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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Consist v0.1.0

A Python library for provenance tracking, intelligent caching, and data virtualization in scientific simulation workflows. It automatically records code, configuration, and input data to skip redundant computations and enables querying results across many runs without manual bookkeeping. Designed to support multi-model simulation workflows like the BEAM CORE toolset at LBL, but designed to be extensible to a wide range of research workflows. Combines lineage tracking features as provided by OpenLineage with deterministic hashing like SnakeMake, and adds powerful analysis tools on model outputs.

Needell, Zachary [Lawrence Berkeley National Labor↗
DOE OSTI2024

Deep Learning Scene Classification Experiments in Automatic Detection of Slums on Planetscope Imagery

Population growth is increasingly happening in slum settlements of the large urban centers in the Global South. The term "slum" encompasses a wide range of communities, located mostly in underserved areas, and often exhibiting distinct structural and functional informalities with a relatively high concentration of marginalized populations. To address the issues confronting slums for effective planning and development, including the realistic estimation of the resident population, identifying them accurately is fundamental. Given the disagreements over a universal definition, diverse characteristic features, and socio-political limitations, global detection of slums is a veritable challenge. In this paper, we present experiments in slum detection using a scene classification algorithm and 3-meter spatial resolution satellite imagery. We train and evaluate the model for slum detection in Mumbai, India for the year 2023 and test the temporal generalization of the trained model on Mumbai in 2020 and 2018. In addition, we explore the pathways toward geographic generalization to Kolkata and Delhi (India). We discuss several limitations in the workflow and model, situate our findings in the existing literature, and suggest improvements and alternatives. With this, we establish baseline methods and experiments as a first step towards developing an image-based global slum detection framework and algorithm. This work adds to the community discussion on methods, data challenges, and open questions related to the detection of slums globally. With this research, we hope to improve our understanding of human settlements, especially in critical areas, improve population estimates, and help measure progress towards the sustainable development goals.

Arndt, Jacob↗
DOE OSTI2023

Predictive Capability Maturity Model Demonstration for Cylindrical Cavity Coupling Using Gemma in the Next Generation Workflow

The predictive capability maturity model (PCMM) uses the expert elicitation process to generate credibility evidence for a particular analysis. To ensure Gemma has the capability to efficiently produce this credibility evidence, next generation workflows (NGW) are created for the solution verification, calibration/validation, and input uncertainty quantification portions of the PCMM assessment. These workflows are then used on the Higgins cylinder problem, which is representative of applications involving external-to-internal electromagnetic field coupling through a slot. The uncertainties calculated using these workflows are then used to calculate the validation comparison error and the validation uncertainty for the model following the American Society of Mechanical Engineers (ASME) verification and validation (V&V) 20 standard. These workflows will enable analysts to iterate each element of PCMM more efficiently than if completed without using a NGW workflow. An example of this iterative process is shown in Section 7.2.

42 ENGINEERING↗
DOE OSTI2024

Modeling and Design of the Triton OWC System (Final Report)

Two versions of the Triton Oscillating Water Column type device will be modeled in WEC Sim. First, a model of a wave-tank scale device which can be tuned and validated against tank test data. Second, a model of the deployment-scale device will be constructed following the method of the tank-scale device to ensure that relevant physics are captured. In the latter case, the geometry, PTO architecture, and other design details are not yet finalized, so the model will serve as a platform for design iteration as time and budget allows. A subset of this iteration will be automated using existing WEC-Sim capabilities. A primary focus of this work will be familiarizing Triton personnel with the WEC-Sim workflow and model details so that the model of the deployment device can continue to be enhanced after project end.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

An Open-Source Frequency-Domain Model for Floating Wind Turbine Design Optimization

A new frequency-domain dynamics model has been developed that uses open-source components to efficiently represent a complete floating wind turbine system. The model, called RAFT (Response Amplitudes of Floating Turbines), incorporates quasi-static mooring reactions, strip-theory and potential-flow hydrodynamics, blade-element-momentum aerodynamics, and linear turbine control. The formulation is compatible with a wide variety of support structure configurations and no manual or time-domain preprocessing steps are required, making RAFT very practical in design and optimization workflows. The model is applied to three reference floating wind turbine designs and its predictions are compared with results from time-domain OpenFAST simulations. There is good agreement in mean offsets as well the statistics and spectra of the dynamic response, verifying RAFT’s general suitability for floating wind analysis. Follow-on work will include verification of potential-flow and turbine-control features and application to optimization problems.

17 WIND ENERGY↗
DOE OSTI2025

An Open-source Llm Enhanced-tool Specialized In Helping Moose Related Problems And Tasks

MOOSEenger is an open-source, terminal-first chat application for the MOOSE ecosystem that couples specialized parsing of MOOSE documentation and “.i” input files with retrieval-augmented generation to deliver grounded answers about multiphysics modeling and workflows. It includes dedicated readers for MOOSE-style HTML and a pyhit-based parser that uses the MOOSE syntax tree to preserve block structure and attach retrieval metadata. A data-ingestion pipeline performs semantic chunking into atomic facts and stores them hierarchically in a local Chroma vector database that maintains parent–child relationships across documents; the system can ingest directories, individual files, and single-page web content, and it provides CRUD operations (insert, update, delete) to manage the corpus. At query time, relevant chunks are embedded, retrieved, and fused into the model context, with interactive features such as token streaming, persistent chat history, and dynamic RAG (retrieval triggered by user input or intermediate model output). Deployment is flexible: MOOSEenger runs with local Ollama models or remote Hugging Face/OpenAI backends—typically coordinating generation, lightweight tagging/summarization, and embeddings across three models—and it also supports a server mode and integration with the VS Code Continue interface.

Li, Mengnan [Idaho National Laboratory (INL), Idah↗
DOE OSTI2022

Multiscale modeling of hydrogenolysis of ethane and propane on Ru(0001): Implications for plastics recycling

Plastic waste presents an environmental threat. Chemical recycling via hydrogenolysis can convert plastic waste into waxes, lubricants, and fuels. Among catalysts, Ru stands out for its superior activity and selectivity. The chemistry of light alkane hydrogenolysis can help understanding plastics deconstruction. Here, we perform first-principles calculations, develop descriptor-based relations, and conduct microkinetic modeling and analysis on ethane and propane. Predictions are in excellent agreement with experimental data. We identify a similar cracking pattern for both hydrocarbons entailing a deeply dehydrogenated species with the removal of four hydrogen atoms: CHCH*+* → 2CH* for ethane and CH 3 CCH*+2* → CH 3 C* + CH* for propane. We find that the rate-determining step is the C-C cracking for ethane and the first dehydrogenation from the terminal carbon (CH 3 CH 2 CH 3 *+* → CH 3 CH 2 CH 2 *+H*) for propane. The vinyl species CH 2 CH* produced from propane cracking is responsible for whether a single or multiple cracking events occur and effectively controls the selectivity. Specifically, ethane formation in propane hydrogenolysis is suppressed at elevated temperatures due to over-cracking via multiple (two here) cracking events being preferred over hydrogenation and desorption of ethane from the catalyst. Our workflow and models provide a baseline for future studies on heavier hydrocarbons. Insights into recent experimental studies of polyethylene over Ru-based catalysts are discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

WAVES (Wind Asset Value Estimation System) [SWR-23-81]

The Wind Asset Value Estimation System (WAVES) model is a coupling framework for core NREL techno economic analysis software models to estimate capital expenditures (ORBIT), operational expenditures (WOMBAT), and energy production (FLORIS) for offshore wind power plants. Existing workflows to couple the three models for lifecycle performance and cost estimation require a large amount of manual and error-prone setup to combine both shared inputs and dependent outputs, as such WAVES's primary functionality is to wrap the core logic for running standard modeling workflows to ensure shared settings and entangled results are correctly and efficiently combined every time. SEE ALSO: https://pypi.org/project/WAVES/

Hammond, Robert↗
DOE OSTI2025

Open-Source and FAIR Research Software for Proteomics

Scientific discovery relies on innovative software as much as experimental methods, especially in proteomics, where computational tools are essential for mass spectrometer setup, data analysis, and interpretation. Since the introduction of SEQUEST, proteomics software has grown into a complex ecosystem of algorithms, predictive models, and workflows, but the field faces challenges, including the increasing complexity of mass spectrometry data, limited reproducibility due to proprietary software, and difficulties integrating with other omics disciplines. Closed-source, platform-specific tools exacerbate these issues by restricting innovation, creating inefficiencies, and imposing hidden costs on the community. Open-source software (OSS), aligned with the FAIR Principles (Findable, Accessible, Interoperable, Reusable), offers a solution by promoting transparency, reproducibility, and community-driven development, which fosters collaboration and continuous improvement. In this manuscript, we explore the role of OSS in computational proteomics, its alignment with FAIR principles, and its potential to address challenges related to licensing, distribution, and standardization. Drawing on lessons from other omics fields, we present a vision for a future where OSS and FAIR principles underpin a transparent, accessible, and innovative proteomics community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

A Field Guide to Corralling the Chaos: A Conceptual Framework for Using Models to Guide Opportunistic Field Studies of Natural Disturbances

Watersheds regulate biogeochemical processes and provide ecosystem services to human societies, but disturbances can fundamentally alter these processes across space and time. Determining when and where to sample to capture disturbance impacts in watersheds remains a central challenge. Manipulation studies and long-term monitoring are often constrained by scope, and opportunistic studies often lack pre-disturbance data needed to statistically determine disturbance impacts. We identify a persistent knowledge gap: the absence of a clear, transferable framework to guide opportunistic disturbance research where pre-disturbance data collection is not a feasible option. To address this gap, we present a conceptual framework that intentionally integrates modeling and empirical observation in an iterative, stepwise model–experiment workflow. We demonstrate its application through two contrasting case studies: wildfire impacts on headwater streams using a pre-disturbance preparedness approach, and saltwater flooding impacts on coastal forests using an ‘ex-post-facto’ approach. From these applications, we assess strengths, limitations, and the critical role of team science for transferability across disturbance types and study designs. Broadly, this framework offers a scalable path towards more rigorous, timely, and actionable disturbance science that can inform watershed management, hazard risk reduction, and ecosystem resilience.

Coastal Biogeochemistry↗
DOE OSTI2022

Particle-based fast jet simulation at the LHC with variational autoencoders

Abstract We study how to use deep variational autoencoders (VAEs) for a fast simulation of jets of particles at the Large Hadron Collider. We represent jets as a list of constituents, characterized by their momenta. Starting from a simulation of the jet before detector effects, we train a deep VAE to return the corresponding list of constituents after detection. Doing so, we bypass both the time-consuming detector simulation and the collision reconstruction steps of a traditional processing chain, speeding up significantly the events generation workflow. Through model optimization and hyperparameter tuning, we achieve state-of-the-art precision on the jet four-momentum, while providing an accurate description of the constituents momenta, and an inference time comparable to that of a rule-based fast simulation.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Analysing Scales of Precipitation

The Analysing Scales of Precipitation (ASoP) package is being interfaced with the DOE Coordinated Model Evaluation Capabilities (CMEC) project. Users will be able to use the cmec-driver software to easily run ASoP and generate precipitation metrics as part of climate model evaluation workflows. These metrics provide information about the spatial and temporal patterns of precipitation at different timescales.

Ordonez, AnaC.↗
DOE OSTI2025

POWER DATA PIPELINE

SF-25-081 Utility software for creating high-performance data pipelines to extract, load, and transform raw electric power systems measurements. For use with anomaly detection models training workflows. The software supports the project: Adaptive Cybersecurity for DER: A Game-Theoretic and Machine Learning approach for Real-Time Threat Detection and Mitigation

Plathottam, Silby Jose [Argonne National Laborator↗
DOE OSTI2023

Compensating attenuation effects in full-waveform inversion with dissipation-dispersion decoupling

Seismic attenuation poses challenges to velocity model building from acoustic/elastic full-waveform inversion (FWI). In particular, when constructing the FWI gradient for velocity inversion, recent studies have indicated that the high-attenuation structure could distort the gradient by damping the amplitudes and shifting the kinematic phases, resulting in an imbalanced update and thus unreliable velocity model. These Q effects are particularly significant in the reflection acquisition geometry due to the “double-damping” issue. Here, we develop a Q-compensated FWI algorithm for constructing a Q-free FWI gradient. By using a recently developed viscoacoustic wave propagator, this compensation can be done conveniently by keeping the dispersion term and flipping the dissipation term in the wave equation when we simulate the forward and time-reversed adjoint wavefields. The resultant gradient obtained by interacting these two wavefields has correct kinematics and Q-free amplitudes. This Q-compensated FWI can balance the update between Q- and no- Q-areas, which we determine using synthetic examples. In addition, we illustrate how to take advantage of the dissipation-dispersion decoupling to determine the anomalous Q value in the Q model building workflow via Q-compensated reverse time migration.

Geochemistry & Geophysics↗
DOE OSTI2021

CrossCheck: Rapid, Reproducible, and Interpretable Model Evaluation

Evaluation beyond aggregate performance metrics, e.g. F1-score, is crucial to both establish an appropriate level of trust in machine learning models and identify future model improvements. In this paper we demonstrate CrossCheck, an interactive visualization tool for rapid crossmodel comparison and reproducible error analysis. We describe the tool and discuss design and implementation details. We then present three use cases (named entity recognition, reading comprehension, and clickbait detection) that show the benefits of using the tool for model evaluation. CrossCheck allows data scientists to make informed decisions to choose between multiple models, identify when the models are correct and for which examples, investigate whether the models are making the same mistakes as humans, evaluate models’ generalizability and highlight models’ limitations, strengths and weaknesses. Furthermore, CrossCheck is implemented as a Jupyter widget, which allows rapid and convenient integration into data scientists’ model development workflows.

Arendt, Dustin L.↗
DOE OSTI2024

Density functional theory (DFT) study of UF 6 hydrolysis: reaction pathways, spectroscopy, and chemical kinetics

Depleted uranium hexafluoride (UF 6 ), a stockpiled byproduct of the nuclear fuel cycle, reacts readily with atmospheric humidity, but the gas-phase reaction mechanism and associated chemical kinetics are poorly understood. During the performance period we undertook development of a state-of-the-art ab initio gas-phase chemical kinetics simulation workflow to model the hydrolysis of uranium hexafluroride (UF 6 ). In doing so, we addressed several outstanding issues in the theoretical treatment of uranium-containing systems. At the outset it was unclear how to generate accurate estimates of kinetic and thermodynamic data for U-containing chemical reactions. Generation of such data has been made routine. Prior to our work, the literature associated with UF 6 hydrolysis were disparate and inaccurate. This body of work provides a modern and comprehensive theoretical assessment of the reaction mechanism, molecular clustering towards deposition, and chemical kinetics. New methodological implementations and software integrations resulting from this work are also highlighted. As much as possible, our predictions were validated against experimental data including particle morphologies, vibrational spectroscopy, atomization enthalpies, and kinetic rate constants. Nevertheless, we were unable to reconcile kinetic measurements with high-accuracy simulations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Modeling based assessment of salt cavern performance for underground hydrogen storage

This poster is about the recent progress in my LDRD project of underground hydrogen storage in multiple salt caverns. The main progress of my work is to develop modeling-based workflow to evaluate the salt cavern performance and assess the potential of geomechanical risk for hydrogen storage.

Chang, Kyung Won [Sandia National Laboratories (SN↗