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All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Core-Noise

This presentation is a technical progress report and near-term outlook for NASA-internal and NASA-sponsored external work on core (combustor and turbine) noise funded by the Fundamental Aeronautics Program Subsonic Fixed Wing (SFW) Project. Sections of the presentation cover: the SFW system level noise metrics for the 2015, 2020, and 2025 timeframes; the emerging importance of core noise and its relevance to the SFW Reduced-Noise-Aircraft Technical Challenge; the current research activities in the core-noise area, with some additional details given about the development of a high-fidelity combustion-noise prediction capability; the need for a core-noise diagnostic capability to generate benchmark data for validation of both high-fidelity work and improved models, as well as testing of future noise-reduction technologies; relevant existing core-noise tests using real engines and auxiliary power units; and examples of possible scenarios for a future diagnostic facility. The NASA Fundamental Aeronautics Program has the principal objective of overcoming today's national challenges in air transportation. The SFW Reduced-Noise-Aircraft Technical Challenge aims to enable concepts and technologies to dramatically reduce the perceived aircraft noise outside of airport boundaries. This reduction of aircraft noise is critical for enabling the anticipated large increase in future air traffic. Noise generated in the jet engine core, by sources such as the compressor, combustor, and turbine, can be a significant contribution to the overall noise signature at low-power conditions, typical of approach flight. At high engine power during takeoff, jet and fan noise have traditionally dominated over core noise. However, current design trends and expected technological advances in engine-cycle design as well as noise-reduction methods are likely to reduce non-core noise even at engine-power points higher than approach. In addition, future low-emission combustor designs could increase the combustion-noise component. The trend towards high-power-density cores also means that the noise generated in the low-pressure turbine will likely increase. Consequently, the combined result from these emerging changes will be to elevate the overall importance of turbomachinery core noise, which will need to be addressed in order to meet future noise goals.

Hultgren, Lennart S.↗
DOE OSTI2022

OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasets: Preprint

Increasing levels of renewable generation motivate a growing interest in data-driven approaches for AC optimal power flow (AC OPF) to manage uncertainty. However, a lack of disciplined dataset creation and benchmarking prohibits useful comparison between approaches in the literature. To instigate confidence, models must be able to reliably predict solutions across a wide range of operating conditions. This paper develops the OPF-Learn package for Julia and Python which uses a computationally efficient approach to create representative datasets that span a wide spectrum of the AC OPF feasible region. Load profiles are uniformly sampled from a convex set that contains the AC OPF feasible set. For each infeasible point found, the convex set is reduced using infeasibility certificates, found by utilizing properties of a relaxed formulation. The framework is shown to generate datasets which are more representative of the entire feasible space versus traditional techniques seen in the literature, improving machine learning model performance.

dataset↗
DOE OSTI2023

Environmental and Economic Implications of Emerging Plastic Recycling Technologies

Recycling will play a crucial role in mitigating plastic pollution and promoting a circular economy for polymers. Quantitative analysis can help guide the optimization and implementation of these plastic management strategies, enabling increased circularity while minimizing costs, energy use, greenhouse gas emissions, and other environmental impacts. This talk will outline the Bio-Optimized Technologies to keep Thermoplastics out of Landfills and the Environment (BOTTLE) Consortium's approach to analysis-guided plastic recycling research. Stringent process modelling, life cycle assessment, and techno-economic analysis are used to benchmark the technical feasibility, environmental impacts, and costs of chemical and biological recycling technologies in comparison to conventional fossil fuel-based plastic manufacturing. Across emerging recycling strategies, several key challenges become apparent - including plastic feedstock pretreatment requirements, high energy or chemical use, and recycled plastic yields and quality - highlighting opportunities for future innovation. Through this consistent and in-depth analysis approach, BOTTLE aims to provide the plastics community with key metrics and insights to drive plastic recycling innovation towards a more sustainable and circular economy.

circular economy↗
DOE OSTI2025

Detecting Process Equipment Failures Using Acoustic Data and Machine Learning

Nuclear power plant (NPP) process equipment such as fans, motors, valves, and pumps generate frequent or continuous noise, and deviations from the normal operational sounds made by this equipment can indicate potential issues. These deviations can be identified via automated acoustic anomaly detection, which involves using acoustic sensors (i.e., microphones) alongside detection algorithms to continuously monitor for changes in acoustic signatures. This task is made challenging by the substantial background noise that exists, such as operators opening and closing doors, manipulating valves, and conversing—in addition to typical plant noises. In collaboration with a nuclear power utility partner, this effort assessed the efficacy of acoustic anomaly detection when using a specific acoustic sensor that compresses data into a fixed set of features that are transferable over a standard Internet of Things communication protocol, thereby improving usability but potentially degrading detection performance. Two methods of performing automated acoustic anomaly detection were evaluated: one-class support vector machine (OC-SVM) and isolation forest (iForest). To enable the use of high-quality acoustic data encompassing both normal and anomalous conditions, the study utilized the publicly available Malfunctioning Industrial Machine Investigation and Inspection dataset, which includes real measured acoustic sensor data for a range of equipment types, model numbers, and signal-to-noise ratios (SNRs), along with a benchmark set of detection results. Using this dataset, the methods were tested and then compared against the benchmark results. The results indicated that although the specific acoustic sensor did not enable as rich a feature set extraction, the proposed methods with the limited feature set performed just as well. This provides solid justification for both the methods and the use of the proposed acoustic sensor.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2023

HTTF Benchmarking Activities in FY 2023

This report describes the work conducted in FY23 within the High Temperature Gas-Cooled Reactor (HTGR) Applications Drivers activity under the Multiphysics Applications technical area in the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program within DOE-NE. The focus of this past year’s activity was on the OECD/NEA thermal hydraulic benchmark for high temperature gas-cooled reactors using HTTF data (HTGR T/H). The activities focus on code-to-code comparisons for two benchmark problems: (1) Depressurized Conduction Cooldown using SAM, and (2) Lower Plenum Mixing using Nek5000/RS.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

An investigation on machine learning predictive accuracy improvement and uncertainty reduction using VAE-based data augmentation

The confluence of ultrafast computers with large memory, rapid progress in Machine Learning (ML) algorithms, and the availability of large datasets place multiple engineering fields at the threshold of dramatic progress. However, a unique challenge in nuclear engineering is data scarcity because experimentation on nuclear systems is usually more expensive and time-consuming than most other disciplines. One potential way to resolve the data scarcity issue is deep generative learning, which uses certain ML models to learn the underlying distribution of existing data and generate synthetic samples that resemble the real data. In this way, one can significantly expand the dataset to train more accurate predictive ML models. In this study, our objective is to evaluate the effectiveness of data augmentation using variational autoencoder (VAE)-based deep generative models. We investigated whether the data augmentation leads to improved accuracy in the predictions of a deep neural network (DNN) model trained using the augmented data. Additionally, the DNN prediction uncertainties are quantified using Bayesian Neural Networks (BNN) and conformal prediction (CP) to assess the impact on predictive uncertainty reduction. To test the proposed methodology, we used TRACE simulations of steady-state void fraction data based on the NUPEC Boiling Water Reactor Full-size Fine-mesh Bundle Test (BFBT) benchmark. Here, we found that augmenting the training dataset using VAEs has improved the DNN model’s predictive accuracy, improved the prediction confidence intervals, and reduced the prediction uncertainties.

Bayesian neural network↗
DOE OSTI2020

Power System Event Classification and Localization Using a Convolutional Neural Network

Detection and timely identification of power system disturbances are essential for situation awareness and reliable electricity grid operation. Because records of actual events in the system are limited, ensemble simulation-based events are needed to provide adequate data for building event-detection models through deep learning; e.g., a convolutional neural network (CNN). An ensemble numerical simulation-based training data set have been generated through dynamic simulations performed on the Polish system with various types of faults in different locations. Such data augmentation is proven to be able to provide adequate data for deep learning. The synchronous generators’ frequency signals are used and encoded into images for developing and evaluating CNN models for classification of fault types and locations. With a time-domain stacked image set as the benchmark, two different time-series encoding approaches, i.e., wavelet decomposition-based frequency-domain stacking and polar coordinate system-based Gramian Angular Field (GAF) stacking, are also adopted to evaluate and compare the CNN model performance and applicability. The various encoding approaches are suitable for different fault types and spatial zonation. With optimized settings of the developed CNN models, the classification and localization accuracies can go beyond 84 and 91%, respectively.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

CISME report on experiments with HMX powder, PBX 9501 prills, and heated PBX 9501

The ability to predict post-ignition explosive response is relevant to explosives safety, particularly weapons assembly/disassembly operations that must operate under a sufficiently conservative safety basis to meet risk mitigation criteria for inadvertent nuclear detonation (IND). In particular, hazard determinations hinge on whether the PBX 9501 main charge can ultimately attain detonation from an initial, non-shock, ignition stimulus. The CISME experiment uses a spherical explosive charge, which is thermally ignited in the center using a fiber-delivered infrared laser pulse. This ignition mechanism - i.e. a localized thermal hotspot - is representative of the ignition-causing insults typical in accident scenarios, including low-velocity impact, drop, skid, and punch events. In fiscal year 2018 CISME experiments were conducted with pristine PBX 9501 spheres, confined and unconfined. Sub-detonative, low-violence reactions were observed. Here we report experiments conducted since 2018. The explosive charge was varied: experiments were conducted with room temperature pour-density HMX powder and prills of PBX 9501, and heated, thermally damaged PBX 9501. Experiments were conducted in a confined configuration, with two 4 mm-thick stainless-steel hemispherical shells bolted around the sphere. Detonation, via DDT (deflagration-to-detonation transition) was observed in the 3-inch diameter powdered HMX and the 6-inch diameter heated and thermally-damaged PBX 9501 tests. No transition to DDT was observed in smaller diameters for those configurations, and no DDT was observed for pour-density prills of PBX 9501 of any diameter. The original test series demonstrated that the response of pristine, undamaged PBX 9501 to a central ignition is relatively benign. This new test series shows that increased porosity, lack of polymer binders and thermal damage can greatly increase the violence of the post-ignition response, up to and including detonation. These results bridge the explosive response of PBX 9501 from sub-detonative through detonation as a function of diameter and provide valuable test cases for modeling efforts that strive to capture the DDT threshold. By exploring explosive response in a more representative 3D geometry, we can also benchmark these new results to more plentiful DDT datasets generated from testing in 1D tube confinement (the historical test configuration)–the configuration upon which almost all DDT models were developed.

42 ENGINEERING↗
DOE OSTI2023

R –8 Dispersion Interaction: Derivation and Application to the Effective Fragment Potential Method

The anisotropic and isotropic R –8 dispersion contributions (disp8) are derived and implemented within the framework of the effective fragment potential (EFP) method formulated with imaginary frequency-dependent Cartesian polarizability tensors distributed at the centroids of the localized molecular orbitals (LMOs). Two forms of damping functions, intermolecular overlap-based and Tang–Toennies, are extended for disp8. To obtain LMO polarizability tensors centered at LMO centroids, an origin-shifting transformation is derived and implemented for the dipole–octopole polarizability tensor and the quadrupole–quadrupole polarizability tensor. The analytic gradient is derived and implemented for the isotropic disp8 contribution. Relative to the previously implemented empirical EFP disp8 energy, the isotropic disp8 component of the interaction energy improves the overall agreement of the EFP dispersion energies with the symmetry-adapted perturbation theory (SAPT) benchmarks, reducing the mean absolute errors (MAEs) and mean absolute percentage errors for most of the databases examined in this work. Further, while the anisotropic disp8 can further enhance the accuracy of the EFP dispersion energy and yield smaller MAEs, significantly overbound dispersion energies are predicted by the anisotropic disp8 when the maximum element in the intermolecular overlap matrix is greater than 0.1, possibly due to the breakdown of the approximations made in the EFP dispersion derivation at a short range. For potential energy scan databases, the newly developed EFP dispersion model with isotropic disp8 yields the overall correct curvature and good agreement with SAPT benchmarks around equilibrium and longer but overestimates the dispersion interactions at a short range. While the overlap-based dispersion-damping functions produce better MAEs than Tang–Toennies damping functions, further improvement is needed to better screen the large attractive dispersion energies at a short range (overlap >0.1).

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

Machine learning of 27Al NMR electric field gradient tensors for crystalline structures from DFT

NMR crystallography has emerged as a promising technique for the determination and refinement of atomic coordinates in crystal structures. The crystal structure of compounds containing quadrupolar nuclei, such as 27Al, can be improved by directly comparing solid-state NMR measurements to DFT computations of the electric field gradient (EFG) tensor. The non-negligible computational cost of these first-principles calculations limits the applicability of this method to all but the most well-defined structures. We developed a fast, low-cost machine learning model to predict EFG parameters based on local structural motifs and elemental parameters. We computed 8081 EFG tensors from 1681 27Al crystalline solids using DFT and benchmarked them against 105 experimentally measured 27Al sites. Surprisingly, simple local geometric features dominate the predictive performance of the resulting random-forest model, yielding an R2 value of 0.98 and an RMSE of 0.61 MHz for CQ, the quadrupolar coupling constant. This model accuracy should enable pre-refining future structural assignments before finally validating with first-principles calculations. Such a catalogue of 27Al NMR tensors can serve as a tool for researchers assigning complex NMR spectra influenced by the nuclear electric quadrupole interaction.

Sun, He↗
DOE OSTI2023

Evaluating Production Implications of Pressure Maintenance in Unconventional Oil and Gas Wells using a Machine Learning Modeling Approach: Case Study in the Permian Basin

This study implements the proprietary machine learning-based model (model) developed under the 2022 study titled “Evaluating the Impact of Proprietary Oil & Gas Data on Machine Learning Model Performance Using a Quasi-Experimental Analytical Approach” for forecasting unconventional oil and gas production using well data from the Permian Basin. The model, developed using an exclusive dataset that includes time series production data from an operator in the Permian Basin, is designed to jointly predict daily oil, gas, and water production for horizontal wells as a function of bottomhole pressure drawdown, spatial placement across the study domain, and well-completion attributes. In this study, the model was explicitly applied to explore its utility to evaluate the impact of varying drawdown strategies on the production forecast of a well in the Permian Basin dataset. Managing pressure drawdown has been identified as a way to improve estimated ultimate recovery (EUR) from unconventional shale wells due to the stress-dependent nature of fractures in shale reservoirs. Research has shown that applying a lower pressure drawdown helps to maintain the reservoir conductivity, resulting in higher productivity over the life of a well. Historic bottomhole pressure data from the well over time was used as a benchmark from which to set more and less aggressive pressure decline rates as bounding modeling cases. All pressure decline rates/strategies were forecasted over 5 years, and the model was used to generate oil, water, and gas prediction over the same timeframe. This report presents the results from the production forecast and discusses potential operational and economic implications, with contrasting perspectives between well productivity and profitability given typical oil and gas economics and the volatility in the oil and gas market.

02 PETROLEUM↗
DOE OSTI2026

ChemGraph as an agentic framework for computational chemistry workflows

Atomistic simulations are essential in chemistry and materials science but remain challenging to run due to the expert knowledge required for the setup, execution, and validation stages of these calculations. We present ChemGraph, an agentic framework powered by artificial intelligence and state-of-the-art simulation tools to streamline and automate computational chemistry and materials science workflows. ChemGraph leverages graph neural network-based foundation models for accurate yet computationally efficient calculations and large language models (LLMs) for natural language understanding, task planning, and scientific reasoning to provide an intuitive and interactive interface. We evaluate ChemGraph across 13 benchmark tasks and demonstrate that smaller LLMs (GPT-4o-mini, Claude-3.5-haiku, Qwen-2.5-14B) perform well on simple workflows, while more complex tasks benefit from using larger models. Importantly, we show that decomposing complex tasks into smaller subtasks through a multi-agent framework enables GPT-4o to reach perfect accuracy and smaller LLMs to match or exceed single-agent GPT-4o's performance in these benchmarks.

Computational chemistry↗
DOE OSTI2022

Pair production of charged IDM scalars at high energy CLIC

The Compact Linear Collider (CLIC) was proposed as the next energy-frontier infrastructure at CERN, to study e ^+ + e ^- − collisions at three centre-of-mass energy stages: 380,GeV, 1.5,TeV and 3,TeV. The main goal of its high-energy stages is to search for the new physics beyond the Standard Model (SM). The Inert Doublet Model (IDM) is one of the simplest SM extensions and introduces four new scalar particles: H ^\pm ± , A and H; the lightest, H, is stable and hence a natural dark matter (DM) candidate. A set of benchmark points is considered, which are consistent with current theoretical and experimental constraints and promise detectable signals at future colliders. Prospects for observing pair-production of the IDM scalars at CLIC were previously studied using signatures with two leptons in the final state. In the current study, discovery reach for the IDM charged scalar pair-production is considered for the semi-leptonic final state at the two high-energy CLIC stages. Full simulation analysis, based on the current CLIC detector model, is presented for five selected IDM scenarios. Results are then extended to the larger set of benchmarks using the Delphes fast simulation framework. The CLIC detector model for Delphes has been modified to take pile-up contribution from the beam-induced \gamma\gamma γ γ interactions into account, which is crucial for the presented analysis. Results of the study indicate that heavy, charged IDM scalars can be discovered at CLIC for most of the proposed benchmark scenarios, with very high statistical significance.

Klamka, Jan↗
NASA NTRS2001

Worldwide Historical Estimates of Leaf Area Index, 1932-2000

Approximately 1000 published estimates of leaf area index (LAI) from nearly 400 unique field sites, covering the period 1932-2000, have been compiled into a single data set. LA1 is a key parameter for global and regional models of biosphere/atmosphere exchange of carbon dioxide, water vapor, and other materials. It also plays an integral role in determining the energy balance of the land surface. This data set provides a benchmark of typical values and ranges of LA1 for a variety of biomes and land cover types, in support of model development and validation of satellite-derived remote sensing estimates of LA1 and other vegetation parameters. The LA1 data are linked to a bibliography of over 300 originalsource references.This report documents the development of this data set, its contents, and its availability on the Internet from the Oak Ridge National Laboratory Distributed Active Archive Center for Biogeochemical Dynamics. Caution is advised in using these data, which were collected using a wide range of methodologies and assumptions that may not allow comparisons among sites.

Scurlock, J. M. O.↗
DOE OSTI2022

Towards a Benchmark Experiment with the Compact Nuclear Power Source

The Compact Nuclear Power Source (CNPS) was a high-assay low enriched uranium (HALEU) tristructural isotropic (TRISO)-fueled, graphite-moderated microreactor constructed in 1987 at Los Alamos National Laboratory. The reactor, conceived as a power source for short-range radar stations, was designed to be "walk-away safe," and was cooled by heat pipes and ambient air. Though the project was formally cancelled after the fuel and moderator material had been received, its potential to meaningfully advance the body of critical and integral data was evident, and critical experiments with a mock-up of the reactor proceeded at TA-18 until its disassembly in 1991. The reflector and some components of the core would later go on to see service as part of the New Production Reactor Modular High-Temperature Gas-Cooled Reactor (NP-MHTGR) critical experiments. In light of the progress made by groups like Westinghouse and X-Energy towards contemporary graphite-moderated microreactors, the system remains an attractive candidate for the basis of a benchmark experiment even today. Uncertainties remain, however, in some features of the system–inconsistencies in the dimensions and composition of the reactor’s components as described in literature and implemented in current computational models. The identification, quantification, and to the extent possible, the minimization of these uncertainties is a crucial task on the path towards creating a benchmark based on the CNPS system. The present work seeks to initiate this process with a survey of available documentation and improvements to the accuracy of CNPS neutronics models.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Compressing Vision Transformers in Geospatial Transfer Learning with Manifold-Constrained Optimization

Deploying geospatial foundation models on resource-constrained edge devices demands compact architectures that maintain high downstream performance. However, their large parameter counts and the accuracy loss often induced by compression limit practical adoption.In this work, we leverage manifold-constrained optimization framework DLRT to compress large vision transformer–based geospatial foundation models during transfer learning. By enforcing structured low-dimensional parameterizations aligned with downstream objectives, this approach achieves strong compression while preserving task-specific accuracy. We show that the method outperforms of-the-shelf low-rank methods as LoRA. Experiments on diverse geospatial benchmarks confirm substantial parameter reduction with minimal accuracy loss, enabling high-performing, on-device geospatial models.

Snyder, Thomas [Yale University]↗
DOE OSTI2022

Benchmark Specification for FFTF LOFWOS Test #13

The Fast Flux Test Facility (FFTF) at the Hanford site in Washington was designed by the Westinghouse Electric Corporation for the U.S. Department of Energy. FFTF was a 400 MW thermal, oxide-fueled, liquid sodium cooled test reactor, built to assist development and testing of advanced fuels and materials for fast breeder reactors. After reaching criticality in 1980, FFTF operated until 1992, providing the U.S. Department of Energy (DOE) with the means to test fuels, materials, and other components in a fast neutron flux environment. In July 1986, a series of unprotected transients (with the plant protection system intentionally disabled) were performed in FFTF as part of the passive safety demonstration program. Among these were thirteen loss of flow without scram (LOFWOS) tests. The goals of this program included confirming the liquid metal reactor safety margins, providing data for computer code validation, and demonstrating the inherent and passive safety benefits of specific design features. The test defined in this benchmark is LOFWOS Test #13, which was initiated at 50% power and 100% flow with the pump pony motors turned off. This benchmark specification is intended to support collaborative efforts within international partnerships on the validation of simulation tools and models in the area of Sodium-cooled Fast Reactor (SFR) safety. Validated tools and models are needed to evaluate SFR inherent safety characteristics and assess the impact of passive design features in response to accident initiators. Comparisons with experimental data and the results of safety analyses from other groups create unique opportunities to improve predictive capabilities of computational codes and methods for SFR modeling and simulation. The conditions of the LOFWOS test along with the feedback from FFTF’s limited free bow core restraint system and the novel passive reactivity control Gas-Expansion Modules (GEMs) pose a very challenging and uniquely valuable benchmark exercise.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

FINETUNA: fine-tuning accelerated molecular simulations

Abstract Progress towards the energy breakthroughs needed to combat climate change can be significantly accelerated through the efficient simulation of atomistic systems. However, simulation techniques based on first principles, such as density functional theory (DFT), are limited in their practical use due to their high computational expense. Machine learning approaches have the potential to approximate DFT in a computationally efficient manner, which could dramatically increase the impact of computational simulations on real-world problems. However, they are limited by their accuracy and the cost of generating labeled data. Here, we present an online active learning framework for accelerating the simulation of atomic systems efficiently and accurately by incorporating prior physical information learned by large-scale pre-trained graph neural network models from the Open Catalyst Project. Accelerating these simulations enables useful data to be generated more cheaply, allowing better models to be trained and more atomistic systems to be screened. We also present a method of comparing local optimization techniques on the basis of both their speed and accuracy. Experiments on 30 benchmark adsorbate-catalyst systems show that our method of transfer learning to incorporate prior information from pre-trained models accelerates simulations by reducing the number of DFT calculations by 91%, while meeting an accuracy threshold of 0.02 eV 93% of the time. Finally, we demonstrate a technique for leveraging the interactive functionality built in to Vienna ab initio Simulation Package (VASP) to efficiently compute single point calculations within our online active learning framework without the significant startup costs. This allows VASP to work in tandem with our framework while requiring 75% fewer self-consistent cycles than conventional single point calculations. The online active learning implementation, and examples using the VASP interactive code, are available in the open source FINETUNA package on Github.

97 MATHEMATICS AND COMPUTING↗