Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “performance data”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Reshock analysis for PMMA driven above the threshold for chemical decomposition

Polymethyl methacrylate (PMMA) is a transparent thermoplastic often used in shock compression science. Previous proceedings (AIP Conf. Proc. 845, 131 (2006)) described plate impact experiments in which PMMA was reshocked to pressures of up to ~130 GPa, well above that at which it decomposes on its principal Hugoniot. Because some of the conclusions of the original analysis were surprising, the results were reanalyzed in a later proceeding (ibid. 1426, 771 (2012)). In addition to our own reanalysis of the data, we performed hydrodynamic simulations of the experiments based on a new equation of state for PMMA shocked above its threshold for decomposition.

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

GIScience in the era of Artificial Intelligence: a research agenda towards Autonomous GIS

The advent of generative AI exemplified by large language models (LLMs) opens new ways to represent and compute geographic information and transcends the process of geographic knowledge production, driving geographic information systems (GIS) towards autonomous GIS. Leveraging LLMs as the decision core, autonomous GIS can independently generate and execute geoprocessing workflows to perform spatial analysis. In this vision paper, we further elaborate on the concept of autonomous GIS and present a conceptual framework that defines its five autonomous goals, five levels of autonomy, five core functions, and three operational scales. We demonstrate how autonomous GIS could perform geospatial data retrieval, spatial analysis, and map making with four proof-of-concept GIS agents. We conclude by identifying critical challenges and future research directions, including fine-tuning and self-growing decision-cores, autonomous modelling, and examining the societal and practical implications of autonomous GIS. By establishing the groundwork for a paradigm shift in GIScience, this paper envisions a future where GIS moves beyond traditional workflows to autonomously reason, derive, innovate, and advance geospatial solutions to pressing global challenges. Meanwhile, we emphasize that as we design and deploy increasingly intelligent geospatial systems, we carry a responsibility to ensure they are developed in socially responsible ways, serve the public good, and support the continued value of human geographic insight in an AI-augmented future.

Autonomous GI↗
DOE OSTI2021

Application of machine learning for the estimation of electron energy distribution from optical emission spectra

Abstract This paper discusses the use of probabilistic deep neural networks for the prediction of the electron energy probability function in low-temperature non-thermal plasmas. The neural networks are trained using optical emission spectroscopy and Langmuir probe measurements, with the goal of providing a reliable estimate of the electron energy probability function solely from optical emission data. The performance of both non-Bayesian and Bayesian networks is evaluated. It is found that Bayesian models are preferable as they assign a higher level of uncertainty to their prediction especially when the dataset used to train them is small. This work describes one of the many potential applications of machine learning in plasma science and technology.

Physics↗
DOE OSTI2024

Benchmarking machine learning strategies for phase-field problems

Abstract We present a comprehensive benchmarking framework for evaluating machine-learning approaches applied to phase-field problems. This framework focuses on four key analysis areas crucial for assessing the performance of such approaches in a systematic and structured way. Firstly, interpolation tasks are examined to identify trends in prediction accuracy and accumulation of error over simulation time. Secondly, extrapolation tasks are also evaluated according to the same metrics. Thirdly, the relationship between model performance and data requirements is investigated to understand the impact on predictions and robustness of these approaches. Finally, systematic errors are analyzed to identify specific events or inadvertent rare events triggering high errors. Quantitative metrics evaluating the local and global description of the microstructure evolution, along with other scalar metrics representative of phase-field problems, are used across these four analysis areas. This benchmarking framework provides a path to evaluate the effectiveness and limitations of machine-learning strategies applied to phase-field problems, ultimately facilitating their practical application.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Scenario adaptive disruption prediction study for next generation burning-plasma tokamaks

Next generation High Performance (HP) tokamaks risk damage from unmitigated disruptions at high current and power. Achieving reliable disruption prediction for a device's HP operation based on its Low Performance (LP) data is a key to its success. In this letter, through explorative data analysis and dedicated numerical experiments on multiple existing tokamaks, we demonstrate how the operational regimes of tokamaks can affect the power of a trained disruption predictor. First, our results suggest data-driven disruption predictors trained on abundant LP discharges work poorly on the HP regime of the same tokamak, which is a consequence of the distinct distributions of the tightly correlated signals related to disruptions in these two regimes. Second, we find that matching operational parameters among tokamaks strongly improves cross-machine accuracy which implies our model learns from the underlying scalings of dimensionless physics parameters like q 95 , β p and confirms the importance of these parameters in disruption physics and cross machine domain matching from the data-driven perspective. Finally, our results show in the absence of HP data from the target devices, the best predictivity of the HP regime for the target machine can be achieved by combining LP data from the target with HP data from other machines. Furthermore, these results provide a possible disruption predictor development strategy for next generation tokamaks, such as ITER and SPARC, and highlight the importance of developing baseline scenario discharges of future tokamaks on existing machines to collect more relevant disruptive data.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Using AI to predict calibration constants for the central drift chamber in GlueX at Jefferson Lab

The AI for Experimental Controls project team at Jefferson Lab has developed an AI system to control and calibrate a large drift chamber system in near-real time. The AI system will monitor environmental and experimental variables to recommend voltage settings that maintain consistent dE/dx gain and optimal resolution throughout the experiment. At present, calibrations are performed after data have been recorded and require a considerable amount of time and attention from experts. The calibrations currently require multiple iterations and depend on accurate tracking information. Our approach uses environmental data, such as atmospheric pressure and gas temperature, and beam conditions, such as the flux of incident particles, as inputs to a Gaussian Process Regression (GPR) model. For the data taken during the GlueX 2020 run period, the GPR is able to predict the existing gain correction factors to within 3.5%. This talk will briefly describe the development, testing, and future plans for this system at Jefferson Lab.

Jeske, Torri↗
DOE OSTI2025

Evaluation of a generalized least squares algorithm for infrasound beamforming with coherent background noise

Infrasonic signals of interest can occur during periods with persistent, coherent, background noise, which may be natural or anthropogenic. For high signal-to-noise (SNR) ratio transient signals, an ‘overprinting’ of the coherent background may occur, and the signal may still be detected. However, this approach fails for low SNR signals of interest, which may be obscured by coherent noise. An infrasound beamforming method based on generalized least squares (GLS) is investigated for detecting transient signals of interest in the presence of coherent and incoherent background noise. This approach relies on an estimate of the noise covariance, captured in a covariance matrix, to effectively null contributions to the array response from noisy directions of arrival. Synthetic array data is used to investigate the performance of the GLS beamformer compared to the Bartlett beamformer when coherent and incoherent backgrounds are present. Additionally, the effects of array element number and relative strength of the interfering signal on the GLS estimates is investigated. GLS empirical area under the curve estimates suggest that the beamformer can recover coherent power for a signal of interest lower in amplitude than the coherent background, but this effectiveness degrades more quickly with SNR for a four element array compared to a six or eight element infrasound array. Finally, infrasound from the Forensic Surface Experiment, a bolide signal observed at IMS array I37NO, and a volcanic signal recorded at the Alaska Volcano Observatory array ADKI are used to evaluate GLS performance on recorded data. A ten minute window was used to capture the background noise, and the coherent background signal was nulled in all three examples.

58 GEOSCIENCES↗
DOE OSTI2021

A composite likelihood approach for inference under photometric redshift uncertainty

ABSTRACT Obtaining accurately calibrated redshift distributions of photometric samples is one of the great challenges in photometric surveys like LSST, Euclid, HSC, KiDS, and DES. We present an inference methodology that combines the redshift information from the galaxy photometry with constraints from two-point functions, utilizing cross-correlations with spatially overlapping spectroscopic samples, and illustrate the approach on CosmoDC2 simulations. Our likelihood framework is designed to integrate directly into a typical large-scale structure and weak lensing analysis based on two-point functions. We discuss efficient and accurate inference techniques that allow us to scale the method to the large samples of galaxies to be expected in LSST. We consider statistical challenges like the parametrization of redshift systematics, discuss and evaluate techniques to regularize the sample redshift distributions, and investigate techniques that can help to detect and calibrate sources of systematic error using posterior predictive checks. We evaluate and forecast photometric redshift performance using data from the CosmoDC2 simulations, within which we mimic a DESI-like spectroscopic calibration sample for cross-correlations. Using a combination of spatial cross-correlations and photometry, we show that we can provide calibration of the mean of the sample redshift distribution to an accuracy of at least 0.002(1 + z), consistent with the LSST-Y1 science requirements for weak lensing and large-scale structure probes.

(cosmology:) large-scale structure of Universe↗
DOE OSTI2023

Sky subtraction in an era of low surface brightness astronomy

ABSTRACT The Vera C. Rubin Observatory Wide-Fast Deep sky survey will reach unprecedented surface brightness depths over tens of thousands of square degrees. Surface brightness photometry has traditionally been a challenge. Current algorithms which combine object detection with sky estimation systematically oversubtract the sky, biasing surface brightness measurements at the faint end and destroying or severely compromising low surface brightness light. While it has recently been shown that properly accounting for undetected faint galaxies and the wings of brighter objects can in principle recover a more accurate sky estimate, this has not yet been demonstrated in practice. Obtaining a consistent spatially smooth underlying sky estimate is particularly challenging in the presence of representative distributions of bright and faint objects. In this paper, we use simulations of crowded and uncrowded fields designed to mimic Hyper Suprime-Cam data to perform a series of tests on the accuracy of the recovered sky. Dependence on field density, galaxy type, and limiting flux for detection are all considered. Several photometry packages are utilized: source extractor, gnuastro, and the LSST science pipelines. Each is configured in various modes, and their performance at extreme low surface brightness analysed. We find that the combination of the source extractor software package with novel source model masking techniques consistently produce extremely faint output sky estimates, by up to an order of magnitude, as well as returning high fidelity output science catalogues.

Astronomy & Astrophysics↗
DOE OSTI2025

Search for 𝛾⁢𝐻 production and constraints on the Yukawa couplings of light quarks to the Higgs boson

A search for 𝛾⁢𝐻 production is performed with data from the CMS experiment at the LHC corresponding to an integrated luminosity of 138 fb −1 at a proton-proton center-of-mass collision energy of 13 TeV. The analysis focuses on the topology of a boosted Higgs boson recoiling against a high-energy photon. The final states of 𝐻 → $b\bar{b}$ and 𝐻 → 4⁢ℓ are analyzed. This study examines effective 𝐻⁡𝑍⁢𝛾 and 𝐻⁡𝛾⁢𝛾 anomalous couplings within the context of an effective field theory. In this approach, the production cross section is constrained to be 𝜎 𝛾⁢𝐻 < 16.4 fb at 95% confidence level (CL). Simultaneous constraints on four anomalous couplings involving 𝐻⁡𝑍⁢𝛾 and 𝐻⁡𝛾⁢𝛾 are provided. Additionally, the production rate for 𝐻 → 4⁢ℓ is examined to assess potential enhancements in the Yukawa couplings between light quarks and the Higgs boson. Assuming the standard model values for the Yukawa couplings of the bottom and top quarks, the following simultaneous constraints are obtained: 𝜅 𝑢 =(0.0 ± 1.5) ×10 3 , 𝜅 𝑑 = (0.0$^{+6.7}_{−6.8}$) × 10 2 , 𝜅 𝑠 = 0$^{+30}_{−32}$, and 𝜅 𝑐 =0.0$^{+2.3}_{−2.8}$. This rules out the hypothesis that up- or down-type quarks in the first or second generation have the same Yukawa couplings as those in the third generation, with a CL greater than 95%.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Search for charged lepton flavor violating 𝑍 and 𝑍′ boson decays in proton-proton collisions at $\sqrt{s}$ = 13 TeV

A search for flavor violating decays of the 𝑍 boson to charged leptons is performed using data from proton-proton collisions at $\sqrt{s}$ =13 TeV collected with the CMS detector at the LHC, corresponding to an integrated luminosity of 138 fb −1 . Each of the decays 𝑍 → 𝑒⁢𝜇, 𝑍 → 𝑒⁢𝜏, and 𝑍 → 𝜇⁢𝜏 is considered. The data are consistent with the backgrounds expected from standard model processes. For the 𝑍 → 𝑒⁢𝜇 channel the observed (expected) 95% confidence level upper limit on the branching fraction is 1.9⁢(2.0) × 10 −7 , which is the most stringent direct limit to date on this process; the corresponding limits for the 𝑍 → 𝑒⁢𝜏 and 𝑍 → 𝜇⁢𝜏 channels are 13.8⁢(11.4) × 10 −6 and 12.0⁢(5.3) × 10 −6 , respectively. Additionally, the 𝑒⁢𝜇 final state is used to search for lepton flavor violating decays of 𝑍′ resonances in the mass range from 110 to 500 GeV. No significant excess is observed above the predicted background levels.

Hayrapetyan, A. [Yerevan Physics Institute]↗
DOE OSTI2026

Simultaneous Probe of the Charm and Bottom Quark Yukawa Couplings Using $t\bar{t}$𝐻 Events

A search for the standard model Higgs boson decaying to a charm quark-antiquark pair, 𝐻→$c\bar{c}$, produced in association with a top quark-antiquark pair ($t\bar{t}$𝐻) is presented. The search is performed with data from proton-proton collisions at √𝑠 =13 TeV, corresponding to an integrated luminosity of 138 fb−1. Advanced machine learning techniques are employed for jet flavor identification and event classification. The Higgs boson decay to a bottom quark-antiquark pair is measured simultaneously and the observed $t\bar{t}$𝐻(𝐻→$b\bar{b}$) event rate relative to the standard model expectation is 0.91$^{+0.26}_{−0.22}$. The observed (expected) upper limit on the product of production cross section and branching fraction 𝜎⁡($t\bar{t}$𝐻)⁢ℬ⁡(𝐻→$c\bar{c}$) is 0.11 (0.13) pb at 95% confidence level, corresponding to 7.8 (8.7) times the standard model prediction. When combined with the previous search for 𝐻 →$c\bar{c}$ via associated production with a 𝑊 or 𝑍 boson, the observed (expected) 95% confidence interval on the Higgs-charm Yukawa coupling modifier, 𝜅 𝑐 , is |𝜅 𝑐 | < 3.5 (2.7), the most stringent constraint to date.

Bottom quark↗
DOE OSTI2025

First Measurement of the Electron-Neutrino Charged-Current Pion Production Cross Section on Carbon with the T2K Near Detector

The T2K Collaboration presents the first measurement of electron neutrino-induced charged-current pion production on a predominantly carbon target in a restricted kinematical phase space. This is performed using data from the 2.5° off-axis near detector, ND280. The differential cross sections with respect to the outgoing electron and pion kinematics, in addition to the total flux-integrated cross section, are obtained. Comparisons between the measured and predicted cross-section results using the neut, genie, and nuwro Monte Carlo event generators are presented. The measured total flux-integrated cross section is [2.52 ± 0.52⁢(stat) ± 0.30⁢(syst)] × 10 −39 cm 2 nucleon −1 , which is lower than the event generator predictions.

Neutrinos↗
DOE OSTI2021

Local integrals of motion and the quasiperiodic many-body localization transition

We study the many-body localization (MBL) transition for interacting fermions subject to quasiperiodic potentials by constructing the local integrals of motion (LIOMs) in the MBL phase as time-averaged local operators. Here, we study numerically how these time-averaged operators evolve across the MBL transition. We find that the norm of such time-averaged operators drops discontinuously to zero across the transition; as we discuss, this implies that LIOMs abruptly become unstable at some critical localization length of order unity. We analyze the LIOMs using hydrodynamic projections and isolating the part of the operator that is associated with interactions. Equipped with these data we perform a finite-size scaling analysis of the quasiperiodic MBL transition. Our results suggest that the quasiperiodic MBL transition occurs at considerably stronger quasiperiodic modulations and has a larger correlation-length critical exponent than previous studies had found.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Antiferromagnetic character of the quantum phase transition in the Hubbard model on the honeycomb lattice

In this work we provide a unified, comprehensive treatment of all operators that contribute to the antiferromagnetic, ferromagnetic, and charge-density-wave structure factors and order parameters of the hexagonal Hubbard Model. We use the Hybrid Monte Carlo algorithm to perform a systematic, carefully controlled analysis in the temporal Trotter error and of the thermodynamic limit. We expect our findings to improve the consistency of Monte Carlo determinations of critical exponents. We perform a data collapse analysis and determine the critical exponent β = 0.898 (37) for the semimetal-Mott insulator transition in the hexagonal Hubbard Model. Our methods are applicable to a wide range of lattice theories of strongly correlated electrons.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Constraining $\mathcal{f(R)}$ gravity with a $\mathcal{k}$-cut cosmic shear analysis of the Hyper Suprime-Cam first-year data

Here, using Subaru Hyper Suprime-Cam (HSC) year 1 data, we perform the first k-cut cosmic shear analysis constraining both Λ CDM and f(R) Hu-Sawicki modified gravity. To generate the f(R) cosmic shear theory vector, we use the matter power spectrum emulator trained on COLA (COmoving Lagrangian Acceleration) simulations [Phys. Rev. D 103, 123525 (2021)]. The k-cut method is used to significantly down-weight sensitivity to small scale (k > 1h Mpc -1 ) modes in the matter power spectrum where the emulator is less accurate, while simultaneously ensuring our results are robust to baryonic feedback model uncertainty. We have also developed a test to ensure that the effects of poorly modeled small scales are nulled as intended. For Λ CDM we find S 8 =σ 8 (Ω m /0.3) 0.5 =0.789$^{+0.039}_{-0.022}$, while the constraints on the f(R) modified gravity parameters are prior dominated. In the future, the k-cut method could be used to constrain a large number of theories of gravity where computational limitations make it infeasible to model the matter power spectrum down to extremely small scales.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Are there correlations in the HAWC and IceCube high energy skymaps outside the Galactic plane?

We use publicly available data to perform a search for correlations of high energy neutrino candidate events detected by IceCube and high-energy photons seen by the HAWC Collaboration. Our search is focused on unveiling such correlations outside of the Galactic plane. This search is sensitive to correlations in the neutrino candidate and photon skymaps which would arise from a population of unidentified point sources. We find no evidence for such a correlation, but suggest strategies for improvements with new datasets. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2024

Impact of dark matter on tidal signatures in neutron star mergers with the Einstein Telescope

If dark matter (DM) accumulates inside neutron stars (NS), it changes their internal structure and causes a shift of the tidal deformability from the value predicted by the dense-matter equation of state (EOS). In principle, this shift could be observable in the gravitational-wave (GW) signal of binary neutron star (BNS) mergers. We investigate the effect of fermionic, noninteracting DM when observing a large number of GW events from DM-admixed BNSs with the precision of the proposed Einstein telescope (ET). Specifically, we study the impact on the recovery of the baryonic EOS and whether DM properties can be constrained. For this purpose, we create event catalogs of BNS mock events with DM fraction up to 1%, from which we reconstruct the posterior uncertainties with the Fisher matrix approach. Using this data, we perform joint Bayesian inference on the baryonic EOS, DM particle mass, and DM particle fraction in each event. Here, our results reveal that when falsely ignoring DM effects, the EOS posterior is biased toward softer EOSs, though the offset is rather small. Further, we find that within our assumptions of our DM model and population, ET will likely not be able to test the presence of DM in BNSs, even when combining many events and adding Cosmic Explorer (CE) to the next-generation detector network. Likewise, the potential constraints on the DM particle mass will remain weak because of degeneracies with the fraction and EOS.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗