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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Design Studies for Deep-Water Floating Offshore Vertical Axis Wind Turbines

Deep-water offshore sites are an untapped opportunity to bring large-scale offshore wind energy to coastal population centers. The primary challenge has been the projected high costs for floating offshore wind systems. This work presents a comprehensive investigation of a new opportunity for deep-water offshore wind using large-scale vertical axis wind turbines. Owing to inherent features of this technology, there is a potential transformational opportunity to address the major cost drivers for floating wind using vertical axis wind turbines. The focus of this report is to evaluate the technical potential for this new technology. The approach to evaluating this potential was to perform system design studies focused on improving the understanding of technical performance parameters while looking for cost reduction opportunities. VAWT design codes were developed in order to perform these design studies. To gain a better understanding of the design space for floating VAWT systems, a comprehensive design study of multiple rotor configuration options was carried out. Floating platforms and moorings were then sized and evaluated for each of the candidate rotor configurations. Preliminary LCOE estimates and LCOE ranges were produced based on the design study results for each of the major turbine and system components. The major outcomes of this study are a comprehensive technology assessment of VAWT performance and preliminary LCOE estimates that demonstrate that floating VAWTs may have favorable performance and costs in comparison to conventional HAWTs in the deep-water offshore environment where floating systems are required, indicating that this new technology warrants further study.

17 WIND ENERGY↗
DOE OSTI2018

Deep Convection: Onset, Diurnal Cycle, and Role in Climate Variability and Sensitivity (Final Report)

The ASR SGP testbed for continuous modeling offers new opportunities to improve GCM parameterizations. We conducted an ASR investigation of convective and cloud microphysical processes that used existing ARM data as a testing ground for meaningful approaches to model evaluation and set the stage for later strategic use of the testbed to improve cumulus parameterizations and better understand deep convective cloud feedback. Our research consisted of the following tasks: Diurnal cycle of continental convection Deep convection at the SGP occurs in two forms: Organized mesoscale convection that originates upstream and produces peak precipitation at night; and local shallow convection that develops into deep convection that peaks in late afternoon. Local transitions are a logical next step from the shallow convection that will be the early focus of the testbed. This transition constrains entrainment and cold pools, since GCMs produce a continental rain peak near noon. We studied ARSCL-observed transition cases using the WRF, determined the sources of SCM diurnal cycle errors and made parameterization changes as appropriate. Factors controlling convective detrainment and anvil evolution Convective detrainment is a major source of high cloud ice water content and cloud forcing. The GISS GCM diagnoses a convective updraft speed profile, assumes a particle size distribution (PSD), and uses size-fallspeed relations to determine the fraction of condensate carried upward vs. precipitating. We used SCM case studies for TWP-ICE and MC3E for which vertical velocity has been retrieved to test whether entrainment improvements produce realistic updraft speeds and then improved the updraft microphysics parameterization. We compared detrained ice water contents and fall speeds to ARM and other field experiment data to refine the GCM’s fall speed and PSD assumptions. Role of convective cloud radiative processes in the Madden-Julian Oscillation (MJO) Changes in entrainment and rain evaporation and a cold pool parameterization have produced an MJO in the GISS GCM for the first time and allowed us to reproduce the KAZR dependence of convection depth on humidity during AMIE-Gan. We determined whether recent model changes including cold pools improve or degrade the MJO and made adjustments accordingly.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Deep Borehole Laboratory and Borehole Testing Strategy: Generic Drilling and Testing Plan

This report presents a generic (i.e., site-independent) preliminary plan for drilling, testing, sampling, and analyzing data for a deep characterization borehole drilled into crystalline basement for the purposes of assessing the suitability of a site for deep borehole disposal (DBD). This research was performed as part of the deep borehole field test (DBFT). Based on revised U.S. Department of Energy (DOE) priorities in mid-2017, the DBFT and other research related to a DBD option was discontinued; ongoing work and documentation were closed out by the end of fiscal year (FY) 2017. This report was initiated as part of the DBFT and documented as an incomplete draft at the end of FY 2017. The report was finalized by Sandia National Laboratories in FY2018 without DOE funding, subsequent to the termination of the DBFT, and published in FY2019. This report presents a possible sampling, testing, and analysis campaign that could be carried out as part of a future project to quantify geochemical, geomechanical, geothermal, and geohydrologic conditions encountered at depths up to 5 km in crystalline basement.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

A Bottom-up Approach to Improve the Representation of Deep Convective Clouds in Weather and Climate Models (Final Report)

A fundamental yet outstanding question about deep cumulus convection regards how convective updrafts, downdrafts, and surface-based cold pools are inter-related within organized storms, and how these three convective components are modulated by external and internal factors. We considered this question through numerical model simulations as well as data collected during the MC3E field campaign. One of our basic hypotheses was that deep cold pools are forced by wide downdrafts which arise from wide updrafts. This “bottom-up” perspective required consideration of the external controls on updraft width, and then the internal controls on cold-pool characteristics including depth. Model experiments showed that environmental vertical wind shear (an external factor) exerts a particularly strong control on updraft width; analyses of linear and nonlinear dynamic pressure forcing readily explain this control. The experiments also showed that increases in updraft width are accompanied by corresponding increases in downdraft width and cold-pool depth. The updraft–downdraft connection is through precipitation: the wide area of precipitation that will be initiated in a wide updraft will in turn lead to a wide downdraft. The downdraft–cold pool connection is through mass continuity: at the downdraft terminus, the large downdraft mass flux through a wide downdraft must lead to a deep cold pool. Model experiments also showed that the cold-pool depth and other characteristics can be significantly modulated by microphysical processes (internal factors). Specifically, the cold-pool expansion rate, depth, and intensity was found to depend most strongly on the amount of rain evaporation near the ground. However, the sustenance of the cold pool itself depended upon the downdrafts strengthened by sublimation of graupel higher in the storm. Finally, field campaign data revealed compelling inter-relationships between the cold-pool characteristics, and downdraft transience in terms of the microphysics. For example, pulses of hail and graupel (also internal factors) within downdrafts resulted in short-lived increases in the cold-pool depth. However, the cold-pool speed appeared to be relatively independent of the cold-pool depth and intensity, suggesting limitations of density current theory to convective cold pools in nature.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Deeply Uncertain: Comparing Methods of Uncertainty Quantification in Deep Learning Algorithms [Slides]

Deep learning is used in many applications in the physical sciences. In those sciences we are used to having uncertainty on every measurement or prediction. In the last years, many methods have been put forth for uncertainty quantification: Bayesian Neural Networks, Concrete Dropout, and Deep Ensembles. Uncertainty in deep learning is often divided into aleatoric or irreducible and epistemic or reducible. The problem remains: Which uncertainty quantification method should be chosen? How are those results interpreted?

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Low-Power Deep Learning Inference using the SpiNNaker Neuromorphic Platform

n this presentation we will discuss recent results on using the SpiNNaker neuromorphic platform (48-chip model) for deep learning neural network inference. We use the Sandia Labs developed Whet stone spiking deep learning library to train deep multi-layer perceptrons and convolutional neural networks suitable for the spiking substrate on the neural hardware architecture. By using the massively parallel nature of SpiNNaker, we are able to achieve, under certain network topologies, substantial network tiling and consequentially impressive inference throughput. Such high-throughput systems may have eventual application in remote sensing applications where large images need to be chipped, scanned, and processed quickly. Additionally, we explore complex topologies that push the limits of the SpiNNaker routing hardware and investigate how that impacts mapping software-implemented networks to on-hardware instantiations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

South Dakota Region Scientific Deep Dive

EPOC uses the Deep Dive process to discuss and analyze current and planned science use cases and anticipated data output of a particular use case, site, or project to help inform the strategic planning of a campus or regional networking environment. This includes understanding future needs related to network operations, network capacity upgrades, and other technological service investments. A Deep Dive comprehensively surveys major research stakeholders’ plans and processes in order to investigate data management requirements over the next 5–10 years. Questions crafted to explore this space include the following: 1) How, and where, will new data be analyzed and used? 2) How will the process of doing science change over the next 5–10 years? and 3) How will changes to the underlying hardware and software technologies influence scientific discovery? Deep Dives help ensure that key stakeholders have a common understanding of the issues and the actions that a campus or regional network may need to undertake to offer solutions. The EPOC team leads the effort and relies on collaboration with the hosting site or network, and other affiliated entities that participate in the process. EPOC organizes, convenes, executes, and shares the outcomes of the review with all stakeholders

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

A Lagrangian Study of the Transition from Shallow to Deep Convection Using ASR Observations and Les Simulations (Final Report)

With the support from this grant, we have made the following accomplishments. We have found that a formula for fractional entrainment rate, a key aspect for parameterization moist convection, that works for both shallow and deep convection, which provide a promising direction for unified treatment of entrainment for both shallow and deep convection. We also examined the formulation of vertical velocity and showed that the effect of non-hydrostatic pressure and entrainment on vertical velocity are not constants, as assumed in past studies, but can be computed using a simple model for the cloudy updrafts. These results also apply to both deep and shallow convection, which opens the door for a more physically based parameterization of the updraft vertical velocity. We have also conducted extensive analysis of the RACORO aircraft data and have published the results in Wei et al. (2020). We have made extensive effort scrutinizing the data in detail and examining the various hypotheses with the aid of the LES simulations. This was the first application of the modified Paluch diagram on modern aircraft data and the first comprehensive analysis of mixing in clouds using real data (instead of only LES output) in a very long time. These analyses enhanced the science return of the ASR aircraft measurements. This grant also supported a study of the collisions among cold pools and an effort to build parameterizations of moist convection using tools developed in control theory.

42 ENGINEERING↗
DOE OSTI2021

Kennesaw State University Scientific Deep Dive

EPOC uses the Deep Dive process to discuss and analyze current and planned science, research, or education activities and the anticipated data output of a particular use case, site, or project to help inform the strategic planning of a campus or regional networking environment. This includes understanding future needs related to network operations, network capacity upgrades, and other technological service investments. A Deep Dive comprehensively surveys major research stakeholders’ plans and processes in order to investigate data management requirements over the next 5–10 years. Between August 2021 and October 2021, staff members from the Engagement and Performance Operations Center (EPOC) met with researchers and staff from Kennesaw State University (KSU) for the purpose of a Deep Dive into scientific and research drivers. The goal of this activity was to help characterize the requirements for a number of campus use cases, and to enable cyberinfrastructure support staff to better understand the needs of the researchers within the KSU community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Continual Learning for Pattern Recognizers using Neurogenesis Deep Learning

Deep neural networks have emerged as a leading set of algorithms to infer information from a variety of data sources such as images and time series data. In their most basic form, neural networks lack the ability to adapt to new classes of information. Continual learning is a field of study attempting to give previously trained deep learning models the ability to adapt to a changing environment. Previous work developed a CL method called Neurogenesis for Deep Learning (NDL). Here, we combine NDL with a specific neural network architecture (the Ladder Network) to produce a system capable of automatically adapting a classification neural network to new classes of data. The NDL Ladder Network was evaluated against other leading CL methods. While the NDL and Ladder Network system did not match the cutting edge performance achieved by other CL methods, in most cases it performed comparably and is the only system evaluated that can learn new classes of information with no human intervention.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Deep Learning for Fish Identification from Sonar Data: CRADA 481 [Abstract only]

To help solve the challenges of hydropower energy production related to the potential for eel injury and mortality from passage through hydropower turbines, we will develop a deep learning method for identifying migrating eels from imaging sonar. This project continues with a prior project conducted by the Pacific Northwest National Laboratory (PNNL) and the Electric Power Research Institute (EPRI) in FY2018-2019. The proposed method employs Convolution Neural Network (CNN), a powerful deep learning method for image classification, to distinguish between images of eels and non-eel moving objects. We propose to collect more laboratory data and add more existing field data to train a powerful deep learning model. In addition to eels and sticks as classified in previous studies, we will add images containing several non-eel fish species and macrophyte mats to the training data. A multi-class classification model will be developed to distinguish these objects. Object detection algorithm will be explored and developed to locate and identify multiple objects in each sonar frame. Motion analysis will be performed to track the movement of objects in sonar video clips. We will also improve the data conversion algorithm so that it can read in both DIDSON and ARIS (both are imaging sonars developed by Sound Metrics Corp) data files and convert them to images with comparably high resolution, regardless of the varying detection ranges in different environments. The developed algorithms will be packaged as a software with a graphic user interface. The software will be evaluated by external collaborators in the field. The developed framework can be generalized for automatic monitoring of fish passage and migration using other imaging sonars like ARIS and will benefit the design and operation of ecologically friendly hydroelectric projects. The developed wavelet and CNN model configuration parameters can potentially be transferred to lamprey detection in similar riverine environments.

13 HYDRO ENERGY↗
DOE OSTI2022

Grid Energy Storage: Supply Chain Deep Dive Assessment

The report “America’s Strategy to Secure the Supply Chain for a Robust Clean Energy Transition” lays out the challenges and opportunities faced by the United States in the energy supply chain as well as the Federal Government plans to address these challenges and opportunities. It is accompanied by several issue-specific deep dive assessments, including this one, in response to Executive Order 14017 “America’s Supply Chains,” which directs the Secretary of Energy to submit a report on supply chains for the energy sector industrial base. The Executive Order is helping the Federal Government to build more secure and diverse U.S. supply chains, including energy supply chains. To combat the climate crisis and avoid the most severe impacts of climate change, the U.S. is committed to achieving a 50 to 52 percent reduction from 2005 levels in economy-wide net greenhouse gas pollution by 2030, creating a carbon pollution-free power sector by 2035, and achieving net zero emissions economy-wide by no later than 2050. The U.S. Department of Energy (DOE) recognizes that a secure, resilient supply chain will be critical in harnessing emissions outcomes and capturing the economic opportunity inherent in the energy sector transition. Potential vulnerabilities and risks to the energy sector industrial base must be addressed throughout every stage of this transition. The DOE energy supply chain strategy report summarizes the key elements of the energy supply chain as well as the strategies the U.S. Government is starting to employ to address them. Additionally, it describes recommendations for Congressional action. DOE has identified technologies and crosscutting topics for analysis in the one-year time frame set by the Executive Order. Along with the capstone policy report, DOE is releasing 11 deep dive assessment documents, including this one, covering the following technology sectors: carbon capture materials; electric grid including transformers and high voltage direct current (HVDC); energy storage; fuel cells and electrolyzers; hydropower including pumped storage hydropower (PSH); neodymium magnets; nuclear energy; platinum group metals and other catalysts; semiconductors; solar photovoltaics (PV); and wind. DOE is also releasing two deep dive assessments on the following crosscutting topics: Commercialization and competitiveness; and cybersecurity and digital components. More information can be found at www.energy.gov/policy/supplychains.

25 ENERGY STORAGE↗
DOE OSTI2020

A Non-Invasive Approach for Elucidating the Spatial Distribution of In Situ Stress in Deep Subsurface Geologic Formations Considered for CO 2 Storage (Task 2 Report Extracting Stress Data from Seismic Data)

This report describes research accomplishments achieved with funding provided through Department of Energy (DOE) Contract DE-FE0031686. The purpose of this DOE funding is to develop methods that can provide key information about stress fields that act on deep rocks without invading the earth to acquire that information. The research objective was to demonstrate methods that extract the azimuth directions of SHmax and SHmin stress in deep rocks from traditional seismic reflection data like the data that are used to explore for deep oil and gas reservoirs. Battelle performed two seismic investigations and achieved estimates of SHmax and SHmin azimuths in both efforts that agreed with local, non-seismic, ground-truth measurements of SHmax orientation. The first procedure utilized vertical seismic profiling (VSP) data; the second effort utilized three-dimensional (3D) seismic data.

58 GEOSCIENCES↗
DOE OSTI2024

CubeSat Interplanetary Exploration: A Deep Dive into Nuclear Propulsion and Astrodynamics for Small Satellites

CubeSats are small satellites most commonly used by universities, however, nowadays places like NASA are looking to CubeSats for interplanetary missions. Because of their universal form fit it makes them cost effective, economical, and therefor desirable for deep space. To achieve deep space exploration, we must explore the applications of propulsion and astrodynamics on small satellites. This paper will touch on one scaling Ion and chemical propulsion as well as nuclear kilo reactors. Two choreographing a flight path for these small satellites and lastly designing an efficient cost-effective platform for CubeSats to get to deep space territory.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

The Role of Deep Convection and Large-scale Circulation in Driving Model Spread in Low Cloud Feedback and Equilibrium Climate Sensitivity

This project aims to advance the understanding of the processes that drive the large uncertainties in climate change projections, use observations to constrain model physics and reduce the inter-model spread in equilibrium climate sensitivity (ECS). There are three major goals: 1) Characterize the representation of the physical pathways that link deep convection, large-scale circulation and low cloud feedback in CMIP6 model simulations and determine the relative contribution of each pathway to the CMIP6 model spread in low cloud feedback and ECS; 2) Use process-oriented diagnostics and multiple observations to evaluate CMIP6 model performance in capturing the observed cloud-circulation relation and deep convection characteristics including convective transition statistics and the bulk properties of mesoscale convective systems (MCSs). Error decomposition in CMIP6 models will be performed. 3) Conduct E3SM short-range hindcasts following the DOE Cloud-Associated Parameterizations Testbed (CAPT) protocol to pinpoint specific model parameters/processes that are crucial to the representation of deep convection, circulation, clouds and the pathways that connect them. We will modify convective parameters in E3SM and analyze the perturbed physics experiments (PPEs) to isolate model parameters that are critical to the uncertainty of ECS.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Embracing the unknown: Why uncertainty matters in deep learning

Most cutting-edge deep learning models are focused on only one thing: improving prediction accuracy. But in the real world, especially in high-stakes domains like science, medicine, and safety (e.g. self-driving cars), knowing what your model doesn't know can be just as important. My goal for this talk is to convince you that uncertainty quantification (UQ) is essential in deep learning. We’ll explore how existing UQ methods work, what they still get wrong, and how ideas from disciplines like astrophysics and environmental science are key to driving the development of better (more trustworthy) deep learning methods.

Nevin, Becky [Fermilab]↗
DOE OSTI2024

Deep Learning for Fish Identification from Sonar Data (CRADA 481 Final Report)

In eastern regions of the United States, the American eel is a species of management and regulatory concern because of significant population declines, despite the species’ previous abundance in all tributaries of rivers flowing into the Atlantic Ocean. The American eel is also a candidate for listing under the U.S. Endangered Species Act. While hydropower construction and operation are only one of several factors contributing to this population decline, such a listing could impose additional regulatory challenges for a large number of hydropower projects. In this CRADA project, we improved technologies for identifying migrating eels with the goal of reducing the cost and time required for future American eel hydropower impact assessment and mitigation studies, while maintaining accuracy. We built on results from a previous FOA project (FOA# DE-FOA-0001662), led by the Electric Power Research Institute (EPRI), which developed a highly accurate, deep-learning method for identifying migrating eels from imaging sonar data. The current study aimed to further optimize this deep-learning model, originally designed for image classification, and to develop an object detection software capable of identifying fish from sonar videos in real time, enabling the detection of events like fish migrations and specific species, such as the American eel, at hydropower dams. The data conversion algorithms were packaged as software with a graphical user interface, and the software is evaluated by external collaborators. We focused on the American eel in this project and explored the transferability of the developed deep learning models to the sea lamprey, given the similar body shape and swimming behavior between the two species.

13 HYDRO ENERGY↗
DOE OSTI2026

Synthetic Atmospheric River Ensembles Generated by Deep-AR

This dataset contains 35,850 synthetic landfalling atmospheric river (AR) realizations generated by the Deep-AR two-stage deep-learning framework over the Northeast Pacific and U.S. West Coast. The archive contains 25 stochastic ensemble members for each of 1,434 held-out observed seed events. Each synthetic realization is initialized from conditions 48 hours before the corresponding observed AR landfall and is generated autoregressively at 6-hour intervals over a 144-hour period. Deep-AR combines a deterministic residual network (ResNet) that advances the large-scale atmospheric state with a Wasserstein generative adversarial network (WGAN) that produces stochastic, high-resolution fields. Each HDF5 file contains 0.25° gridded synthetic integrated vapor transport components (qu, qv), 10 m wind components (u10, v10), and 6-hour accumulated precipitation on a common 200 × 480 grid. The files also include coordinate and datetime arrays. This dataset supports AR hazard analysis, ensemble-based uncertainty characterization, precipitation-extremes research, and regional stress testing. Synthetic files follow the naming convention deepar.model.YYYYMMDD.HHMMSS.vNN.h5. YYYYMMDD.HHMMSS identifies the UTC initial-condition timestamp, which occurs 48 hours before the diagnosed observed landfall, and vNN identifies the zero-padded ensemble member, ranging from v01 through v25. Each synthetic file can be paired with its corresponding observed file by matching the initial-condition timestamp. The paired observed file follows the naming convention deepar.obs.YYYYMMDD.HHMMSS.h5 and is available in the separately registered oracle/deepar.obs dataset at https://wdh.energy.gov/ds/oracle/deepar.obs (DOI: https://doi.org/10.21947/3377671).

17 WIND ENERGY↗