Results for “deep well” — Page 33

NASA NTRS1972

Concatenated coding for low date rate space communications.

In deep space communications with distant planets, the data rate as well as the operating SNR may be very low. To maintain the error rate also at a very low level, it is necessary to use a sophisticated coding system (longer code) without excessive decoding complexity. The concatenated coding has been shown to meet such requirements in that the error rate decreases exponentially with the overall length of the code while the decoder complexity increases only algebraically. Three methods of concatenating an inner code with an outer code are considered. Performance comparison of the three concatenated codes is made.

Chen, C. H.↗

NASA NTRS1984

Blue clusters, populous clusters, and globulars

The introduction of new photometric techniques for faint objects has tremendously increased our understanding of the MC clusters, especially at the old end of the age spectrum. Many of the well-studied clusters of the past have gone through several transformations, starting as true globulars in 1960, becoming intermediate-age objects in the 1970's, but now returning to the true globular classification in the 1980's. LW 868 is an example of a cluster with such a history. Better photometric accuracy is also greatly improving our understanding of younger clusters, allowing detailed comparisons with theoretical evolutionary models, and a more reliable determination of the age-abundance relationship. The dynamical history of MC clusters can also be examined from the new data. The cluster formation rate, as well as the destruction rate, can be reconstructed from modern, deep cluster surveys.

Hodge, P. W.↗

NASA NTRS1989

The winds of O-stars. II - The terminal velocities of stellar winds of O-type stars

The SEI method (Lamers et al., 1987) is used to obtain P Cygni profiles of the UV resonance lines of C IV, N V, and S IV and of the subordinate UV lines of N IV and C III observed in the spectra of 27 O-type stars. Theoretical profiles which include the turbulence effects agree well with the observations, and they can account for the deep absorption troughs, the shape of the violet absorption wings, and the wavelength of the emission peak. The resulting terminal velocities of the stellar winds are found to be systematically lower by about 400 km/s than previous estimates obtained using the Sobolev approximation (Castor and Lamers, 1979), suggesting that the narrow absorption components, observed in the UV resonance lines of O and B stars, reach the terminal velocity of the winds.

Groenewegen, M. A. T.↗

NASA NTRS1991

The discovery of a young radio galaxy at z = 2.390 - Probing initial star formation at z less than approximately 3.0

The discovery of a weak radio galaxy from the Leiden Berkeley Deep Survey at a redshift of 2.390 is presented, as well as nine-band photometry for the galaxy and for surrounding objects. The source 53W002 is not variable on the time scales of years. Its rest-frame UV continuum is compared with IUE spectra of various nearby galaxies with relatively recent starbursts, and with nearby AGNs. It is inferred from the C IV/Ly-alpha and N V/Ly-alpha ratios that 53W002 has a Seyfert 1-like AGN, and that the ratios constrain the nonthermal component to about 35 percent of the observed UV continuum. Several independent age estimates yield a consistent value of 0.25-0.32 Gyr. The available data are consistent with 53W002 being a genuinely young galaxy seen at a redshift of 2.390 during its first major starburst. It likely started forming most of its current stars at redshifts between 2.5 and 3.0, suggesting that radio galaxies do not form the bulk of their stars coevally, but start doing so over a lengthy period of cosmic time.

Windhorst, Rogier A.↗

NASA NTRS2011

Auto-Gopher: A Wire-Line Rotary-Hammer Ultrasonic Drill

Developing technologies that would enable NASA to sample rock, soil, and ice by coring, drilling or abrading at a significant depth is of great importance for a large number of in-situ exploration missions as well as for earth applications. Proven techniques to sample Mars subsurface will be critical for future NASA astrobiology missions that will search for records of past and present life on the planet, as well as, the search for water and other resources. A deep corer, called Auto-Gopher, is currently being developed as a joint effort of the JPL's NDEAA laboratory and Honeybee Robotics Corp. The Auto-Gopher is a wire-line rotary-hammer drill that combines rock breaking by hammering using an ultrasonic actuator and cuttings removal by rotating a fluted bit. The hammering mechanism is based on the Ultrasonic/Sonic Drill/Corer (USDC) that has been developed as an adaptable tool for many of drilling and coring applications. The USDC uses an intermediate free-flying mass to transform the high frequency vibrations of the horn tip into a sonic hammering of a drill bit. The USDC concept was used in a previous task to develop an Ultrasonic/Sonic Ice Gopher. The lessons learned from testing the ice gopher were implemented into the design of the Auto-Gopher by inducing a rotary motion onto the fluted coring bit. A wire-line version of such a system would allow penetration of significant depth without a large increase in mass. A laboratory version of the corer was developed in the NDEAA lab to determine the design and drive parameters of the integrated system. The design configuration lab version of the design and fabrication and preliminary testing results are presented in this paper

life dectection↗

NASA NTRS2014

SWIM: A Semi-Analytical Ocean Color Inversion Algorithm for Optically Shallow Waters

In clear shallow waters, light that is transmitted downward through the water column can reflect off the sea floor and thereby influence the water-leaving radiance signal. This effect can confound contemporary ocean color algorithms designed for deep waters where the seafloor has little or no effect on the water-leaving radiance. Thus, inappropriate use of deep water ocean color algorithms in optically shallow regions can lead to inaccurate retrievals of inherent optical properties (IOPs) and therefore have a detrimental impact on IOP-based estimates of marine parameters, including chlorophyll-a and the diffuse attenuation coefficient. In order to improve IOP retrievals in optically shallow regions, a semi-analytical inversion algorithm, the Shallow Water Inversion Model (SWIM), has been developed. Unlike established ocean color algorithms, SWIM considers both the water column depth and the benthic albedo. A radiative transfer study was conducted that demonstrated how SWIM and two contemporary ocean color algorithms, the Generalized Inherent Optical Properties algorithm (GIOP) and Quasi-Analytical Algorithm (QAA), performed in optically deep and shallow scenarios. The results showed that SWIM performed well, whilst both GIOP and QAA showed distinct positive bias in IOP retrievals in optically shallow waters. The SWIM algorithm was also applied to a test region: the Great Barrier Reef, Australia. Using a single test scene and time series data collected by NASA's MODIS-Aqua sensor (2002-2013), a comparison of IOPs retrieved by SWIM, GIOP and QAA was conducted.

shallow waters↗

NASA NTRSDate not supplied

NASA's Space Launch System Begins Moving To The Launch Site

NASA is accelerating plans for a human return to the Moon. NASA was directed by the White House in 2019 to land the first woman and next man on the Moon by 2024. NASA’s backbone for future deep space exploration is the Space Launch System (SLS), as well as the Orion crew spacecraft, Gateway outpost, and Human Landing System as part of the Artemis program. While the SLS upper stage, the Interim Cryogenic Propulsion Stage (ICPS), based on an existing commercial stage was shipped to Kennedy Space Center (KSC) in 2017, major completed components of SLS will soon begin their eastward journey that will see them at KSC in 2020 to be integrated for their history-making launch back to the Moon. Core Stage prime contractor Boeing completed the Artemis I core stage in 2019 at NASA’s Michoud Assembly Facility (MAF) and shipped it to NASA Stennis Space Center (SSC) for stage green run testing in 2020 and then to KSC. Northrop Grumman, prime contractor for the 5-segment solid rocket boosters, is scheduled to begin overland shipment of the Artemis I motor segments from Utah to KSC in 2020 to await integration. This paper will discuss SLS progress to date and planned 2020 milestones.

Bruce R Askins↗

DOE OSTI2024

Estimating irrigation water use from remotely sensed evapotranspiration data: Accuracy and uncertainties at field, water right, and regional scales

Irrigated agriculture is the dominant user of water globally, but most water withdrawals are not monitored or reported. As a result, it is largely unknown when, where, and how much water is used for irrigation. Here, we evaluated the ability of remotely sensed evapotranspiration (ET) data, integrated with other datasets, to calculate irrigation water withdrawals and applications in an intensively irrigated portion of the United States. We compared irrigation calculations based on an ensemble of satellite-driven ET models from OpenET with reported groundwater withdrawals from hundreds of farmer irrigation application records and a statewide flowmeter database at three spatial scales (field, water right group, and management area). At the field scale, we found that ET-based calculations of irrigation agreed best with reported irrigation when the OpenET ensemble mean was aggregated to the growing season timescale (bias = 1.6–4.9%, R 2 = 0.53–0.74), and agreement between calculated and reported irrigation was better for multi-year averages than for individual years. At the water right group scale, linking pumping wells to specific irrigated fields was the primary source of uncertainty. At the management area scale, calculated irrigation exhibited similar temporal patterns as flowmeter data but tended to be positively biased with more interannual variability. Disagreement between calculated and reported irrigation was strongly correlated with annual precipitation, and calculated and reported irrigation agreed more closely after statistically adjusting for annual precipitation. The selection of an ET model was also an important consideration, as variability across ET models was larger than the potential impacts of conservation measures employed in the region. From these results, we suggest key practices for working with ET-based irrigation data that include accurately accounting for changes in soil moisture, deep percolation, and runoff; careful verification of irrigated area and well-field linkages; and conducting application-specific evaluations of uncertainty.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

A Public Data Set of Auto-Generated Geotagged PV Site Equipment, Generated via Deep Learning

In this research, we present a data set over 100 photovoltaic (PV) sites in TX, which have been automatically geotagged via a fully autonomous deep learning (DL) pipeline. Specifically, locations of inverters, tracker/fixed tilt rows, batteries, and substations are labeled algorithmically. To ensure high data quality, all systems have been reviewed manually and any deep learning errors have been corrected. This public data set, as well as the open-sourced pipeline used to generate it, is valuable for site planning, modelling, and insurance purposes. Given time and resources, we hope to extend the data set to additional states/regions in the US.

14 SOLAR ENERGY↗

DOE OSTI2023

Quantifying the Relevance of Long-Range Forces for Crystal Nucleation in Water

Understanding nucleation from aqueous solutions is of fundamental importance in a multitude of fields, ranging from materials science to biophysics. The complex solvent-mediated interactions in aqueous solutions hamper the development of a simple physical picture, elucidating the roles of different interactions in nucleation processes. In this study, we make use of three complementary techniques to disentangle the role played by short- and long-range interactions in solvent-mediated nucleation. Specifically, the first approach we utilize is the local molecular field (LMF) theory to renormalize long-range Coulomb electrostatics. Second, we use well-tempered metadynamics to speed up rare events governed by short-range interactions. Third, the deep learning-based State Predictive Information Bottleneck approach is employed in analyzing the reaction coordinate of the nucleation processes obtained from the LMF treatment coupled with well-tempered metadynamics. We find that the two-step nucleation mechanism can largely be captured by the short-range interactions, while the long-range interactions further contribute to the stability of the primary crystal state under ambient conditions. Furthermore, by analyzing the reaction coordinate obtained from the combined LMF-metadynamics treatment, we discern the fluctuations on different time scales, highlighting the need for long-range interactions when accounting for metastability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRSDate not supplied

BioSentinel: Forging the path for Deep Space CubeSat Missions

The BioSentinel mission was launched in 2022 aboard the SLS launch vehicle as part of the Artemis-I campaign and continues mission operations into 2024. The 6U CubeSat has been characterizing deep space radiation at large distances from Earth. This presentation gives a status of the mission performance to date, as well as some of the lessons learned from project. BioSentinel has achieved unprecedented performance as an SLS secondary payload due to preparation, planning, and a robust design. Pre-launch antenna and interface testing with both DSN and ESA confirmed command and data pathways and allowed for operational flexibility in the critical early hours post-deployment. Mission Operations simulations prior to launch identified potential risks and trained operators to respond in flight, preparing the team to react quickly and successfully to detumble the spacecraft and enter a power-positive state. The spacecraft would not have survived without the inclusion of the trailblazing 3D-printed composite cold gas propulsion system. The non-standard tank geometry enabled efficient use of the limited space available in the CubeSat form factor as well as the capability to detumble the spacecraft and manage momentum for extended mission durations, while providing sufficient margin to execute potential delta-V maneuvers. Following the conclusion of the primary science mission, the Linear Energy Transfer (LET) Spectrometer has continued to collect solar and galactic radiation data from its unique location in heliocentric orbit. The free space dataset offered by the BioSentinel LET is a valuable source of data for both model validation and future mission planning. As the spacecraft travels farther from Earth it is poised to provide longitudinally distributed measurements of solar particle events during solar maximum. NASA Ames led development of the BioSentinel spacecraft to operate for long durations in deep space. The novel subsystems and COTS components that comprise the BioSentinel bus can serve as a template for future deep space missions, while the lessons the team has learned from well over a year of continuous operations will enable improved performance in the generation of deep space CubeSat missions.

BioSentinel↗

DOE OSTI2023

Recent Developments in Hydrogeologic Applications for Strain Tensor Analyses

Changes in fluid pressure deform porous media and this effect occurs in a variety of hydrogeologic processes, from the change in storage during pumping or injection to fluctuations in water levels caused by barometric pressure. We have developed instruments for measuring small strains in porous media, and we have used the resulting strain data to evaluate well testing, hydraulic fracturing, manual loading at the ground surface, and ambient hydrologic processes, like rainfall and evaporation. A particularly important application is the use of strain tensor data measured at shallow depths to analyze well tests or hydraulic fractures conducted at much greater depths. An early demonstration of this technique was conducted at the North Avant Field north of Tulsa, Oklahoma, where Pennsylvanian sandstone creates a confined aquifer and oil reservoir at a depth of 530m. We have showed that the strains caused by injecting into the aquifer could be measured at a depth of 30m and used to evaluate the properties of the aquifer. We recently expanded the array of strainmeters at the North Avant Field by deploying three more instruments at shallow depth (30m) along with a deep instrument at 520m depth in the winter, 2021. The deep instrument is deployed in shale caprock slightly above the aquifer. To our knowledge, the deep strainmeter at the North Avant Field is the deepest strainmeter ever deployed and this required refining methods originally developed for shallow deployments. The instrument was lowered to depth on oil field tubing and cemented in place using techniques and materials developed for use in oil wells. Optical fiber used to communicate with the instrument was cemented in the annulus on the outside of tubing. This is significant because the techniques we used could readily be extended to greater depth, suggesting that strainmeters can be deployed over a wide range of depths for monitoring critical subsurface processes. For example, it suggests that strainmeters could be deployed through the caprock to monitor for leaks from underlying CO2 storage reservoirs. The strainmeter array at the North Avant Field has been used to characterize deformation during a series of injection tests in the spring and summer, 2021. All the new strainmeters respond to pumping, and the strainmeters we installed earlier also responded. To our knowledge, this is the first application of well testing in a deep aquifer that was monitored by an array of strainmeters—our earlier work used strainmeters at a single location. We are currently analyzing the strain data using an analytical solution, a proxy-based Bayesian inversion algorithm, and other methods. Strainmeter data has also been used to characterize periodic pumping tests by us and Riley Blais. A periodically varying pumping rate causes both hydraulic head and strain signals that vary with the same period as the pumping. The peaks and troughs of the head in monitoring wells lag behind the peaks and troughs of the head in the pumping well, and this lag time increases with distance from the pumping well. The lag time of the pressure and the distance to the monitoring well can be used in a simple analysis to estimate the hydraulic diffusivity of the aquifer. The lag time determined from strain data can be used to estimate aquifer properties using the same analysis that works for the pressure only for strain data measured at particular locations. That is because the strain field in a confining unit advances upward, laterally and then downward even though the pressure in the underlying aquifer only advances laterally, according to our recent simulations. We have field data showing that a small periodic signal superimposed on an injection rate at the North Avant Field will create a periodic strain signal at shallow strainmeters. The field data and the recent simulations suggest that including a periodic component to injection or pumping and then monitoring the resulting strain signal could be a way to monitor the subsurface.

Murdoch, Larry↗

DOE OSTI2023

Recent Developments in Hydrogeologic Applications for Strain Tensor Analyses

Changes in fluid pressure deform porous media and this effect occurs in a variety of hydrogeologic processes, from the change in storage during pumping or injection to fluctuations in water levels caused by barometric pressure. We have developed instruments for measuring small strains in porous media, and we have used the resulting strain data to evaluate well testing, hydraulic fracturing, manual loading at the ground surface, and ambient hydrologic processes, like rainfall and evaporation. A particularly important application is the use of strain tensor data measured at shallow depths to analyze well tests or hydraulic fractures conducted at much greater depths. An early demonstration of this technique was conducted at the North Avant Field north of Tulsa, Oklahoma, where Pennsylvanian sandstone creates a confined aquifer and oil reservoir at a depth of 530m. We have showed that the strains caused by injecting into the aquifer could be measured at a depth of 30m and used to evaluate the properties of the aquifer. We recently expanded the array of strainmeters at the North Avant Field by deploying three more instruments at shallow depth (30m) along with a deep instrument at 520m depth in the winter, 2021. The deep instrument is deployed in shale caprock slightly above the aquifer. To our knowledge, the deep strainmeter at the North Avant Field is the deepest strainmeter ever deployed and this required refining methods originally developed for shallow deployments. The instrument was lowered to depth on oil field tubing and cemented in place using techniques and materials developed for use in oil wells. Optical fiber used to communicate with the instrument was cemented in the annulus on the outside of tubing. This is significant because the techniques we used could readily be extended to greater depth, suggesting that strainmeters can be deployed over a wide range of depths for monitoring critical subsurface processes. For example, it suggests that strainmeters could be deployed through the caprock to monitor for leaks from underlying CO2 storage reservoirs. The strainmeter array at the North Avant Field has been used to characterize deformation during a series of injection tests in the spring and summer, 2021. All the new strainmeters respond to pumping, and the strainmeters we installed earlier also responded. To our knowledge, this is the first application of well testing in a deep aquifer that was monitored by an array of strainmeters—our earlier work used strainmeters at a single location. We are currently analyzing the strain data using an analytical solution, a proxy-based Bayesian inversion algorithm, and other methods. Strainmeter data has also been used to characterize periodic pumping tests by us and Riley Blais. A periodically varying pumping rate causes both hydraulic head and strain signals that vary with the same period as the pumping. The peaks and troughs of the head in monitoring wells lag behind the peaks and troughs of the head in the pumping well, and this lag time increases with distance from the pumping well. The lag time of the pressure and the distance to the monitoring well can be used in a simple analysis to estimate the hydraulic diffusivity of the aquifer. The lag time determined from strain data can be used to estimate aquifer properties using the same analysis that works for the pressure only for strain data measured at particular locations. That is because the strain field in a confining unit advances upward, laterally and then downward even though the pressure in the underlying aquifer only advances laterally, according to our recent simulations. We have field data showing that a small periodic signal superimposed on an injection rate at the North Avant Field will create a periodic strain signal at shallow strainmeters. The field data and the recent simulations suggest that including a periodic component to injection or pumping and then monitoring the resulting strain signal could be a way to monitor the subsurface.

Murdoch, Larry↗

NASA NTRS2025

Components Refurbishment and Chemical Analysis Facility, SWMU #041 - Per- and Polyfluoroalkyl Substances Site Assessment Report Kennedy Space Center, Florida

This PFAS Site Assessment (SA) Report presents the activities and results associated with PFAS investigations at the Components Refurbishment and Chemical Analysis (CRCA) facility located at Kennedy Space Center (KSC), Florida. In 2022, CRCA was identified as an Area of Potential Concern because the facility stores several potential PFAS-containing chemicals. A groundwater sample collected from an onsite monitoring well detected PFOA and PFOS at concentrations greater than State of Florida provisional Groundwater Cleanup Target Levels (pGCTLs). PFAS SA activities were conducted between March 2022 and February 2024. During this timeframe, a total of 16 direct-push technology (DPT) locations and 82 discrete samples were collected from these locations, along with 90 monitoring well samples. The analytical data screening process focused on State of Florida pGCTLs and United States Environmental Protection Agency (USEPA) Regional Screening Level (RSLs) from November 2023 to evaluate the data. Analytical results identified PFOS, PFOA, and PFBA at concentrations exceeding their respective RSLs at each depth interval (shallow, intermediate, and deep). PFOS and PFOA had the largest footprint of RSL exceedances in each depth interval, but pGCTL exceedances were only observed at limited locations in the shallow and intermediate intervals. PFBA had the highest detections of any PFAS compound, with concentrations exceeding 180,000 nanograms per liter (ng/L), which is 100-times the RSL of 1,800 ng/L in the shallow and intermediate intervals and 10-times the RSL in the deep interval. The maximum PFBA detection was 841,000 ng/L at shallow monitoring well, MW0006. The PFAS SA also included samples collected from the onsite hydraulic containment system (HCS) which was installed to control and treat the onsite chlorinated volatile organic compound (CVOC) plume. Influent and effluent aqueous samples were collected monthly from the system. PFAS concentrations were relatively the same for both influent and effluent samples, indicating that while the HCS has been effective for CVOC treatment, it does not provide any additional treatment for PFAS compounds. Since the HCS has achieved its objectives, the system was shut down in December 2024. PFAS data gaps still exist, to include surface water and soil, which were not sampled during this SA. Soil sampling near the Chemical Process Area is recommended. Surface water and soil sampling at select stormwater outfalls is also recommended. Additionally, further groundwater sampling is recommended (DPT and monitoring well) in all depth intervals to delineate the extent of PFAS impacts at CRCA.

K Alex Murphy↗

NASA NTRS2005

Neptune Polar Orbiter with Probes

The giant planets of the outer solar system divide into two distinct classes: the gas giants Jupiter and Saturn, which consist mainly of hydrogen and helium; and the ice giants Uranus and Neptune, which are believed to contain significant amounts of the heavier elements oxygen, nitrogen, and carbon and sulfur. Detailed comparisons of the internal structures and compositions of the gas giants with those of the ice giants will yield valuable insights into the processes that formed the solar system and, perhaps, other planetary systems. By 2012, Galileo, Cassini and possibly a Jupiter Orbiter mission with microwave radiometers, Juno, in the New Frontiers program, will have yielded significant information on the chemical and physical properties of Jupiter and Saturn. A Neptune Orbiter with Probes (NOP) mission would deliver the corresponding key data for an ice giant planet. Such a mission would ideally study the deep Neptune atmosphere to pressures approaching and possibly exceeding 1000 bars, as well as the rings, Triton, Nereid, and Neptune s other icy satellites. A potential source of power would be nuclear electric propulsion (NEP). Such an ambitious mission requires that a number of technical issues be investigated, however, including: (1) atmospheric entry probe thermal protection system (TPS) design, (2) probe structural design including seals, windows, penetrations and pressure vessel, (3) digital, RF subsystem, and overall communication link design for long term operation in the very extreme environment of Neptune's deep atmosphere, (4) trajectory design allowing probe release on a trajectory to impact Neptune while allowing the spacecraft to achieve a polar orbit of Neptune, (5) and finally the suite of science instruments enabled by the probe technology to explore the depths of the Neptune atmosphere. Another driving factor in the design of the Orbiter and Probes is the necessity to maintain a fully operational flight system during the lengthy transit time from launch through Neptune encounter, and throughout the mission. Following our response to the recent NASA Research Announcement (NRA) for Space Science Vision Missions for mission studies by NASA for implementation in the 2013 or later time frame, our team has been selected to explore the feasibility of such a Neptune mission.

Bienstock, Bernard↗

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 OSTI2023

Improving Deep Neural Networks’ Training for Image Classification With Nonlinear Conjugate Gradient-Style Adaptive Momentum

Momentum is crucial in stochastic gradient-based optimization algorithms for accelerating or improving training deep neural networks (DNNs). In deep learning practice, the momentum is usually weighted by a well-calibrated constant. However, tuning the hyperparameter for momentum can be a significant computational burden. In this article, we propose a novel adaptive momentum for improving DNNs training; this adaptive momentum, with no momentum-related hyperparame- ter required, is motivated by the nonlinear conjugate gradient (NCG) method. Stochastic gradient descent (SGD) with this new adaptive momentum eliminates the need for the momentum hyperparameter calibration, allows using a significantly larger learning rate, accelerates DNN training, and improves the final accuracy and robustness of the trained DNNs. For example, SGD with this adaptive momentum reduces classification errors for training ResNet110 for CIFAR10 and CIFAR100 from 5.25% to 4.64% and 23.75% to 20.03%, respectively. Furthermore, SGD, with the new adaptive momentum, also benefits adversarial training and, hence, improves the adversarial robustness of the trained DNNs.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Forecasting Solar-Thermal Systems Performance under Transient Operation Using a Data-Driven Machine Learning Approach Based on the Deep Operator Network Architecture

Modeling and prediction of the dynamic behavior of thermal systems operating under intermittent energy input and variable load requirements represent one of the greatest challenges in the development of efficient and reliable renewable-based power generation technologies. In this work, a data-driven machine learning modeling framework was developed based on a modified version of the Deep Operator Network architecture where the time coordinate in the trunk net is replaced with historical data of the predicting quantity. The modeling framework can be used to accurately predict the performance of renewable-based energy conversion technologies including wind- and solar-based power plants. This novel framework was applied on a solar-thermal system that consists of a solar collection loop using a flat plate collector, a power generation loop comprising an Organic Rankine Cycle, and a thermal energy storage tank connecting both loops. Variable solar irradiance, air temperature, and power load profiles were used by the Deep Operator Network to predict the State-of-Charge and the efficiency of the thermal system for several days. The results were compared with the State-of-Charge and efficiency functions calculated using a physics-based model. For a simple operation scenario, characterized by a clear sky solar irradiance profile and constant load, the standard deviation in the State-of-Charge prediction by Deep Operator Network is below 0.9% during a seven-day prediction time horizon. For the most realistic operation scenario that considers real solar irradiance and a rough load profile, the maximum standard deviation in the predictions for the State-of-Charge and efficiency are below 6.8% and 2.5%, respectively. A comparison between Deep Operator Network and Long Short Term Memory network was also performed. In general, both networks predict very well the State-of-Charge for different data density conditions; however, a higher accuracy, with a standard deviation below 2.0%, is obtained by the Deep Operator Network during three and half days using sparser training data of 20-minute points. The same accuracy for the State-of-Charge prediction with the Long Short Term Memory network is achieved only for 14 h. Average standard deviations for the State-of-Charge prediction of 1.1% with the Deep Operator Network and 1.5% with the Long Short Term Memory network are obtained for a four-day prediction time using a denser training data of 5-minute points.

DeepONet↗