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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 1,459 records · Page 81
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Towards a Neutronic and Thermal-Hydraulic Safety Analysis of PWR Fuel Bow with SIMULATE5 and COBRA-TF

In the 1990's, collective fuel assembly bow was observed in the Ringhals PWRs in Sweden. Back then fuel vendors reinforced the fuel assemblies' dashpot, decreased hold-down spring forces, increased guide tube wall thickness, and introduced guide tube materials with higher creep resistance in order to improve dimensional stability. Nevertheless, twenty years later in 2010, the same kind of collective bow with S and C shape patterns started to appear in most of Germany's PWRs. At this time the SIMULATE5 (S5) core simulator with its delta-gap bow model (DGA) which was originally developed for safety analysis at Ringhals, could be used as an off-the-shelf solution to assess the impact of bow on Fq and FH. However, these two quantities are of secondary importance in German PWRs in normal operation because the most restricting licensing criteria set limits on DNBRmin values. For this purpose, a method was developed to extract S5 pin powers from DGA calculations and automatically generate COBRA-TF input files (CTF) to analyze DNBR values in the cores. In the following two sections, it is shown how the S5 model for DGA calculations has been verified with independent MCNP6 calculations in order to check its predictions on pin power changes for delta gaps up to 1 cm in size and how the existing licensed DNBR calculation methodology has been updated to bowed fuel assembly situations with the help of CTF. Finally, the applicability of the Doroschuk CHF correlation, which is the licensing standard in most German PWRs, is discussed. For the fuel bow patterns considered, it is assumed that only the gap between fuel assemblies change and that the pitch between fuel rods inside the assemblies remains largely unchanged. This approximation can be justified by the visual inspections conducted at the plants during the outages. Every calculation of the pin-power impact of delta-gaps in the core under operating conditions is only as good as the knowledge of the bow pattern. Measurements of fuel assembly bow at cold conditions have been made for some German PWRs for all the fuel assemblies in the core during plant outages. Together with methods developed by the fuel assembly vendors, a best-estimate bow pattern during operation could be reconstructed. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier–Stokes simulations: A data-driven, physics-informed Bayesian approach

Despite their well-known limitations, Reynolds-Averaged Navier–Stokes (RANS) models are still the workhorse tools for turbulent flow simulations in today's engineering analysis, design and optimization. While the predictive capability of RANS models depends on many factors, for many practical flows the turbulence models are by far the largest source of uncertainty. As RANS models are used in the design and safety evaluation of many mission-critical systems such as airplanes and nuclear power plants, quantifying their model-form uncertainties has significant implications in enabling risk-informed decision-making. In this work we develop a data-driven, physics-informed Bayesian framework for quantifying model-form uncertainties in RANS simulations. Uncertainties are introduced directly to the Reynolds stresses and are represented with compact parameterization accounting for empirical prior knowledge and physical constraints (e.g., realizability, smoothness, and symmetry). An iterative ensemble Kalman method is used to assimilate the prior knowledge and observation data in a Bayesian framework, and to propagate them to posterior distributions of velocities and other Quantities of Interest (QoIs). We use two representative cases, the flow over periodic hills and the flow in a square duct, to evaluate the performance of the proposed framework. Both cases are challenging for standard RANS turbulence models. Simulation results suggest that, even with very sparse observations, the obtained posterior mean velocities and other QoIs have significantly better agreement with the benchmark data compared to the baseline results. At most locations the posterior distribution adequately captures the true model error within the developed model form uncertainty bounds. The framework is a major improvement over existing black-box, physics-neutral methods for model-form uncertainty quantification, where prior knowledge and details of the models are not exploited. This approach has potential implications in many fields in which the governing equations are well understood but the model uncertainty comes from unresolved physical processes. - Highlights: • Proposed a physics–informed framework to quantify uncertainty in RANS simulations. • Framework incorporates physical prior knowledge and observation data. • Based on a rigorous Bayesian framework yet fully utilizes physical model. • Applicable for many complex physical systems beyond turbulent flows.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

SmileyLlama: modifying large language models for directed chemical space exploration

Here we show that large language models (LLMs) can be transformed via supervised fine-tuning of engineered prompts into SmileyLlama for exploring the chemical space of drug molecules. We benchmark SmileyLlama against pretrained LLMs and chemical language models trained from scratch for generating valid and novel drug-like molecules, and use direct preference optimization to both improve SmileyLlama’s adherence to a prompt and as part of the iMiner reinforcement learning framework to predict molecules with optimized three-dimensional conformations and high binding affinity to drug targets. By training an LLM to speak directly as a chemical language model, while retaining most of its natural language capabilities, we show that SmileyLlama can reliably generate molecules with user-specified properties rather than acting only as a chatbot with knowledge of chemistry or as a virtual assistant. While SmileyLlama is geared toward drug discovery, the supervised fine-tuning/direct preference optimization/LLM framework can be extended to other chemical, biological and materials applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Treatment of brackish water reverse osmosis brine using only solar energy

Post-treatment of brine produced by reverse osmosis (RO) is a great challenge as it often requires high energy input and works under extreme operating conditions. In this study, brine from a RO plant in El Paso, TX USA was successfully treated using a pilot-scale nanophotonics enhanced solar membrane distillation (NESMD) system. The novel NESMD reactor has a nanophotonic membrane surface area of 0.2 m 2 and an internal heat recovery system to recover the latent heat released during vapor condensation. By utilizing a sweeping gas operational mode under real solar irradiation (585–827 W m -2 ), the NESMD realized successful desalination of RO brine with the membrane flux reaching 0.45–0.65 kg m -2 h -1 and total dissolved solids (TDS) removal greater than 99.5% without an external heat condenser. The decrease in the feed flow rate to the evaporation channel of the NESMD system led to an increase in the gained output ratio (GOR) from 0.35 to 0.62. We report to the best of our knowledge, this is the largest photothermal reactor utilized for the desalination of real RO brine under practical solar irradiation. Compared with conventional brine treatment processes that require high temperature or pressure, the NESMD desalinates RO brine at near-ambient temperature and pressure with free solar energy, providing a promising approach for water desalination, and RO brine post treatment.

42 ENGINEERING↗
DOE OSTI2020

Quantifying Load Uncertainty Using Real Smart Meter Data

As we get closer to customers in distribution systems, load stochasticity increases. In the past, due to lack of real-time data, the comprehensive knowledge of load behavior was limited, and simplistic assumptions had to be made for distribution system modeling and analysis, especially in the processes of network design and expansion. With the deployment of Advanced Metering Infrastructure (AMI), ample real-time smart meter data has become available to utilities. In this paper, using real hourly smart meter data, we have quantified load uncertainty in terms of average, maximum and maximum noncoincident demands on a daily basis, as well as load factor and diversity factor. These uncertainty metrics are examined for individual residential, commercial and industrial customers, as well as distribution transformers serving residential customers. This paper provides a benchmark on load uncertainty quantification for practicing engineers and researchers.

Bu, Fankun↗
DOE OSTI2021

Workshop on Using a Git-based Repository for ASTM Committee Technical Knowledge Capture [Slides]

Consensus technical standards maintained by ASTM International rely on individual contributors to act as technical contacts, effectively stewards, for a particular standard. As a particular standard’s technical content evolves over time, the contact must update relevant tables, graphics, and supporting discussion. However, the details of the data sources and/or processes used to perform such updates may be separate enough from the standard such that expert knowledge is required to perform these updates. As a result of this need for expert knowledge, and the desire to capture that knowledge, subcommittee E10.05 (Nuclear Radiation Metrology) has experimented with using a git-based repository to capture data and processes to reproduce figures needed by E10.05 to maintain the ASTM standards under their jurisdiction. This workshop will provide an overview of the git-based repository site’s organization, operation, and outputs. It will then lead interested workshop participants through adding content, not yet already included in the repository to the site.

42 ENGINEERING↗
DOE OSTI2025

Performance Evaluation of LPBF Manufactured 316H Components

This work represents the continuation of a benchmark study that includes modeling, fabrication and characterization as demonstration to support industry’s adoption of advanced manufacturing processes in a variety of structures. This comprehensive study investigated the feasibility of using additive manufacturing (AM) technologies, specifically Laser Powder Direct Energy Deposition (LP-DED) and Laser Powder Bed Fusion (LPBF), to produce complex nuclear microreactor components using 316H stainless steel. The research focused on manufacturing an expanded elbow pipe component with transitioning sections, which are traditionally difficult and costly to produce through conventional manufacturing methods. The overall study’s primary objectives are therefore demonstrating AM viability for nuclear applications, optimizing process parameters, developing comprehensive material characterization protocols, validating computational modeling approaches, and establishing manufacturing guidelines for complex geometries. Although the initial work included the phased approach of cubical, upscaled cylindrical components, it is to enable to obtain more knowledge for the printing of the expanded elbow structure. The project achieved significant progress in process development by successfully optimizing LP-DED parameters to achieve 99.16-99.97% relative density in 316H stainless steel components. Through systematic evaluation of sixteen cube samples with varied laser powers (400-700W) and scan speeds (600-900 mm/min), optimal processing windows were identified at 500-550W with 600-700 mm/min or 650-700W with 650-900 mm/min scan speeds. The DED manufactured 316H demonstrated mechanical properties comparable or superior to wrought materials, with Young's modulus ranging from 153-208 GPa and controlled microstructural characteristics including greater than 95% face-centered cubic (FCC) phases and engineered cellular structures with sizes between 3.23-6.17 µm.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Transformers and Long Short-Term Memory Transfer Learning for GenIV Reactor Temperature Time Series Forecasting

Automated monitoring of the coolant temperature can enable autonomous operation of generation IV reactors (GenIV), thus reducing their operating and maintenance costs. Automation can be accomplished with machine learning (ML) models trained on historical sensor data. However, the performance of ML usually depends on the availability of large amount of training data, which is difficult to obtain for GenIV, as this technology is still under development. We propose the use of transfer learning (TL), which involves utilizing knowledge across different domains, to compensate for this lack of training data. TL can be used to create pre-trained ML models with data from small-scale research facilities, which can then be fine-tuned to monitor GenIV reactors. In this work, we develop pre-trained Transformer and long short-term memory (LSTM) networks by training them on temperature measurements from thermal hydraulic flow loops operating with water and Galinstan fluids at room temperature at Argonne National Laboratory. The pre-trained models are then fine-tuned and re-trained with minimal additional data to perform predictions of the time series of high temperature measurements obtained from the Engineering Test Unit (ETU) at Kairos Power. The performance of the LSTM and Transformer networks is investigated by varying the size of the lookback window and forecast horizon. The results of this study show that LSTM networks have lower prediction errors than Transformers, but LSTM errors increase more rapidly with increasing lookback window size and forecast horizon compared to the Transformer errors.

LSTM↗
DOE OSTI2018

The Development of the Corrosion Control Limits for Direct Feed Low-Activity Waste Melter-Off Gas Returns to Hanford Tank Farms - 18196

The Savannah River National Laboratory has been developing a corrosion model to facilitate implementation of Hanford's Direct Feed Low Waste (DFLAW) flowsheet. Laboratory testing and statistical analysis have been used to determine the necessary corrosion controls for returning effluent from the Hanford Waste Treatment and Immobilization Plant's Low Activity Waste Vitrification melter off-gas treatment system to the tank farms. The stream compositions are projected to contain components at relative concentrations that are significantly more corrosive toward carbon steel, specifically halide and sulfate anions, than the current waste compositions in the tank farms. Administrative programs are in place to mitigate potential corrosion mechanisms and thereby maintain the structural and leak integrity functions of these waste tanks throughout their intended service life. Waste chemistry and temperature control are the means by which corrosion degradation of the waste tanks is minimized. The current administrative specification for chemical composition and temperature for the underground carbon steel waste tanks is not designed to control the higher halide or sulfate composition. The content of the DFLAW waste streams will be diverse and depend on several factors, such as the melter feed composition and melter idle time. Other factors, such as effluent management of the off gas water collections, will impact the variability of the return streams even more. The work performed by the Savannah River National Laboratory has confirmed that the halide ions are the most aggressive species in the return stream compositions projected using a flowsheet simulation, and nitrite is the most effective inhibitor at pH 10 and 40 deg. C. This test program has employed statistically designed test matrices to determine the dependent variables and the interaction terms between the variables that are significant for the corrosion model and definition of the corrosion control specifications. The first phase of testing focused on identifying aggressive species in off-gas condensate return compositions that present a propensity for localized corrosion attack, namely pitting corrosion. The second phase of testing focused on developing a corrosion model from the interaction terms that will predict the susceptibility of pitting in the double shell tank system based on waste composition and the required amount of corrosion inhibitor. The data collected from this testing program in conjunction with other testing programs, both at the Savannah River Site and the Hanford Site, along with operations knowledge, has determined the chemistry ranges and chemical species requirements for corrosion control in the Hanford double-shell tank system for the effluents generated in the Hanford Waste Treatment and Immobilization Plant's Low Activity Waste facility during the DFLAW phase of operations. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

A microfluidic spore chamber for long-term imaging of single-spore hyphal development.

Understanding the life cycle of fungal spores is essential for elucidating their roles in pathogenesis, dispersal, and survival. However, studying spore development under controlled, spatially defined conditions remains challenging. Here, we present the Spore Chamber, a custom-built microfluidic platform engineered for parallel trapping and long-term imaging of individual spores under defined media conditions, enabling real-time visualization of hyphal development. Using Aspergillus fumigatus as a model organism, we demonstrate that sparse trapping of individual spores within size-matched trap geometries enables long-term time-lapse imaging of key developmental stages, including germination, polarized hyphal elongation, branching, and conidiophore formation. To assess the device's capacity to resolve morphogenetic responses to exogenous signals, we introduced lipochitooligosaccharides (LCOs) and short-chain chitooligosaccharides (COs). Rhizobium-derived, non-sulfated LCO (nsLCO) mixtures induced enhanced secondary branching (hyperbranching), a response not previously reported in A. fumigatus under these signal conditions, to our knowledge, whereas sulfated LCOs and CO4 did not significantly alter branching patterns. In addition, long-term confinement and imaging revealed rare developmental morphologies previously described primarily in mutant strains, including split conidiophore formation, elongated phialides, and stress-associated phenomena such as microcyclic conidiation, and chlamydospore development. Together, these results establish the Spore Chamber as a targeted microfluidic platform for single-spore phenotyping and long-term developmental analysis, with applications in fungal biology, chemical signaling studies, and host–microbe interaction research.

Antifungal screening↗
DOE OSTI2016

Safety envelope for load tolerance of structural element design based on multi-stage testing

Structural elements, such as stiffened panels and lap joints, are basic components of aircraft structures. For aircraft structural design, designers select predesigned elements satisfying the design load requirement based on their load-carrying capabilities. Therefore, estimation of safety envelope of structural elements for load tolerances would be a good investment for design purpose. In this article, a method of estimating safety envelope is presented using probabilistic classification, which can estimate a specific level of failure probability under both aleatory and epistemic uncertainties. An important contribution of this article is that the calculation uncertainty is reflected in building a safety envelope using Gaussian process, and the effect of element test data on reducing the calculation uncertainty is incorporated by updating the Gaussian process model with the element test data. It is shown that even one element test can significantly reduce the calculation uncertainty due to lacking knowledge of actual physics, so that conservativeness in a safety envelope is significantly reduced. The proposed approach was demonstrated with a cantilever beam example, which represents a structural element. The example shows that calculation uncertainty provides about 93% conservativeness against the uncertainty due to a few element tests. As a result, it is shown that even a single element test can increase the load tolerance modeled with the safety envelope by 20%.

42 ENGINEERING↗
DOE OSTI2025

Dataset for "A Microfluidic Spore Chamber for Long-Term Imaging of Single-Spore Hyphal Development"

Understanding the life cycle of fungal spores is essential for elucidating their roles in pathogenesis, dispersal, and survival. However, studying spore development under controlled, spatially defined conditions remains challenging. Here, we present the Spore Chamber, a custom-built microfluidic platform engineered for parallel trapping and long-term imaging of individual spores under defined media conditions, enabling real-time visualization of hyphal development. Using Aspergillus fumigatus as a model organism, we demonstrate that sparse trapping of individual spores within size-matched trap geometries enables long-term time-lapse imaging of key developmental stages, including germination, polarized hyphal elongation, branching, and conidiophore formation. To assess the device’s capacity to resolve morphogenetic responses to exogenous signals, we introduced lipochitooligosaccharides (LCOs) and short-chain chitooligosaccharides (COs). Rhizobium-derived, non-sulfated LCO (nsLCO) mixtures induced enhanced secondary branching (hyperbranching), a response not previously reported in A. fumigatus under these signal conditions, to our knowledge, whereas sulfated LCOs and CO4 did not significantly alter branching patterns. In addition, long-term confinement and imaging revealed rare developmental morphologies previously described primarily in mutant strains, including split conidiophore formation, elongated phialides, microcyclic conidiation, and chlamydospore development. Together, these results establish the Spore Chamber as a targeted microfluidic platform for single-spore phenotyping and long-term developmental analysis, with applications in fungal biology, chemical signaling studies, and host–microbe interaction research. Videos of the observed phenomena are included in this data set.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Experimental and Computational Analysis of NEAMS Pebble Bed Reactors

The Advanced High-Temperature Pebble Bed Reactor concept leverages a particle-based fuel format consisting of discrete spherical graphite pebbles arrayed in a packed bed architecture. Thermal regulation achieved via flow of gas (e.g., helium) or liquid (e.g., molten salt) coolants through the void spaces between pebbles in the bed (characteristic pebble diameters: ~ 6 cm, gas cooled; ~ 3 cm, liquid cooled). This design is of interest owing to inherent advantages including passive safety features and highly efficient heat transfer characteristics that enable high power densities to be achieved. Fully leveraging these advantages requires a fundamental understanding of the complex coolant flow structure within the pebble-shaped fuel bed. Unfortunately, complexities associated with non-invasively probing the micro-scale flow phenomena within the complex network of void spaces between pebbles have hindered efforts to characterize the underlying transport phenomena. Without this fundamental foundation, it becomes challenging to rationally construct macroscopic transport models based upon volume averages of micro-scale parameters that accurately capture the observed flow behavior. Consequently, this lack of fundamental knowledge has limited previous work to coarse comparisons between theory and extremely limited experimental data. Moreover, these comparisons have been made only in terms of integral or macroscopic results. Therefore, multipoint measurements with a high level of spatial and temporal resolution are critically needed to fully map the complex flow patterns and to provide data at high spatial density to permit accurate volume averaging in the pebble bed. For this project, we performed a coordinated experimental and computational effort to quantitatively map the full-field velocity and temperature fields in the interstitial spaces within the pebble bed. The major accomplishments of this project included the important knowledge acquired for the flow mixing and heat transfer in the interstitial pore scales, the effects of flow mixing in the near-wall and far-wall regions, and the by-pass flows of pebble bed systems. The produced high-resolution experimental data (not broadly available in existing literature) were then used for the numerical studies enabling the refinement and validation of standard simulation tools (Nek5000).

42 ENGINEERING↗
DOE OSTI2017

Benchmarks of Global Clean Energy Manufacturing: Summary of Findings

The Benchmarks of Global Clean Energy Manufacturing will help policymakers and industry gain deeper understanding of global manufacturing of clean energy technologies. Increased knowledge of the product supply chains can inform decisions related to manufacturing facilities for extracting and processing raw materials, making the array of required subcomponents, and assembling and shipping the final product. This brochure summarized key findings from the analysis and includes important figures from the report. The report was prepared by the Clean Energy Manufacturing Analysis Center (CEMAC) analysts at the U.S. Department of Energy's National Renewable Energy Laboratory.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2012

German experiences in local fatigue monitoring

The ageing management of nuclear power plants (NPP) has gained an increasing importance in the last years. The reasons are mainly due to the international context of extending period of plants operation. Moreover, new scientific discoveries, such as the corrosive influence of the medium on the fatigue process (environmentally assisted fatigue - EAF) play an important role and influence the code development (ASME, EAF code cases). The fatigue damage process takes a central position in ageing mechanisms of components. It must be ensured through appropriate evidence that facilities are being operated under allowable boundary conditions. In the design phase of NPP, fatigue analyses are still based on theoretical considerations and empirical values, which are summarized in the design transient catalogue, necessary for licensing. These analyses aim at proving the admissibility of the loads in terms of stress and fatigue usage. These analyses will also provide the fatigue-relevant positions in the NPP and give a basis for future design improvements and optimization of operating modes. The design transients are in practice conservatively correlated with the real transients occurring during operation. Uncertainties reveal very conservative assumptions regarding forecast temperatures, temperature gradients and frequencies of events. During operation of the plant, it has to be recurrently proved, that the plant is being operated under designed boundary conditions. Moreover, operating signals are constantly acquired to enable a fatigue evaluation. For example, in Germany fatigue evaluation is based on decades of experience and regulatory requirements. The rule KTA 3201.4 [1] establishes the rules for qualified fatigue monitoring. The rule DIN 25475-3 [2] on fatigue monitoring systems is available in draft version. Experience shows that some significant differences occur between the design transients and the real occurred transients during plant operation. The reasons for it are the various manual control options and also different operating modes. It is clear that showing the covering of real loads by design loads, requires a relatively complex and well-qualified detection process. The difficulty of this task is increased due to the lack of data or incomplete information and the exclusive reliance on existing operation plant data. The strategy of employing local fatigue monitoring is a straightforward solution enabling the direct measurement of loads on the fatigue-sensitive zones. Nowadays a direct derivation of the complete stress tensor at the fatigue-relevant locations is enabled thanks to the recorded local loads and combination with finite element (FE) analyses. So, additionally to the recorded temperature curves, a representation of the time evolution of the six stress components for each monitored component is possible. This allows the application of the simplified elasto-plastic fatigue check according to design codes. The fatigue level can be realistically analyzed with a suitable cycle-counting method. Furthermore, the knowledge of the time evolution of the stresses and strains enables to take into account an environmental factor to include the corrosive fluid influence in the calculations. Without local recording, it is impossible to calculate realistic fatigue usage. AREVA offers the AREVA fatigue concept (AFC) and the new fatigue monitoring system integrated (FAMOSi), necessary tools to monitor local fatigue and to provide realistic assessment. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Analytical method for nitroaromatic explosives in radiologically contaminated soil for ISO/IEC 17025 accreditation

Here, unique hazards are presented in the analysis of radiologically contaminated samples. Strenuous safety and security precautions must be in place to protect the analyst, laboratory, and instrumentation used to perform analyses. A validated method has been optimized for the analysis of select nitroaromatic explosives and degradative products using gas chromatography/mass spectrometry via sonication extraction of radiologically contaminated soils, for samples requiring ISO/IEC 17025 laboratory conformance. Target analytes included 2-nitrotoluene, 4-nitrotoluene, 2,6-dinitrotoluene, and 2,4,6-trinitrotoluene, as well as the degradative product 4-amino-2,6-dinitrotoluene. Analytes were extracted from soil in methylene chloride by sonication. Administrative and engineering controls, as well as instrument automation and quality control measures, were utilized to minimize potential human exposure to radiation at all times and at all stages of analysis, from receiving through disposition. Though thermal instability increased uncertainties of these selected compounds, a mean lower quantitative limit of 2.37 µg/mL and mean accuracy of 2.3% relative error and 3.1% relative standard deviation were achieved. Quadratic regression was found to be optimal for calibration of all analytes, with compounds of lower hydrophobicity displaying greater parabolic curve. Blind proficiency testing (PT) of spiked soil samples demonstrated a mean relative error of 9.8%. Matrix spiked analyses of PT samples demonstrated that 99% recovery of target analytes was achieved. To the knowledge of the authors, this represents the first safe, accurate, and reproducible quantitative method for nitroaromatic explosives in soil for specific use on radiologically contaminated samples within the constraints of a nuclear analytical lab.

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

RAG for FLAG: AI Assistance for a Physics Code

Artificial intelligence (AI) has quickly become an important tool in scientific research, where significant efforts are underway to develop tools that will expedite the research process. One area of particular impact is scientific software, which can be particularly complex, and therefore time consuming to learn and use effectively. AI assistants are increasingly helping to streamline the process by performing tasks such as interactively answering user questions or suggesting solutions. Los Alamos National Laboratory (LANL) develops several advanced scientific codes, such as FLAG, which can be used to run multiphysics simulations. With this study, our goal was to develop an AI assistant for FLAG that could help make the process of understanding the software and running physics simulations more efficient. To develop an AI assistant for FLAG, we used a method called retrieval-augmented generation (RAG), which is a technique that uses information from relevant data sources to enhance the accuracy of large language models (LLMs). We used the FLAG user manual and other FLAG documentation as the knowledge base for the RAG system. When a user provides a query, RAG retrieves relevant sections from the knowledge base in response, then uses those excerpts to generate grounded and contextually rich answers. We found that our AI assistant was able to provide context aware answers and source references to user queries. To evaluate performance, we developed a set of 40 benchmark questions and compared the accuracy of the responses to those of two standard LLMs without retrieval. Our AI assistant significantly outperformed the standard LLMs at answering FLAG-related questions, with an 82.5% accuracy rate, compared to 47.5% for both of the standard LLMs. This has the potential to make the process of learning and using FLAG much easier, especially for new users. Ultimately, it supports LANL’s broader mission by empowering scientists and engineers to focus more on discovery and analysis rather than on navigating complex software systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

The hidden structure of hydrodynamic transport in random fracture networks

We study the large-scale dynamics and prediction of hydrodynamic transport in random fracture networks. The flow and transport behaviour is characterized by first passage times and displacement statistics, which show heavy tails and anomalous dispersion with a strong dependence on the injection condition. The origin of these behaviours is investigated in terms of Lagrangian velocities sampled equidistantly along particle trajectories, unlike classical sampling strategies at a constant rate. The velocity series are analysed by their copula density, the joint distribution of the velocity unit scores, which reveals a simple, albeit hidden, correlation structure that can be described by a Gaussian copula. Based on this insight, we derive a Langevin equation for the evolution of equidistant particle speeds. In this framework, particle motion is quantified by a stochastic time-domain random walk, the joint density of particle position, and speed satisfies a Klein–Kramers equation. The upscaled theory quantifies particle motion in terms of the characteristic fracture length scale and the distribution of Eulerian flow velocities. That is, it is predictive in the sense that it does not require the a priori knowledge of transport attributes. The upscaled model captures non-Fickian transport features, and their dependence on the injection conditions in terms of the velocity point statistics and average fracture length. It shows that the first passage times and displacement moments are dominated by extremes occurring at the first step. The presented approach integrates the interaction of flow and structure into a predictive model for large-scale transport in random fracture networks.

42 ENGINEERING↗