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

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Quantification of Soil Organic Carbon in Biochar-Amended Soil Using Ground Penetrating Radar (GPR)

The application of biochar amendments to soil has been proposed as a strategy for mitigating global carbon (C) emissions and soil organic carbon (SOC) loss. Biochar can provide additional agronomic benefits to cropping systems, including improved crop yield, soil water holding capacity, seed germination, cation exchange capacity (CEC), and soil pH. To maximize the beneficial effects of biochar amendments towards the inventory, increase, and management of SOC pools, nondestructive analytical methods such as ground penetrating radar (GPR) are needed to identify and quantify belowground C. The use of GPR has been well characterized across geological, archaeological, engineering, and military applications. While GPR has been predominantly utilized to detect relatively large objects such as rocks, tree roots, land mines, and peat soils, the objective of this study was to quantify comparatively smaller, particulate sources of SOC. This research used three materials as C sources: biochar, graphite, and activated C. The C sources were mixed with sand—12 treatments in total—and scanned under three moisture levels: 0%, 10%, and 20% to simulate different soil conditions. GPR attribute analyses and Naïve Bayes predictive models were utilized in lieu of visualization methods because of the minute size of the C particles. Significant correlations between GPR attributes and both C content and moisture levels were detected. The accuracy of two predictive models using a Naïve Bayes classifier for C content was trivial but the accuracy for C structure was 56%. The analyses confirmed the ability of GPR to identify differences in both C content and C structure. Beneficial future applications could focus on applying GPR across more diverse soil conditions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1975

Summary of space imagery studies in Utah and Nevada

LANDSAT-1, Skylab, and RB-57 imagery acquired within days of each other of the San Rafael swell enabled geological mapping of individual formations of the southern portion of this broad anticlinal feature in eastern Utah. Mapping at a scale of 1/250,000 on an enhanced and enlarged S-190B image resulted in a geological map showing correlative mappable features that are indicated on the geological map of Utah at the same scale. An enhanced enlargement of an S-190B color image at a scale of 1/19,200 of the Bingham Porphyry Copper deposit allowed comparison of a geological map of the area with the space imagery map as fair for the intrusion boundaries and total lack of quality for mapping the sediments. Hydrothermal alteration is only slightly evident on space imagery at Bingham but in the Tintic mining district and the volcanic piles of the Keg and Thomas ranges, Utah, hydrothermal alteration is readily mapped on color enlargements of S-190B (SL-3, T3-3N Tr-2). A mercury soil-gas analyzer was developed for locating hidden mineralized zones which were suggested from space imagery.

Jensen, M. L.↗
DOE OSTI2018

Fluorescence Color by Data-Driven Design of Genomic Silver Clusters

DNA nucleobase sequence controls the size of DNA-stabilized silver clusters, leading to their well-known yet little understood sequence-tuned colors. The enormous space of possible DNA sequences for templating clusters has challenged the understanding of how sequence selects cluster properties and has limited the design of applications that employ these clusters. Here, we investigate the genomic role of DNA sequence for fluorescent silver clusters using a data-driven approach. Employing rapid parallel silver cluster synthesis and fluorimetry, we determine the fluorescence spectra of silver cluster products stabilized by 1432 distinct DNA oligomers. By applying pattern recognition algorithms to this large experimental data set, we discover certain DNA base patterns, or “motifs,” that correlate to silver clusters with similar fluorescence spectra. These motifs are employed in machine learning classifiers to predictively design DNA template sequences for specific fluorescence color bands. Our method improves selectivity of templates by 330% for silver clusters with peak emission wavelengths beyond 660 nm. The discovered base motifs also provide physical insights into how DNA sequence controls silver cluster size and color. Finally, this predictive design approach for color of DNA-stabilized silver clusters exhibits the potential of machine learning and data mining to increase the precision and efficiency of nanomaterials design, even for a soft-matter-inorganic hybrid system characterized by an extremely large parameter space.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Developing an experimental-computational framework to investigate the deformation mechanisms and mechanical properties of Al-8Ce-10Mg alloys at micro and macroscales

There is a promising future for the use of aluminum-cerium-magnesium alloys in a broad range of applications, including devices that operate at high temperatures. With cerium currently considered a waste product of rare earth mining validation studies of possible applications are essential to reduce the environmental waste. Here, a computational-experimental framework is developed to investigate the role both the intermetallic and matrix have on the mechanical properties of these alloys. A set of experiments, including SEM/EBSD imaging, nanoindentation, in-situ SEM tensile testing, and in-situ SEM-DIC tests are performed to characterize the microstructure and mechanical properties of these alloys. Furthermore, the elastic, plastic, and failure deformation mechanisms of the microstructure, and their correlation with the bulk scale mechanical properties are investigated. Experimental results are also used to calibrate parameters for a crystal plasticity finite element model, by performing a computational framework that minimizes the error between the computational and experimental results. This model is then utilized to investigate how the area percentage of intermetallics and the crystallographic texture control the mechanical properties of the alloy. Simulation results show that an increase in the percentage of intermetallics increase the strength but decrease the ductility of the alloy. Also, a change in material texture improves strength and reduces damage that leads to material failure. The development of the crystal plasticity model, as discussed in this work, opens opportunities for future investigations of similar aluminum-cerium-magnesium alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Tetrahedral Tilting and Lithium‐Ion Transport in Halide Argyrodites Prepared by Rapid, Microwave‐Assisted Synthesis

Abstract This study demonstrates a rapid, dry, microwave‐assisted (MW) synthesis method that enables preparation of halide argyrodites ( , , ) in less than 20 min. The structures and ion transport properties of the resulting materials are compared with those synthesized by conventional solid‐state synthesis methods. The microwave‐assisted method leads to increased site disorder and elevated Arrhenius prefactors (), which lead to an order of magnitude improvement in the 30 ionic conductivity of MW‐. X‐ray pair distribution function analysis (XPDF) reveals significant rotational disorder of the units, which is impacted by the synthesis method, choice of halide, and presence of / site disorder. These rotational displacements are strongly correlated with ion transport, specifically and entropy of migration (). Overall, this study demonstrates a rapid synthesis route for preparing high‐quality halide argyrodite solid‐state electrolytes in less than 20 min, and further unravels atomistic insights into the interplay of structural disorder, rotational dynamics, and ion transport mechanisms.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Data Analysis Approaches for the Risk-Informed Safety Margins Characterization Toolkit

In the past decades, several numerical simulation codes have been employed to simulate accident dynamics (e.g., RELAP5-3D, RELAP-7, MELCOR, MAAP). In order to evaluate the impact of uncertainties into accident dynamics, several stochastic methodologies have been coupled with these codes. These stochastic methods range from classical Monte-Carlo and Latin Hypercube sampling to stochastic polynomial methods. Similar approaches have been introduced into the risk and safety community where stochastic methods (such as RAVEN, ADAPT, MCDET, ADS) have been coupled with safety analysis codes in order to evaluate the safety impact of timing and sequencing of events. These approaches are usually called Dynamic PRA or simulation-based PRA methods. These uncertainties and safety methods usually generate a large number of simulation runs (database storage may be on the order of gigabytes or higher). The scope of this paper is to present a broad overview of methods and algorithms that can be used to analyze and extract information from large data sets containing time dependent data. In this context, “extracting information” means constructing input-output correlations, finding commonalities, and identifying outliers. Some of the algorithms presented here have been developed or are under development within the RAVEN statistical framework.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

High-Dimensional Similarity Search with Quantum-Assisted Variational Autoencoder

Recent progress in quantum algorithms and hardware indicates the potential importance of quantum computing in the near future. However, finding suitable application areas remains an active area of research. Quantum machine learning is touted as a potential approach to demonstrate quantum advantage within both the gate-model and the adiabatic schemes. For instance, the QVAE has been proposed as a quantum enhancement to the discrete VAE. We extend on previous work and study the real-world applicability of a QVAE by presenting a proof-of-concept for similarity search in large-scale high-dimensional datasets. While exact and fast similarity search algorithms are available for low dimensional datasets, scaling to high-dimensional data is non-trivial. We show how to construct a space-efficient search index based on the latent space representation of a QVAE. Our experiments show a correlation between the Hamming distance in the embedded space and the Euclidean distance in the original space on the MODIS dataset. Further, we find real-world speedups compared to linear search and demonstrate memory-efficient scaling to half a billion data points.

Data mining, similarity search, quantum machine le↗
NASA NTRS2021

Peru Health & Air Quality: Land Use Change in the Rapidly Developing Peruvian Amazon and Implications on Zoonotic Disease Incidence

In the Madre de Dios region of the Peruvian Amazon, forests are being cleared for mining, timber harvesting, road construction, and hydroelectric dam development. These rapid land use changes are increasing human presence in previously sparsely populated areas, disrupting ecosystems and increasing the proximity of human settlement to zoonotic disease vectors. Dengue fever and leishmaniasis are two neglected tropical diseases which are prevalent in Madre de Dios and have been associated with urbanization and road construction. In partnership with the Peruvian Ministries of Health (MINSA) and the Environment (MINAM) and other in-country collaborators, our team examined Land Use Land Cover (LULC) correlations with reported dengue and leishmaniasis incidence in the Madre de Dios region to help partners understand the spatial relationship between land use change and zoonotic disease incidence. We created a LULC classification script using Google Earth Engine with Landsat 5 Thematic Mapper and Landsat 8 Operational Land Imager imagery to classify land cover in 2010, 2015, and 2020 and evaluate changes over this time period. We then used the quantified results of the LULC assessment in conjunction with reported disease cases to evaluate correlations between disease incidence and key land cover changes across Madre de Dios’s 11 districts. In the second term, the team will use these products to develop more detailed disease incidence risk maps and models. High risk areas will then be classified, using PeruSat-1 that allow for even higher resolution mapping at less than 3 meters. These products will allow the partners to understand hotspots of land cover change in Peru and the relationship with outbreaks to inform public health decision making and environmental policy.

Elizabeth Stapleton↗
DOE OSTI2018

Biogeochemical controls on the speciation and aquatic toxicity of vanadium and other metals in sediments from a river reservoir

Effects of hydrologic variability on reservoir biogeochemistry are relatively unknown, particularly for less studied metals like vanadium (V). Further, few studies have investigated the fate and effects of sediment-associated V to aquatic organisms in hydrologically variable systems. Our primary objective was to assess effects of hydrologic manipulation on speciation and toxicity of V (range: 635 to 1620 mg kg- 1) and other metals to Hyalella azteca and Daphnia magna. Sediments were collected from a reservoir located in a former mining area and microcosm experiments were conducted to emulate 7-day drying and inundation periods. Despite high sediment concentrations, V bioavailability remained low with no significant effects to organism survival, growth, or reproduction. The lack of V toxicity was attributed to reduced speciation (III, IV), non-labile complexation, and sorption to Al/Fe/Mn-oxyhydroxides. Zinc (Zn) increased in surface and porewater with inundation, for some sediments exceeding the U.S. EPA threshold for chronic toxicity. While no effects of Zn to organism survival or growth were observed, Zn body concentrations were negatively correlated with H. azteca growth. Results from this study indicate that V bioavailability and environmental risk is dependent on V-speciation, and V is less influenced by hydrologic variability than more labile metals such as Zn.

58 GEOSCIENCES↗
NASA NTRS2008

2007 Research and Engineering Annual Report

Selected research and technology activities at NASA Dryden Flight Research Center are summarized. These following activities exemplify the Center's varied and productive research efforts: Developing a Requirements Development Guide for an Automatic Ground Collision Avoidance System; Digital Terrain Data Compression and Rendering for Automatic Ground Collision Avoidance Systems; Nonlinear Flutter/Limit Cycle Oscillations Prediction Tool; Nonlinear System Identification Using Orthonormal Bases: Application to Aeroelastic/Aeroservoelastic Systems; Critical Aerodynamic Flow Feature Indicators: Towards Application with the Aerostructures Test Wing; Multidisciplinary Design, Analysis, and Optimization Tool Development Using a Genetic Algorithm; Structural Model Tuning Capability in an Object-Oriented Multidisciplinary Design, Analysis, and Optimization Tool; Extension of Ko Straight-Beam Displacement Theory to the Deformed Shape Predictions of Curved Structures; F-15B with Phoenix Missile and Pylon Assembly--Drag Force Estimation; Mass Property Testing of Phoenix Missile Hypersonic Testbed Hardware; ARMD Hypersonics Project Materials and Structures: Testing of Scramjet Thermal Protection System Concepts; High-Temperature Modal Survey of the Ruddervator Subcomponent Test Article; ARMD Hypersonics Project Materials and Structures: C/SiC Ruddervator Subcomponent Test and Analysis Task; Ground Vibration Testing and Model Correlation of the Phoenix Missile Hypersonic Testbed; Phoenix Missile Hypersonic Testbed: Performance Design and Analysis; Crew Exploration Vehicle Launch Abort System-Pad Abort-1 (PA-1) Flight Test; Testing the Orion (Crew Exploration Vehicle) Launch Abort System-Ascent Abort-1 (AA-1) Flight Test; SOFIA Flight-Test Flutter Prediction Methodology; SOFIA Closed-Door Aerodynamic Analyses; SOFIA Handling Qualities Evaluation for Closed-Door Operations; C-17 Support of IRAC Engine Model Development; Current Capabilities and Future Upgrade Plans of the C-17 Data Rack; Intelligent Data Mining Capabilities as Applied to Integrated Vehicle Health Management; STARS Flight Demonstration No. 2 IP Data Formatter; Space-Based Telemetry and Range Safety (STARS) Flight Demonstration No. 2 Range User Flight Test Results; Aerodynamic Effects of the Quiet Spike(tm) on an F-15B Aircraft; F-15 Intelligent Flight Controls-Increased Destabilization Failure; F-15 Integrated Resilient Aircraft Control (IRAC) Improved Adaptive Controller; Aeroelastic Analysis of the Ikhana/Fire Pod System; Ikhana: Western States Fire Missions Utilizing the Ames Research Center Fire Sensor; Ikhana: Fiber-Optic Wing Shape Sensors; Ikhana: ARTS III; SOFIA Closed-Door Flutter Envelope Flight Testing; F-15B Quiet Spike(TM) Aeroservoelastic Flight Test Data Analysis; and UAVSAR Platform Precision Autopilot Flight Results.

Stoliker, Patrick↗
DOE OSTI2014

Integrated Analyses of Multiple Worldwide Aerosol Mass Spectrometer Datasets for Improved Understanding of Aerosol Sources and Processes and for Comparison with Global Models

The AMS is the only current instrument that provides real-time, quantitative, and size-resolved data on submicron non-refractory aerosol species with a time resolution of a few minutes or better. The AMS field data are multidimensional and massive, containing extremely rich information on aerosol chemistry, microphysics and dynamics—basic information that is required to evaluate and quantify the radiative climate forcing of atmospheric aerosols. The high time resolution of the AMS data also reveals details of aerosol dynamic variations that are vital to understanding the physico-chemical processes of atmospheric aerosols that govern aerosol properties relevant to the climate. There are two primary objectives of this 3-year project. Our first objective is to perform highly integrated analysis of dozens of AMS datasets acquired from various urban, forested, coastal, marine, mountain peak, and rural/remote locations around the world and synthesize and inter-compare results with a focus on the sources and the physico-chemical processes that govern aerosol properties relevant to aerosol climate forcing. Our second objective is to support our collaboration with global aerosol modelers, in which we will supply the size-resolved aerosol composition and temporal variation data (via a public web interface) and our analysis results for use in model testing and validation and for translation of the rich AMS database into model constraints that can improve climate forcing simulations. Several prominent global aerosol modelers have expressed enthusiastic support for this collaboration. The specific tasks that we propose to accomplish include 1) to develop, validate, and apply multivariate analysis techniques for improved characterization and source apportionment of organic aerosols; 2) to evaluate aerosol source regions and relative contributions based on back-trajectory integration (PSCF method); 3) to summarize and synthesize submicron aerosol information, including composition, concentration, size distribution and (inferred) shape and mixing state in various environments and their regional and seasonal variations within the context of regional and global modeling; and 4) to quantitatively evaluate important processes in various atmospheric environments and during different seasons, focusing on acid-catalyzed SOA formation, new particle growth, and photochemical processes of atmospheric organic aerosols (i.e., SOA production and POA oxidation). We will also examine the correlations and compile the ratios between important pairs of aerosol and gas phase species using region-specific and season-specific correlations and as a function of photochemical age and compare them with the ratios produced by various models. To enable our collaborations with the modelers, we will supply (via a public web interface) AMS data and our analysis results for use in model testing and validation and facilitate the use of the AMS information to constrain calculations of radiative forcing. Model output and AMS measurements and derived parameters will be compared with a focus on regional variability of model/measurement discrepancies and their causes. Finally we will share results, insights and data mining algorithms through peer-reviewed publications, presentations/tutorials at conferences/workshops, and web dissemination of analysis results and in-house developed software packages.

58 GEOSCIENCES↗
NASA NTRS2016

Risk of Performance Decrements and Adverse Health Outcomes Resulting from Sleep Loss, Circadian Desynchronization, and Work Overload

Sleep loss, circadian desynchronization, and work overload occur to some extent for ground and flight crews, prior to and during spaceflight missions. Ground evidence indicates that such risk factors may lead to performance decrements and adverse health outcomes, which could potentially compromise mission objectives. Efforts are needed to identify the environmental and mission conditions that interfere with sleep and circadian alignment, as well as individual differences in vulnerability and resiliency to sleep loss and circadian desynchronization. Specifically, this report highlights a collection of new evidence to better characterize the risk and reveals new gaps in this risk as follows: Sleep loss is apparent during spaceflight. Astronauts consistently average less sleep during spaceflight relative to on the ground. The causes of this sleep loss remain unknown, however ground-based evidence suggests that the sleep duration of astronauts is likely to lead to performance impairment and short and long-term health consequences. Further research is needed in this area in order to develop screening tools to assess individual astronaut sleep need in order to quantify the magnitude of sleep loss during spaceflight; current and planned efforts in BHP's research portfolio address this need. In addition, it is still unclear whether the conditions of spaceflight environment lead to sleep loss or whether other factors, such as work overload lead to the reduced sleep duration. Future data mining efforts and continued data collection on the ISS will help to further characterize factors contributing to sleep loss. Sleep inertia has not been evaluated during spaceflight. Ground-based studies confirm that it takes two to four hours to achieve optimal performance after waking from a sleep episode. Sleep inertia has been associated with increased accidents and reduced performance in operational environments. Sleep inertia poses considerable risk during spaceflight when emergency situations necessitate that crewmembers wake from sleep and make quick decisions. A recently completed BHP investigation assesses the effects of sleep inertia upon abrupt awakening, with and without hypnotics currently used in spaceflight; results from this investigation will help to inform strategies relative to sleep inertia effects on performance. Circadian desynchrony has been observed during spaceflight. Circadian desynchrony during spaceflight develops due to schedule constraints requiring non-24 operations or 'slam-shifts' and due to insufficient or mis-timed light exposure. In addition, circadian misalignment has been associated with reduced sleep duration and increased medication use. In ground-based studies, circadian desynchrony has been associated with significant performance impairment and increased risk of accidents when operations coincide with the circadian nadir. There is a great deal of information available on how to manage circadian misalignment, however, there are currently no easily collected biomarkers that can be used during spaceflight to determine circadian phase. Current research efforts are addressing this gap. Work overload has been documented during current spaceflight operations. NASA has established work hour guidelines that limit shift duration, however, schedule creep, where duty requirements necessitate working beyond scheduled work hours, has been reported. This observation warrants the documentation of actual work hours in order to improve planning and in order to ensure that astronauts receive adequate down time. In addition to concerns about work overload, ground based evidence suggests that work underload may be a concern during deep space missions, where torpor may develop and physically demanding workload will be exchanged for monitoring of autonomous systems. Given that increased automation is anticipated for exploration vehicles, fatigue effects in the context of such systems needs to be further understood. Performance metrics are needed to evaluate fitness-for-duty during spaceflight. Although ground-based evidence supports the notion that sleep loss, circadian desynchronization and work overload lead to performance impairment, inconsistency in the measures used to evaluate performance during spaceflight make it difficult to evaluate the magnitude of performance impairment during spaceflight. Work is underway to standardize measures of performance evaluation during spaceflight. Once established, such performance indicators need to be correlated with operational performance. Individual differences in sleep need and circadian preference, phase shifting ability and period have been documented in ground-based studies. Individual differences in response to sleep loss and circadian misalignment have also been documented and are presumed to be associated with genetic polymorphisms. No studies have systematically reported individual differences in sleep or circadian-related outcomes during spaceflight. More work is needed in this area in order to identify genetic or phenotypic biomarkers that predict resilience or vulnerability to sleep loss in order to personalize countermeasure strategies and mitigate performance impairment during spaceflight. Two laboratory and field investigations specific to this topic are currently ongoing; additional efforts, including an effort to mine existing biological data from spaceflight relative to sleep and circadian outcomes, are planned. Sex differences in sleep need and circadian period and phase have been reported in ground-based studies. The impact of these sex differences on performance is unclear. Sex differences in sleep need and circadian rhythms have not been systematically studied during spaceflight, presumably due to the small number of women that have flown in space. More research is needed in this area to evaluate whether any of the observed sex differences in physiology lead to altered performance in spaceflight and on the ground.

Flynn-Evans, Erin↗
DOE OSTI2018

Policy, Permit, Perform: Using City Benchmarking Data and Building Construction Permit History to Identify Energy Performance Improvements

The City of Portland (City) and Multnomah County 2015 Climate Action Plan targets a 40 percent reduction in carbon emissions below 1990 levels by 2030. To reach this goal, the plan includes a key objective to reduce the total energy use of existing buildings by 25 percent. Buildings are responsible for one-half of carbon emissions in Portland, Oregon, U.S., and improving their performance is critical to achieving the City’s climate goals. As part of its climate action planning, the City passed an ordinance on April 22, 2015, requiring commercial buildings 20,000 ft2 and larger to benchmark and disclose annual energy performance metrics through ENERGY STAR® Portfolio Manager® (ESPM), including greenhouse gas emissions, building energy use intensity (EUI) and ENERGY STAR scores. Twenty-five cities in the U.S. have adopted similar policies requiring energy benchmarking. These policies are generating a treasure trove of data about the drivers of building performance, but practitioners are only beginning to link benchmarking data to other datasets to produce actionable insight into improving building performance. In 2016, the City received a financial assistance award from the U.S. Department of Energy (DOE) through the Cities Leading through Energy Analysis and Planning project (Award Number DE-0007737). Portland’s project evaluated the application of the DOE Building Energy Asset Score Tool and rating system developed by Pacific Northwest National Laboratory (PNNL) to commercial buildings that report to the City. The City used building construction permit history to develop Asset Scores for a set of office buildings that reported energy performance for 2015 – the first year the City mandated energy benchmarking and reporting for commercial buildings 50,000 ft2 and larger. The primary project objective was to link energy benchmarking data and commercial building permit data to analyze commercial buildings systems that present the best, specific opportunities to improve energy performance. Integrating these datasets from two City departments, the Bureau of Development Services and the Bureau of Planning and Sustainability, could expand Portland’s use of analytics to inform climate policy decisions and strategic targeting of financial incentives for building owners and managers who are best positioned to improve the performance of their buildings. The project began with the commercial benchmarking data received for 340 commercial buildings that submitted calendar year 2015 energy reports. The City retained Research Into Action and Energy 350 (RIA team) to evaluate this self-reported benchmarking data. Based on a survey of 53 building managers, the RIA team found that most respondents used the City’s compliance tools, including a How-to Guide and Help Desk, and found access to utility data to be straightforward. However, almost one-third of the respondents encountered challenges when estimating the gross floor area (GFA), and some experienced confusion regarding the input of property use types and details into ESPM. Based on the RIA team’s recommendations, the City refined its How-to Guide, created online answers to frequently asked questions, and hosted office hour sessions to help building managers accurately characterize their building’s physical characteristics and operations in ESPM. Concurrently with the RIA team’s evaluation, the City worked with PNNL to estimate DOE Asset Score Preview (Preview) scores for 181 of the 340 commercial buildings that submitted ESPM reports to the City. Asset Score Tool is a web-based tool developed by PNNL for DOE to evaluate and rate the as-built energy efficiency of a building. Asset Score Tool’s full analysis capability runs a detailed energy model based on building geometry, age, envelope, lighting and mechanical systems using standard operating assumptions to give the building a score, similar to a miles per gallon rating for a car. Preview, a simplified deployment of the full Asset Score Tool, provides a preliminary score range using only seven data points, six of which are readily available in the ESPM benchmarking data. Analysis of the combined Preview scores and the operational ESPM scores identified buildings with a high potential for energy improvements through either retro-commissioning and retuning of existing operations or capital investments into building systems. The City contracted Whole Building Solutions (WBS) to mine building permit history and develop full Asset Scores for a subset of 26 office buildings that were eligible for both ENERGY STAR scores and Asset Scores. WBS researched permits and building plans to identify mechanical, lighting and envelope systems and other building characteristics necessary to complete Asset Scores. This historical data was recorded using the web-based Asset Score Tool, which evaluates the as designed energy efficiency of existing buildings. Much of this City information was available online after 2010; however, WBS had to search through microfiche files and the City’s database to obtain permit and plan history prior to 2010. Although building geometry, mechanical system type and basic envelope information was consistently identifiable, WBS found permit data gaps that prevented complete and accurate Asset Scores for most of the office buildings. If the City decides to develop Asset Scores based on building permit information in the future, WBS recommended requiring lighting plans, control diagrams and as-built drawings that reflect how the buildings are actually constructed. In addition, WBS recommended uploading plans and permits from microfiche storage into a more comprehensive, searchable database consistent with the City’s current online permit information. To identify the actual lighting types, WBS and City staff conducted site visits of all 26 office buildings. As part of managing the flow of ESPM and Asset Score data for the project, Earth Advantage, a sub-recipient of the Cities-LEAP award, created a DOE Standard Energy Efficiency Data (SEED) (USDOE 2018) instance for the City and used Asset Score’s Application Programming Interface (API) (Asset Score 2018) capabilities to develop two new features in the SEED Platform™: (1) automated Asset Score Preview ranges calculated based on benchmarking data and the City’s estimate of number of building floors; and (2) storage of full Asset Score information. Leveraging the construction permit history and site visit information for the 26 office buildings, The Cadmus Group (Cadmus) analyzed benchmarking data and Asset Score information to identify opportunities and drivers to improve performance. Correlation and regression analysis of the combined dataset was completed to determine whether building operations or specific systems – mechanical, lighting or envelope – present the best opportunities to improve energy performance. The central outcome of this project was Cadmus’ detailed analysis of the richer dataset that was created by combining the City of Portland’s commercial building permit activity and building-specific energy performance information based on metrics from ESPM and Asset Score Tool. Some of their findings collaborate with well-known facts, such as buildings with less efficient lighting systems are good targets for efficiency improvements through lighting upgrades. This analysis also helped identify that individual heating, ventilation, and air conditioning (HVAC) equipment efficiency does not necessarily correlate with building performance. However, overall HVAC system performance, as measured with the Total System Performance Ratio (TSPR) metric, is a better indicator of building performance (Goel et al. 2014). As a metric, TSPR considers whole system performance that includes equipment efficiency and system controls, such as resets for static pressure and chilled water setpoints, which can be more effective and financially feasible options for improving building performance. Given the high cost associated with envelope retrofits, opportunities to upgrade envelope insulation are likely to be limited. However, buildings with single-pane and non-thermal break windows are good candidates for window replacements, such as upgrading to double-pane windows with thermal breaks. This research project determined that developing Asset Scores based on site visits is preferable to mining City building permit data. In addition, the use of benchmarking data to develop Asset Score Preview Scores is recommended as a first step in engaging building managers to develop full Asset Scores. Asset Score Tool outputs can help building managers quickly identify opportunities for improvement and then work with the utility incentive administrator and energy service providers to develop their retrofit plan. Where full Asset Scores are available, analysis using a quadrant matrix with ENERGY STAR scores is recommended to identify buildings that present the best opportunities for operational-behavioral improvements versus ones with opportunities for upgrades to physical building systems. This workflow and the tools will help cities, utility incentive administrators, building portfolio managers and energy service providers develop energy programs and strategies for screening and analysis of buildings with the greatest energy savings potential. This report describes the project approach and research results to inform future coordination of ESPM and Asset Score data, including the use of benchmarking data to generate Preview scores. Given better awareness of energy performance, building managers can make more informed decisions to reduce energy consumption and carbon emissions. With cities, states and jurisdictions adopting benchmarking and auditing ordinances, there are substantial amounts of data submitted to cities that could be used to inform investments in energy efficiency.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Seafloor Seismic Noise Patterns Across the Pacific Basin

Seismic hazard monitoring and global tomography efforts are improved by recording signals at a variety of distances and azimuths to maximize subsurface sampling. Although seismic networks provide good to excellent coverage on land, seafloor stations are still sparse. Inclusion of ocean-based data would greatly improve the global coverage of seismic networks, but the use of seafloor seismic data to complement land-based detection and characterization of events is complicated by the generally much higher ambient noise level in the ocean compared to that observed on land. This noise is driven primarily by sea surface waves and tides, but how seismic noise levels vary with location in the oceans is not well described. Here, in this work, we analyze the relationship between ocean surface wave height and seismic noise in the 0.4–4 Hz frequency band at ocean-bottom seismometer deployments across the Pacific basin. We find that a noise-to-responsiveness ratio (NRR)—the median noise level at a station divided by its sea surface wave height responsiveness—correlates negatively with detection success for large teleseismic earthquakes. Stations that are close to land, with relatively shallow ocean and low wind speed, often have lower NRR than open-ocean stations, but the connection between geographic location and earthquake detection success is imperfect.

58 GEOSCIENCES↗
DOE OSTI2020

Mining a human transcriptome database for chemical modulators of NRF2

Nuclear factor erythroid-2 related factor 2 (NRF2) encoded by the NFE2L2 gene is a transcription factor critical for protecting cells from chemically-induced oxidative stress. We developed computational procedures to identify chemical modulators of NRF2 in a large database of human microarray data. A gene expression biomarker was built from statistically-filtered gene lists derived from microarray experiments in primary human hepatocytes and cancer cell lines exposed to NRF2-activating chemicals (oltipraz, sulforaphane, CDDOIm) or in which the NRF2 suppressor Keap1 was knocked down by siRNA. Directionally consistent biomarker genes were further filtered for those dependent on NRF2 using a microarray dataset from cells after NFE2L2 siRNA knockdown. The resulting 143-gene biomarker was evaluated as a predictive tool using the correlation-based Running Fisher algorithm. Using 59 gene expression comparisons from chemically-treated cells with known NRF2 activating potential, the biomarker gave a balanced accuracy of 93%. The biomarker was comprised of many well-known NRF2 target genes (AKR1B10, AKR1C1, NQO1, TXNRD1, SRXN1, GCLC, GCLM), 69% of which were found to be bound directly by NRF2 using ChIP-Seq. NRF2 activity was assessed across ~9840 microarray comparisons from ~1460 studies examining the effects of ~2260 chemicals in human cell lines. A total of 260 and 43 chemicals were found to activate or suppress NRF2, respectively, most of which have not been previously reported to modulate NRF2 activity. Using a NRF2-responsive reporter gene in HepG2 cells, we confirmed the activity of a set of chemicals predicted using the biomarker. The biomarker will be useful for future gene expression screening studies of environmentally-relevant chemicals.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Comparative genomics reveals phylogenetic distribution patterns of secondary metabolites in Amycolatopsis species

Background: Genome mining tools have enabled us to predict biosynthetic gene clusters that might encode compounds with valuable functions for industrial and medical applications. With the continuously increasing number of genomes sequenced, we are confronted with an overwhelming number of predicted clusters. In order to guide the effective prioritization of biosynthetic gene clusters towards finding the most promising compounds, knowledge about diversity, phylogenetic relationships and distribution patterns of biosynthetic gene clusters is necessary. Results: Here, we provide a comprehensive analysis of the model actinobacterial genus Amycolatopsis and its potential for the production of secondary metabolites. A phylogenetic characterization, together with a pan-genome analysis showed that within this highly diverse genus, four major lineages could be distinguished which differed in their potential to produce secondary metabolites. Furthermore, we were able to distinguish gene cluster families whose distribution correlated with phylogeny, indicating that vertical gene transfer plays a major role in the evolution of secondary metabolite gene clusters. Still, the vast majority of the diverse biosynthetic gene clusters were derived from clusters unique to the genus, and also unique in comparison to a database of known compounds. Our study on the locations of biosynthetic gene clusters in the genomes of Amycolatopsis’ strains showed that clusters acquired by horizontal gene transfer tend to be incorporated into non-conserved regions of the genome thereby allowing us to distinguish core and hypervariable regions in Amycolatopsis genomes. Conclusions: Using a comparative genomics approach, it was possible to determine the potential of the genus Amycolatopsis to produce a huge diversity of secondary metabolites. Furthermore, the analysis demonstrates that horizontal and vertical gene transfer play an important role in the acquisition and maintenance of valuable secondary metabolites. Our results cast light on the interconnections between secondary metabolite gene clusters and provide a way to prioritize biosynthetic pathways in the search and discovery of novel compounds.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Two-Fluid and Discrete Element Modeling of a Parallel Plate Fluidized Bed Heat Exchanger for Concentrating Solar Power

A novel high-temperature particle solar receiver is developed using a light trapping planar cavity configuration. As particles fall through the cavity, the concentrated solar radiation warms the boundaries of the receiver and in turn heats the particles. Particles flow through the system, forming a fluidized bed at the lower section, leaving the system from the bottom at a constant flowrate. Air is introduced to the system as the fluidizing medium to improve particle heat transfer and mixing. A laboratory scale cavity receiver is built by collaborators at the Colorado School of Mines and their data are used for model validation. In this experimental setup, near IR quartz lamp is used to provide flux to the vertical wall of the heat exchanger. The system is modeled using the discrete element method and a continuum two-fluid method. The computational model matches the experimental system size and the particle size distribution is assumed monodisperse. A new continuum conduction model that accounts for the effects of solid concentration is implemented, and the heat flux boundary condition matches the experimental setup. Radiative heat transfer is estimated using a widely used correlation during the post-processing step to determine an overall heat transfer coefficient. The model is validated against testing data and achieves less than 30% discrepancy and a heat transfer coefficient greater than 1000 W/m2 K.

concentrating solar power↗
NASA NTRS1991

Flight test and numerical simulation of transonic flow around YAV-8B Harrier II wing

A computational fluid dynamics (CFD) method is used to study the aerodynamics of the YAV-8B Harrier II wing in the transonic region. A numerical procedure is developed to compute the flow field around the complicated wing-pylon-fairing geometry. The surface definition of the wing and pylons were obtained from direct measurement using theodolite triangulation. A thin-layer Navier-Stokes code with the Chimera technique is used to compute flow solutions. The computed pressure distributions at several span stations are compared with flight test data and show good agreement. Computed results are correlated with flight test data that show the flow is severely separated in the vicinity of the wing-pylon junction. Analysis shows that shock waves are induced by pylon swaybrace fairings, that the flow separation is much stronger at the outboard pylon and that the separation is caused mainly by the crossflow passing the geometry of wing-pylon junction.

Gea, Lie-Mine↗