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At least 1,261 records · Page 70

Topological Relationship–Based Flow Direction Modeling: Mesh–Independent River Networks Representation

River networks are important features in surface hydrology. However, accurately representing river networks in spatially distributed hydrologic and Earth system models is often sensitive to the model's spatial resolution. Specifically, river networks are often misrepresented because of the mismatch between the model's spatial resolution and river network details, resulting in significant uncertainty in the projected flow direction. In this study, we developed a topological relationship-based river network representation method for spatially distributed hydrologic models. This novel method uses (a) graph theory algorithms to simplify real-world vector-based river networks and assist in mesh generation; and (b) a topological relationship-based method to reconstruct conceptual river networks. The main advantages of our method are that (a) it combines the strengths of vector-based and DEM raster-based river network extraction methods; and (b) it is mesh-independent and can be applied to both structured and unstructured meshes. This method paves a path for advanced terrain analysis and hydrologic modeling across different scales.

54 ENVIRONMENTAL SCIENCES↗

Performance characterization of x-ray crystal spectroscopy highly oriented pyrolytic graphite reflectors based on x-ray diffractometry experiments

The use of Highly Oriented Pyrolytic Graphite (HOPG) reflectors is often proposed in the design of X-ray Crystal Spectroscopy (XCS) diagnostic systems for the next-generation tokamak devices, including the ITER project. Here, this study introduces an experimental study based on the X-Ray Diffractometry (XRD) method to evaluate the performance of HOPG reflectors. The experimental method provides both the angular responses and the reflectivities of the HOPG reflectors. A demonstrative XRD experiment is conducted, and the details of the experiment are introduced. This method enables precise studies on HOPG reflectors, facilitating the design of XCS diagnostic systems for future tokamaks.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Validation of a synthetic fast ion loss detector model for Wendelstein 7-X

Abstract We present the first validated synthetic diagnostic for fast ion loss detectors (FILDs) in the Wendelstein 7-X (W7-X) stellarator. This model has been developed on, and validated against experimental data from, a FILD provided by the National Institute for Fusion Science (NIFS-FILD), with potential future applicability to the existing Faraday Cup FILD (FC-FILD) on W7-X as well as the scintillating FILD (S-FILD) currently under development. A workflow combining Monte Carlo codes BEAMS3D and ASCOT5 is used to track fast ions produced by neutral beam injection from the moment of ionization until they are thermalized or lost from the last closed flux surface, and from there to a virtual plane which serves as a projection of the entrance aperture to the FILD. Simulations in ASCOT5 are analyzed via a geometric method to determine the probability of transmission through the FILD aperture and onto the detector as a function of normalized momentum, pitch angle, gyrophase, and position at the virtual plane. This probability is then applied to the simulated ions arriving from the plasma, producing a simulated signal from a computationally tractable number of simulated fast ions. Simulated signals are presented for two W7-X experiments with neutral beam injection and quantitatively compared with experimental measurements from the NIFS-FILD diagnostic. An estimate of the frequency of charge-exchange with neutral particles in the edge is performed, and it is found that this process may have a significant impact on the measured signals.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Variational study of the Kitaev-Heisenberg-Gamma model

Here we compute the low-energy excitation spectrum and the dynamical spin structure factor of the Kitaev-Heisenberg-Gamma model through a variational approach based on the exact fractionalized excitations of the pure Kitaev honeycomb model. This novel approach reveals the physical reason for the asymmetric stability of the Kitaev spin liquid phases around the ferromagnetic and antiferromagnetic Kitaev limits. Moreover, we demonstrate that the fractionalized excitations form bound states in specific regions of each Kitaev spin liquid phase and that certain phase transitions induced by Heisenberg and Gamma interactions are driven by the condensation of such a bound state. Remarkably, this bound state appears as a sharp mode in the dynamical spin structure factor, while its condensation patterns at the appropriate phase transitions provide a simple explanation for the magnetically ordered phases surrounding each Kitaev spin liquid phase.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Atomic-Site-Specific Surface Valence-Band Structure from X-Ray Standing-Wave Excited Photoemission

X-ray standing-wave (XSW) excited photoelectron emission was used to measure the site-specific valence band (VB) for ½ monolayer (ML) Pt grown on a SrTiO 3 (001) surface. The XSW induced modulations in the core level (CL), and VB photoemission from the surface and substrate atoms were monitored for three ℎ⁡kl substrate Bragg reflections. The XSW CL analysis shows the Pt to have a face-centered-cubic-like cube-on-cube epitaxy with the substrate. The XSW VB information compares well to a density functional theory calculated projected density of states from the surface and substrate atoms. Altogether, this Letter represents a novel method for determining the contribution to the density of states by valence electrons from specific atomic surface sites.

74 ATOMIC AND MOLECULAR PHYSICS↗

Incorporating Operational Uncertainties into the Dispatch of an Integrated Solar and Storage System

The economic assessment of hybrid energy systems (HES) pairing battery energy storage systems (BESSs) and photovoltaics (PV) is highly important for advancing their deployment in power systems. This paper presents an innovative assessment framework, including an optimal control policy for dispatch under uncertainty and procedures for exploring control parameters that maximize economic benefits. The proposed dispatch policy consists of two steps using system forecast information. The first step is to determine whether a BESS will be used within an operational scheduling time frame based on the probability of events and their thresholds. Once the dispatch of BESS is triggered, a model predictive control (MPC) is carried out in the second step for scheduling using the expected value of system information. By exercising this policy with different thresholds, one can explore the trade-offs between short-term benefits and battery lifetime, and identify an optimal threshold that maximizes the total economic benefits within the battery lifetime. An evaluation study in a real-world HES project is presented to illustrate the proposed framework. Compared with traditional optimal dispatch algorithms, the proposed method can significantly improve the economic benefits of an HES scheduled under forecast uncertainties.

Ma, Xu↗

Matilda v1.0: An R package for probabilistic climate projections using a reduced complexity climate model

A primary advantage to using reduced complexity climate models (RCMs) has been their ability to quickly conduct probabilistic climate projections, a key component of uncertainty quantification in many impact studies and multisector systems. Providing frameworks for such analyses has been a target of several RCMs used in studies of the future co-evolution of the human and Earth systems. In this paper, we present Matilda, an open-science R software package that facilitates probabilistic climate projection analysis, implemented here using the Hector simple climate model in a seamless and easily applied framework. The primary goal of Matilda is to provide the user with a turn-key method to build parameter sets from literature-based prior distributions, run Hector iteratively to produce perturbed parameter ensembles (PPEs), weight ensembles for realism against observed historical climate data, and compute probabilistic projections for different climate variables. This workflow gives the user the ability to explore viable parameter space and propagate uncertainty to model ensembles with just a few lines of code. The package provides significant freedom to select different scoring criteria and algorithms to weight ensemble members, as well as the flexibility to implement custom criteria. Additionally, the architecture of the package simplifies the process of building and analyzing PPEs without requiring significant programming expertise, to accommodate diverse use cases. We present a case study that provides illustrative results of a probabilistic analysis of mean global surface temperature as an example of the software application.

54 ENVIRONMENTAL SCIENCES↗

Nanophotonic quantum sensing with engineered spin-optic coupling

Nitrogen vacancy centers in diamond provide a spin-based qubit system with long coherence time even at room temperature, making them suitable ambient-condition quantum sensors for quantities including electromagnetic fields, temperature, and rotation. The optically addressable level structures of NV spins allow transduction of spin information onto light-field intensity. The sub-optimal readout fidelity of conventional fluorescence measurement remains a significant drawback for room-temperature ensemble sensing. Here, we discuss nanophotonic interfaces that provide opportunities to achieve near-unity readout fidelity based on IR absorption via resonantly enhanced spin-optic coupling. Spin-coupled resonant nanophotonic devices are projected to particularly benefit applications that utilize micro- to nanoscale sensing volume and to outperform present methods in their volume-normalized sensitivity.

42 ENGINEERING↗

Full loop thermodynamic corrosion inhibition and sensing in molten chloride systems. Final report

The objective of this project is to increase the lifetime of Concentrated Solar Power (CSP) systems by identifying corrosion mitigation methods for heat transfer loops in the temperature range of 500 - 800 °C. Coupling of experimental data with CFD modeling has provided greater insight into corrosion mitigation methods and how they will be applied in molten salt systems with convective flows. The overall goal of the program is to demonstrate techniques that control corrosion while having limited impact on system alloys.

14 SOLAR ENERGY↗

Improving NO x Entitlement with Axial Staging

Manufacturers are constantly under pressure to increase the efficiency of air breathing gas turbines for the next generation of ground based combined cycle power plants without a significant increase in NO x emissions. Axially staging some of the fuel and air is one method manufacturers are exploring to increase the firing temperature with a minimal NO x increase. This project used a high-pressure combustion facility to systematically explore the axial stage design space at conditions relevant to engine manufacturers. Chemiluminescence and PIV images of the reacting jets were obtained as well as exit emissions. This data was used to create correlations for reacting jets-in-crossflow, analogous to what currently exists for non-reacting jets-in-crossflow. Two CFD programs were used to model these results to quantifying the accuracy of the codes and help elucidate the underlying physics that governs the high-pressure reacting jet in crossflow. This report indicates a change in flame/flow-field behavior, and emissions characteristics with increasing pressure from 1-5.5 atm. Additionally, the effects of preheating and premixing the axial jet are explored at various conditions and show increased jet liftoff leads to a decrease in NO x emissions.

03 NATURAL GAS↗

Applying the Risk Management Framework: The Distributed Energy Resource Risk Manager

As part of a multiyear effort, the National Renewable Energy Laboratory (NREL) has dedicated resources to understand and identify cybersecurity weaknesses in distributed energy resources (DERs) by performing assessments. Due to a lack of standardization and rapidly increasing adoption of DERs, there is a critical need to address cybersecurity needs for DER systems in an interactive way. Furthermore, federal agencies, which are required to obtain an authority to operate, are challenged by the complexities of including their DERs. To help meet this need, in early 2020, NREL released the Distributed Energy Resources Cybersecurity Framework (DERCF) and accompanying Web application. This process is supported by the Risk Management Framework (RMF) developed by the National Institute of Standards and Technology. This project, referred to as the DERCF RMF application, expands on the existing DERCF work to include methods that support walking a user through the seven RMF steps. The tool will be available for download at no cost from [link ]. The purpose of this paper is to describe the steps the DERCF team at NREL took to understand Steps 1-5 of the RMF process. Additionally, this document will identify future work on the first five steps as well as a plan for Steps 6 and 7.

24 POWER TRANSMISSION AND DISTRIBUTION↗

A Modular Oxygen-Selective System for Combustion Systems

Small-scale modular systems for oxygen generation offer clear benefits, including improved energy efficiency, reduced operating costs, and the flexibility to meet location-specific needs. We propose that a compact modular system could be developed using a solvent-based method for capturing and releasing oxygen, enabling “oxygen on demand” for various combustion technologies. In this project, we aim to develop and demonstrate a modular oxygen-selective approach based on Battelle IPID 31740. The use of designer solvents, which are reversible, single-component solvents capable of dissolving and separating oxygen from air, offers the advantage of unlimited chemical tunability to achieve desirable properties for efficient oxygen separation. We filed U.S. Patent (32845-E, iEdison No. 0685901-23-0184).

36 MATERIALS SCIENCE↗

SISGR: The regulation of carbon fixation in plant and green algae: Rubisco activase and the origin of heat inactivation of CO 2 assimilation

Rubisco activase (Rca) is a critical AAA+ ATPase protein complex that remodels and promotes the Rubisco enzyme, a key player in photosynthetic performance and carbon fixation. The assembly and function of the Rca protein complex are regulated by a range of factors, including subunit concentration, nucleotide-binding states, thermal conditions, metal-ion coordination, and post-translational modifications, such as phosphorylation. Despite its importance in photosynthesis, the detailed molecular mechanisms underlying the regulation of plant Rca and how it activates Rubisco remain elusive. This project aims to bridge this knowledge gap by integrating sophisticated enzymology tools with single-molecule methods and high-resolution electron microscopy to elucidate the structure and function of plant Rca. Through these multiple approaches, we have systematically investigated how the activity of plant Rca is impacted by various factors, such as phosphorylation and metal-ion coordination. The Rca complex assembly/disassembly dynamics were captured using anti-Brownian electrokinetic (ABEL) trap-based measurements, providing unprecedented insight into its structural flexibility and diverse assembly states. Furthermore, the structural analysis of Rca through electron crystallography and single-particle cryogenic electron microscopy (cryo-EM) reveals novel assembly states of the spinach Rca, providing insight into the mechanistic action for Rubisco remodeling. By combining cutting-edge tools and approaches, this work uncovers critical aspects of Rca’s regulation and assembly, paving the way for a deeper understanding of its role in photosynthetic efficiency and the potential for enhancing carbon fixation in crops.

59 BASIC BIOLOGICAL SCIENCES↗

Catalytic water splitting kinetics and optimization

This project was motivated primarily by the opportunity of turning photocatalysis into a cost efficient hydrogen generation method for domestic energy and chemical production, and by the need to provide detailed mechanisms of photocatalytic reactions of broad scientific and technological interest. Specifically, we focused on the development of a new technique for analysis of (photo)catalytic reaction mechanisms to guide photocatalyst design. Our approach was based on applying velocity map imaging (VMI) to surface catalyzed reactions in order to identify photocatalytic reaction products, intermediates, and relate these to photocatalyst composition, dimensions, size and other characteristics. By coupling VMI with other spectroscopic and microstructural characterization, we aimed to reveal the features that limit performance of photocatalysis. As a first step to achieve this goal, we constructed a custom VMI microscope specifically designed for investigation of surface photocatalytic reactions and validated it’s design using a photodissociatinon of MnBr(CO) 5 adsorbed on a metallic surface. Using both 230 nm and 266 nm pulsed laser excitations, we observed that there are likely three main pathways for photodissociation of Mn(CO)5Br: from the gas phase, the surface, and ejected Mn complex from the surface increasing the gas phase concentration.

08 HYDROGEN↗

Advanced Computing Annual Report 2025 [Slides]

In Fiscal Year (FY) 2025, the National Laboratory of the Rockies (NLR) continued to advance computing as a cornerstone of energy innovation, expanding the Kestrel high-performance computing (HPC) system to 56 peak petaflops. This growth strengthened Kestrel's role as a national asset for applied energy research, enabling larger, more complex simulations and accelerating the integration of artificial intelligence (AI) methods across the laboratory's computing portfolio. In FY 2025, AI was a component of most projects running on Kestrel, underscoring its central role in modern energy science and engineering. Kestrel supported a broad and diverse set of 507 modeling and simulation projects, engaging 855 researchers across the U.S. Department of Energy's (DOE's) Office of Critical Minerals and Energy Innovation (CMEI) portfolio and other offices, as well as partners from industry, academia, and utilities. These efforts span critical materials discovery, energy systems modeling, grid modernization, advanced manufacturing, and other areas essential to strengthening U.S. energy security and competitiveness. Together, these collaborations produced 708 technical outputs, including 293 peer-reviewed publications, reflecting both the depth and impact of the science enabled by NLR's computing capabilities. This year's report highlights the growing importance and benefit of AI throughout NLR's research programs and features work by early career researchers who are helping shape the future of computing-enabled energy innovation. Explore these sections and the many project successes captured in the pages that follow.

97 MATHEMATICS AND COMPUTING↗

Quantifying Leaf Chlorophyll Concentration of Sorghum from Hyperspectral Data Using Derivative Calculus and Machine Learning

Leaf chlorophyll concentration (LCC) is an important indicator of plant health, vigor, physiological status, productivity, and nutrient deficiencies. Hyperspectral spectroscopy at leaf level has been widely used to estimate LCC accurately and non-destructively. This study utilized leaf-level hyperspectral data with derivative calculus and machine learning to estimate LCC of sorghum. We calculated fractional derivative (FD) orders starting from 0.2 to 2.0 with 0.2 order increments. Additionally, 43 common vegetation indices (VIs) were calculated from leaf spectral reflectance factor to make comparisons with reflectance-based data. Within the modeling pipeline, three feature selection methods were assessed: Pearson’s correlation coefficient (PCC), partial least squares based variable importance in the projection (VIP), and random forest-based mean decrease impurity (MDI). Finally, we used partial least squares regression (PLSR), random forest regression (RFR), support vector regression (SVR), and extreme learning regression (ELR) to estimate the LCC of sorghum. Results showed that: (1) increasing derivative order can show improved model performance until certain order for reflectance-based analysis; however, it is inconclusive to state that a particular order is optimal for estimating LCC of sorghum; (2) VI-based modeling outperformed derivative augmented reflectance factor-based modeling; (3) mean decrease impurity was found effective in selecting sensitive features from large feature space (reflectance-based analysis), whereas simple Pearson’s correlation coefficient worked better with smaller feature space (VI-based analysis); and (4) SVR outperformed all other models within reflectance-based analysis; alternatively, ELR with VIs from original reflectance yielded slightly better results compared to all other models.

47 OTHER INSTRUMENTATION↗

Investigating lab-scaled offshore wind aerodynamic testing failure and developing solutions for early anomaly detections

As offshore wind systems become more complex, the risk of human error or equipment malfunction increases during experimental testing. This study investigates a lab-scale incident involving a 1 : 50 scale 5 MW wind turbine, where a generator failure led to rotor overspeed and a blade–tower strike. To improve early fault detection, we propose a data-driven method based on multivariate long short-term memory (LSTM) models. High-frequency measurements are projected onto principal components, and anomalies are identified using reconstruction error and its time derivative. Two models are trained on different healthy datasets and tested using single- and multi-principal component (1PC and MPC) variations. Results show that combining both error and error derivative improves detection accuracy. The 1PC model detects faults faster, has a higher recall rate, and achieves a 43 % improvement in anomaly detection accuracy, while the MPC model yields higher precision. This approach provides a simple and effective tool for early anomaly detection in lab-scale experiments, helping to reduce the risk of future failures during the testing of new technologies.

17 WIND ENERGY↗

Systems and methods for vehicle cruise speed recommendation

A method for providing a cruising speed recommendation to an operator of a vehicle includes determining a projected route; receiving route characteristic data including route elevation data; determining a sampling resolution; sampling the route elevation data at the sampling resolution to generate sampled route elevation data; determining at least one start of uphill position and at least one start of downhill position; determining at least one cruise speed route segment based at least in part on the at least one start of uphill position and the at least one start of downhill position; determining a corresponding cruising speed for the at least one cruise speed route segment based at least in part on one or more of the route elevation data and the sampled route elevation data; and communicating the corresponding cruising speed for the at least one cruise speed route segment.

Aggoune, Karim↗