intensity values are questionable. With respect to other parameters, many mistakes have been found. The above claims are consistent with a well known fact that values of these parameters in HITRAN contain larger uncertainties. Furthermore, supplements of the missing line list consisting of line assignments and positions can be developed from the screening results.

spectroscopy↗