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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Curifactory: A research experiment manager

Curifactory is a command line tool and framework for organizing Python experiment code, configuration parameters, and results. It is an opinionated and lightweight approach to workflow management infrastructure and is primarily intended to support researchers conducting experiments on one machine. This software was developed to support the reproducibility of results for several data science projects in the Nuclear Nonproliferation Division at Oak Ridge National Laboratory. Curifactory is intended to be a general framework and is not specific to machine learning or data science. It can aid in any field in which experiments are primarily computation-based studies and can be implemented in Python (e.g., high-energy physics, astronomy, computational chemistry). Here, the design emphasizes the automated caching of intermediate data analysis artifacts to speed up development involving computationally intensive tasks. It also allows for data provenance and experiment reproduction. Individual experiment runs are tracked through logs and their output reports, and entire copies of a run with all cached data and metadata can be exported for others to run using Curifactory on another machine. Curifactory experiments can either be integrated into a project from the beginning or can be written on top of an existing codebase without needing significant modification. A few important views of the Curifactory library can be seen in Figure 1.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Lessons Learned from Ecosystem-Scale Experimental Field Studies (Workshop Report)

Efforts to understand and predict ecosystem responses to environmental change require long-term, large-scale, spatially representative experiments and observations that capture natural variability, test predictive models, and generate transferable knowledge. Such studies are indispensable for unraveling the complexities of terrestrial ecosystems and their responses to disturbances and evolving environmental conditions, while generating the data necessary for developing mechanistic models and predictive tools that inform decision-making processes. Having a rich history of designing and executing large-scale ecosystem experiments, the U.S. Department of Energy’s Environmental System Science program convened a workshop in January 2025 that brought together leaders in the field to distill critical lessons from decades of experience in large-scale experiments. The workshop aimed to (1) provide an ecosystem experiment primer for best practices, thus ensuring a high scientific return on investment for funding agencies, and (2) offer a robust framework for the design and management of future research initiatives. This report synthesizes insights and experiences from workshop participants and is structured to capture the entire research life cycle, from goal setting and design to operations, adaptive management, team dynamics, collaborations, and the often overlooked aspect of decommissioning. By synthesizing decision-making and lessons learned across diverse research approaches, the report aims to provide a template of essential factors to consider when designing successful long-term, large-scale ecosystem experiments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Structure and Process of Managing the UO2 Dry In-Pile Fracture Test Irradiation Experiment

The management and structure of a novel research and development (R&D) project can be hard to navigate. This is especially true when it involves nuclear fuel. Familiarity with institutional safety and procedural requirements, a well defined scope, and accurate cost estimates can all help ensure successful project management from start to finish. The Dry In-Pile Fracture Test (DRIFT) experiments performed at the Transient Test Reactor Facility are an excellent example of managing an R&D project within its scope and budget constraints. The objective of the DRIFT test was to develop fracture propagation data in a manner consistent with light-water reactors to validate and improve the fracture propagation models of uranium oxide fuels in the MOOSE (Multiphysics Object-Oriented Simulation Environment)-BISON-Marmot code framework [1].

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Transport Modeling of As-Run ATR Cycles to Support U-10Mo Research Reactor Fuel Qualification Experiment

The Department of Energy’s (DOE) Office of Materials Management and Minimization has been tasked with converting the five remaining United States High Performance Research Reactors (U.S. HPRR) from highly enriched uranium to low-enriched uranium. The Nuclear Regulatory Commission (NRC) regulates Massachusetts Institute of Technology Reactor (MITR), Missouri University Research Reactor (MURR), and National Bureau of Standards Reactor (NBSR); and DOE regulates the High Flux Isotope Reactor and Advanced Test Reactor (ATR). To meet the high demands of these reactors, the U.S. HPRR program has chosen to use 90% uranium - 10% molybdenum (U-10Mo) monolithic fuel. This plate-type fuel will undergo multiple irradiation experiment campaigns in ATR, from mini-plates to full element tests, over a large range of operating conditions. This will provide data in support of the fuel qualification of each reactor. This summary focuses on the as-run neutronic analysis of the first series of mini-plate (MP-1) experiments, which have been irradiated in the ATR. MP-1 experiment’s main goal is to demonstrate the fabrication process and meet the fuel irradiation performance requirements with primary focus on the fuel plates associated with the three NRC reactors, with a small focus on the low power requirements of the ATR fuel. The MP-1 experiments are planned to be the basis of the monolithic U-10Mo fuel for qualification through the NRC.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Automated Network Services for Exascale Data Movement

The Large Hadron Collider (LHC) experiments distribute data by leveraging a diverse array of National Research and Education Networks (NRENs), where experiment data management systems treat networks as a “blackbox” resource. After the High Luminosity upgrade, the Compact Muon Solenoid (CMS) experiment alone will produce roughly 0.5 exabytes of data per year. NREN Networks are a critical part of the success of CMS and other LHC experiments. However, during data movement, NRENs are unaware of data priorities, importance, or need for quality of service, and this poses a challenge for operators to coordinate the movement of data and have predictable data flows across multi-domain networks. The overarching goal of SENSE (The Software-defined network for End-to-end Networked Science at Exascale) is to enable National Labs and universities to request and provision end-to-end intelligent network services for their application workflows leveraging SDN (Software-Defined Networking) capabilities. This work aims to allow LHC Experiments and Rucio, the data management software used by CMS Experiment, to allocate and prioritize certain data transfers over the wide area network. In this paper, we will present the current progress of the integration of SENSE, Multi-domain end-to-end SDN Orchestration with QoS (Quality of Service) capabilities, with Rucio, the data management software used by CMS Experiment.

Balcas, Justas↗
DOE OSTI2025

A Practical Comparison of Data-Driven Prognostics Methods for Energy Systems

This study explores data-driven prognostics for nuclear power plant (NPP) condensers, focusing on tube fouling. We utilized the Asherah nuclear power plant simulator (ANS) to compare four methods: Random Forest (RF), Support Vector Regressor (SVR), Fully Connected Neural Network (FCNN), and Long Short-Term Memory Neural Network (LSTM). By simulating various fouling scenarios in the ANS, we generated data with different degradation rates under transient operations. The models were trained and tested on these data, with performance evaluated visually and numerically including uncertainty assessment. The LSTM model excelled, exhibiting minimal prediction noise and the most accurate remaining useful life estimates across all degradation levels. Its ability to capture long-term dependencies and produce cleaner outputs makes it a strong candidate, although accurate training data across the entire component lifespan are crucial. The RF model emerged as a robust alternative, providing reliable predictions with high confidence. The FCNN and SVR models, while less effective overall, showed potential under specific conditions. FCNN offers a less complex alternative to LSTM and might benefit from larger datasets. SVR excels in precision when the quality of the training data is high. Furthermore, this study highlights the operational benefits of advanced prognostics in the energy sector and emphasizes the need for further research in NPP condenser health management through real-life experiments.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Perennializing marginal croplands: going back to the future to mitigate climate change with resilient biobased feedstocks

Managing annual row crops on marginally productive croplands can be environmentally unsustainable and result in variable economic returns. Incorporating perennial bioenergy feedstocks into marginally productive cropland can engender ecosystem services and enhance climate resiliency while also diversifying farm incomes. We use one of the oldest bioenergy-specific field experiments in North America to evaluate economically and environmentally sustainable management practices for growing perennial grasses on marginal cropland. This long-term field trial called 9804 was established in 1998 in eastern Nebraska and compared the productivity and sustainability of corn (Zea mays L.)—both corn grain and corn stover—and switchgrass (Panicum virgatum L.) bioenergy systems under different harvest strategies and nitrogen (N) fertilizer rates. This experiment demonstrated that switchgrass, compared to corn, is a reliable and sustainable bioenergy feedstock. This experiment has been a catalyst for other bioenergy projects which have also expanded our understanding of growing and managing bioenergy feedstocks on marginal cropland. We (1) synthesize research from this long-term experiment and (2) provide perspective concerning both the knowledge gained from this experiment and knowledge gaps and how to fill them as well as the role switchgrass will play in the future of bioenergy.

09 BIOMASS FUELS↗
DOE OSTI2024

Improving Self-Driving Labs: Quantifying System-Level Experiment Repeatability and Broadening Instrument-Level Compatibility

Modular Autonomous Research System (MARS) is a self-driving laboratory (SDL) which performs wet-lab science with peptide-lanthanide combinations in an automated and, ultimately, an autonomous manner to aid in soil analysis for domestic lithium mining. Autonomous experimentation involves automated experimentation, experiment planning, and active learning. MARS consists of a 6-axis robotic arm (UR5e) on a linear rail, pipette robots (Opentrons 2), and microplate readers. These components transport, operate on, and collect data with chemical solutions in standard labware. For effective autonomy, MARS must perform system-level labware operations repeatably, plan experiments autonomously, and be portable between research-domains. Repeatability is evaluated by labware placement precision, such that future operations can properly locate labware, as well as the elapsed time, so that low variance mean estimates of experiment duration can inform high-level researcher decision making. Autonomous experiment planning is the next step to decouple experimentation from human management; however, there is a conflict between the ideal system-level experiment goals and the constraints imposed by instruments’ limitations. Sub-domain portability is a long-term goal to extend MARS’ research beyond the chemistry of peptide-lanthanide binding to other sub-domains without having to invest significant overhead to system retrofitting. To address these goals, we manually trained the robotic arm labware placement and modelled statistical failurerate and uncertainty Additionally, we benchmarked the duration and variance of each experiment sub-operation as a heuristic for research decision making. Next, we use a parameterized geometric program (PGP) approach to design experiments that optimize system-level objectives and satisfy instrument-level constraints. Lastly, we proposed a Python framework to maximize MARS’ extensibility to other scientific sub-domains through a JSON-based experiment specification.

36 MATERIALS SCIENCE↗
DOE OSTI2024

An open-source data storage and visualization platform for collaborative qubit control

Developing collaborative research platforms for quantum bit control is crucial for driving innovation in the field, as they enable the exchange of ideas, data, and implementation to achieve more impactful outcomes. Furthermore, considering the high costs associated with quantum experimental setups, collaborative environments are vital for maximizing resource utilization efficiently. However, the lack of dedicated data management platforms presents a significant obstacle to progress, highlighting the necessity for essential assistive tools tailored for this purpose. Current qubit control systems are unable to handle complicated management of extensive calibration data and do not support effectively visualizing intricate quantum experiment outcomes. In this paper, we introduce Qubit Control Storage and Visualization ( QubiCSV ), a platform specifically designed to meet the demands of quantum computing research, focusing on the storage and analysis of calibration and characterization data in qubit control systems. As an open-source tool, QubiCSV facilitates efficient data management of quantum computing, providing data versioning capabilities for data storage and allowing researchers and programmers to interact with qubits in real time. The insightful visualization are developed to interpret complex quantum experiments and optimize qubit performance. QubiCSV not only streamlines the handling of qubit control system data but also improves the user experience with intuitive visualization features, making it a valuable asset for researchers in the quantum computing domain.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Demonstration of a Novel Technology to Manage Electricity Demand in Grid-Independent Military Microgrids

This research was conducted by the National Renewable Energy Laboratory (NREL) in collaboration with the S&C Electric Inc. through funding provided by the ESTCP. The project demonstrates use of cybersecure Automated Demand Response (ADR) technology to effectively manage microgrid loads during grid-independent, also known as "islanded," operation. When military microgrids become isolated from the main electrical grid, they are required to balance electricity supply and demand locally. Given that local generation may be constrained, the prevailing strategy involves shedding all but the most critical loads by tripping smart circuit breakers, which then necessitate manual resetting. This approach is generally implemented at the building level, which means that the buildings with mission-critical activities are exempt from load management and remain fully powered, whereas those deemed non-critical can experience a complete loss of service. In this research we developed a method that allows building automation systems to selectively control their assets in response to load shedding request from a microgrid controller, avoiding total loss of service in contrast to the conventional control approach. A commercial OpenADR client server by GridFabric is used for communication between the microgrid controller and the building management system (BMS). The microgrid controller monitors both generation capacity and various assets within the microgrid and issues a demand reduction request when necessary. This request is communicated to the OpenADR server via Modbus. Upon receiving the request, the OpenADR server forwards it to the BMS utilizing the OpenADR protocol. The BMS is pre-configured with various levels of load reduction strategies based on the controllable assets available, allowing for a nuanced approach to demand reduction. Both lab and field tests were performed that considered load shedding needed to achieve closed transition into island mode and to accommodate changing loads and power source availability while islanded. A commercial microgrid controller was used for these tests with normal programming within the expected constraints of the system capabilities. That is, the solution did not require any specialized modification to the code base of the controller. Given the latency of the round-trip communication path between the microgrid controller and the various devices involved with the load shed processes, there are certain scenarios for which the demonstrated solution are appropriate and some which are not. The methods described in this report can be used for load shedding/restoration during transitions between islanded and grid-tied modes of operation, as well as accommodating normal variations in load and the need to remove a power source from operation for maintenance. These methods should not be used for scenarios that require load shedding within a second or two such as sudden and unanticipated significant load increases or loss of power sources through equipment faults.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Sustainable Water Management: Understanding the Socioeconomic and Cultural Dimensions

With the pressing challenges of water scarcity and pollution, achieving sustainable water management is imperative for promoting long-term development. Therefore, this paper aims to examine the socio-economic and cultural factors that shape the sustainability of water management strategies in Brazil and Portugal. This study highlights various factors that influence water management, including robust legal frameworks, socio-economic disparities, cultural practices, agricultural water usage, knowledge sharing, public participation, climate change resilience, water scarcity risks, industrial water consumption, and urbanization. By conducting a SWOT analysis of water management strategies, this research synthesizes information through an extensive literature review, encompassing the legal frameworks, policies, and implemented strategies in both countries. Additionally, it investigates comparative studies among Brazil, Portugal, and other European nations to facilitate the exchange of knowledge and experiences in water management practices. The findings of this study offer valuable insights into the strengths, weaknesses, opportunities, and threats associated with water management strategies in Brazil and Portugal, thereby guiding the development of tailored policies and strategies that foster sustainability in water resource management. Additionally, the research highlights the role of digital transformation in optimizing water management practices. By integrating socio-economic, cultural, and digital factors, this study contributes to effective and sustainable water management in Brazil and Portugal, ensuring responsible utilization and preservation of water resources.

Santos, Eleonora↗
DOE OSTI2023

Managing Software Provenance to Enhance Reproducibility in Computational Research

Scientific processes rely on software as an important tool for data acquisition, analysis, and discovery. Over the years, sustainable software development practices have made progress in being considered as an integral component of research. However, management of computation-based scientific studies is often left to individual researchers who design their computational experiments based on personal preferences and the nature of the study. Here, we believe that the quality, efficiency, and reproducibility of computation-based scientific research can be improved by explicitly creating an execution environment that allows researchers to provide a clear record of traceability. This is particularly relevant to complex computational studies in high-performance computing (HPC) environments. In this article, we review the documentation required to maintain a comprehensive record of HPC computational experiments for reproducibility. We also provide an overview of tools and practices that we have developed to perform such studies around Flash-X, a multiphysics scientific software.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Development of Laboratory Testbeds Simulating Real-World Greenhouse Gas Emissions

Climate change leads to drastic variations in the environment that can threaten food security. To protect the security and prosperity of the environment, new agricultural practices that mitigate greenhouse gas emissions need to be developed. Agriculture plays a large role in the emissions of greenhouse gases, thus a need for new practices. At present, testing landscape management practices for carbon sequestration and reduction of greenhouse gas emissions are expensive because such tests often require intricate field experiments. Laboratory tests on landscape management practices often fail to simulate and accurately represent the natural systems researchers intend to study. There are methods that can be used to make these laboratory tests more closely replicate natural systems for the improvement of agricultural lands used in biofuel and food production. The goal of this project is to develop a laboratory testbed replicating field conditions that will mimic field greenhouse gas emissions. To achieve this goal, we used soil cores taken from agricultural and marginal lands, then attached fiberglass wicks to improve the drainage system in the soil core to simulate a more natural hydraulic system. The soil cores with the wicks were used to compared to greenhouse gas emissions observed in the field. Our preliminary results suggest that the soil carbon dioxide and nitrous oxide flux from the agricultural land site were comparable to the soil cores equipped with the drainage wicks. The data suggests that laboratory testbeds can be used to mimic field greenhouse gas emissions if parameters, such as soil water content were in a set range. The laboratory testbed produces similar greenhouse gas emissions to the agricultural site when the soil water content was between 15-25%.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Transport Modeling of As-Run ATR Cycles for U-10Mo Fuel Qualification Experiment

The Department of Energy’s (DOE) Office of Materials Management and Minimization has been tasked with converting the five remaining United States High Performance Research Reactors (U.S. HPRR) from highly enriched uranium to low-enriched uranium. The Nuclear Regulatory Commission (NRC) regulates Massachusetts Institute of Technology Reactor (MITR), Missouri University Research Reactor (MURR), and National Bureau of Standards Reactor (NBSR); and DOE regulates the High Flux Isotope Reactor and Advanced Test Reactor (ATR). To meet the high demands of these reactors, the U.S. HPRR program has chosen to use 90% uranium - 10% molybdenum (U-10Mo) monolithic fuel. This plate-type fuel will undergo multiple irradiation experiment campaigns in ATR, from mini-plates to full element tests, over a large range of operating conditions. This will provide data in support of the fuel qualification of each reactor. This summary focuses on the as-run neutronic analysis of the first series of mini-plate (MP-1) experiments, which have been irradiated in the ATR. MP-1 experiment’s main goal is to demonstrate the fabrication process and meet the fuel irradiation performance requirements with primary focus on the fuel plates associated with the three NRC reactors, with a small focus on the low power requirements of the ATR fuel. The MP-1 experiments are planned to be the basis of the monolithic U-10Mo fuel for qualification through the NRC.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

The Advanced Dimensional Depletion for Engineering of Reactors (ADDER) Software for Depletion and Fuel Management

The Advanced Dimensional Depletion for Engineering of Reactors (ADDER) software is being developed in the Research and Test Reactor (RTR) Program at Argonne National Laboratory to meet the reactor design and analysis needs of the Conversion Program. ADDER is a flexible tool that (1) provides a depletion capability through coupling external neutronics codes with a built-in CRAM solver or external depletion code and (2) provides a user-friendly interface to perform fuel management and criticality search operations. The ADDER software is a Python 3 application written using modern software development practices subject to a compliant implementation of NQA-1 and applicable Department of Energy software quality assurance standards. This report is the user guide for the software release referred to as ADDER v1.1.0. The motivation for a software to have flexible capabilities that ADDER possesses is the need to support a wide variety of geometries that are commonly required in analysis of research and test reactors. These reactors can have complex fuel, experiment, or control material shuffling patterns that persist over several years with many fuel management and partial refueling intervals. The scale of fuel management analysis can require tracking of an inventory that is multiple times the core loading. Many reactors, both power and non-power reactors of various types, will find the features of ADDER useful to facilitate key tasks that a fuel or core design engineer must perform with the convenience of concise input and validated functionality.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

The Advanced Dimensional Depletion for Engineering of Reactors (ADDER) Software for Depletion and Fuel Management

The Advanced Dimensional Depletion for Engineering of Reactors (ADDER) software is being developed in the Research and Test Reactor (RTR) Program at Argonne National Laboratory to meet the reactor design and analysis needs of the Conversion Program. ADDER is a flexible tool that (1) provides a depletion capability through coupling external neutronics codes with a built-in CRAM solver or external depletion code and (2) provides a user-friendly interface to perform fuel management and criticality search operations. The ADDER software is a Python 3 application written using modern software development practices subject to a compliant implementation of NQA-1 and applicable Department of Energy software quality assurance standards. This report is the user guide for the software release referred to as ADDER v1.1.0. The motivation for a software to have flexible capabilities that ADDER possesses is the need to support a wide variety of geometries that are commonly required in analysis of research and test reactors. These reactors can have complex fuel, experiment, or control material shuffling patterns that persist over several years with many fuel management and partial refueling intervals. The scale of fuel management analysis can require tracking of an inventory that is multiple times the core loading. Many reactors, both power and non-power reactors of various types, will find the features of ADDER useful to facilitate key tasks that a fuel or core design engineer must perform with the convenience of concise input and validated functionality.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Nuclear Physics Network Requirements Review (Final Report)

The Energy Sciences Network (ESnet) is the high-performance network user facility for the US Department of Energy (DOE) Office of Science (SC) and delivers highly reliable data transport capabilities optimized for the requirements of data-intensive science. In essence, ESnet is the circulatory system that enables the DOE science mission by connecting all its laboratories and facilities in the US and abroad. ESnet is funded and stewarded by the Advanced Scientific Computing Research (ASCR) program and managed and operated by the Scientific Networking Division at Lawrence Berkeley National Laboratory (LBNL). ESnet is widely regarded as a global leader in the research and education networking community. ESnet interconnects DOE national laboratories, user facilities, and major experiments so that scientists can use remote instruments and computing resources as well as share data with collaborators, transfer large datasets, and access distributed data repositories. ESnet is specifically built to provide Between July 2023 and October 2023, ESnet and the Nuclear Physics program (NP) of the DOE SC organized an ESnet requirements review of NP-supported activities. Preparation for these events included identification of key stakeholders: program and facility management, research groups, and technology providers. Each stakeholder group was asked to prepare formal case study documents about its relationship to the NP program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Multiscale Modeling and Experimental Insights into High-Temperature Soil Biodegradation Dynamics of Semi-Crystalline Poly(Lactic Acid) Nonwoven Fabrics

This study investigates the biodegradation of semi-crystalline poly(lactic acid) (PLA) nonwovens (NWs) in soil at 58 °C using both experimental and mathematical modeling approaches. The model utilizes a system of parabolic diffusion-reaction partial differential equations (PDEs) to elucidate chemical transformations over time and in space. It accounts for phenomena such as the diffusion of water and lactic acid monomers through the polymer matrix and into the surrounding soil, along with their microbial breakdown. It also accounts for the initial PLA crystallinity and predicts its evolution in time. The model is solved numerically for a single filament, and the results were used to shed light on PLA NW transformations observed in soil over a 180-day incubation period. Various characterization techniques, including scanning electron microscopy (SEM), differential scanning calorimetry (DSC), and Raman spectroscopy, were employed to assess morphological changes, crystallinity, and molecular changes in the NWs throughout the experiment. By comparing the experimental data with the model predictions, the hydrolysis rate coefficient was found to be 3.37 × 10 -7 s -1 , while the rate of microbial degradation of lactic acid monomers was faster, of the order of 9.63 × 10 -7 s -1 . The findings highlight the significant role of crystallinity in the biodegradation process. The PLA degradation ceases when no amorphous material remains, and the crystallinity reaches 0.8, as observed in the experiments by day 120. Furthermore, this research contributes to a deeper understanding of PLA biodegradation dynamics and offers insights for effectively managing biodegradable materials in environmental settings.

Diffusion−reaction modeling↗