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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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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Producing 236 U reference standards for Accelerator Mass Spectrometry at the University of Notre Dame

36 U is a rare isotope of uranium, naturally occurring in ores with an abundance of 236 U/ 238 U$<$ 1 x 10 -9 . The ability to detect it and make isotopic ratio measurements has applications ranging from nuclear forensics and nonproliferation to energy production and environmental protection. Currently, Accelerator Mass Spectrometry (AMS) is the only technique sensitive enough to accurately measure 236 U/ 238 U isotopic ratios as they exist in naturally occurring ores in the range of 236 U/ 238 U = 10 -12 $-$ 10 -9 . Some AMS facilities have demonstrated their capabilities to make these measurements. Historically, the lack of commercially available reference standards covering the range of naturally occurring 236 U/ 238 U abundances has necessitated the use of absolute measurements, notoriously difficult to do using AMS, resulting in increased uncertainties in measurements and a reliance on knowledge of systematic effects. To mitigate these issues, various AMS facilities have sought to develop their own reference standards. Using a reference standard prepared for other forms of mass spectrometry, a series of AMS suitable standards was created through dilution with low-background natural uranium. The techniques used to produce and characterize these materials as well as analysis of them using AMS will be discussed.

236U↗
DOE OSTI2025

Artificial Intelligence for Data Center Operations (AIOps): Cooperative Research and Development (Final Report)

High performance computing data centers will increasingly need to rely on automation to keep pace with exascale growth in compute capability and to manage and optimize the data center environment and facility resources. Artificial intelligence and machine learning approaches provide the means to improve HPC data center operational efficiency, by learning historical trends and training models to operate on real-time data collected from both IT and facilities sources. NREL has developed methods of real-time collection, aggregation and streaming of these data in the ESIF HPC Data Center and has collected a significant dataset of relevant metrics across computer systems, racks, environmental, building and utility sources for research into various predictive analytics problems. HPE's Advanced Technology Group (ATG) is doing comprehensive research into exascale monitoring and management for High Performance Computing (HPC) systems (hereinafter HPE's Data Monitoring/ Management Technology). NREL and HPE will collaborate to add Artificial Intelligence (AI) to NREL's real-time data collection/ aggregation/ streaming system and HPE's Data Monitoring/ Management System, with the goal of improving the operational efficiency of NREL's Energy Systems Integration Facility (ESIF) HPC Data Center through data analytics on both historical and real-time data from IT systems and facilities operations. This collaboration will consist of efforts in Data Management, Data Analytics, and AI/ML Optimization for both manual and autonomous intervention in data center operations. This will be a multi-year, multi-staged effort with a goal towards building capabilities for an Advanced Smart Facility, and demonstration of these techniques in the NREL ESIF HPC Data Center.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

SuperLab 2.0 Showcase: Connecting Five Labs to Tackle Grid Complexity and Unlock Unique Grid Asset Potential

SuperLab 2.0 (5-Lab Demo) is a collaborative, national-scale experiment showcasing the coordination of geographically distributed energy assets in real time. The demonstration integrates 25 physical and digital assets, spanning wind, PV, batteries, electrolyzers, DC fast chargers, microgrid controllers, building automation systems, small modular reactor (SMR), control centers, and gas turbines, across five DOE national laboratories-NLR, INL, NETL, LBNL, and SNL. These assets are unified using Energy Sciences Network (ESnet), a low-latency, high-performance U.S. Department of Energy's (DOE) network, and controlled via a centralized energy controller hosted at NLR's ARIES facility. The demonstration validates the ability to stress-test hybrid energy systems under dynamic scenarios to de-risk advanced control strategies for greater resilience and flexibility. SuperLab 2.0 (5-Lab Demo) showcased a major advancement in federated national laboratory collaboration, enabling real-time, cross-laboratory experimentation to coordinate geographically dispersed distributed energy resources (DERs) using various communication protocols and networks. SuperLab 2.0 (5-Lab Demo) built on previous demonstrations conducted between NLR-PNNL and NLR-INL connecting diverse assets including distant protection devices, a SMR simulator, and a high temperature electrolyzer (HTE). Previous demos were based on a single connection between two labs with minimal coordination challenges. The 5-Lab demo with a centralized controller, distributed testbeds across different geographical locations, and use of protocols-based communication represents a scenario closer to real-world grid operations that coordinate resources across a region to meet system needs. This experiment studied how local DER controllers interact with a centralized energy controller during normal and abnormal events to maintain reliability. The SuperLab team across the five labs implemented a notional power system model equivalent of transmission and distribution lines, represented by the data networks interconnecting the labs. Each lab continuously exchanged local parameters (such as P and Q) from its Hardware-In-Loop (CHIL) and Power Hardware-In-Loop (PHIL) assets through centralized energy controller at NLR, enabling real-time interaction and coordination across sites. By leveraging ESnet as the communication backbone, the team successfully operated the distributed assets as a unified power system, with each bus represented by a different laboratory. This setup mirrors how assets interact in real-world power systems across dispersed locations with various protocols and latencies. At each lab site, assets were operated using their own local controllers which were coordinated through an overarching operation and control layer of centralized energy controller, equivalent to how an energy management system (EMS) orchestrates assets across a regional or national grid. SuperLab's federated connectivity utilized a Digital Real-Time Simulators (DRTS)-type gateway to connect Controller Hardware-In-Loop (CHIL) and PHIL assets between labs. To enable this federated connection through ESnet, a deterministic network was established where latency variations were consistent. This consistency allowed the development of digital filters for the power system assets across CHIL and PHIL interfaces to avoid unstable and unreliable grid conditions. This report provides an overview of the cross-laboratory configuration and offers insights into interconnecting geographically distributed research assets to test them as if they were co-located. This experiment represents a step toward linking nine DOE national laboratories, enabling nation-wide simulations that can address utility-driven challenges with grid resilience, flexibility, and modernization.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Ionic liquid mediated one-step perpendicular assembly in block copolymer thin films for ultrafiltration membrane applications

The rapid rise of oil and gas, petrochemical, food processing, and pharmaceutical industries has added a complex mixture of contaminants like oil, particulates, metals, and other organic compounds to wastewater. Multipurpose membranes with precise pore structure can offer a solution to this problem and block copolymers (BCP) can be used to achieve such structures. Achieving vertical assembly of BCP domains provides a perfect template for developing uniform through film channels for separation and transport of material, besides being useful for the rectification of defects in photolithography patterns. Producing such morphologies may require extensive film/substrate processing and is not always feasible for scale-up. With this article, we demonstrate a facile solution casting method to induce vertical domain assembly in as-cast diblock copolymer thin films in the presence of an ionic liquid (IL) additive that preferentially segregates to one block and neutralizes interfacial interactions. We also show the tunability of domain sizes by controlling the additive concentration. These vertically aligned morphologies are important for the development of next-generation lithographic techniques and form excellent templates for ultrafiltration membranes with uniform pore sizes. In conclusion, this article demonstrates how IL additives can be used to obtain stable non-equilibrium morphologies in as-cast BCP films and the effect of BCP molecular mass, block volume fractions, and IL content on self-assembly.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

SuperLab 2.0 Showcase: Connecting Five Labs to Tackle Grid Complexity and Unlock Unique Grid Asset Potential

SuperLab 2.0 (a five-lab demonstration) is a collaborative, national-scale experiment showcasing the coordination of geographically distributed energy assets in real time. The demonstration integrates 25 physical and digital assets, spanning wind, photovoltaics, batteries, electrolyzers, DC fast chargers, microgrid controllers, building automation systems, small modular reactors, control centers, and gas turbines, across five U.S. Department of Energy (DOE) national laboratories: National Laboratory of the Rockies (NLR), Idaho National Laboratory, National Energy Technology Laboratory, Lawrence Berkeley National Laboratory, and Sandia National Laboratories. These assets are unified using Energy Sciences Network (ESnet), a low-latency, high-performance DOE network, and are controlled via a centralized energy controller hosted at NLR's Advanced Research on Integrated Energy Systems facility. The demonstration validates the ability to stress-test hybrid energy systems under dynamic scenarios to de-risk advanced control strategies for greater resilience and flexibility.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Phase-resolving spin-wave microscopy using infrared strobe light

The need for sensitively and reliably probing magnetization dynamics has been increasing in various contexts such as studying novel hybrid magnonic systems, in which the spin dynamics strongly and coherently couple to other excitations, including microwave photons, light photons, or phonons. Recent advances in quantum magnonics also highlight the need for employing the magnon phase as quantum state variable, which is to be detected and mapped out with high precision in on-chip micro- and nanoscale magnonic devices. Here, in this study, we demonstrate a facile optical technique that can directly perform concurrent spectroscopic and imaging functionalities with spatial and phase resolutions, using infrared strobe light operating at 1550-nm wavelength. To showcase the methodology, we spectroscopically studied the phaseresolved spin dynamics in a bilayer of Permalloy and yttrium iron garnet Y 3 Fe 5 O 12 (YIG), and spatially imaged the backward-volume spin-wave modes of YIG in the dipolar spin-wave regime. Using the strobe light probe, the detected precessional phase contrast can be directly used to construct the map of the spin wave's wave front, in the continuous-wave regime of spin-wave propagation and in the stationary state, without needing any optical reference path. By selecting the applied field, frequency, and detection phase, the spin-wave images can be made sensitive to the precession amplitude and phase. Our results demonstrate that infrared optical strobe light can serve as a versatile platform for magneto-optical probing of magnetization dynamics, with potential implications in investigating hybrid magnonic systems.

Xiong, Yuzan [University of North Carolina, Chapel↗
DOE OSTI2025

Mastering the Copolymerization Behavior of Ethyl Cyanoacrylate as Gel Polymer Electrolyte for Lithium‐metal Battery Application

Polymers with strong electron-withdrawing groups (e.g., cyano-containing polymers) are attractive for a wide range of applications due to their high dielectric constant and outstanding electrochemical stability. However, the polymerization of such monomers is difficult to control with trace of water affording instant reactions, and copolymerization with other monomers without using strong acid is even more challenging. The present study demonstrates a facile approach enabling efficient and controllable copolymerization of ethyl cyanoacrylate (ECA) without adding undesired additives, achieving mechanically robust and high ion-conduction gel polymer electrolyte (GPE) for safe and long cycle-life lithium-metal batteries (LMBs). The incorporated dual-lithium salts, i.e., lithium difluoro(oxalato)borate (LiDFOB) and lithium bis(trifluoromethanesulphonyl)imide (LiTFSI) not only facilitate radical polymerization of ECA monomers by suppressing their anionic polymerization, but also promote the formation of high-ionic conducting GPE. The incorporated methyl methacrylate (MMA) monomer accelerates the radical polymerization of ECA (confirmed by DFT calculations), achieving controlled copolymerization of ECA-based copolymers. Here, the mechanically robust polymer network made by the ECA copolymer enables LMBs with both LFP cathodes and high-voltage LCO cathodes (4.5 V) operatable at different temperatures with ultra-long cycle life at 1 C (capacity retention of 81.1 % and 83.8 %, respectively, over 1000 cycles).

Min, Weixing [Nankai University, Tianjin (China)]↗
DOE OSTI2026

Alcoholysis of nylon 6 waste to ε-caprolactam promoted by phosphoric acid

Nylon 6 is widely used in high-performance materials. However, the inherent structural rigidity of nylon 6 inhibits catalytic depolymerization to its monomer, ε-caprolactam. Here, in this study, we demonstrate an acid-catalyzed alcoholysis process that depolymerizes nylon 6 to ε-caprolactam, achieving yields up to 74% using n-propanol as solvent and phosphoric acid as catalyst at 220°C for 2 h. We further applied the process to commercial nylon-containing products, demonstrating ε-caprolactam yields of 67% to 76%. Process modeling and techno-economic analysis estimated a minimum selling price of recycled nylon 6 at $\$$1.79/kg, 30% lower than the 5-year average market price of virgin nylon 6. These estimates depend on assumed costs and process performance and may vary with future conditions. Life cycle assessment indicated that nylon 6 produced via alcoholysis can reduce greenhouse gas emissions by up to 63% compared with primary production. Overall, this study demonstrates a facile, cost-effective, and environmentally beneficial process for nylon 6 chemical recycling.

09 BIOMASS FUELS↗
DOE OSTI2025

Delamination-informed lifecycle decisions: A dielectric and machine learning framework for composite sorting and recycling

Composite materials are widely used in aerospace, marine, and automotive sectors due to their high strength-to-weight ratio and durability. However, their long-term reliability can be compromised by damage accumulation. Specifically, delamination initiation serves as a precursor to structural failure, which is often difficult to detect during damage inspection. Identifying and sorting delamination initiation in samples not only increases operational safety while providing critical information for end-of-life decisions, which influences both the service life extension value and the efficiency of fiber extraction during recycling. This research addresses two challenges: (1) developing a nondestructive, ex-situ framework to sort composite materials based on damage severity, particularly delamination, and (2) understanding how damage in composites influences resin removal during pyrolysis. Both experimental work and finite element analysis were performed to predict critical stress levels that are associated with delamination onset. Based on these results, three loading levels 50 %, 75 %, and 90 % of maximum stress, were selected for controlled experiments, generating composite samples with varying extents of damage for machine learning model training. Microscopic imaging of these samples confirmed the damage progression from matrix cracking to delamination, validating the computational predictions. We explored supervised machine learning using dielectric measurements to classify damage states. Preliminary results show an artificial neural network can identify early delamination which is a potential precursor to failure, with 94.44 % accuracy on our dataset. A parallel investigation into the effect of damage severity on pyrolysis recycling showed that heavily delaminated samples required significantly less energy for comparable matrix removal than undamaged samples.

dielectric variables↗
DOE OSTI2026

A machine learning pipeline for identifying infiltration managed aquifer recharge locations from satellite imagery in the San Joaquin Valley, California

This study focuses on an agricultural region in California’s Central Valley, USA, where Managed Aquifer Recharge (MAR) is widely implemented to mitigate groundwater depletion under increasing water demand and climate variability. A deep learning and machine learning framework was developed to identify infiltration-MAR locations using satellite imagery and environmental data. The framework integrates surface water detection from Sentinel-2 imagery, geospatial delineation of water bodies, spatiotemporal tracking of water body dynamics, and supervised classification using meteorological, environmental, and topographic variables. The framework was applied to a 2379 km² study area southwest of Fresno, where 765 water bodies were detected, including 139 identified MAR sites based on publicly available datasets and expert knowledge. The classification model achieved an accuracy of 0.94 and an F1 score of 0.85. Feature importance analysis indicates that cropland, normalized difference vegetation index (NDVI), and evaporation are among the most influential predictors for infiltration-MAR. Notably, the framework suggests that engineered water management in infiltration-MAR systems can disrupt or even reverse the expected positive correlation between surface water extent and precipitation. These findings provide physically interpretable insights into the characteristics of existing infiltration-MAR facilities and demonstrate the potential of the proposed framework as a reproducible, interpretable, and potentially transferable tool for data-driven infiltration-MAR identification and inventory development under growing climatic and hydrological uncertainty.

Classification↗
DOE OSTI2025

Single-Ion Conducting Polymer Electrolyte Enabled via Aza-Michael Addition

Single-ion conducting polymer electrolytes present a possible route for achieving high energy density next-generation batteries due to their flexible nature and high cation transport number. The trifluoromethanesulfonimide (TFSI) functional group represents one of the most efficient tethered anions for Li-ion conductivity. However, the covalent attachment of TFSI groups into polymer electrolytes has been challenging and costly due to its synthetic difficulty and limited commercial availability of building blocks. Here, we present a new polymer electrolyte synthesized by the Michael addition reaction between poly(allylamine) (PAA) and a vinyl TFSI anion under mild reaction conditions. The resulting PAATFSI exhibits a lower glass transition temperature and several orders of magnitude higher Li-ion conductivity than similar TFSI-based single-ion conducting homopolymers in the dry state. Moreover, PAATFSI exhibits excellent Li-ion conductivity (2.7 × 10 –4 S/cm at 30 °C) with the addition of a plasticizer (60 wt % of carbonate solvent), which enables stable lithium–metal battery performance. In conclusion, this study demonstrates a facile route for the synthesis of new single-ion conducting polymer electrolytes that opens a library of possibilities to enable the realization of polymer-based batteries.

25 ENERGY STORAGE↗
DOE OSTI2025

Scalable Bottom-Up Synthesis of Nanoporous Hexagonal Boron Nitride ( h -BN) for Large-Area Atomically Thin Ceramic Membranes

Nanopores embedded within monolayer hexagonal boron nitride (h-BN) offer possibilities of creating atomically thin ceramic membranes with unique combinations of high permeance (atomic thinness), high selectivity (via molecular sieving), increased thermal stability, and superior chemical resistance. However, fabricating size-selective nanopores in monolayer h-BN via scalable top-down processes remains nontrivial due to its chemical inertness, and characterizing nanopore size distribution over a large area remains extremely challenging. Here, we demonstrate a facile and scalable approach of exploiting the chemical vapor deposition (CVD) process temperature to enable direct incorporation of subnanometer/nanoscale pores into the monolayer h-BN lattice, in combination with manufacturing compatible polymer casting to fabricate centimeter-scale nanoporous atomically thin ceramic membranes. We leverage diffusive transport of analytes including size-selective Ficoll sieving to characterize subnanometer-scale and nanoscale defects that manifest as pores in centimeter-scale h-BN membranes, overcoming previous limitations in large-area characterization of nanoscale defects in h-BN. Our approach opens a new frontier to advance atomically thin membranes to 2D ceramic materials, such as h-BN via facile and direct formation of nanopores, for size-selective separations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Elucidating the reversible exsolution–dissolution behaviour of high-entropy oxides in crystalline and amorphous phases

High-entropy oxides (HEOs), as a subclass of high-entropy materials (HEMs), offer a versatile platform for catalysis by leveraging entropy-stabilized solid solutions with tunable compositions, lattice structures, and electronic properties. While exsolution–dissolution of metal species in crystalline HEOs has emerged as a promising strategy for reversible active sites regeneration, the dynamic behaviour of HEOs possessing amorphous nature remains under-explored, particularly the difference with crystalline counterparts. In this work, we systematically investigate the architecture-dependent exsolution–dissolution behavior of HEOs by comparing a crystalline-phase HEO (c-HEO) and an amorphous-phase HEO (a-HEO), both comprising Ni, Mg, Cu, Zn, and Co as principal metal elements. Using a combination of in situ variable-temperature X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), electron microscopy, and in situ CO diffuse reflectance infrared Fourier transform spectroscopy (CO-DRIFTS), the structural evolution of the two HEO phases under redox conditions was elucidated. Both materials exhibit reversible exsolution of metallic species or alloys in reducing environments, followed by re-incorporation into the host lattice upon oxidation. Remarkably, the a-HEO demonstrates more facile and dynamic self-healing behavior, with alloy exsolution and dissolution occurring under milder conditions because of its enhanced reducibility and structural disorder. This study provides critical insights into the design of next-generation regenerable catalysts based on amorphous HEOs, highlighting the role of phase structure in governing reversible metal-site formation dynamics and catalytic performance.

Wang, Qingju [Univ. of Tennessee, Knoxville, TN (U↗
DOE OSTI2025

Direct Conversion of MnO2 into Atomic Mn Sites for Oxygen Reduction

Development of platinum group metal (PGM)-free catalysts has been investigated to replace the platinum group metal catalyst in future inexpensive polymer electrolyte membrane (PEM) fuel cells. Usually, synthetic methods for these PGM-free catalysts involve introducing transition-metal salts or molecules. Herein, we demonstrate a facile synthetic method to prepare PGM-free Mn-N-C catalysts by directly converting manganese oxides into highly active MnN4 sites. Typically, MnO2 is used as a Mn source. Ammonium chloride and benzimidazole are introduced during high-temperature treatment to enhance catalytic activity and stability further. Ammonia generated from the decomposition of ammonium chloride can improve the intrinsic ORR activity of MnNx moieties through chemical or electronic effects by introducing additional nitrogen groups. The Mn-N-C catalyst exhibits promising ORR activity, achieving a half-wave potential of 0.83 V in 0.5 M H2SO4, outperforming most PGM-free ORR catalysts. The robust carbon structure resulting from organic-molecule treatment is also verified by electrochemical and physical characterization, thereby improving the catalyst's durability.

Yang, Xiaoxuan↗
DOE OSTI2024

Proposed muon collider R&D at SNS

Generation of a muon beam at a Muon Collider requires relatively short, high-charge proton bunches. They are produced in a high-average-power proton driver by first accumulating a proton beam from a super-conducting linac, then bunching the beam and finally compressing and combining the bunches into a single high-intensity proton pulse. All of these beam formation stages involve handling of unprecedentedly high beam charges. Validation of these intricate beam manipulations requires better understanding of extreme space-charge effects and experimental demonstration. A facility perhaps most closely resembling the proton driver configuration and beam parameters is the Spallation Neutron Source (SNS) accelerator complex at Oak Ridge National Laboratory (ORNL). Considering the energy scaling of the space-charge parameters, many of the beam formation steps planned for the proton driver can be experimentally checked at the SNS at the relevant space-charge interaction levels. This paper discusses potential proton driver and other muon-collider-related R\&D at the SNS.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Optimizing Simulation Fidelity in Direct-Drive Inertial Confinement Fusion with Cassio

Recently, the National Ignition Facility (NIF) demonstrated that inertial confinement fusion (ICF) is capable to achieve thermonuclear (TN) ignition in the laboratory, making it a crucial method on the path to replicate the Sun’s power production mechanism on Earth. However, the physics governing the high-energy density environments is very complex and remains a challenge to fully understand and model. For example, dopants in the TN fuel are important diagnostic tools to extract the thermodynamic conditions of the plasma. However, if their concentration is chosen too high, they can significantly degrade the performance of an ICF capsule. In this study, we use the Los Alamos National Laboratory radiation-hydrodynamics code Cassio to model ICF implosions of capsules which contain deuterium fuel with high-Z dopants like Krypton and Argon from the high-Z campaign conducted 15 years ago. We focus on how chosen computational and physics parameters influence the implosion outcomes. By systematically changing the resolution of the computational mesh and the photon energies as well as modifying settings for the laser drive and TN fuel pre-heat effects, we assess the impact on experimentally measured performance metrics like neutron production from TN burn and x-ray emission during the implosion. Our results will help to improve the fidelity of simulations and guide future numerical studies and experimental designs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Pilot Development Progress to Demonstrate Electric Thermal Energy Storage (ETES) Using Low-Cost Particles

The rapid growth of variable renewable power generation increases the need to economically store electrical energy over durations up to several days in long-duration energy storage (LDES) applications. LDES can bridge the intermittency of increasing variable renewable energy (VRE) and facilitate emission-free dispatchable electricity to improve the resilience of the grid and provide reliable and potentially cost-effective energy. Several energy storage approaches including mechanical, chemical and electrochemical methods are currently deployed or under development. However, LDES requirements pose unique challenges for scalability, energy capacity, and cost. Electro-thermal energy storage (ETES) can store a large capacity of energy with site flexibility and has attracted significant interest for LDES purposes. This paper shows the progress of developing a demonstration test facility for particle ETES technology including pilot-scale component design and fabrication.

25 ENERGY STORAGE↗
DOE OSTI2026

Large Language Model for Validation, Optical Calibration, and Learning (VOCAL) Distributed Temperature Sensing Interface

Distributed temperature sensing (DTS) using fiber optic sensors (FOS) offers a promising method for temperature measurements in advanced reactors, such as sodium fast reactors and molten salt cooled reactors. To support the calibration and validation of DTS measurements, Argonne National Laboratory developed the Validation, Optical Calibration, and Learning (VOCAL) software package. This report describes the integration of a local large language model (LLM) with a retrieval-augmented generation (RAG) system into the VOCAL interface to serve as an interactive user assistant. The LLM framework enhances the VOCAL platform’s accessibility to users by explaining interface components, clarifying inputs and outputs, and answering user queries dynamically in real-time. The accuracy of the LLM assistant performance was evaluated with 20 queries regarding the interface and its parameters using experimental data from the Thermal Hydraulic Experimental Test Article (THETA) facility. Results demonstrate that the LLM achieved a 95% accuracy rate, with a BERTScore of 0.8816 and SBERT value of 0.7417. Furthermore, validation of the RAG system within the LLM framework showed optimal accuracy with k-values between 1 and 2 using the k-refinement convergence test. The prompt perturbation analysis demonstrated good initial consistency for the RAG system, exhibiting the highest accuracy under punctuation variations and the greatest sensitivity under query reordering. Notably, the model’s errors were limited to data retrieval failures rather than factual hallucinations, reinforcing its baseline reliability. The integration of LLM provides a highly accurate, userfriendly enhancement to the VOCAL platform without disrupting its core computational capabilities for FOS calibration and validation.

Hong, Evan↗