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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 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

DeepMC: a deep learning method for efficient Monte Carlo beamlet dose calculation by predictive denoising in magnetic resonance-guided radiotherapy

Abstract Emerging magnetic resonance (MR) guided radiotherapy affords significantly improved anatomy visualization and, subsequently, more effective personalized treatment. The new therapy paradigm imposes significant demands on radiation dose calculation quality and speed, creating an unmet need for the acceleration of Monte Carlo (MC) dose calculation. Existing deep learning approaches to denoise the final plan MC dose fail to achieve the accuracy and speed requirements of large-scale beamlet dose calculation in the presence of a strong magnetic field for online adaptive radiotherapy planning. Our deep learning dose calculation method, DeepMC, addresses these needs by predicting low-noise dose from extremely noisy (but fast) MC-simulated dose and anatomical inputs, thus enabling significant acceleration. DeepMC simultaneously reduces MC sampling noise and predicts corrupted dose buildup at tissue-air material interfaces resulting from MR-field induced electron return effects. Here we demonstrate our model’s ability to accelerate dose calculation for daily treatment planning by a factor of 38 over traditional low-noise MC simulation with clinically meaningful accuracy in deliverable dose and treatment delivery parameters. As a post-processing approach, DeepMC provides compounded acceleration of large-scale dose calculation when used alongside established MC acceleration techniques in variance reduction and graphics processing unit-based MC simulation.

Engineering↗
DOE OSTI2020

Physics-guided machine learning approaches to predict the ideal stability properties of fusion plasmas

One of the biggest challenges to achieve the goal of producing fusion energy in tokamak devices is the necessity of avoiding disruptions of the plasma current due to instabilities. The disruption event characterization and forecasting (DECAF) framework has been developed in this purpose, integrating physics models of many causal events that can lead to a disruption. Two different machine learning approaches are proposed to improve the ideal magnetohydrodynamic (MHD) no-wall limit component of the kinetic stability model included in DECAF. First, a random forest regressor (RFR), was adopted to reproduce the DCON computed change in plasma potential energy without wall effects for a large database of equilibria from the national spherical torus experiment (NSTX). This tree-based method provides an analysis of the importance of each input feature, giving an insight into the underlying physics phenomena. Secondly, a fully-connected neural network has been trained on sets of calculations with the DCON code, to get an improved closed form equation of the no-wall β limit as a function of the relevant plasma parameters indicated by the RFR. The neural network has been guided by physics theory of ideal MHD in its extension outside the domain of the NSTX experimental data. The estimated value has been incorporated into the DECAF kinetic stability model and tested against a set of experimentally stable and unstable discharges. Moreover, the neural network results were used to simulate a real-time stability assessment using only quantities available in real-time. Finally, the portability of the model was investigated, showing encouraging results by testing the NSTX-trained algorithm on the mega ampere spherical tokamak (MAST).

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

A practical guide to replica-exchange Wang—Landau simulations

This paper is based on a series of tutorial lectures about the replica-exchange Wang-Landau (REWL) method given at the IX Brazilian Meeting on Simulational Physics (BMSP 2017). It provides a practical guide for the implementation of the method. A complete example code for a model system is available online. In this paper, we discuss the main parallel features of this code after a brief introduction to the REWL algorithm. The tutorial section is mainly directed at users who have written a single-walker Wang–Landau program already but might have just taken their first steps in parallel programming using the Message Passing Interface (MPI). In the last section, we answer "frequently asked questions" from users about the implementation of REWL for different scientific problems.

Vogel, Thomas↗
DOE OSTI2018

Liquid Argon Scintillation Detection Utilizing Wavelength-Shifting Plates and Light Guides

In DUNE, the event timing provided by the detection of the relatively prompt scintillation photons will improve spatial resolution in the drift direction of the time-projection chamber (TPC) and is especially useful for non-beam physics topics such as supernova neutrinos and nucleon decay. The baseline design for the first 10kt single phase TPC fits the photon detector system in the natural gap between the wire planes of adjacent TPC volumes. A prototype photon detector design utilizes wavelength-shifter coated plates to convert the vacuum ultraviolet scintillation light to the optical and commercially-produced wavelength-shifting light guides to trap some of this light and transport it to an array of silicon photomultipliers at the end. This system and the testing performed to characterize the system and determine the efficiency are discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

A novel construct for scaling groundwater–river interactions based on machine-guided hydromorphic classification

Hydrologic exchange between river channels and adjacent subsurface environments is a key process that influences water quality and ecosystem function in river corridors. Predictive numerical models are needed to understand responses of river corridors to environmental change and to support sustainable watershed management. We posit that systematic hydromorphic classification provides a scaling construct that facilitates extrapolation of outputs from local-scale mechanistic models to reduced-order models applicable at reach and watershed scales. This in turn offers the potential to improve large-scale predictions of river corridor hydrobiogeochemical processes. Here we present a new machine-guided hydromorphic classification methodology that addresses the key requirements of this objective, and we demonstrate its application to a segment of the Columbia River in the northwestern United States. The resulting hydromorphic classes form spatially coherent and physically interpretable hydromorphic units that exhibit distinct behaviors in terms of distributions of subsurface residence times (a primary control on critical biogeochemical reactions). This approach forms the basis of ongoing research that is evaluating the formulation of reduced-order models and transferability of results to other river reaches and larger scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Continental-Scale Controls on Hyporheic Respiration Revealed by Knowledge-Guided Machine Learning

Hyporheic zone sediments regulate organic matter turnover and in-stream respiration, yet controls on sediment respiration remain poorly constrained across heterogeneous river networks, limiting prediction of stream metabolism and carbon processing at continental scales. Here, we integrate observations from ~90 river corridors across the United States in the WHONDRS consortium with a knowledge-guided machine learning (KGML) framework that couples thermodynamic rate theory with machine learning to identify dominant controls on hyporheic respiration. Diagnostic analyses show that organic matter concentration and thermodynamic favorability define an upper bound on respiration potential, whereas biological catalytic capacity and physical accessibility jointly govern realized respiration rates through interaction effects. To represent unmeasurable accessibility constraints, we use the mechanistic model as a scaffold for KGML, allowing machine learning to target residual structure not explained by process theory. This hybrid framework improves predictive skill relative to both the mechanistic model alone and fully data-driven models while preserving interpretability. These results indicate that variability in hyporheic respiration is largely mechanistically structured and demonstrate how integrating process theory with explainable AI enhances predictive performance while enabling scalable synthesis of river corridor observations.

Zheng, Jianqiu↗
DOE OSTI2024

Spectroscopy-guided discovery of three-dimensional structures of disordered materials with diffusion models

Spectroscopy techniques such as x-ray absorption near edge structure (XANES) provide valuable insights into the atomic structures of materials, yet the inverse prediction of precise structures from spectroscopic data remains a formidable challenge. In this study, we introduce a framework that combines generative artificial intelligence models with XANES spectroscopy to predict three-dimensional atomic structures of disordered systems, using amorphous carbon (a-C) as a model system. In this work, we introduce a new framework based on the diffusion model, a recent generative machine learning method, to predict 3D structures of disordered materials from a target property. For demonstration, we apply the model to identify the atomic structures of a-C as a representative material system from the target XANES spectra. We show that conditional generation guided by XANES spectra reproduces key features of the target structures. Furthermore, we show that our model can steer the generative process to tailor atomic arrangements for a specific XANES spectrum. Finally, our generative model exhibits a remarkable scale-agnostic property, thereby enabling generation of realistic, large-scale structures through learning from a small-scale dataset (i.e. with small unit cells). Our work represents a significant stride in bridging the gap between materials characterization and atomic structure determination; in addition, it can be leveraged for materials discovery in exploring various material properties as targeted.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Deep-learning-guided high-resolution subsurface reflectivity imaging with application to ground-penetrating radar data

Subsurface reflectivity imaging is one of the most important geophysical characterization methods for revealing subsurface structures. In many cases, accurate subsurface reflectivity imaging is challenging because of, for example, random or coherent noise in the data and sparse source-receiver observation geometry. Here, we develop a deep-learning-guided iterative imaging method to improve subsurface structure imaging. Specifically, we train a supervised neural network to infer a noise-free, high-resolution image from a noisy, low-resolution image and use this estimated image as guidance to regularize least-squares imaging. We develop a systematic method to generate high-quality synthetic training data (data-label pairs) to train the guidance neural network. The trained neural network can provide high-fidelity predictions even for field-data images that are not in the training data. We validate our new imaging method using one synthetic and two field ground-penetrating radar data examples, and find that our method can produce clean, high-resolution subsurface reflectivity images where existing single-pass and least-squares imaging methods fail due to noise and insufficient data coverage.

58 GEOSCIENCES↗
DOE OSTI2021

A best-practice guide to predicting plant traits from leaf-level hyperspectral data using partial least squares regression

Partial least squares regression (PLSR) modelling is a statistical technique for correlating datasets, and involves the fitting of a linear regression between two matrices. One application of PLSR enables leaf traits to be estimated from hyperspectral optical reflectance data, facilitating rapid, high-throughput, non-destructive plant phenotyping. This technique is of interest and importance in a wide range of contexts including crop breeding and ecosystem monitoring. The lack of a consensus in the literature on how to perform PLSR means that interpreting model results can be challenging, applying existing models to novel datasets can be impossible, and unknown or undisclosed assumptions can lead to incorrect or spurious predictions. We address this lack of consensus by proposing best practices for using PLSR to predict plant traits from leaf-level hyperspectral data, including a discussion of when PLSR is applicable, and recommendations for data collection. Further, we provide a tutorial to demonstrate how to develop a PLSR model, in the form of an R script accompanying this manuscript. This practical guide will assist all those interpreting and using PLSR models to predict leaf traits from spectral data, and advocates for a unified approach to using PLSR for predicting traits from spectra in the plant sciences.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Mechanism of CRISPR-RNA guided recognition of DNA targets in Escherichia coli

In bacteria and archaea, short fragments of foreign DNA are integrated into Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR) loci, providing a molecular memory of previous encounters with foreign genetic elements. In Escherichia coli, short CRISPR-derived RNAs are incorporated into a multi-subunit surveillance complex called Cascade (CRISPR-associated complex for antiviral defense). Recent structures of Cascade capture snapshots of this seahorse-shaped RNA-guided surveillance complex before and after binding to a DNA target. Here we determine a 3.2 Å x-ray crystal structure of Cascade in a new crystal form that provides insight into the mechanism of double-stranded DNA binding. Molecular dynamic simulations performed using available structures reveal functional roles for residues in the tail, backbone and belly subunits of Cascade that are critical for binding double-stranded DNA. Structural comparisons are used to make functional predictions and these predictions are tested in vivo and in vitro. Collectively, the results in this study reveal underlying mechanisms involved in target-induced conformational changes and highlight residues important in DNA binding and protospacer adjacent motif recognition.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Machine learning guided discovery of superconducting calcium borocarbides

Pursuit of superconductivity in light-element systems at ambient pressure is of great experimental and theoretical interest. In this work, we combine a machine learning (ML) method with first-principles calculations to efficiently search for the energetically favorable ternary Ca-B-C compounds. Three layered borocarbides (stable CaBC 5 and metastable Ca 2 BC 11 and CaB 3 C 3 ) are predicted to be phonon-mediated superconductors at ambient pressure. The stable CaBC 5 and the low-energy metastable Ca 2 BC 11 (with formation energy only 9.5 meV/atom above the convex hull) have a superconducting T c of 5.2 and 8.9 K, respectively. While the hexagonal CaB 3 C 3 possesses a T c of 26.1 K, it is metastable with formation energy of 153 meV/atom above the convex hull. The ML-guided approach opens up a way for greatly accelerating the discovery of new high-T c superconductors.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Guiding thermomagnetic avalanches with soft magnetic stripes

Here, we demonstrate the potential for manipulating the ultrafast dynamics of thermomagnetic flux avalanches (TMA) in superconducting films with soft magnetic stripes deposited on the film. By tuning the in-plane magnetization of the stripes, we induce lines of strong magnetic potentials for Abrikosov vortices, resulting in guided slow motion of vortices along the stripe edges and preferential bursts of TMA along the stripes. Furthermore, we show that transversely polarized stripes can reduce the TMA size by diverting magnetic flux away from the major trunk of the TMA into interstripe gaps. Our data indicate that TMAs are launched from locations with enhanced vortex entry barrier, where flux accumulation followed by accelerated vortex discharge significantly reduces the threshold of the applied field ramping speed required for the creation of TMAs. Finally, vortex-antivortex annihilation at the moving front of an expanding TMA can account for the enhanced TMA activity in the receding branches of the sample's magnetization cycle and the preferred propagation of TMAs into maximum trapped flux regions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2011

Realization of a Distributed Bragg Reflector for Propagating Guided Matter Waves

We report on the experimental study of a Bragg reflector for guided, propagating Bose-Einstein condensates. A one-dimensional attractive optical lattice of finite length created by red-detuned laser beams selectively reflects some velocity components of the incident matter wave packet. We find quantitative agreement between the experimental data and one-dimensional numerical simulations and show that the Gaussian envelope of the optical lattice has a major influence on the properties of the reflector. In particular, it gives rise to multiple reflections of the wave packet between two symmetric locations where Bragg reflection occurs. Our results are a further step towards integrated atom-optics setups for quasi-cw matter waves.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Enhancement of maximum attainable ion energy in the radiation pressure acceleration regime using a guiding structure

Radiation Pressure Acceleration is a highly efficient mechanism of laser driven ion acceleration, with the laser energy almost totally transferrable to the ions in the relativistic regime. There is a fundamental limit on the maximum attainable ion energy, which is determined by the group velocity of the laser. In the case of a tightly focused laser pulses, which are utilized to get the highest intensity, another factor limiting the maximum ion energy comes into play, the transverse expansion of the target. Transverse expansion makes the target transparent for radiation, thus reducing the effectiveness of acceleration. Utilization of an external guiding structure for the accelerating laser pulse may provide a way of compensating for the group velocity and transverse expansion effects.

43 PARTICLE ACCELERATORS↗
DOE OSTI2017

Population Mixing in Asymmetric Magnetic Reconnection with a Guide Field

We investigate how population mixing leads to structured electron distribution functions in asymmetric guide-field magnetic reconnection based on particle-in-cell simulations. The change of magnetic connectivity patches populations from different inflow regions to form multicomponent distributions in the exhaust, illustrating the direct consequence of the breaking and rejoining of magnetic flux tubes. Finite Larmor radius (FLR) effects of electrons accelerated by the perpendicular electric fields result in crescent-type nongyrotropic distributions. A new type of nongyrotropy is found to be caused by the combined effects of the FLR and velocity dispersion of electrons accelerated by the parallel electric field. The patching together of populations and the effects of acceleration and the FLR form the first steps of mixing in the exhaust and separatrix regions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Refractive Guide Switching a Regenerative Amplifier Free-Electron Laser for High Peak and Average Power Hard X Rays

In this work, we present an x-ray regenerative amplifier free-electron laser design capable of producing fully coherent hard x-ray pulses across a broad tuning range at a high steady state repetition rate. The scheme leverages a strong undulator taper and an apertured diamond output-coupling cavity crystal to produce both high peak and average spectral brightness radiation that is 2 to 3 orders of magnitude greater than conventional single-pass self-amplified spontaneous emission free-electron laser amplifiers. Refractive guiding in the postsaturation regime is found to play a key role in passively controlling the stored cavity power. The scheme is explored both analytically and numerically in the context of the Linac Coherent Light Source II High Energy upgrade.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Using Direct Laboratory Measurements of Electron Temperature Anisotropy to Identify the Heating Mechanism in Electron-Only Guide Field Magnetic Reconnection

Anisotropic electron heating during electron-only magnetic reconnection with a large guide magnetic field is directly measured in a laboratory plasma through in situ measurements of electron velocity distribution functions. Electron heating preferentially parallel to the magnetic field is localized to one separatrix, and anisotropies of 1.5 are measured. The mechanism for electron energization is identified as the parallel reconnection electric field because of the anisotropic nature of the heating and spatial localization. These characteristics are reproduced in a 2D particle-in-cell simulation and are also consistent with numerous magnetosheath observations. A measured increase in the perpendicular temperature along both separatrices is not reproduced by our 2D simulations. Here, this work has implications for energy partition studies in magnetosheath and laboratory reconnection.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Anomalous Resistivity and Electron Heating by Lower Hybrid Drift Waves during Magnetic Reconnection with a Guide Field

The lower hybrid drift wave (LHDW) has been a candidate for anomalous resistivity and electron heating inside the electron diffusion region of magnetic reconnection. In a laboratory reconnection layer with a finite guide field, quasielectrostatic LHDW (ES-LHDW) propagating along the direction nearly perpendicular to the local magnetic field is excited in the electron diffusion region. ES-LHDW generates large density fluctuations ($δn_{\text e}$, about 25% of the mean density) that are correlated with fluctuations in the out-of-plane electric field ($δE_Y$, about twice larger than the mean reconnection electric field). With a small phase difference (~ 30°) between two fluctuating quantities, the anomalous resistivity associated with the observed ES-LHDW is twice larger than the classical resistivity and accounts for 20% of the mean reconnection electric field. After we verify the linear relationship between $δn_{\text e}$ and $δE_Y$, anomalous electron heating by LHDW is estimated by a quasilinear analysis. The estimated electron heating is about 2.6 ± 0.3 MW / m 3 , which exceeds the classical Ohmic heating of about 2.0 ± 0.2 MW / m 3 . This LHDW-driven heating is consistent with the observed trend of higher electron temperatures when the wave amplitude is larger. Finally, presented results provide the first direct estimate of anomalous resistivity and electron heating power by LHDW, which demonstrates the importance of wave-particle interactions in magnetic reconnection.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗