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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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This document describes the theory and function of the Model for the Assessment of Reprocessing and Recycle with Innovative Execution (MARIE) software developed by Oak Ridge National Laboratory (ORNL). Specifically presented are the supported unit operations, underlying theory for processes such as dissolution and generic solvent extraction, execution, and supported output capabilities
The VA Environmental Determinants of Health (EDH) Advanced Software Pipeline Framework is designed to enhance the efficiency, scalability, and security of geospatial data processing workflows. This framework integrates modern data orchestration and containerization technologies, including Prefect for workflow automation, Docker for containerization, and PostgreSQL/PostGIS for geospatial data storage and analysis. It ensures standardized, reproducible, and automated data processing, supporting VA objectives related to substance use risk assessment and recovery research. The pipeline addresses key scalability and performance challenges through horizontal and vertical scaling, high-performance computing (HPC) integration, parallel processing, task caching, and dynamic resource allocation. These optimizations improve throughput and reduce latency, allowing the system to efficiently manage large and complex datasets. Additionally, security and compliance measures—such as data encryption (SSL), Role-Based Access Control (RBAC), and adherence to GDPR and HIPAA standards—safeguard sensitive information throughout data transmission and storage. A key implementation of this framework includes the automation of shelter list geolocation workflows, ensuring that up-to-date data is readily available for VA decision-making. Lessons learned from this project include the transition from in-memory processing to incremental storage writes, improving resource management and reliability. Future enhancements aim to expand automation, integrate AI-driven anomaly detection, and incorporate high-performance computing resources. This framework provides a scalable, secure, and adaptable solution for managing geospatial datasets, reinforcing the VA’s ability to support clinical and strategic initiatives through data-driven decision-making.
The CRADA work involved 1) the development of a machine learning software architecture to automatically detect defects within Electroluminescence images of solar panels, and 2) demonstrations of the usefulness of UV Fluorescence (UVF) defect detection for a wide variety of defect types across many different solar panel technologies with different field or environmental chamber histories.
Recent research has highlighted the potential for solar to act as a zero-marginal-cost and zero-emission flexibility resource on the bulk power system when operated with advanced control systems. To increase the performance of these systems, leading technologies, including machine learning (ML) and hierarchical inverter set point allocation, have been developed by Latimer Controls, Inc. to estimate the headroom of large PV plants for grid operation and control; however, these technologies lack comprehensive validation under real-world application scenarios. Latimer Controls, Inc. received two voucher awards for research at a national laboratory from the Department of Energy American Made Solar Prize Round 6. The National Renewable Energy Laboratory (NREL) was selected to collaborate with Latimer staff to conduct a performance evaluation of Latimer PV control software. The NREL team will develop a hardware-in-the-loop (HIL) testbed to perform testing and validation of the Latimer PV control technology in a de-risked yet realistic testbed environment. Latimer and NREL worked together to analyze the test data, draw conclusions from the results, and disseminate the resulting scientific findings. In this CRADA work, we propose to test and validate the real-world application of the Latimer Control solution in an HIL environment. We evaluate the performance of different flexible solar technologies in responding to automatic generation control signals in a closed-loop fashion. In particular, a data-driven potential high limit (PHL) estimation is developed for large solar plants to accurately estimate their headroom so that they have fast and short-time regulation and control capability to participate in grid services and respond to grid signals in real time (e.g., AGC). This PHL estimation algorithm is embedded in a hardware power plant controller (PPC) and tested with an IEEE-39 bus system model developed in RTDS. To account for the varying cloud conditions and diverse inverter dispatches, we developed a 135-MW PV plant with detailed modeling of 27 individual PV modules and inverters using RTDS. The real-world communications used in such big plants, such as ModBus TCP/IP for inverter level and DNP3 for plant level, were developed to emulate the real-world applications in big PV plants. The ML-based PHL estimation method is tested under nine separate weather scenarios against the ‘reference-control’ solution, hereafter referred to as the baseline solution. The baseline method reserves a subset of inverters (reference group) to operate at their PHL at all times and dispatches only the remaining inverters (control group) at curtailed levels to fulfill the flexibility need. Despite being successfully piloted by NREL in California in 2017 and Chile in 2020, there exist two gaps in the state of the art to fully unlock the flexibility of PV plants: a. There is a trade-off between the PHL estimation accuracy and the flexibility range. b. There lacks granularity in the PHL estimation to capture the variation across inverters. The Latimer solution seeks to address these gaps by applying machine learning methods to improve PHL estimation accuracy while accounting for variability at every inverter. Performance metrics were taken from the 2023 Georgia Power CARES utility-scale RFP. The results demonstrate that the ML-based approach outperforms the traditional baseline method in PHL estimation accuracy for 7 of 9 scenarios. The average PHL error across the nine scenarios was 7.40% for the ML-based method, 2.06% less than the 9.46% PHL error average across scenarios that was exhibited by the baseline method. Additionally, the PHL error was below 5% for at least 95% of the testing interval for 3 of 9 tested intervals with the ML approach, whereas it did not achieve this metric for any of the baseline tests. Overall, simulation results indicate the superior performance of an ML-based approach compared to the conventional baseline reference-control approach, showcasing its potential to support grid stability and operational efficiency. This laboratory HIL testing using real PPC, representative power system simulation models in real-time with detailed PV plant and inverter models, and real-world communication protocols gives us confidence that this machine learning based PHL estimation algorithm works well in the hardware PPC and therefore de-risks future field commissioning. The end goal of this project is to advance grid technology to address the grid operation challenges brought by solar plant’s variability and uncertainties in power generation.
This COG Software Architecture Design Description Document describes the organization and functionality of the COG Multiparticle Monte Carlo Transport Code for radiation shielding and criticality calculations, at a level of detail suitable for guiding a new code developer in the maintenance and enhancement of COG. The intended audience also includes managers and scientists and engineers who wish to have a general knowledge of how the code works. This Document is not intended for end-users.
As part of its commitment to advancing safety and reliability assessment methodologies, Argonne National Laboratory pioneered the use of an evaluation method called the Success Path Method (SPM) to improve risk management for offshore oil and gas operations. The development of the SPM at Argonne has been driven by the need to improve existing risk assessment methodologies by focusing on the steps necessary for success rather than failure modes alone. This is particularly important for industrial environments like offshore facilities that perform multiple functions under a continuously evolving set of operational conditions – such as water depth and temperature, currents, and weather conditions. In these dynamic environments, the traditional Probabilistic Risk Assessment (PRA) approach is far too complex as it focuses on what can go wrong – which comprises an infinite failure space that must be fully explored and understood. By shifting the focus to a finite space of success paths, the SPM enables operators and decision makers to prioritize a manageable number of steps that must go right to ensure success. Building on its five decades of experience in safety assessments for the nuclear industry, Argonne made major adaptations to existing risk assessment methods utilizing features similar to fault trees that are traditionally used in PRA to map all pathways in which the system can malfunction. In contrast, SPM identifies the components and processes that must function correctly to achieve specific outcomes – such as preventing the uncontrolled release of hydrocarbons during drilling operations. The SPM framework integrates equipment, procedures, software, processes, and human actions to ensure that physical barriers meet critical safety functions in dynamic operational conditions. This approach helps identify failure modes and improve operational risk management by narrowing the focus to key success elements, which in turn reduces uncertainty and helps users understand, manage, and respond to failures.
The CRADA work involved 1) the development of a machine learning software architecture to automatically detect defects within Electroluminescence images of solar panels, and 2) demonstrations of the usefulness of UV Fluorescence (UVF) defect detection for a wide variety of defect types across many different solar panel technologies with different field or environmental chamber histories.
Ensuring that data on long-term environmental remediation at the Hanford Site is high-quality, traceable, and easily accessible is an ongoing challenge, complicated by decades of data collection, multiple contractors maintaining data sources, and the wide range of data types. A centralized data catalog, known as the Hanford Environmental Information and Data Index (HEIDI), has been under development as part of the Hanford Environmental Data Management (HEDM) program to address these challenges. HEIDI fulfills a critical need to bring together a wide range of data types and sizes from multiple authoritative data sources, while documenting the data pedigree and quality information (i.e., traceable to the data source/originator). This document describes additional development and maturation of the HEIDI prototype. Key accomplishments included deploying the catalog software, Esri Geoportal Server, on a server accessible to Hanford Local Area Network users, conducting cybersecurity evaluations, investigating integrated authentication solutions, and conducting functional testing of the catalog prototype. The server-based deployment enabled targeted feedback, leading to enhancements including improved accessibility features and an expanded metadata schema. Specifications for the server-based deployment of the prototype catalog and the HEIDI metadata schema are provided in this document to support subsequent HEIDI deployment by the U.S. Department of Energy Richland Operations Office.
The purpose of this document is for the verification and validation results of ANSYS Mechanical on an ORNL workstation. The ANSYS Mechanical verification and validation was performed in accordance with the ANSYS Mechanical software quality assurance plan.
The purpose of this document is to show the verification and validation results of ANSYS LSDYNA 2023R1 an ORNL workstation. The ANSYS LSDYNA verification and validation was done in accordance with the ANSYS LSDYNA 2023R1 software quality assurance plan.
This report summarizes the University of Washington’s contributions to the ComPort project, Rigorous Testing Methods to Safeguard Software Porting, funded under the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research (ASCR), award number DE-SC0022081. UW’s work addressed numerics-driven correctness challenges arising in heterogeneous and rapidly evolving computing environments. Our efforts focused on developing interactive analysis tools, correctness-preserving rewriting systems, semantic-aware code-transformation infrastructure, and multi-language support for reproducible numerical experiments.
Fixed-energy Response-function Analysis with Multiple efficiency (FRAM) is a software code designed primarily for plutonium and uranium isotopic analysis. It is widely used in both the domestic and international safeguards community. FRAM can quickly and accurately determine the isotopic compositions of plutonium, uranium, and mixed oxides (MOX) when measuring with a high-purity germanium (HPGe), cadmium zinc telluride (CZT), or lanthanum bromide (LaBr 3 ) detector. The capabilities of FRAM have been enhanced to analyze the data of the pixelated CZT detector (made by H3D) and to measure the mass of plutonium, uranium, and MOX. Both the isotopic composition and mass of the item can be quickly determined with one measurement using a gamma detector with FRAM v.7.1.
This poster provides an overview of the application of the Solar Uncertainty Integrator (SUNI) software to atmospheric radiation measurement (ARM) solar radiation measurements.
Automated prediction techniques like simulation-based inference (SBI) are important tasks for science experiments that produce large amounts of complex, raw data. However, their development remains in its early stages because the uncertainties of these techniques lack sufficient trustworthiness and interpretability. Packages for SBI provide a growing set of diagnostics; however, the software requirements are substantial, as they are tied to the inference technology itself, and the APIs lack adaptability. We introduce the DeepDiagnostics package for diagnosing posteriors from analytic likelihood-based methods and SBI methods, such as neural posterior estimation. DeepDiagnostics produces a comprehensive set of high-quality visualizations and metrics in a highly accessible, easy-to-use, and flexible package. We address all of these goals by providing a command-line inference tool and a Python API that is controlled through a configuration file. The package includes common diagnostics, such as parity plots, corner (covariance) plots, simulation-based calibration (SBC) diagnostics (including posterior coverage and rank histograms), Lemos et al. s PQMass and TARP, Masserano et al. s WALDO, Linhart et al. s LC2ST, as well as credible region diagnostics developed by our group.
Containerization technology has emerged as a transformative tool for software engineers, offering consistent development and deployment environments, simplifying dependency management, and enhancing scalability and portability across diverse systems. However, its application in High-Performance Computing (HPC) presents unique challenges, including the management of virtualization overhead, the need for efficient resource allocation, and the maintenance of optimal performance for compute-intensiv
The Software Defined Architectures for Portability and Performance (SODAPOP) project developed a co-design framework to partition and map converged applications on specialized heterogeneous architectures. We started from key domain applications that combine scientific simulation with data analytics and machine learning as drivers to integrate our framework. The framework includes high-level compilers that interfaces with high-level programming frameworks, domain-specific optimization passes, and hardware-oriented optimizations. The framework leverages hardware generators to enable specialization and facilitate exploration of custom system designs.
The National Synchrotron Light Source II operating at Brookhaven National Laboratory since 2014 for the US Department of Energy is one of the newest and brightest storage-ring synchrotron facility in the world. NSLS-II, like other facilities, provides pre-processing of the raw data and some analysis capabilities to its users. We describe the research collaborations and open source infrastructure developed at large instrument facilities such as NSLS-II for the purpose of curating high value scientific data along the early stages of the data lifecycle. Data acquisition and curation tasks include storing experiment configuration, detector metadata, raw data acquisition with infrastructure that converts proprietary instrument formats to industry standards. In addition, we describe a specific effort for discovering sample information at NSLS-II and tracing the provenance of analysis performed on acquired images. We show that curation tasks must be embedded into software along the data life cycle for effectiveness and ease of use, and that loosely defined collaborations evolve around shared open source tools. Finally we discuss best practices for experimental metadata capture in such facilities, data access and the new challenges of scale and complexity posed by AI-based discovery for the synthesis of new materials.
We introduce Mitiq, a Python package for error mitigation on noisy quantum computers. Error mitigation techniques can reduce the impact of noise on near-term quantum computers with minimal overhead in quantum resources by relying on a mixture of quantum sampling and classical post-processing techniques. Mitiq is an extensible toolkit of different error mitigation methods, including zero-noise extrapolation, probabilistic error cancellation, and Clifford data regression. The library is designed to be compatible with generic backends and interfaces with different quantum software frameworks. We describe Mitiq using code snippets to demonstrate usage and discuss features and contribution guidelines. We present several examples demonstrating error mitigation on IBM and Rigetti superconducting quantum processors as well as on noisy simulators.