Enabling model-based scenario control in EAST by fast surrogate modeling within COTSIM
Not Available
Wang, Z. (ORCID:0000000317620663)↗
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Results for “Surrogates”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Operational risk quantification of power grids using graph neural network surrogates of the DC OPF
Not Available
Zhang, Yadong↗
Direct Deoxygenation of Phenol over Fe-Based Bimetallic Surfaces Using On-the-Fly Surrogate Models
We present an accelerated nudged elastic band (NEB) study of phenol direct deoxygenation (DDO) on Fe-based bimetallic surfaces using a recently developed Gaussian process regression (GPR) calculator. Our test calculations demonstrate that the GPR calculator achieves up to 3 times speedup compared to conventional density functional theory calculations while maintaining high accuracy, with energy barrier errors below 0.015 eV. Using GPR-NEB, we systematically examine the DDO mechanism on pure Fe(110) and surfaces modified with Co and Ni in both top and subsurface layers. Our results show that subsurface Co and Ni substitutions preserve favorable thermodynamics and kinetics for both C–O bond cleavage and C–H bond formation, comparable to those on the pure Fe(110) surface. In contrast, top-layer substitutions generally increase the C–O bond cleavage barrier, render the step endothermic, and result in significantly higher reverse reaction rates, making DDO unfavorable on these surfaces. This work demonstrates the effectiveness of GRR-accelerated transition state searches for complex surface reactions and provides insights into rational design of bimetallic catalysts for selective deoxygenation.
Aromatic compounds↗
Porous Iron Electrodes Reduce Energy Consumption During Electrocoagulation of a Virus Surrogate: Insights into Performance Enhancements Using Three-Dimensional Neutron Computed Tomography
Electrocoagulation has attracted significant attention as an alternative to conventional chemical coagulation because it is capable of removing a wide range of contaminants and has several potential advantages. In contrast to most electrocoagulation research that has been performed with nonporous electrodes, in this study, we demonstrate energy-efficient iron electrocoagulation using porous electrodes. In batch operation, investigation of the external pore structures through optical microscopy suggested that a low porosity electrode with sparse connection between pores may lead to mechanical failure of the pore network during electrolysis, whereas a high porosity electrode is vulnerable to pore clogging. Electrodes with intermediate porosity, instead, only suffered a moderate surface deposition, leading to electrical energy savings of 21% and 36% in terms of electrocoagulant delivery and unit log virus reduction, respectively. Neutron computed tomography revealed the critical role of electrode porosity in utilizing the electrode’s internal surface for electrodissolution and effective delivery of electrocoagulant to the bulk. Energy savings of up to 88% in short-term operation were obtained with porous electrodes in a continuous flow-through system. Further investigation on the impact of current density and porosity in long-term operation is desired as well as the capital cost of porous electrodes.
42 ENGINEERING↗
Production and radiochemistry of the in vivo PET generator 140 Nd/ 140 Pr as an imaging surrogate for F-block therapeutic radionuclides
Theranostics, a combined approach of diagnostics and therapeutics, often employs F-block therapeutic radionuclides including 225 Ac, 177 Lu, and 161 Tb. While there is a lack of F-block PET imaging radionuclides, the in vivo PET generator pair 140 Nd/ 140 Pr can act as a theranostic imaging counterpart to the F-block therapeutic radionuclides. In this study, we explored the production and separation of high purity 140 Nd via the 141 Pr(p,2n) 140 Nd reaction route. Monoisotopic 141 Pr targets irradiated with 20 MeV protons for 10 min with 10 µA beam current yielded 21.45 ± 0.82 MBq (580 ± 22 µCi) of 140 Nd. A two-step separation method was developed for the purification of 140 Nd from the 141 Pr target material. Recoveries of 27.4 ± 2.1% 140Nd were obtained upon separation with < 20 ppb of 141 Pr target material in the final product. Radiolabeling of Macropa and DOTA chelators with 140 Nd resulted in [ 140 Nd]Nd-Macropa with a molar activity of 74.0 MBq/µmol (2.0 mCi/µmol) and [ 140 Nd]Nd-DOTA with a molar activity of 70.3 MBq/µmol (1.9 mCi/µmol). An imaging study with a phantom indicated the PET spatial resolution of 140 Nd/ 140 Pr was distinguishable down to 2.4 mm. This study sets the stage for the 140 Nd/ 140 Pr in vivo PET generator to be explored in radiopharmaceutical applications.
F-block↗
Diagnosing hot-spot symmetry in surrogate ignition experiments via secondary DT-neutron spectroscopy at the NIF
The directional energy spectrum of neutrons generated from the in-flight fusion reaction of 1-MeV tritons contains information about the hot-spot symmetry. The National Ignition Facility (NIF) fields Symmetry Capsule (Symcap) implosions, which have historically measured the symmetry of the radiation, drive by measuring the hot-spot shape via x-ray self-emission. Symcaps are used to tune the hot-spot symmetry for ignition experiments at the NIF. This work shows the relationship between directional secondary DT-n spectra and x-ray imaging data for a large database of Symcap implosions. A correlation is observed between the relative widths of the DT-n spectra measured with nTOFs and the shape measured with x-ray imaging. A Monte Carlo model, which computes the directional secondary DT-n spectrum, is used to interpret the results. A comparison of the x-ray and secondary DT-n data with the Monte Carlo model indicates that 56% of the variance between the two datasets is explained by a P2 asymmetry. More advanced simulations using HYDRA suggest that the unaccounted variance is due to P1 and P4 asymmetries present in the hot spot. The comparison of secondary DT-n data and x-ray imaging data to the modeling shows the DT-n data contain important information that supplements current P2 measurements and contain new information about the P1 asymmetry.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
An efficient surrogate model of secondary electron formation and evolution
This work extends the adjoint-deep learning framework for runaway electron (RE) evolution, developed by McDevitt et al. [Phys. Plasmas 32, 042503 (2025)], to account for large-angle collisions. By incorporating large-angle collisions, the framework allows the avalanche of REs to be captured, an essential component of RE dynamics. This extension is accomplished by using a Rosenbluth–Putvinski approximation to estimate the distribution of secondary electrons generated by large-angle collisions. By evolving both the primary and multiple generations of secondary electrons, the present formulation can capture both the detailed temporal evolution of a RE population beginning from an arbitrary initial momentum space distribution, along with providing approximations to the saturated growth and decay rates of the RE population. Predictions of the adjoint-deep learning framework are verified against a traditional RE solver, with good agreement present across a broad range of parameters.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Transformer-powered surrogates close the ICF simulation-experiment gap with extremely limited data
Abstract Recent advances in machine learning, specifically transformer architecture, have led to significant advancements in commercial domains. These powerful models have demonstrated superior capability to learn complex relationships and often generalize better to new data and problems. This paper presents a novel transformer-powered approach for enhancing prediction accuracy in multi-modal output scenarios, where sparse experimental data is supplemented with simulation data. The proposed approach integrates transformer-based architecture with a novel graph-based hyper-parameter optimization technique. The resulting system not only effectively reduces simulation bias, but also achieves superior prediction accuracy compared to the prior method. We demonstrate the efficacy of our approach on inertial confinement fusion experiments, where only 10 shots of real-world data are available, as well as synthetic versions of these experiments.
97 MATHEMATICS AND COMPUTING↗
Dynamic Validation of CNN-Based Surrogate Models for Inverter-Based Resources in Open-Source Solvers
Traditionally, distribution system planning has focused on steady-state analyses, with limited consideration of dynamic behavior. However, as large or medium-scale inverter-based resources (IBRs), particularly grid-following (GFL) inverters in commercial or industry buildings, become more prevalent, understanding their dynamic impact is essential for grid planning and operation. This article presents an innovative deep-learning (DL)-approach using convolutional neural networks technique to model the GFL inverters. Developed from real grid-tied commercial IBR transient data, these dynamic DL models overcome proprietary constraints by requiring minimal knowledge of internal converter physics while maintaining high accuracy and flexibility. To demonstrate their applicability, the models were incorporated into GridLAB-D, an open-source, three-phase distribution analysis tool. This integration enables dynamic simulations of large-scale distribution networks with high IBR penetration stability analysis. Rigorous testing and validation, aligned with industry standards, confirmed the reliability and efficiency of this approach, paving the way for enhanced planning and operational assessments of modern power systems.
Deep-learning↗
KSTAR_UEDGE_Dataset: UEDGE Simulation Dataset for Training the KSTAR DivControlNN Surrogate Model
Explore the source record for details and available documents.
Zhao, Menglong [Lawrence Livermore National Labora↗
mphys-surrogate-model
This repository contains python scripts for building and studying reduced-order-modeling representations of droplet coalescence for eventual use in atmospheric models. The included data are generated from high-fidelity superdroplet methods and are utilized by machine learning pipelines to build data-driven models of droplet size distributions that evolve under coalescence. This repository further includes scripts to determine prediction (uncertainty) intervals on the data-driven model products based on conformal prediction.
Katona, JonasE [Lawrence Livermore National Labora↗
The Completion of Surrogate Testing for Low-Temperature TEX and a Look Towards the Future
To address the mounting need for below room temperature nuclear data validation, the Low-Temperature Thermal Epithermal eXperiments (LT-TEX) have been designed. Validation of low-temperature neutron cross sections is necessary to verify any operation at temperatures below room temperature which is typically observed in environments far from the equator. For example, a fissile material transportation truck may routinely observe ambient temperatures down to -40°C, which is the lower temperature bound of the normal conditions of transportation defined in the United States Title 10 Code of Federal Regulations §71.71c2. Additionally, sub-room temperature benchmarks can validate newly produced cross sections, that include novel thermal scattering laws, from North Carolina State University.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Rapid Tuning of Synchrotron Surrogate Model at the Recycler Ring
The 8 GeV proton-storage Recycler Ring (RR) is essential for reaching megawatt beam intensity goals for the DUNE neutrino beam at Fermilab. Custom shims on each RR permanent magnet were designed to cancel manufacturing defects and bring magnetic fields to the design values. Remaining imperfections cause the observed tune variation vs energy to deviate from what is calculated using the design fields. Using the POUNDERS (“Practical Optimization Using No Derivatives for sums of Squares”) optimization method with Synergia in the loop, we demonstrate rapid convergence to a set of additive, higher-order multipole moments of these magnetic shims which reproduce that observed variation, and show that the convergence advantage grows with the parameter-space dimensionality.
43 PARTICLE ACCELERATORS↗
Cesium Removal from Surrogate Pyroprocessing Salt by Electrodeposition
Active metals in used nuclear fuel dissolve into the salt during pyroprocessing and are not removed by electrorefining or drawdown operations. The buildup of 137 Cs over time increases the heat load and ionizing radiation level of the salt such that it must be replaced frequently, resulting in a significant amount of salt waste. An effective means of managing cesium in the molten salt electrolyte would increase the efficiency of pyroprocessing and decrease the volume of salt waste requiring disposal. A previous report summarized issues that must be addressed when developing a removal strategy and assessed the suitability of existing methods and remaining technological gaps to their application (Rose and Thomas 2023). Cesium is extremely stable in molten salt as a chloride—even more stable than the LiCl-KCl eutectic base salt used for pyroprocessing fuel—which makes removing cesium a challenge. However, sufficiently strong atomic interactions occur between active metal species and liquid metals that make the electrodeposition of active metal fission products into liquid metal electrodes energetically favorable. The feasibility of recovering cesium from LiCl-KCl pyroprocessing salt through electrodeposition into liquid metals is eing assessed by identifying potentially effective liquid metals and performing tests to determine the effectiveness of electrodepositing cesium from a LiCl/KCl salt into these liquid metals. Previous studies investigating the electrodeposition of Sr 2+ , and Ba 2+ into zinc, cadmium, bismuth, lead, tin and antimony have shown that alkali and alkaline earth metals can be electrodeposited at liquid metal cathodes (Kim et al., 2018). The removal of Ba 2+ and Sr 2+ was measured to be more efficient than the removal of monovalent cations due to the greater thermochemical driving force for alloying those elements with the liquid metal (Jang et al. 2022). Because the equilibrium potentials are dependent on the interactions of the active metal in the liquid metal, it is likely that the other alkali metals Li + , and K + , will deposit from LiCl/KCl salt with the Cs + . Therefore, application of this method to recover active metals from pyroprocessing salt will benefit from the use of a liquid metal and set of operating conditions that sufficiently increase the reduction potential of cesium to remove cesium from the waste salt with an acceptable amount of co-deposited lithium and potassium.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Property Measurements of LiF-NaF-KF Molten Salts Doped with Surrogate Fission Products
Measurements of molten salt properties including transition temperatures, phase behavior, heat capacity, density, volumetric thermal expansion, surface tension, viscosity thermal diffusivity, thermal conductivity, and vapor pressure are performed at Argonne. The properties of several fuel and coolant salts including eutectic LiF-NaF-KF (FLiNaK) have been measured. These properties are suitable for use in evaluating reactor performance during startup and early operating conditions.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
In situ Characterization of Soot Aggregates Produced by Burning Contaminated Liquid Fuels with Nuclear Waste Surrogate
The motivation of this work was to complement other studies intended to enhance and update DOE-HDBK-3010-94 for hazard scenarios like nuclear waste fires. In this work, a uniquely designed combustion chamber was successfully integrated with a light scattering technique to characterize soot aggregates. The chamber design allowed for soot investigation within the flame as well as after soot left the incandescent flame region.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗