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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Efficient machine-learning model for fast assessment of elastic properties of high-entropy alloys

We combined descriptor-based analytical models for stiffness-matrix and elastic-moduli with mean-field methods to accelerate assessment of technologically useful properties of high-entropy alloys, such as strength and ductility. Model training for elastic properties uses Sure-Independence Screening (SIS) and Sparsifying Operator (SO) method yielding an optimal analytical model, constructed with meaningful atomic features to predict target properties. Computationally inexpensive analytical descriptors were trained using a database of elastic properties determined from density functional theory for binary and ternary subsets of Nb-Mo-Ta-W-V refractory alloys. The optimal Elastic-SISSO models, extracted from an exponentially large feature space, give an extremely accurate prediction of target properties, similar to or better than other models, with some verified from existing experiments. Here we also show that electronegativity variance and elastic-moduli can directly predict trends in ductility and yield strength of refractory HEAs, and reveals promising alloy concentration regions.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Fast and accurate machine learning prediction of phonon scattering rates and lattice thermal conductivity

Abstract Lattice thermal conductivity is important for many applications, but experimental measurements or first principles calculations including three-phonon and four-phonon scattering are expensive or even unaffordable. Machine learning approaches that can achieve similar accuracy have been a long-standing open question. Despite recent progress, machine learning models using structural information as descriptors fall short of experimental or first principles accuracy. This study presents a machine learning approach that predicts phonon scattering rates and thermal conductivity with experimental and first principles accuracy. The success of our approach is enabled by mitigating computational challenges associated with the high skewness of phonon scattering rates and their complex contributions to the total thermal resistance. Transfer learning between different orders of phonon scattering can further improve the model performance. Our surrogates offer up to two orders of magnitude acceleration compared to first principles calculations and would enable large-scale thermal transport informatics.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Application of machine learning techniques for fast MeV x-ray spectra unfolding from filter stack spectrometer data

Recovery of MeV x-ray spectra from detector signals is difficult because the response matrix inversion is ill-conditioned and current methods are too slow for high-repetition-rate experiments. In this work, we make use of neural networks to unfold MeV x-ray spectra from measurements obtained with a filter stack spectrometer at rates of near 40 Hz. The neural network was trained on synthetic data and tested on both synthetic and experimental data, the latter obtained in two separate experiments performed at the Omega EP laser facility. We show here that this unfolding method has good performance on synthetic data and that it is a promising option for experimental data of up to 40 MeV. The accuracy on experimental data is verified by using a simple forward model to compare against measured values.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Symplectic machine learning model for fast simulation of space-charge effects

Symplectic simulation of space-charge effects is crucial for the design and operation of high-intensity particle accelerators. Traditional methods for simulating these effects are often computationally expensive, resulting in significant overhead. In this work, we introduce a generative model based on a U-Net architecture within a generative adversarial network framework to efficiently simulate space-charge effects. The model is trained to predict the transverse multiparticle space-charge Hamiltonian, which can be physically computed using a gridless spectral method. The one-step symplectic transverse transfer map for the particles is then obtained by differentiating the predicted Hamiltonian. Benchmarking results demonstrate that this generative model achieves an order of magnitude higher computational efficiency compared to the spectral method, providing a highly efficient alternative for simulating space-charge effects with a large number of particles. By maintaining symplecticity, the model effectively preserves the phase-space structure and mitigates nonphysical errors in long-term simulations. This model has been integrated into jutrack, a novel autodifferentiable accelerator modeling code developed in the julia programming language.

Beam code development & simulation techniques↗
NASA NTRS2015

Taxi Time Prediction at Charlotte Airport Using Fast-Time Simulation and Machine Learning Techniques

Accurate taxi time prediction can be used for more efficient runway scheduling to increase runway throughput and reduce taxi times and fuel consumptions on the airport surface. This paper describes two different approaches to predicting taxi times, which are a data-driven analytical method using machine learning techniques and a fast-time simulation-based approach. These two taxi time prediction methods are applied to realistic flight data at Charlotte Douglas International Airport (CLT) and assessed with actual taxi time data from the human-in-the-loop simulation for CLT airport operations using various performance measurement metrics. Based on the preliminary results, we discuss how the taxi time prediction accuracy can be affected by the operational complexity at this airport and how we can improve the fast-time simulation model for implementing it with an airport scheduling algorithm in real-time operational environment.

Lee, Hanbong↗
DOE OSTI2021

Multiscale CFD simulation of biomass fast pyrolysis with a machine learning derived intra-particle model and detailed pyrolysis kinetics

Coupling particle and reactor scale models is as essential as reactor fluid dynamics and particle motion for accurate Computational Fluid Dynamic (CFD) simulations of biomass fast pyrolysis reactors due to intraparticle heat transfer and chemical reactions controlling conversion time and product distributions. Direct online coupling of a particle model with a reactor model is computationally expensive, while offline coupling is case-dependent. In this research, solutions from a series of particle pyrolysis simulations were regressed with Artificial Neural Network (ANN). Furthermoer, this machine learning-derived model predicted the same temperature and conversion profiles compared with particle resolved simulation while the isothermal approach overpredicted the temperature by 130 K and underpredicted the conversion time by 30 s. The ANN model was then integrated into CFD simulations of fluidized bed biomass fast pyrolysis with varied feedstocks via coupling PyTorch and MFiX. The averaged error of simulation predicted bio-oil yields with four feedstocks is 6.4%. This multi-scale approach provides an efficient tool for the coupled particle and reactor scale simulations of biomass pyrolysis.

09 BIOMASS FUELS↗
DOE OSTI2024

NEML2: A High Performance Library for Constitutive Modeling

NEML2, the New Engineering Material model Library, version 2, is an offshoot of NEML, an earlier material modeling code developed at Argonne National Laboratory. NEML2 extends the key philosophy of its predecessor, i.e., material models are flexible, modular, and can be built from smaller blocks. It also provides modern features that do not exist in the framework of its predecessor such as material model vectorization, automatic differentiation, device-portable just-in-time compilation, operator fusion, lazy tensor evaluation, etc. Moreover, NEML2 can seamlessly integrate with the popular machine learning package PyTorch to take advantage of modern and fast-growing machine learning techniques. In this fiscal year, the development of core library features and capabilities are complete. The purpose of this report is not to serve as a verbatim copy of the software API reference (which is available online at https://reverendbedford.github.io/neml2/). Instead, this report documents the motivation, implementation, design choices, and usage of each core capability as well as their applications in solving practical engineering problems. This report is compiled based on the NEML2 major release 2.0.0.

36 MATERIALS SCIENCE↗
DOE OSTI2025

An integrated approach to examine fuel-cladding chemical interaction in HT9/U-10Zr metallic fast reactor fuels: Coupling machine learning with electron microscopy and local mechanical properties analysis

The metallic U-Zr nuclear fuel alloy has garnered renewed interest as a promising candidate for next-generation sodium-cooled fast reactors. Recent studies and technology assessments have identified several areas requiring improvements, enhanced knowledge, and reliable data to strengthen the U-Zr fuel design basis for qualification and commercial applications. One of the most challenging phenomena impacting this fuel system’s performance is fuel-cladding chemical interaction (FCCI). This work aimed to harvest FCCI data by examining selected HT9/U-10Zr (wt. %) fuel samples of prototypic full-length fuel pins through an integrated approach. This approach integrated scanning electron microscopy (SEM) microstructure characterization with localized mechanical properties examination to deepen understanding of FCCI phenomenon in HT9/U-10Zr fuel system. Particularly, this study focused on MFF fuel pins irradiated at Fast Flux Test Facility (FFTF), which aimed to qualify metallic fuel as a driver fuel for FFTF and to assess its viability for larger-scale fast reactors. Electron microscopy provided high confidence in detecting and distinguishing the different FCCI layers, while small-scale mechanical testing (SSMT) probed the mechanical properties of these layers. SEM examination of a MFF-2 pin 192167, with a time averaged inner cladding temperature (TICT) slightly over 500°C, revealed minimal cladding-side FCCI (cladding wastage). In contrast, significantly thicker cladding wastage comprising two distinct sublayers was observed in samples from the thermally hot MFF-3 pin 193045 and MFF-5 pin 195011 where the TICT ranged from 610-635°C. SSMT indicated complete embrittlement in the sublayer adjacent to the fuel and a tendency toward embrittlement in the other sublayer. Additionally, a new machine learning method was developed, validated, and used to quantify cladding wastage thickness. The machine learning method reliably predicted the wastage thickness across various fuel pins and sample cross-sections. Furthermore, the available cladding wastage data from HT9/U-10Zr fuel system demonstrated a strong temperature dependency. However, the dataset remains small, and ongoing research activities are essential to further understand the FCCI phenomenon and develop a reliable FCCI model for enhanced fuel performance simulation under various conditions.

36 - MATERIALS SCIENCE↗
NASA NTRS2016

Taxi Time Prediction at Charlotte Airport Using Fast-Time Simulation and Machine Learning Techniques

Accurate taxi time prediction is required for enabling efficient runway scheduling that can increase runway throughput and reduce taxi times and fuel consumptions on the airport surface. Currently NASA and American Airlines are jointly developing a decision-support tool called Spot and Runway Departure Advisor (SARDA) that assists airport ramp controllers to make gate pushback decisions and improve the overall efficiency of airport surface traffic. In this presentation, we propose to use Linear Optimized Sequencing (LINOS), a discrete-event fast-time simulation tool, to predict taxi times and provide the estimates to the runway scheduler in real-time airport operations. To assess its prediction accuracy, we also introduce a data-driven analytical method using machine learning techniques. These two taxi time prediction methods are evaluated with actual taxi time data obtained from the SARDA human-in-the-loop (HITL) simulation for Charlotte Douglas International Airport (CLT) using various performance measurement metrics. Based on the taxi time prediction results, we also discuss how the prediction accuracy can be affected by the operational complexity at this airport and how we can improve the fast time simulation model before implementing it with an airport scheduling algorithm in a real-time environment.

airport surface traffic↗
DOE OSTI2023

Machine Learning-Based Model Predictive Control of Two-Time-Scale Systems

In this study, we present a general form of nonlinear two-time-scale systems, where singular perturbation analysis is used to separate the dynamics of the slow and fast subsystems. Machine learning techniques are utilized to approximate the dynamics of both subsystems. Specifically, a recurrent neural network (RNN) and a feedforward neural network (FNN) are used to predict the slow and fast state vectors, respectively. Moreover, we investigate the generalization error bounds for these machine learning models approximating the dynamics of two-time-scale systems. Next, under the assumption that the fast states are asymptotically stable, our focus shifts toward designing a Lyapunov-based model predictive control (LMPC) scheme that exclusively employs the RNN to predict the dynamics of the slow states. Additionally, we derive sufficient conditions to guarantee the closed-loop stability of the system under the sample-and-hold implementation of the controller. A nonlinear chemical process example is used to demonstrate the theory. In particular, two RNN models are constructed: one to model the full two-time-scale system and the other to predict solely the slow state vector. Both models are integrated within the LMPC scheme, and we compare their closed-loop performance while assessing the computational time required to execute the LMPC optimization problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Quantum optimal control of superconducting qubits based on machine-learning characterization

Implementing fast and high-fidelity quantum operations using open-loop quantum optimal control relies on having an accurate model of the quantum dynamics. Any deviations between this model and the complete dynamics of the device, such as the presence of spurious modes or pulse distortions, can degrade the performance of optimal controls in practice. Here, we propose an experimentally simple approach to realize optimal quantum controls tailored to the device parameters and environment while specifically characterizing this quantum system. Concretely, we use physics-inspired machine learning to infer an accurate model of the dynamics from experimentally available data and then optimize our experimental controls on this trained model. We show the power and feasibility of this approach by optimizing arbitrary single-qubit operations in detailed numerical simulations of a superconducting transmon qubit. Furthermore, we demonstrate that this framework produces an accurate description of the device dynamics under arbitrary controls, together with the precise pulses achieving arbitrary single-qubit gates with a high fidelity of ∼99.99%.

Artificial neural networks↗
DOE OSTI2022

Introduction to Special Section: Machine Learning for Image-based Geologic Interpretation

Image-based geological interpretation has been a labor-intensive and time-consuming process because it requires well-trained geoscientists to identify geological structures, features, and textures from various types of images. These images include scanning electron microscopic images, optical microscopic images, optical photos, resistivity images, seismic volumes, remote-sensing images, etc. With fast-evolving machine learning (ML) technology and computing power in recent decades, computers can achieve nearhuman-level to super-human-level performance with scalable high efficiency in the computer vision field. These technological revolutions facilitated image-based geological interpretation in petroleum exploration and production. For example, a fault picking method applied to 3-D seismic volume data using deep learning can achieve superior performance in comparison to conventional auto-picking methods. In addition, under the new normal of low oil prices, the petroleum industry seeks cost-effective strategies such as automating traditionally labor-intensive processes. Nevertheless, the potential of applying ML to geological image interpretation is still facing a few key challenges including data scarcity, data distribution, poor data and/or label quality, data leakage, learning algorithms, model architecture, training methodologies, testing and evaluation metrics, hyper-parameters optimization, model drift, production deployment, and the like.

58 GEOSCIENCES↗
DOE OSTI2024

Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors

Abstract Efficient and accurate algorithms are necessary to reconstruct particles in the highly granular detectors anticipated at the High-Luminosity Large Hadron Collider and the Future Circular Collider. We study scalable machine learning models for event reconstruction in electron-positron collisions based on a full detector simulation. Particle-flow reconstruction can be formulated as a supervised learning task using tracks and calorimeter clusters. We compare a graph neural network and kernel-based transformer and demonstrate that we can avoid quadratic operations while achieving realistic reconstruction. We show that hyperparameter tuning significantly improves the performance of the models. The best graph neural network model shows improvement in the jet transverse momentum resolution by up to 50% compared to the rule-based algorithm. The resulting model is portable across Nvidia, AMD and Habana hardware. Accurate and fast machine-learning based reconstruction can significantly improve future measurements at colliders.

Physics↗
DOE OSTI2024

Event Detection and Classification Using Machine Learning Applied to PMU Data for the Western US Power System

Smart grid technology enhances our comprehension and reliability of the power grid, leveraging Phasor Measurement Unit (PMU) data—time-synchronized, high-frequency measurements gathered across the US power grid. This paper employs machine learning techniques to effectively analyze the vast PMU data in Wide Area Monitoring Systems (WAMS) for power grid event detection and classification. Analyzing several months of real-world PMU data, the paper focuses on machine learning for fast, precise event detection and classification, corroborated by utility event logs. Practical challenges like feature extraction, dimensionality reduction, and model selection are addressed. A novel feature yielding improved results is discovered, and a supplementary algorithm for detecting small power grid faults is developed. The final algorithm is validated using a month-long real PMU data set, demonstrating its capability in accurately identifying power grid events in near real-time.

machine learning, event detection, PMU↗
DOE OSTI2024

Data Science Enabled Enabled Discovery of Superconductors (Final Progress Report)

This Final Technical Report describes efforts by 4 PIs at the University of Florida (Peter Hirschfeld, Richard Hennig, Greg Stewart and James Hamlin), over the period September 2019-August 2023, to use data science and machine learning techniques to discover new conventional superconductors. The PIs constructed a discovery loop with two theorists and two experimentalists to: develop algorithms to machine learn descriptors correlating strongly with the critical temperature Tc (PI's Peter Hirschfeld, UF Physics and Richard Hennig, UF Materials Science and En), synthesize and measure properties of promising materials, and feed back the knowledge gained into the prediction algorithm. This work was motivated by the theoretical prediction and experimental discovery of high-pressure, high-pressure hydride superconductors, and to find ways to recreate the high critical temperatures in these systems at ambient pressure. Highlights from the grant include: 1) a new equation for Tc in terms of moments of the electron-phonon spectral function, improving on the so-called Allen-Dynes equation (1975); 2) study of the metastable A15 superconductor Nb3Si, formed under explosive compression at ~1000GPa to determine the kinetic barrier to the ground state structure; 3) the development of ultra-fast machine-learned atomic potentials for molecular dynamics, and 4) the discovery of superconductivity at 19K in WB2 arising from metastable defect structures in the crystal.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

New tool for kinematic regime estimation in semi-inclusive deep-inelastic scattering

We introduce a new phenomenological tool based on momentum region indicators to guide the analysis and interpretation of semi-inclusive deep-inelastic scattering measurements. The new tool, referred to as “affinity”, is devised to help visualize and quantify the proximity of any experimental kinematic bin to a particular hadron production region, such as that associated with transverse momentum dependent factorization. We apply the affinity estimator to existing HERMES and COMPASS data and expected data from Jefferson Lab and the future Electron-Ion Collider. We also provide an interactive notebook based on Machine Learning for fast evaluation of affinity.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗