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DOE OSTI2021

Response of U.S. West Coast Mountain Snowpack to Local Sea Surface Temperature Perturbations: Insights from Numerical Modeling and Machine Learning

Sea surface temperature (SST) significantly modulates the precipitation and temperature over land, with important consequences on land surface processes such as snowpack. Compared to the impact of remote SST, the effect of nearshore/local SST is less well understood. In this study, the impact of local SST on the mountain snowpack of the U.S. West Coast is investigated using two 6-km regional climate simulations driven by the same lateral boundary conditions but with time-varying versus time-invariant and warmer local SSTs during 2003–15. Results show that local SST warming leads to warmer winter with more precipitation over the mountains. Meanwhile, the removal of SST temporal variability results in reduced temperature variability but increased precipitation variability. As a result, winter snow accumulation decreases by ~200 mm per season in the Cascade Mountains in the north but increases by ~100 mm per season in the Sierra Nevada in the south. Such a dipole response results from the competing effects of precipitation and temperature change at different elevations and are amplified by the enhanced atmospheric river moisture transport. To further delineate the relative contributions of different meteorological factors to the snowpack response, two neural network models were developed to predict the snow behaviors at seasonal and monthly scales. These models reveal the dominant influence of the total amount and the average temperature of precipitation on the snowpack response. Furthermore, these findings highlight the sensitivity of mountain snowpack to local SST in the western United States and underscore the importance of local SST and atmospheric rives to accurate snowpack estimations for water management.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Physics Design of Proton/Ion SRF Accelerators

Superconducting Radio Frequency (SRF) technology has become foundation of the modern high energy particle accelerators. The technology enables a higher accelerating field with minimal power dissipation and a stronger compact magnetic field. These advancements have broadened the operational horizon of the particle accelerators making a higher duty factor and Continuous Wave (CW) beam operations both feasible and economically viable. The intensity frontier research founded on generation of intense beam of exotic particles including but not limited to neutrinos, muons, kaons and neutrons as well as practical applications involving transmutation of nuclear reactor waste, rare isotopes generation, sub-critical nuclear power generation etc. could be accomplished efficiently only by using superconducting proton/ions particle accelerators. As a result, many newly constructed or under construction high energy accelerator facilities around the world such as Proton Improvement Plan-II (PIP-II) at Fermilab, FRIB at MSU, ESS at Sweden are utilizing SRF accelerators. This talk presents the design philosophy of SRF proton/ion particle accelerator, with a focus on optimizing its performance while addressing physics and operational challenges including the fault scenarios, machine availability and preservation of crucial beam parameters.

Saini, Arun↗

DOE OSTI2015

Joint neutron measurements on the flat-top HEU critical assembly machine - 14388

Flat-Top is a critical assembly with a spherical core (either ∼17 kg of HEU or ∼6 kg of δ-phase Pu) surrounded by a spherical 1000 kg natural uranium reflector. In 2013, joint LANL and CEA measurements were conducted to measure Flat-Top configurations with the HEU core using He-3 detector systems. Similar experiments were previously documented in which the Caliban and Godiva critical assemblies were measured. The core and reflector of Flat-Top have a glory hole in which nuclear material or non-nuclear material may be placed. Five configurations were measured and included subcritical configurations at various reactivity states as well as a configuration at delayed critical. The various reactivity states were achieved by varying the placement of the safety blocks and control rods (composed of natural uranium). A description of the assembly and the specific configurations measured will be presented. The subcritical and delayed critical measurements were analyzed using the Hage-Cifarelli formalism of the Feynman Variance-to-Mean method. This analysis can be used to calculate the multiplication of each configuration, from which the multiplication factor and reactivity are determined. These results are compared to the reactivity states based upon control rod worth curves and MCNP simulations. For this work, a new MCNP model was made to accurately represent the measured configurations. The ICSBEP handbook contains a benchmark of Flat-Top but all models are very simple and do not have the required fidelity to accurately represent the measured configurations. This work also examines the number of neutrons emitted per fission for this assembly (which has a significant contribution of induced fissions in U-238 due to the large natural uranium reflector). (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2022

Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning and Graph Neural Networks

We present a novel method for graph partitioning, based on reinforcement learning and graph convolutional neural networks. Our approach is to recursively partition coarser representations of a given graph. The neural network is implemented using SAGE graph convolution layers, and trained using an advantage actor critic (A2C) agent. We present two variants, one for finding an edge separator that minimizes the normalized cut or quotient cut, and one that finds a small vertex separator. The vertex separators are then used to construct a nested dissection ordering to permute a sparse matrix so that its triangular factorization will incur less fill-in. The partitioning quality is compared with partitions obtained using METIS and SCOTCH, and the nested dissection ordering is evaluated in the sparse solver SuperLU. Our results show that the proposed method achieves similar partitioning quality as METIS and SCOTCH. Furthermore, the method generalizes across different classes of graphs, and works well on a variety of graphs from the SuiteSparse sparse matrix collection.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

Investigations of Dual-Purpose Canister Direct Disposal Feasibility (FY14)

Results reported here continue to support the FY13 conclusion that direct disposal of DPCs is technically feasible, at least for some DPCs, and for some disposal concepts (geologic host media). Much of the work performed has reached a point where site-specific information would be needed for further resolution. Several activities in FY14 have focused on clay/shale media because of potential complications resulting from low thermal conductivity, limited temperature tolerance, and the need to construct hundreds of kilometers of emplacement drifts that will remain stable for at least 50 years. Technologies for rapid excavation and liner installation have significantly advanced in the past 20 years. Tunnel boring machines are the clear choice for large-scale excavation. The first TBM excavations, including some constructed in clay or shale media, are now approaching 50 years of service. Open-type TBMs are a good choice but the repository host formation would need to have sufficient compressive strength for the excavation face to be self-supporting. One way to improve the strength-stress relationship is to reduce the repository depth in soft formations (e.g., 300 m depth). The fastest construction appears to be possible using TBMs with a single-pass liner made of pre-fabricated concrete segments. Major projects have been constructed with prefabricated segmented liner systems, and with cast-in-place concrete liners. Cost comparisons show that differences in project management and financing may be larger cost factors than the choice of liner systems. Costs for large-scale excavation and construction in clay/shale media vary widely but can probably be limited to $10,000 per linear meter, which is similar to previous estimates for repository construction. Concepts for disposal of DPC-based waste packages in clay/shale media are associated with thermal management challenges because of the relatively low thermal conductivity and limited temperature tolerance. Peak temperature limits of 100°C or lower for clay-rich materials have been selected by some international programs, but a limit above 100°C could help to shorten the duration of surface decay storage and repository ventilation. The effects of locally higher peak temperatures on repository performance need to be evaluated (in addition to the effects at lower temperatures). This report describes a modeling approach that couples the TOUGH2 and FLAC3D codes to represent thermally driven THM processes, as a demonstration of the types of models needed.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

Physics-informed graph neural networks for predicting cetane number with systematic data quality analysis

Designing alternative fuels for advanced compression ignition engines necessitates a predictive model for cetane number (CN). In this study, the physics-informed graph neural networks are introduced for a reliable CN prediction by considering molecular features pertinent to the physical properties of molecules that affect CN. The reliability of measured data is another key factor to consider for improving the predictive model. Various experimental instruments for measuring CN exist, including standard and non-standard methods. In this regard, a systematic data quality analysis was carried out for the total 630 CNs collected from literature and new measurements in this study using Advanced Fuel Ignition Delay Analyzer (AFIDA). The results from this data curation process were reflected in the model by imposing lower sample weights on the data coming from less reliable measurement techniques. This approach effectively maximized the prediction accuracy while incorporating data from all available sources. Using the sample weights decreased the mean absolute error (MAE) up to 0.8 CN units. The accuracy was also improved by introducing the CN-related physical properties (the number of hydrogen bond donors and acceptors); the test set MAE is 5.74 and 7.01 for the model with and without such properties, respectively. Investigating molecular structural effects on CN was also carried out to gain chemical insights into factors used to design new fuel candidates. The dimensionality reduction analysis of feature vectors showed a clear clustering in terms of functional groups and CN and the structural effect derived from the model was consistent with the physicochemical insights. Finally, this physics-informed model and data curation would be helpful for accurate CN prediction and inform rational fuel design.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Understanding and Controlling Photoexcited Molecules in Complex Environments (Final Technical Report)

Predicting the outcome of a photochemical reaction is complicated by factors outside the scope of tradition theoretical chemistry. On such small scales, fluctuations of the environment render outcomes stochastic, nonadiabatic dynamics blur the separation between nuclear and electronic motion, and ultrafast relaxation renders typical assumptions of equilibrium inappropriate. This project aimed to elucidate and control nonadiabatic molecular dynamics in condensed phases. We developed accurate, efficient simulation tools to study irreversible molecular processes computational across a range of scales. We applied these methodological advances to study electron transfer under exotic conditions where spin and topology opened new pathways for reactivity. We also brought these ideas to bare on light harvesting systems, where energy and charge transport are coupled, and where long lifetimes can be deleterious to function. These advances helped to establish general principles for chemical efficiency and guide the design of nanoscale energy materials, molecular machines, and related technologies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Critical problems of energy frontier Muon Colliders: optics, magnets and radiation

This White Paper brings together our previous studies on a Muon Collider (MC) and presents a design concept of the 6 TeV MC optics, the superconducting (SC) magnets, and a preliminary analysis of the protection system to reduce magnet radiation loads as well as particle backgrounds in the detector. The SC magnets and detector protection considerations impose strict limitations on the lattice choice, hence the design of the collider optics, magnets and Machine Detector Interface (MDI) are closely intertwined. As a first approximation we use the Interaction Region (IR) design with beta-star=3 mm, whereas for the arcs we re-scale the arc cell design of the 3 TeV MC. Traditional cos-theta coil geometry and Nb3Sn superconductor were used to provide field maps for the analysis and optimization of the arc lattice and IR design, as well as for studies of beam dynamics and magnet protection against radiation. The stress management in the coil will be needed to avoid large degradation or even damage of the brittle SC coils. In the assumed IR designs, the dipoles close to the Interaction Point (IP) and tungsten masks in each IR (to protect magnets) help reducing background particle fluxes in the detector by a substantial factor. The tungsten nozzles in the 6 to 600 cm region from the IP, assisted by the detector solenoid field, trap most of the decay electrons created close to the IP as well as most of the incoherent electron-positron pairs generated in the IP. With sophisticated tungsten, iron, concrete and borated polyethylene shielding in the MDI region, the total reduction of background loads by more than three orders of magnitude can be achieved.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

Machine-Learning-Based Mapping and Modeling of Solar Energy with Ultra-High Spatiotemporal Granularity

Despite the rapid growth of solar energy, we still lack a dynamic, high-fidelity database that tracks the spatiotemporal variations of solar PVs and their associated infrastructures across different places at a spatially resolved scale. The absence of such data presents a barrier to various applications such as solar PV growth projection, solar energy integration, solar incentive design, and climate risk assessment. In this project, we aim to bridge this gap by developing AI-based algorithms to extract granular information about solar PV installations and their associated infrastructures (i.e., distribution grids) from widely available unstructured data like remote sensing images and street views. As a result, we have built the Solar Energy Atlas, a fine-grained, large-scale geospatial overlay of distributed solar PVs and distribution grids. On top of it, we have advanced the understanding of solar adoption and distribution grid vulnerability to climate-induced extremes. Our major contributions can be summarized as follow: (1) By developing new AI algorithms, we have built the most comprehensive solar PV spatiotemporal database covering the entire US. This is the first time we obtained the exact GPS locations, size, subtype, and installation year information for rooftop solar PVs across the US. This database can be used for solar PV growth projection, solar energy integration, solar energy policy analysis and design, and spatially-resolved climate risk assessment. (2) Leveraging this database, we have uncovered the socioeconomic driving factors that are correlated with earlier onset of solar adoption and higher saturated adoption levels. We have identified the heterogeneity in the effects of different types of financial incentives on solar adoption and provided implications for tailoring incentive design based on local income levels to promote equitable solar adoption. (3) We have developed a distribution grid GIS mapping algorithm which can obtain granular geospatial and topology information about distribution grids using multi-modal open data, reducing the dependency on hard-to-obtain smart meter data of conventional approaches. It shows effectiveness in both the U.S. and Sub-Saharan Africa. Using this algorithm, we have uncovered the non-uniform vulnerability of distribution grids to wildfires in California in the aspects of undergrounding protection and Distributed Energy Resources (DER) preparedness. This has provided important implications for improving the affordability and equity of grid adaptation approaches. (3) We have made our produced database publicly available and provided user-friendly interface to enable various stakeholders and the general public to interact with the data. We have also integrated the produced data into the Data Commons platform to enable the public to access the data and correlate it with other location-specific characteristics simply using natural language as queries. The impact of our project is three-fold: (1) New algorithms for mapping solar PVs and distribution grids across space and time, which are open source to facilitate researchers and industry; (2) New databases of solar PVs and distribution grids that have been made publicly available for engineering, social, and policy applications; (3) New understandings and actionable insights on the potential approaches to promoting solar adoption and reducing energy infrastructure vulnerabilities. In this report, we start by discussing the project background and motivation (section 5), followed by the overview of project objectives (section 6). Results and discussion for each task are presented in section 7. Significant accomplishments are summarized in section 8. This report will be concluded by discussing the paths forwards (section 9), products (section 10), and team roles (section 11).

14 SOLAR ENERGY↗

DOE OSTI2024

Integrating Experiments and Well Logs to Predict Caney Shale Static Mechanical Properties During Production with Supervised Machine Learning

Caney shale is one of the emerging oil reservoirs in Oklahoma. Understanding the impact of effective stress on its mechanical properties is critical for predicting hydraulic fracture geometry and overall hydrocarbon production. The objective of our study is to evaluate the impact of effective stress on the dynamic Young’s modulus using ultrasonic velocity measurements for Caney shale samples. A triaxial cell was utilized to measure ultrasonic (P-wave and S-wave) velocities for ten downhole Caney shale samples under various effective stresses to indirectly assess the impact of pore pressure change. The dynamic Young’s moduli estimated from these measurements were integrated with available conventional well logs (excluding sonic logs) and triaxial test results from Benge et al. (2021) to predict the static Young’s modulus using Random Forest (RF) and Extreme Gradient Boosting (XGBoost) models. The results showed that the estimated dynamic Young’s moduli from ultrasonic measurements were higher than the corresponding static Young’s modulus of cores from the same vertical well at similar depths. With increasing effective stress, the dynamic Young’s modulus increased for all samples. The estimated dynamic-to-static correction factor tended to be higher in zones of high neutron porosity (PHIN) and low density compared to other zones. Finally, SHapley Additive exPlanations (SHAP) for RF and XGBoost models identified depth, gamma ray (GR), and PHIN as key features for predicting the static Young’s modulus. This study enhances our understanding of the dynamic and static Young’s moduli for the Caney shale interval, as a function of effective stress and conventional well logs. The findings from this study can improve predictions of production throughout the well's lifespan by offering insights into the mechanical property degradation resulting from pore pressure depletion.

Kholy, Sherif M.↗

DOE OSTI2025

Stable Machine‐Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection‐Permitting Simulations

Modern climate projections often suffer from inadequate spatial and temporal resolution due to computational limitations, resulting in inaccurate representations of sub-grid processes. A promising technique to address this is the multiscale modeling framework (MMF), which embeds a kilometer-resolution cloud-resolving model (CRM) within each atmospheric column of a host climate model to replace traditional convection and cloud parameterizations. Machine learning offers a unique opportunity to make MMF more accessible by emulating the embedded CRM and reducing its substantial computational cost. Although many studies have demonstrated proof-of-concept success of achieving stable hybrid simulations, it remains a challenge to achieve near operational-level success with real geography and comprehensive variable emulation that includes, for example, explicit cloud condensate coupling. In this study, we present a stable hybrid model capable of integrating for at least 5 years with near operational-level complexity, including coarse-grid geography, seasonality, explicit cloud condensate and wind predictions, and land coupling. Our model demonstrates skillful online performance, achieving a 5-year zonal mean tropospheric temperature bias within 2 K, water vapor bias within 1 g/kg, and a precipitation root mean square error of 0.96 mm/day. Key factors contributing to our online performance include an expressive U-Net architecture and physical thermodynamic constraints for microphysics. With microphysical constraints mitigating unrealistic cloud formation, our work is the first to demonstrate realistic multi-year cloud condensate climatology under the MMF framework. Despite these advances, online diagnostics reveal persistent biases in certain regions, highlighting the need for innovative strategies to further optimize online performance.

Hu, Zeyuan [NVIDIA Corporation, Santa Clara, CA (U↗

DOE OSTI2022

Predicting and Controlling Corrosion (Abbreviated Final Report)

Corrosion accumulates significant replacement costs in the transportation, utility, manufacturing, and infrastructure sectors. It also underpins several of LLNL’s core missions in stockpile stewardship, defense, and energy security. Whereas most corrosion and aging models are empirically parameterized to describe well-defined conditions late in the reaction, the factors that determine the early stages of corrosion—during which mitigation could be most impactful— are poorly understood. This project addressed the critical need for new approaches to predict the kinetics of corrosion initiation based on firm physical and chemical understanding. The activities encompassed degradation of relevant metals in both hydrogen-rich and environmental corrosion scenarios. The team integrated state-of-the-art multiscale simulation, in situ characterization, and data science within three technical thrusts: hydriding of Ti alloys; aqueous corrosion of Al and Ni-Cr alloys; and degradation of additively manufactured 316L stainless steel. In each case, novel capabilities were developed to identify and track the impacts of key atomistic, compositional, and microstructural features on the metal systems. For hydriding, protocols were developed to tightly integrate multiscale models, advanced multimodal characterization, and machine learning to determine how hydrogen interacts with native passivating surface oxides and nucleates new undesired phases, shedding new light on the critical role of grain boundaries, interfaces, and atomically disordered regions. For aqueous corrosion, the project demonstrated methods to predict dissolution rates of metal surfaces in corrosive solutions, to measure and understand microstructural and grain orientation effects on corrosion susceptibility, and to investigate competing growth and dissolution kinetics of surface oxides. For additively manufactured metals, analysis using state-of-the-art microscopy techniques revealed the role of specific heterogeneities invoked during laser processing, including cellular structure, dislocations, and precipitates, on corrosion susceptibility. In addition to new capabilities and understanding, the project provided an avenue for workforce development, as well as key partnerships with stakeholders in corrosion science.

08 HYDROGEN↗

DOE OSTI2017

Microstructural Effects on Creep-Fatigue Life of Alloy 709

Cyclic testing has been carried out to characterize the fatigue and creep-fatigue behavior of Alloy 709, an advanced austenitic structural alloy. Specimens were machined from several plates of 709, subjected to different processing conditions and resulting in varying microstructures in the specimens, dependent on both the plate used for the machining and the location the specimen originated from within the plate. The cyclic testing has been conducted primarily at a single fatigue and creep-fatigue condition, which was at 650 °C and 1.0% total strain (+/-0.5% strain). For creep-fatigue, a 30 min. hold time at peak tensile strain was introduced. The combined results of the cyclic testing and post-test metallographic characterization suggest that the investigated microstructures, ultimately controlled by the processing conditions, have limited influence on the Alloy 709 fatigue cycle life. In contrast, some effect on the creep-fatigue cycle life due to the microstructure was observed, specifically that the presence of large grains may negatively impact the creep-fatigue resistance. The number of cycles to failure at the selected creep-fatigue condition varied by up to a factor of 5 for the specimens tested. Specimens with an optimum microstructure, a uniform distribution of grains with diameters less than approximately 200 ?m, resulted in a creep-fatigue resistance similar to that of 316-type stainless steel. The majority of the Alloy 709 cyclic test data has been previously reported in milestone reports - and the microstructural and processing details for the specific heats have been reported by Yamamoto et al. - This summary report includes creep-fatigue test results on three additional heats (011502F, 011502H, and 011539 (note, same plate as ORNL 011593)) for which the processing and microstructural details are available in previous reports.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

Affordability of Household Transportation Fuel Costs by Region and Socioeconomic Factors

Transportation energy is an important component of household budgets. Household vehicle fuel, one part of total transportation energy costs is over 3% of total household expenditures nationwide in the U.S. The average annual expenditure of over $2,000 for vehicle fuel (mostly gasoline) is comparable to the total average household expenditures for electricity and natural gas combined. However, these average values vary geographically, and lower income households can face higher energy cost burdens. The highly resolved data developed in this study reveals the variation of household energy burdens across the country and provides localized data to support local decision making.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

EBSD seed LDRD project: Does Corona Virus – 2019 (COVID-19) and Seasonal Flu have similar meteorology and air quality controls driving their spread?

Seasonal influenza and Influenza like Illnesses (ILI) pose a serious public health risk and in turn affect the economy. Various factors affect ILI cases and mortality, including the pathogen and its interaction with the host, as well as environmental and socioeconomic factors such as meteorology, household structure, air pollution, urbanization, and population. Despite the growing number of studies on influenza and ILI, challenges remain in forecasting the timing of seasonal onset, outbreak patterns, and key factors affecting transmission. In particular, the impacts of meteorology and air quality on ILI have been challenging to understand, with linear regression studies focused on different geographic regions producing contradictory results. For example, influenza seasonality has been associated with cold-dry conditions in temperate mid-latitudes, but with humid-rainy conditions in tropical climates. These apparently contradictory results imply that the relationships between influenza cases and atmospheric variables may be too complex to be captured by linear regression models. In this seed project, we analyze meteorology and air quality variables from numerical models to determine which atmospheric variables are most helpful in predicting weekly changes in recorded flu cases. In contrast to most previous studies that relied on linear regression analysis to predict the timing of the flu onset or peak, we employ a robust machine learning algorithm to evaluate the contribution of atmospheric variables to weekly changes in recorded ILI cases. These results may also be relevant to the spread of other respiratory illnesses such as Corona Virus Disease – 2019 (COVID-19).

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

Incorporating multislice imaging into x-ray CT polymer gel dosimetry

Purpose: To evaluate multislice computed tomography (CT) scanning for fast and reliable readout of radiation therapy (RT) dose distributions using CT polymer gel dosimetry (PGD) and to establish a baseline assessment of image noise and uniformity in an unirradiated gel dosimeter. Methods: A 16-slice CT scanner was used to acquire images through a 1 L cylinder filled with water. Additional images were collected using a single slice machine. The variability in CT number (N{sub CT}) associated with the anode heel effect was evaluated and used to define a new slice-by-slice background subtraction artifact removal technique for CT PGD. Image quality was assessed for the multislice system by evaluating image noise and uniformity. The agreement in N{sub CT} for slices acquired simultaneously using the multislice detector array was also examined. Further study was performed to assess the effects of increasing x-ray tube load on the constancy of measured N{sub CT} and overall scan time. In all cases, results were compared to the single slice machine. Finally, images were collected throughout the volume of an unirradiated gel dosimeter to quantify image noise and uniformity before radiation is delivered. Results: Slice-by-slice background subtraction effectively removes the variability in N{sub CT} observed across images acquired simultaneously using the multislice scanner and is the recommended background subtraction method when using a multislice CT system. Image noise was higher for the multislice system compared to the single slice scanner, but overall image quality was comparable between the two systems. Further study showed N{sub CT} was consistent across image slices acquired simultaneously using the multislice detector array for each detector configuration of the slice thicknesses examined. In addition, the multislice system was found to eliminate variations in N{sub CT} due to increasing x-ray tube load and reduce scanning time by a factor of 4 when compared to imaging a large volume using a single slice scanner. Images acquired through an unirradiated, active gel revealed N{sub CT} varies between the top and bottom of the 1 L cylinder as well as across the diameter of the cylinder by up to 7 HU. Conclusions: Multislice CT imaging has been evaluated for CT PGD and found to be the superior technique compared to single slice imaging in terms of the time required to complete a scan and the tube load characteristics associated with each scanning method. The implementation of multislice scanning is straightforward and expected to facilitate routine gel dosimetry measurements for complex dose distributions in modern RT centers.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Side-by-Side Comparison of Subhourly Clipping Models: Preprint

Over the past several years there have been numerous attempts at quantifying the inherent power clipping of inverters due to inter-hourly irradiance variability that is not captured in hourly PV performance models. Different models have been proposed to correct for these clipping losses in PV performance estimates, including matrix lookup models, distribution modeling of the PV power performance within a given hour, and machine learning methods. To date, there have been few comprehensive quantitative comparisons of these inverter clipping correction modeling approaches to evaluate the effectiveness of said approaches in predicting the actual behavior of PV system inverter clipping. In this study, we perform such a comparison, evaluating two different clipping correction loss modeling approaches recently implemented in the System Advisor Model (SAM) against clipping losses modeled with 1-minute climate data. These comparisons will be performed across a variety of climate locations and inverter loading ratios to thoroughly analyze the effectiveness of these modeling approaches relative to each other. Results from this analysis reveal that both clipping correction approaches improve annual energy accuracy to within 2% of 1-minute modeled energy yield. The models can improve accuracy up to 3% in systems with ILR of 2.0, showing the importance of this modeling factor in energy yield estimates.

clipping↗

DOE OSTI2024

Low-index mesoscopic surface reconstructions of Au surfaces using Bayesian force fields

Metal surfaces have long been known to reconstruct, significantly influencing their structural and catalytic properties. Many key mechanistic aspects of these subtle transformations remain poorly understood due to limitations of previous simulation approaches. Using active learning of Bayesian machine-learned force fields trained from ab initio calculations, we enable large-scale molecular dynamics simulations to describe the thermodynamics and time evolution of the low-index mesoscopic surface reconstructions of Au (e.g., the Au(111)-‘Herringbone,’ Au(110)-(1 × 2)-‘Missing-Row,’ and Au(100)-‘Quasi-Hexagonal’ reconstructions). This capability yields direct atomistic understanding of the dynamic emergence of these surface states from their initial facets, providing previously inaccessible information such as nucleation kinetics and a complete mechanistic interpretation of reconstruction under the effects of strain and local deviations from the original stoichiometry. We successfully reproduce previous experimental observations of reconstructions on pristine surfaces and provide quantitative predictions of the emergence of spinodal decomposition and localized reconstruction in response to strain at non-ideal stoichiometries. A unified mechanistic explanation is presented of the kinetic and thermodynamic factors driving surface reconstruction. Furthermore, we study surface reconstructions on Au nanoparticles, where characteristic (111) and (100) reconstructions spontaneously appear on a variety of high-symmetry particle morphologies.

36 MATERIALS SCIENCE↗