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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Unsupervised classification for region of interest in X-ray ptychography

X-ray ptychography offers high-resolution imaging of large areas at a high computational cost due to the large volume of data provided. To address the cost issue, we propose a physics-informed unsupervised classification algorithm that is performed prior to reconstruction and removes data outside the region of interest (RoI) based on the multimodal features present in the diffraction patterns. The preprocessing time for the proposed method is inconsequential in contrast to the resource-intensive reconstruction process, leading to an impressive reduction in the data workload to a mere 20% of the initial dataset. This capability consequently reduces computational time dramatically while preserving reconstruction quality. Through further segmentation of the diffraction patterns, our proposed approach can also detect features that are smaller than beam size and correctly classify them as within the RoI.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Soil nitrogen concentration mediates the relationship between leguminous trees and neighbor diversity in tropical forests

Legumes provide an essential service to ecosystems by capturing nitrogen from the atmosphere and delivering it to the soil, where it may then be available to other plants. However, this facilitation by legumes has not been widely studied in global tropical forests. Demographic data from 11 large forest plots (16–60 ha) ranging from 5.25° S to 29.25° N latitude show that within forests, leguminous trees have a larger effect on neighbor diversity than non-legumes. Where soil nitrogen is high, most legume species have higher neighbor diversity than non-legumes. Where soil nitrogen is low, most legumes have lower neighbor diversity than non-legumes. No facilitation effect on neighbor basal area was observed in either high or low soil N conditions. The legume–soil nitrogen positive feedback that promotes tree diversity has both theoretical implications for understanding species coexistence in diverse forests, and practical implications for the utilization of legumes in forest restoration.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Federated benchmarking of medical artificial intelligence with MedPerf

Medical artificial intelligence (AI) has tremendous potential to advance healthcare by supporting and contributing to the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving both healthcare provider and patient experience. Unlocking this potential requires systematic, quantitative evaluation of the performance of medical AI models on large-scale, heterogeneous data capturing diverse patient populations. Here, to meet this need, we introduce MedPerf, an open platform for benchmarking AI models in the medical domain. MedPerf focuses on enabling federated evaluation of AI models, by securely distributing them to different facilities, such as healthcare organizations. This process of bringing the model to the data empowers each facility to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status and real-world deployment, our roadmap and, importantly, the use of MedPerf with multiple international institutions within cloud-based technology and on-premises scenarios. Finally, we welcome new contributions by researchers and organizations to further strengthen MedPerf as an open benchmarking platform.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Astronomical image time series classification using CONVolutional attENTION (ConvEntion)

Aims: The treatment of astronomical image time series has won increasing attention in recent years. Indeed, numerous surveys following up on transient objects are in progress or under construction, such as the Vera C. Rubin Observatory Legacy Survey for Space and Time (LSST), which is poised to produce huge amounts of these time series. The associated scientific topics are extensive, ranging from the study of objects in our galaxy to the observation of the most distant supernovae for measuring the expansion of the universe. With such a large amount of data available, the need for robust automatic tools to detect and classify celestial objects is growing steadily. Methods: This study is based on the assumption that astronomical images contain more information than light curves. In this paper, we propose a novel approach based on deep learning for classifying different types of space objects directly using images. We named our approach ConvEntion, which stands for CONVolutional attENTION. It is based on convolutions and transformers, which are new approaches for the treatment of astronomical image time series. Our solution integrates spatio-temporal features and can be applied to various types of image datasets with any number of bands. Results: In this work, we solved various problems the datasets tend to suffer from and we present new results for classifications using astronomical image time series with an increase in accuracy of 13%, compared to state-of-the-art approaches that use image time series, and a 12% increase, compared to approaches that use light curves.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Arbitrarily accurate, nonparametric coarse graining with Markov renewal processes and the Mori–Zwanzig formulation

Stochastic dynamics, such as molecular dynamics, are important in many scientific applications. However, summarizing and analyzing the results of such simulations is often challenging due to the high dimension in which simulations are carried out and, consequently, due to the very large amount of data that are typically generated. Coarse graining is a popular technique for addressing this problem by providing compact and expressive representations. Coarse graining, however, potentially comes at the cost of accuracy, as dynamical information is, in general, lost when projecting the problem in a lower-dimensional space. This article shows how to eliminate coarse-graining error using two key ideas. First, we represent coarse-grained dynamics as a Markov renewal process. Second, we outline a data-driven, non-parametric Mori–Zwanzig approach for computing jump times of the renewal process. Numerical tests on a small protein illustrate the method.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Toward ultra-efficient high-fidelity predictions of wind turbine wakes: Augmenting the accuracy of engineering models with machine learning

This study proposes a novel machine learning (ML) methodology for the efficient and cost-effective prediction of high-fidelity three-dimensional velocity fields in the wake of utility-scale turbines. The model consists of an autoencoder convolutional neural network with U-Net skipped connections, fine-tuned using high-fidelity data from large-eddy simulations (LES). The trained model takes the low-fidelity velocity field cost-effectively generated from the analytical engineering wake model as input and produces the high-fidelity velocity fields. The accuracy of the proposed ML model is demonstrated in a utility-scale wind farm for which datasets of wake flow fields were previously generated using LES under various wind speeds, wind directions, and yaw angles. Comparing the ML model results with those of LES, the ML model was shown to reduce the error in the prediction from 20% obtained from the Gauss Curl hybrid (GCH) model to less than 5%. In addition, the ML model captured the non-symmetric wake deflection observed for opposing yaw angles for wake steering cases, demonstrating a greater accuracy than the GCH model. The computational cost of the ML model is on par with that of the analytical wake model while generating numerical outcomes nearly as accurate as those of the high-fidelity LES.

Mechanics↗
DOE OSTI2021

Transmission of Images on High-Temperature Nuclear-Grade Metallic Pipe with Ultrasonic Elastic Waves

Transmission of information using elastic ultrasonic waves on existing metallic pipes provides an alternative communication option for a nuclear facility. The advantages of this approach consist of transmitting information through barriers, such as the containment building wall, with minimal modification of the existing hardware. Because bit rates on the order of kilobits per second are achievable, relatively large volumes of data, such as images, can be transmitted. A viable candidate for an ultrasonic communication channel is a stainless steel pipe of the chemical volume control system (CVCS) that penetrates through the reactor containment building wall through a sealed tunnel. To study ultrasonic communication under simulated nuclear facility conditions of high temperature, a test article was developed by installing heating tapes, temperature controllers, and thermal insulation on a laboratory CVCS-like stainless steel pipe. High temperature and radiation-resilient lithium niobate ultrasonic transducers were utilized for information transmission on the heated pipe. The amplitude shift keying (ASK) digital communication protocol was developed and implemented in a GNU Radio software-defined radio environment. A root-raised-cosine filter was introduced to suppress ultrasonic transducer ringing and thus reduce inter-symbol interference. This resulted in the enhancement of the data transmission bit rate compared to information encoding with square pulses. Demonstrations of communication at high temperature included transmission of a 90-KB image at the bit rate of 10 Kbps with a bit error rate of 10 -3 across a 6-ft-long straight pipe heated up to 230 degrees C. Additional preliminary studies were conducted to evaluate ultrasonic communication system resilience to environmental degradation and damage.

42 ENGINEERING↗
DOE OSTI2021

A modified many-body dissipative particle dynamics model for mesoscopic fluid simulation: methodology, calibration, and application for hydrocarbon and water

The many-body dissipative particle dynamics (mDPD) is a prominent mesoscopic multiphase model for fluid transport in mesoconfinement. However, it has been a long-standing challenge for mDPD (and other multiphase-enabled DPD models) to accurately predict real-fluid static and dynamic properties simultaneously. We have developed a modified mDPD model to overcome the issue and a rigorous calibration approach that uses reference data, including experimental and/or molecular dynamics (MD) simulations to parameterise the modified mDPD for real fluids. We choose heptane as a representative example of hydrocarbon in source rocks to demonstrate the model's capability to accurately predict the equation of state (EOS), free surface tension, diffusivity, and viscosity. Our timing test shows that the modified mDPD is 400–500 times faster than its MD counterpart for simulating bulk heptane in equivalent volumes. To further demonstrate the robustness of the model, we revisited the benchmark problem of mesoscopic modelling of water, in which all the previous works on DPD reported only a limited portion of the water properties. Here, we show that the modified mDPD can provide accurate modelling of water static and dynamic properties and an EOS that matches the experimental data to a large range of confinement pressure.

42 ENGINEERING↗
DOE OSTI2024

Do black holes remember what they are made of?

We study the ringdown signal of black holes formed in prompt-collapse binary neutron star mergers. We analyze data from 47 numerical relativity simulations. We show that the ($l$ = 2, $m$ = 2) and ($l$ = 2, $m$ = 1) multipoles of the gravitational wave signal are well fitted by decaying damped exponentials, as predicted by black-hole perturbation theory. We show that the ratio of the amplitude in the two modes depends on the progenitor binary mass ratio q and reduced tidal parameter $\overline{Λ}$. Unfortunately, the numerical uncertainty in our data is too large to fully quantify this dependency. If confirmed, these results will enable novel tests of general relativity in the presence of matter with next-generation gravitational-wave observatories.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Electron emission from fluorene (C 13 H 10 ) upon 3.5 MeV/u Si 8+ ion impact: double differential distributions

In this work, we have studied the electron emission from one of the polycyclic aromatic hydrocarbon (PAH) molecules namely, fluorene (C13H10), upon 3.5 MeV/u Si8+ ion impact. The experimentally measured absolute double differential cross sections (DDCS) are compared with the continuum distorted wave-eikonal initial state (CDW-EIS) model and the first Born approximation including correct boundary conditions (CB1). The measurements are carried out in the ejected e⁻-energy range of 1 eV–400 eV and in the angular range of 20°–160°. We have obtained the single differential and the total cross sections (TCSs) of e⁻-emission as well. The CB1 calculation largely underestimates the data. The CDW-EIS model, which is applied for the PAH molecule for the first time, provides an overall better agreement with the double differential, single differential and TCS data. The DDCS data for fluorene has also been compared with that for CH4 molecule, at a few angles. The forward–backward angular asymmetry shows a relatively flatter distribution compared to the theoretical predictions. The contribution due to the giant plasmon resonance could not be clearly observed except a mild indication in the asymmetry parameter. The angular distribution of the carbon KLL Auger electron cross section shows certain variations. The study of the KLL hyper-satellite component indicates the double K-ionization cross section is about 8.6% of the single K-ionization one.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2026

The DESI DR1 peculiar velocity survey: growth rate measurements from the maximum likelihood fields method

We present the constraint on the growth rate of structure from the combination of DESI DR1 BGS sample, Fundamental Plane, and Tully-Fisher peculiar velocity catalogues using the maximum likelihood fields method. The combined catalogue contains 415,523 galaxy redshifts and 76,616 peculiar velocity measurements. To handle the large amount of data in the DESI DR1 peculiar velocity catalogue, we significantly improve the computational efficiency by rewriting the algorithm with JAX. After removing outliers and Tully-Fisher galaxies that are affected by systematics, we find fσ 8 = 0.483 -0.043 +0.080 (stat) ± 0.018(sys), consistent within 1σ with the power spectrum and correlation function analysis using the same dataset. Combining all three measurements with appropriate correlations, the consensus measurement is fσ 8 (z eff = 0.07) = 0.450±0.055, consistent with Planck +ΛCDM cosmology (fσ 8 = 0.449±0.008). Combining with the high redshift growth rate of structure measurements from DESI ShapeFit, the constraint on the growth index is γ = 0.58±0.11, consistent with GR.

cosmic flows↗
DOE OSTI2020

Comparison of Rotor Wake Identification and Characterization Methods for the Analysis of Wake Dynamics and Evolution

Optimal wind power plant design requires understanding of wind turbine wake physics and validation of engineering wake models under wake-controlled operating conditions. In this work, we have developed and investigated several different wake identification and characterization methods for analyzing wake evolution and dynamics. The accuracy and robustness of these methods, based on Gaussian function fitting and adaptive contour identification, have been assessed by application to a large-eddy simulation data set. A new contour-based method based on downstream momentum deficit has been considered. Uncertainties arising from wake-identification errors result in characterizations of the wake expansion, recovery, and meandering motion that differ by 19% of the rotor area, 4% of the freestream, and 15% rotor diameter, respectively.

17 WIND ENERGY↗
DOE OSTI2024

Analysis and Benchmarking of feature reduction for classification under computational constraints

Abstract Machine learning is most often expensive in terms of computational and memory costs due to training with large volumes of data. Current computational limitations of many computing systems motivate us to investigate practical approaches, such as feature selection and reduction, to reduce the time and memory costs while not sacrificing the accuracy of classification algorithms. In this work, we carefully review, analyze, and identify the feature reduction methods that have low costs/overheads in terms of time and memory. Then, we evaluate the identified reduction methods in terms of their impact on the accuracy, precision, time, and memory costs of traditional classification algorithms. Specifically, we focus on the least resource intensive feature reduction methods that are available in Scikit-Learn library. Since our goal is to identify the best performing low-cost reduction methods, we do not consider complex expensive reduction algorithms in this study. In our evaluation, we find that at quadratic-scale feature reduction, the classification algorithms achieve the best trade-off among competitive performance metrics. Results show that the overall training times are reduced 61%, the model sizes are reduced 6×, and accuracy scores increase 25% compared to the baselines on average with quadratic scale reduction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A source of gamma rays coincident with the shell of the supernova remnant CTB 80

ABSTRACT CTB 80 (G69.0+2.7) is a relatively old (50–80 kyr) supernova remnant (SNR) with a complex radio morphology showing three extended radio arms and a radio and X-ray nebula near the location of the pulsar PSR B1951+32. We report on a study of the GeV emission in the region of CTB 80 with Fermi-Large Area Telescope data. An extended source with a size of 1.3°, matching the size of the infrared shell associated to the SNR, was discovered. The GeV emission, detected up to an energy of ∼20 GeV, is more significant at the location of the northern radio arm where previous observations imply that the SNR shock is interacting with ambient material. Both hadronic and leptonic scenarios can reproduce the multiwavelength data reasonably well. The hadronic cosmic ray energy density required is considerably larger than the local Galactic value and the gamma-ray leptonic emission is mainly due to bremsstrahlung interactions. We conclude that GeV particles are still trapped or accelerated by the SNR producing the observed high-energy emission when interacting with ambient material.

Araya, M.↗
DOE OSTI2021

Predicting images for the dynamics of stellar clusters ( π-DOC ): a deep learning framework to predict mass, distance, and age of globular clusters

ABSTRACT Dynamical mass estimates of simple systems such as globular clusters (GCs) still suffer from up to a factor of 2 uncertainty. This is primarily due to the oversimplifications of standard dynamical models that often neglect the effects of the long-term evolution of GCs. Here, we introduce a new approach to measure the dynamical properties of GCs, based on the combination of a deep-learning framework and the large amount of data from direct N-body simulations. Our algorithm, π-DOC (Predicting Images for the Dynamics Of stellar Clusters) is composed of two convolutional networks, trained to learn the non-trivial transformation between an observed GC luminosity map and its associated mass distribution, age, and distance. The training set is made of V-band luminosity and mass maps constructed as mock observations from N-body simulations. The tests on π-DOC demonstrate that we can predict the mass distribution with a mean error per pixel of 27 per cent, and the age and distance with an accuracy of 1.5 Gyr and 6 kpc, respectively. In turn, we recover the shape of the mass-to-light profile and its global value with a mean error of 12 per cent, which implies that we efficiently trace mass segregation. A preliminary comparison with observations indicates that our algorithm is able to predict the dynamical properties of GCs within the limits of the training set. These encouraging results demonstrate that our deep-learning framework and its forward modelling approach can offer a rapid and adaptable tool competitive with standard dynamical models.

Chardin, Jonathan↗
DOE OSTI2022

The relation between accretion rate and jet power in early-type galaxies with thermally unstable hot atmospheres

ABSTRACT We use Chandra X-ray data and Very Large Array radio observations for a sample of 20 nearby, massive, X-ray bright, early-type galaxies to investigate the relation between the Bondi accretion rates and the mechanical jet powers. We find a strong correlation ($\rho = 0.96^{+0.03}_{-0.09}$; BF10 > 100) between the Bondi accretion power, PBondi, and the mechanical jet power, Pjet, for a subsample of 14 galaxies, which also host cool H α+[N ii] line emitting gas and thus likely have thermally unstable atmospheres. The relation between the Bondi accretion power and the mechanical jet power for this subsample is well described by a power-law model $\log \frac{P_{\mathrm{Bondi}}}{{10^{43} \, \mathrm{erg \, s^{-1}}}} = \alpha + \beta \log \frac{P_{\mathrm{jet}}}{{10^{43} \, \mathrm{erg \, s^{-1}}}}$, where α = 1.10 ± 0.25 and β = 1.10 ± 0.24 with an intrinsic scatter $\sigma = 0.08^{+0.14}_{-0.06}$ dex. The results indicate that in all galaxies with thermally unstable atmospheres the cooling atmospheric gas feeds the central black holes at a similar jet-to-Bondi power ratio. For the full sample of 20 galaxies, the correlation is weaker and in a subset of galaxies with no signs of H α+[N ii] emission, we see a hint for a systematically lower jet-to-Bondi power ratio. We also investigate the dependence of jet power on individual quantities in the Bondi formula such as the supermassive black hole mass (M•) and the specific entropy of the gas (K) at the Bondi radius. For the subsample of H α+[N ii] emitting galaxies, we find a very tight correlation of Pjet with M• ($\rho = 0.91^{+0.06}_{-0.11}$; BF10 > 100) and, although poorly constrained, a hint of an anticorrelation for Pjet and K ($\rho = -0.47^{+0.60}_{-0.37}$; BF10 = 1.1).

79 ASTRONOMY AND ASTROPHYSICS↗