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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 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Uncertainty Modeling for Robustness Analysis of Control Upset Prevention and Recovery Systems

Formal robustness analysis of aircraft control upset prevention and recovery systems could play an important role in their validation and ultimate certification. Such systems (developed for failure detection, identification, and reconfiguration, as well as upset recovery) need to be evaluated over broad regions of the flight envelope and under extreme flight conditions, and should include various sources of uncertainty. However, formulation of linear fractional transformation (LFT) models for representing system uncertainty can be very difficult for complex parameter-dependent systems. This paper describes a preliminary LFT modeling software tool which uses a matrix-based computational approach that can be directly applied to parametric uncertainty problems involving multivariate matrix polynomial dependencies. Several examples are presented (including an F-16 at an extreme flight condition, a missile model, and a generic example with numerous crossproduct terms), and comparisons are given with other LFT modeling tools that are currently available. The LFT modeling method and preliminary software tool presented in this paper are shown to compare favorably with these methods.

Belcastro, Christine M.↗
NASA NTRS2011

Contributions of Precipitation and Soil Moisture Observations to the Skill of Soil Moisture Estimates in a Land Data Assimilation System

The contributions of precipitation and soil moisture observations to the skill of soil moisture estimates from a land data assimilation system are assessed. Relative to baseline estimates from the Modern Era Retrospective-analysis for Research and Applications (MERRA), the study investigates soil moisture skill derived from (i) model forcing corrections based on large-scale, gauge- and satellite-based precipitation observations and (ii) assimilation of surface soil moisture retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E). Soil moisture skill is measured against in situ observations in the continental United States at 44 single-profile sites within the Soil Climate Analysis Network (SCAN) for which skillful AMSR-E retrievals are available and at four CalVal watersheds with high-quality distributed sensor networks that measure soil moisture at the scale of land model and satellite estimates. The average skill (in terms of the anomaly time series correlation coefficient R) of AMSR-E retrievals is R=0.39 versus SCAN and R=0.53 versus CalVal measurements. The skill of MERRA surface and root-zone soil moisture is R=0.42 and R=0.46, respectively, versus SCAN measurements, and MERRA surface moisture skill is R=0.56 versus CalVal measurements. Adding information from either precipitation observations or soil moisture retrievals increases surface soil moisture skill levels by IDDeltaR=0.06-0.08, and root zone soil moisture skill levels by DeltaR=0.05-0.07. Adding information from both sources increases surface soil moisture skill levels by DeltaR=0.13, and root zone soil moisture skill by DeltaR=0.11, demonstrating that precipitation corrections and assimilation of satellite soil moisture retrievals contribute similar and largely independent amounts of information.

Reichle, Rolf H.↗
NASA NTRSDate not supplied

Identification of Presolar Grains in Quick Look Samples of Asteroid Bennu Returned By the OSIRIS-REx Mission

The OSIRIS-REx spacecraft returned regolith sample from the B-type asteroid Bennu on September 24, 2023. The first samples to be analyzed, termed the Quick Look samples, are comprised of material collected from the avionics deck of the OSIRIS-REx sample canister. Initial mineralogical analysis of the Quick Look sample revealed a diversity of components, including hydrated and anhydrous silicates, sulfides, magnetite, phosphates, and abundant organic matter. Each constituent has their own origin and history, and their study can inform us of the history of Bennu and its parent asteroid. The analysis plan for asteroid Bennu samples is guided by hypotheses that address specific periods of Bennu’s history, from the pre-accretionary epoch to the OSIRIS-REx epoch. The pre-accretionary epoch studies aim to characterize materials that predate the formation of the solar system. One hypothesis that falls under this epoch is that Bennu contains presolar materials derived from diverse sources. These presolar materials include stardust grains that condensed from the outflows of evolved stars or the ejecta of novae and supernovae (SN), as well as organic matter that formed in the interstellar medium. Here we report the identification of C-rich presolar stardust grains in the Bennu Quick Look sample. Isotopically anomalous organic matter in this sample is reported in.

A N Nguyen↗
DOE OSTI2025

Data-driven Community-centered Resilient Assessment and Planning Toolkit for Nexus of Energy and Water (DCRAPT-NEW)

Urban areas, including Detroit and Pittsburgh, have suffered significant dual outages of the electrical and water infrastructure in the past decade due, in part, to the increasing number of extreme weather events. With increasing temperatures and rainfall intensity, these regions need to prepare for increasing extreme events through community-based energy and water resilience analysis, planning, and enhancement. This project developed a suite of open-source, open-access, community-centered, data-driven assessment and distributed energy resource (DER) and planning tools for energy and water resilience enhancement in urban areas. Through establishing a multi-level community awareness and engagement mechanism and a comprehensive collection of power outage and flooding data, an innovative group of community energy and water resilience assessment and planning tools have been developed for a wide range of users with differing and variable sets of data available to them. The developed tools include (1) DOE EAGLE-I data-driven, deep-learning assisted resilience assessment and DER planning tools at the county level with socioeconomic factors incorporated; (2) Utility annual power outage data-driven tools for long term resilience assessment and DER planning and 15-min power outage data-driven tools for short term resilience assessment and planning; (3) Detailed engineering tools for energy and water systems resilience assessment and planning when the system topology and component fragility curves are available; (4) Alternative Resiliency Metric Calculation that extracts and separates outage and restoration processes; and (5) Co-optimization tools that evaluate the resilience of the power and sewage system and allow users to conduct joint planning with energy and wastewater systems. The developed tools provide planners, decision-makers, and stakeholders with powerful capabilities to systematically evaluate system/community resilience and optimal and actionable guidance for enhancing resilience while prioritizing DER investments. The tools have been used and validated in Detroit and Pittsburgh and can be used in other areas of the nation. In addition, this project will (1) advance the knowledge and applications of machine-learning methods in analyzing and fusing different layers of information and generating meaningful data points such as generating rare weather events; (2) significantly improve the energy and water resilience of the identified communities in Detroit and Pittsburgh and prepare for more frequent and severe weather conditions; (3) help communities assess extreme weather event impacts and address short-term and long-term resilience-related issues The developed tools have been made public via GitHub and demonstrated to community stakeholders and utility companies via the two annual workshops and numerous community engagement meetings. The project outcomes are also disseminated through publications in various journals and conference proceedings, and presentations at top conferences.

13 HYDRO ENERGY↗
NASA NTRS1984

Particle excitation, airglow and H2 vibrational disequilibrium in the atmosphere of Jupiter

The extreme ultraviolet EUV emission produced by particle excitation of the hydrogen atmospheres of Jupiter and Saturn is examined using model calculations to determine the nature of the energy deposition process and the effect of such processes on atmospheric structure. Tasks ranging from examination of phenomenologically related processes on Saturn and Titan to analysis of experimental laboratory data required to allow accurate modeling of emissions from hydrogenic atmospheres are investigated. An explanation of the hydrogen H Ly(alpha) bulge in Jupiter's emission from the equatorial region is presented. It is proposed that Saturn, rather then Titan is the major source of the extended hydrogen cloud. The atomic hydrogen detected at the rings of Saturn may originate predominantly from the same source. A cross calibration is obtained between the Pioneer 10 EUV photometer and the Voyager EUV spectrometers, thus providing a direct measure of the temporal morphology of Jupiter between a minimum and a maximum in solar activity. Atomic and molecular data required for the research program are analyzed. An extrapolation of conditions in the upper atmospheres of Jupiter and Saturn produces a predicted condition at Uranus in terms of excitation and hydrogen escape rates that may be observed at Voyager-Uranus encounter.

Shemansky, D. E.↗
DOE OSTI2022

Data and scripts associated with the manuscript "Encoding Diel Hysteresis and the Birch Effect in Dryland Soil Respiration Models through Knowledge-Guided Deep Learning"

This package contains the data and scripts used in "Encoding Diel Hysteresis and the Birch Effect in Dryland Soil Respiration Models through Knowledge-Guided Deep Learning" (Jiang et al., 2022). The data.zip file contains the flux tower and automated chamber observations used for developing the deep learning model for modeling soil respiration. The scripts.zip file contains the Jupyter notebooks and python scripts for preprocessing the data, training the deep learning models, and postprocessing the results. The src.zip contains the source code for training the deep learning model, performing mutual information analysis, and plotting functions. The trained_models.zip contains multiple folders used for hosting the trained deep-learning models and the associated soil respiration predictions. The whole process is performed using python. We include the REAMD.md to document the python package requirements.Soil respiration in dryland ecosystems is challenging to model due to its complex interactions with environmental drivers. Knowledge-guided deep learning provides a much more effective means of accurately representing these complex interactions than traditional Q10-based models. Mutual information analysis revealed that future soil temperature shares more information with soil respiration than past soil temperature, consistent with their clockwise diel hysteresis. We explicitly encoded diel hysteresis, soil drying, and soil rewetting effects on soil respiration dynamics in a newly designed Long Short Term Memory (LSTM) model. The model takes both past and future environmental drivers as inputs to predict soil respiration. The new LSTM model substantially outperformed three Q10-based models and the Community Land Model when reproducing the observed soil respiration dynamics in a semi-arid ecosystem. The new LSTM model clearly demonstrated its superiority for temporally extrapolating soil respiration dynamics, such that the resulting correlation with observational data is up to 0.7 while the correlations of both Q10-based models and the Community Land Model (CLM) are less than 0.4. Our results underscore the high potential for knowledge-guided deep learning to replace Q10-based soil respiration modules in Earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Emerging Energy Market Analysis Initiative, Methodological Framework

Planning and operations of the electric power sector are undergoing radical changes. Climate change mitigation efforts have forced rapid changes to the technology mix. Technologies like wind and solar have experienced rapid growth, while investment in fossil sources has peaked or is declining. These foundational changes are forcing changes to energy systems. Demand-side adoption of electrified technologies, including electric vehicles, is changing load profiles and opening up new avenues for consumer participation in the power systems. The implications of an evolving power system pertain to more than environmental and technical dimensions. Changes to the generation mix and its consequent upstream and downstream impacts such as fuel production have significant and highly concentrated consequences on economies and employment. Shifts towards distributed (or decentralized) generating assets offer the potential to reshape economic and employment opportunities associated with the energy sector across space and socioeconomic groups. The Emerging Energy Market Analysis (EMA) initiative aims to identify sustainable, regionally acceptable, and high-value energy solutions that are secure and equitable. Unlike short-term, least-cost choices that can narrowly account for traditional options, EMA’s focus on emerging energy markets recognizes that new or adapted practices and technologies can alter the frontier of solutions and advance a community’s social, economic, and natural pathways. Such change requires a more comprehensive analysis of societal input, resources, capabilities, and infrastructure. These considerations lay the foundation for community decision-making models that are responsive to community values as well as the history and drivers. The result is a community-based decision and engagement model that will be valuable to decisionmakers and developers of advanced and emerging energy solutions, seeking a social license to operate prior to project development.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

Conus-Wide Mapping of Water Availability and Cost

Background: New demands for water can be satisfied through a variety of source options. In some basins surface and/or groundwater may be available through permitting with the state water management agency (termed unappropriated water), alternatively water might be purchased and transferred out of its current use to another (termed appropriated water), or non-traditional water sources can be captured and treated (e.g., wastewater). The relative availability and cost of each source are key factors in the development decision. Unfortunately, these measures are location dependent with no consistent or comparable set of data available for evaluating competing water sources. With the help of water managers, water availability was mapped for over 2500 watersheds throughout CONUS. These values are unique in that they account for institutional factors (i.e., water rights, compacts, treaties, instream flows) that locally limit water use. Five water sources were individually examined, including unappropriated surface water, unappropriated groundwater, appropriated water, municipal wastewater and brackish groundwater. Associated costs to acquire, convey and treat the water, as necessary, for each of the five sources were estimated. These metrics were developed to support regional water planning and policy analysis with initial application to electric transmission planning in the western US. Problem: These data were originally developed in 20141 and updated in 20182. New datasets on which these data depend have been published. As such, there is need to update the water availability and cost data. There is also need to map this same data to a county resolution and to publish the data in a more accessible place. Organization of the dataset: Data are organized according to five primary water sources: • Unappropriated fresh surface water • Unappropriated fresh groundwater • Appropriated water • Recycled wastewater, and • Brackish water

Tidwell, Vincent [Pacific Northwest National Labor↗
NASA NTRS2022

A Modular Framework for Integrating and Visualizing Telemetry for Mars 2020 Rover Mechanism Operations

The analysis of mechanism telemetry requires a wide variety of tools to quickly and effectively assess spacecraft state, capture long-term trends in system performance, and identify and track anomalous events. Such analysis often requires spacecraft telemetry to first be transformed into derived fields and aggregated statistics before operators can begin their analysis. In past missions, aspects of this process have been automated, but operators were expected to use their own tools and procedures to understand and visualize the data, which led to redundant and inconsistent tools and processes. The Mech Data Tools Python library (MDT) was developed to provide a flexible, unified tool set for operators to extract and analyze mechanism telemetry over the life of the Mars 2020 surface mission. MDT consists of a set of configurable components that implement standard interfaces for ingesting input and producing output. Components can be chained together to form a data processing pipeline. Data are ingested from several sources within the greater Mars 2020 cloud infrastructure and stored in pandas DataFrames, which allows users to leverage the data manipulation capabilities present within the widely-used pandas library. Visualization capabilities are provided through the Plotly library, which generates interactive plots for users to interpret. Following the beginning of Mars 2020 surface operations, usage of MDT has spread to all mechanism-focused subsystems and has demonstrated great utility in analyzing early surface activities. This paper describes MDT’s evolution from heritage mechanism telemetry tools, the critical architecture decisions and challenges faced over MDT’s two years of development, and current applications of MDT in support of mechanism operations.

Wolsieffer, Ben↗
DOE OSTI2021

The incidence of X-ray selected AGN in nearby galaxies

ABSTRACT We present the identification and analysis of an unbiased sample of active galactic nuclei (AGN) that lie within the local galaxy population. Using the MPA-JHU catalogue (based on SDSS DR8) and 3XMM DR7 we define a parent sample of 25 949 local galaxies (z ≤ 0.33). After confirming that there was strictly no AGN light contaminating stellar mass and star-formation rate calculations, we identified 917 galaxies with central, excess X-ray emission likely originating from an AGN. We analysed their optical emission lines using the BPT diagnostic and confirmed that such techniques are more effective at reliably identifying sources as AGN in higher mass galaxies: rising from 30 per cent agreement in the lowest mass bin to 93 per cent in the highest. We then calculated the growth rates of the black holes powering these AGN in terms of their specific accretion rates (∝LX/M*). Our sample exhibits a wide range of accretion rates, with the majority accreting at rates $\le 0.5\ \mathrm{ per \, cent}$ of their Eddington luminosity. Finally, we used our sample to calculate the incidence of AGN as a function of stellar mass and redshift. After correcting for the varying sensitivity of 3XMM, we split the galaxy sample by stellar mass and redshift and investigated the AGN fraction as a function of X-ray luminosity and specific black hole accretion rate. From this we found the fraction of galaxies hosting AGN above a fixed specific accretion rate limit of 10−3.5 is constant (at $\approx 1\ \mathrm{ per \, cent}$) over stellar masses of 8 < log M*/M⊙ < 12 and increases (from $\approx 1\ \mathrm{ per \, cent}$ to 10 per cent) with redshift.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Understanding Biomass Burning Aerosol via Integrated Analyses of Aerosol Mass Spectrometry Data from DOE Campaigns and ACRF Long Term Measurements

Biomass burning (BB) is one of the largest sources of aerosol particles in the atmosphere and a significant source of trace gases important to atmospheric chemistry. Organic aerosols from biomass burning sources (BBOA) are an important but poorly characterized component of the earth’s climate system. BBOA composition and life cycle processes are associated with enormous complexities and contribute significantly to model uncertainties. Hence it is important to improve the ability to simulate the concentration and properties (chemical, optical, and hygroscopic) of BBOA and the atmospheric aging processes that affect these properties. Here, we propose to analyze ambient data collected from the DOE Biomass Burning Observation Project (BBOP) field campaign and from a long term measurement site of the Atmospheric Radiation Measurement (ARM) program – the Southern Great Plains (SGP), where transported BB plumes and elevated BBOA episodes were frequently observed. This project is aimed at 1) gaining detailed, quantitative understanding of the chemical and physical properties of BBOA and their chemical changes during atmospheric aging via advanced analysis of aerosol measurement data acquired at the summit of Mt. Bachelor (MBO) during the BBOP campaign and through integration with concurrent measurements of trace gases, particle properties, and meteorological conditions performed at MBO and from the G-1 aircraft; 2) gaining better understanding of BBOA properties and its roles in radiative forcing via analysis of routine data from the ARM long-term measurement sites; and 3) integrating the results from 1) and 2) into a global database of aerosol mass spectrometry and collaborate with modelers on evaluating and improving numerical model performance as part of the ASR program.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Validation of OpenPronghorn for Periodic Hill Flow Separation

OpenPronghorn is an open-source, MOOSE-based thermal-hydraulics simulation tool used for advanced reactor analysis. As an open-source code, it offers a transparent framework for validating governing equations, assumptions, and numerical methods against established benchmarks. This study evaluates OpenPronghorn's RANS turbulence model against the ERCOFTAC Case 81 periodic hill benchmark, a standard test case for separated turbulent flow featuring curved-wall separation, recirculation, shear-layer development, and reattachment. Streamwise velocity profiles predicted by OpenPronghorn were compared to reference LES data at multiple x/h locations. Results show that OpenPronghorn captures the overall trend of the velocity profiles, but the largest discrepancies occur in the separated-flow region, where turbulence is highly anisotropic and strongly affected by adverse pressure gradients and wall curvature—conditions that are inherently difficult for standard RANS models to resolve. Future work will test alternative k-e model variants and correction terms to improve prediction accuracy in this region.

42 - ENGINEERING↗
DOE OSTI2021

Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data With Spatial Information

Efficient processing of large-scale time series data is an intricate problem in machine learning. Conventional sensor signal processing pipelines with hand engineered feature extraction often involve huge computational cost with high dimensional data. Deep recurrent neural networks have shown promise in automated feature learning for improved time-series processing. However, generic deep recurrent models grow in scale and depth with increased complexity of the data. This is particularly challenging in presence of high dimensional data with temporal and spatial characteristics. Consequently, this work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to efficiently process complex multi-dimensional time series data with spatial information. Here, the cellular recurrent architecture in the proposed model allows for location-aware synchronous processing of time series data from spatially distributed sensor signal sources. Extensive trainable parameter sharing due to cellularity in the proposed architecture ensures efficiency in the use of recurrent processing units with high-dimensional inputs. This study also investigates the versatility of the proposed DCRNN model for classification of multi-class time series data from different application domains. Consequently, the proposed DCRNN architecture is evaluated using two time-series datasets: a multichannel scalp EEG dataset for seizure detection, and a machine fault detection dataset obtained in-house. The results suggest that the proposed architecture achieves state-of-the-art performance while utilizing substantially less trainable parameters when compared to comparable methods in the literature.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Time Delay of Wide Area Damping Control in Urban Power Grid: Model-Based Analysis and Data-Driven Compensation

Due to the rapid development of economies, large urban cities consume an increasing amount of energy and have a higher requirement for power quality. Voltage source converter based high voltage direct current (VSC-HVDC) is a promising device to transmit clean power from remote regions to urban power systems, while also providing wide area damping control (WADC) for frequency stabilization. However, the time-delay naturally existing in the VSC-HVDC system may degrade the performance of WADC and even result in instability. To address this issue, this paper develops a time-delay correction control strategy for VSC-HVDC damping control in urban power grids. First, a small signal model of WADC is built to analyze the negative impacts of time delay. Then, a data-driven approach is proposed to compensate for the inherent time delay in VSC-HVDC damping control. The extensive training data will be generated under various disturbances. After offline training, the long short-term memory network (LSTM) can be implemented online to predict the actual frequency deviation based on real-time measurements. Finally, the proposed method is validated through MATLAB-Simulink in a two-area four-machine system. The results indicate that the data-driven compensation has a strong generalization ability for random delay time constants and can improve the performance of WADC significantly.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Implications of an emission trading scheme for India’s net-zero strategy: a modelling-based assessment

To help meet its near-term NDC goals and long-term net-zero 2070 target, the Government of India has planned to establish a Carbon Credit Trading Scheme (CCTS), i.e. a domestic emission trading scheme (ETS). An ETS is an inherently cost-effective policy instrument for emission reduction, providing the greatest flexibility to reduce emissions from within and across sectors. An effective ETS requires design features that consider country-specific challenges and reflect its role within the larger policy package to achieve long-term emission reduction. Within the Indian context and in this study we therefore investigate—(i) what might be the role of the ETS in achieving India’s long-term mitigation targets? (ii) How might the various sectors interact under an emissions cap? (iii) How might the ETS interact with existing energy and climate policies? We do this analysis by running four main scenarios using the integrated assessment model GCAM (v6.0), adapted to India-specific assumptions and expectations. These scenarios are—(i) NZ (net-zero), (ii) NZ + ETS, (iii) NZ + CC (command and control), and (iv) NZ + RPO (renewables purchase obligations) + ETS. The NZ scenario assumes India’s near-term and long-term climate commitments of net zero by 2070. Scenarios with ETS (ii) and (iv) apply an emissions cap on four sectors—electricity, iron and steel, cement, and fertilizer. The scenario with CC applies a homogenous emission cap on each of the chosen sectors but does not allow cross-sectoral trading. The last scenario includes renewables purchase obligations (RPOs along with an ETS. We show that under a specific ETS emissions cap: (i) the electricity sector emerges as the largest source of cost-effective greenhouse gas (GHG) reduction options; (ii) ETS with trading across sectors is around 24% more cost-effective than ETS with trading only within sectors, (iii) RPOs can be complementary to an ETS although the impact of RPOs on GHG reductions in the electricity sector would need to be considered when setting the level of the ETS cap (or emissions intensity targets) or the RPO targets to avoid low carbon prices, and (iv) the direction and volume of financial transfers across sectors depends on allocation targets set by the government. Based on these results we provide design recommendations for India’s ETS.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Understanding Dynamics and Thermodynamics of ENSO and Its Complexity Simulated by E3SM and Other Climate Models

Despite the seeming success of most state-of-the-art climate models in simulating the El Niño-Southern Oscillation (ENSO), there is strong evidence that models achieve realistic levels of ENSO activity do so often for wrong reasons. This is owing to an often occurred near cancelation of large errors in terms of contributions to ENSO growth rate from coupled dynamic and thermodynamic feedback processes. Climate models remain deficient in simulating the observed ENSO’s spatial and temporal complexity that involves interplays of coupled dynamic and thermodynamic feedbacks, interactions across multiple scales, nonlinear processes in the tropical atmosphere and ocean system, biases in mean sate and physical processes, and influences external to equatorial Pacific coupled ENSO dynamics. Our proposed research aims at advancing predictive and process-level understandings of ENSO simulated in E3SM and other climate models under current and future climate conditions with two main objectives: (i) better understanding the aforementioned broad range interactive processes and sources that control fundamental properties of ENSO in E3SM and CIMP6 outputs as well as in observational (reanalysis) data sets, using a hierarchical of coupled dynamical frameworks consisting of theoretical analysis, intermediate complexity modeling, and coupled dynamic diagnostics; (ii) to use this understanding to explore pathways towards improving E3SM’s capability of simulating ENSO and its complexity. More specifically, we will focus on four main thrusts of research: (1) ENSO’s dynamic and thermodynamic feedbacks; (2) the across-scale interactions of ENSO with annual cycle and MJO/WWB/TIW (Madden Julian Oscillation/Westerly Wind Burst/Tropical Instability Wave) activity; (3) key nonlinear processes of ENSO involving atmospheric convective thresholds, nonlinear ocean dynamic heating, and thermocline outcropping; and (4) the impacts of climate mean-state biases/changes and perturbed physical processes on simulated ENSO and its complexity.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1983

Telescope-optical system performance analysis for the Cryogenic Limb Array Etalon Spectrometer (CLAES) on the Upper Atmospheric Research Satellite

A first-order performance analysis of the CLAES telescope-optical system is presented. The experiment involves the passive measurement of earth-limb radiance over a 10-60 km tangent altitude range, and is based on a solid Fabry-Perot spectrometer which provides spectral resolution of 0.25/cm for atmospheric emission spectroscopy over the 3.5-12 micron IR range. The optical system is required to provide a high degree of off-axis rejection and stray-light control, primarily to suppress intense emission from the earth surface. The astigmatism and other geometric aberrations are corrected by a secondary mirror which produces an excellent image of the primary one, allowing for location of a diffraction control or Lyot stop. The off-axis scattering performance of the telescope is examined in terms of the mirror scatter coefficient and point source rejection ratio. A mirror bidirectional reflectance distribution function of 0.0001 at 1 deg with a 1/theta-squared roll-off between 1 and 0.2 deg is realizable based on recent measurements. This results in an off-axis radiance term that is generally small in comparison with the system-limiting NER.

Roche, A. E.↗
NASA NTRS2012

Pt, Au, Pd and Ru Partitioning Between Mineral and Silicate Melts: The Role of Metal Nanonuggets

The partition coefficients of Pt and other Pt Group Elements (PGE) between metal and silicate D(sub Metal-Silicate) and also between silicate minerals and silicate melts D(sub Metal-Silicate) are among the most challenging coefficients to obtain precisely. The PGE are highly siderophile elements (HSE) with D(sub Metal-Silicate) >10(exp 3) due to the fact that their concentrations in silicates are very low (ppb to ppt range). Therefore, the analytical difficulty is increased by the possible presence of HSE-rich-nuggets in reduced silicate melts during experiments). These tiny HSE nuggets complicate the interpretation of measured HSE concentrations. If the HSE micro-nuggets are just sample artifacts, then their contributions should be removed before calculations of the final concentration. On the other hand, if they are produced during the quench, then they should be included in the analysis. We still don't understand the mechanism of nugget formation well. Are they formed during the quench by precipitation from precursor species dissolved homogeneously in the melts, or are they precipitated in situ at high temperature due to oversaturation? As these elements are important tracers of early planetary processes such as core formation, it is important to take up this analytical and experimental challenge. In the case of the Earth for example, chondritic relative abundances of the HSE in some mantle xenoliths have led to the concept of the "late veneer" as a source of volatiles (such as water) and siderophiles in the silicate Earth. Silicate crystal/liquid fractionation is responsible for most, if not all, the HSE variation in the martian meteorite suites (SNC) and Pt is the element least affected by these fractionations. Therefore, in terms of reconstructing mantle HSE abundances for Mars, Pt becomes a very important player. In the present study, we have performed high temperature experiments under various redox conditions in order to determine the abundances of Pt, Au, Ru and Pd in minerals (olivine and diopside) and in silicate melts, but also to characterize the sizes, density and chemistry of HSE nuggets when present in the samples.

Malavergne, V.↗