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At least 991 records · Page 55

CSP Systems Analysis 2022-2024 (Final Technical Report)

The CSP Systems Analysis project estimates the current market cost of CSP subsystems and technologies for use by the DOE Program and within NREL analysis tools. The project addresses upgrades to NREL's System Advisor Model (SAM) related to CSP, as well as development of new modules within SAM to expand the types of CSP systems that can be simulated and new tools for evaluating CSP subsystem performance or optimizing CSP system layouts, for example, SolarPILOT and SolTrace. The project evaluates the potential cost and performance of new CSP-relevant technologies and generally includes assessment of CSP technologies in support of the DOE subprogram. A primary goal of the CSP System Analysis project is to provide timely and accurate cost data to the DOE to assess the current state of CSP technologies as well as predict performance and cost for pre-commercial and emerging technologies that may impact the industry. Prior work in this task has produced technology cost reports and roadmaps for DOE and highlighted promising research paths that could reduce overall CSP cost, such as the supercritical CO2 power cycle and molten-salt trough systems. CSP Systems Analysis produces modeling tools to evaluate the performance of CSP systems, subsystems, or components, and journal articles, conference papers, technical reports and databases to disseminate information to the CSP industry. CSP technology costs are estimated and tracked using these tools as well as analysis of literature and industry sources. The CSP modeling tools and NREL analysis results are used by researchers; government, financial, and industry analysts; and other CSP stakeholders. The purpose of this final technical report is to summarize the key accomplishments of the past three years of this project. The project was divided into seven separate tasks, and in what follows, we describe the background, objectives, key accomplishments, and path forward for each task within the project, with one section of this report summarizing each task.

14 SOLAR ENERGY↗

Pushing the frontiers in climate modelling and analysis with machine learning

Climate modelling and analysis are facing new demands to enhance projections and climate information. Here, in this study, we argue that now is the time to push the frontiers of machine learning beyond state-of-the-art approaches, not only by developing machine-learning-based Earth system models with greater fidelity, but also by providing new capabilities through emulators for extreme event projections with large ensembles, enhanced detection and attribution methods for extreme events, and advanced climate model analysis and benchmarking. Utilizing this potential requires key machine learning challenges to be addressed, in particular generalization, uncertainty quantification, explainable artificial intelligence and causality. This interdisciplinary effort requires bringing together machine learning and climate scientists, while also leveraging the private sector, to accelerate progress towards actionable climate science.

54 ENVIRONMENTAL SCIENCES↗

Enhanced critical axial tensile strain limit of CORC ® wires: FEM and analytical modeling

Abstract Conductor on Round Core (CORC ® ) cables and wires are composed of spiraled high-temperature superconducting (HTS) rare-earth barium copper oxide (REBCO) tapes, wound in multiple layers, and can carry very high currents in background magnetic fields of more than 20 T. They combine isotropic flexibility and high resilience to electromagnetic and thermal loads. The brittle nature of HTS tapes limits the maximum allowable axial tensile strain in superconducting cables. An intrinsic tensile strain above about 0.45% will introduce cracks in the REBCO layer of straight HTS tapes resulting in irreversible damage. The helical fashion at which the REBCO tapes are wound around the central core allows tapes to experience only a fraction of the total axial tensile strain applied to the CORC ® wire. As a result, the critical strain limit of CORC ® wires can be increased by a factor of more than 10 that of REBCO tapes. Finite element (FE) and analytical models are developed to predict the performance of CORC ® wires under axial tensile strain. A parametric analysis is carried out by varying the winding angle, the Poisson’s ratio of the CORC ® wire core, the core diameter, and the tape width. The results show that a small variation in winding angle can have a significant impact on the cable’s axial tensile strain tolerance. While the radial contraction of the helically wound tapes in a CORC ® wire under axial tensile strain depends on its winding angle, it is mostly driven by the Poisson’s ratio of the central core, affecting the tape strain state and thus its performance. Contact pressure from multiple layers within the CORC ® wire also affects the CORC ® wire performance. The FE model can be used to optimize the cable design for specific application conditions, resulting in an irreversible strain limit of CORC ® cables and wires as high as 7%.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Integrating very-high-resolution UAS data and airborne imaging spectroscopy to map the fractional composition of Arctic plant functional types in Western Alaska

Widespread changes in vegetation cover and composition are driving strong impacts on Arctic ecosystem functioning and global climate feedbacks. An accurate characterization of tundra vegetation composition is required to understand how the Arctic will respond to future climate change. However, quantifying tundra vegetation composition over large areas is challenging as commonly-used satellite observations are too coarse, spatially and spectrally, to differentiate low-lying tundra vegetation types. Recent airborne and spaceborne imaging spectroscopy platforms provide better data to characterize vegetation composition. Yet, our ability to characterize vegetation composition with imaging spectroscopy remains largely unexplored in the Arctic, particularly due to a lack of ground observations needed to train and test classification models. To address this problem, we collected very-high-resolution (VHR, ~5 cm) unoccupied aerial system (UAS) imagery at three low-Arctic tundra sites located on the Seward Peninsula, western Alaska. In this paper, we examine the feasibility of integrating imagery from the UAS and the hyperspectral Airborne Visible/Infrared Imaging Spectrometer, Next Generation (AVIRIS-NG) airborne instrument to map the fractional composition of 12 key Arctic plant functional types (PFTs). To this end, we first mapped the 12 PFTs from our VHR UAS imagery using random forest classification. We then used these UAS-derived PFT maps as ground truth to develop partial least squares regression (PLSR) models to predict the fractional cover (FCover) of each PFT from AVIRIS-NG imagery. Further, we evaluated the performance of our PLSR models using reserved UAS samples, as well as by mapping PFT FCover and dominant PFT for large tundra landscapes. Our results show that 1) Arctic PFTs can be effectively mapped using VHR UAS imagery, with overall accuracy between 86% and 92%, 2) when the UAS mapped PFTs were used to inform PLSR scaling models, the FCover of the 12 PFTs could be effectively estimated from AVIRIS-NG imagery with a mean absolute error (MAE) <0.13, and 3) our PLSR models outperformed traditional, fully constrained least-squares (FCLS) linear mixture analysis and produced high-quality, spatially contiguous PFT FCover and PFT maps that captured vegetation spatial patterns with similar accuracy to those developed from UAS imagery. The developed PLSR models have the potential to be broadly applied for quantifying vegetation composition with AVIRIS-NG images to help monitor tundra vegetation dynamics and improve process-based modeling of tundra ecosystems.

54 ENVIRONMENTAL SCIENCES↗

Light-Duty Vehicle Choice Modeling and Benefits Analysis

The U.S. Department of Energy's Vehicle Technologies and Hydrogen and Fuel Cell Technologies Offices (VTO and HFTO) support research and development of efficient and sustainable transportation technologies that will improve energy efficiency, minimize emissions, and enable America to use less petroleum. This presentation steps through the approach, updates to the approach, and preliminary estimates of light-duty vehicle energy and emissions benefits from the continuation and success of VTO and HFTO programs. The programs include research on batteries, electric drive technologies (EDT), combustion, materials, fuel cells, and hydrogen storage. Preliminary results for battery and EDT program success show an annual 24% reduction in petroleum use and 13% reduction in carbon emissions.

47 OTHER INSTRUMENTATION↗

Life-cycle analysis of greenhouse gas emissions from hydrogen delivery: A cost-guided analysis

The cost of hydrogen delivery for transportation accounts for most of the current H 2 selling price; delivery also requires substantial amounts of energy. In this work, we developed harmonized techno-economic and life-cycle emissions models of current and future H 2 production and delivery pathways. Our techno-economic analysis of dispensed H 2 costs guided our selection of pathways for the life-cycle analysis. In this paper, we present the results of market expansion scenarios using existing capabilities (for example, those that use H 2 from steam methane reforming, chlor-alkali, and natural gas liquid cracker plants), as well as results for future electrolysis plants that use nuclear, solar, and hydroelectric power. Reductions in greenhouse gas emissions for fuel cell electric vehicles compared to conventional gasoline pathways vary from 40% reduction for fossil-derived H 2 to 20-fold for clean H 2 . Supplemental tables with greenhouse gas emissions data for each step in the H 2 pathways enable readers to evaluate additional scenarios.

08 HYDROGEN↗

Modeling Carbon-Based Nanomaterials (CNMs) and Derived Composites and Devices

A review of different modeling techniques, specifically in the framework of carbon-based nanomaterials (CNMs, including nanoparticles such as graphene and carbon nanotubes—CNTs) and the composites and devices that can be derived from them, is presented. The article emphasizes that the overall performance of these materials depends on mechanisms that operate across different time and spatial scales, requiring tailored approaches based on the material type, size, internal structure/configuration, and the specific properties of interest. Far from attempting to cover the entire spectrum of models, this review examines a wide range of analysis and simulation techniques, highlighting their potential use, some of their weaknesses and strengths, and presenting the latest developments and some application examples. In this way, it is shown how modeling can provide key information for tailoring or designing new materials for specific components or systems or to obtain certain functionalities. At the same time, it is revealed to be an area constantly undergoing development and improvement, as evidenced by the progress made by various of these techniques and the new modeling approaches that have emerged in recent years.

Chiminelli, Agustίn (ORCID:0000000230312107)↗

Model Advancements to Enable Impact Analysis of Climate Change on Streamflow Temperature

With support from the Department of Energy’s Water Power Technologies Office, Pacific Northwest National Laboratory (PNNL) has developed new tools that incorporate cutting-edge climate and hydrological science capabilities to assess the potential long-term impacts of future climate conditions on unregulated streamflow and water temperature within watershed-river-reservoir systems. The objectives of this project were achieved by enhancing key hydrologic and hydrodynamic models and transferring them to a high-performance computing environment to provide a high-spatiotemporal resolution, multi-scale modeling framework. The new modeling framework has the potential to quantify risks related climate change impacts on runoff, unregulated streamflow, and water temperature. Initial development and demonstration of the modeling framework was conducted under historical and future climate conditions in the Columbia River Basin in the Pacific Northwest and the Connecticut River Basin in New England.

13 HYDRO ENERGY↗

Use of Machine Learning on PMU Data for Transmission System Fault Analysis

Synchrophasor technology has been used for monitoring, control, and protection of bulk power system for over 10 years. Deployment of phasor measurement units (PMUs) in the USA power system has surpassed 3000 units installed in the transmission substations as stand-alone intelligent electronic devices (IEDs) or as a software add-on to other devices such as digital protective relays (DPRs) or digital fault recorders (DFRs). By now, thousands of terabytes of PMU data may have been captured and stored by various transmission system operators (TSOs) and independent system operators (ISOs). This creates an opportunity to deploy advanced machine learning (ML) techniques to detect and classify faults recorded by PMUs automatically to be used by the system operators for rapid, critical decision-making when manual analysis of the past or unfolding events is not feasible. In this paper we offer a brief background on how the automated fault analysis may be done using DPR and/or DFR data, and compare some of the legacy approaches to the new ML approaches in the context of the system-wide PMU recordings. We then offer insights from developing practical ML solutions that have been applied on field recordings captured by close to 450 PMUs from all three US interconnections (Western, Eastern and ERCOT) over two years (2016-2017). We identify and illustrate ML challenges we addressed: inaccurate data, data with scarce and temporally imprecise fault labels, data recorded by PMUs sparsely located at substations resulting in the fault records taken afar from the ends of the faulted lines, data containing only positive sequence values, and data taken at different voltage levels. We then illustrate the ML model results for fault analysis under different application scenarios. The novelty of this study is not only in the design, implementation, and performance analysis of the ML algorithms, but also in the use of advanced fault modelling and simulation approaches to improve the training results when developing supervised ML models for fault detection and classification. Extensive simulations of faults were conducted on a 14-bus power system to create a training dataset with over 1400 accurately labelled faults. This dataset was applied to enhance the accuracy of fault detection and classification of machine learning-based models trained with small number of labelled faults in large datasets recorded in the grid interconnections ranging from 5,000 to 70,000 buses.

Synchrophasors, Machine Learning, Fault Analysis, ↗

Closures for multicomponent reacting flows based on dispersion analysis

This work presents algebraic closure models associated with advective transport and nonlinear reactions in a Reynolds-averaged Navier-Stokes context for a system of species subject to binary reactions and transport by advection and diffusion. Expanding upon analysis originally developed for non-reactive transport in the context of Taylor dispersion of scalars, this work extends the modified gradient diffusion model explicated by Peters [N. Peters, Turbulent Combustion, Cambridge Monographs on Mechanics (Cambridge University Press, Cambridge, 2000)] and based on work by Corrsin [S. Corrsin, The reactant concentration spectrum in turbulent mixing with a first-order reaction, J. Fluid Mech. 11, 407 (1961)] beyond single-component transport phenomena and involving nonlinear reactions. The presented model forms, from this weakly nonlinear extension of the original dispersion theory, lead to an analytic expression for the eddy diffusivity matrix that explicitly captures the influence of the reaction kinetics on the closure operators. Furthermore, we demonstrate that the derived model form directly translates between flow topologies through a priori and a posteriori testing of a binary species system subject to homogeneous isotropic turbulence. Using two- and three-dimensional direct numerical simulations involving laminar and turbulent flows, it is shown that this framework improves prediction of mean quantities compared to previous results. Lastly, the presented model form, collapses to the earlier gradient diffusion and its modified version derived by Corrsin in the limits of nonreactive species and linear reactions, respectively.

42 ENGINEERING↗

Uncovering New Physics in the Cosmic Microwave Background: Developing Novel Theoretical Models and Machine-Learning-Powered Constraints

The search for evidence of new physics via its signatures in the cosmos is a cornerstone goal of the Office of High Energy Physics in the DOE Office of Science. Indeed, the current concordance cosmological model provides intriguing hints for beyond-the-standard-model (BSM) physics, such as dark matter and dark energy. Recently, a potential breakdown has appeared in this model, which could be initial evidence toward a further important revision in our fundamental theoretical understanding of cosmology. This breakdown is reflected in disagreements between inferences of the current expansion rate of the universe, H 0 (the Hubble constant), based on indirect, cosmological data (e.g., from the early universe) and based on direct, local measurements. Despite significant effort, a compelling new concordance cosmological model has yet to be found; achieving significant progress on this front was the first major focus of the project. Theoretical considerations indicate that if the observational discrepancies are not due to systematic errors, they strongly suggest new physics operating in the redshift range just prior to recombination, when cosmic microwave background (CMB) photons last scattered. Crucially, almost all such models produce unique signatures in the CMB temperature and polarization power spectra, which will be measured with unprecedented precision by ongoing and upcoming experiments, including the DOE-supported CMB-S4 project. However, these subtle hints of new physics must be uncovered from beneath a swath of Galactic and extragalactic foreground contamination. Current CMB analysis methods, although powerful, do not optimally infer the CMB power spectrum in the presence of non-Gaussian foregrounds. There is thus scope for theoretical improvement in this foundational challenge of cosmological inference, which formed the second major focus of the project. The primary objectives of the project were two-fold: (1) to develop new theoretical models in cosmology that can restore concordance amongst the full suite of cosmological data sets, thereby potentially providing evidence of novel BSM physics; (2) to develop new theoretical machinery to enable significant sensitivity improvements in searches for new physics in cosmology, particularly via the CMB power spectrum. The two objectives are intertwined, as the analysis methodology improvements in (2) will enable the tightest possible constraints on the signatures of new physics predicted by the novel scenarios in (1). The theoretical approaches to restore concordance focused on models involving novel scalar field dynamics in the pre-recombination universe (the “early dark energy” scenario and modifications thereof), as well as couplings between this field and other components in the standard cosmological model, such as dark matter. We also studied a model featuring a generalization of the decaying dark matter scenario, in which a sub-component of dark matter converts into dark radiation at late times in cosmic history. While these ideas are mostly driven by phenomenological considerations, this tactic has proven extremely successful in cosmology throughout the past few decades, including in the early history of evidence for dark matter and dark energy. The new theoretical machinery envisioned in (2) is undergirded by developments in signal processing and machine learning, which will enable improvements in CMB power spectrum estimation in the presence of non-Gaussian foreground contaminants. In turn, this will yield optimal sensitivity in searches for new physics in the CMB, by maximizing the cosmological information that is extracted from this observable. The most ambitious outcome of this work would be the construction of a new cosmological model that restores concordance amongst data sets. Although the individual models studied here did not fully achieve that goal, significant progress in narrowing down the model space was made, as described below. Moreover, the outcome of the methodological improvements in (2) will significantly impact a wide range of theoretical cosmology, by enabling the tightest possible constraints on any model that leaves novel signatures in the CMB temperature and polarization power spectra.

79 ASTRONOMY AND ASTROPHYSICS↗

Evolution of dislocation loops in irradiated α-Uranium: An atomistically-informed cluster dynamics investigation

An atomistically-informed mean field cluster dynamics model has been developed to investigate the nucleation and growth of prismatic loops in irradiated a-Uranium. TEM analysis of neutron irradiated a-Uranium shows the evolution of self-interstitial atom and vacancy loops on (010) and (100) crystallographic planes, respectively, resulting in an anisotropic lattice swelling of its face-centered orthorhombic crystal. To provide model parameters, the crystallography of loops and the binding energy of point defects to these loops were studied using an angular dependent EAM potential and classical molecular dynamics (MD) simulations. Furthermore, using the bond-boost hyperdynamics method, the anisotropic diffusion of interstitials and vacancies in a-Uranium was investigated. Here, the mechanisms of point defect diffusion and the associated migration energies were reported and compared with previous DFT studies. The energetics and kinetic quantities mentioned above were used in the cluster dynamics model to predict the cluster density as a function of dose rate, dose and temperature and the results were compared to the reported neutron irradiation experiments. The model predictions reveal an accumulation of small sized vacancy loops along with a population of large and growing self-interstitial loops, which closely corresponds to the TEM observations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Preliminary Nuclear Containment Vessel Modeling for Multi-Hazard Probabilistic Risk Assessment under Seismic Hazards and Concrete Degradation

The current practice for natural phenomena hazards (NPH) risk assessment of nuclear facilities is to compute the risk for each hazard independently and then compound the total risk as a combination of single hazard risks. This state of practice does not consider correlations between hazards and the cascading impacts to structures, systems, and components (SSCs), and could thus underestimate the NPH risk or overestimate the nuclear facility safety. Events such as the Fukushima Daiichi accident have highlighted the importance of multi-hazard risk considerations to nuclear power plants (NPPs) that quantify the cascading damage effects to SSCs in the risk models. Moreover, the current fleet of NPPs in the United States is aging; these NPPs are now expected to operate well beyond their initially planned design life. Aging-related deterioration can potentially decrease the capacity of critical structures such as containment vessels to withstand NPH. Such aging considerations may not be adequately accounted for by the current NPH risk assessment guidelines. This paper presents a preliminary modeling and simulation of a representative reinforced concrete containment vessel subjected to seismic mainshock and aftershock considering concrete degradation due to alkali silica reaction. The broader aim is to develop multi-hazard time-dependent fragility functions that could be subsequently used in the probabilistic risk assessment (PRA) model. The multi-hazard component comes into play due to the consideration of damage to the containment vessel under seismic loads and concrete degradation. Consideration of concrete degradation also brings into play the time-dependent nature of the containment vessel response. The response of the containment vessel under varying degrees of concrete degradation to seismic loads is investigated. The results presented are simulated using the Multi-hazard Analysis for STOchastic time-DOmaiN phenomena (MASTODON) software for seismic analysis and the Blackbear software for concrete degradation and damage modeling. Both software are open source and developed within the Multiphysics Object-Oriented Simulation Environment (MOOSE).

42 ENGINEERING↗

Optimal PMU design based on sampling model and sensitivity analysis

The precise measurements of the synchrophasor and frequency from phasor measurement units (PMUs) are widely used in power grid applications. With the improvement of the technique, applications always require stable dynamic performance and higher accuracy for the synchrophasor and frequency measurements, which is challenging for PMU development. To evaluate the contribution of PMU hardware to measurement accuracy, this paper proposes a general-purpose sampling model to analyze the measurement error. In the proposed sampling model, a strict mathematical derivation is derived, where its error is purely determined by the parameters of the PMU hardware. The sensitivity analysis is carried out by three methods, including mathematical analysis, computer simulation, and variance-based sensitivity analysis. Through the sensitivity analysis, this paper establishes the systematic formulation and the inclusion of synchrophasor, frequency, and ROCOF. Experimental results based on the real-world testbench involving distribution-level PMUs match the mathematical analysis conclusion, which verifies the correctness of the general-purpose sampling model. Furthermore, a strategy for the optimal PMU design is proposed, which could guide PMU design in the future.

42 ENGINEERING↗

The r -modes of slowly rotating, stratified neutron stars

ABSTRACT The only r-modes that exist in a globally barotropic, rotating, Newtonian star are the fundamental l = |m| solutions, where l and m are the indices of the spherical harmonic $Y_l^m$ that describe the mode’s angular dependence. This is in stark contrast to a stellar model that is non-barotropic throughout its interior, which hosts all the l ≥ |m| perturbations including radial overtones. In reality, neutron stars are stratified with locally barotropic regions. Therefore, we explore how stratification alters a star’s ability to support r-modes. We consider the globally stratified case and examine the behaviour of the modes as the star gets close to barotropicity. In this limit, we find that all but the fundamental l = |m| perturbations change character and become generic inertial modes. Restricting the analysis to l = |m| perturbations, we develop the r-mode equations in order to consider stellar models that exhibit local barotropicity. Our results for such models show that the r-mode overtones diverge and join the inertial modes. In order to see which r-modes persist and retain their character in realistic neutron stars, these calculations will need to be brought into full general relativity.

79 ASTRONOMY AND ASTROPHYSICS↗

Transforming Energy Through Computational Excellence: A View From NREL

At the National Renewable Energy Laboratory (NREL)—a U.S. Department of Energy laboratory—computational science, high-performance computing, applied mathematics, advanced computer science, visualization, and data play a pivotal role in advancing energy abundance, affordability, security, and reliability. From fundamental scientifc discovery to systems engineering and analysis, NREL researchers tackle market-relevant challenges to develop solutions for an independent energy system that is reliable, resilient and secure. Collaborative partnerships with industry, government, and academia ensure that our research remains cutting edge, impactful, applicable, and aligned with real-world energy needs. This special issue of Computing in Science & Engineering highlights exemplary NREL projects where computational tools and methodologies drive discovery and accelerate innovation in scalable and integrated energy systems. The featured articles explore the role of computational modeling, high-performance computing, generative AI, and adaptive computing in advancing independent energy solutions, optimizing sustainability research, and enhancing decision-making for energy solutions using a broad mix of energy technologies. Here, these contributions demonstrate how NREL’s computational research bridges the gap between theoretical advancements and practical implementation, emphasizing interdisciplinary collaboration and a commitment to innovation, with a focus on translating computational excellence into real-world impact, thus accelerate progress toward national energy goals. By showcasing cutting-edge research at the intersection of computational science and energy systems, this issue aims to inspire and inform researchers, practitioners, and policymakers dedicated to shaping a more reliable energy future.

97 MATHEMATICS AND COMPUTING↗

Hydrogen Production Cost with Alkaline Electrolysis

Rigorous stakeholder-vetted techno-economic analysis was performed to assess the cost of hydrogen (H 2 ) produced using state-of-the-art Liquid Alkaline (LA) electrolysis. Projected high-volume, untaxed levelized cost of hydrogen (LCOH) range from 2020US $\$ 1.84$ to $\$ 2.88$/kg-H 2 depending on technology year, process design, and electrolyzer project scale, assuming an electricity price of $\$ 0.03$/kWh. The total installed capital cost for a LA electrolysis plant was estimated from bottom-up stack and installed cost models that account for purchased equipment, installation costs, site preparation, and general overhead costs. For this study, the LA electrolysis plant is assumed to be a stick-built, greenfield project developed by an EPC firm with electrolysis stacks purchased directly from an electrolysis stack manufacturer. The price of the electrolysis stacks is based on a bottom-up cost assessment with business markup for the electrolysis company fabricator. Methods from the Hydrogen Analysis (H 2 A) production model, a peer-reviewed national laboratory-developed discounted cash flow model, were used to calculate the LCOH production in 2020$/kg-H 2 . The baseline electricity price case ($\$ 0.03$/kWh) corresponds to average wholesale electricity prices currently possible in U.S. markets with plentiful wind. Similar low-cost electricity pricing is possible from solar Power Purchase Agreements (PPA) although these prices are typically limited by renewable energy capacity factors.

08 HYDROGEN↗