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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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Energy Efficiency Package for Tenant Fit-Out: Laboratory Testing and Validation of Energy Savings and Indoor Environmental Quality
Approximately 40% of the total U.S. office floor space of 1.5 billion sq.m (16 billion sq.ft.) is leased space occupied by tenants. Tenant fit-out presents a key opportunity to incorporate energy efficiency within the real estate business cycle. We designed a package of energy efficiency measures tailored to the scope of a tenant fit-out. This tenant fit-out package (TFP) includes advanced lighting and heating, ventilating and air-conditioning (HVAC) controls as core measures, with ceiling fans, automated shading, and plug load controls as additional optional measures. We conducted laboratory testing of six configurations of the package to evaluate energy savings, indoor environmental quality, and identify installation, commissioning, and operational issues. Combined savings for HVAC, lighting, and plug loads ranged from 33–40%. Lighting savings ranged from 69–83%, and HVAC savings from 20–40%. The laboratory testing also revealed some minor but tractable challenges with installation and commissioning of HVAC controls. Overall, the results demonstrate that significant savings can be realized in existing office buildings by incorporating relatively low-risk, proven measures at the time of a tenant fit-out.
Benefits of digitalizing and employing simulation to increase plant system performance and ensure compliance with technical specifications - 109
The essential services water system in a BWR nuclear power plant was affected by corrosion, soiling and blockages due to ageing. Those episodes affected safety margins hindering the Technical Specifications compliance during periodic system testing. Tecnatom designed, implemented and tested a design modification for the on-line monitoring of this system by integrating it in the existing Digital Control System (DCS). Key system parameters were acquired in real time to be displayed in the Human System Interface, used in performing calculations and stored their historical evolution. The objective was to optimize monitoring and surveillance of the essential service water system. Furthermore, Tecnatom developed an engineering simulator ('What if' simulator) consisting of a hydraulic model of the system. This simulator takes as inputs each heat exchanger performance parameters from the plant DCS, allows the user to change the essential services water system configuration (valves position, UHS level...) and calculates theoretical process values predicting the system real behavior. In a second stage, more system instruments were wired to the DCS for Technical Specifications compliance automatic surveillance regarding opening and closing times of system valves. Operating experience shows important benefits after the digitalization. It enhances on-line information of system operation and automates calculation of load loss factor of each heat exchanger. It eases the evaluation of blockages and soiling in the system allowing optimization of cleaning proceedings. System Surveillance Requirements and Test Requirements reports have been automated optimizing the operation workload. The simulator use enables to identify optimal system configuration increasing safety margins of the system and ensuring Technical Specifications fulfillment. (authors)
Automated Detection of Instability-Inducing Channel Geometry Transitions in Saint-Venant Simulation of Large-Scale River Networks
A new sweep-search algorithm (SSA) is developed and tested to identify the channel geometry transitions responsible for numerical convergence failure in a Saint-Venant equation (SVE) simulation of a large-scale open-channel network. Numerical instabilities are known to occur at “sharp” transitions in discrete geometry, but the identification of problem locations has been a matter of modeler’s art and a roadblock to implementing large-scale SVE simulations. The new method implements techniques from graph theory applied to a steady-state 1D shallow-water equation solver to recursively examine the numerical stability of each flowpath through the channel network. The SSA is validated with a short river reach and tested by the simulation of ten complete river systems of the Texas–Gulf Coast region by using the extreme hydrological conditions recorded during hurricane Harvey. The SSA successfully identified the problematic channel sections in all tested river systems. Subsequent modification of the problem sections allowed stable solution by an unsteady SVE numerical solver. The new SSA approach permits automated and consistent identification of problem channel geometry in large open-channel network data sets, which is necessary to effectively apply the fully dynamic Saint-Venant equations to large-scale river networks or for city-wide stormwater networks.
Graph-Based Approach to Systematic Molecular Coarse-Graining
A novel methodology is introduced here to generate coarse-grained (CG) representations of molecular models for simulations. The proposed strategy relies on basic graph-theoretic principles and is referred to as graph-based coarse-graining (GBCG). It treats a given system as a molecular graph and derives a corresponding CG representation by using edge contractions to combine nodes in the graph, which correspond to atoms in the molecule, into CG sites. A key element of this methodology is that the nodes are combined according to well-defined protocols that rank-order nodes based on the underlying chemical connectivity. By iteratively performing these operations, successively coarser representations of the original atomic system can be produced to yield a systematic set of CG mappings with hierarchical resolution in an automated fashion. These capabilities are demonstrated in the context of several test systems, including toluene, pentadecane, a polysaccharide dimer, and a rhodopsin protein. In these examples, GBCG yields multiple, intuitive structures that naturally preserve the chemical topology of the system. Importantly, these representations are rendered from algorithmic implementation rather than an arbitrary ansatz, which, until now, has been the conventional approach for defining CG mapping schemes. Overall, the results presented here indicate that GBCG is efficient, robust, and unambiguous in its application, making it a valuable tool for future CG modeling.
Abstraction hierarchy to define biofoundry workflows and operations for interoperable synthetic biology research and applications
Lack of standardization in biofoundries limits the scalability and efficiency of synthetic biology research. Here, we propose an abstraction hierarchy that organizes biofoundry activities into four interoperable levels: Project, Service/Capability, Workflow, and Unit Operation, effectively streamlining the Design‑Build‑Test‑Learn (DBTL) cycle. This framework enables more modular, flexible, and automated experimental workflows. It improves communication between researchers and systems, supports reproducibility, and facilitates better integration of software tools and artificial intelligence. Our approach lays the foundation for a globally interoperable biofoundry network, advancing collaborative synthetic biology and accelerating innovation in response to scientific and societal challenges.
Machine-learning for designing nanoarchitectured materials by dealloying
Machine learning-augmented materials design is an emerging method for rapidly developing new materials. It is especially useful for designing new nanoarchitectured materials, whose design parameter space is often large and complex. Metal-agent dealloying, a materials design method for fabricating nanoporous or nanocomposite from a wide range of elements, has attracted significant interest. Here, a machine learning approach is introduced to explore metal-agent dealloying, leading to the prediction of 132 plausible ternary dealloying systems. A machine learning-augmented framework is tested, including predicting dealloying systems and characterizing combinatorial thin films via automated and autonomous machine learning-driven synchrotron techniques. This work demonstrates the potential to utilize machine learning-augmented methods for creating nanoarchitectured thin films.
A semblance measure for model comparison
Algorithmic and computational advances have made it possible that geophysical survey and earth model design can be aided by many systematic trial inverse-modelling runs with synthetic data. Such may, for example, come up in machine-learning approaches. Automated image appraisal pertaining to such applications will involve common statistical tests for goodness-of-data fit as a primary evaluation method. However, solution non-uniqueness may render multiple images equivalent in terms of their data fit, requiring secondary categorizers. A logical choice for classifying synthetic-imaging results quantifies the goodness of model fit where a known reference model replaces the observational input. The task of model intercomparison in terms of measuring the resemblance to the reference model poses challenges to common distance-based metrics like root mean square error and mean absolute error. First, distance-based metrics can introduce spurious contributions when smooth models with fuzzy target contours are to be compared against a sharp reference. Second, large differences due to parameter-estimation overshoots can dominate distance metrics. Here, we propose a remedy that is referred to as semblance and is based on the idea of logistic functions, where a binary-dependent variable adds non-zero or zero accumulation terms for the, respectively, passing or failing of preset target thresholds. This classifying approach is amenable to an objective where model feature recognition is primary. Numerical comparisons to distance-based metrics provide evidence for the advantages of the semblance in view of this objective. Geophysical imaging in conjunction with machine-learning is seen as a benefitting upcoming application area.
Cybersecurity Certification Requirements for Distributed Energy Resources: A Survey of SunSpec Alliance Standards
This survey paper explores the cybersecurity certification requirements defined by the SunSpec Alliance for Distributed Energy Resource (DER) devices, focusing on aspects such as software updates, device communications, authentication mechanisms, device security, logging, and test procedures. The SunSpec cybersecurity standards mandate support for remote and automated software updates, secure communication protocols, stringent authentication practices, and robust logging mechanisms to ensure operational integrity. Furthermore, the paper discusses the implementation of the SAE J3072 standard using the IEEE 2030.5 protocol, emphasizing the secure interactions between electric vehicle supply equipment (EVSE) and plug-in electric vehicles (PEVs) for functionalities like vehicle-to-grid (V2G) capabilities. This research also examines the SunSpec Modbus standard, which enhances the interoperability among DER system components, facilitating compliance with grid interconnection standards. This paper also analyzes the existing SunSpec Device Information Models, which standardize data exchange formats for DER systems across communication interfaces. Finally, this paper concludes with a detailed discussion of the energy storage cybersecurity specification and the blockchain cybersecurity requirements as proposed by SunSpec Alliance.
Safeguards and security modeling for electrochemical plants
Safeguards and security design for reprocessing plants can lead to excessive costs if not incorporated early in the design process. The design for electrochemical plants is somewhat uncertain since these plants have not been built at a commercial scale in the past. The Separation and Safeguards Performance Model (SSPM), developed at Sandia National Laboratories, has been used for safeguards design and evaluation for multiple reprocessing plant types. The SSPM includes the following capabilities: -) spent fuel source term library, -) mass tracking of elements 1-99 and bulk solid/liquids, -) tracking of heat load and activity, -) customisable measurement points, -) automated calculation of ID and error propagation, -) alarm conditions and statistical tests, and -) user-defined diversion scenarios. Materials accountancy and process monitoring data can provide more timely detection of material loss specifically to protect against the insider threat. While the SSPM is capable of determining detection probabilities and examining detection times for material loss scenarios, it does not model the operations or spatial effects for a plant design. The STAGE software was chosen to model the physical protection system. STAGE provides a framework to create end-to-end scalable force-on-force combat simulations. It allows for a complete 3D model of a facility to be designed along with the design of physical protection elements. This software, then, can be used to model operations and response for various material loss scenarios. The future integration of the SSPM model data with the STAGE software will provide a more complete analysis of diversion scenarios to assist plant designers.
Development of programmable logic in a rapid, automated, and repeatable fashion for safety-related applications - 385
Digital safety platform considerations for power plant modernization or new build efforts involve a burdensome task of implementing a large number of software-based functions onto a designated platform to match the plant's architecture. This problem is not unique to any particular safety-related platform solution, but is prevalent across the landscape of current Qualified and Licensed Safety-related Instrumentation and Control (I and C) Solutions. As an example when implementing a RG 1.97 compliant Post Accident Monitoring System (PAMS) solution with the Lockheed Martin Nuclear Protection and Control (NuPAC) safety platform, 72 unique software loads are needed to configure the Qualified Display System (QDS). Large scale development and customization efforts to upgrade existing plant functions or systems require the licensee to assume the cost and technical risk necessary to undergo a digital based upgrade. Often the cost, schedule, and technical risk outweighed the benefit of modernization efforts due to the unknown hurdles and past projects that overran cost and schedule projections. Lockheed Martin's Engineering Production Suite (EPS) minimizes the licensee risk and expense associated with modernization efforts through a developer's toolkit for rapid, automated, and repeatable development of Programmable Logic used to implement plant functions and architectures for use on the Lockheed Martin NuPAC platform. This paper describes a use case where by EPS can create system level representations, simulate system functions, partition logic designs to accommodate hardware constraints through logic partitioning, automate the assembly of Application Specific Programmable Logic (ASPL), generate system test cases, program the NuPAC hardware, and perform testing throughout the design workflow. (authors)
"Hierarchical Speed Planner for Automated Vehicles: A Framework for Lagrangian Variable Speed Limit in Mixed Autonomy Traffic"
This paper introduces a novel control framework for Lagrangian variable speed limits in hybrid traffic flow environments utilizing automated vehicles (AVs). The framework was validated using a fleet of 100 connected automated vehicles as part of the largest coordinated open-road test designed to smooth traffic flow. The framework includes two main components: a high-level controller deployed on the server side, named Speed Planner, and low-level controllers called vehicle controllers deployed on the vehicle side. The Speed Planner designs and updates target speeds for the vehicle controllers based on real-time Traffic State Estimation (TSE) [1]. The Speed Planner comprises two modules: a TSE enhancement module and a target speed design module. The TSE enhancement module is designed to minimize the effects of inherent latency in the received traffic information and to improve the spatial and temporal resolution of the input traffic data. The target speed design module generates target speed profiles with the goal of improving traffic flow. The vehicle controllers are designed to track the target speed meanwhile responding to the surrounding situation. The numerical simulation indicates the performance of the proposed method: the bottleneck throughput has increased by 5.01%, and the speed standard deviation has been reduced by a significant 34.36%. We further showcase an operational study with a description of how the controller was implemented on a field-test with 100 AVs and its comprehensive effects on the traffic flow.
Dyno-in-the-Loop: An Innovative Hardware-in-the-Loop Development and Testing Platform for Emerging Mobility Technologies
Today’s transportation is quickly transforming with the nascent advent of connectivity, automation, shared-mobility, and electrification. These technologies will not only affect our safety and mobility, but also our energy consumption, and environment. As a result, it is of unprecedented importance to understand the overall system impacts due to the introduction of these emerging technologies and concepts. Existing modeling tools are not able to effectively capture the implications of these technologies, not to mention accurately and reliably evaluating their effectiveness with a reasonable scope. To address these gaps, a dynamometer-in-the-loop (DiL) development and testing approach is proposed which integrates test vehicle(s), chassis dynamometer, and high fidelity traffic simulation tools, in order to achieve a balance between the model accuracy and scalability of environmental analysis for the next generation of transportation systems. With this DiL platform, a connected eco-operation system for the plug-in hybrid electric bus (PHEB) has been developed and tested, which can optimize the vehicle dynamics (and potentially powertrain control via smart energy management) to reduce the operational energy consumption as well as tailpipe emissions of the target PHEB. The system performance has been evaluated on the DiL platform with respect to a variety of traffic congestion levels. The results have shown that the developed system can save fuel by more than 13% while reducing the electricity consumption by 2% in the test scenarios.
Artificial intelligence models, photos, and data associated with the manuscript “Quantifying Streambed Grain Size, Uncertainty, and Hydrobiogeochemical Parameters Using Machine Learning Model YOLO” (v2)
This data package is associated with the manuscript “Quantifying Streambed Grain Size, Uncertainty, and Hydrobiogeochemical Parameters Using Machine Learning Model YOLO” published in Water Resources Research (Chen et al., 2024). This data package includes the training, validation, testing, and prediction data used by the artificial intelligence (AI) model for automated grain size and hydro-biogeochemistry quantification using streambed photos. The grain size data are extracted for each photo using You Look Only Once (YOLO), a pre-trained object detection model. This data package was originally published in October 2023. It was updated August 2025 (v2; new and modified files). File and folder names were not revised to indicate changes. See the change history section in the readme for more details. Please see flmd.csv for a list of all files contained in this data package and descriptions for each. Please see dd.csv for a data dictionary that defines the column headers of .csv files in the data package. This dataset is comprised of one data folder containing (1) file-level metadata; (2) data dictionary; (3) readme; and (4) six subfolders. Subfolders 1 to 4 include the training, validation, testing, and prediction data. Subfolder 5_Summary includes the summary results of different combinations of training, validation, testing, and prediction data. Subfolder 6_SupplementalData includes additional data downloaded from public sources (Kaufman et al., 2023a; Kaufman et al., 2023b; Garefalakis et al., 2023; Mair et al., 2024; https://github.com/river-corridors-sfa/Geospatial_variables). In total, the data package includes 110 folders and 44,283 files. These files include 9,047 .jpg photos, 1 .png photo, 3 .tif photos; 26,639 photo labels and individual grain sizes and probability from AI (.txt); 8,447 grain size distribution data (.dat); and 126 CSV files for results summary, and 14 required metadata files (.xlsx). The summary CSV files contain 68 columns and approximately 2,200 rows that represent photo names, site locations, recording time, GPS coordinates, grains sizes (D10, D50, D60, and D84), number of grains, and additional hydro-biogeochemical data such as water depth, flow velocity, Manning’s coefficient, friction factor, hydraulic conductivity, permeability, streambed interstitial velocity magnitude, mass transfer rate, and nitrate uptake velocity. The photos were obtained from 75 sites in the Yakima River Basin and the Columbia River shorelines, and other associated data from samples and sensors obtained when the photos were taken are publicly available (Fulton et al. 2022; Grieger et al. 2023). All files are .csv, .txt, .dat, .jpg, or .pdf. We acknowledge the Yakama Nation as owners and caretakers of the lands where we collected some of these data. We thank the Confederated Tribes and Bands of the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview.
LLM Benchmarking with LLaMA2: Evaluating Code Development Performance Across Multiple Programming Languages
The rapid evolution of large language models (LLMs) has opened new possibilities for automating various tasks in software development. This paper evaluates the capabilities of the LLaMA 2-70B model in automating these tasks for scientific applications written in commonly used programming languages. Using representative test problems, we assess the model's capacity to generate code, documentation, and unit tests, as well as its ability to translate existing code between commonly used programming languages. Our comprehensive analysis evaluates the compilation, runtime behavior, and correctness of the generated and translated code. Additionally, we assess the quality of automatically generated code, documentation, and unit tests. Here, our results indicate that while LLaMA 2-70B frequently generates syntactically correct and functional code for simpler numerical tasks, it encounters substantial difficulties with more complex, parallelized, or distributed computations, requiring considerable manual corrections. We identify key limitations and suggest areas for future improvements to better leverage AI-driven automation in scientific computing workflows.
From text to test: AI-generated control software for materials science instruments
AI-crafted control software for automating scientific instruments can democratize and further accelerate materials research.
Fuel Performance Analysis of Fast Flux Test Facility MFF-3 and -5 Fuel Pins Using BISON with Post Irradiation Examination Data
Using the BISON fuel-performance code, simulations were conducted of an automated process to read initial and operating conditions from the Pacific Northwest National Laboratory (PNNL) database and reports, which contain metallic-fuel data from the Fast Flux Test Facility (FFTF) MFF Experiments. This work builds on previous modeling efforts involving 1977 EBR-II metallic fuel pins from experiments. Coupling the FFTF PNNL reports to BISON allowed for all 338 pins from MFF-3 and MFF-5 campaigns to be simulated. Each BISON simulation contains unique power and flux histories, axial power and flux profiles, and coolant-channel flow rates. Fission-gas release (FGR), fuel axial swelling, cladding profilometry, and burnup were all simulated in BISON and compared to available post-irradiation examination (PIE) data. Cladding profilometry, FGR, and fuel axial swelling simulation results for full-length MFF metallic pins were found to be in agreement with PIE measurements using FFTF physics and models used previously for EBR-II simulations. The main two peaks observed within the cladding profilometry were able to be simulated, with fuel-cladding mechanical interaction (FCMI), fuel-cladding chemical interaction (FCCI), and thermal and irradiation-induced creep being the cause. A U-Pu-Zr hot-pressing model was included in this work to allow pore collapse within the fuel matrix. This allowed better agreement between BISON-simulated cladding profilometry and PIE measurements for the peak caused by FCMI. This work shows that metallic fuel models used to accurately represent fuel performance for smaller EBR-II pins may be used for full-length metallic fuel, such as FFTF MFF assemblies and the Versatile Test Reactor (VTR). As new material models and PIE measurements become available, FFTF MFF assessment cases will be reassessed to further BISON model development.
Understanding and modeling current and future coastal wetland methane dynamics (Final Report)
The coastal terrestrial-aquatic interface (TAI) is a highly dynamic component of the Earth system that plays a critical role in biogeochemical cycling. Due to its dynamic nature, the processes that regulate decomposition and methane (CH 4 ) emissions are of greater significance at the TAI than in upland systems. Despite this, we have limited mechanistic understanding of how climate stressors interact to regulate the electron acceptors and donors that determine decomposition pathways within TAIs, including the generation of hot spots and hot moments. Accurately modeling these processes is critical for incorporating the coastal TAI into Earth systems models, such as DOE’s E3SM. With previous DOE support, we adapted an aerobic terrestrial representation of decomposition using PFLOTRAN, a reactive flow and transport model, and added anerobic decomposition pathways, salinity, and oxygen (O 2 ) that fluctuates independently of water table level. However, because this model (PFLOTRAN TAI ) is based on decomposition rates and organic matter carbon to nitrogen ratios from terrestrial systems, its performance in TAI systems is currently limited by the lack of empirical data to properly parameterize variables. In addition, while PFLOTRAN TAI can simulate movement of O 2 into sediments, it is not currently capable of tracking the movement of CH 4 gas through plant tissues due to both current model structure and lack of available data. Our overall objective of the project was the increase our mechanistic understanding of CH 4 dynamics in response to environmental change, such that we can improve the representation of these dynamics in PFLOTRAN. We installed automated flux chambers in a new field-scale active soil warming experiment and set up marsh organs (mesocosms) to test effects of warming, flooding, and salinity. This resulted in a new dataset consisting of chamber-level CH 4 flux measurements across multiple sites, ecological conditions, and timeframes, as well as corresponding measurements on porewater chemistry, soil carbon quality, plant biomass, and redox reaction rates. Using these data, we improved PFLOTRAN TAI to more accurately model CH 4 dynamics and successfully tested our hypotheses. This grant contributed to the professional development of 3 postdocs, 4 undergraduate interns, 4 teacher interns, 17 technicians, and 15 participatory scientists. The automated chamber technology designed for this grant has also been shared with multiple new projects.