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SAND2021-15055 O Limbo provides a programmatic interface for accessing computer vision training data. It is organized in the Limbo format. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
SAND2021-15065 O This release contains Python code for two distribution system model calibration algorithms as well as some sample data and documentation for the algorithms and code. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
SAND2025-01899O pyTriBeam is a software tool that creates automated processes for a scanning electron microscope including workflows for 3D serial sectioning dataset collection, high-res image montaging, and support for custom script use. This includes integration for 3D chemical mapping (EDS) and crystallographic (EBSD) data collection with select supported detectors. The application allows end users to setup and run customizable data collection workflows without requiring expertise in programming. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
Microbial oils are a sustainable biomass-derived substitute for liquid fuels and vegetable oils. Oilcane, an engineered sugarcane with superior feedstock characteristics for biodiesel production, is a promising candidate for bioconversion. This study describes the processing of oilcane stems into juice and hydrothermally pretreated lignocellulosic hydrolysate and their valorization to ethanol and microbial oil using Saccharomyces cerevisiae and engineered Rhodosporidium toruloides strains, respectively. A bioethanol titer of 106 g/L was obtained from S. cerevisiae grown on oilcane juice in a 3 L fermenter, and a lipid titer of 8.8 g/L was obtained from R. toruloides grown on oilcane hydrolysate in a 75 L fermenter. Oil was extracted from the R. toruloides cells using supercritical CO2, and the observed fatty acid profile was consistent with previous studies on this strain. These results demonstrate the feasibility of pilot-scale lipid production from oilcane hydrolysate as part of an integrated bioconversion strategy.
From neutron user principal investigator: We kindly request the public release of three neutron imaging datasets through ONCat. All datasets were collected from two forensic specimens, B12W and M8N, sectioned from zinc-filled steel-wire sockets recovered from the collapsed Arecibo Telescope. The dataset titled “Neutron radiographs of B12W and M8N socket sections of the Arecibo telescope” contains normalized two-dimensional (2D) neutron radiographs of the specimens, showing the geometry and spatial distribution of the steel wires embedded within the zinc matrix, as well as internal features such as voids and cracks. The dataset titled “Neutron computed tomography of B12W and M8N socket sections of the Arecibo telescope” contains normalized 2D neutron projection images acquired over a range of specimen rotation angles for one selected region of each specimen. These projection images were used to reconstruct three-dimensional (3D) tomographic volumes that reveal the embedded-wire geometry and internal defects. The dataset titled “Bragg edge imaging (BEI) of B12W and M8N socket sections of the Arecibo telescope” contains six time-of-flight (TOF) neutron imaging datasets, three from each specimen, acquired at regions of interest selected based on the radiographs. The spatially resolved 2D TOF images show the zinc matrix and embedded steel wires, and the wavelength-dependent neutron transmission data were used to characterize crystallographic texture within the zinc. All components and their condition are in the public domain as they are the property of the National Science Foundation (NSF). The neutron imaging data, part geometries, and detailed forensic information have been widely published in the Arecibo Telescope Collapse Forensic Report by Thornton Tomasetti Engineers and others (NASA report and NASEM report).
Generative Adversarial Networks (GANs) have been used as a deep learning approach to solving physics and engineering problems. Using deep learning for these problems is attractive in that reasonably accurate models can be inferred from only raw data, eliminating the need to define the exact physical equations governing a problem. We expand on previous work using GANs to generate steady-state solutions to the two-dimensional heat equation. Using a basic conditional GAN (cGAN), we generate accurate solutions for rectangular domains conditioned on four edge boundary conditions (MAE < 0.5%). For finding steady-state solutions over arbitrary two-dimensional domains (not constrained to rectangles), we use a cGAN designed for image-to-image translation. We train this GAN on various types of geometric domains (circles, squares, triangles, shapes with one circular or rectangular hole), achieving accurate results on test data made up of geometries similar to those in training (MAE < 1%). For both of these GANs, we experiment with different loss function terms, showing that a term using the gradients of solution images significantly improves the basic cGAN but not the image-to-image GAN. Lastly, we show that the image-to-image GAN performs poorly when applied to two-dimensional geometries that vary in structure from training data (MAE < 8% for shapes with multiple holes or different shaped holes). This demonstrates the cGAN's lack of generalizability. While the cGAN is an accurate and computationally efficient method when trained and tested on similarly structured data, it is a much less reliable method when applied to data that is slightly different in structure from the training data.
Researchers at Los Alamos have extensive capabilities in design, testing and optimization of: (1) new capture materials based on membrane separation, amine and other solvent and sorbent separation, and porous materials; (2) biological capture methods, particularly algae and plants; (3) full-scale systems using science-based virtual learning; and (4) system performance under specific meteorological conditions, impacts of CO2 capture via atmospheric data and simulations, and availability of energy and storage capacity. The goal for Los Alamos is advancing our physical technologies and engineered systems approaches from the development phase to a minimal viable product in the energy sector for commercial purposes. Los Alamos is seeking strategic commercialization partners to work with us in advancing these capabilities and technologies for commercial viability.
This project targeted advanced low NOx combustion for advanced gas turbines capable of 65%, or greater, efficiency in combined cycle application. This technology advancement has further potential to benefit gas turbines used in coal based IGCC applications with pre-combustion carbon capture and hydrogen as the resulting fuel. The program developed and synthesized the most advanced combustion system capable of achieving low NOX emissions up to turbine inlet temperatures of 3100F while also supporting the load-following needs of a modern grid. The combustion system contributes to the overall gas turbine efficiency goal by setting the maximum cycle temperature achievable for a given NOX level and by minimizing the through-combustor air flow pressure drop. Focus areas for this project targeted maximizing the turbine inlet temperature entitlement, as constrained by emissions considerations. The design also minimized parasitic air flow pressure drop by using advanced cooling techniques and performance materials selections and by minimizing hot surface area. These two technology objectives (maximum, emissions-compliant cycle temperature and minimum air flow pressure drop) were integrated into a prototype design. The primarily analytical project sought to identify the most promising technologies to meet these objectives. Additional critical “jugular” data were obtained from multi-tube mixer tests to realize the potential of leveraging “micro flames” for minimizing overall hot surface area. This data was used, in conjunction with an understanding of advanced material and cooling design technologies, to analytically develop multiple design concepts. Phase I focused on in-depth engineering analysis and design, with minimal supporting laboratory testing to enable a selection of the top three combustion architectures for achieving these overall objectives. Phase II of the program developed the selected design through a combination of sub-scale testing and analytical efforts. Early tests included a cold-flow cascade to establish aerodynamic performance characteristics and a sub-scale fired test at GE Global Research in Niskayuna, NY, to establish cooling and heat transfer characteristics in conjunction with combustion performance. The data from these tests validated the analytical models to ultimately design a full-scale, test article to evaluate at prototypical pressure and temperature conditions at GE Gas Power’s Gas Turbine Technology Laboratory in Greenville, SC. GE Gas Power also developed, tested, and recommended a suitable seal design to be applied to the unique features of the combustor. To assess the technology challenges from prospective future production of the combustor from a ceramic matrix composite material, screening tests of Environmental Barrier Coatings were completed.
The Axis II Downstream Transport (DST) of the Dual Axis Radiographic Hydrodynamic Testing (DARHT) Facility is the final subassembly of the particle accelerator whose primary objective is to focus the 4-pulse beam as it enters the Target Region. Before the beam reaches the Target Region, it passes through two imaging stations located in the DST. At Imaging Station E (ISE) and Imaging Station C (ISC), diagnostics are conducted and experimenters study beam physics using specialized targets. Different targets are used depending on the objective of the study. The desire to conduct specific experiments has driven a need for a multi-foil target. This new design has been engineered to allow for new capabilities, and diagnostics to be studied at ISC. To accommodate new parameters, three different configurations have been conceived. Although each of the three designs differ from each other, their function and purpose remain consistent. Each target is engineered to secure a variable amount of target foils at variable spacing while Imaging Station experiments are conducted. Beam diagnostics and data from the targets are analyzed through various sized viewports on the imaging stations. The objective of this technical note is to document the purpose and functionality of the targets. Design considerations, assembly instructions, and maintenance recommendations are also discussed. Additionally, each target and its respective advantages and disadvantages are covered in detail.
The US government has identified 17 critical elements, including most rare earth elements (REEs), and metals, which are used extensively in consumer electronics as well as military and national security hardware. We are developing techniques for biomining these elements, the process of using microorganisms to extract critical elements from water, ores, and mine waste to fill these needs. Bioextraction is focusing on: 1) building on our first-year successes of identifying microbes that generate exudates that enhance the extraction of multivalent cations, 2) evaluating available phosphate solubilizing microbes to dissolve apatite and monazite, and 3) and biosurfactant producing microbes. Bioaccumulation efforts involves testing microbes that: 1) naturally release complex exudates that contain organic chelating agents, and 2) naturally accumulate or hyperaccumulate (wt-% levels) metals. Results from year one includes proof of concept that biosurfactant producing microorganisms can release REEs from select ores and that release of REEs is increased by the addition of glucose. Molecular results for microorganism profiling proved a population shift after addition of microorganisms. The study will culminate in a proof-of-concept demonstration, manuscripts, and the data needed for scaledup biomining of REEs.
Precise knowledge of nuclear fission is a critical component for nuclear energy and stockpile stewardship research. Despite having been discovered over 80 years ago, fission is not fully described by a predictive theory, so thorough and accurate nuclear data libraries are necessary. These libraries provide information about the fission process in studied isotopes that is used in scientific and engineering models.
Agenda : Vessel Repair History; Data Collection; Exploring for better tools; Where we are headed.
To address the data gap crucial for updating existing viscoplastic constitutive material models for the Class A materials in the American Society of Mechanical Engineers (ASME) Boiler and Pressure Vessel Code, Section III, Division 5, High Temperature Reactors (ASME, 2023) for inelastic design analysis, Oak Ridge National Laboratory (ORNL) conducted experimental studies on Alloy 800H and Alloy 617 across a range of temperatures up to their maximum temperature limits in Section III, Division 5. The studies aim to characterize the materials' deformation behavior under both strain-controlled mechanical cyclic loading and thermal cycling conditions. This report summarizes ORNL's FY 2024 experimental findings on Alloy 800H and Alloy 617, focusing on pure fatigue tests, cyclic stress-strain curves, thermomechanical fatigue experiments, and verification of the high temperature cyclic damage summation design rules.
Recent advances in artificial intelligence (AI) and development of large language models (LLMs) present the opportunity to develop a new generation of power systems applications. In contrast with early power system AI applications based on structured numerical data, LLMs offer unique capabilities to perform logical reasoning using text documents, unstructured data, and application programming interface (API) calls to computational software. This paper seeks to bridge the knowledge gap between power systems engineers and LLM developers through a crosscutting explanation of use cases, characteristics, requirements, practical considerations from the perspectives of both LLM capabilities and industry needs. Specific focus is given to applications that can be realistically deployed by electric utilities. After introducing the architecture of LLMs and unique challenges of the power systems domain, this paper proposes twenty representative LLM applications grouped into categories of 1) power system operations, 2) asset management, 3) system planning and analytics, and 4) energy management and protection systems. Five use cases are presented within each category with descriptions of the motivation, objectives, approaches, example inputs / outputs, and benefits of each use case.
Understanding and quantifying microstructural features such as fiber orientation and porosity is critical for predicting the mechanical behavior and performance of fiber-reinforced polymer composites. Traditional manual analysis is time-consuming, subjective, and unsuitable for high-throughput datasets. We present a graphical user interface (GUI) application that automates the analysis of microscopy images to extract key microstructural metrics, including fiber orientation tensors, fiber orientation distribution, porosity and pore size distribution. The app integrates multiple image segmentation techniques including global and local thresholding, clustering, and region-based approaches, offering flexibility for different types of image qualities and features. Users can load microstructural images, select regions of interest and segmentation techniques tailored to their image dataset. It also addresses a critical challenge in fiber orientation analysis: the ambiguities caused by touching, overlapping, or partially cut fibers. It supports autorun examples for standardized workflows, enabling reproducible analysis and facilitating training and benchmarking. This tool significantly reduces manual intervention, enhances consistency, and accelerates data generation for structure–property modeling, process optimization, and digital materials research. The tool is intended for use by materials scientists, engineers, and researchers engaged in composite characterization, quality control, and machine learning-based microstructural studies.
Heavy-duty vehicles dominate global freight movement and primarily rely on fossil-derived diesel fuel. However, fluctuations in crude oil prices and evolving emissions regulations have prompted interest in alternative powertrains to enhance fleet energy resiliency. This study paired real-world operational data from a large commercial fleet with high-fidelity vehicle models to evaluate the potential for replacing diesel internal combustion engine (ICE) trucks with alternative powertrain architectures. The baseline vehicle for this analysis is a diesel-powered ICE truck. Alternatives include ICE trucks fueled by bio- and renewable diesel, compressed natural gas (CNG) or hydrogen (H 2 ), as well as plug-in hybrid (PHEV), fuel cell electric (FCEV), and battery electric vehicles (BEV). While most alternative powertrains resulted in some payload capacity loss, the overall fleetwide impact was negligible due to underutilized payload capacity for the specific fleet considered in this study. For sleeper cab trucks, CNG-powered trucks achieved the highest replacement potential, covering 85% of the fleet. In contrast, H 2 and BEV architectures could replace fewer than 10% and 1% of trucks, respectively. Day cab trucks, with shorter daily routes, showed higher replacement potential: 98% for CNG, 78% for H 2 , and 34% for BEVs. However, achieving full fleet replacement would still require significant operational changes such as route reassignment and enroute refueling, along with considerable improvements to onboard energy storage capacity. Additionally, the higher total cost of ownership (TCO) for alternative powertrains remains a key challenge. This study also evaluated lifecycle impacts across various fuel sources, both fossil and bio-derived. Bio-derived synthetic diesel fuels emerged as a practical option for diesel displacement without disrupting operations. Conversely, H 2 and electrified powertrains provide limited lifecycle impacts under the current energy scenario. This analysis highlights the complexity of replacing diesel ICE trucks with admissible alternatives while balancing fleet resiliency, operational demands, and emissions goals. These results reflect a US-based fleet’s duty cycles, payloads, GVWR allowances, and an assumption of depot-only refueling/recharging. Applicability to other fleets and regions may differ based on differing routing practices or technical features such as battery swapping.
Abstract. This paper provides a summary of the work done within Phase IV of the Offshore Code Comparison Collaboration, Continued with Correlation and unCertainty (OC6) project, under International Energy Agency Wind Technology Collaboration Programme Task 30. This phase focused on validating the loading on and motion of a novel floating offshore wind system. Numerical models of a 3.6 MW horizontal-axis wind turbine atop the TetraSpar floating support structure were compared using measurement data from a 1:43-Froude-scale test performed in the University of Maine's Alfond Wind–Wave (W2) Ocean Engineering Laboratory. Participants in the project ran a series of simulations, including system equilibrium, surge offsets, free-decay tests, wind-only conditions, wave-only conditions, and a combination of wind and wave conditions. Validation of the models was performed by comparing the aerodynamic loading, floating support structure motion, tower base loading, mooring line tensions, and keel line tensions. The results show a relatively good estimation of the aerodynamic loading and a reasonable estimation of the platform motion and tower base fore–aft bending moment. However, there is a significant dispersion in the dynamic loading for the upwind mooring line. Very good agreement was observed between most of the numerical models and the experiment for the keel line tensions.
A sampling circuit automatically resamples the data from another timing domain until the sampled data is represented correctly in the new domain by assuring that no metastable states exist. If a metastable state exists, a sampling signal recirculates through the sampling circuit until the metastable state no longer exists. A comparison of input data to sampled data is used to determine the existence of a metastable state.