Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “deep potential”

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 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Phonon spectra of two-dimensional liquid dusty plasmas on a one-dimensional periodic substrate

We investigate the phonon spectra of two-dimensional liquid dusty plasmas on a one-dimensional periodic substrate using numerical simulations. The propagation of the waves across the potential wells of the substrate is inhibited due to the confinement of the dust particles by the substrate minima. If the substrate wells are narrow or deep, one-dimensional chains of particles are formed in each minimum, and the longitudinal motion of an individual chain dominates the propagation of waves along the potential wells of the substrate. Increasing the width or decreasing the depth of the substrate minima allows the particles to buckle into a zigzag structure, and the resulting spectra develop two branches, one for sloshing motion and one for breathing motion. Lastly, the repulsion between neighboring dust particles produces backward propagation of the sloshing wave for small wave numbers.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Signatures of hot carriers and hot phonons in the re-entrant metallic and semiconducting states of Moiré-gapped graphene

Abstract Stacking of graphene with hexagonal boron nitride (h-BN) can dramatically modify its bands from their usual linear form, opening a series of narrow minigaps that are separated by wider minibands. While the resulting spectrum offers strong potential for use in functional (opto)electronic devices, a proper understanding of the dynamics of hot carriers in these bands is a prerequisite for such applications. In this work, we therefore apply a strategy of rapid electrical pulsing to drive carriers in graphene/h-BN heterostructures deep into the dissipative limit of strong electron-phonon coupling. By using electrical gating to move the chemical potential through the “Moiré bands”, we demonstrate a cyclical evolution between metallic and semiconducting states. This behavior is captured in a self-consistent model of non-equilibrium transport that considers the competition of electrically driven inter-band tunneling and hot-carrier scattering by strongly non-equilibrium phonons. Overall, our results demonstrate how a treatment of the dynamics of both hot carriers and hot phonons is essential to understanding the properties of functional graphene superlattices.

42 ENGINEERING↗
DOE OSTI2024

Deep Multitask Learning Models for Radiation Estimation at High Energy Accelerator Facility

Controlling the dose of radiation exposure in potential radioactive facilities is critical for ensuring the safety of staff and the public. Here, in this paper, we developed machine learning models to estimate radiation exposure efficiently at the Thomas Jefferson National Accelerator Facility (JLab), aiming to enhance safety in both accelerator facilities and public areas. Multiple sensors were deployed around the three experimental halls at JLab. Data on single-beam currents, energy levels, and radiation values at the sensor locations were collected during accelerator operation. We proposed a multi-task learning model for radiation estimation, utilizing either one-dimensional convolutional neural networks (1-D CNNs) or long short-term memory networks (LSTMs) as the backbone. The proposed model was trained to simultaneously estimate radiation levels at the sensor locations. Experimental results demonstrated that the proposed model with LSTM backbone achieved the best estimation performance, with an average R 2 score of 0.7557 for estimation within the same year and 0.7157 for estimation across different years. These results significantly surpassed those of competing models.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2001

The Effect of Deep, Tropical Convection on the Tropical Tropopause Layer

There is evidence that most convective detrainment occurs below 355-K potential temperature (14 km). On the other hand, it is also generally accepted that convection does occasionally reach the altitude of the tropopause, typically approx. 16 km, or higher. The question is whether the relatively infrequent penetrations above 14 km are sufficient to influence the composition of the atmosphere above 14 km. Using measurements of O3 and CO as tracers of convective activity, I quantify the height distribution of mass detraining from convection near the tropopause by means of a simple mixing model similar to those used to calculate mixing into the stratospheric "tropical pipe". I calculate that significant detrainment from convection is occurring as high as approx. 380 K (16 km), an altitude typical of the tropopause. Further, I calculate that as much as 40% of the mass crossing the 380-K surface at this time detrained above 370 K. I conclude that the "mixing barrier" at 14 km is more porous than previously thought.

Dessler, A. E.↗
NASA NTRS2012

Deep Space Mission Trend Analyses: A Briefing to the Next Generation EBRE Study Team

Determination of stakeholder needs for next generation implementations necessitates a multi ]pronged approach. . Future mission set analyses provide a lower gbound h for some of these needs. . Earth ]based analogies provide an upper gbound h for some of these needs. . Interpreting the results requires being mindful of both the near ]term contextual factors and long ]term factors that are in play. . In the context of last year fs analyses, the current budget environment, the potential Pu ]238 shortage, and SMD fs gsingle 34m only h policy may, collectively, create a future deep space mission set that, from a capacity and end ]to ]end link difficulty standpoint, is no more challenging than it is today. . Nonetheless, data rates and volumes continue to increase, suggesting capability and spectrum challenges ahead. These results agree with the results from the Earthbased analogies. . Emerging developments such as smallsats and distributed spacecraft could significantly change the capacity and end ]to ]end link difficulty picture.

future mission analysis↗
NASA NTRS2006

High Efficiency Power Combining of Ka-Band TWTs for High Data Rate Communications

Future NASA deep space exploration missions are expected in some cases to require telecommunication systems capable of operating at very high data rates (potentially 1 Gbps or more) for the transmission back to Earth of large volumes of scientific data, which means high frequency transmitters with large bandwidth. Among the Ka band frequencies of interest are the present 500 MHz Deep Space Network (DSN) band of 31.8 to 32.3 GHz and a broader band at 37-38 GHz allocated for space science [1]. The large distances and use of practical antenna sizes dictate the need for high transmitter power of up to 1 kW or more. High electrical efficiency is also a requirement. The approach investigated by NASA GRC is a novel wave guide power combiner architecture based on a hybrid magic-T junction for combining the power output from multiple TWTs [1,2]. This architecture was successfully demonstrated and is capable of both high efficiency (90-95%, depending on frequency) and high data rate transmission (up to 622 Mbps) in a two-way power combiner circuit for two different pairs of Ka band TWTs at two different frequency bands. One pair of TWTs, tested over a frequency range of 29.1 to 29.6 GHz, consisted of two 110-115W TWTs previously used in uplink data transmission evaluation terminals in the NASA Advanced Communications Technology Satellite (ACTS) program [1,2]. The second pair was two 100W TWTs (Boeing 999H) designed for high efficiency operation (greater than 55%) over the DSN frequency band of 31.8 to 32.3 GHz [3]. The presentation will provide a qualitative description of the wave guide circuit, results for power combining and data transmission measurements, and results of computer modeling of the magic-T and alternative hybrid junctions for improvements in efficiency and power handling capability. The power combiner results presented here are relevant not only to NASA deep space exploration missions, but also to other U.S. Government agency programs.

Wintucky, E. G.↗
DOE OSTI2022

Bioplastic design using multitask deep neural networks

Abstract Non-degradable plastic waste jeopardizes our environment, yet our modern lifestyle and current technologies are impossible to sustain without plastics. Bio-synthesized and biodegradable alternatives such as polyhydroxyalkanoates (PHAs) have the potential to replace large portions of the world’s plastic supply with cradle-to-cradle materials, but their chemical complexity and diversity limit traditional resource-intensive experimentation. Here, we develop multitask deep neural network property predictors using available experimental data for a diverse set of nearly 23,000 homo- and copolymer chemistries. Using the predictors, we identify 14 PHA-based bioplastics from a search space of almost 1.4 million candidates which could serve as potential replacements for seven petroleum-based commodity plastics that account for 75% of the world’s yearly plastic production. We also discuss possible synthesis routes for the identified promising materials.

36 MATERIALS SCIENCE↗
NASA NTRS2011

Qualification of Bonding Process of Temperature Sensors to Extreme Temperature Deep Space Missions

A process has been explored based on the state-of-the-art technology to bond the platinum resistance thermometer (PRT) on to potential aerospace material such as a flat aluminum surface and a flexible copper tube to simulate coaxial cable for the flight applications. Primarily, PRTs were inserted into a metal plated copper braid to avoid stresses on the sensor while attaching the sensor with braid to the base material for long duration deep space missions. Appropriate pretreatment has been implemented in this study to enhance the adhesion of the PRTs to the base material. NuSil product has been chosen in this research to attach PRT to the base materials. The resistance (approx.1.1 k(Omega)) of PRTs has been electrically monitored continuously during the qualification thermal cycling testing from -150 C to +120 C and -100 C to -35 C. The test hardware has been thermal cycled three times the mission life per JPL design principles for JUNO project. No PRT failures were observed during and after the PRT thermal cycling qualification test for extreme temperature environments. However, there were some failures associated with staking of the PRT pig tails as a result of thermal cycling qualification test.

Extreme temperatures↗
DOE OSTI2023

Identification and preliminary characterization of conserved uncharacterized proteins from Chlamydomonas reinhardtii , Arabidopsis thaliana , and Setaria viridis

Abstract The rapid accumulation of sequenced plant genomes in the past decade has outpaced the still difficult problem of genome‐wide protein‐coding gene annotation. A substantial fraction of protein‐coding genes in all plant genomes are poorly annotated or unannotated and remain functionally uncharacterized. We identified unannotated proteins in three model organisms representing distinct branches of the green lineage (Viridiplantae): Arabidopsis thaliana (eudicot), Setaria viridis (monocot), and Chlamydomonas reinhardtii (Chlorophyte alga). Using similarity searching, we identified a subset of unannotated proteins that were conserved between these species and defined them as Deep Green proteins. Bioinformatic, genomic, and structural predictions were performed to begin classifying Deep Green genes and proteins. Compared to whole proteomes for each species, the Deep Green set was enriched for proteins with predicted chloroplast targeting signals predictive of photosynthetic or plastid functions, a result that was consistent with enrichment for daylight phase diurnal expression patterning. Structural predictions using AlphaFold and comparisons to known structures showed that a significant proportion of Deep Green proteins may possess novel folds. Though only available for three organisms, the Deep Green genes and proteins provide a starting resource of high‐value targets for further investigation of potentially new protein structures and functions conserved across the green lineage.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Assessment of desalination technologies for treatment of a highly saline brine from a potential CO 2 storage site

Brine extraction is a promising strategy for the management of increased reservoir pressure, resulting from carbon dioxide (CO 2 ) injection in deep saline reservoirs. The extracted brines usually have high concentrations of total dissolved solids (TDS) and various contaminants, and require proper disposal or treatment. In this article, first by conducting a critical review, we evaluate the applicability, limits, and advantages or challenges of various commercially available and emerging desalination technologies that can potentially be employed to treat the highly saline brine (with TDS values >70.000 ppm) and those that are applicable to a ~200,000 ppm TDS brine extracted from the Mt. Simon Sandstone, a potential CO 2 storage site in Illinois, USA. Based on the side-by-side comparison of technologies, evaporators are selected as the most suitable existing technology for treating Mt. Simon brine. Process simulations are then conducted for a conceptual design for desalination of 454 m 3 /h (2000 gpm) pretreated brine for near-zero liquid discharge by multi-effect evaporators. In conclusion, the thermal energy demand is estimated at 246kWh perm 3 of recoveredwater, ofwhich 212kWh/m 3 is required for multiple-effect evaporation and the remainder for salt drying. The process also requires additional electrical power of ~2 kWh/m 3 .

42 ENGINEERING↗
DOE OSTI2019

Stripping off the implementation complexity of physics-based model predictive control for buildings via deep learning

Over the past decade, model predictive control (MPC) has been considered as the most promising solution for intelligent building operation. Despite extensive effort, transfer of this technology into practice is hampered by the need to obtain an accurate controller model with minimum effort, the need of expert knowledge to set it up, and the need of increased computational power and dedicated software to run it. A promising direction that tackles the last two problems was proposed by approximate explicit MPC where the optimal control policies are learned from MPC data via a suitable function approximator, e.g., a deep learning (DL) model. The main advantage of the proposed approach stems from simple evaluation at execution time leading to low computational footprints and easy deployment on embedded HW platforms. We present the energy savings potential of physics-based (also called 'white-box') MPC applied to an office building in Belgium. Moreover, we demonstrate how deep learning approximators can be used to cut the implementation and maintenance costs of MPC deployment without compromising performance. We also critically assess the presented approach by pointing out the major challenges and remaining open-research questions.

Drgona, Jan↗
NASA NTRS2016

A Quantum-Assisted Algorithm for Sampling Applications in Machine Learning

An increase in the efficiency of sampling from Boltzmann distributions would have a significant impact in deep learning and other machine learning applications. Recently, quantum annealers have been proposed as a potential candidate to speed up this task, but several limitations still bar these state-of-the-art technologies from being used effectively. One of the main limitations is that, while the device may indeed sample from a Boltzmann-like distribution, quantum dynamical arguments suggests it will do so with an instance-dependent effective temperature, different from the physical temperature of the device. Unless this unknown temperature can be unveiled, it might not be possible to effectively use a quantum annealer for Boltzmann sampling. In this talk, we present a strategy to overcome this challenge with a simple effective-temperature estimation algorithm. We provide a systematic study assessing the impact of the effective temperatures in the learning of a kind of restricted Boltzmann machine embedded on quantum hardware, which can serve as a building block for deep learning architectures. We also provide a comparison to k-step contrastive divergence (CD-k) with k up to 100. Although assuming a suitable fixed effective temperature also allows to outperform one step contrastive divergence (CD-1), only when using an instance-dependent effective temperature we find a performance close to that of CD-100 for the case studied here. We discuss generalizations of the algorithm to other more expressive generative models, beyond restricted Boltzmann machines.

Perdomo-Ortiz, Alejandro↗
DOE OSTI2019

Investigation of Hydrometeor Distributions in Continental and Maritime Storm Systems: Application of Cloud-Resolving Simulations, a Polarimetric Scanning Radar Simulator and Polarimetric Radar Observations

Detailed observations from DOE ARM field campaigns were used to evaluate and improve microphysical and dynamic fields simulated by 3D Cloud Resolving Models (CRMs), employing both bulk and size-resolved bin microphysics. One of the main challenges in bridging the gap between observations and models is placing the analysis in a common framework. To address this, a polarimetric radar simulator was built, consisting of a forward operator to calculate model-consistent polarimetric radar fields, and an analysis package to calculate microphysical and dynamic fields from radar data in a self-consistent manner. The assumptions required for the transformation from model output to radar parameters were carefully considered and tested. It was found that although rain is fairly well captured by the forward model, ice characteristics are difficult to constrain. This polarimetric radar simulator, POLArimetric Radar Retrieval and Instrument Simulator (POLARRIS), was then applied to WRF simulations and polarimetric C-band radar observations from Darwin, Australia and the SGP site in Oklahoma. These two locations contrast maritime and mid-latitude continental convection respectively. POLARRIS was used with NASA Unified WRF simulations of the same storms in order to compare the observed land and ocean contrasts in microphysics and dynamics. An improved model framework for dynamic downscaling of large-scale aerosol products was also developed and applied to the NASA Unified WRF (NU-WRF) cloud resolving model. This improved framework was used to explore the effects of thermodynamic and aerosol invigoration in deep land and ocean convection through a series of sensitivity studies by perturbating aerosol (CCN) and convective available potential energy (CAPE). It was found that increases in aerosol concentrations yielded increases in low-density graupel in the maritime case and high-density hail in the continental case through riming by increased supercooled water. This result implies that native differences in aerosol concentrations may enhance the inherent contrast in riming effects between deep convective systems in the maritime and continental conditions. Formal publications were generated describing this research.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1977

Diterpenoid compounds and other lipids in deep-sea sediments and their geochemical significance

Cyclic diterpenoid compounds have been found by various investigators in the geosphere (e.g., fossil resins, coals, soil, shale, and deep-sea sediments). These compounds occur in significant amounts only in higher plants and are therefore potential markers of terrigenous plant lipids. Diterpenoids with the abietane skeleton (mainly dehydroabietic acid) have been identified in the lipids of sediment samples from the northeast Pacific Ocean, Black Sea, and North Atlantic Ocean. The presence of these resin-derived compounds was correlated with the terrigenous clay components and with the presence of pollen. The presence of polycyclic diterpenoids was also correlated with the distribution patterns and inferred sources of other sediment lipid constituents. Potamic transport, followed by turbidite redistribution, is the probable input mechanism of these resin-derived compounds to the deep-sea sediments. These diterpenoids appear to be excellent biological markers of resinous higher plants.

Simoneit, B. R. T.↗
DOE OSTI2013

Deep Sludge Gas Release Event Analytical Evaluation

Long Abstract. Full Text. The purpose of the Deep Sludge Gas Release Event Analytical Evaluation (DSGRE-AE) is to evaluate the postulated hypothesis that a hydrogen GRE may occur in Hanford tanks containing waste sludges at levels greater than previously experienced. There is a need to understand gas retention and release hazards in sludge beds which are 200 -300 inches deep. These sludge beds are deeper than historical Hanford sludge waste beds, and are created when waste is retrieved from older single-shell tanks (SST) and transferred to newer double-shell tanks (DST).Retrieval of waste from SSTs reduces the risk to the environment from leakage or potential leakage of waste into the ground from these tanks. However, the possibility of an energetic event (flammable gas accident) in the retrieval receiver DST is worse than slow leakage. Lines of inquiry, therefore, are (1) can sludge waste be stored safely in deep beds; (2) can gas release events (GRE) be prevented by periodically degassing the sludge (e.g., mixer pump); or (3) does the retrieval strategy need to be altered to limit sludge bed height by retrieving into additional DSTs? The scope of this effort is to provide expert advice on whether or not to move forward with the generation of deep beds of sludge through retrieval of C-Farm tanks. Evaluation of possible mitigation methods (e.g., using mixer pumps to release gas, retrieving into an additional DST) are being evaluated by a second team and are not discussed in this report. While available data and engineering judgment indicate that increased gas retention (retained gas fraction) in DST sludge at depths resulting from the completion of SST 241-C Tank Farm retrievals is not expected and, even if gas releases were to occur, they would be small and local, a positive USQ was declared (Occurrence Report EM-RP--WRPS-TANKFARM-2012-0014, "Potential Exists for a Large Spontaneous Gas Release Event in Deep Settled Waste Sludge"). The purpose of this technical report is to (1) present and discuss current understandings of gas retention and release mechanisms for deep sludge in U.S. Department of Energy (DOE) complex waste storage tanks; and (2) to identify viable methods/criteria for demonstrating safety relative to deep sludge gas release events (DSGRE) in the near term to support the Hanford C-Farm retrieval mission. A secondary purpose is to identify viable methods/criteria for demonstrating safety relative to DSGREs in the longer term to support the mission to retrieve waste from the Hanford Tank Farms and deliver it to the Waste Treatment and Immobilization Plant (WTP). The potential DSGRE issue resulted in the declaration of a positive Unreviewed Safety Question (USQ). C-Farm retrievals are currently proceeding under a Justification for Continued Operation (JCO) that only allows tanks 241-AN-101 and 241-AN-106 sludge levels of 192 inches and 195 inches, respectively. C-Farm retrievals need deeper sludge levels (approximately 310 inches in 241-AN-101 and approximately 250 inches in 241-AN-106). This effort is to provide analytical data and justification to continue retrievals in a safe and efficient manner.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2016

Strategic Technologies for Deep Space Transport

Deep space transportation capability for science and exploration is fundamentally limited by available propulsion technologies. Traditional chemical systems are performance plateaued and require enormous Initial Mass in Low Earth Orbit (IMLEO) whereas solar electric propulsion systems are power limited and unable to execute rapid transits. Nuclear based propulsion and alternative energetic methods, on the other hand, represent potential avenues, perhaps the only viable avenues, to high specific power space transport evincing reduced trip time, reduced IMLEO, and expanded deep space reach. Here, key deep space transport mission capability objectives are reviewed in relation to STMD technology portfolio needs, and the advanced propulsion technology solution landscape is examined including open questions, technical challenges, and developmental prospects. Options for potential future investment across the full compliment of STMD programs are presented based on an informed awareness of complimentary activities in industry, academia, OGAs, and NASA mission directorates.

Technology↗
DOE OSTI2024

Advancing the Limits of InSAR to Detect Crustal Displacement from Low-Magnitude Earthquakes through Deep Learning

Detecting surface deformation associated with low-magnitude (M w ≤ 5) seismicity using interferometric synthetic aperture radar (InSAR) is challenging due to the subtlety of the signal and the often challenging imaging environments. However, low-magnitude earthquakes are potential precursors to larger seismic events, and thus characterizing the crustal displacement associated with them is crucial for regional seismic hazard assessment. We combine InSAR time-series techniques with a Deep Learning (DL) autoencoder denoiser to detect the magnitude and extent of crustal deformation from the M w = 3.4 Gallina, New Mexico earthquake that occurred on 30 July 2020. Although InSAR alone cannot detect event-related deformation from such a low-magnitude seismic event, application of the DL method reveals maximum displacements as small as (±2.5 mm) in the vicinity of both the fault and earthquake epicenter without prior knowledge of the fault system. This finding improves small-scale displacement discernment with InSAR by an order of magnitude relative to previous studies. We additionally estimate best-fitting fault parameters associated with the observed deformation. The application of the DL technique unlocks the potential for low-magnitude earthquake studies, providing new insights into local fault geometries and potential risks from higher-magnitude earthquakes. This technique also permits low-magnitude event monitoring in areas where seismic networks are sparse, allowing for the possibility of global fault deformation monitoring.

58 GEOSCIENCES↗
DOE OSTI2020

Simulated Precipitation Diurnal Variation With a Deep Convective Closure Subject to Shallow Convection in Community Atmosphere Model Version 5 Coupled With CLUBB

In order to improve the physical consistency between shallow and deep convection, we modify the deep convective closure in the Community Atmosphere Model version 5 (CAM5) coupled with a third-order turbulence closure parameterization (i.e., Cloud Layers Unified by Binormals; CLUBB). The revised closure reserves a portion of the total convective available potential energy for shallow convection via utilizing the heating and moistening profiles from CLUBB to distribute moisture and energy between shallow and deep convection. Simulations at two resolutions (i.e. 2° and 0.5°, respectively) are conducted to investigate the impacts of convective closure on the simulated precipitation diurnal variations. Results from low-resolution simulations show that the revised closure suppresses deep convection until the lower troposphere is sufficiently moistened by shallow convection, which improves the precipitation diurnal variation simulations compared to the default closure, with the precipitation diurnal peak over tropical lands delayed from 12LST to 19LST. The revised closure better simulates the diurnal variations for precipitation over the Asian monsoon region, such as the delayed precipitation onset, but still fails to well capture the nighttime peak for precipitation there. This deficiency is alleviated to some extent when applying the revised closure in highresolution simulations, but nighttime precipitation is still underestimated probably because key processes responsible for nighttime convection are missing. Altogether, our results indicate that establishing the consistency between shallow and deep convection is critical for the precipitation diurnal cycle simulations.

54 ENVIRONMENTAL SCIENCES↗