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DOE OSTI2022

Solar Irradiance Ramp Forecasting Based on All-Sky Imagers

Solar forecasting constitutes a critical tool for operating, producing and storing generated power from solar farms. In the framework of the International Energy Agency’s Photovoltaic Power Systems Program Task 16, the solar irradiance nowcast algorithms, based on five all-sky imagers (ASIs), are used to investigate the feasibility of ASIs to foresee ramp events. ASIs 1–2 and ASIs 3–5 can capture the true ramp events by 26.0–51.0% and 49.0–92.0% of the cases, respectively. ASIs 1–2 provided the lowest (<10.0%) falsely documented ramp events while ASIs 3–5 recorded false ramp events up to 85.0%. On the other hand, ASIs 3–5 revealed the lowest falsely documented no ramp events (8.0–51.0%). ASIs 1–2 are developed to provide spatial solar irradiance forecasts and have been delimited only to a small area for the purposes of this benchmark, which penalizes these approaches. These findings show that ASI-based nowcasts could be considered as a valuable tool for predicting solar irradiance ramp events for a variety of solar energy technologies. The combination of physical and deep learning-based methods is identified as a potential approach to further improve the ramp event forecasts.

14 SOLAR ENERGY↗

NASA NTRS1986

Theoretical studies of diatomic and triatomic systems containing the group IB atoms Cu, Ag, and Au

Selected portions of the ground state potential energy surfaces of the Cu3, Ag3, AgCu2, and AuCu2 trimers are studied at the single-reference singles plus doubles configuration interaction and couple pair functional levels correlating 33 electrons. The calculations use the effective core potentials of Hay and Wadt (1985) to replace the deep core levels. The Cu3 and Ag3 molecules are found to have 2B2 obtuse-angled ground states, with low-lying 2A1 acute-angled excited states. The AgCu2 and AuCu2 molecules have 2A1 acute-angled ground states. The Cu3 molecule has a smaller 3d population than Ag3, and Cu3 has a smaller s electron density in the open-shell orbital than does Ag3, in agreement with recent ESR experiments.

Walch, Stephen P.↗

NASA NTRS1990

Space radiation dose analysis for solar flare of August 1989

Potential dose and dose rate levels to astronauts in deep space are predicted for the solar flare event which occurred during the week of August 13, 1989. The Geostationary Operational Environmental Satellite (GOES-7) monitored the temporal development and energy characteristics of the protons emitted during this event. From these data, differential fluence as a function of energy was obtained in order to analyze the flare using the Langley baryon transport code, BRYNTRN, which describes the interactions of incident protons in matter. Dose equivalent estimates for the skin, ocular lens, and vital organs for 0.5 to 20 g/sq cm of aluminum shielding were predicted. For relatively light shielding (less than 2 g/sq cm), the skin and ocular lens 30-day exposure limits are exceeded within several hours of flare onset. The vital organ (5 cm depth) dose equivalent is exceeded only for the thinnest shield (0.5 g/sq cm). Dose rates (rem/hr) for the skin, ocular lens, and vital organs are also computed.

Nealy, John E.↗

NASA NTRS2002

Tides and Modern Geodesy

In modem high-precision geodesy, and especially in modem space geodesy, every measurement that one makes contains tidal signals. Generally these signals are considered noise and must somehow be eliminated. The stringent requirements of the latest space geodetic missions place severe demands on tidal models. On the other hand, these missions provide the strongest data for improving tidal models. In particular, TOPEX/POSEIDON altimetry and LAGEOS laser ranging have improved models to such an extent that new geophysical information about the ocean and the solid Earth are coming to light. Presumably GRACE intersatellite ranging data will also add to this information. This paper discusses several of these new geophysical results, with special emphasis given to the dissipation of tidal energy. Strong constraints have recently been placed on the partitioning of energy dissipation among the ocean, atmosphere, and solid earth and between the deep and shallow ocean. The dissipation in deep water is associated with internal tides and has potentially important implications for understanding the ocean's thermohaline circulation.

Ray, Richard D.↗

NASA NTRS2003

The Ability to Probe the Martian Polar Subsurface Via Ground-penetrating Radar

Ground-penetrating radar (GPR) offers the exciting possibility of remote sensing below the Martian surface for trapped aquifers. A GPR is currently heading to Mars onboard Mars Express (MEX) and a GPR is in consideration to be onboard Mars Reconnaissance Orbiter (MRO) in 2005. While such orbital systems offer great potential for polar stratigraphy studies, their ability to penetrate deep into the Martian polar ice is a function of both the intervening ionospheric density and the overlying ground ice conductivity. The influence of both signal-altering layers will be discussed. Polar Ice and Water: Clifford1,2 has suggested

Farrell, W. M.↗

NASA NTRS2012

Missions to Near-Earth Asteroids: Implications for Exploration, Science, Resource Utilization, and Planetary Defense

Introduction: In 2009 the Augustine Commission identified near-Earth asteroids (NEAs) as high profile destinations for human exploration missions beyond the Earth-Moon system as part of the Flexible Path. More recently the U.S. presidential administration directed NASA to include NEAs as destinations for future human exploration with the goal of sending astronauts to a NEA in the mid to late 2020s. This directive became part of the official National Space Policy of the United States of America as of June 28, 2010. NEA Space-Based Survey and Robotic Precursor Missions: The most suitable targets for human missions are NEAs in Earth-like orbits with long synodic periods. However, these mission candidates are often not observable from Earth until the timeframe of their most favorable human mission opportunities, which does not provide an appropriate amount of time for mission development. A space-based survey telescope could more efficiently find these targets in a timely, affordable manner. Such a system is not only able to discover new objects, but also track and characterize objects of interest for human space flight consideration. Those objects with characteristic signatures representative of volatile-rich or metallic materials will be considered as top candidates for further investigation due to their potential for resource utilization and scientific discovery. Once suitable candidates have been identified, precursor spacecraft are required to perform basic reconnaissance of a few NEAs under consideration for the human-led mission. Robotic spacecraft will assess targets for potential hazards that may pose a risk to the deep space transportation vehicle, its deployable assets, and the crew. Additionally, the information obtained about the NEA's basic physical characteristics will be crucial for planning operational activities, designing in-depth scientific/engineering investigations, and identifying sites on the NEA for sample collection. Human Exploration Considerations: These missions would be the first human expeditions to interplanetary bodies beyond the Earth-Moon system and would prove useful for testing technologies required for human missions to Mars, Phobos and Deimos, and other Solar System destinations. Current analyses of operational concepts suggest that stay times of 15 to 30 days may be possible at a NEA with total mission duration limits of 180 days or less. Hence, these missions would undoubtedly provide a great deal of technical and engineering data on spacecraft operations for future human space exploration while simultaneously conducting detailed investigations of these primitive objects with instruments and equipment that exceed the mass and power capabilities delivered by robotic spacecraft. All of these activities will be vital for refinement of resource characterization/identification and development of extraction/utilization technologies to be used on airless bodies under low- or micro-gravity conditions. In addition, gaining enhanced understanding of a NEA s geotechnical properties and its gross internal structure will assist the development of hazard mitigation techniques for planetary defense. Conclusions: The scientific, resource utilization, and hazard mitigation benefits, along with the programmatic and operational benefits of a human venture beyond the Earth-Moon system, make a piloted sample return mission to a NEA using NASA s proposed human exploration systems a compelling endeavor.

Abell, P. A.↗

NASA NTRS2016

Architecture and CONOPS of Next-Generation Ground Network for Communications and Tracking of Interplanetary SmallSats

As small spacecraft venture out of Earth orbit, they will encounter challenges not experienced or addressed by the numerous low Earth orbit (LEO) CubeSat and Smallsat missions staged to date. The LEO CubeSats typically use low-cost, proven CubeSat radios, antennas, and university ground stations with small apertures. As more ambitious yet cost-constrained space mission concepts to the Moon and beyond are being developed, CubeSats and smallsats have the potential to provide a more affordable platform for exploring deep space and performing the associated science. Some of the challenges that have, so far, slowed the proliferation of small interplanetary spacecraft are those of communications and navigation.

Cheung, Kar-ming↗

NASA NTRS2019

NASA's Space Launch System: Payload Opportunities for Lunar Exploration, Science Missions

Nearing its first launch, the Space Launch System (SLS), NASA’s new super heavy-lift launch vehicle, offers highly energetic launches that deliver more mass and provide more volume in 8.4 m-diameter and potentially larger fairings to make a new generation of deep space missions possible. An evolvable launcher available in crew, crew with a co-manifested payload (CPL) and cargo-only configurations, SLS is a crucial capability to enable astronauts to return to the Moon, but it also offers key benefits for science missions. NASA’s 21st-century return to the Moon recently received a formal name: the Artemis program. In addition to the core enabling capabilities of SLS and the Orion crew spacecraft, NASA will also enlist international and commercial partnerships for Artemis. The Agency intends to build a scientific outpost in lunar orbit, the Gateway, from which human and robotic missions to and from the surface can rendezvous. SLS will launch Orion on a series of missions leading to landing the first woman and the next man on the Moon as part of Artemis. SLS uses proven propulsion systems: two solid rocket boosters and four RS-25s engines that have been upgraded to provide more thrust and operate in the SLS environment. SLS Block 1 uses a modified Delta IV Heavy upper stage, called the Interim Cryogenic Propulsion Stage (ICPS) and lifts at least 26 metric tons (t) to trans-lunar injection (TLI). The Block 1 vehicle can also be outfitted with a 5 m-diameter fairing. Block 1B, the next major variant, also uses solid rocket boosters and RS-25 engines to achieve Earth escape velocity, but replaces the single-engine liquid hydrogen (LH2)/liquid oxygen (LOX) ICPS with a four-engine LH2/LOX Exploration Upper Stage (EUS) to increase mass to TLI to 34-37 t, depending on crew or cargo configuration. In the Block 1B crew configuration, a 10 m-tall Universal Stage Adapter (USA) connects the vehicle to Orion and can carry a CPL up to 10 t. The USA provides 286 m3 of unpressurized volume for payloads. For large payloads, 8.4 m- and 10 m-diameter cargo fairings in 19.1 m and 27.4 m lengths are possible. The ultimate SLS vehicle, Block 2, incorporates evolved boosters to reach a lift capacity of more than 45 t to TLI. The capabilities of SLS not only make new missions to the Moon possible, but also game-changing science missions, such as deployment of large-aperture space telescopes, spacecraft to the ice giants or even probes to the interstellar medium. This paper will discuss the capabilities of SLS, the vehicle’s planned evolution, missions that can effectively utilize the vehicle and manufacturing status of the vehicle.

Creech, Stephen D.↗

NASA NTRS2019

NASA's Space Launch System: Payload Opportunities for Lunar Exploration, Science Missions

Creech, Steve↗

NASA NTRSDate not supplied

Developing Autonomous Technologies for Biological Missions to Deep Space

In upcoming biological missions beyond low Earth orbit (LEO), the use of autonomous instrumentation will allow scientists to perform a variety of experiments, including the characterization of the response to different space environments (Moon, Mars, interplanetary space) using biological models like microbes, plants, organoids, and tissue chips. BioSentinel is an ongoing deep space mission, currently at over 50 million kilometers from Earth and the first instrument developed to perform biological experiments beyond LEO. Even though the primary objective of this CubeSat mission was to investigate the effects of the deep space radiation environment on budding yeast, the spacecraft bus (i.e., all the subsystems that support the biological payload like power, thermal, data telemetry, navigation, etc.) can accommodate a variety of biological (and physical) experiments and model organisms. LEIA, an upcoming CLPS mission to the lunar surface, uses a microfluidic and optical instrument based on BioSentinel to study the effects of the lunar environment on different cellular processes and on bioproduction of antioxidants. A new series of science mission concepts are being proposed to be accommodated into platforms like BioSentinel. These missions will investigate the response of a variety of organisms to the deep space environment, including but not limited to single-cell eukaryotes, cyanobacteria, plants (including crops), organoids, and tissue chips. In addition to optical absorbance measurements like the ones performed in BioSentinel (and LEIA), we are investigating the use of fluorescence detection, microscopy, sequencing devices, etc. Thus, instruments like the ones proposed here can be adapted to a variety of platforms like free-flyers, deployable payloads, landers, rovers, and the lunar Gateway. These technologies can be used as steppingstones for establishing a sustained human presence on the Moon and in deep space while providing knowledge for the development of potential countermeasures.

Sergio R Santa Maria↗

NASA NTRS2003

Optical ground station site diversity for Deep Space Optical Communications the Mars Telecom Orbiter optical link

Future NASA deep space missions will fly advanced high resolution imaging instruments that will require high bandwidth links to return the huge data volumes generated by these instruments. Optical communications is a key technology for returning these large data volumes from deep space probes. Yet to cost effectively realize the high bandwidth potential of the optical link will require deployment of ground receivers in diverse locations to provide high link availability. A recent analysis of GOES weather satellite data showed that a network of ground stations located in Hawaii and the Southwest continental US can provide an average of 90% availability for the deep space optical link. JPL and AFRL are exploring the use of large telescopes in Hawaii, California, and Albuquerque to support the Mars Telesat laser communications demonstration. Designed to demonstrate multi-Mbps communications from Mars, the mission will investigate key operational strategies of future deep space optical communications network.

laser communications↗

DOE OSTI2023

Deep Learning for Modeling Enhanced Geothermal Systems

Enhanced Geothermal Systems (EGS) offer a vast potential to expand the use of geothermal energy. Heat is extracted from this engineered system by injecting cold water into a subsurface fractures, which are in contact with the hot dry rock, and pulled through the production wells. Creating EGS requires improving the natural permeability of hot crystalline rocks. To develop economically viable EGS reservoirs, significant technical barriers (e.g., better stimulation technologies without adequate water and/or permeability) and non-technical barriers (e.g., land access and permitting) must be overcome. In this short conference paper, we present a workflow to address a part of this challenge – “How to develop economically viable EGS using existing technologies?”. Our workflow called the GeoThermalCloud (GTC) for EGS, leverages recent advances in machine learning, deep learning, and cloud computing. This GTC framework is open-source and available at https://github.com/SmartTensors/GeoThermalCloud.jl. The GTC framework provides trained deep learning (DL) models to estimate the net present value of a given EGS design scenario. The Geothermal Design Tool (https://github.com/GeoDesignTool/GeoDT.git), a fast and simplified multi-physics solver, is used to develop a database for training DL models. The database consists of EGS design parameters (inputs to DL model) and their net present value (output of DL model) in uncertain geologic systems. The EGS design parameters for constructing this training database are based on Utah FORGE but include the options of more wells and deeper depths. The DL models are trained by ingesting the EGS design parameters and estimating the corresponding net present value. Such an emulation allows us to screen various EGS designs quickly and identify good development strategies by coupling them with optimization techniques. Our preliminary results show promise in DL emulation of net present value. However, a lot more work is needed to improve the predictive capability of DL models (i.e., extensive hyperparameter tuning is necessary). This will be the primary focus of our future work.

artificial neural networks, geothermal↗

DOE OSTI2022

Real-time deep-learning inversion of seismic full waveform data for CO 2 saturation and uncertainty in geological carbon storage monitoring

Deep-learning inversion has recently drawn attention in geological carbon storage research due to its potential of imaging and monitoring carbon storage in real time, significantly improving efficiency and safety of carbon storage operations. We present a deep-learning full waveform inversion method that after the neural network has been trained can image CO 2 saturation and its uncertainty in real time. Our deep-learning inversion method is based on the U-Net architecture with the neural network trained on pairs of synthetic seismic data and CO 2 saturation models. Accordingly, our training establishes a mapping relationship between seismic data and CO 2 saturation models and once fully trained directly estimates CO 2 saturation as a function of subsurface location. We further quantify uncertainties of CO 2 saturation estimates using the Monte Carlo dropout method and a bootstrap aggregating method. For this proof-of-concept study, the CO 2 training models and data are derived from the Kimberlina 1.2 model, a hypothetical 3D geological carbon storage model that is constructed based on various geological and hydrological data from the Southern San Joaquin Basin, California. We perform deep-learning inversion experiments using noise-free and noisy training and test data sets and compare the results. Our modelling experiments show that (1) the deep-learning inversion can estimate 2D distributions of CO 2 fairly well even in the presence of Gaussian random noise and (2) both CO 2 saturation imaging and uncertainty quantification can be done in real time. Our results suggest that the deep-learning inversion method can serve as a robust real-time monitoring tool for geological carbon storage and/or other time-varying reservoir/aquifer properties that result from injection, extraction, and/or other subsurface transport phenomena.

58 GEOSCIENCES↗

DOE OSTI2020

Deep anomaly detection for industrial systems: a case study

We explore the use of deep neural networks for anomaly detection of industrial systems where the data are multivariate time series measurements. We formulate the problem as a self-supervised learning where data under normal operation are used to train a deep neural network autoregressive model, i.e., use a window of time series data to predict future data values. The aim of such a model is to learn to represent the system dynamic behavior under normal conditions, while expect higher model vs. measurement discrepancies under faulty conditions. In real world applications, many control settings are discrete in nature. In this paper, vector embedding and joint losses are employed to deal with such situations. Both LSTM and CNN based deep neural network backbones are studied on the Secure Water Treatment (SWaT) testbed datasets. Also, Support Vector Data Description (SVDD) method is adapted to such anomaly detection settings with deep neural networks. Evaluation methods and results are discussed based on the SWaT dataset along with potential pitfalls.

anomaly detection, deep neural network↗

DOE OSTI2024

Research Priorities and Opportunities in U.S. Wholesale Electricity Markets: Market Design under Deep Decarbonization

This report provides a comprehensive review of challenges, research needs, and potential solutions for competitive wholesale electricity market design in deeply decarbonized power systems. We provide context regarding how competitive wholesale electricity markets can evolve in a longer-term perspective to ensure that they still operate efficiently throughout the transition to a deeply decarbonized future. We organize the discussion across seven topics: operational reliability, emerging technology integration, adequacy and resilience, price formation, interactions across transmission and distribution systems, transmission planning, achieving clean energy objectives, and challenges associated with cost-effectively achieving clean energy objectives. In each section we first identify key associated challenges before proposing a set of corresponding solutions and research needs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS2000

Design and Preliminary Testing of a High Performance Antiproton Trap (HiPAT)

Antimatter represents the pinnacle of energy density, offering the potential to enhance current fusion/fission concepts enabling various classes of deep space missions. Current production rates are sufficient to support proof-of-concept evaluation of many key technologies associated with antimatter-derived propulsion. Storage has been identified as a key enabling technology for all antimatter-related operations, and as such is the current focus of this NASA-MSFC effort to design and fabricate a portable device capable of holding up to 10(exp 12) particles. Hardware has been assembled and initial tests are underway to evaluate the trap behavior using electron gun generated, positive hydrogen ions. Ions have been stored for tens of minutes, limited by observed interaction with background gas. Additionally, radio frequency manipulation is being tested to increase lifetime by stabilizing the stored particles, potentially reducing their interaction with background gas, easing requirements on ultimate trap vacuum and precision mechanical alignment.

Martin, James↗

DOE OSTI2023

Insights into Methodologies and Stochastic Optimization of Thermal Energy Storage-Coupled Advanced Reactor Systems: A Comparison of Methods for Accessing Long-Term Sub-System Sizing Adequacy

This paper investigates the potential of coupling Thermal Energy Storage (TES) with Advanced Reactors (ARs) to address uncertainties posed by climate change in deep decarbonized power systems. The TES Use-case Team at Idaho National Laboratory (INL) has examined the potential of storing thermal energy from ARs during low demand periods and optimizing discharge during peak-priced hours, in both steady-state and transient conditions. Building on this groundwork, this study bridges the gaps in optimal sizing of the sub-system of TES-coupled AR systems using Risk Analysis Virtual Environment (RAVEN) and Holistic Energy Resource Optimization Network (HERON), INL?s framework for grid optimization. By applying this framework, we present statistically-robust optimal charge, discharge including balance of plant (BOP), and storage sizing for the High-Temperature Gas-Cooled Reactor (HTGR) with 203 MWth output. To this end, we generated synthetic price samples for 30 years using 2018 ? 2021 real-time market data from ERCOT, PJM and MISO. Our results reveals that the TES-coupled HTGR system is highly effective in maximizing revenue from electricity sales. We observed a substantial increase of 40 % in ERCOT and a noteworthy 15 % increase in PJM and MISO when compared to the conventional BOP without TES. This improvement is achieved through regionally-tailored sub-system sizing, which ranges from 398 to 416 MWth for the discharge system and 610 to 1029 MWth for the TES. We find that the average electricity price directly impacts the overall economics, while price volatility influences storage size. Additional sensitivity analyses were performed to access the impact of key assumptions on system economics and sizing, differentiating the optimization window (i.e., 24 ? 219 hours of chronological observations) and by imposing storage continuity condition in tracking TES cycles. We observed that at the 120-hour of the optimization window, a reasonable balance between computation time and accuracy was achieved. Our analysis also highlights the significance of conducting multi-day cycle analysis (> 120-hour) for TES to capture interaction between electricity prices and storage dynamics, providing a comprehensive understanding of TES behavior that AR developers should integrate into their plant designs.

25 ENERGY STORAGE↗

DOE OSTI2025

Anomalous electroweak physics unraveled via evidential deep learning

The ever-growing ecosystem of beyond standard model (BSM) calculations and parametrizations has motivated the development of systematic methods for making quantitative cross-comparisons over the wide range of possible models, especially with controllable uncertainties. In this setting, the language of uncertainty quantification (UQ) furnishes useful metrics for assessing statistical overlaps and discrepancies among BSM and related models. In this study, we leverage recent machine learning (ML) developments in evidential deep learning (EDL) for UQ to separate data (aleatoric) and knowledge (epistemic) uncertainties in a model-discrimination setting. We construct several potentially BSM-motivated scenarios for the anomalous electroweak interaction (AEWI) of neutrinos with nucleons in deep inelastic scattering ( v DIS). These scenarios are then quantitatively mapped, as a demonstration, alongside Monte Carlo replicas of the CT18 PDFs used to calculate the $\varDelta \chi ^{2}$ statistic for a typical multi-GeV v DIS experiment, CDHSW. Our framework effectively highlights areas of model agreement and provides a classification of out-of-distribution (OOD) samples. By offering the opportunity to quantitatively understand model overlaps, the approach presented in this work can help facilitate efficient BSM model exploration and exclusion for future New Physics searches.

AI↗