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At least 1,351 records · Page 75

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DOE OSTI2023

Magnetoelectronic phenomena due to quantum magnetization fluctuations

The goal of the project was to identify techniques to study magnetoelectronic phenomena such as spin transfer (ST) that stem from the quantum-mechanical nature of magnetization, and utilize them to characterize these quantum phenomena. The first such technique, electronic measurements based on the dependence of resistance on the population of spin-wave quanta (magnons) in a magnetic nanostructure, was shown by detailed measurements to be significantly influenced by the current-driven generation of a highly nonequilibrium phonon distribution by electrical current. This previously unrecognized phenomenon adds to the understanding of current-induced heating phenomena, qualitatively changes the methodology of analysis of current-induced heating common in scientific studies and engineering of current-carrying nanostructures. It benefits the public by providing a new direction for mitigation of Joule heating effects in electronic devices. The project also included quantum simulations of current-induced effects in magnetic nanostructures, which revealed that linear momentum and energy conservation are important for ST. Only angular momentum conservation was previously believed to be important. This finding is important for the fundamental understanding of ST, and for the design of efficient magnetoelectronic devices taking advantage of these conservation laws. Simulations also showed that non-classical ST is dominant in antiferromagnets, which will transform the understanding of ST in these systems. Finally, experimental studies of spin-transfer in ultrathin transition metal films revealed very large non-classical effects which were traced to the quantum origins of magnetism associated in the studied systems with the formation of an orbital liquid state. These findings i) transform the understanding of "conventional" transition-metal magnetism, ii) open a new field of studies in magnetism and a new method for the electronic control of magnetic properties and magnetism itself, and iii) provide a new approach to the development of efficient magnetoelectronic devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

National Demonstration Reactor Siting Study—Phase I (Revision 1)

In 2020, the Department of Energy Office of Nuclear Energy (DOE-NE) initiated the Advanced Reactor Demonstration Program (ARDP), with the goal of demonstrating two advanced reactor designs within the next five to seven years. When evaluating challenges in meeting this deadline, the newly established National Reactor Innovation Center (NRIC) determined that there was a limited number of well-characterized sites that could support this time-sensitive demonstration requirement. To close this gap, NRIC initiated a two-phase joint analytic effort including DOE, National Laboratory, and academic partners to examine demonstration reactor siting alternatives. The first phase of the study, which is documented in this report, describes the development of a methodology for site assessment and a preliminary assessment of a limited number of known siting alternatives. Revision 1 of this report includes 3 additional (13 total) siting alternatives and 2 additional (4 total) example scenarios.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Joint LLNL, LANL, SNL, and IRSN High Multiplication Subcritical (Multiplicity) Benchmark Experiments Execution Plan (IER-518 CED-3a)

As part of the experiment design and planning, the critical experiment design team (CEDT), as well as additional stakeholders, convened a series of meetings to discuss the goals and requirements of execution for this experiment. The slides from these meetings are attached in Appendix A. The following sections summarize the outcome of those discussions and present the planned experimental configurations and measurements. The stated goals of this experiment are as follows: 1) Measure time-tagged list-mode data for configurations exceeding neutron multiplication of 100; 2) Provide intercomparison between LLNL, LANL, and IRSN detector systems and methodologies; 3) Generate experiment execution report(s) useful to a fundamental physics benchmark for the ICSBEP; 4) Leverage existing critical experiment and detector system benchmarks to limit required modeling and uncertainty analysis.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Ensemble Manufacturing Techniques for Steam Turbine Components Across Length Scales

Faster design to manufacturing requirements were sought for steam turbine components to meet the changing demands of today’s power grid of improved efficiency through operating temperature increases and enhanced operational flexibility from baseload to cyclic operations. Advances in multiple advanced manufacturing (AM) process enabled redesign of turbine components for extreme environments with the potential to reduce cost. AM is of particular interest to improve component functionality, higher temperature capability, and superior durability in large scale steam turbine applications. AM methods have an immense potential to open-up the design space by working directly with the 3D model to produce near-net shapes, enable fast design-manufacturing iterations, thereby significantly reducing product cost and lead-time up to 25 % from current baseline. However, these benefits cannot be fully realized due to the potential for unknown AM processing defects and their resultant effect upon component performance in service. Siemens is partnering with Oak Ridge National Laboratory (ORNL), Electric Power Research Institute (EPRI), and Connecticut Center for Advanced Technology (CCAT) to advance the knowledge of complex process-material interactions for desired microstructures and properties that are closely interlinked to component geometries across different length scales. The proposed program utilized an ensemble of multidisciplinary technologies to accelerate the development of materials, high-throughput experiments for their qualification, and design flexibility/topology optimization for repair/redesign of components to address critical failure mechanisms for improved performance and increased reliability of existing power plant components. The proposed activities, if successfully demonstrated for identified components, will enable paradigm shift in customized manufacturing and accelerated qualification/certification towards increased steam turbine component durability, increased turbine efficiency, and reduced CO 2 emissions in load-following environments compared to today’s technology. Technology maturation is built into the project as successful research will include customized process-component down-selection enabling AM methodologies to be incorporated directly into the existing supply chain. The specific activities of the proposed effort are: 1. Topology optimization of down-selected steam turbine parts that are amenable to additive and hybrid manufacturing for cost/performance improvement. 2. Process-structure-property relationships for five AM processes for steam turbine materials of interest to compare with conventional materials. 3. Perform part/assembly build process using advanced additive/hybrid machine tools followed by quality inspection of the built components for insight into qualification for production scale-up. Steam turbine rig testing of printed components under targeted, well monitored and characterized environmental conditions of for performance comparison of baseline and redesigned components.

36 MATERIALS SCIENCE↗

DOE OSTI2023

A New Reflected Target Optical Assessment System: Stage 1 Development Results

NREL has completed stage 1 development of an indoor optical measurement tool for fully assembled heliostats and single facets. This tool began as an indoor version of NREL’s outdoor Non-Intrusive Optical (NIO) measurement technique [1]. It uses similar techniques to other available tools (deflectometry, photogrammetry, etc.), but is designed to require very little infrastructure, labor, and time to set up and collect surface slope and canting measurements, making it a valuable tool for quality assurance and laboratory measurement of heliostat optics. It accomplishes this by using computer vision, photogrammetry, and multiple images stitched together to minimize the printed target size and required setup precision. This adaptable setup is useful for taking measurements at a variety of heliostat pointing angles, and for measuring fully assembled heliostats on the assembly line. In this paper, we describe the methodology behind the measurement system, present an initial analysis of its uncertainty and sensitivity, and compare it with established optical measurement systems.

Kesseli, Devon (ORCID:0000000193113036)↗

DOE OSTI2022

Optimizing DER Deployment for Climate, Health, Resilience, and Energy Bill Benefits Using the REopt Model

Climate change, public health, and resilience to power outages are of critical concern to local governments, federal agencies, and the private sector, and are increasingly motivating investments in distributed energy resources (DERs). However, designing a solar-plus-storage system to co-optimize for climate, health, resilience, and energy bill benefits requires complex trade-offs. To address this need, the National Renewable Energy Laboratory has integrated climate and health impacts of grid-purchased electricity and on-site fuel consumption into the publicly-available REopt web tool and API - a techno-economic model that determines the cost-optimal DER system sizes and dispatch strategy. This presentation will provide an overview of the tool's methodology, datasets, and capabilities, including costing emissions into system sizing and dispatch and setting emissions reductions targets. Results of previous work will demonstrate the use of new emissions accounting capabilities by quantifying the impact of including climate and health costs on the optimal resilient microgrid configurations for a hospital, school, and warehouse across 14 U.S. cities.

climate↗

DOE OSTI2022

Predicting the rheology of limestone calcined clay cements (LC 3 ): Linking composition and hydration kinetics to yield stress through Machine Learning

The physicochemical characteristics of calcined clay influence yield stress of limestone calcined clay cements (LC 3 ), but the independent influences the clay's physical and chemical characteristics as well as the effect of other variables on LC 3 rheology are less well-understood. Further, a relationship between LC 3 hydration kinetics and yield stress – important for informing mixture design – has not yet been established. Here, rheological properties were determined in pastes with varying water-to-solid ratio (w/s), constituent mass ratios (PC:metakaolin:limestone), limestone particle size and gypsum content. From these data, an ML model developed allowed the independent examination of the different mechanisms by which metakaolin fraction influences yield stress of LC 3 , identifying four predictors – packing index, Al 2 O 3 /SO 3 , total particle density and metakaolin fraction relative to limestone (MK/LS) – most significant for predicting LC 3 yield stress. A methodology based on kernel smoothing also identified hydration kinetics parameters best correlated with yield stress.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Multi deep learning-based stochastic microstructure reconstruction and high-fidelity micromechanics simulation of time-dependent ceramic matrix composite response

A multi deep learning-based framework is developed for efficient, automated microstructure reconstruction and generation of stochastic representative volume elements (SRVEs) with periodic boundary conditions (PBCs) for accurate modeling of ceramic matrix composite (CMC) response. The methodology comprises a convolutional neural network coupled with regression layers to act as a vanilla regression network for semantic segmentation of the microstructure, allowing accurate characterization of the phases and their distributions at the microscale. Scanning electron microscope and confocal microscope are used to obtain C/SiNC and SiC/SiNC CMCs micrographs for vanilla regression testing. Microstructure variability in terms of fiber volume fraction and porosity are quantified through the output regression layer, ensuring accurate representation of material variability in SRVE construction. Generative adversarial network (GAN) and its variants are designed to produce high-fidelity SRVE, spanning CMCs microstructure variability space. A circular padding algorithm is developed to generate SRVEs with PBCs during training of GANs. The accuracy of the generated SRVEs is established through micromechanics simulations, where an efficient formulation of the high-fidelity generalized methods of cells (HFGMC) approach is used to compute the effective mechanical properties. Furthermore, an iterative algorithm is implemented in the HFGMC solver to simulate time-dependent deformation of SiC/SiNC subjected to creep loading conditions.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Physics-based modeling and information-theoretic sensor and settings selection for tool wear detection in precision machining

Precision machining of metals is an energy intensive process with applications and impacts across the manufacturing industry. The energy efficiency, product yield, and maintenance of the precision machine require a digital twin that can assist with prognostics and health management. In this report a physics-based model is developed and validated against face milling data, and then used for the timely and precise inference of machining faults that cannot be measured directly. Computer numerical control (CNC) measurements of power and force are used through this physics-based machining model to predict deviations of the outputs of power consumption and cutting forces during normal operation. A model-based fault detection and isolation methodology is applied to determine the optimal (traditional and available) sensor suite and the test settings (admissible input values) that improve the inference of tool wear in face milling. The optimal sensor suite and input test settings are obtained by solving a mixed integer non-linear program that optimizes information-theoretic metrics relevant to the detection and isolation of tool wear from steady-state or transient machining measurements. Dynamic time warping and k—NN classification are then used to validate the robustness of the optimal design for fault detection test design, including the optimal sensor suite.

42 ENGINEERING↗

DOE OSTI2022

Quantification of the effect of uncertainty on impurity migration in PISCES-A simulated with GITR

A Bayesian inference strategy has been used to estimate uncertain inputs to global impurity transport code (GITR) modeling predictions of tungsten erosion and migration in the linear plasma device, PISCES-A. This allows quantification of GITR output uncertainty based on the uncertainties in measured PISCES-A plasma electron density and temperature profiles (n e , T e ) used as inputs to GITR. The technique has been applied for comparison to dedicated experiments performed for high (4 × 10 22 m –2 s –1 ) and low (5 × 10 21 m –2 s –1 ) flux 250 eV He–plasma exposed tungsten (W) targets designed to assess the net and gross erosion of tungsten, and corresponding W impurity transport. The W target design and orientation, impurity collector, and diagnostics, have been designed to eliminate complexities associated with tokamak divertor plasma exposures (inclined target, mixed plasma species, re-erosion, etc) to benchmark results against the trace impurity transport model simulated by GITR. The simulated results of the erosion, migration, and re-deposition of W during the experiment from the GITR code coupled to materials response models are presented. Specifically, the modeled and experimental W I emission spectroscopy data for a 429.4 nm line and net erosion through the target and collector mass difference measurements are compared. Furthermore, the methodology provides predictions of observable quantities of interest with quantified uncertainty, allowing estimation of moments, together with the sensitivities to plasma temperature and density.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Bidding Curve Design for Hybrid Power Plants with Uncertain Solar Forecast

This paper presents a novel bidding curve design algorithm tailored for hybrid power plants (HPPs) to participate in the wholesale electricity market. Utilizing forecasts for photovoltaic (PV) generation and available battery power, our algorithm strategically computes the bidding curve to maximize HPP profit while adeptly managing the inherent uncertainty associated with PV power generation. In addition, the introduction of the penalty cost in HPP bidding curves provides the system operator a tool to effectively manage the system-level uncertainty that caused by HPPs. Numerical analysis through Monte Carlo simulations confirms that our bidding curve methodology outperforms the benchmark across various scenarios.

bidding curve↗

DOE OSTI2024

Bidding Curve Design for Hybrid Power Plants with Uncertain Solar Forecast: Preprint

bidding curve↗

DOE OSTI2025

Empowering Geothermal Research: The Geothermal Data Repository's New AI Research Assistant

The Department of Energy's (DOE) Geothermal Data Repository (GDR) team has integrated a Large Language Model (LLM) with the metadata and supporting documents associated with GDR datasets to create an Artificially Intelligent (AI) research assistant. By leveraging work done to make GDR metadata machine-readable and an open-source LLM integration model called the Energy Language Model, developed by the National Renewable Energy Laboratory, AskGDR serves as a virtual research assistant to GDR users. It provides answers to a variety of user-provided questions using natural language processing and generative machine learning. Users can get answers to questions about specific datasets, including inquiries about the equipment, assumptions and methodologies used in the origination of the data; or more abstract questions, such as the applicability of data to specific research fields. AskGDR improves the discoverability of geothermal data by helping guide users to datasets beyond simple keyword searches. It enables users to find data based on properties of the data, discover information contained within supporting documents, and explore data from projects related to their research objectives. This paper will outline the development, integration, output, and efficacy of the AskGDR LLM, including adherence to scientific rigor through improvements designed to increase the accuracy of generated answers, avoid speculation, and provide proper references for all resources used.

access↗

DOE OSTI2022

Distribution System Planning for Growth in Residential Electric Vehicle Adoption

Anticipated growth in Electric Vehicles (EV) adoption could stress distribution system circuits beyond their original design limits. Uncertainties related to when households will begin buying EVs in large numbers challenges existing distribution system planning approaches and complicates efforts to assess grid impacts. Anticipated location, timing, and demand for EV charging is critical information for distribution system planners as it guides investment strategies for infrastructure upgrades to ensure continued reliability. This research makes two significant contributions: an EV adoption model that uses socioeconomic data to forecast location and year-specific adoption patterns through a bottoms-up approach, and an EV hosting capacity assessment methodology that offers improvements to current utility planning and asset management practices for infrastructure investments. Both contributions are applied to a Southern California Edison feeder in 7 adoption years from 2025 to 2050, with the results indicating that they are likely valuable additions to distribution systems planning capabilities.

Sridhar, Siddharth↗

DOE OSTI2026

Making an s1G Self-Preserving Environmental DNA (eDNA) Filter from Sustainable Materials

Environmental DNA (eDNA) sampling of aquatic environments is a powerful new tool for resource managers to use in detecting the presence of rare and endangered species. The use of eDNA for species detection is quickly becoming an industry-standard methodology in environmental science, and the technology sector is moving quickly to create purpose-built tools for eDNA sampling. Smith-Root, Inc. pioneered the new technology of self-preserving eDNA filters that automatically preserve the DNA captured on filters by desiccating the internal filter membrane. This capability saves the sampling technician significant time in the field and drastically reduces the potential for sample contamination. One main constraint of this new technology, however, is the necessity of single-use plastics for eDNA sampling. Single-use designs are employed because sampling equipment must be devoid of potential contaminating DNA, and the time required to sterilize equipment is often not cost-effective for practitioners. Thus, this technology is creating a significant new source of plastic waste—something that eDNA practitioners, and society, would like to avoid. This project explores the use of alternative bio based polymers and composites as replacements for incumbent industrial standards for eDNA filtration systems, with an emphasis on ease of processing and biodegradable options. 8 candidate materials, 3 for the top filter housing and 5 for the bottom filter housing, were investigated to replace incumbent housing materials. Of the 8 candidates, 2 materials were identified for future investigation which met performance requirements, were bio-based and could potentially offer better disposal options like soil degradation or composting, features sought by the customers of these products. The top housing candidate, material 1, has comparable performance to the incumbent material and equally manufacturable. The bottom housing candidate, material 6, improved desiccation performance by 81% after 1 hour in comparison to the incumbent material, is 100% bio-based, compostable and can be sourced domestically throughout the U.S.A.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Chapter 16: Connecting Microbial Genotype with Phenotype in the Omics Era

Although rational design-based metabolic engineering has been applied widely to obtain promising microbial biocatalysts, conventional strategies such as adaptive laboratory evolution (ALE) and mutagenesis are still efficient approaches to improve microorganisms for exceptional features such as a broad spectrum of substrate utilization, robustness of cell growth, as well as high titer, yield, and productivity of the target products. In this chapter, we describe the procedure to generate mutant strains with desired phenotypes using ALE and a new mutagenesis approach of Atmosphere and Room Temperature Plasma (ARTP). In addition, we discuss the methodology to combine next-generation sequencing (NGS)-based genome-resequencing and RNA-Seq transcriptomics approaches to characterize the mutant strains and connect the phenotypes with their corresponding genotypic changes.

adaptive laboratory evolution (ALE)↗

DOE OSTI2024

Multiscale modeling of metal-hydride interphases—quantification of decoupled chemo-mechanical energies

Abstract The quantification of interphase properties between metals and their corresponding hydrides is crucial for modeling the thermodynamics and kinetics of the hydrogenation processes in solid-state hydrogen storage materials. In particular, interphase boundary energies assume a pivotal role in determining the kinetics of nucleation, growth, and coarsening of hydrides, alongside accompanying morphological evolution during hydrogenation. The total interphase energy arises from both chemical bonding and mechanical strains in these solid-state systems. Since these contributions are usually coupled, it is challenging to distinguish via conventional computational approaches. Here, a comprehensive atomistic modeling methodology is developed to decouple chemical and mechanical energy contributions using first-principles calculations, of which feasibility is demonstrated by quantifying chemical and elastic strain energies of key interfaces within the FeTi metal-hydride system. Derived materials parameters are then employed for mesoscopic micromechanical analysis, predicting crystallographic orientations in line with experimental observations. The multiscale approach outlined verifies the importance of the chemo-mechanical interplay in the morphological evolution of growing hydride phases, and can be generalized to investigate other systems. In addition, it can streamline the design of atomistic models for the quantitative evaluation of interphase properties between dissimilar phases and allow for efficient predictions of their preferred phase boundary orientations.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Modeling the impact of structure and coverage on the reactivity of realistic heterogeneous catalysts

Adsorbates often cover the surfaces of catalysts densely as they carry out reactions, dynamically altering their structure and reactivity. Understanding adsorbate-induced phenomena and harnessing them in our broader quest for improved catalysts is a substantial challenge that is only beginning to be addressed. Here, in this work, we chart a path toward a deeper understanding of such phenomena by focusing on emerging in silico modeling methodologies, which will increasingly incorporate machine learning techniques. We first examine how adsorption on catalyst surfaces can lead to local and even global structural changes spanning entire nanoparticles, and how this affects their reactivity. We then evaluate current efforts and the remaining challenges in developing robust and predictive simulations for modeling such behavior. Last, we provide our perspectives in four critical areas—integration of artificial intelligence, building robust catalysis informatics infrastructure, synergism with experimental characterization, and adaptive modeling frameworks—that we believe can help surmount the remaining challenges in rationally designing catalysts in light of these complex phenomena.

catalytic mechanisms↗