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DOE OSTI2022

Experimental study of the partitioning of some platinum group elements (Pd and Ir) between orthopyroxene and silicate melt

Past experiments and observations on natural samples have largely focused on the roles of olivine and chromite in controlling the behaviour of the platinum-group elements (PGE) during melting and solidification, whereas other phases, such as pyroxene, have gone largely uncharacterized. Here, to address this, experiments have been done to measure the partitioning of Pd (with a subset of results for Ir), between orthopyroxene and silicate melt at 1340 °C, 0.1 MPa and log fO 2 of FMQ - 1 to FMQ + 6 (FMQ = Fayalite-Magnetite-Quartz). The X-ray Absorption Near-Edge Structure (XANES) was measured in a subset of experiment glasses. Glass concentrations of Pd (corrected to unit Pd activity) increase from ~6 to ~650 ug/g over the fO 2 range of experiments. The slope of the solubility-fO 2 relation is consistent with Pd 1+ as the dominant oxidation state, with evidence for Pd 0 and Pd 2+ at the lowest and highest experiment fO 2 , respectively. Consistent with this result, the XANES reveal spectral features similar to Pd 0 and Pd 2+ spectral reference materials (specRM) at the most reduced and oxidized synthesis conditions, respectively. Other lines of evidence require the presence of a third melt species, here interpreted to be Pd 1+ . Values of orthopyroxene/melt partition coefficients for Pd (D Pd Opx/melt ) are 0.0051 (+/-0.006) at log fO 2 < ΔFMQ + 3, increasing with fO 2 to a maximum of 0.013 at ~FMQ + 6. Sodium partition coefficients, expected to be similar to Pd, range from 0.0061 (+/-0.00061) at FMQ + 3, increase to 0.007–0.009 at higher fO 2 , but with no clear systematic trend. A value for D Ir Opx-melt of ~0.6 was measured at ~FMQ + 4, indicating significantly more compatible behaviour for Ir relative to Pd. Partitioning results are interpreted in the context of the Blundy-Wood elastic strain model in which the variation in partitioning is related to ionic radius mismatch to an optimal crystallographic site size. Based on the trend in ionic radius with oxidation state, the estimated ionic radius of Pd 1+ in octahedral coordination is similar to Na 1+ , and comparison to previous orthopyroxene-melt partitioning experiments suggests D Pd1+ opx/melt and DNaopx/melt should be nearly identical, consistent with the results of this study. The ionic radius of VI-fold Pd 2+ is close to the optimal M2 site size, so an increased proportion of this species with fO 2 accounts for the larger values of D Pd opx/melt at the highest fO 2 investigated. The much larger partition coefficient for Ir is consistent with the presence of Ir 2+ , whose estimated ionic radius is close to Fe 2+ and Mg 2+ , as well as predictions for the optimal M1 site size. With the assumption that D Pd opx/melt = D Na opx/melt , combined with a revised value for the Pd content of the primitive mantle, a melting model is presented that better reproduces the Pd concentration of high degree melts from sulfide-free mantle sources.

58 GEOSCIENCES↗

DOE OSTI2017

Heat deposition analysis for the High Flux Isotope Reactor’s HEU and LEU core models

The High Flux Isotope Reactor at Oak Ridge National Laboratory is an 85 MW th pressurized light-water-cooled and -moderated flux-trap type research reactor. The reactor is used to conduct numerous experiments, advancing various scientific and engineering disciplines. As part of an ongoing program sponsored by the US Department of Energy National Nuclear Security Administration Office of Material Management and Minimization, studies are being performed to assess the feasibility of converting the reactor’s highly enriched uranium fuel to low-enriched uranium fuel. To support this conversion project, reference models with representative experiment target loading and explicit fuel plate representation were developed and benchmarked for both fuels to (1) allow for consistent comparison between designs for both fuel types and (2) assess the potential impact of low-enriched uranium conversion. These high-fidelity models were used to conduct heat deposition analyses at the beginning and end of the reactor cycle and are presented herein. This article (1) discusses the High Flux Isotope Reactor models developed to facilitate detailed heat deposition analyses of the reactor’s highly enriched and low-enriched uranium cores, (2) examines the computational approach for performing heat deposition analysis, which includes a discussion on the methodology for calculating the amount of energy released per fission, heating rates, power and volumetric heating rates, and (3) provides results calculated throughout various regions of the highly enriched and low-enriched uranium core at the beginning and end of the reactor cycle. These are the first detailed high-fidelity heat deposition analyses for the High Flux Isotope Reactor’s highly enriched and low-enriched core models with explicit fuel plate representation. Lastly, these analyses are used to compare heat distributions obtained for both fuel designs at the beginning and end of the reactor cycle, and they are essential for enabling comprehensive thermal hydraulics and safety analyses that require detailed estimates of the heat source within all of the reactor’s fuel element regions.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2024

Identifying Meteorological Drivers for Errors in Modeled Winds along the Northern California Coast

Abstract An accurate wind resource dataset is required for assessing the potential energy yield of floating offshore wind farms that are expected along the California outer continental shelf. The National Renewable Energy Laboratory has developed and disseminated an updated wind resource dataset offshore of California, using the Weather Research and Forecasting Model, referred to as the CA20 dataset. As compared to buoy lidar measurements that have become available recently, the CA20 dataset showed significant positive biases for 100-m wind speeds along Northern California wind energy lease areas. To investigate the meteorological drivers for the model errors, we first consider two 1-yr simulations run with two different planetary boundary layer (PBL) parameterizations: the Mellor–Yamada–Nakanishi–Niino (MYNN) PBL scheme (the chosen configuration in the CA20 dataset) and the Yonsei University PBL scheme (which significantly reduces the bias in modeled winds). By comparing the 1-yr simulations to the concurrent lidar buoy observations, we find that errors are larger with the MYNN PBL scheme in warm seasons. We then dive deeper into the analysis by running simulations for short-term (3-day) case studies to evaluate the sensitivity of initial/boundary condition forcings on model results. By analyzing the short-term simulations, we find that during synoptic-scale northerly flows driven by the North Pacific high and inland thermal low, a coastal warm bias in the MYNN simulation is mainly responsible for the modeled wind speed bias by altering the boundary layer thermodynamics. The results of our analysis will help guide the creation of an updated version of the CA20 dataset.

17 WIND ENERGY↗

NASA NTRS2000

Mars Global Reference Atmospheric Model 2000 Version (Mars-GRAM 2000): Users Guide

This report presents Mars Global Reference Atmospheric Model 2000 Version (Mars-GRAM 2000) and its new features. All parameterizations for temperature, pressure, density, and winds versus height, latitude, longitude, time of day, and L(sub s) have been replaced by input data tables from NASA Ames Mars General Circulation Model (MGCM) for the surface through 80-km altitude and the University of Arizona Mars Thermospheric General Circulation Model (MTGCM) for 80 to 170 km. A modified Stewart thermospheric model is still used for higher altitudes and for dependence on solar activity. "Climate factors" to tune for agreement with GCM data are no longer needed. Adjustment of exospheric temperature is still an option. Consistent with observations from Mars Global Surveyor, a new longitude-dependent wave model is included with user input to specify waves having 1 to 3 wavelengths around the planet. A simplified perturbation model has been substituted for the earlier one. An input switch allows users to select either East or West longitude positive. This memorandum includes instructions on obtaining Mars-GRAM source code and data files and for running the program. It also provides sample input and output and an example for incorporating Mars-GRAM as an atmospheric subroutine in a trajectory code.

Justus, C. G.↗

DOE OSTI2015

Complik: A versatile tool for automatizing the representation of material data in MCNP models - 14412

At the Paul Scherrer Institut, the CASMO/SIMULATE code system is used as reference scheme for core analyses of the Swiss reactors. Besides, Monte Carlo (MC) based neutron transport codes are in parallel being established both for out-of-core applications and full core analyses, in order to provide higher fidelity solutions to problems beyond the modelling capabilities of the standard core modelling methods. In this perspective, a necessity is to be able to conduct the MC calculations at any operating conditions, requiring thus proper initialisation of the thermal-hydraulic feedback as well as at any burnup, prompting the need for a detailed modelling of all material zones in the irradiated fuel assemblies. To tackle the later challenge, the development of the COMPLINK tool, aimed at initialising large-scale MC models of burned core configurations, using as basis upstream 2-D assembly/ 3-D core analyses results, was initiated and is presented in this paper. A main feature is the capability to extend in an automatic manner, a user given MC model in order to represent material data with a higher resolution, keeping track of the location of such materials also within nested repeated structures. This capability prevents the user from the burden of developing specific computational framework for a target application. Since the same functionalities are required to analyse out-of-core spent fuel managements systems, a first verification of COMPLINK is illustrated on the basis of a criticality safety evaluation (CSE) with MCNP6.1 of a Swiss PWR wet storage pool loaded with fresh and burned fuel assemblies. Starting from CASMO-5 depletion calculations at each axial layer of the depleted fuel assemblies and reconstructed based on realistic irradiation histories, COMPLINK is then applied to load the isotopic compositions into the material zones of the corresponding MCNP6.1 assembly models. On this basis, the CSE analyses are conducted with focus on investigating axial and radial modelling effects. All in all, the obtained results are found to be well in line with the impact on CSE expected at the considered burnup levels, providing thus confidence in the correct implementation of the COMPLINK functionalities and confirming its effectiveness as well as efficiency for the initialization of all material zones in large-scale MC models of complex and strongly heterogeneous burned configurations. (authors)

42 ENGINEERING↗

DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Trail Creek in Taylor River Watershed, Colorado 2024-2025

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors near Trail Creek. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format TR-X-Y, where TR refers to Trail Creek, X is the treatment block identifier, and Y is the location identifier. Specifically, TR-ASCC1 is the control treatment block under the Adaptive Silviculture for Climate Change (ASCC) project, and TR-ASCC2 is the clear-cut treatment block. TR-ASCC-EHSn is associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and TR-ASCC-ERTn (upslope n=1) are ecohydrology sites along the electrical resistivity tomography transects. The sample and location information can be found in metadata.csv, and the data from the soil sensors will be included in a future data version when the observation period becomes sufficiently long for data analysis. Sampling and Measurements Each sample falls into one of the two sampling methods – (1) intact cores or (2) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. The intact cores were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

EARTH SCIENCE > LAND SURFACE > SOILS↗

NASA NTRS2018

Prediction Skill of U.S. Flash Droughts in Subseasonal Experiment (SubX) Model Hindcasts

Droughts that establish themselves over a short period of time (weeks to a few months), referred to as flash droughts, can have devastating impacts on agriculture, water resources, and ecosystems. The ability to predict such droughts in advance would greatly enhance our preparation for them and potentially reduce their impacts. The sub-seasonal time scale at which flash droughts occur emphasizes the importance of producing forecasts at weekly or finer intervals that extend beyond the numerical weather prediction time frame. Here we assess the ability of eight global forecast systems, each participating in the Sub-seasonal Experiment project (SubX), to predict key features associated with rapidly developing droughts over the United States during the last two decades. MERRA2 reanalysis is used as observations. Prediction skill for temperature and precipitation anomalies during these events is limited to the first 1-2 weeks after initialization for most hindcasts. However, there are some hindcasts in which large anomalies are well predicted 3-4 weeks or more in advance. The physical mechanisms that are key to the development of surface anomalies, including quasi-stationary atmospheric waves, were also evaluated. Most hindcasts were unable to capture the development or progression of such drought-inducing circulation features more than 1-2 weeks in advance.

Deangelis, Anthony M.↗

DOE OSTI2015

TU-G-BRA-03: Predicting Radiation Therapy Induced Ventilation Changes Using 4DCT Jacobian Calculations

Purpose: Longitudinal changes in lung ventilation following radiation therapy can be mapped using four-dimensional computed tomography(4DCT) and image registration. This study aimed to predict ventilation changes caused by radiation therapy(RT) as a function of pre-RT ventilation and delivered dose. Methods: 4DCT images were acquired before and 3 months after radiation therapy for 13 subjects. Jacobian ventilation maps were calculated from the 4DCT images, warped to a common coordinate system, and a Jacobian ratio map was computed voxel-by-voxel as the ratio of post-RT to pre-RT Jacobian calculations. A leave-one-out method was used to build a response model for each subject: post-RT to pre-RT Jacobian ratio data and dose distributions of 12 subjects were applied to the subject’s pre-RT Jacobian map to predict the post-RT Jacobian. The predicted Jacobian map was compared to the actual post-RT Jacobian map to evaluate efficacy. Within this cohort, 8 subjects had repeat pre-RT scans that were compared as a reference for no ventilation change. Maps were compared using gamma pass rate criteria of 2mm distance-to-agreement and 6% ventilation difference. Gamma pass rates were compared using paired t-tests to determine significant differences. Further analysis masked non-radiation induced changes by excluding voxels below specified dose thresholds. Results: Visual inspection demonstrates the predicted post-RT ventilation map is similar to the actual map in magnitude and distribution. Quantitatively, the percentage of voxels in agreement when excluding voxels receiving below specified doses are: 74%/20Gy, 73%/10Gy, 73%/5Gy, and 71%/0Gy. By comparison, repeat scans produced 73% of voxels within the 6%/2mm criteria. The agreement of the actual post-RT maps with the predicted maps was significantly better than agreement with pre-RT maps (p<0.02). Conclusion: This work validates that significant changes to ventilation post-RT can be predicted. The differences between the predicted and actual outcome are similar to differences between repeat scans with equivalent ventilation. This work was supported by NIH grant CA166703 and a Pilot Grant from University of Iowa Carver College of Medicine.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

EOSPAC User's Manual: Version 6.5 Second Edition (Rev. 3)

The EOSPAC utility package is a collection of interface routines, which can be used to access the SESAME data library and perform various data adjustments and interpolations on the SESAME data. The SESAME data library contains both thermodynamic (e.g., equation of state) and transport coefficients (e.g., opacity and conductivity). Note, for simplicity, the term EOS (equation of state) used herein includes both thermodynamic variables and transport coefficients. The EOSPAC utility package is designed to be used by physics codes (henceforth ”host codes”) written in multiple languages and on multiple platforms. The remainder of this manual is organized into several sections. Chapter 2 discusses conventions such as data organization and routine names. Chapter 3 provides a general overview of basic theory and models implemented within EOSPAC. Chapter 4 provides a general overview of how to use the EOSPAC interface library. Chapters 5 to 7 describe the public interfaces of EOSPAC in detail. Chapter 8 provides a brief introduction to some related tools, which may be of use to the user. Chapter 9 provides details related to some selected numerical features of EOSPAC. Chapter 10 gives examples for using the interface routines described in chapters 5 to 7. Chapter 11 provides technical support contact information. Chapter 12 contains a brief set of acknowledgments. Chapter 13 contains a list of referenced documents. Finally, chapter 14 lists the “table types: mnemonic conventions”, “table types: grouped by category, sorted by name”, “table types: eospac version 5 cross reference”, “options: setup phase”, “data information parameters”, “meta-data information parameters”, “options: interpolation phase”, and the “error codes”.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Active Optical Control with Machine Learning: A Proof of Concept for the Vera C. Rubin Observatory

Abstract The Active Optics System of the Vera C. Rubin Observatory (Rubin) uses information provided by four wave front sensors to determine deviations between the reconstructed wave front and the ideal wave front. The observed deviations are used to adjust the control parameters of the optical system to maintain image quality across the 3.°5 field of view. The baseline approach from the project is to obtain amplitudes of the Zernike polynomials describing the distorted wave front from out-of-focus images collected by the wave front sensors. These Zernike amplitudes are related via an “influence matrix” to the control parameters necessary to correct the wave front. In this paper, we use deep-learning methods to extract the control parameters directly from the images captured by the wave front sensors. Our neural net model uses anti-aliasing pooling to boost performance, and a domain-specific loss function to aid learning and generalization. The accuracy of the control parameters derived from our model exceeds Rubin requirements even in the presence of full-moon background levels and mis-centering of reference stars. Although the training process is time consuming, model evaluation requires only a few milliseconds. This low latency should allow for the correction of the optical configuration during the readout and slew interval between successive exposures.

Astronomy & Astrophysics↗

NASA NTRS1963

Lunar Flight Study Series: Volume 7. Earth-Moon Transit Studies Based on Ephemeris Data and Using Best Available Computer Program: Principles for Reducing Earth-Moon Trajectory Analysis to Fundamentals - 2

This report presents an empirical investigation of earth-to-moon trajectories. The primary objective is to uncover relationships that result in the reduction of mission analysis or trajectory analysis problems to fundamentals. It is considered imperative that the results be accurate to the extent that any relationships that are uncovered are not brought about by simplifying or unrealistic assumptions. Consequently, the model used is as accurate as is presently available for use and the computations are performed under strict error controls. Procedures are reasonable from the engineering viewpoint. Some very helpful principles are uncovered. These may be briefly stated as follows: 1) Trajectories of constant flight time to the moon (in the 66 hour to 90 hour range), and arriving within a few minutes of the same time pass over a common point on the moon regardless of the arrival altitude and inclination, This common point of passage for such a family of trajectories is arbitrarily referred to as a VERTEX point; 2) These VERTEX points lie within a bounded region on the lunar surface as arrival time varies throughout the years, for a given flight time or flight time spread; 3) Increasing flight time results primarily in a longitude shift of the VERTEX point for a given arrival time; 4) Launch azimuth should be treated as a strong parameter regarding its influence on the vertex. Launch azimuth variations up to +/- 20 degrees about 90 degrees can shift the vertex as much as 3 degrees in latitude even when one is free to pick launch time, coast time, and S-IVB burn time appropriately with launch azimuth; and 5) The locus of periselenum, as arrival inclination takes on all possible values, is nearly circular about the vertex point for a given arrival altitude. A publication is now in preparation that applies these principles to much advantage in the solution of various trajectory analysis problems.

LUNAR FLIGHT↗

NASA NTRS1993

Deucalionis Regio, Mars: Evidence for a unique mineralogic endmember and a crusted surface

A small equatorial region south of Sinus Meridiani, Deucalionis Regio, has been found spectrally distinct from other regions as seen in a high spectral resolution telescopic image of the meridian hemisphere of Mars. Analysis of Viking IRTM and other related data suggest that Deucalionis Regio has a crusted surface. The crust-bonding minerals may contribute to the spectral uniqueness of this region. Two independent analyses of spectral images, linear spectral mixing and supervised classification based on the spectral shapes, showed that in addition to the well-known spectral endmember regions in this image (western Arabia, south Acidalia, and Sinus Meridiani), Deucalionis Regio has spectral properties that are unique enough to make it a principle endmember unit. In those earlier works, Deucalionis Regio was referred to as 'Meridiani Border.' Analysis of thermal inertia, rock abundance, and albedo information derived from Viking images and Infrared Thermal Mapper (IRTM) data obtained 1977-80 also indicate that Deucalionis Regio has a surface of distinctly different physical properties when compared to Arabia, Sinus Meridiani, and Acidalia. Deucalionis Regio has a thermal inertia equivalent to the Martian average, a low rock abundance (less than 5 percent), and an intermediate albedo and color. Considerable effort by previous investigators has revealed a consistent model for the surface (upper few cm) properties of the endmember reigons Arabia, Sinus Meridiani, and Acidalia. Compared with these regions, we consider that Deucalionis Regio is not a region of either (1) unconsolidated, fine bright dust like Arabia, (2) considerable windblown unconsolidated sand like Sinus Meridiani, or (3) a rocky-and-sandy surface like Acidalia. Thus, we are forced to consider that either the surface of Deucalionis Regio is made of unconsolidated fine to medium sand (about 250 microns) of an unusual and previously unreported color and albedo, or that the surface is crusted, fine-grained weathered soil, and the thermal inertia is an indicator of the degree to which the surface sediments have become indurated. We favor the latter.

Merenyi, E.↗

NASA NTRS2015

Human Exploration of Phobos

This study developed, analyzed, and compared mission architectures for human exploration of Mars' Moons within the context of an Evolvable Mars Campaign. METHODS: All trades assumed conjunction class missions to Phobos (approximately 500 days in Mars system) as it was considered the driving case for the transportation architecture. All architectures assumed that the Mars Transit Habitat would remain in a High Mars Orbit with crewmembers transferring between HMO and Phobos in a small crew taxi vehicle. A reference science / exploration program was developed including performance of a standard set of tasks at 55 locations on the Phobos surface. Detailed EVA timelines were developed using realistic flight rules to accomplish the reference science tasks using exploration systems ranging from jetpacks to multi-person pressurized excursion vehicles combined with Phobos surface and orbital (L1, L4/L5, 20km Distant Retrograde Orbit) habitat options. Detailed models of propellant mass, crew time, science productivity, radiation exposure, systems and consumables masses, and other figures of merit were integrated to enable quantitative comparison of different architectural options. Options for pre-staging assets using solar electric propulsion (SEP) vs. delivering all systems with the crew were also evaluated. Seven discrete mission architectures were evaluated. RESULTS: The driving consideration for habitat location (Phobos surface vs. orbital) was radiation exposure, with an estimated reduction in cumulative mission radiation exposure of up to 34% (vs. Mars orbital mission) when the habitat is located on the Phobos surface, compared with only 3-6% reduction for a habitat in a 20km DRO. The exploration utility of lightweight unpressurized excursion vehicles was limited by the need to remain within 20 minutes of Solar Particle Event radiation protection combined with complex GN&C systems required by the non-intuitive and highly-variable gravitational environment. Two-person pressurized excursion vehicles as well as mobile surface habitats offer significant exploration capability and operational benefits compared with unpressurized EVA mobility systems at the cost of increased system and propellant mass. Mechanical surface translation modes (i.e. hopping) were modeled and offer potentially significant propellant savings and the possibility of extended exploration operations between crewed missions. Options for extending the utilization of the crew taxi vehicle were examined, including use as an exploration asset for Phobos surface exploration (when combined with an alternate mobility system) and as an EVA platform, both on Phobos and for contingency EVA on the Mars Transit Habitat. CONCLUSIONS: Human exploration of Phobos offers a scientifically meaningful first step towards human Mars surface missions that develops and validates transportation, habitation, and exploration systems and operations in advance of the Mars landing systems.

Abercromby, Andrew F. J.↗

NASA NTRSDate not supplied

Microstructural Characterization of Felsite Fragments From the Apollo Next Generation Sample Analysis (ANGSA) Double Drive Tube 73001/73002

While the Moon’s surface is dominated by mafic igneous products formed through the crystallization of the lunar magma ocean, and the subsequent eruption of mare basalts – felsic magmatic fragments (variably referred to as felsites, granites, rhyolites or granophyres) have been identified from a number of Apollo samples (e.g. 12013[1], 14321[2], 15405[3] and 73215[4]). Magmatic edifices like the Gruithuisen Domes – silicic constructs sometimes found proximal to the basaltic mare provinces filling nearside basins, may represent a petrogenetic origin. However, this is, is complicated by the fact that they apparently predate mare volcanism (e.g. [5]). Additionally, crater-counting indicates that these silicic surface features also postdate radiogenic ages of Apollo felsites [6]. Various modes of felsic magmatism have been invoked to explain the presence of felsic lithologies on the Moon, including: 1) fractional crystallization and silicate liquid immiscibility; 2) partial melting of lunar crust through basaltic underplating; and 3) fractional crystallization of the mare parent magmas. Although these models are plausible, based on known Apollo samples and lunar meteorites, they are complicated by the lack of sample lithologies with intermediate composition between basaltic magmas and the felsic components, and the fact that partial melting of many crustal rock types on the Moon (e.g., anorthosite, troctolites) are unlikely to form granites. Along with other unique magmatic lithologies, new felsites have been identified within the < 1 mm size fractions of the ANGSA double drive core tube (73001/73002) from Apollo 17 station 3, sampling the light mantle landslide deposit from the South Massif [7]. These additional examples of lunar felsite will help us to better constrain the processes that lead to the formation of these evolved lithologies. To do this, we have employed a gambit of high-resolution scanning electron microscopy (SEM) and scanning transmission electron microscopy (TEM) analytical techniques, including electron backscatter diffraction (EBSD), cathodoluminescence and high-angle annular dark field (HAADF) imaging. These data provide unique insights into the felsite mineralogy and microstructures including the coexistence of quartz and tridymite in many of the fragments. In addition, these analyses provide petrological context and assist targeted in situ secondary ion mass spectrometry (SIMS) measurements of the U-Pb systematics of accessory minerals, and the volatile abundances and D/H ratios of apatite grains identified within the clasts.

lunar↗

DOE OSTI2021

Combined Mesonet and Tracker

Title: Combined Mesonet and Trackers (UNL Mobile Mesonets) Authors University of Nebraska PI: Adam Houston, UNL Professor (ahouston2@unl.edu) Mailing Address: 126 Bessey Hall P.O. Box 880340 Lincoln, NE 68588-0340 CoMeT Overview The University of Nebraska-Lincoln operates three Combined Mesonet and Tracker (CoMeTs). CoMeTs are Ford Explorers (model years 2015, 2017, and 2019) with forward-mounted suites of meteorological sensors and dual moonroofs, combining the capability of a mobile mesonet to collect near-surface observations with the capability of an unmanned aircraft systems (UAS) tracker vehicle, which enables an observer in the second row of seats to see the aircraft and maintain compliance with Federal Aviation Administration policies on UAS operation. The CoMeTs collect observations of slow temperature and humidity at ~2 m above ground level (AGL) using a Vaisala HMP155A, fast temperature at ~2 m AGL using a Campbell Scientific 109SS-L thermistor, pressure at ~2.5 m AGL using a Vaisala PTB210, wind speed and direction at ~3.25 m AGL using an R.M. Young 05103 propeller anemometer, and vehicle heading using a KVH Industries C-100 fluxgate compass (Barbieri et al. 2019). The HMP155A and 109SS-L thermistor are shielded and aspirated within a U-tube (Waugh and Frederickson 2010; Houston et al. 2016). This list of sensors is also included in the CoMeT data file metadata. Manufacturer specifications for these instruments are given in Table 1 of Hanft and Houston (2018). The reported measured quantities are summarized below.CoMeT-3 was funded through an equipment allocation included in the NSF TORUS award (AGS-1824649). Instrument Description The specific sensors included on each CoMeT are summarized in the table at the end of this section. In general each CoMeT collects observations of slow temperature and humidity at ~2 m above ground level (AGL) using a Vaisala HMP155, fast temperature at ~2 m AGL using a Campbell Scientific 109SS-L thermistor, pressure at ~2.5 m AGL using a Vaisala PTB210 barometer with a Gill pressure port, wind speed and direction at ~3.25 m AGL using an R.M. Young 05103 propeller anemometer, position using a Garmin 19x HVS receiver, and vehicle heading using a KVH Industries C-100 fluxgate compass. The HMP155 and 109SS are shielded and aspirated within a U-tube (Waugh and Frederickson 2010; Houston et al. 2016). Fast temperature and corrected RH measurements (using sensors housed within the U-tube) have a time constant of 10-12 s based on data collected across a temperature and RH shock during the CLOUD-MAP 2017 calibration/validation tests on June 26, 2017. Vehicle speed was < 10 kts for this test. CoMeT-1 CoMeT-2 CoMeT-3 Slow Temperature Slow RH Vaisala HMP155A-L20-PT Part #: 22280-7 Vaisala HMP155E Part #: E1AA11A0B1A1A0A Vaisala HMP155E Part #: E1AA11A0B1A1A0A Fast temperature Campbell Scientific 109SS-L20-PT Part #: 21448-3 Campbell Scientific 109SS-L12-PW Part #: 21448-109 Campbell Scientific 109SS-L12-PW Part #: 21448-150 Pressure Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Wind RM Young 05103-L20-PT Part #: 18435-310 RM Young 05103-L20-PW Part #: 18435-244 RM Young 05103-L20-PW Part #: 18435-244 GPS Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Compass KVH C-100 Part #: 01-0177-15 KVH C-100 Part #: 01-0177-15 KVH C-100 Part #: 01-0177-15 Logger Campbell Scientific CR6-NA-XT-SW Part #: 28385-9 Campbell Scientific CR6-WIFI-XT-SW Part #: 28385-6 Campbell Scientific CR6-WIFI-XT-SW Part #: 28385-6 Data Collection and Real-Time Processing The reported measured quantities are summarized in the table below. Quantity Units Source Epoch time Seconds GPS Latitude and longitude Degrees GPS Altitude m GPS Pressure hPa PTB210 Temperature (fast) deg C 109SS-L Temperature (slow) deg C HMP155 RH (slow) % HMP155 Vehicle speed m/s GPS Vehicle heading deg C-100 and GPS In addition to the measured variables, several derived variables are calculated. Corrected/fast relative humidity (%) Relative humidity is adjusted to the fast temperature following Richardson et al. (1998) and Houston et al. (2016). Water vapor mixing ratio (g/kg) Dew point temperature (°C) Potential temperature (Kelvin) Virtual potential temperature (Kelvin) Equivalent potential temperature (Kelvin) Regular intercomparisons between all three CoMeTs were performed during TORUS 2019. Comparisons were also conducted between CoMeT-1 and CoMeT-2 during LAPSE-RATE (2018) on 14 July. In these intercomparisons, the vehicles were parked adjacent to each other aligned perpendicular to (and facing into) the wind. To minimize engine heating effects, intercomparisons were only conducted when the wind speed was >10 kts. Data Format Original data files for each deployment are saved as text files and then converted to NetCDF. NetCDF versions have units that are CF compliant and may not match the original units in the txt files. The naming convention for the NetCDF files is as follows: UNL.CoMeT3.{deployment date YYYYMMDD}.{start time of observation collection in UTC HHMM}.L2.{post-processing codes}.cdf example: UNL.CoMeT3.20190627.1931.L2.g1.f1.cdf Post-processing codes are included to track modifications to the raw data. These codes are closely connected to error flags associated with each record. Each letter corresponds to a particular instrument: g: GPS p: Barometer tf: Fast temperature ts: Slow temperature rh: Relative humidity f: Compass w: Wind monitor a: All instruments Each number corresponds to a particular post-processing action described more below. Measured and derived variables are included in the following table. Variable Heading Standard Name Units time Time seconds since 00:00:00, 01-01-1970 Alt Altitude meters lat Latitude degrees north lon Longitude degrees east fast_temp Air Temperature kelvin slow_temp Air Temperature kelvin pressure Air Pressure pascals logger_RH Relative Humidity percent calc_corr_RH Relative Humidity percent wind_speed Wind Speed meters per second wind_dir Wind From Direction degrees vehicle_dir Vehicle Direction degrees dewpoint Dew Point Temperature kelvin mixing_ratio Humidity Mixing Ratio g/g theta Air Potential Temperature kelvin theta_v Virtual Potential Temperature kelvin theta_e Equivalent Potential Temperature Kelvin error_flag The error_flag variable is a string that matches the post-processing codes listed above. All instruments will have an associated code, but will have a “0” if the datum is unchanged from the initial processed value. Error Codes The following table summarizes the error codes for data collected before 2020: Error Code Relevant CoMeT Description g1 1,2,3 Exact correction. GPS position and time reprocessed from raw data g2 1 As far as we can tell this is an exact correction to an error in the GPS time. During the correct time periods the time suddenly went backwards ~250s and stayed at this offset for 750s when it corrected itself. The offset was applied to the “time warp” period. p1 2 Approximate correction. Hole in the pressure tube connecting the pressure port to the barometer. Resulted in erroneously low air pressure measurements when the vehicle was in motion. Derived variables recalculated (dew point temperature [e depends on qv and p], water vapor mixing ratio, potential temperature, virtual potential temperature, equivalent potential temperature) a1 3 Exact correction. Missing data reprocessed from raw data a2 1 Bug fix to bias correction for ts1, ts2, and rh1: water vapor mixing ratio was off by a factor of 10 and virtual potential temperature was wrong because of this. f1 3 No correction, missing data. Fluxgate compass inoperable. Wind speed and direction calculated using GPS-derived vehicle heading instead. rh1 1 Approximate correction. Constant bias of +1.7% removed from relative humidity. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) ts1 1 Approximate correction. Constant bias of +0.6 K removed from slow temperature. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) ts2 1 Approximate correction. Constant bias of +1.0 K removed. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) References Bolton, D., 1980: The Computation of Equivalent Potential Temperature. Mon. Wea. Rev., 108, 1046–1053, https://doi.org/10.1175/1520-0493(1980)108<1046:TCOEPT>2.0.CO;2. Hanft, W., and A. L. Houston, 2018: An Observational and Modeling Study of Mesoscale Air Masses with High Theta-E. Mon. Wea. Rev., 146, 2503–2524, https://doi.org/10.1175/MWR-D-17-0389.1.Wexler Houston, A. L., R. J. Laurence III, T. W. Nichols, S. Waugh, B. Argrow, and C. L. Ziegler, 2016: Intercomparison of unmanned aircraft-borne and mobile mesonet atmospheric sensors. Journal of Atmospheric and Oceanic Technology. 33, 1569-1582, doi: 10.1175/JTECH-D-15-0178.1. Lowe, P. R., 1977: An Approximating Polynomial for the Computation of Saturation Vapor Pressure. J. Applied Meteorology, 16, 100–103. Richardson, S. J., S. E. Frederickson, F. V. Brock, and J. A. Brotzge, 1998: Combination temperature and relative humidity probes: Avoiding large air temperature errors and associated relative humidity errors. Preprints, 10th Symp. On Meteorological Observations and Instrumentation, Phoenix, AZ, Amer. Meteor. Soc., 278–283. Waugh, S., and S. E. Frederickson, 2010: An improved aspirated temperature system for mobile meteorological observations, especially in severe weather. 25th Conf. on Severe Local Storms, Denver, CO, Amer. Meteor. Soc., P5.2. [Available online at https://ams.confex.com/ams/25SLS/techprogram/paper_176205.htm.]

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

WE-AB-BRA-08: Correction of Patient Motion in C-Arm Cone-Beam CT Using 3D-2D Registration

Purpose: Intraoperative C-arm cone-beam CT (CBCT) is subject to artifacts arising from patient motion during the fairly long (∼5–20 s) scan times. We present a fiducial free method to mitigate motion artifacts using 3D-2D image registration that simultaneously corrects residual errors in geometric calibration. Methods: A 3D-2D registration process was used to register each projection to DRRs computed from the 3D image by maximizing gradient orientation (GO) using the CMA-ES optimizer. The resulting rigid 6 DOF transforms were applied to the system projection matrices, and a 3D image was reconstructed via model-based image reconstruction (MBIR, which accommodates the resulting noncircular orbit). Experiments were conducted using a Zeego robotic C-arm (20 s, 200°, 496 projections) to image a head phantom undergoing various types of motion: 1) 5° lateral motion; 2) 15° lateral motion; and 3) 5° lateral motion with 10 mm periodic inferior-superior motion. Images were reconstructed using a penalized likelihood (PL) objective function, and structural similarity (SSIM) was measured for axial slices of the reconstructed images. A motion-free image was acquired using the same protocol for comparison. Results: There was significant improvement (p < 0.001) in the SSIM of the motion-corrected (MC) images compared to uncorrected images. The SSIM in MC-PL images was >0.99, indicating near identity to the motion-free reference. The point spread function (PSF) measured from a wire in the phantom was restored to that of the reference in each case. Conclusion: The 3D-2D registration method provides a robust framework for mitigation of motion artifacts and is expected to hold for applications in the head, pelvis, and extremities with reasonably constrained operative setup. Further improvement can be achieved by incorporating multiple rigid components and non-rigid deformation within the framework. The method is highly parallelizable and could in principle be run with every acquisition. Research supported by National Institutes of Health Grant No. R01-EB-017226 and academic-industry partnership with Siemens Healthcare (AX Division, Forcheim, Germany).

60 APPLIED LIFE SCIENCES↗

NASA NTRS2018

The PMIP4 Contribution to CMIP6 - Part 1: Overview and Over-Arching Analysis Plan

This paper is the first of a series of four GMD (Geoscientific Model Development) papers on the PMIP4-CMIP6 (Paleoclimate Modelling Intercomparison Project - Phase 4 -- Coupled Model Intercomparison Project - Phase 6) experiments. Part 2 (Otto-Bliesner et al., 2017) gives details about the two PMIP4-CMIP6 interglacial experiments, Part 3 (Jungclaus et al., 2017) about the last millennium experiment, and Part 4 (Kageyama et al., 2017) about the Last Glacial Maximum experiment. The mid-Pliocene Warm Period experiment is part of the Pliocene Model Intercomparison Project (PlioMIP) - Phase 2, detailed in Haywood et al. (2016). The goal of the Paleoclimate Modelling Intercomparison Project (PMIP) is to understand the response of the climate system to different climate forcings for documented climatic states very different from the present and historical climates. Through comparison with observations of the environmental impact of these climate changes, or with climate reconstructions based on physical, chemical, or biological records, PMIP also addresses the issue of how well state-of-the-art numerical models simulate climate change. Climate models are usually developed using the present and historical climates as references, but climate projections show that future climates will lie well outside these conditions. Palaeoclimates very different from these reference states therefore provide stringent tests for state-of-the-art models and a way to assess whether their sensitivity to forcings is compatible with palaeoclimatic evidence. Simulations of five different periods have been designed to address the objectives of the sixth phase of the Coupled Model Intercomparison Project (CMIP6): the millennium prior to the industrial epoch (CMIP6 name: past1000); the mid-Holocene, 6000 years ago (midHolocene); the Last Glacial Maximum, 21,000 years ago (lgm); the Last Interglacial, 127,000 years ago (lig127k); and the mid-Pliocene Warm Period, 3.2 million years ago (midPliocene-eoi400). These climatic periods are well documented by palaeoclimatic and palaeoenvironmental records, with climate and environmental changes relevant for the study and projection of future climate changes. This paper describes the motivation for the choice of these periods and the design of the numerical experiments and database requests, with a focus on their novel features compared to the experiments performed in previous phases of PMIP and CMIP. It also outlines the analysis plan that takes advantage of the comparisons of the results across periods and across CMIP6 in collaboration with other MIPs.

climate↗

DOE OSTI2020

Microbial potentiometric sensor: A new approach to longstanding challenges

The underlying hypothesis of this study is that simple potentiometric measurements between sensing electrodes and a shared reference electrode - Microbial Potentiometric Sesnor (MPS) system - can be employed in a long-term, continuous mode of operation to resolve the spatial and temporal changes in environmental systems. To address the hypothesis, (1) a conceptual description of the MPS system and its postulated principle of operation are provided; (2) the MPS system performance is documented under controlled laboratory conditions; and (3) the capabilities of the MPS system are documented under quiescent and dynamic field condition. In a laboratory setting, the variability among different MPS signals was insignificant confirming the postulated high accuracy and reproducibility of the sensor performance. It also demonstrated statistically significant correlations with dissolved oxygen (DO) and oxidation-reduction potential (ORP) sensors, while showing capabilities of operating under anoxic and anaerobic conditions. Regardless of their locations in the model wetland system, three MPS sensors functioned without interruption and cleaning for a period >2 years, and thus demonstrating long-term durability of the MPS technology. In real batch-wastewater treatment facility, the deployed MPS system signals were able to describe the organic carbon trends and correlate with each treatment phase in a cycle. Data reproducibility and reliability exceeded the expectations better describing the carbon treatment levels than the DO and ORP sensors (p < 4.4 × 10 –162 vs phase adjusted p < 3.0 × 10 –58 ). While MPS signals correlate with specific parameters that describe the local process or environments, it is more prudent to employ both the magnitude and pattern of a composite signal like the MPS signal describe the change to reflect any shift in the local environment. Finally, when compared to a baseline pattern, this change in signal magnitude and pattern reveals important information that can be employed to tailor and optimize any condition or process which involves microorganisms.

47 OTHER INSTRUMENTATION↗