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At least 397 records · Page 22

All sources NASA NTRS DOE OSTI

DOE OSTI2025

MTUQ: a framework for estimating moment tensors, point forces, and their uncertainties

SUMMARY We introduce MTUQ, an open-source Python package for seismic source estimation and uncertainty quantification, emphasizing flexibility and operational scalability. MTUQ provides MPI-parallelized grid search and global optimization capabilities, compatibility with 1-D and 3-D Green’s function database formats, customizable data processing, C-accelerated waveform and first-motion polarity misfit functions, and utilities for plotting seismic waveforms and visualizing misfit and likelihood surfaces. Applicability to a range of full- and constrained-moment tensor, point force, and centroid inversion problems is possible via a documented application programming interface, accompanied by example scripts and integration tests. We demonstrate the software using three different types of seismic events: (1) a 2009 intraslab earthquake near Anchorage, Alaska; (2) an episode of the 2021 Barry Arm landslide in Alaska; and (3) the 2017 Democratic People’s Republic of Korea underground nuclear test. With these events, we illustrate the well-known complementary character of body waves, surface waves, and polarities for constraining source parameters. We also convey the distinct misfit patterns that arise from each individual data type, the importance of uncertainty quantification for detecting multimodal or otherwise poorly constrained solutions, and the software’s flexible, modular design.

58 GEOSCIENCES↗

DOE OSTI2025

Fragme∩t: An Open‐Source Framework for Multiscale Quantum Chemistry Based on Fragmentation

Fragment-based quantum chemistry offers a means to circumvent the nonlinear computational scaling of conventional electronic structure calculations, by partitioning a large calculation into smaller subsystems then considering the many-body interactions between them. Variants of this approach have been used to parameterize classical force fields and machine learning potentials, applications that benefit from interoperability between quantum chemistry codes. However, there is a dearth of software that provides interoperability yet is purpose-built to handle the combinatorial complexity of fragment-based calculations. To fill this void we introduce “Fragme∩t”, an open-source software application that provides a tool for community validation of fragment-based methods, a platform for developing new approximations, and a framework for analyzing many-body interactions. Fragme∩t includes algorithms for automatic fragment generation and structure modification, and for distance- and energy-based screening of the requisite subsystems. Checkpointing, database management, and parallelization are handled internally and results are archived in a portable database. Interfaces to various quantum chemistry engines are easy to write and exist already for Q-Chem, PySCF, xTB, Orca, CP2K, MRCC, Psi4, NWChem, GAMESS, and MOPAC. Applications reported here demonstrate parallel efficiencies around 96% on more than 1000 processors but also showcase that the code can handle large-scale protein fragmentation using only workstation hardware, all with a codebase that is designed to be usable by non-experts. Fragme∩t conforms to modern software engineering best practices and is built upon well established technologies including Python, SQLite, and Ray. The source code is available under the Apache 2.0 license.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

optimas v0.1

optimas is a Python library that can coordinate a large number of numerical simulations on high-performance computing resources, with the aim of optimizing a given simulation result. More specifically, this library is currently used for design optimization of laser-plasma particle accelerators. In this context, the performance of particle accelerator designs are often evaluated with large-scale simulation codes, and many separate simulations need to be run - with different design parameters - in order to find the most performant design. optimas facilitates this process by providing a convenient interface to advanced optimizers (e.g. Bayesian optimization), and by coordinating the execution of the different simulations on HPC resources. (This is done by leveraging the library libensemble.) Compared to other open-source optimization libraries (e.g. Ax), optimas is more tailored towards execution on DOE HPC resources (e.g. Perlmutter, Summit, etc.) and is specialized for the type of simulation codes and workflows that are used in the community of laser-plasma acceleration.

Lehe, Remi↗

DOE OSTI2025

kokkos-fft: A shared-memory FFT for the Kokkos ecosystem

kokkos-fft provides a unified, performance-portable interface for Fast Fourier Transforms (FFTs) within the Kokkos ecosystem (C. Trott et al., 2021). It seamlessly integrates with leading local FFT libraries including FFTW, cuFFT, rocFFT, and oneMKL. Designed for simplicity and efficiency, kokkos-fft offers a user experience akin to numpy.fft for in-place and out-of-place transforms, while leveraging the raw speed of vendor-optimized libraries. A demonstration solving 2D Hasegawa-Wakatani turbulence with the Fourier spectral method illustrates how kokkos-fft can deliver significant speedups over Python-based alternatives without drastically increasing code complexity, empowering researchers to perform high-performance FFTs simply and effectively.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Hydroscanner: Instrumentation for Water Ingress Imaging in Photovoltaic Module Packaging Materials

A new iteration of the Hydroscanner prototype was designed and built during the execution of the contract. Compared to the previous designs, the communication port was changed from ethernet to USB C, since it provides a more seamless integration as a peripheral unit. An additional advantage is that one single port can be used for both communication and power up of the infrared camera. Although the power requirements are higher than the typical amount provided by a laptop computer, the camera can be run as a peripheral from a docking station when connected to a thunderbolt charging port. The software interface was modified to accommodate two imaging modes, an 8-bit and 14-bit image display. The 8-bit images use a custom colorization scheme that divides all pixels in the image into bins of equal width, such that images containing a wide range of brightness display the intermediate pixel intensities in greyscale values that enhance the contrast. The 14-bit images provide the direct readout from the focal plane array of the camera and can be saved as a text file for later processing in other software packages (MATLAB, Python).

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

JHTDB-wind: a web-accessible large-eddy simulation database of a wind farm with virtual sensor querying

This paper introduces JHTDB-wind (https://turbulence.idies.jhu.edu/datasets/windfarms, last access: 11 November 2025), a publicly accessible database containing large-eddy simulation (LES) data from wind farms. Building on the framework of the Johns Hopkins Turbulence Database (JHTDB), which hosts direct numerical simulation (DNS) and some LES datasets of canonical turbulent flows, JHTDB-wind stores the 4D space–time history of the flow and provides users the ability to access and query the data via a web-based virtual sensor interface. The initial dataset comprises LES results from a large wind farm with 10×6 turbines, modeled using a filtered actuator line method, under conventionally neutral atmospheric conditions. These data comprise 1 h (hour) of flow field data (velocity, pressure, potential temperature deviation, subgrid-scale (SGS) eddy viscosity, and turbine forces, approximately 15 TB (terabytes) and wind turbine data – including both turbine-level operational quantities and blade-level aerodynamic quantities (approximately 1.3 TB) – stored in Zarr and Parquet formats, respectively. Data retrieval is facilitated by the giverny Python package, allowing remote users to query the database in Python or MATLAB (C and Fortran support are available for flow field data). This paper details the simulation setup and demonstrates data access through examples that analyze wind farm flow structures and turbine performance. The framework is extensible to future datasets, including the JHTDB-wind diurnal cycle simulation analyzed in Xiao et al. (2025).

17 WIND ENERGY↗

DOE OSTI2023

HLA-Clus: HLA class I clustering based on 3D structure

In a previous paper, we classified populated HLA class I alleles into supertypes and subtypes based on the similarity of 3D landscape of peptide binding grooves, using newly defined structure distance metric and hierarchical clustering approach. Compared to other approaches, our method achieves higher correlation with peptide binding specificity, intra-cluster similarity (cohesion), and robustness. Here we introduce HLA-Clus, a Python package for clustering HLA Class I alleles using the method we developed recently and describe additional features including a new nearest neighbor clustering method that facilitates clustering based on user-defined criteria. The HLA-Clus pipeline includes three stages: First, HLA Class I structural models are coarse grained and transformed into clouds of labeled points. Second, similarities between alleles are determined using a newly defined structure distance metric that accounts for spatial and physicochemical similarities. Finally, alleles are clustered via hierarchical or nearest-neighbor approaches. We also interfaced HLA-Clus with the peptide:HLA affinity predictor MHCnuggets. By using the nearest neighbor clustering method to select optimal allele-specific deep learning models in MHCnuggets, the average accuracy of peptide binding prediction of rare alleles was improved. The HLA-Clus package offers a solution for characterizing the peptide binding specificities of a large number of HLA alleles. This method can be applied in HLA functional studies, such as the development of peptide affinity predictors, disease association studies, and HLA matching for grafting. HLA-Clus is freely available at our GitHub repository (https://github.com/yshen25/HLA-Clus).

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

TUMME: Tsinghua University Minnesota Master Equation program

We report that TUMME is a program for assembling and solving master equations for gas-phase chemical kinetics based on chemically significant eigenmodes. TUMME has interfaces to the Gaussian, Polyrate, and/or MSTor output files that allow the master equation code to obtain the microcanonical flux coefficients needed for the coefficient matrix of the master equation. The flux coefficients for reactions with barriers can be calculated by multi-structural variational transition state theory with small-curvature tunneling (MS-VTST/SCT) or by simpler approximations to this such as conventional transition state theory without tunneling (also called RRKM theory). The flux coefficients for barrierless reactions are provided by a hard-sphere model. TUMME is written in double precision with Python 3; quadruple and octuple precision are also available for some subtasks in C++. The Python code can run in serial or parallel (MP or MPI), and the C++ code can run on a single processor or on multiple processors with OpenMP.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

GridDS: Data Science Toolkit for Energy Grid Data

According to the U.S. Energy Information Administration (EIA), the demand for energy is expected to increase 50% by the year 20501. While energy standards, such as the Institute of Electrical and Electronics Engineers (IEEE) Standard 1547, (Basso 2015) and monitoring with wide area management systems (WAMS) (Liu 2017, Zhou 2016) have enabled large scale data collection and storage, the application of this data in mitigating costs associated with increased consumer demand is an ongoing focus for energy research. This ubiquitous data collection presents a promising opportunity for machine learning and data science to improve efficiency of distributed energy resources (DERs). The GridDS software toolkit is designed to leverage advanced metering infrastructure (AMI), outage management systems data (OMS), Supervisory control Data Acquisition (SCADA), and geographic information systems (GIS) to forecast future energy demands and detect incipient grid failures. GridDS is a python software library designed to be modular and generalizable to data recorded by DERs. In adapting to disparate datasets recorded by various WAMS, GridDS provides a range of unique functionality not presently implemented in current WAMS which have highly specific software infrastructure by design. GridDS functionality ranges from data specification and preparation, to training and validation for state of the art machine learning, to interactive data visualization. For data intake, GridDS combines: Pandera: a library for creating data specifications. TimeScaleDB: a postgresSQL database infrastructure for efficient storage of timeseries data. Dataset class: A custom dataset class / interface that ensures modularity between a range of synthetic and live recorded datasets. Is

Ladd, Alexander↗

NASA NTRSDate not supplied

Building a Real-Time Flood Prediction Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the use of applied remote sensing analyses is increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP National Program partnered with the local government of Howard County, Maryland, to investigate the use of machine learning for advanced flood risk detection, and to test the feasibility of integrating this approach into the county’s flood early warning system. To strengthen the efforts of the Howard County Office of Emergency Management (OEM), the project developed a statistical model capable of hindcasting the two severe flash flood events that devastated Ellicott City and transitioned to a ‘Long Short-Term Memory’ based sequence-to-sequence deep learning model with 8-hour forecast capability. The team combined data inputs from public sources including river and precipitation gauges, NASA and NOAA Earth observations, and numerical weather model products using scripts written in the Google Colaboratory Python scripting environment. In addition to designing the deep learning architecture, the team trained and tested the model, and evaluated its performance using Nash-Sutcliffe Efficiency. The final product, the Sequentially Trained Real-time EstimAted Model (STREAM) predicts stage height for the Hudson Branch gauge in Ellicott City using data products available in near real-time, including the High-Resolution Rapid Refresh model’s accumulated precipitation forecasts supplemented by stream gauge data from the OEM and the U.S. Geological Survey. STREAM was incorporated into an online dashboard in a user-friendly interface capable of triggering the alarms that initiate emergency response protocols up to 8 hours in advance of a predicted severe flood event. The project demonstrated the potential for the integration of open data and Earth observations into a flood risk forecasting tool capable of informing near real-time decision making.

NASA DEVELOP↗

DOE OSTI2022

GIS-Based Graphical User Interface Tools for Analyzing Solar Thermal Desalination Systems & High-Potential Implementation

This project developed a user-friendly, open-source, software that enables a comparative evaluation of solar thermal desalination technology options and employs geospatial data layers to identify regions of high-potential for solar thermal desalination. This was accomplished by integrating solar models with desalination models and enhancing their utility by providing GIS-based data inputs. The developed Solar Energy Desalination Analysis Tool (SEDAT) enables techno-economical evaluation of desalination technologies and selection of regions with the highest potential for using solar energy to power desalination plants. It simplifies the planning, design, and valuation of solar thermal and solar hybrid desalination systems in the U.S. and worldwide. SEDAT uses Dash for integrating various layers of large volumes of GIS data with Python-based models of solar energy generation and desalination technologies. It derives time-series of energy generation and water production, with details of plant performance and suggestions for improving the solar-desalination coupling. It is a one of-a-kind tool of analysis representing a definitive advancement in the state-of-the-art. Of solar desalination modeling This report summarizes the various phases of the tool’s development, and presents examples of the results.

14 SOLAR ENERGY↗

DOE OSTI2026

GRIDAPPSD/distopf (33583-E)

DistOPF is an open-source Python package providing a three-phase, asymmetric optimal power flow (OPF) tool specifically designed for distribution systems. The key inventive features include: - Asymmetrical 3-phase OPF modeling for distribution systems with unbalanced phases - Comprehensive control optimization supporting both active (P) and reactive (Q) power control variables - Built-in visualization and validation tools - Standard test system benchmarking platform for algorithm development and comparison - Modular CSV-based input system using Pandas DataFrames for flexible model specification - Standard power distribution model importer enabling direct conversion from CIM and OpenDSS format to optimization-ready models - Multiple solve interface compatibility (PYOMO, CVXPY, SciPy) with automatic solver selection based on problem type

Gray, Nathan [Pacific Northwest National Laborator↗

DOE OSTI2020

HFIRCON Version 1.0.5 User Guide

The High Flux Isotope Reactor (HFIR) Controller (HFIRCON) code is a collection of python routines and C plugins that automate the workflow for fuel and single- or multicycle target depletion analyses for HFIR at Oak Ridge National Laboratory (ORNL). This code calls the LAVAMINT (LAVA Model Interrogator) parallel (MCNP) Monte Carlo N-Particle model interrogator to stochastically calculate cell volumes and bounding boxes, the ADVANTG (Automatic Variance Reduction Generation) code package for all variance reduction and source biasing calculations, the ORNL-Transformative Neutronics/MCNP5 transport solver for all transport solutions, and the MSX_DEPLETE module to perform all depletion calculations via the ORIGEN (Oak Ridge Isotope Generation) application programming interface. It also performs a robust set of postprocessing functions to automatically provide summaries of several key metrics that are common to a wide variety of typical HFIR design and safety-basis analyses.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Nanoporous Tio2 Water Training Data

Data and input files used to train a Deep Potential (DP) model for the nanoporous TiO2-water interface. The DeepMD-kit code was used to train the DP. Information about data format, and how to use DeepMD-kit can be found at https://docs.deepmodeling.com/projects/deepmd/en/master/

08 HYDROGEN↗

NASA NTRSDate not supplied

AMMPER: a user-friendly agent-based model that recapitulates simple metabolic responses of yeast to deep-space radiation

For humans venturing to deep space, radiation exposure poses a major health risk. Fundamental research into the biological effects of space radiation are essential for enabling exploration, and the first experimental organisms we send to deep space will be microbial. Yet there are many ways in which microorganisms are likely to experience the effects of high-energy particle radiation (such as Galactic Cosmic Rays) differently from multicellular animals, partly due to the simple fact that microbes are small and unicellular-- less likely to get hit in the first place, and less likely to communicate damage between cells. Computational modeling can aid in designing experiments and predicting the biological effects of radiation, but thus far particle radiation models have not focused on microbes. Here we present the latest developments in AMMPER, the Agent-based Model for Microbial Populations Exposed to Radiation. Originally written in 2021, AMMPER is a Python-based model that incorporates radiation track data from NASA's RITRACKS software and simulates the growth, damage, and death of yeast cells in 3D. It is now freely available as an open-source package on NASA's GitHub repository. Recent improvements include the ability to simulate the dynamics of alamarBlue, a color-changing redox dye commonly used to track metabolic activity in microbial spaceflight experiments. We demonstrate that a simple blue-pink-clear transition model is able to recapitulate key features observed in empirical data from ground studies. AMMPER also includes a new graphical user interface and introductory tutorial to facilitate ease of use by a wider audience. AMMPER can help us to understand how spatially heterogeneous particle radiation damage at the single-cell level can translate to growth differences at the population level, ultimately allowing us to better interpret experiments using microbes as model organisms and how well their results apply to humans.

yeast↗

NASA NTRSDate not supplied

Building a Real-Time Predictive Flood Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the use of applied remote sensing analyses is increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP National Program partnered with the Howard County government in Maryland to investigate the use of machine learning for advanced flood risk detection, and to test the feasibility of integrating this approach into the county’s flood early warning system. To strengthen the efforts of the Howard County Office of Emergency Management (OEM), the project developed a prediction model capable of hindcasting the two severe flash flood events that devastated Ellicott City, and transitioned to an Long Short-Term Memory (LSTM) based sequence-to-sequence deep learning model with 8-hour forecast capability. The team combined data inputs from public sources including river and precipitation gauges, NASA and NOAA Earth observations, and numerical weather model products using scripts written in the Google Colaboratory Python scripting environment. In addition to designing the deep learning architecture, the team trained and tested the model, and evaluated its performance using the Nash-Sutcliffe Efficiency (NSE). The final product, called the Sequentially Trained Real-time EstimAted Model (STREAM), predicts stage height for the Hudson Branch gauge in Ellicott City using data products available in near real-time, including the High-Resolution Rapid Refresh (HRRR) model’s accumulated precipitation forecasts supplemented by stream gauge data from the OEM and the U.S. Geological Survey. STREAM was incorporated into an online dashboard in a user-friendly interface capable of triggering the alarms that initiate the OEM’s emergency response protocols up to 8 hours in advance of a predicted severe flood event. The project demonstrated the potential for the integration of open data and Earth observations into a flood risk forecasting tool capable of informing near real-time decision making.

Ryan Hammock↗

DOE OSTI2022

Co-Design of Marine Energy Converters for Autonomous Underwater Vehicle Docking and Recharging - Software and Data

Software and testing data from the OH Hinsdale Wave lab for DOE-funded project on Co-Design of Marine Energy Converters for Autonomous Underwater Vehicle Docking and Recharging. This project will perform foundational research and testing to accelerate the sector-wide development and deployment of marine energy converters to provide Power-At-Sea. Specifically, we seek to overcome known challenges and knowledge gaps for the successful co-design of coupled Wave Energy Converter (WEC)-Autonomous Underwater Vehicles (AUV) systems; systems designed and tested for WEC array system health and environmental monitoring applications. This project brings together an experienced, multi-institution, and multi-disciplinary team to focus on the co-design of marine energy (ME) technologies and AUV docking systems, including multi-body hydrodynamic modeling, active control, autonomy, and hardware interfaces necessary to enable new WEC-focused understanding, and allow for robust and ubiquitous AUV docking and recharging in real-world conditions.

16 TIDAL AND WAVE POWER↗

NASA NTRS2018

Rotorcraft Optimization Tools: Incorporating Rotorcraft Design Codes into Multi-Disciplinary Design, Analysis, and Optimization

One of the goals of NASA's Revolutionary Vertical Lift Technology Project (RVLT) is to provide validated tools for multidisciplinary design, analysis and optimization (MDAO) of vertical lift vehicles. As part of this effort, the software package, RotorCraft Optimization Tools (RCOTOOLS), is being developed to facilitate incorporating key rotorcraft conceptual design codes into optimizations using the OpenMDAO multi-disciplinary optimization framework written in Python. RCOTOOLS, also written in Python, currently supports the incorporation of the NASA Design and Analysis of RotorCraft (NDARC) vehicle sizing tool and the Comprehensive Analytical Model of Rotorcraft Aerodynamics and Dynamics II (CAMRAD II) analysis tool into OpenMDAO-driven optimizations. Both of these tools use detailed, file-based inputs and outputs, so RCOTOOLS provides software wrappers to update input files with new design variable values, execute these codes and then extract specific response variable values from the file outputs. These wrappers are designed to be flexible and easy to use. RCOTOOLS also provides several utilities to aid in optimization model development, including Graphical User Interface (GUI) tools for browsing input and output files in order to identify text strings that are used to identify specific variables as optimization input and response variables. This paper provides an overview of RCOTOOLS and its use

Analysi↗