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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

CCSI Toolset 3.18 Release

CCSI Toolset 3.18 Release Highlights FOQUS was updated to allow installation for users using MacOS on Apple silicon. FOQUS Cloud support was added for user plugins. The Optimality-Based Design of Experiments tutorials were updated to reflect the latest changes in the user interface flow. The plot discrete sliders were fixed for CDF and 3D plots within Uncertainty Quantification, which were not working due to a matplotlib depreciation. The installation was updated to set the default location for the PSUADE executable if found in the environment. Updates were made to allow compatibility with NumPy 1.25. Additional documentation changes were made to fix typographical errors and fix a broken link to optional software.

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CCSI Toolset 3.19 Release

CCSI Toolset 3.19 Release Highlights A gradient generation tool was developed to support GENN models in FOQUS. Certain machine learning tools train gradient-enhanced neural network (GENN) models which can be more accurate for complex datasets given a priori knowledge of model derivatives. However, the derivatives must be known beforehand and are not often available for process data. This tool automatically predicts the gradients for a training dataset in a form usable by common GENN trainers, such as Surrogate Modeling Toolbox. Support was added for Surrogate Modeling Toolbox GENN models in FOQUS, including updates to the run methods, node properties, test framework, documentation and optional dependencies list. Users can train/save Surrogate Modeling Toolbox gradient-enhanced neural network (GENN) models with custom objects and produce .pkl files compatible with the Machine Learning/Artificial Intelligence Plugin in FOQUS. A simpler implementation of the ordering algorithm in the Sequential Design of Experiments (SDOE) module was included. The SDOE examples documentation was updated. The Optimality-Based Design of Experiments was updated to improve the error handling when the results are None.

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CCSI Toolset 3.20 Release

CCSI Toolset 3.20 Release Highlights Minimum Viable Product surrogate plugin was added for creating Machine Learning/Artificial Intelligence models. Corresponding documentation was added for the plugin. Sequential Design of Experiments plots were updated to eliminate an issue with the window stack ordering upon closure of the plots. Support for Python 3.7 was removed. Documentation was improved by adding new mandatory section to the ReadTheDocs configuration and adding installation instructions back for NLOpt. TurbineLite was updated to 3.0.0, which is compatible with SimSinter 3.0.0. The developer environment was updated and 32-bit support was removed. SimSinter was updated to 3.0.0. This version removed gPROMS support and included security updates.

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CCSI Toolset 3.21 Release

CCSI Toolset 3.21 Release Highlights Parallelization support was added for Sequential Design of Experiments (SDOE) computations using Dask (preliminary). Input type dependent ordering capability was added to the SDOE module. With this implementation the user can specify the level of difficulty to change an input (Easy or Hard) and FOQUS will generate the appropriate ordered design depending on the input difficulty combination. Python version support was extended. FOQUS is now compatible with Python 3.8 through 3.12. Platforms used for automated testing were expanded to include macOS ARM (Apple Silicon). Updates to the FOQUS documentation to include information on how to set paths for SimSinter and TurbineLite. Turbine configuration section was added to Debugging Documentation.

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CCSI Toolset 3.22 Release

CCSI Toolset 3.22 Release Highlights The Sequential Design of Experiments user interface was updated to resolve an issue where the results would fail to plot in some cases (e.g., Non-Uniform Space Filling designs). The Machine Learning/Artificial Intelligence module was updated to support Keras 3 and to reflect changes made to dependencies’ syntax. A check was added to ensure PSUADE is installed and available at FOQUS startup. If PSUADE is not installed, a link to the FOQUS documentation is displayed and FOQUS is closed. The copyright year was updated to include 2024 in places where it had not previously been updated. Typographical errors were corrected to improve clarity in variable names and documentation. The FOQUS documentation was updated to reflect the fact that ALAMO can have two executables and indicates the correct executable to add to the Settings path. SimSinter was updated to version 3.1.0. This version removed gPROMS support and included security updates.

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CCSI Toolset 3.23 Release

CCSI Toolset 3.23 Release Highlights The user interface was updated to allow timeouts in Aspen Custom Modeler and AspenPlus. The installation was updated to use 64-bit versions of TurbineLite and SimSinter by default. The documentation was improved for clarity.

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CCSI Toolset 3.24 Release

CCSI Toolset 3.24 Release Highlights Support for Python 3.8 was removed. Extraneous and wildcard imports were removed.

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CCSI Toolset 3.25 Release

CCSI Toolset 3.25 Release Highlights The copyright in the CCSI Toolset was updated to include the year 2025. The code and notes within it were revised to correct minor typographical errors and clarify meanings of variables. The configuration of the FOQUS documentation via Read the Docs was updated to explicitly set the path.

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The Digital Engineering Vision for DOME: Facilitating Design, Deployment, and Operations [Poster]

DOME is a planned microreactor test facility at INL’s Materials and Fuels Complex. It is a complex system with several interdependent sub-systems such as the reactor (up to 20 MWth), radioactive confinement, temperature and pressure regulation system, ventilation system, etc. The engineering design process for such a system traditionally involves several documents from various sources and the system information is scattered across these documents. Digital engineering represents a paradigm shift through which systems are designed using digital models and integrated data. The digital engineering vision for DOME utilizes a model-based systems engineering (MBSE) approach. The system architecture, physical components, control logic, and verification experiments are all designed using MathWorks MATLAB and Simulink. This hierarchical model can combine data from multiple sources at various levels of abstraction. It can be used to simulate the facility’s operations and to test the system using different sets of parameters. Its capabilities can be expanded by interfacing it with high-fidelity multi-physics models, risk analysis tools, etc. The same model can evolve into a digital twin that can monitor operations and conduct predictive analysis using real-time sensor data from the facility. The eventual goal of this effort is to transform the end-to-end engineering of nuclear facilities in every phase of their lifecycle, including design, deployment, and operations.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

SinhaRoy_TechPresentation_2024 [Slides]

DOME is a planned microreactor test facility at INL’s Materials and Fuels Complex. It is a complex system with several interdependent sub-systems such as the reactor (up to 20 MWth), radioactive confinement, temperature and pressure regulation system, ventilation system, etc. The engineering design process for such a system traditionally involves several documents from various sources and the system information is scattered across these documents. Digital engineering represents a paradigm shift through which systems are designed using digital models and integrated data. The digital engineering vision for DOME utilizes a model-based systems engineering (MBSE) approach. The system architecture, physical components, control logic, and verification experiments are all designed using MathWorks MATLAB and Simulink. This hierarchical model can combine data from multiple sources at various levels of abstraction. It can be used to simulate the facility’s operations and to test the system using different sets of parameters. Its capabilities can be expanded by interfacing it with high-fidelity multi-physics models, risk analysis tools, etc. The same model can evolve into a digital twin that can monitor operations and conduct predictive analysis using real-time sensor data from the facility. The eventual goal of this effort is to transform the end-to-end engineering of nuclear facilities in every phase of their lifecycle, including design, deployment, and operations.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Development of a Novel DIW-PuSL Printer: The xPuSL Project

During my internship at Lawrence Livermore National Laboratory, I contributed to the xPuSL project. The xPuSL project aimed to revolutionize multi-material 3D printing by combining DIW and PuSL techniques to produce complex geometries with functional materials. My role encompassed process engineering, control systems, experimentation, and CAD design, leading to a streamlined workflow from CAD models to xPuSL prints.

36 MATERIALS SCIENCE↗

Carbon Capture and Utilization for Protein and Fatty Acids

The unlimited release version of the final report for the "Carbon Capture and Utilization for Protein and Fatty Acids" project. This project advanced an integrated open raceway algae cultivation and processing system to engineering scale for carbon capture and utilization (CCU) from the flue gas of a naphtha-fired power plant.

02 PETROLEUM↗

To What Extent Will Decarbonization Deepen the Conversation Between Industry and the Grid?

Decarbonization - the transition away from un-mitigated fossil fuel combustion throughout the economy - requires big changes from both power and process systems. On the power system side, those changes are expected to include large increases in variable generation, e.g., from wind and solar, which has near-zero marginal costs and at large shares can produce infrequent but consequential energy droughts. On the process systems side, industries are investigating their options for direct and indirect electrification, the latter exemplified by replacing fossil fuel inputs with zero-carbon, energy-carrying chemicals like hydrogen and ammonia produced via electrochemical processes. The economic features of these changes within the larger context of power and process systems suggest that their realization could be accompanied by a paradigm shift in how industrial facilities interact with the grid. For example, the dominant type of demand participation in power markets could change from today's focus on load reductions at peak times to a new focus on shifting electricity use, enabled in part by large-scale product storage, to take advantage of renewable energy that would otherwise be curtailed and to avoid consumption during high-price energy droughts. This talk will describe these and other possible design and operational approaches from grid and industrial economic perspectives, culminating in an enumeration of open problems that lie at the interface of today and tomorrow's power and process systems.

co-design↗

Addressing Human and Organizational Factors in Nuclear Industry Modernization: A Sociotechnically Based Strategic Framework

The modernization of nuclear power plants will require an advanced concept of operations, involving an integrated set of tightly coupled systems in which all stakeholders act in a coordinated manner. For this modernization effort to be enabled, we developed a human and organizational factors approach based on a broad sociotechnical framework. Starting from core human factors principles, we conducted a literature review of the methods and approaches relevant to the modernization problem. These included not only core disciplines such as cognitive systems engineering, systems theoretic accident modeling and processes, human systems integration, resilience engineering, and macroergonomics but also related topics of safety culture and organizational change. From this literature, we developed a conceptual framework centered around the work system with its four interacting components: people, technology, process, and governance. In an effective work system, these four components are jointly optimized according to three systems criteria: efficiency, effectiveness, and safety. System failure may result from excessive emphasis on any one criterion. The actual work of attaining joint optimization in a given work system can be accomplished by utilizing three high-level functions: knowledge elicitation, knowledge representation, and cross-functional integration. Finally, we illustrated the utility of this approach by applying it to practical problems and case studies.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Addressing Human and Organizational Factors In Nuclear Industry Modernization: A Sociotechnically-Based Strategic Framework

The modernization of nuclear power plants will require an advanced concept of operations, involving an integrated set of tightly coupled systems in which all stakeholders act in a coordinated manner. For this modernization effort to be enabled, we developed a human and organizational factors approach, based on a broad sociotechnical framework. Starting from core human factors principles, we conducted a literature review of the methods and approaches relevant to the modernization problem. These included core disciplines such as cognitive systems engineering, systems theoretic accident modeling and processes (STAMP), human systems integration, resilience engineering, and macroergonomics but also related topics of safety culture and organizational change. From this literature, we developed a conceptual framework centered around the work system with its four interacting components: people, technology, process, and governance. In an effective work system, these four components are jointly optimized according to three systems criteria: efficiency, effectiveness, and safety. System failure may result from excessive emphasis on any one criterion. The actual work of attaining joint optimization in a given work system can be accomplished by utilizing three high level functions: knowledge elicitation, knowledge representation, and cross-functional integration. We illustrated the utility of this approach by applying it to practical problems and case studies. The complete report is available in [1]; this paper is a description of our approach and report contents.

99 GENERAL AND MISCELLANEOUS↗

Data movement engine

A memory system of a data processing system includes one or more storage devices and a data rearrangement engine for moving data between memory regions of the plurality of memory regions. The data rearrangement engine is configured to rearrange data stored at non-contiguous addresses in a source memory region into contiguous address in a destination region responsive to a rearrangement specified by a host processing unit of the data processing system. A description of the rearranged data is maintained in a metadata memory region. Rearranged data may be accessed by one or more host processing units. Write-back of data from the destination to the source region may be reduced by use of Bloom filter or the like.

Beard, Jonathan Curtis↗

Using a coupled agent-based modeling approach to analyze the role of risk perception in water management decisions

Abstract. Managing water resources in a complex adaptive natural–human system is a challenge due to the difficulty of modeling human behavior under uncertain risk perception. The interaction between human-engineered systems and natural processes needs to be modeled explicitly with an approach that can quantify the influence of incomplete/ambiguous information on decision-making processes. In this study, we two-way coupled an agent-based model (ABM) with a river-routing and reservoir management model (RiverWare) to address this challenge. The human decision-making processes is described in the ABM using Bayesian inference (BI) mapping joined with a cost–loss (CL) model (BC-ABM). Incorporating BI mapping into an ABM allows an agent's psychological thinking process to be specified by a cognitive map between decisions and relevant preceding factors that could affect decision-making. A risk perception parameter is used in the BI mapping to represent an agent's belief on the preceding factors. Integration of the CL model addresses an agent's behavior caused by changing socioeconomic conditions. We use the San Juan River basin in New Mexico, USA, to demonstrate the utility of this method. The calibrated BC-ABM–RiverWare model is shown to capture the dynamics of historical irrigated area and streamflow changes. The results suggest that the proposed BC-ABM framework provides an improved representation of human decision-making processes compared to conventional rule-based ABMs that do not take risk perception into account. Future studies will focus on modifying the BI mapping to consider direct agents' interactions, up-front cost of agent's decision, and upscaling the watershed ABM to the regional scale.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗