A new model for ionospheric total electron content: the impact of solar flux proxies and indices
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Larisa P. Goncharenko↗
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Controls on Highly Siderophile Element Concentrations in Martian Basalt: Sulfide Saturation and Under-Saturation
Highly siderophile elements (HSE; Re, Au and the platinum group elements) in shergottites exhibit a wide range from very high, similar to the terrestrial mantle, to very low, similar to sulfide saturated mid ocean ridge basalt (e.g., [1]). This large range has been difficult to explain without good constraints on sulfide saturation or under-saturation [2]. A new model for prediction of sulfide saturation places new constraints on this problem [3]. Shergottite data: For primitive shergottites, pressure and temperature estimates are between 1.2-1.5 GPa, and 1350-1470 C [4]. The range of oxygen fugacities is from FMQ-2 to IW, where the amount of Fe2O3 is low and thus does not have a significant effect on the S saturation values. Finally, the bulk compositions of shergottites have been reported in many recent studies (e.g., [5]). All of this information will be used to test whether shergottites are sulfide saturated [3]. Modeling values and results: The database for HSE partition coefficients has been growing with many new data for silicates and oxides [6-8] to complement a large sulfide database [9- 11]. Combining these data with simple batch melting models allows HSE contents of mantle melts to be estimated for sulfide-bearing vs. sulfide-free mantle. Combining such models with fractional crystallization modeling (e.g., [12]) allows HSE contents of more evolved liquids to be modeled. Most primitive shergottites have high HSE contents (and low S contents) that can be explained by sulfide under-saturated melting of the mantle. An exception is Dhofar 019 which has high S contents and very low HSE contents suggesting sulfide saturation. Most evolved basaltic shergottites have lower S contents than saturation, and intermediate HSE contents that can be explained by olivine, pyroxene, and chromite fractionation. An exception is EET A79001 lithology B, which has very low HSE contents and S contents higher than sulfide saturation values . evidence for sulfide saturation during late fractional crystallization. These results show that shergottite HSE contents are controlled by silicates, oxides, and sulfides. In addition, the mantle producing the most primitive shergottites did not contain near chondritic relative ratios of the HSEs like the terrestrial mantle, and did not experience a late chondritic veneer.
Righter, Kevin↗
Agricultural soil moisture experiment, Colby, Kansas 1978: Measured and predicted hydrological properties of the soil
Predictive procedures for developing soil hydrologic properties (i.e., relationships of soil water pressure and hydraulic conductivity to soil water content) are presented. Three models of the soil water pressure-water content relationship and one model of the hydraulic conductivity-water content relationship are discussed. Input requirements for the models are indicated, and computational procedures are outlined. Computed hydrologic properties for Keith silt loam, a soil typer near Colby, Kansas, on which the 1978 Agricultural Soil Moisture Experiment was conducted, are presented. A comparison of computed results with experimental data in the dry range shows that analytical models utilizing a few basic hydrophysical parameters can produce satisfactory data for large-scale applications.
Arya, L. M.↗
Using AI to build a hydrobiogeochemical soil model
Soil water content is a function of inputs from precipitation and outputs via evaporation, transpiration, lateral flow, and vertical percolation, and is sensitive to biogeochemical processes. As such, soils serve as an ideal integrator of atmospheric, hydrological, and biogeochemical processes affecting the water cycle. In addition, soil water retention capacity, infiltration rates, and hydraulic conductivity can buffer or exacerbate the effects of extreme precipitation events (e.g., flooding, runoff, subsurface transport, erosion, greenhouse gas emissions) and mitigate the impact of droughts and heat waves on land systems (e.g., fire, crop failure). However, integrating water cycle measurements spanning different land atmosphere compartments across scales is a fundamental barrier for numerical model predictability. A significant challenge is that each domain (soil, hydrology, biology, and atmosphere) typically collects different sets of data at different temporal and spatial frequencies/scales, and even different dimensionalities (2D vs 3D). To implement soil as an integrator of the water cycle in land models, we suggest that novel machine learning (ML) tools can be developed to effectively simulate complex landscapes across various domains and scales, extended to regions with sparse or no data. The ultimate goals are to improve predictive understanding of land-atmosphere interactions and to extend the predictability of current Earth System Models (ESMs) through better integration of hydrological and biogeochemical data. We envision a framework in which: (1) ML-aided data reconstructions enable the merger of data sources into a unified geospatial product; (2) automated detection techniques are used to improve the knowledge of complex soil processes and interactions; and (3) this knowledge is leveraged and incorporated into models through AI-based emulators to distinctly connect the land and atmospheric compartments of the water cycle in models.
54 ENVIRONMENTAL SCIENCES↗
Enhancing the Useability of the IAEA's Physical Model: An Analysis of User Feedback
This study presents the results of Physical Model user interviews conducted with 12 people who have International Atomic Energy Agency (IAEA) safeguards experience. The goal of this report is to provide additional context for policy makers when prioritizing the needs of the IAEA regarding support for enhancements to the Physical Model. The interviews covered the general usability of the Physical Model, its effectiveness as a technical reference, and the scope and utility of the Physical Model's indicators and content. The key conclusions from these interviews are as follows: 1) The Physical Model is difficult to find and not well advertised. 2) The Physical Model can be difficult to use due to its format, which makes searching for information difficult, and lack of visual content. Therefore, the Physical Model should be supported in multiple formats (print, PDF, web) to maximize its usability for different stakeholders. 3) The scope of the Physical Model's content and indicators is likely sufficient, and translating indicators into other languages is unlikely to be worth the effort. 4) Despite its inefficiencies and other issues, the Physical Model in its current form is a vital (albeit under-utilized) resource for key stakeholders. 5) Despite its broad use case the Physical Model is not a catch-all tool and force-fitting the Physical Model into functions for which it has not been designed for would likely diminish its value. 6) It can be difficult to disentangle challenges related to the Physical Model and its usability from broader challenges at the Agency, such as general issues with communication and coordination. Based on these conclusions and considering the results of past studies on the Physical Model funded by NA-241, the analysis team developed a list of recommendations and associated actions for IAEA consideration. These recommendations are prioritized based on the assessed level of difficulty for the IAEA to implement a recommendation, emphasizing low hanging fruit then building to more ambitious changes to the Physical Model's format and content.
96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
Initialization of soil-water content in regional-scale atmospheric prediction models
The purpose of this study is to demonstrate the feasibility of determining the soil-water content fields required as initial conditions for land surface components within atmospheric prediction models. This is done using a model of the hydrologic balance and conventional meteorological observations, land cover, and soils information. A discussion is presented of the subgrid-scale effects, the integration time, and the choice of vegetation type on the soil-water content patterns. Finally, comparisons are made between two The Pennsylvania State University/National Center for Atmospheric Research mesoscale model simulations, one using climatological fields and the other one using the soil-moisture fields produced by this new method.
Smith, Christopher B.↗
Field and Model Data Associated with the Manuscript “Drivers of Streamflow Intermittency in Humid Regions: 2. Evaluating Controls on Flow Persistence in an Urbanized Catchment”
This package contains field data, modeling files, and scripts supporting the investigation of the drivers of streamflow intermittency in an urbanized catchment. It includes the field data collected from electrical resistivity tomography (ERT) surveys, distributed temperature sensing (DTS), continuous self-potential (SP) monitoring, groundwater and stilling well. In addition, it contains the data and results of the coupled water- and electrical-flow model developed using the COMSOL Multiphysics and Advanced Terrestrial Simulator (ATS), as well as software files and Jupyter notebooks used to process the data and generate figures in the manuscript submitted for peer review. The data archive is organized in the following directories: 1) Climate Includes hourly precipitation and daily evapotranspiration time series (2024 – 2025) provided as CSV files, alongside a text file detailing dataset units. 2) Coupled_model Field_Application subfolder contains the ATS XML input scripts, data files, output data for the SP site. It also contains the Jupyter notebook (Plot_final_calib.ipynb) to visualize the results of the modeled SP, stream-groundwater exchange and moisture content. The flow model simulation is executed using the ATS XML scripts and the included Python script (generate_data_set.py) to convert ATS output to COMSOL-ready input. COMSOL Multiphysics template (.m can only be used with COMSOL with MATLAB) is executed using the ATS output data to simulate the potential field. 3) Discharge Includes the electrical conductivity (EC) time series (provided as CSV files) from salt slug injections. It also includes the Jupyter notebook (Discharge_process.ipynyb) used to estimate discharge. All discharge measurements collated into rating_curve_processed.csv 4) DTS Contains collated DTS data including raw Stokes and anti-Stokes measurement (provided as .h5 file). It also includes DTS processing.ipynb, a Jupyter notebook for calibrating the DTS data using dts_calibration Python package. cooler_calibration.csv is the DTS calibration CSV used in the calibration sequence. 5) ERT Contains raw resistivity data (provided as CSV files), spatial location of each of the electrodes (provided as CSV files), and files used for the resistivity inversion. 6) Slug_test Includes the slug test data at all the groundwater wells provided as CSV files, as well as the Jupyter notebook (Slug_test.ipynb) for calculating hydraulic conductivity. 7) SP Contains the SP data collected in field at the SP sites (provided as CSV files). 8) Well_data Contains two subfolders: 1) Raw, which provides unprocessed pressure, electrical conductivity and temperature timeseries downloaded from the loggers in all the groundwater and stilling wells, and 2) Processed, which contains sorted, QA/QC timeseries data for each well. The data archive also contains data_process.ipynb, a Jupyter notebook used for field data analysis and generating figures (plotting well, SP, climate, and discharge data, as well as calculating head gradient at sites with nested groundwater wells). Note: Code files (.ipynb, .py, .xml) can be opened in any standard code editor, .exo file can be viewed using Paraview, .h5 files can be opened using HDFView software and h5py Python package, and .resipy file can be opened with the open-source ResIPy software.
ATS↗
Modeling of Different Fiber Type and Content SiC/SiC Minicomposites Creep Behavior
Silicon Carbide based Ceramic Matrix Composites (CMCs) are attractive materials for use in high-temperature applications in the aerospace and nuclear industries. However, creep damage mechanism in CMCs is the most dominant mechanism at elevated temperatures. Consequently, the tensile creep behavior of Hi-Nicalon, Hi-Nicalon Type S SiC fibers and Chemical vapor infiltrated Silicon Carbide matrix (CVI-SiC) were characterized and creep parameters were extracted from creep experiments. Some fiber creep tests were performed in inert environment at 1200 C on individual fibers. Creep behavior of different fiber content pristine and precracked Hi-Nicalon and Hi-Nicalon Type S reinforced minicomposites with BN interphases and CVI-SiC matrix were then modelled using the creep data found in this study and the literature and compared with creep experiments results for the pristine and precracked Hi-Nicalon and Hi-Nicalon Type S minicomposites. Finally, the effects of load-sharing and matrix cracking on CMC creep behavior will be discussed.
ceramic matrix minicomposites↗
Physical retrieval of precipitation water contents using multisensor microwave data and model constraints
A physical retrieval method is proposed for estimating precipitation water distributions and other geophysical parameters from the Special Sensor Microwave/Imager (SSM/I) data. In the proposed method, sensor antenna functions are explicitly included to accommodate varying channel resolution. An embedded one-dimensional cloud model is utilized to generate vertical distributions of precipitating and nonprecipitating water, and spectral solutions are sought for certain background parameters, such as humidity, which vary more slowly in the horizontal than the cloud and precipitation water contents. The method allows the incorporation of measurements from SSM/T and geostationary infrared measurements in addition to SSM/I data, as well as information from conventional sources (e.g., radiosondes) or numerical forecast model fields.
Olson, William S.↗
Presenting Model-Based Systems Engineering Information to Non-Modelers
NASA’s Human Research Program’s (HRP) Exploration Medical Capability (ExMC) Element adopted Systems Engineering (SE) principles and Model Based Systems Engineering (MBSE) tools to capture the system functions, system architecture, requirements, interfaces, and clinical capabilities for a future exploration medical system. There are many different stakeholders who may use the information in the model: systems engineers, clinicians (physicians, nurses, and pharmacists), scientists, and program managers. Many of these individuals do not have access to MBSE modeling tools or have never used these tools. Many of these individuals (clinicians, scientists, even program managers) may have no experience with SE in general let alone interpreting a systems model. The challenge faced by ExMC was how to present the content in the model to non-modelers in a way they could understand with limited to no training in MBSE or the Systems Modeling Language (SysML) without using the modeling tool. Therefore, from the model, ExMC created an HTML report that is accessible to anyone with a browser. When creating the HTML report, the ExMC SE team talked to stakeholders and received their feedback on what content they wanted and how to display this content. Factoring in feedback, the report arranges the content in a way that not only directs readers through the SE process taken to derive the requirements, but also helps them to understand the fundamental steps in an SE approach. The report includes links to source information (i.e., NASA documentation that describes levels of care) and other SE deliverables (e.g., Concept of Operations). These links were provided to aid in the understanding of how the team created this content through methodical SE approach. This paper outlines the process used to develop the model, the data chosen to share with stakeholders, many of the model elements used in the report, the review process stakeholders followed, the comments received from the stakeholders, and the lessons ExMC learned through producing this HTML report.
Jeffrey R. Cohen↗
Presenting Model-Based Systems Engineering Information to Non-Modelers
NASA’s Human Research Program’s (HRP) Exploration Medical Capability (ExMC) Element adopted Systems Engineering (SE) principles and Model Based Systems Engineering (MBSE) tools to capture the system functions, system architecture, requirements, interfaces, and clinical capabilities for a future exploration medical system. There are many different stakeholders who may use the information in the model: systems engineers, requirement engineers, clinicians (doctors, nurses, and pharmacists), scientists, and program managers. Many of these individuals do not have access to MBSE modeling toolsor have never used these tools. Many of these individuals (clinicians, scientists, even program managers)may have no experience with SE in general let alone interpreting a systems model. The challenge faced by ExMC was how to present the content in the model to non-modelers in a way they could understand with limited to no training in MBSE or the Systems Modeling Language (SysML) without using the modeling tool. Therefore, from the model, ExMC created an HTML report that is accessible to anyone with a browser. When creating the HTML report, the ExMC SE team talked to stakeholders and received their feedback on what content they wanted and how to display this content. Factoring in feedback, the report arranges the content in a way that not only directs readers through the SE process taken to derive the requirements, but also helps them to understand the fundamental steps in an SE approach. The report includes links to source information (i.e., NASA documentation that describes levels of care) and other SE deliverables (e.g., Concept of Operations). These links were provided to aid in the understanding of how the team created this content through a methodical SE approach. This paper outlines the process used to develop the model, the data chosen to share with stakeholders, many of the model elements used in the report, the review process stakeholders followed, the comments received from the stakeholders, and the lessons ExMC learned through producing this HTML report.
Jeff Cohen↗
Presenting Model-Based Systems Engineering Information to Non-Modelers
NASA’s Human Research Program’s (HRP) Exploration Medical Capability (ExMC) Element adopted Systems Engineering (SE) principles and Model Based Systems Engineering (MBSE) tools to capture the system functions, system architecture, requirements, interfaces, and clinical capabilities for a future exploration medical system. There are many different stakeholders who may use the information in the model: systems engineers, clinicians (physicians, nurses, and pharmacists), scientists, and program managers. Many of these individuals do not have access to MBSE modeling tools or have never used these tools. Many of these individuals (clinicians, scientists, even program managers) may have no experience with SE in general let alone interpreting a systems model. The challenge faced by ExMC was how to present the content in the model to non-modelers in a way they could understand with limited to no training in MBSE or the Systems Modeling Language (SysML) without using the modeling tool. Therefore, from the model, ExMC created an HTML report that is accessible to anyone with a browser. When creating the HTML report, the ExMC SE team talked to stakeholders and received their feedback on what content they wanted and how to display this content. Factoring in feedback, the report arranges the content in a way that not only directs readers through the SE process taken to derive the requirements, but also helps them to understand the fundamental steps in an SE approach. The report includes links to source information (i.e., NASA documentation that describes levels of care) and other SE deliverables (e.g., Concept of Operations). These links were provided to aid in the understanding of how the team created this content through a methodical SE approach. This paper outlines the process used to develop the model, the data chosen to share with stakeholders, many of the model elements used in the report, the review process stakeholders followed, the comments received from the stakeholders, and the lessons ExMC learned through producing this HTML report.1Trade names and trademarks are used in this report for identification only. Their usage does not constitute an official endorsement, either expressed or implied, by the National Aeronautics and Space Administration.
Jeffrey R. Cohen↗
Use of Lorente de No's neuron circuit model for describing acceleratory nystagmus
Results of a previous metric analysis and an electronic simulation of acceleratory nystagmus are given. On this basis, a tentative mathematical model for describing acceleratory nystagmus is reported. The essential content of the model is Lorente de No's neuron circuit, to which the two-factor theory of excitation has been applied.
Valentinuzzi, M.↗
I Can’t Read All That! Improving the Usability of Semantic Models Using Concise, Ontology-Agnostic, Building-Specific Schemas
Semantic ontologies have enabled the creation of formalized, machine-readable descriptions of heterogenous building systems by providing dictionaries of well defined concepts that can be applied to model them. Within a semantic model of a particular building, a subset of an ontology's concepts may be applied in different ways to represent a particular perspective of the building's systems. How the concepts were applied can only be understood by examining the large amount of instance data within a semantic model, which leads to usability challenges. We propose a concise, ontology-agnostic method for defining building-specific schema (b-schema) graphs that summarize the structure and content of a semantic model. This approach provides a queryable and concise representation of the model's contents, separate from the instance data within a model, that can mitigate the challenges posed by the size and complexity of semantic models in processes such as visualization, querying, validation, and the use of large language models (LLMs). We validate our approach on semantic models based on the Brick and ASHRAE S223 ontologies. Results demonstrate that b-schemas significantly reduce the complexity of visual interpretation, accelerate SPARQL queries and SHACL validation, and improve LLM-based knowledge graph question answering.
Paul, Lazlo [Lawrence Berkeley National Laboratory↗
Megaregolith insulation, internal temperatures, and bulk uranium content of the moon
Finite-difference models are used to study the effects of insulation by the megaregolith on lunar thermal evolution. Results indicate that the megaregolith has two important influences on heat flow: (1) Because the megaregolith is exceptionally thin in mare regions, heat passes more readily through them than through highland regions, and even flows laterally from the highland toward the mare. As a result, heat flow is exceptionally high along a boundary between highland and mare regions. (2) On a global scale, megaregolith insulation combined with lithosphere insulation causes slow cooling, which as a cumulative effect results in high present-day mantle temperatures and heat flow. Assuming that the global mean megaregolith thickness is 2 km, a heat flow of 12 mW/sq m is best matched by models with bulk moon U contents of 20-21 ng/g. Independent constraints on lunar internal temperatures derived from magnetic and tectonic data are best matched by models with about 14 ng/g U. Thus the bulk moon U content is roughly 17 ng/g. These results imply that the bulk moon contents of U, and related refractory lithophile elements such as Th, Al, Ca, etc., must be considerably lower than commonly assumed.
Warren, Paul H.↗
Ground-based_transient_electromagnetic_data_and_resistivity_models_for_SubTER_BelleCreek_2017-2018
Ground-based transient electromagnetic (TEM) data were acquired in selected locations around Belle Creek, Montana to define the resistivity structure of the near surface. Data were acquired using the ABEM WalkTEM system (Guideline Geo Ab, Sundbyberg, Sweden). TEM data were processed and numerically inverted to derive one-dimensional resistivity structure using SPIA (Aarhus Geosoftware, Aarhus, Denmark). This release includes raw and processed TEM data and resistivity models for each data location using the following structure: Ground-based_transient_electromagnetic_data_and_resistivity_models_for_SubTER_BelleCreek_2017-2018.xml SBC_TEM_2019_SoundingLocationsModels.gdb.zip - file geodatabase with all models and plots attached. This is a mirror of the contents of the model directory. Ground-based_transient_electromagnetic_data_and_resistivity_models_for_SubTER_BelleCreek_2017-2018.zip Data SBC_USF - raw data USF_format_description.pdf - manual describing the USF file format SBC_SoundingID-USF_lookup.csv - table describing the USF file naming convention and channel mapping *.usf files - raw, unprocessed data files SBC_TEM - processed data AarhusInv_manual.pdf - manual describing the TEM file format *.tem files - processed data files Models SBC_Model_Plots - plots of the data and the models for each sounding location TEM_model_data_dictionary.csv - data dictionary describing tabulated models SBC_TEM_models.csv - tabulated models
geophysical surveying↗
Safety Analysis Report For Packaging, Y-12 National Security Complex, Model ES-3100 Package with Bulk HEU Contents
This safety analysis report for packaging (SARP) presents the results of the safety analysis prepared in support of the Consolidated Nuclear Security, LLC (CNS) request for licensing of the Model ES-3100 package with bulk highly enriched uranium (HEU) contents and issuance of a Type B(U) Fissile Material Certificate of Compliance. This SARP, published in the format specified in the U.S. Nuclear Regulatory Commission (NRC) Regulatory Guide 7.9 and using information provided in UCID-21218 and NRC Regulatory Guide 7.10, demonstrates that the Y-12 National Security Complex (Y-12) ES-3100 package with bulk HEU contents meets the established NRC regulations for packaging, preparation for shipment, and transportation of radioactive materials given in Title 10, Part 71, of the Code of Federal Regulations (CFR) [10 CFR 71] as well as U.S. Department of Transportation (DOT) regulations for packaging and shipment of hazardous materials given in Title 49 CFR.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Filling the Gaps: A Bayesian Mixture Model for Imputing Missing Soil Water Content Data
ABSTRACT Soil water content (SWC) data are central to evaluating how soil moisture varies over time and space and influences critical plant and ecosystem functions, especially in water‐limited drylands. However, sensors that record SWC at high frequencies often malfunction, leading to incomplete timeseries and limiting our understanding of dryland ecosystem dynamics. We developed an analytical approach to impute missing SWC data, which we tested at six eddy flux tower sites along an elevation gradient in the southwestern United States. We impute missing data as a mixture of linearly interpolated SWC between the observed endpoints of a missing data gap and SWC simulated by an ecosystem water balance model (SOILWAT2). Within a Bayesian framework, we allowed the relative utility (mixture weight) of each component (linearly interpolated vs. SOILWAT2) to vary by depth, site and gap characteristics. We explored “fixed” weights versus “dynamic” weights that vary as a function of cumulative precipitation, average temperature, and time since the start of the gap. Both models estimated missing SWC data well ( R 2 = 0.70–0.88 vs. 0.75–0.91 for fixed vs. dynamic weights, respectively), but the utility of linearly interpolated versus SOILWAT2 values depended on site and depth. SOILWAT2 was more useful for more arid sites, shallower depths, longer and warmer gaps and gaps that received greater precipitation. Overall, the mixture model reliably gap‐fills SWC, while lending insight into processes governing SWC dynamics. This approach to impute missing data could be adapted to accommodate more than two mixture components and other types of environmental timeseries.
Ogle, Kiona [School of Informatics, Computing, and↗