A new process-based cotton model, CPM, has been developed to simulate the growth and development of upland cotton (Gossypium hirsutum L.) throughout the growing season with minimal data input. CPM predicts final cotton yield for any combination of soil, weather, cultivar and sequence of management actions. Over the last 30 years, the U.S. Department of Agriculture's (USDA) Agricultural Research Service (ARS) has conducted a wide range of research on cotton, including work to develop a series of "production models" designed to serve as decision aids to cotton producers. In 1996, ARS decided to develop a new "second generation" Cotton Production Model (CPM) that would retain the best features of the earlier versions in a new, more versatile, and more user friendly framework. The development process was completed to the stage of beta-testing, when the need to redirect limited resources to other priorities caused ARS to decide not to complete the validation process. ARS believes that CPM, while only partially validated, has the potential to make useful contributions to American cotton producers when completed. For these reasons, ARS decided to make the model available for further development and commercialization. The Cotton Production Model (CPM) was developed with a modular structure using an object-oriented programming language, C++. The model draws upon the latest scientific knowledge available, and is intended to be used with a wide variety of cotton types across the entire US Cotton Belt. CPM is written in C++ using a new modular structure that allows flexibility and adaptability. This object-oriented structure should allow modules to be incorporated into process-based models of other crop species (see Acock, B. and V. R. Reddy. 1977. Designing an object-oriented structure for crop models. Ecological Modeling 94: 33-44). In addition to being modular and generic, CPM has other advantages over earlier models. Compared to previous cotton models, CPM is more robust, more user-friendly, more easily maintained, and more easily updated with future advances in science. The algorithms that simulate crop growth are derived in part from the best of each of the previous models, and they incorporate new physiological information as well. A new feature of CPM is that it incorporates 2DSOIL, an excellent up-to-date soil and root process model (see Timlin, D. J., Y. Pachepsky, and B. Acock. 1996. A design for a modular, generic soil simulator to interface with plant models. Agronomy Journal 88:162-169 ). 2DSOIL tracks water movement through the soil-plant-atmosphere continuum with hourly time-steps. It also incorporates a new model of plant water relations that responds realistically to water stress. CPM has updated treatments of carbon and nitrogen stresses compared to previous models, and it is designed for easy addition of responses to phosphorus and potassium. Because the growth of each leaf, inter-node and fruit is simulated separately, CPM should be easily linked to pest or disease models. CPM has the potential to be useful as a decision aid for cotton farmers and crop production consultants. If fully developed, it would be a valuable tool to optimize management inputs such as irrigation, fertilization, plant growth regulators, and defoliant application prior to harvest. In its current version, however, CPM has not yet been fully validated to be useful as a decision aid. The released version of CPM should be considered an advanced model suitable for research purposes. ARS does not endorse its use for any other purpose at this time. Of particular importance to a decision aid model is the user interface. The interface under which CPM has been developed and tested is one that was earlier developed for the soybean model, GLYCIM, and has been documented elsewhere (Acock, B., Pachepsky, Y. A., Mironenko, E. V., Whisler, F. D., and Reddy, V. R. 1999. GUICS: A Generic User Interface for On-Farm Crop Simulations. Agronomy Journal. 91:657-665). CPM is part of the current release of GUICS.

This submission contains slow strain rates summed to radians over 30 second intervals [rad/s] derived from horizontal distributed acoustic sensing measurements (DASH) of Brady geothermal field during PoroTomo deployment (2016-Mar-14 to 2016-Mar-26). There is one file corresponding to each day written in *.mat format for use with Matlab. The format for the binary Matlab .mat files are defined at: https://www.mathworks.com/help/pdf_doc/matlab/matfile_format.pdf. One such file includes the following variables: 'flist': list of raw DASH files used in the summation 'time_tag_mdt': sample time tag in datetime format with hours given in 24-hr format (yyyy/MM/dd HH:mm:ss.SSSSSSS) 'time_tag_uts': sample time tag in Unix time 'strain_rate_summed_over30s_in_radians_per_second': slow strain rates summed over 30 second intervals in units rad/s 'sample_standard_deviation_in_radians_per_second': corresponding sample standard deviation of slow strain rates in units rad/s The PoroTomo final technical report, raw DASH data, and software repository are also available through the links below.

A DOE software services platform and search tool for DOE-funded code. DOE CODE provides functionality for collaboration, archiving, and discovery of scientific and business software. DOE CODE replaces OSTI's old software center, the Energy Science and Technology Software Center (ESTSC).

The link points to a website at NCEDC to download the full moment tensors inversion software The moment tensor analysis conducted in the current project is based on the full moment tensor model described in Minson and Dreger (2008). The software including source, examples and tutorial can be obtained from ftp://ncedc.org/outgoing/dreger (download file pasi-nov282012.tar.gz). Performance criteria, mathematics and test results are provided by Minson and Dreger (2008), Ford et al. (2008, 2009, 2010, 2012) and Saikia (1994). References: Ford, S., D. Dreger and W. Walter (2008). Source Characterization of the August 6, 2007 Crandall Canyon Mine Seismic Event in Central Utah, Seism. Res. Lett., 79, 637-644. Ford, S. R., D. S. Dreger and W. R. Walter (2009). Identifying isotropic events using a regional moment tensor inversion, J. Geophys. Res., 114, B01306, doi:10.1029/2008JB005743. Ford, S. R., D. S. Dreger and W. R. Walter (2010). Network sensitivity solutions for regional moment tensor inversions, Bull. Seism. Soc. Am., 100, p. 1962-1970. Ford, S. R., W. R. Walter, and D. S. Dreger (2012). Event discrimination using regional moment 665 tensors with teleseismic-P constraints, Bull. Seism. Soc. Am. 102, 867-872. Minson, S. and D. Dreger (2008), Stable Inversions for Complete Moment Tensors, Geophys. J. Int., 174, 585-592. Saikia, C.K. (1994), Modified Frequency-Wavenumber Algorithm for Regional Seismograms using Filons Quadrature: Modeling of Lg Waves in Eastern North America. Geophys. J. Int., 118, 142-158.

This file contains a zipped file that contains many files required to run GEO2D. GEO2D is a computer code for simulating ground source heat pump (GSHP) systems in two-dimensions. GEO2D performs a detailed finite difference simulation of the heat transfer occurring within the working fluid, the tube wall, the grout, and the ground. Both horizontal and vertical wells can be simulated with this program, but it should be noted that the vertical wall is modeled as a single tube. This program also models the heat pump in conjunction with the heat transfer occurring. GEO2D simulates the heat pump and ground loop as a system. Many results are produced by GEO2D as a function of time and position, such as heat transfer rates, temperatures and heat pump performance. On top of this information from an economic comparison between the geothermal system simulated and a comparable air heat pump systems or a comparable gas, oil or propane heating systems with a vapor compression air conditioner. The version of GEO2D in the attached file has been coupled to the DOE heating and cooling load software called ENERGYPLUS. This is a great convenience for the user because heating and cooling loads are an input to GEO2D. GEO2D is a user friendly program that uses a graphical user interface for inputs and outputs. These make entering data simple and they produce many plotted results that are easy to understand. In order to run GEO2D access to MATLAB is required. If this program is not available on your computer you can download the program MCRInstaller.exe, the 64 bit version, from the MATLAB website or from the Geothermal Data Repository. This is a free download which will enable you to run GEO2D.

Data about the Prototype receiver’s hardware design, its software design and the testing performed to verify the receiver meets the performance requirements for the various services. Includes Notice Regarding Use of ETTUS Corp Documentation.

The Geothermal Resource Portfolio Optimization and Reporting Technique (GeoRePORT) was developed with funding from the U.S. Department of Energy Geothermal Technologies Office to assist in identifying and pursuing long-term investment strategies through the development of a resource reporting protocol. GeoRePORT provides scientists and nonscientists a comprehensive and quantitative means of reporting: (1) features intrinsic to geothermal sites (project grade) and (2) maturity of the development (project readiness). Because geothermal feasibility is not determined by any single factor (e.g., temperature, permeability, permitting), a site?s project grade and readiness are evaluated on 12 attributes pertaining to geological, technical, or socio-economic feasibility. In this submission, we present the geological, socio-economic, and technical protocols as well as the spreadsheet template for easy data entry and reporting of the GeoRePORT protocol.

The main objective of the developed software is to reduce the cost per foot during drilling, in other words, optimize the drilling operational parameters in achieving optimum ROP while avoiding critical operational parameters due to either low ROP, drillstring vibration, accelerated cutter wear, or low MSE. The developed software can also be used for post-well analysis to provide insight and lessons learned for future drilling operations. Several functions are available in the software to help the user perform drilling analysis, optimization, and simulation.

A set of PYTHON programs to implement image processing of ground and aerial images by offering via graphical user interface (GUI) 1) plot-level metrics extraction through a series of algorithms for image conversion, band math, radiometric/geometric calibrations, segmentation, masking, adaptive region of interest (ROI), gridding, heatmap, and batch process, 2) GIS interface for GeoTIFF pixels to Lat/Lon, UTM conversion, read/write shapefile, Lat/Lon to ROI, grid to polygon, and 3) utility GUI functions for zooming, panning, rotation, images to video, file I/O, and histogram.

Software developed in LabVIEW for the Modular Ocean Instrumentation System (MOIS) is provided. Two documents: MOIS User's Guide and MOIS Software Developer's Guide are included in the submission.

The New River Geothermal Exploration (DOE Award No. EE0002843) is located approximately 25km south of the Salton Sea, near town of Brawley in Imperial County and approximately 150km east of San Diego, California. A total of 182 MT Logger sites were completed covering the two separate Mesquite and New River grids. The data was collected over a frequency range of 320Hz to 0.001Hz with variable site spacing. A number of different inversion algorithms in 1D, 2D and 3D were used to produce resistivity-depth profiles and maps of subsurface resistivity variations over the survey area. For 2D inversions, a total of eighteen lines were constructed in east-west and north-south orientations crossing the entire survey area. For MT 3D inversion, the New River property was divided in two sub-grids, Mesquite and New River areas. The report comprises of two parts. For the first part, inversions and geophysical interpretation results are presented with some recommendations of the potential targets for future follow up on the property. The second part of the report describes logistics of the survey, survey parameters, methodology and the survey results (data) in digital documents. The report reviews a Spartan MT survey carried out by Quantec Geoscience Limited over the New River Project in California, USA on behalf of Ram Power Inc. Data was acquired over a period of 29 days from 2010/06/26 to 2010/07/24.

Contains the AIRMaster+ tool as well as 5 pdfs. AIRMaster+ is a free online software tool that helps users analyze energy use and savings opportunities in industrial compressed air systems. Use AIRMaster+ to baseline existing and model future system operations improvements, and evaluate energy and dollar savings from many energy efficiency measures. AIRMaster+ provides a systematic approach to assessing compressed air systems, analyzing collected data, and reporting results. Internet Archive URL: https://web.archive.org/web/*/https://www.energy.gov/eere/amo/downloads/airmaster

The Soil and Water Hub is jointly developed by USDA Agricultural Research Service (USDA-ARS) and Texas A&M AgriLife Research, part of The Texas A&M University System. Modeling dataset resources are available for download for use with software tools Agricultural Policy/Environmental eXtender Model (APEX), Soil and Water Assessment Tool (SWAT), ArcSWAT, and related Conservation practices.

STA Tool – a virtual research assistant designed to: • Organize and visualize disparate big data and knowledge resources • Simplify geologic domain formation • Provide and execute multi-dimensional statistical analyses and validation • Utilize ML to characterize property trends and predictions The Subsurface Trend Analysis (STA) Tool utilizes the STA method as a framework for integrating machine learning and artificial intelligence to accelerate subsurface analyses. The Subsurface Trend Analysis method (Rose et al., 2020) is a validated methodology that improves prediction of subsurface properties, even in areas with little to no data. This download includes the 2D version of the STA Tool. A 3D version is forthcoming. This work was conducted under the Advanced Offshore Research Portfolio, FWP Number 1022409 at National Energy Technology Laboratory, U.S. Dept. of Energy. For more information: https://edx.netl.doe.gov/offshore/portfolio-items/geohazards-subsurface-uncertainty-modeling/ Rose, K., Bauer, J.R., and Mark-Moser, M., “A Systematic, Science-Driven Approach for Predicting Subsurface Properties,” Interpretation, 8:1 (2020), 167–181, https://doi.org/10.1190/INT-2019-0019.1.

This tool evaluates the potential occurrence of URC resources using a series of validated heuristics as outlined in "Creason, C.G., Justman, D., Rose, K. et al. A Geo-Data Science Method for Assessing Unconventional Rare-Earth Element Resources in Sedimentary Systems. Nat Resour Res (2023). https://doi.org/10.1007/s11053-023-10163-x"

WinSRFR is a hydraulic analysis tool for surface irrigation systems. The software combines simulation, evaluation, operational analysis, and design functionalities. Intended users are irrigation specialists, extension agents, researchers, consultants, students, and farmers with moderate to advanced knowledge of surface irrigation hydraulics. WinSRFR 5.1 is the fifth major release of the software. The new version offers a reprogrammed simulation engine, an application programming interface, batch simulation capabilities, and enhancements to the user interface.

GUI-based software coded in PYTHON to automate image stitching and alignment processes from a set of tile images for the high throughput image analytics by implementing a series of algorithms: 1) deskewing the image acquired in an oblique view angle, 2) row alignment of the geometrically drifted image due to acquisition errors by detecting the crop row using Hough Transformation, and 3) options for omnidirectional overlap trimming and resizing.