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 1,801 records · Page 100
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Updating algal evolutionary relationships through plastid genome sequencing: did alveolate plastids emerge through endosymbiosis of an ochrophyte?

Algae with secondary plastids of a red algal origin, such as ochrophytes (photosynthetic stramenopiles), are diverse and ecologically important, yet their evolutionary history remains controversial. We sequenced plastid genomes of two ochrophytes, Ochromonas sp. CCMP1393 (Chrysophyceae) and Trachydiscus minutus (Eustigmatophyceae). A shared split of the clpC gene as well as phylogenomic analyses of concatenated protein sequences demonstrated that chrysophytes and eustigmatophytes form a clade, the Limnista, exhibiting an unexpectedly elevated rate of plastid gene evolution. Our analyses also indicate that the root of the ochrophyte phylogeny falls between the recently redefined Khakista and Phaeista assemblages. Taking advantage of the expanded sampling of plastid genome sequences, we revisited the phylogenetic position of the plastid of Vitrella brassicaformis, a member of Alveolata with the least derived plastid genome known for the whole group. The results varied depending on the dataset and phylogenetic method employed, but suggested that the Vitrella plastids emerged from a deep ochrophyte lineage rather than being derived vertically from a hypothetical plastid-bearing common ancestor of alveolates and stramenopiles. Thus, we hypothesize that the plastid in Vitrella, and potentially in other alveolates, may have been acquired by an endosymbiosis of an early ochrophyte.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Curvature constraints from large scale structure

We modified the CLASS code in order to include relativistic galaxy number counts in spatially curved geometries; we present the formalism and study the effect of relativistic corrections on spatial curvature. The new version of the code is now publicly available. Using a Fisher matrix analysis, we investigate how measurements of the spatial curvature parameter Ω {sub K} with future galaxy surveys are affected by relativistic effects, which influence observations of the large scale galaxy distribution. These effects include contributions from cosmic magnification, Doppler terms and terms involving the gravitational potential. As an application, we consider angle and redshift dependent power spectra, which are especially well suited for model independent cosmological constraints. We compute our results for a representative deep, wide and spectroscopic survey, and our results show the impact of relativistic corrections on spatial curvature parameter estimation. We show that constraints on the curvature parameter may be strongly biased if, in particular, cosmic magnification is not included in the analysis. Other relativistic effects turn out to be subdominant in the studied configuration. We analyze how the shift in the estimated best-fit value for the curvature and other cosmological parameters depends on the magnification bias parameter, and find that significant biases are to be expected if this term is not properly considered in the analysis.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Via Machinae 2.0 : Full-sky, model-agnostic search for stellar streams in Gaia DR2

ABSTRACT We present an update to Via Machinae, an automated stellar stream-finding algorithm based on the deep learning anomaly detector ANODE. Via Machinae identifies stellar streams within Gaia, using only angular positions, proper motions, and photometry, without reference to a model of the Milky Way potential for orbit integration or stellar distances. This new version, Via Machinae 2.0, includes many improvements and refinements to nearly every step of the algorithm, that altogether result in more robust and visually distinct stream candidates than our original formulation. In this work, we also provide a quantitative estimate of the false positive rate of Via Machinae 2.0 by applying it to a simulated Gaia-mock catalogue based on galaxia, a smooth model of the Milky Way that does not contain substructure or stellar streams. Finally, we perform the first full-sky search for stellar streams with Via Machinae 2.0, identifying 102 streams at high significance within the Gaia Data Release 2, of which only 10 have been previously identified. While follow-up observations for further confirmation are required, taking into account the false positive rate presented in this work, we expect approximately 90 of these stream candidates to correspond to real stellar structures.

Astronomy & Astrophysics↗
DOE OSTI2021

New method for detecting fast neutrino flavor conversions in core-collapse supernova models with two-moment neutrino transport

Fast-pairwise neutrino oscillations potentially affect many aspects of core-collapse supernovae (CCSNe): the explosion mechanism, neutrino signals, and nucleosynthesis in the ejecta. This particular mode of collective neutrino oscillations has a deep connection to the angular structure of neutrinos in momentum space; for instance, the appearance of electron-neutrino lepton number (ELN) angular crossings in momentum space is a good indicator of the occurrence of flavor conversions. However, many multidimensional CCSN simulations are carried out with approximate neutrino transport (such as two-moment methods), which limits access to the angular distributions of neutrinos, i.e., inhibits ELNcrossing searches. In this paper, we develop a new method of searching for ELN crossing in these CCSN simulations. The required data is the zeroth and first angular moments of neutrinos and the matter profile, all of which are available in CCSN models with the two-moment method. One of the novelties of our new method is the use of a ray-tracing neutrino transport to determine ELNs in the direction of the stellar center. It is designed to compensate for shortcomings of the crossing searches with only two angular moments. We assess the capability of the method by carrying out a detailed comparison with results of full Boltzmann neutrino transport in 1D and 2D CCSN models. In this work, we find that the ray-tracing neutrino transport improves the accuracy of crossing searches; indeed, the appearance/disappearance of the crossings is accurately detected even in the region of forward-peaked angular distributions. The new method is computationally cheap and has the benefit of efficient parallelization; hence, it will be useful for ELN-crossing searches in any CCSN models that employ two-moment neutrino transport.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks

Abstract Inferring model parameters from experimental data is a grand challenge in many sciences, including cosmology. This often relies critically on high fidelity numerical simulations, which are prohibitively computationally expensive. The application of deep learning techniques to generative modeling is renewing interest in using high dimensional density estimators as computationally inexpensive emulators of fully-fledged simulations. These generative models have the potential to make a dramatic shift in the field of scientific simulations, but for that shift to happen we need to study the performance of such generators in the precision regime needed for science applications. To this end, in this work we apply Generative Adversarial Networks to the problem of generating weak lensing convergence maps. We show that our generator network produces maps that are described by, with high statistical confidence, the same summary statistics as the fully simulated maps.

Mustafa, Mustafa (ORCID:0000000291770283)↗
DOE OSTI2020

Using Reinforcement Learning to Optimize Quantum Circuits in thePresence of Noise

Many quantum computing frameworks currently use noise aware algorithms for implementing quantum circuits which do not scale efficiently as the size of the hardware architecture increases. As we move towards devices which utilize more qubits, it becomes increasing more important to map quantum circuits in a way that uses resources efficiently as well as maximizes the reliability of the results of that circuit. However, as the hardware increases to the point where Quantum supremacy is attainable, it will infeasible for a brute-force algorithm to find the most optimal circuit layout for circuits of medium to large depth sizes. To this end, we will to rely on reinforcement learning (RL) as a method of building quantum circuits based on observations of the noise characteristics in its environment. In this work, we create a working reinforcement learning environment in which an agent is able to make action which will build the class of circuits which creates the GHZ state. In addition to this, we also get preliminary results of the performance of a Deep Q Neural Network, which initially does not perform as well as we believe it can. In the future, we want to improve the performance of the agent and potentially generalize this environment to more classes of circuits.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Flavour Decomposition with Precision Pion and Kaon Cross Sections

Key properties of the nucleon are encoded in the correlation between their longitudinal momentum and their transverse position, such as the orbital angular momentum. This correlation is expressed through Generalized Parton Distributions (GPDs), which can be understood as spatial densities at different values of the longitudinal momentum of the quark. Validation of our understanding of hard exclusive reactions ($\pi^+$, $K^+$, $\pi^0$, $\gamma$) is an essential aspect towards such 3D hadron imaging and potential future flavor decomposition. The key to extracting GPDs from experiment is QCD factorization theorems. To validate the meson factorization theorems and potentially extract flavor separated GPDs from experiment, one has measure the separated longitudinal and transverse cross sections and their $t$ and $Q^2$ dependencies. Recent data and prospects for deep exclusive pion, both charged and neutral, and kaon electroproduction are presented, which conceptually would allow for flavor separations.

Horn, Tanja↗
DOE OSTI2017

Extreme Radio Flares and Associated X-Ray Variability from Young Stellar Objects in the Orion Nebula Cluster

Young stellar objects are known to exhibit strong radio variability on timescales of weeks to months, and a few reports have documented extreme radio flares with at least an order of magnitude change in flux density on timescales of hours to days. However, there have been few constraints on the occurrence rate of such radio flares or on the correlation with pre-main sequence X-ray flares, although such correlations are known for the Sun and nearby active stars. Here we report simultaneous deep VLA radio and Chandra X-ray observations of the Orion Nebula Cluster, targeting hundreds of sources to look for the occurrence rate of extreme radio variability and potential correlation with the most extreme X-ray variability. We identify 13 radio sources with extreme radio variability, with some showing an order of magnitude change in flux density in less than 30 minutes. All of these sources show X-ray emission and variability, but we find clear correlations with extreme radio flaring only on timescales <1 hr. Strong X-ray variability does not predict the extreme radio sources and vice versa. Radio flares thus provide us with a new perspective on high-energy processes in YSOs and the irradiation of their protoplanetary disks. Finally, our results highlight implications for interferometric imaging of sources violating the constant-sky assumption.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1997

A Technology Plan for Enabling Commercial Space Business

The National Aeronautics and Space Administration's (NASA) Advanced Space Transportation Program is a customer driven, focused technology program that supports the NASA Strategic Plan and considers future commercial space business projections. The initial cycle of the Advanced Space Transportation Program implementation planning was conducted from December 1995 through February 1996 and represented increased NASA emphasis on broad base technology development with the goal of dramatic reductions in the cost of space transportation. The second planning cycle, conducted in January and February 1997, updated the program implementation plan based on changes in the external environment, increased maturity of advanced concept studies, and current technology assessments. The program has taken a business-like approach to technology development with a balanced portfolio of near, medium, and long-term strategic targets. Strategic targets are influenced by Earth science, space science, and exploration objectives as well as commercial space markets. Commercial space markets include those that would be enhanced by lower cost transportation as well as potential markets resulting in major increases in space business induced by reductions in transportation cost. The program plan addresses earth-to-orbit space launch, earth orbit operations and deep space systems. It also addresses all critical transportation system elements; including structures, thermal protection systems, propulsion, avionics, and operations. As these technologies are matured, integrated technology flight experiments such as the X-33 and X-34 flight demonstrator programs support near-term (one to five years) development or operational decisions. The Advanced Space Transportation Program and the flight demonstrator programs combine business planning, ground-based technology demonstrations and flight demonstrations that will permit industry and NASA to commit to revolutionary new space transportation systems beginning at the turn of the century and continuing far into the future.

Lyles, Garry M.↗
NASA NTRS2012

On Applications of Disruption Tolerant Networking to Optical Networking in Space

The integration of optical communication links into space networks via Disruption Tolerant Networking (DTN) is a largely unexplored area of research. Building on successful foundational work accomplished at JPL, we discuss a multi-hop multi-path network featuring optical links. The experimental test bed is constructed at the NASA Glenn Research Center featuring multiple Ethernet-to-fiber converters coupled with free space optical (FSO) communication channels. The test bed architecture models communication paths from deployed Mars assets to the deep space network (DSN) and finally to the mission operations center (MOC). Reliable versus unreliable communication methods are investigated and discussed; including reliable transport protocols, custody transfer, and fragmentation. Potential commercial applications may include an optical communications infrastructure deployment to support developing nations and remote areas, which are unburdened with supporting an existing heritage means of telecommunications. Narrow laser beam widths and control of polarization states offer inherent physical layer security benefits with optical communications over RF solutions. This paper explores whether or not DTN is appropriate for space-based optical networks, optimal payload sizes, reliability, and a discussion on security.

Hylton, Alan Guy↗
NASA NTRS2013

Advanced Multifunctional MMOD Shield: Radiation Shielding Assessment

Deep space missions must contend with a harsh radiation environment Impacts to crew and electronics. Need to invest in multifunctionality for spacecraft optimization. MMOD shield. Goals: Increase radiation mitigation potential. Retain overall MMOD shielding performance.

Rojdev, Kristina↗
NASA NTRS2019

Soil Displacement Terramechanics for Wheel-Based Trenching with a Planetary Rover

Planetary exploration rovers are expensive, weight constrained, and cannot be serviced once deployed. Here, we explore one way to increase their capabilities while avoiding the cost, mass, and complexity leading to these issues. We propose to re-use the large wheel actuators for trenching and other digging operations, which will enable a range of missions such as sampling deeper layers of soil. We present a new, closed-form model of the soil displaced by an angled, spinning wheel to analyze the trenching potential of a driving strategy and inform the control of the wheel. The model is demonstrated with single wheel experiments under different driving conditions. The model suggests: that a deep trench does not require large tractive efforts; that the shape of the trench can be controlled; and that a rear wheel has a lower risk of entrapment when trenching than a front wheel. Ultimately this model could be used in a nonprehensile manipulation planning or learning algorithm to enable autonomous trenching.

Wheels↗
NASA NTRS2022

A Transient Hydrodynamic Model of Screen Channel Liquid Acquisition Devices for In-Space Cryogenic Propellant Management

Screen channel liquid acquisition devices (LADs) will play a crucial role in future deep space travel. It is essential that vapor-free delivery of propellants during tank-to-tank transfer is ensured to maximize yield from storage tanks and prevent potential combustion instabilities. The screen channel LAD utilizes a fine screen wire mesh that can separate phases in a low Bond number (i.e. microgravity) environment using surface tension forces. This study presents the development and verification of a new model for transient screen compliance, one of the influential factors for screen channel LAD design. Screen compliance is crucial during LAD channel outflow transients because the slight deflection of the screen can provide needed mass to satisfy rapid outflow demands and reduce the pressure difference across the screen. The model is successfully verified against CFD simulations. In addition, the characteristic speed for the governing screen compliance equations is derived which allows for numerical stability criteria to be established. As shown in this study, the transient maximum pressure difference across the screen can greatly exceed the steady state maximum pressure difference across the screen in many cases.

Hydrodynamics Simulations↗
DOE OSTI2019

Single-object Imaging and Spectroscopy to Enhance Dark Energy Science from LSST

Single-object imaging and spectroscopy on telescopes with apertures ranging from ~4 m to 40 m have the potential to greatly enhance the cosmological constraints that can be obtained from LSST. Two major cosmological probes will benefit greatly from LSST follow-up: accurate spectrophotometry for nearby and distant Type Ia supernovae will expand the cosmological distance lever arm by unlocking the constraining power of high-z supernovae; and cosmology with time delays of strongly-lensed supernovae and quasars will require additional high-cadence imaging to supplement LSST, adaptive optics imaging or spectroscopy for accurate lens and source positions, and IFU or slit spectroscopy to measure detailed properties of lens systems. We highlight the scientific impact of these two science drivers, and discuss how additional resources will benefit them. For both science cases, LSST will deliver a large sample of objects over both the wide and deep fields in the LSST survey, but additional data to characterize both individual systems and overall systematics will be key to ensuring robust cosmological inference to high redshifts. Community access to large amounts of natural-seeing imaging on ~2-4 m telescopes, adaptive optics imaging and spectroscopy on 8-40 m telescopes, and high-throughput single-target spectroscopy on 4-40 m telescopes will be necessary for LSST time domain cosmology to reach its full potential. In two companion white papers we present the additional gains for LSST cosmology that will come from deep and from wide-field multi-object spectroscopy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Deep-Learning-Enabled Crack Detection and Analysis in Commercial Lithium-Ion Battery Cathodes

We report in Li-ion batteries, the mechanical degradation initiated by micro cracks is one of the bottlenecks for enhancing the performance. Quantifying the crack formation and evolution in complex composite electrodes can provide important insights into electrochemical behaviors under prolonged and/or aggressive cycling. However, observation and interpretation of the complicated crack patterns in battery electrodes through imaging experiments are often time-consuming, labor intensive, and subjective. Herein, a deep learning-based approach is developed to extract the crack patterns from nanoscale hard X-ray holo-tomography data of a commercial 18650-type battery cathode. Efficient and effective quantification of the damage heterogeneity with automation and statistical significance is demonstrated. The crack characteristics are further associated with the active particles’ packing densities and a potentially viable architectural design is discussed for suppressing the structural degradation in an industry-relevant battery configuration.

25 ENERGY STORAGE↗
DOE OSTI2022

Training Ultrasound Image Classification Deep-Learning Algorithms for Pneumothorax Detection Using a Synthetic Tissue Phantom Apparatus

Ultrasound (US) imaging is a critical tool in emergency and military medicine because of its portability and immediate nature. However, proper image interpretation requires skill, limiting its utility in remote applications for conditions such as pneumothorax (PTX) which requires rapid intervention. Artificial intelligence has the potential to automate ultrasound image analysis for various pathophysiological conditions. Training models require large data sets and a means of troubleshooting in real-time for ultrasound integration deployment, and they also require large animal models or clinical testing. Here, we detail the development of a dynamic synthetic tissue phantom model for PTX and its use in training image classification algorithms. The model comprises a synthetic gelatin phantom cast in a custom 3D-printed rib mold and a lung mimicking phantom. When compared to PTX images acquired in swine, images from the phantom were similar in both PTX negative and positive mimicking scenarios. We then used a deep learning image classification algorithm, which we previously developed for shrapnel detection, to accurately predict the presence of PTX in swine images by only training on phantom image sets, highlighting the utility for a tissue phantom for AI applications.

Boice, Emily N. (ORCID:0000000171802842)↗
DOE OSTI2024

Neural network methods for radiation detectors and imaging

Recent advances in image data proccesing through deep learning allow for new optimization and performance-enhancement schemes for radiation detectors and imaging hardware. This enables radiation experiments, which includes photon sciences in synchrotron and X-ray free electron lasers as a subclass, through data-endowed artificial intelligence. We give an overview of data generation at photon sources, deep learning-based methods for image processing tasks, and hardware solutions for deep learning acceleration. Most existing deep learning approaches are trained offline, typically using large amounts of computational resources. However, once trained, DNNs can achieve fast inference speeds and can be deployed to edge devices. A new trend is edge computing with less energy consumption (hundreds of watts or less) and real-time analysis potential. While popularly used for edge computing, electronic-based hardware accelerators ranging from general purpose processors such as central processing units (CPUs) to application-specific integrated circuits (ASICs) are constantly reaching performance limits in latency, energy consumption, and other physical constraints. These limits give rise to next-generation analog neuromorhpic hardware platforms, such as optical neural networks (ONNs), for high parallel, low latency, and low energy computing to boost deep learning acceleration (LA-UR-23-32395).

edge computing↗
DOE OSTI2016

Wellbore and groundwater temperature distribution eastern Snake River Plain, Idaho: Implications for groundwater flow and geothermal potential

A map of groundwater temperatures from the Eastern Snake River Plain (ESRP) regional aquifer can be used to identify and interpret important features of the aquifer, including aquifer flow direction, aquifer thickness, and potential geothermal anomalies. The ESRP is an area of high heat flow, yet most of this thermal energy fails to reach the surface, due to the heat being swept downgradient by the aquifer to the major spring complexes near Thousand Springs, ID, a distance of 300 km. Nine deep boreholes that fully penetrate the regional aquifer display three common features: (1) high thermal gradients beneath the aquifer, corresponding to high conductive heat flow in low-permeability hydrothermally-altered rocks; (2) isothermal temperature profiles within the aquifer, characteristic of an actively flowing groundwater; and (3) moderate thermal gradients in the vadose zone with values that indicate that over half of the geothermal heat flow is removed by advective transport in the regional aquifer system. This study utilized temperature data from 250 ESRP aquifer wells to evaluate regional aquifer flow direction, aquifer thickness, and potential geothermal anomalies. Because the thermal gradients are typically low in the aquifer, any measurement of groundwater temperature is a reasonable estimate of temperature throughout the aquifer thickness, allowing the construction of a regional aquifer temperature map for the ESRP. Mapped temperatures are used to identify cold thermal plumes associated with recharge from tributary valleys and adjacent uplands, and warm zones associated with geothermal input to the aquifer. Warm zones in the aquifer can have various causes, including local circulation of groundwater through the deep conductively dominated region, slow groundwater movement in low-permeability regions, or localized heat flow from deeper thermal features.

15 GEOTHERMAL ENERGY↗

Refine your search to explore more results.