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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

merlin , an improved framework for the reconstruction of high-quality genome-scale metabolic models

Abstract Genome-scale metabolic models have been recognised as useful tools for better understanding living organisms’ metabolism. merlin (https://www.merlin-sysbio.org/) is an open-source and user-friendly resource that hastens the models’ reconstruction process, conjugating manual and automatic procedures, while leveraging the user's expertise with a curation-oriented graphical interface. An updated and redesigned version of merlin is herein presented. Since 2015, several features have been implemented in merlin, along with deep changes in the software architecture, operational flow, and graphical interface. The current version (4.0) includes the implementation of novel algorithms and third-party tools for genome functional annotation, draft assembly, model refinement, and curation. Such updates increased the user base, resulting in multiple published works, including genome metabolic (re-)annotations and model reconstructions of multiple (lower and higher) eukaryotes and prokaryotes. merlin version 4.0 is the only tool able to perform template based and de novo draft reconstructions, while achieving competitive performance compared to state-of-the art tools both for well and less-studied organisms.

Capela, João (ORCID:0000000212352922)↗
DOE OSTI2021

rmc-discord : reverse Monte Carlo refinement of diffuse scattering and correlated disorder from single crystals

A user-friendly program has been developed to analyze diffuse scattering from single crystals with the reverse Monte Carlo method. The approach allows for refinement of correlated disorder from atomistic supercells with magnetic or structural (occupational and/or displacive) disorder. The program is written in Python and optimized for performance and efficiency. Refinements of two user cases obtained with legacy neutron-scattering data demonstrate the effectiveness of the approach and the developed program. It is shown with bixbyite, a naturally occurring magnetic mineral, that the calculated three-dimensional spin-pair correlations are resolved with finer real-space resolution compared with the pair distribution function calculated directly from the reciprocal-space pattern. With the triangular lattice Ba 3 Co 2 O 6 (CO 3 ) 0.7 , refinements of occupational and displacive disorder are combined to extract the one-dimensional intra-chain correlations of carbonate molecules that move toward neighboring vacant sites to accommodate strain induced by electrostatic interactions. The program is packaged with a graphical user interface and extensible to serve the needs of single-crystal diffractometer instruments that collect diffuse-scattering data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

The Pixel Anomaly Detection Tool : a user-friendly GUI for classifying detector frames using machine-learning approaches

Data collection at X-ray free electron lasers has particular experimental challenges, such as continuous sample delivery or the use of novel ultrafast high-dynamic-range gain-switching X-ray detectors. This can result in a multitude of data artefacts, which can be detrimental to accurately determining structure-factor amplitudes for serial crystallography or single-particle imaging experiments. Here, a new data-classification tool is reported that offers a variety of machine-learning algorithms to sort data trained either on manual data sorting by the user or by profile fitting the intensity distribution on the detector based on the experiment. This is integrated into an easy-to-use graphical user interface, specifically designed to support the detectors, file formats and software available at most X-ray free electron laser facilities. The highly modular design makes the tool easily expandable to comply with other X-ray sources and detectors, and the supervised learning approach enables even the novice user to sort data containing unwanted artefacts or perform routine data-analysis tasks such as hit finding during an experiment, without needing to write code.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

DXRD : a user-friendly suite of two- and multiple-beam dynamical X-ray diffraction programs

The DXRD program suite consisting of a series of dynamical theory programs is introduced for computing dynamical X-ray diffraction from single crystals. Its interactive graphical user interfaces (GUIs) allow general users to make complicated calculations with minimal effort. It can calculate plane-wave Darwin curves of single crystals (or multiple crystals) for both the Bragg and Laue cases, including grazing-incidence diffraction and backward diffraction (with Bragg angles approaching 90°). It is also capable of simulating rocking curves for divergent incident X-ray beams with finite bandwidths. A unique feature of DXRD is that it provides a convenient GUI-based multiple-beam diffraction program that can accurately compute arbitrary N-beam diffraction of any geometry using a universal 4N × 4N matrix method. DXRD also provides a mapping program for plotting all the multiple-beam diffraction lines (monochromator glitches) in the azimuth–energy coordinate system. All these functions make DXRD a convenient and powerful software tool for designing crystal-based synchrotron/X-ray optics (monochromators, analyzers, polarizers, phase plates etc.) and for crystal characterization, X-ray spectroscopy and X-ray diffraction teaching.

Bragg reflection↗
DOE OSTI2024

TomoPyUI : a user-friendly tool for rapid tomography alignment and reconstruction

The management and processing of synchrotron and neutron computed tomography data can be a complex, labor-intensive and unstructured process. Users devote substantial time to both manually processing their data ( i.e. organizing data/metadata, applying image filters etc. ) and waiting for the computation of iterative alignment and reconstruction algorithms to finish. In this work, we present a solution to these problems: TomoPyUI , a user interface for the well known tomography data processing package TomoPy . This highly visual Python software package guides the user through the tomography processing pipeline from data import, preprocessing, alignment and finally to 3D volume reconstruction. The TomoPyUI systematic intermediate data and metadata storage system improves organization, and the inspection and manipulation tools (built within the application) help to avoid interrupted workflows. Notably, TomoPyUI operates entirely within a Jupyter environment. Herein, we provide a summary of these key features of TomoPyUI , along with an overview of the tomography processing pipeline, a discussion of the landscape of existing tomography processing software and the purpose of TomoPyUI , and a demonstration of its capabilities for real tomography data collected at SSRL beamline 6-2c.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

WE-Validate: An Open-Source Framework For Wind Power Validation

Grid operators rely on historical weather time series at existing and planned wind power plants to make informed decisions when planning for a future power grid with very high penetration of renewable power. While synthetic wind power time series have been developed based on historical weather models, their validation with actual power production data remains complex due to variations in modeling practices and methodologies. This paper introduces the WE-Validate framework, originally designed for wind speed validation and now enhanced for wind power validation with a graphical user interface to support users with minimal programming experience. Validation of wind power with WE-Validate is based on robust metrics consisting of RMSE, centered RMSE, average bias, average percent bias, mean absolute error, mean absolute percent error, cross correlation, and calculation of ramping magnitude, rate, and duration. This paper showcases WE-Validate with validation of synthetically derived power for a wind plant in Washington state for one month in 2018. Validation of the synthetic power from two comparison data sets compared with observations shows both comparison series have strong correlation with observed across weekly and monthly aggregations while suffering from persistent negative bias. The suite of metrics within WE-Validate facilitates immediate insight into the utility of the comparison data sets through compression across multiple axes. This user-friendly, open-source tool can be extended beyond wind power, making it a valuable resource for system planners and operators in different domains.

Moncheur de Rieudotte, Malcolm P.↗
DOE OSTI2025

Decayheatml

This code is designed to predict and analyze the decay heat generated in molten salt reactors (MSRs) using a hybrid approach that combines machine learning and segmented polynomial fitting. The accurate prediction of decay heat is essential for reactor safety and the optimization of spent fuel storage. The code operates through several key components: 1) Data Architecture: It incorporates a modular data architecture that handles various MSR-specific operational parameters such as power density, humidity content, and air ingress. These parameters are sampled using Sobol sequences to ensure comprehensive coverage of operational uncertainties. 2) Machine Learning Framework: The code employs a diverse set of machine learning models, including polynomial regression, decision trees, random forests, gradient boosting, support vector regression, k-nearest neighbors, multi-layer perceptrons, and symbolic regression. These models are trained to predict decay heat over a wide temporal range, from immediate shutdown up to 10,000 years. 3) Region-Optimized Training: The temporal domain is divided into multiple regions, each modeled separately to capture distinct decay heat characteristics across different time scales. This approach significantly improves the accuracy and interpretability of predictions. 4) Segmented Polynomial Interpretation (SPI): The SPI method translates machine learning predictions into piecewise polynomial equations. These equations are physically interpretable and can be directly integrated into existing engineering workflows and safety analyses. 5) Front-End Interfaces: The code includes both a Jupyter notebook interface for research development and a Streamlit web application for operational deployment. These interfaces allow users to interactively explore decay heat predictions, adjust operational parameters, and visualize results in real-time. 6) Applications: The framework supports various applications, including safety system validation and spent fuel container optimization. It enables real-time evaluation of worst-case decay heat scenarios, informing the design of passive safety systems and optimizing container designs for long-term storage. Overall, this code provides a robust, accurate, and user-friendly tool for predicting decay heat in MSRs, enhancing reactor safety, and optimizing spent fuel management.

Retamales, Mauricio Eduardo Tano [Idaho National L↗
DOE OSTI2026

Retrofit Energy Analysis and Central Thermal modeling (REACT) v1.0

REACT is a website designed to simplify the analysis and decision-making process for retrofitting existing central plant heating and cooling systems with advanced heat pump technologies. The tool evaluates the technical and economic viability of replacing traditional boilers with various options including water-to-water or air-to-water heat pumps, which can provide efficient and lower-cost heating and cooling. It allows users to compare current central plant configurations with retrofit scenarios, assessing energy consumption, life-cycle costs, and environmental impact. Retrofitting traditional boiler and chiller systems with water-to-water or air-to-water heat pumps can significantly reduce energy consumption and lower lifecycle costs. The REACT provides: User-Friendly Tools: A user friendly web interface for quick, intuitive analysis accessible to non-experts. Advanced Modeling: A Python-powered engine for detailed parametric studies, optimization, and research applications. Comprehensive Analysis: Lifecycle cost evaluation, energy consumption modeling, and environmental impact assessment. Visual Insights: A variety of plots to visualize system performance and design trade-offs. The engine for the website (REACT) bases on several Python libraries, and the website will be hosted on an ETA server.

Kim, Donghun [Lawrence Berkeley National Laborator↗
DOE OSTI2024

teemi: An open-source literate programming approach for iterative design-build-test-learn cycles in bioengineering

Synthetic biology dictates the data-driven engineering of biocatalysis, cellular functions, and organism behavior. Integral to synthetic biology is the aspiration to efficiently find, access, interoperate, and reuse high-quality data on genotype-phenotype relationships of native and engineered biosystems under FAIR principles, and from this facilitate forward-engineering strategies. However, biology is complex at the regulatory level, and noisy at the operational level, thus necessitating systematic and diligent data handling at all levels of the design, build, and test phases in order to maximize learning in the iterative design-build-test-learn engineering cycle. To enable user-friendly simulation, organization, and guidance for the engineering of biosystems, we have developed an open-source python-based computer-aided design and analysis platform operating under a literate programming user-interface hosted on Github. The platform is called teemi and is fully compliant with FAIR principles. In this study we apply teemi for i) designing and simulating bioengineering, ii) integrating and analyzing multivariate datasets, and iii) machine-learning for predictive engineering of metabolic pathway designs for production of a key precursor to medicinal alkaloids in yeast. The teemi platform is publicly available at PyPi and GitHub.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Development and Commercialization of an IDAES-Based Power Plant Performance Monitoring and Optimization System

DOE and NETL have created an advanced, open-source computational platform through the Institute for the Design of Advanced Energy Systems (IDAES). The IDAES platform is a very extensive modeling environment that can be used for a broad range of power plant and process applications. MapEx Software is developing and commercializing a software application that makes it easier to set-up and run IDAES-based analyses. The MapEx-developed software application will replace the need for custom Python language coding with a user-friendly graphical user interface where the user can construct a flowsheet diagram of the IDAES model by inserting icons representing the plant equipment onto the screen. This application will make the implementation of modeling and optimization of existing fossil-fired power plants more straight-forward and less time-consuming. The effort focuses on performance monitoring and optimization of plant operations for the existing coal-fired power plant fleet but is built on a structure that allows expansion into the broad range of applications where IDAES methods may be applied.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

CO 2 Storage prospeCtive Resource Estimation Excel aNalysis (CO 2 -SCREEN) User’s Manual: Python_V4.1

This user’s manual guides the use of the National Energy Technology Laboratory’s (NETL) CO 2 Storage prospeCtive Resource Estimation Excel aNalysis (CO 2 -SCREEN) tool, which was developed to aid users in screening geologic formations for prospective CO 2 storage resources. This manual is specific to the CO 2 -SCREEN 4.1 version which is based in Python. The 4.1 version of CO 2 -SCREEN adds in newly updated storage efficiency factors for saline formations. CO 2 -SCREEN applies U.S. Department of Energy (DOE) methods and equations for estimating prospective CO 2 storage resources for saline formations, shale formations, and residual oil zones (ROZ). CO 2 -SCREEN was developed to be substantive and user-friendly and provide a consistent method for calculating prospective CO 2 storage resources. CO 2 -SCREEN uses a Java- based graphical user interface (GUI) for data inputs and uses Python to calculate prospective CO 2 storage resources.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The waterSHED Model: User Guide

The ideal design and operation of small hydropower plants is a complex optimization problem with economic, social, and environmental objectives. The waterSHED (Water Allocation Tool Enabling Rapid Small Hydropower Environmental Design) model is a user-friendly tool that allows hydropower stakeholders to model the trade-offs among these objectives using the Standard Modular Hydropower (SMH) framework. The SMH framework employs modular technologies that can be represented as blackbox objects and combined within a river to create a hydropower facility. For a given site, the waterSHED model aims to determine which modules should be placed in a facility and how those modules should be operated. This user guide describes how to use the graphical user interface and related functionalities. This document also summarizes the background research and mathematical formulations that are explained indepth in the accompanying doctoral dissertation. This model is an early step toward a new hydropower design process that employs standardization and modularity to reduce costs, development timelines, and challenges regarding social and environmental mitigation measures for low-head, small hydropower development. The waterSHED model is a Python application that will require the ability to download a GitHub repository, import the necessary packages, and run a set of Python script files using an integrated development environment. The script produces a graphical user interface to coordinate inputs, simulate operation, and visualize results, so no coding experience is needed once the script is running. Additionally, the waterSHED Workbook is a Microsoft Excel file that works with the Python script to facilitate data entry

13 HYDRO ENERGY↗
DOE OSTI2022

FY22 Progress Report: SRNL Analysis of ICCWR LCM and WAMS Data for Corrosion and Cracking

Algorithms for machine learning and data analysis for the 3013 Surveillance Program are being developed in an ongoing collaborative effort by the Savannah River National Laboratory (SRNL) and the University of South Carolina (USC). To detect the presence of corrosion and cracking, data is collected from large binary files generated by a Laser Confocal Microscope (LCM) or Wide Area 3D Measurement System (WAMS). Software is being developed to use the physical attributes in the data files (e.g., height, color, and grayscale values; all as functions of a location in a plane projection) to detect the presence of surface corrosion and cracking. A user-friendly Matlab Graphical User Interface (GUI) that reads data from either LCM or WAMS files was developed to integrate input data with software developed for processing and evaluation. The GUI can selectively download binary data, interrogate data attributes, label data for training ML algorithms, flag significant features, execute Machine Learning (ML) algorithms, output parameters from trained ML algorithms, report ML model accuracy with respect to labeled data, and generate graphical representations for various analyses. Surface defects can be called out by setting user-specified thresholds, feature based analysis or machine learning algorithms. Enhancements to data labeling capability have been developed to address this essential precursor to application of ML routines. Efficient labeling is particularly important in view of the very large volume of data required to train ML algorithms.

3013 Corrosion↗
DOE OSTI2026

Developing a GUI for the Robotic Test Stand

The introduction of this poster explains the technology behind DUNE’s far and near detectors and how passing neutrinos generate electrons that drift into a wire grid. I then explain how 3 ASICs manage signals received from electron interception. Next, the poster states how COLDATA chips are undergoing quality control by a Robotic Test Stand using a state machine. I further explained how earlier tests were done via a command line script and the necessity to implement a user-friendly Graphical User Interface with new features a command line can’t implement. For the implementation section, tools and methods for implementation are listed such as Python, tkinter, and GitHub as well as how multithreading and queue implementation was necessary for GUI functionality. Then, I elaborated on the GUIs new features. Finally, I explain how the GUI will be distributed across multiple institutions and future changes planned for the GUI. Photos of the RTS, far detector cave, diagram of anode assembly plane, COLDATA chips, set up tab, result tab, and legacy command line interface are shown.

Gutierrez Villanueva, Jaziel [DuPage Coll.]↗
DOE OSTI2026

A User-Friendly GUI Tool for Automated Microstructural Analysis of Fiber-Reinforced Composites and Porous Structures

Understanding and quantifying microstructural features such as fiber orientation and porosity is critical for predicting the mechanical behavior and performance of fiber-reinforced polymer composites. Traditional manual analysis is time-consuming, subjective, and unsuitable for high-throughput datasets. We present a graphical user interface (GUI) application that automates the analysis of microscopy images to extract key microstructural metrics, including fiber orientation tensors, fiber orientation distribution, porosity and pore size distribution. The app integrates multiple image segmentation techniques including global and local thresholding, clustering, and region-based approaches, offering flexibility for different types of image qualities and features. Users can load microstructural images, select regions of interest and segmentation techniques tailored to their image dataset. It also addresses a critical challenge in fiber orientation analysis: the ambiguities caused by touching, overlapping, or partially cut fibers. It supports autorun examples for standardized workflows, enabling reproducible analysis and facilitating training and benchmarking. This tool significantly reduces manual intervention, enhances consistency, and accelerates data generation for structure–property modeling, process optimization, and digital materials research. The tool is intended for use by materials scientists, engineers, and researchers engaged in composite characterization, quality control, and machine learning-based microstructural studies.

Chawla, Komal [ORNL] (ORCID:0000000190327565)↗
DOE OSTI2021

Bioinformatic Teaching Resources – For Educators, by Educators – Using KBase, a Free, User-Friendly, Open Source Platform

Over the past year, biology educators and staff at the U.S. Department of Energy Systems Biology Knowledgebase (KBase) initiated a collaborative effort to develop a curriculum for bioinformatics education. KBase is a free web-based platform where anyone can conduct sophisticated and reproducible bioinformatic analyses via a graphical user interface. Here, we demonstrate the utility of KBase as a platform for bioinformatics education, and present a set of modular, adaptable, and customizable instructional units for teaching concepts in Genomics, Metagenomics, Pangenomics, and Phylogenetics. Each module contains teaching resources, publicly available data, analysis tools, and Markdown capability, enabling instructors to modify the lesson as appropriate for their specific course. We present initial student survey data on the effectiveness of using KBase for teaching bioinformatic concepts, provide an example case study, and detail the utility of the platform from an instructor’s perspective. Even as in-person teaching returns, KBase will continue to work with instructors, supporting the development of new active learning curriculum modules. For anyone utilizing the platform, the growing KBase Educators Organization provides an educators network, accompanied by community-sourced guidelines, instructional templates, and peer support, for instructors wishing to use KBase within a classroom at any educational level–whether virtual or in-person.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Seismic Waveform Inversion Capability on Resource-Constrained Edge Devices

Seismic full wave inversion (FWI) is a widely used non-linear seismic imaging method used to reconstruct subsurface velocity images, however it is time consuming, has high computational cost and depend heavily on human interaction. Recently, deep learning has accelerated it’s use in several data-driven techniques, however most deep learning techniques suffer from overfitting and stability issues. In this work, we propose an edge computing-based data-driven inversion technique based on supervised deep convolutional neural network to accurately reconstruct the subsurface velocities. Deep learning based data-driven technique depends mostly on bulk data training. In this work, we train our deep convolutional neural network (DCN) (UNet and InversionNet) on the raw seismic data and their corresponding velocity models during the training phase to learn the non-linear mapping between the seismic data and velocity models. The trained network is then used to estimate the velocity models from new input seismic data during the prediction phase. The prediction phase is performed on a resource-constrained edge device such as Raspberry Pi. Raspberry Pi provides real-time and on-device computational power to execute the inference process. In addition, we demonstrate robustness of our models to perform inversion in the presence on noise by performing both noise-aware and no-noise training and feeding the resulting trained models with noise at different signal-to-noise (SNR) ratio values. We make great efforts to achieve very feasible inference times on the Raspberry Pi for both models. Specifically, the inference times per prediction for UNet and InversionNet models on Raspberry Pi were 22 and 4 s respectively whilst inference times for both models on the GPU were 2 and 18 s which are very comparable. Finally, we have designed a user-friendly interactive graphical user interface (GUI) to automate the model execution and inversion process on the Raspberry Pi.

Manu, Daniel (ORCID:0000000154982677)↗