Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Deep”

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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

DEEP CHANDRA, HST-COS, AND MEGACAM OBSERVATIONS OF THE PHOENIX CLUSTER: EXTREME STAR FORMATION AND AGN FEEDBACK ON HUNDRED KILOPARSEC SCALES

We present new ultraviolet, optical, and X-ray data on the Phoenix galaxy cluster (SPT-CLJ2344-4243). Deep optical imaging reveals previously undetected filaments of star formation, extending to radii of ∼50–100 kpc in multiple directions. Combined UV-optical spectroscopy of the central galaxy reveals a massive (2 × 10{sup 9} M{sub ⊙}), young (∼4.5 Myr) population of stars, consistent with a time-averaged star formation rate of 610 ± 50 M{sub ⊙} yr{sup −1}. We report a strong detection of O vi λλ1032,1038, which appears to originate primarily in shock-heated gas, but may contain a substantial contribution (>1000 M{sub ⊙} yr{sup −1}) from the cooling intracluster medium (ICM). We confirm the presence of deep X-ray cavities in the inner ∼10 kpc, which are among the most extreme examples of radio-mode feedback detected to date, implying jet powers of 2–7 × 10{sup 45} erg s{sup −1}. We provide evidence that the active galactic nucleus inflating these cavities may have only recently transitioned from “quasar-mode” to “radio-mode,” and may currently be insufficient to completely offset cooling. A model-subtracted residual X-ray image reveals evidence for prior episodes of strong radio-mode feedback at radii of ∼100 kpc, with extended “ghost” cavities indicating a prior epoch of feedback roughly 100 Myr ago. This residual image also exhibits significant asymmetry in the inner ∼200 kpc (0.15R{sub 500}), reminiscent of infalling cool clouds, either due to minor mergers or fragmentation of the cooling ICM. Taken together, these data reveal a rapidly evolving cool core which is rich with structure (both spatially and in temperature), is subject to a variety of highly energetic processes, and yet is cooling rapidly and forming stars along thin, narrow filaments.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Measurement of the Target-Normal Single-Spin Asymmetry in Deep-Inelastic Scattering from the Reaction He 3 ↑ ( e , e ' ) X

We report here the first measurement of the target-normal single-spin asymmetry in deep-inelastic scattering from the inclusive reaction He 3 ↑ ( e , e ' ) X on a polarized He 3 gas target. Assuming time-reversal invariance, this asymmetry is strictly zero in the Born approximation but can be nonzero if two-photon-exchange contributions are included. The experiment, conducted at Jefferson Lab using a 5.89 GeV electron beam, covers a range of 1.7 < W < 2.9 GeV , 1.0 < Q 2 < 4.0 GeV 2 and 0.16 < x < 0.65 . Neutron asymmetries were extracted using the effective nucleon polarization and measured proton-to- He 3 cross-section ratios. The measured neutron asymmetries shown are negative with an average value of ( - 1.09 ± 0.38 ) × 1 0 - 2 for invariant mass W > 2 GeV , which is nonzero at the 2.89 σ level. Our measured asymmetry agrees both in sign and magnitude with a two-photon-exchange model prediction that uses input from the Sivers transverse momentum distribution obtained from semi-inclusive deep-inelastic scattering.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

A PDF-Based Parameterization of Subgrid-Scale Hydrometeor Transport in Deep Convection

Abstract A parameterization scheme is proposed for the subgrid-scale transport of hydrometeors in an assumed probability density function (PDF) scheme. Joint distributions of vertical velocity and hydrometeor mixing ratios are typically unknown, but marginal (1D) PDFs of these variables are available. The parameterization is developed using high-resolution simulations of continental and tropical deep convection. A 3D cloud-resolving model (CRM) providing benchmark solutions has a horizontal grid spacing of 250 m and employs the Morrison microphysics scheme, which treats prognostically mass and number mixing ratios for four types of precipitating hydrometeors (rain, graupel, snow, and ice) as well as cloud droplet number mixing ratio. The subgrid-scale hydrometeor transport scheme assumes input given in the form of marginal PDFs of vertical velocity and hydrometeor mixing ratios; in this study, these marginal distributions are provided by the cloud-resolving model. Conditional sampling and scaling are then applied to the marginal distributions to account for subplume correlations. The parameterized fluxes tested for four episodes of deep convection show good agreement with benchmark fluxes computed directly from the CRM output. The results demonstrate the potential use of the subgrid-scale hydrometeor transport scheme in an assumed PDF scheme to parameterize the covariances of vertical velocity and hydrometeor mixing ratios.

Wong, May↗
DOE OSTI2026

Data for FUN-PROSE: A Deep Learning Approach to Predict Condition-Specific Gene Expression in Fungi

mRNA levels of all genes in a genome is a critical piece of information defining the overall state of the cell in a given environmental condition. Being able to reconstruct such condition-specific expression in fungal genomes is particularly important to metabolically engineer these organisms to produce desired chemicals in industrially scalable conditions. Most previous deep learning approaches focused on predicting the average expression levels of a gene based on its promoter sequence, ignoring its variation across different conditions. Here we present FUN-PROSE—a deep learning model trained to predict differential expression of individual genes across various conditions using their promoter sequences and expression levels of all transcription factors. We train and test our model on three fungal species and get the correlation between predicted and observed condition-specific gene expression as high as 0.85. We then interpret our model to extract promoter sequence motifs responsible for variable expression of individual genes. We also carried out input feature importance analysis to connect individual transcription factors to their gene targets. A sizeable fraction of both sequence motifs and TF-gene interactions learned by our model agree with previously known biological information, while the rest corresponds to either novel biological facts or indirect correlations.

Genomics↗
DOE OSTI2022

Deep Realistic Extragalactic Model (DREaM) Galaxy Catalogs: Predictions for a Roman Ultra-deep Field

In the next decade, deep galaxy surveys from telescopes such as the James Webb Space Telescope and Roman Space Telescope will provide transformational data sets that will greatly enhance the understanding of galaxy formation during the epoch of reionization (EoR). In this work, we present the Deep Realistic Extragalactic Model (DREaM) for creating synthetic galaxy catalogs. Our model combines dark matter simulations, subhalo abundance matching and empirical models, and includes galaxy positions, morphologies, and spectral energy distributions. The resulting synthetic catalog extends to redshifts z ~ 12, and galaxy masses log 10 (M/M ⊙ ) = 5 covering an area of 1 deg 2 on the sky. We use DREaM to explore the science returns of a 1 deg 2 Roman ultra-deep field (UDF), and to provide a resource for optimizing ultra-deep survey designs. We find that a Roman UDF to ~30 m AB will potentially detect more than 10 6 M UV < -17 galaxies, with more than 10 4 at redshifts z > 7, offering an unparalleled data set for constraining galaxy properties during the EoR. Our synthetic catalogs and simulated images are made publicly available to provide the community with a tool to prepare for upcoming data.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2021

Scheduling the NASA Deep Space Network with Deep Reinforcement Learning

With three complexes spread evenly across the Earth, NASA’s Deep Space Network (DSN) is the primary means of communications as well as a significant scientific instrument for dozens of active missions around the world. A rapidly rising number of spacecraft and increasingly complex scientific instruments with higher bandwidth requirements have resulted in demand that exceeds the network’s capacity across its 12 antennae. The existing DSN scheduling process operates on a rolling weekly basis and is time-consuming; for a given week, generation of the final baseline schedule of spacecraft tracking passes takes roughly 5 months from the initial requirements submission deadline, with several weeks of peer-to-peer negotiations in between. This paper proposes a deep reinforcement learning (RL) approach to generate candidate DSN schedules from mission requests and spacecraft ephemeris data with demonstrated capability to address real-world operational constraints. A deep RL agent is developed that takes mission requests for a given week as input, and interacts with a DSN scheduling environment to allocate tracks such that its reward signal is maximized. A comparison is made between an agent trained using Proximal Policy Optimization and its random, untrained counterpart. The results represent a proof-of-concept that, given a well-shaped reward signal, a deep RL agent can learn the complex heuristics used by experts to schedule the DSN. A trained agent can potentially be used to generate candidate schedules to bootstrap the scheduling process and thus reduce the turnaround cycle for DSN scheduling.

Wilson, Brian↗
DOE OSTI2014

Evaluation of Cloud-resolving and Limited Area Model Intercomparison Simulations using TWP-ICE Observations. Part 1: Deep Convective Updraft Properties

Ten 3D cloud-resolving model (CRM) simulations and four 3D limited area model (LAM) simulations of an intense mesoscale convective system observed on January 23-24, 2006 during the Tropical Warm Pool – International Cloud Experiment (TWP-ICE) are compared with each other and with observed radar reflectivity fields and dual-Doppler retrievals of vertical wind speeds in an attempt to explain published results showing a high bias in simulated convective radar reflectivity aloft. This high bias results from ice water content being large, which is a product of large, strong convective updrafts, although hydrometeor size distribution assumptions modulate the size of this bias. Snow reflectivity can exceed 40 dBZ in a two-moment scheme when a constant bulk density of 100 kg m-3 is used. Making snow mass more realistically proportional to area rather than volume should somewhat alleviate this problem. Graupel, unlike snow, produces high biased reflectivity in all simulations. This is associated with large amounts of liquid water above the freezing level in updraft cores. Peak vertical velocities in deep convective updrafts are greater than dual-Doppler retrieved values, especially in the upper troposphere. Freezing of large rainwater contents lofted above the freezing level in simulated updraft cores greatly contributes to these excessive upper tropospheric vertical velocities. Strong simulated updraft cores are nearly undiluted, with some showing supercell characteristics. Decreasing horizontal grid spacing from 900 meters to 100 meters weakens strong updrafts, but not enough to match observational retrievals. Therefore, overly intense simulated updrafts may partly be a product of interactions between convective dynamics, parameterized microphysics, and large-scale environmental biases that promote different convective modes and strengths than observed.

clould-resolving↗
DOE OSTI2018

Automated correlative segmentation of large Transmission X-ray Microscopy (TXM) tomograms using deep learning

Highlights: • A unique automated segmentation approach for large 3D nanotomography datasets obtained by Transmission X-ray Microscopy (TXM) in an Al-Cu alloy. • Automated segmentation using a Convolutional Neural Network (CNN) architecture based on a deep learning approach was employed. • This extremely versatile technique is capable of emulating the manual segmentation process effectively. • Quantitative comparison between manual segmentation and CNN architecture established the accuracy of this automated technique. • Its ability to robustly process ultra-large volumes of data in relatively small time frames can exponentially accelerate tomographic data analysis. - Abstract: A unique correlative approach for automated segmentation of large 3D nanotomography datasets obtained using Transmission X-ray Microscopy (TXM) in an Al-Cu alloy has been introduced. Automated segmentation using a Convolutional Neural Network (CNN) architecture based on a deep learning approach was employed. This extremely versatile technique is capable of emulating the manual segmentation process effectively. Coupling this technique with post-scanning SEM imaging ensured precise estimation of 3D morphological parameters from nanotomography. The segmentation process as well as subsequent analysis was expedited by several orders of magnitude. Quantitative comparison between segmentation performed manually and using the CNN architecture established the accuracy of this automated technique. Its ability to robustly process ultra-large volumes of data in relatively small time frames can exponentially accelerate tomographic data analysis, possibly opening up novel avenues for performing 4D characterization experiments with finer time steps.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Bayesian Optimized Deep Ensemble for Uncertainty Quantification of Deep Neural Networks: a System Safety Case Study on Sodium Fast Reactor Thermal Stratification Modeling

Deep neural networks (DNNs) are increasingly important to scientific computing and engineering system simulations. Accurate uncertainty quantification (UQ) for DNNs is critical in safety-sensitive engineering domains. Traditional Deep Ensemble (DE) methods, while easy to implement, frequently suffer from poorly calibrated uncertainty estimates and limited predictive accuracy due to reliance on fixed architectures with varied weight initializations. To address these issues, we introduce a workflow that combines Bayesian Optimization (BO) and DE. The workflow is modular, scalable, and integrates parallel BO initialized with Sobol sequences to individually optimize the hyperparameters of each ensemble member. This method enhances ensemble diversity, improves predictive accuracy, and provides reliable uncertainty estimates. We evaluate the proposed BODE approach in a sodium fast reactor thermal stratification modeling case study, where we used a densely connected convolutional neural network to predict turbulent viscosity during the reactor transient with consideration of data noise. We benchmark its performance against several optimization approaches, including baseline deep ensemble, evolutionary algorithm-optimized ensemble, ensemble formed via random search combined with greedy selection, and a BO ensemble using random initialization. Here, our results demonstrate superior performance of the developed BODE approach. In noise-free scenarios, BODE notably reduces incorrect aleatoric uncertainty and significantly enhances predictive accuracy. Under conditions of 5% and 10% Gaussian noise, BODE adaptively quantifies uncertainty proportional to data noise, achieving up to an 80% reduction in root mean square error compared to baseline methods and producing well-calibrated prediction intervals.

Bayesian optimization↗
DOE OSTI2016

Deep levels in as-grown and electron-irradiated n-type GaN studied by deep level transient spectroscopy and minority carrier transient spectroscopy

Development of high performance GaN-based devices is strongly dependent on the possibility to control and understand defects in material. Important information about deep level defects is obtained by deep level transient spectroscopy and minority carrier transient spectroscopy on as-grown and electron irradiated n-type bulk GaN with low threading dislocation density produced by halide vapor phase epitaxy. One hole trap labelled H1 (E{sub V} + 0.34 eV) has been detected on as-grown GaN sample. After 2 MeV electron irradiation, the concentration of H1 increases and at fluences higher than 5 × 10{sup 14 }cm{sup −2}, a second hole trap labelled H2 is observed. Simultaneously, the concentration of two electron traps, labelled T1 (E{sub C} – 0.12 eV) and T2 (E{sub C} – 0.23 eV), increases. By studying the increase of the defect concentration versus electron irradiation fluence, the introduction rate of T1 and T2 using 2 MeV- electrons was determined to be 7 × 10{sup −3 }cm{sup −1} and 0.9 cm{sup −1}, respectively. Due to the low introduction rate of T1, it is suggested that the defect is associated with a complex. The high introduction rate of trap H1 and T2 suggests that the defects are associated with primary intrinsic defects or complexes. Some deep levels previously observed in irradiated GaN layers with higher threading dislocation densities are not detected in present investigation. It is therefore suggested that the absent traps may be related to primary defects segregated around dislocations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Aerosol transport and wet scavenging in deep convective clouds: a case study and model evaluation using a multiple passive tracer analysis approach

The effect of wet scavenging on ambient aerosols in deep, continental convective clouds in the mid-latitudes is studied for a severe storm case in Oklahoma during the Deep Convective Clouds and Chemistry (DC3) field campaign. A new passive-tracer based transport analysis framework is developed to characterize the convective transport based on the vertical distribution of several slowly reacting and nearly insoluble trace gases. The passive gas concentration in the upper troposphere convective outflow results from a mixture of 47% from the lower level (0-3 km), 21% entrained from the upper troposphere, and 32% from mid-atmosphere based on observations. The transport analysis framework is applied to aerosols to estimate aerosol transport and wet-scavenging efficiency. Observations yield high overall scavenging efficiencies of 81% and 68% for aerosol mass (Dp < 1μm) and aerosol number (0.03< Dp < 2.5μm), respectively. Little chemical selectivity to wet scavenging is seen among observed submicron sulfate (84%), organic (82%), and ammonium (80%) aerosols, while nitrate has a much lower scavenging efficiency of 57% likely due to the uptake of nitric acid. Observed larger size particles (0.15 - 2.5μm) are scavenged more efficiently (84%) than smaller particles (64%; 0.03 - 0.15μm). The storm is simulated using the chemistry version of the WRF model. Compared to the observation based analysis, the standard model underestimates the wet scavenging efficiency for both mass and number concentrations with low biases of 31% and 40%, respectively. Adding a new treatment of secondary activation significantly improves simulation results, so that the bias in scavenging efficiency in mass and number concentrations is reduced to <10%. This supports the hypothesis that secondary activation is an important process for wet removal of aerosols in deep convective storms.

Yang, Qing↗
DOE OSTI2014

Silicate deposition during decomposition of cyanobacteria may promote export of picophytoplankton to the deep ocean

Marine particles transport organic matter through the water column to the sediment where the organic matter can be buried. This pathway is one of the few natural removal mechanisms of CO 2 from the atmosphere over geological time. Picophytoplankton, major primary producers in the ocean, have until recently been thought unimportant regarding particle transport. Here we provide evidence that silicate is deposited on extracellular polymeric substance (EPS) associated with decomposing picophytoplankton. We also find that Si is enriched in a previously unexplored group of marine particles (called micro-blebs) from the deep-water column. The surprising similarity in morphology and composition between EPS-Si and micro-blebs suggests that EPS-Si may be a precursor of micro-blebs observed in the deep ocean. Finally, this previously unexplored source of silicon may be important to silicon cycling and may further enhance export of picophytoplankton to the deep ocean.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2018

Deep crustal heating by neutrinos from the surface of accreting neutron stars

Here, we present a new mechanism for deep crustal heating in accreting neutron stars. Charged pions (π + ) are produced in nuclear collisions on the neutron star surface during active accretion and upon decay they provide a flux of neutrinos into the neutron star crust. For massive and/or compact neutron stars, neutrinos deposit ≈ 1 – 2 MeV of heat per accreted nucleon into the inner crust. The strength of neutrino heating is comparable to the previously known sources of deep crustal heating, such as from pycnonuclear fusion reactions, and is relevant for studies of cooling neutron stars. We model the thermal evolution of a transient neutron star in a low-mass x-ray binary, and in the particular case of the neutron star MXB 1659-29 we show that additional deep crustal heating requires a higher thermal conductivity for the neutron star inner crust. A better knowledge of pion-production cross sections near threshold would improve the accuracy of our predictions.

79 ASTRONOMY AND ASTROPHYSICS↗