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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

3-D Geologic Controls of Hydrothermal Fluid Flow at Brady Geothermal Field, Nevada using PCA

In many hydrothermal systems, fracture permeability along faults provides pathways for groundwater to transport heat from depth. Faulting generates a range of deformation styles that cross-cut heterogeneous geology, resulting in complex patterns of permeability, porosity, and hydraulic conductivity. Vertical connectivity (a through going network of permeable areas that allows advection of heat from depth to the shallow subsurface) is rare and is confined to relatively small volumes that have highly variable spatial distribution. This local compartmentalization of connectivity represents a significant challenge to understanding hydrothermal circulation and for exploring, developing, and managing hydrothermal resources. Here, we present an evaluation of the geologic characteristics that control this compartmentalization in hydrothermal systems through 3-D analysis of the Brady geothermal field in western Nevada. A published 3-D geologic map of the Brady area is used as a basis to develop structural and geological variables that are hypothesized to control or effect permeability or connectivity. The 3-D distribution of these variables is compared to the distribution of productive and non-productive fluid flow intervals along production wells and non-productive wells via principal component analysis (PCA). This comparison elucidates which geologic and structural variables are most closely associated with productive fluid flow intervals. Results indicate that production intervals at Brady are located: (1) within or near to known and stress-loaded macro-scale faults, and (2) in areas of high fault and fracture density. This submission includes the published journal article detailing this work, the published 3-D geologic map of the Brady Geothermal Area used as a basis to develop structural and geological variables that are hypothesized to control or effect permeability or connectivity, 3-D well data, along which geologic data were sampled for PCA analyses, and associated metadata file. This work was done using existing R programs.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Search for the chiral magnetic effect through beam energy dependence of charge separation using event shape selection

High-energy, heavy-ion collisions can create local domains of chirality-imbalanced quarks, reflecting the topological features of quantum chromodynamics. The chiral magnetic effect (CME) predicts an electric charge separation of quarks in such topological domains along the magnetic field ($\vec{B}$) generated by the passing of two high-Z nuclei. Here, we use a correlation observable Δ⁢𝛾$^{112}$ between charged meson pairs to detect the CME-induced charge separation and a novel event shape selection (ESS) method to mitigate the background effects related to elliptic flow (𝑣 2 ). The ESS method classifies events based on the emission pattern of final-state particles and determines Δ⁢𝛾$^{112}_{ESS}$ from the zero-flow limit. We reconstruct the $\vec{B}$ field direction from the spectator nucleons, which minimizes backgrounds unrelated to the collective motion of the system. In this work, we report the measurements of Δ⁢𝛾$^{112}$ and a background indicator Δ⁢𝛾$^{132}$ in Au+Au collisions from the Brookhaven National Laboratory Relativistic Heavy Ion Collider (RHIC) Beam Energy Scan phase II and at the top RHIC energy. After background suppression, Δ⁢𝛾$^{132}_{ESS}$ aligns with zero, and Δ⁢𝛾$^{112}_{ESS}$ is reduced to no more than 20% of Δ⁢𝛾$^{112}$. We observe a finite residual charge separation with 2.5⁢𝜎, 3⁢𝜎, and 3.2⁢𝜎 significance in the 20–50% centrality range of Au + Au collisions at 11.5, 14.6, and 19.6 GeV. The results at 17.3 and 27 GeV also show positive values but with a lower significance of 1.3⁢𝜎 and 1.1⁢𝜎, respectively. The corresponding Δ⁢𝛾$^{112}_{ESS}$ values at 7.7, 9.2, and 200 GeV are consistent with zero within uncertainties.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Applying Particle Swarm Optimization and Extended Kalman Filtering to Model Kaplan Generation Dynamics for Hydropower Systems

Variable renewable generation is increasing the need for hydropower plants to provide fast and flexible grid support, which places new demands on plant-level dynamic models used for monitoring, control, and operational decision-making. This need is especially important for hydroelectric systems, where turbine and generator dynamics are strongly coupled, nonlinear, and time-varying, making accurate real-time representation difficult. To address this problem, this paper develops a digital twin (DT) framework for a synchronous generator–Kaplan turbine system using an explicit separation of slow turbine dynamics and fast generator dynamics. The turbine subsystem is represented by a six-coefficient model, whose parameters are identified offline using particle swarm optimization, while the generator subsystem is updated online through an extended Kalman filter for real-time state and parameter estimation. These models are integrated within a closed-loop simulation that includes a proportional–integral–derivative–double-derivative governor and excitation system, allowing the DT to track plant behavior under realistic operating conditions. Unlike prior studies that treat turbine and generator modeling separately or rely mainly on simulated inputs, the proposed framework is validated using real operational data from a hydropower plant. Results show that the DT reproduces terminal voltage, active power, and reactive power with a normalized root mean square error of approximately 5%. This hybrid offline–online formulation constitutes the main contribution of the work, providing an adaptive and practically deployable DT for hydropower systems with direct relevance to control improvement, performance monitoring, and grid-support applications under high renewable penetration.

13 HYDRO ENERGY↗
DOE OSTI2021

Probabilistic Day-Ahead Forecasting Using an Analog Ensemble Approach for Wind Farm Grid Services

Wind resource assessment and wind power forecasting are used in research and industry to anticipate future power output at scales ranging from individual wind turbines to entire wind farms. Probabilistic day-ahead wind forecasting is useful for anticipating how a wind farm could potentially participate in the day-ahead market by providing upper and lower bounds for expected power generation, thus informing grid operators of its uncertainty. Understanding this uncertainty is part of a larger project focused on building a platform that combines efforts in weather forecasting, aerodynamic and economic modeling to create maximum value of a wind plant to better provide services to the grid. This effort is also known as the Atmosphere to Electrons to Grid (A2E2G) project. One method for producing a probabilistic forecast is through the analog ensemble approach (Delle Monache et al., 2011). This method leverages historical forecasts and their corresponding observations as a training data set from which future forecasts can be made. For some future forecast, the most similar historical forecasts (analogs) are identified on a regular time basis such as once per a 3-hour window. The most similar analogs, based on a metric such as root mean square error (RMSE), are recorded and their corresponding verifying observations are used as an ensemble member for this future forecast. Prior work in this area demonstrates improvements over raw Numerical Weather Prediction (NWP) forecasts and shows skill similar to techniques such as logistic regression and machine learning (Delle Monache et al., 2013; Alessandrini et al., 2015). Here, we take the High-Resolution Rapid Refresh model (HRRR) day-ahead forecast (0-36 hours) to create a probabilistic day-ahead forecast using an analog ensemble approach. The HRRR has an hourly temporal resolution, with a spatial resolution of 3 km. The 12 UTC HRRR model run is downloaded every day for one year from August 2019 - July 2020, with the first 11 months serving as a bank of analogs from which the forecasting algorithm can create a probabilistic forecast. Once downloaded, the original HRRR forecast is temporally interpolated to 5-minutes, aligning with both the temporal resolution of the observations as well as the timescale relevant for day-ahead power forecasts. The forecast is validated at the M2 tower at the Flatirons Campus of the National Renewable Energy Laboratory (NREL) at a typical wind turbine height of 80 m. Variables such as wind speed, wind direction, and turbulence intensity are incorporated into the probabilistic forecast model and weighted according to their relative importance to the forecast. Based on metrics such as mean bias error (MBE), mean absolute error (MAE), and root mean square error, the analog ensemble forecast outperforms the raw HRRR forecast during the testing period of July 2020. Figure 1 illustrates an example day-ahead forecast compared against the verifying observations. The general variability and ramps are captured throughout the day, with potential to further improve the analog ensemble model through machine learning techniques.

numerical weather prediction↗
DOE OSTI2020

Investigation into Reductive Separation of Tc-99 from Low Activity Waste Off-Gas Condensate by Zero Valent Iron Materials - 20311

This study is investigating the potential for reductive separation of technetium-99 (Tc) from off-gas condensate generated from vitrification of legacy tank waste at the Hanford Tank Waste Treatment and Immobilization Plant. Management of Tc is a high-priority activity for the U.S. Department of Energy (DOE) Office of Environmental Management (EM) complex, which must be addressed based on knowledge gaps and technology development needs outlined in the DOE-EM Technetium Management Program Plan [1]. Technetium is one of the main risk drivers in the permanent disposal of low activity waste (LAW) by vitrification at the Hanford Site due to its high volatility and only fractional incorporation into borosilicate glass. Volatilized Tc will be captured by an off-gas treatment system, and the current plan is to continuously recycle generated condensate back to the LAW glass melter feed. Off-gas recycling is effective at increasing Tc loading in the LAW glass, but it also disproportionately increases concentrations of sulfate, halides, and other volatile compounds impeding overall LAW processing leading to significantly greater waste glass volume requirements. Removal of Tc from secondary liquid waste offers an alternative disposal route to continuous off-gas recycling which may result in significant reduction of the volumes of LAW waste glass and substantial cost savings. The objective of this research funded by the DOE-EM Minority Serving Institutions Partnership Program (MSIPP) is to determine the suitability of zero valent iron (ZVI) as a separation technology to remove Tc from the off-gas condensate stream to minimize its recycle. While ZVI has been proposed for various remediation applications with Tc, it has not been studied for its suitability for removing Tc from the LAW off-gas condensate. The goal is to identify the optimal conditions for which Tc{sup (VII)} will be reduced to Tc{sup (IV)} species and incorporated into an iron matrix. This paper presents a programmatic overview of our evaluation of commercial ZVI materials for their efficiency towards Tc{sup (VII)} reductive removal from simplified off-gas simulant solutions under aerobic conditions. Tc concentrations in solutions were monitored for up to 30 days to determine efficiency and kinetics of reductive removal of Tc. The best performing candidate materials were selected for further experiments evaluating the effects of (1) variable ionic strength, (2) the presence of chromium and corrosive anions, and (3) ZVI aging on Tc{sup (VII)} reductive removal in batch experiments. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Mechanistic roles of metal- and ligand-protonated species in hydrogen evolution with [Cp*Rh] complexes

Protonation reactions involving organometallic complexes are ubiquitous in redox chemistry and often result in the generation of reactive metal hydrides. However, some organometallic species supported by η 5 -pentamethylcyclopentadienyl (Cp*) ligands have recently been shown to undergo ligand-centered protonation by direct proton transfer from acids or tautomerization of metal hydrides, resulting in the generation of complexes bearing the uncommon η 4 -pentamethylcyclopentadiene (Cp*H) ligand. Here, time-resolved pulse radiolysis (PR) and stopped-flow spectroscopic studies have been applied to examine the kinetics and atomistic details involved in the elementary electron- and proton-transfer steps leading to complexes ligated by Cp*H, using Cp*Rh(bpy) as a molecular model (where bpy is 2,2′-bipyridyl). Stopped-flow measurements coupled with infrared and UV-visible detection reveal that the sole product of initial protonation of Cp*Rh(bpy) is [Cp*Rh(H)(bpy)] + , an elusive hydride complex that has been spectroscopically and kinetically characterized here. Tautomerization of the hydride leads to the clean formation of [(Cp*H)Rh(bpy)] + . Variable-temperature and isotopic labeling experiments further confirm this assignment, providing experimental activation parameters and mechanistic insight into metal-mediated hydride-to-proton tautomerism. Spectroscopic monitoring of the second proton transfer event reveals that both the hydride and related Cp*H complex can be involved in further reactivity, showing that [(Cp*H)Rh] is not necessarily an off-cycle intermediate, but, instead, depending on the strength of the acid used to drive catalysis, an active participant in hydrogen evolution. Identification of the mechanistic roles of the protonated intermediates in the catalysis studied here could inform design of optimized catalytic systems supported by noninnocent cyclopentadienyl-type ligands.

08 HYDROGEN↗
DOE OSTI2023

Rapid AI-based Dissection of Ashes using Raman and XRF Spectroscopy (RADAR-X)

Waste-to-energy (WTE) facilities incinerate ~35 million tons of municipal solid waste annually in the United States. The incineration process reduces the mass and the volume of the waste fraction by over 75 and 95%, respectively. The fraction left after incineration remains as ash residues and is referred to as WTE ash, compromising of bottom and fly ash. In the United States, ~10 million tons of WTE ashes are generated annually and predominantly landfilled because of the lack of secondary end-use pathways. This incurs a significant financial burden (landfilling costs) on U.S. WTE facilities and also results in the loss of materials to the landfill. The primary objective of this research is to understand better the elemental and mineralogical composition of WTE ashes from diverse origins and find composition dependent upcycling pathways for diverting these ashes from landfills. This primary objective was addressed through three research tasks –(Task I) An AI-led Multi-Modal Approach for Compositional Analysis, (Task II) Developing a Dissolution-Based Test for Real Time Analysis, and (Task III) Establishing composition-dependent end uses. The chemical composition of WTE ash is dependent on two factors, i.e., the input waste composition and the operational parameters of a WTE facility (combustion conditions). Amongst these two factors, the input waste composition will likely show spatial and temporal variation. As a result, the chemical composition of WTE ash will also fluctuate. To understand the spatial and temporal variation in WTE ash composition, in Task I, we collected 128 ash samples (62 bottom ash and 66 fly ash samples) from 11 WTE facilities located in 11 U.S. states and characterized them via X-ray Fluorescence, powder X-ray Diffraction, and Raman Spectroscopy. The findings from this extensive characterization work indicated that the key elements in WTE fly ashes are Ca, Cl (greater than 10 wt. %), Si, S, K, Zn ( between 1 and 10 wt. %), Mg, Al, P, Ti, Fe, Cu, Br, and Pb (between 0.1 and 1 wt. %). Similarly, the key elements in WTE bottom ash fraction finer than 45μm are Ca (greater than 10 wt. %), Mg, Al, Si, S, Cl, K, Ti, Fe, Zn (between 1 and 10 wt. %), P, V, Cr, Mn, Cu, Br, and Pb (between 0.1 and 1 wt. %). Here, we note that the dominant fraction of WTE bottom ash is the coarse fraction. The coarse WTE bottom ash fraction (rich in silicon) was not characterized in this study because of excessive grinding requirements and their unsuitability as a supplementary cementitious material due to their coarse nature. The elements in WTE bottom ashes are present as calcite, anhydrite, vaterite, hydroxyapatite, quartz, bassanite, gehlenite, akermanite, hydrocalumite, and portlandite. Similarly, the mineralogical species present in WTE fly ashes are calcium chloride hydroxide, halite, calcite, anhydrite, sylvite, hydrocalumite, vaterite, hannebachite, quartz, and bassanite. Temporal variation in ash composition may also result in significant fluctuations in chemical compositions. Therefore, a WTE facility may need to monitor the ash composition (elemental and mineralogical composition) in real time. In Task I, we evaluated the possibility of using a portable X-ray fluorescence (XRF) spectrometer to monitor the elemental composition in real-time. Specifically, we collected XRF data on identical specimens via a portable XRF spectrometer (low-end) and a lab-based benchtop XRF spectrometer (high-end). The collected data was used to train an A.I. algorithm (portable XRF data as an input and benchtop XRF data as an output) to predict accurate elemental composition using portable XRF data. Finally, we developed a 2-minute photobleaching protocol to monitor the mineralogical characteristics of WTE ashes via Raman spectroscopy. Overall, the activities in Task I improved our understanding of ash composition and developed techniques to monitor elemental and mineralogical composition in near real-time. Based on the findings of Task I, we find that WTE ashes exhibit wide variability in mineralogy. For ICP-based elemental analysis, all the mineralogical species in WTE ashes must be brought into solution. This is traditionally accomplished with acid digestion using a combination of multiple acids. However, acid digestion with multiple acids is time-consuming and often fails to ensure complete digestion of the ash matrix. To address this limitation, in Task II, we developed an alkali-fusion-based digestion protocol using a combination of lithium tetraborate, lithium metaborate, and their combinations as possible alkali fluxes for digesting WTE ashes entirely and rapidly. The validity of the developed method was evaluated on two standard ash specimens, i.e., SRM 1633c coal fly ash and BCR-176R incineration fly ash specimen. The findings suggest that the developed protocol can ensure complete digestion of elements such as Al, Ba, Ca, Cr, Cu, Mg, Mn, P, Sr, V, Zn, Be, K, and rare earth elements. The recent changes in the energy market towards renewables and increased metal recycling have resulted in reduced supplies of supplementary cementitious materials (coal fly ash and slag). Therefore, in Task III, we evaluated the possibility of employing WTE ashes as SCMs. As the chemical composition of WTE ashes varies temporally (on an hourly basis), there was also a need to develop tests that can evaluate the suitability of material to act as supplementary cementitious material rapidly, i.e., in a few minutes. Therefore, in Task II, we also developed a rapid test to assess the suitability of a material to act as an SCM in 5 minutes. This represents a significant advance over the state-of-the-art R 3 test, which takes ~144 hours. This test was initially validated on amorphous aluminosilicates, such as calcined clays, and could be extended to evaluate other industrial by-products, such as WTE ashes. In Task III, we evaluated the possibility of employing WTE ashes for two applications, i.e., as an SCM and a lime substitute for clay stabilization. The findings from Task I indicated that WTE ashes are enriched in chlorine and, therefore, cannot be used directly as an SCM due to corrosion-related risks and altered hydration kinetics. Accordingly, we developed an ash treatment protocol to reduce the solubility of chlorine-containing species in WTE ashes. The developed treatment protocol also immobilized lead in certain mineral forms. As a result of the treatment, WTE ashes can be used as SCMs without any corrosion or heavy metal leaching concerns. The second application examined in this study was clay stabilization. WTE ashes are calcium-rich and can be an adequate lime replacement for clay stabilization. Our findings reveal that the sum of the concentrations of Ca(OH) 2 and CaClOH controls the clay stabilization capability of WTE ashes. In summary, in this work, we evaluated the elemental and mineralogical characteristics of U.S. WTE ashes from diverse origins and developed tests to evaluate the chemical characteristics of these ashes in real time through a portable XRF and a benchtop Raman spectrometer. Based on the chemical characteristics of these ashes, we developed an ash treatment process to enable the use of WTE ashes as an SCM and also evaluated the possibility of employing these ashes for clay stabilization. Overall, the findings from this work enables the diversion of WTE ashes from landfills for multiple end-uses, i.e., as an SCM or a lime substitute for clay stabilization.

36 MATERIALS SCIENCE↗
NASA NTRS1999

TRMM and Its Connection to the Global Water Cycle

The importance of quantitative knowledge of tropical rainfall, its associated latent heating and variability is summarized in the context of the global hydrologic cycle. Much of the tropics is covered by oceans. What land exists, is covered largely by rainforests that are only thinly populated. The only way to adequately measure the global tropical rainfall for climate and general circulation models is from space. The TRMM orbit is inclined 35 degrees leading to good sampling in the tropics and a rapid precession to study the diurnal cycle of precipitation. The precipitation instrument complement consists of the first rain radar to be flown in space (PR), a multi-channel passive microwave sensor (TMI) and a five-channel VIS/IR (VIRS) sensor. The precipitation radar operates at a frequency of 13.6 GHz. The swath width is 220 km, with a horizontal resolution of 4 km and the vertical resolution of 250 m. The minimum detectable signal from the precipitation radar has been measured at 17 dBZ. The TMI instrument is designed similar to the SSM/I with two important changes. The 22.235 GHz water vapor absorption channel of the SSM/I was moved to 21.3 GHz in order to avoid saturation in the tropics and 10.7 GHz V&H polarized channels were added to expand the dynamic range of rainfall estimates. The resolution of the TMI varies from 4.6 km at 85 GHz to 36 km at 10.7 GHz. The visible and infrared sensor (VIRS) measures radiation at 0.63, 1.6, 3.75, 10.8 and 12.0 microns. The spatial resolution of all five VIRS channels is 2 km at nadir. In addition to the three primary rainfall instruments, TRMM will also carry a Lightning Imaging Sensor (LIS) and a Clouds and the Earth's Radiant Energy System (CERES) instrument. This presentation will focus primarily on the advances in our understanding of tropical rain systems needed to interpret the TRMM data. Global averages, as well as case studies from TRMM radar (PR), the TRMM Microwave Imager (TMI) and Visible and Infrared Sensor (VIRS) will be presented. Comparisons and contrasts among the different sensors will be drawn. Results will also be compared to previous rainfall climatologies generated from the SSM/I instrument. In particular this paper will focus on the synergy between the TRMM radar and passive microwave radiometer and what we have learned from is synergy.

Kummerow, Chiristian↗
NASA NTRSDate not supplied

ISRU Pilot Excavator: Bucket Drum Scaling Experimental Results

NASA’s Space Technology Mission Directorate (STMD) is funding the development of a robotic excavator called the “ISRU Pilot Excavator” which will be a technology demonstration of excavating and transporting 10 metric tons of lunar regolith on the surface of the moon with a 30k-class robotic excavator. ISRU Pilot Excavator will be the next generation of robotic excavator to use bucket drums as excavation tools. This is an evolution of the Regolith Advanced Surface Systems Operations Robot (RASSOR) developed at NASA’s Kennedy Space Center. Bucket drums are hollow cylinders with regularly spaced scoops around the perimeter. The drums rotate in one direction to collect regolith with the scoops. The regolith slides down an internal baffling system inside the drum which prevents the regolith from falling back out of the scoops. The captured regolith can then be transported while held in the drum and then deposited by rotating the drum in the opposite direction allowing the regolith to slide back down the baffling and out of the excavation scoops. Bucket drums were developed by Lockheed Martin in 2008 and used on multiple robotic excavator prototypes ever since. However the forces on a bucket drum and considerations for scaling have not been measured in detail. Bucket drums are challenging to model using classical blade\bucket equations because of their unique geometry. Therefore this experiment was performed to measure the forces on three bucket drums of the same geometry at different scales. Small: 9.4” (239mm) dia. x 8.1” (206mm) width, Medium: 11.6” (294mm) dia. x 10” (254mm) width, and Large: 17” (432mm) dia. x 14.1”(358mm) width. The test stand consisted of an actuated gantry with controlled motion in the vertical (Z) and horizontal (X) axes and a single rotation axis (R). The bucket drums were individually mounted to the rotary axis of the test stand and translated across a prepared bed of BP-1 lunar regolith simulant at a specified linear speed and cutting depth. The test stand was outfitted with a torque sensor in line with the rotation of the drum (R) and a 3 axis (X, Y, and Z) load cell. In addition to the three sizes of bucket drums the linear excavation speed and cutting depth were test variables. The results of these experiments show the relationship between the three scales of bucket drums for factors such as: excavation force, torque due to regolith rotation inside the drum, excavation energy, time to fill, etc. and will be discussed in detail in this paper. This fundamental data will be used in the design of the ISRU Pilot Excavator and can inform the design of future bucket drum excavators.

Jason Michael Schuler↗
NASA NTRS2010

Depth Dose Distribution Study within a Phantom Torso after Irradiation with a Simulated Solar Particle Event at NSRL

The adequate knowledge of the radiation environment and the doses incurred during a space mission is essential for estimating an astronaut's health risk. The space radiation environment is complex and variable, and exposures inside the spacecraft and the astronaut's body are compounded by the interactions of the primary particles with the atoms of the structural materials and with the body itself Astronauts' radiation exposures are measured by means of personal dosimetry, but there remains substantial uncertainty associated with the computational extrapolation of skin dose to organ dose, which can lead to over- or underestimation of the health risk. Comparisons of models to data showed that the astronaut's Effective dose (E) can be predicted to within about a +10% accuracy using space radiation transport models for galactic cosmic rays (GCR) and trapped radiation behind shielding. However for solar particle event (SPE) with steep energy spectra and for extra-vehicular activities on the surface of the moon where only tissue shielding is present, transport models predict that there are large differences in model assumptions in projecting organ doses. Therefore experimental verification of SPE induced organ doses may be crucial for the design of lunar missions. In the research experiment "Depth dose distribution study within a phantom torso" at the NASA Space Radiation Laboratory (NSRL) at BNL, Brookhaven, USA the large 1972 SPE spectrum was simulated using seven different proton energies from 50 up to 450 MeV. A phantom torso constructed of natural bones and realistic distributions of human tissue equivalent materials, which is comparable to the torso of the MATROSHKA phantom currently on the ISS, was equipped with a comprehensive set of thermoluminescence detectors and human cells. The detectors are applied to assess the depth dose distribution and radiation transport codes (e.g. GEANT4) are used to assess the radiation field and interactions of the radiation field with the phantom torso. Lymphocyte cells are strategically embedded at selected locations at the skin and internal organs and are processed after irradiation to assess the effects of shielding on the yield of chromosome damage. The initial focus of the present experiment is to correlate biological results with physical dosimetry measurements in the phantom torso. Further on, the results of the passive dosimetry within the anthropomorphic phantoms represent the best tool to generate reliable data to benchmark computational radiation transport models in a radiation field of interest. The presentation will give first results of the physical dose distribution, the comparison with GEANT4 computer simulations based on a Voxel model of the phantom, and a comparison with the data from the chromosome aberration study.

Berger, Thomas↗
NASA NTRS1999

TRMM and its Connection to the Global Water Cycle

The importance of quantitative knowledge of tropical rainfall, its associated latent heating and variability is summarized in the context of the global hydrologic cycle. Much of the tropics is covered by oceans. What land exists, is covered largely by rainforests that are only thinly populated. The only way to adequately measure the global tropical rainfall for climate and general circulation models is from space. The Tropical Rainfall Measuring Mission (TRMM) orbit is inclined 35 degrees leading to good sampling in the tropics and a rapid precession to study the diurnal cycle of precipitation. The precipitation instrument complement consists of the first rain radar to be flown in space (PR), a multi-channel passive microwave sensor (TMI) and a five-channel VIS/IR (VIRS) sensor. The precipitation radar operates at a frequency of 13.6 GHz. The swath width is 220 km, with a horizontal resolution of 4 km and the vertical resolution of 250 m. The minimum detectable signal from the precipitation radar has been measured at - 17 dBZ. The TMI instrument is designed similar to the SSM/I with two important changes. The 22.235 GHz water vapor absorption channel of the SSM/I was moved to 21.3 GHz in order to avoid saturation in the tropics and 10.7 GHz V&H polarized channels were added to expand the dynamic range of rainfall estimates. The resolution of the TMI varies from 4.6 km at 85 GHz to 36 km at 10.7 GHz. The visible and infrared sensor (VIRS) measures radiation at 0.63, 1.6, 3.75, 10.8 and 12.0 microns. The spatial resolution of all five VIRS channels is 2 km at nadir. In addition to the three primary rainfall instruments, TRMM will also carry a Lightning Imaging Sensor (LIS) and a Clouds and the Earth's Radiant Energy System (CERES) instrument. This presentation will focus primarily on the advances in our understanding of tropical rain systems needed to interpret the TRMM data. Global averages, as well as case studies from TRMM radar (PR), the TRMM Microwave Imager (TMI) and Visible and Infrared Sensor (VIRS) will be presented. Comparisons and contrasts among the different sensors will be drawn. Results will also be compared to previous rainfall climatologies generated from the SSM/I instrument. In particular this paper will focus on the synergy between the TRMM radar and passive microwave radiometer and what we have learned from its synergy.

Kummerow, Christian↗
DOE OSTI2024

Redesigning Photoelectrodes So They Can Work in the Light and Dark

Unassisted photoelectrochemical (PEC) reactions, such as H2 generation and CO2 reduction, are limited by the durability of the immersed photoelectrode. Small band gap semiconductors, like Si, are efficient at utilizing a large portion of the solar spectrum but are not stable in aqueous environments without protection. While great strides have been made to improve stability under constant illumination, dark stability remains relatively unexamined and presents great challenges for durable PEC systems. Cathodic protection is an established electrochemical method for preventing metal electrode degradation in harsh conditions. Similar protection strategies cannot be applied to traditional two-terminal (2T) semiconductor photoelectrodes because of their inability to pass reverse bias current in the dark. New, three-terminal (3T) photovoltaic (PV) architectures introduce additional degrees of freedom in traditional 2T PEC operations by adding an extra electrical contact for an alternative low resistance path to protect the photoelectrode and drive electrochemical reactions, even in the dark. Here, we investigate bare 3T Si PV devices operating as photocathodes in aqueous methyl viologen electrolyte. The 3T architecture provides additional capabilities to PEC systems such as cathodic protection, the ability to drive reactions with or without illumination, and in situ switching between different operational modes. We show that 3T-based Si photocathodes maintain PEC activity after several hours of light/dark cycling. This work helps advance PEC use in real-world conditions where variable illumination must be considered.

cathodic protection↗
DOE OSTI2026

Progressing Analysis of Variable Electric Rates (PAVER) Study

The Progressing Analysis of Variable Electric Rates (PAVER) study analyzed the impact of a range of time-varying electric rates on the performance of a regional electric grid and the resulting costs for participating and non-participating customers. This analysis leveraged and extended the work of PNNL’s Distribution System Operator with Transactive (DSO+T) study. Five different rate designs were included: a flat volumetric energy charge, a typical Time of Use (TOU) rate, a dynamic energy (DE) rate (based on wholesale locational marginal prices), a dynamic energy and capacity (DE+C) rate, and, finally, a Block and Swing (B&S) rate that billed customers based on their average load profile at constant pricing, but used the DE+C dynamic price for load deviations from their average profile. These rates were analyzed in a large-scale co-simulation of an entire regional grid with a customer population representative of the current state. A large fraction (80%) of residential and commercial customers were assumed to participate in these time-varying rates with automatically controlled HVAC, water heaters, electric vehicles, and batteries. This study assumed no industrial sector participation. The DE and DE+C rates saw system peak loads reduced by 6-7%, while the large participation in the TOU rate case saw a significant rebound effect and a resulting peak load increase of >5%. The impacts to the annual and peak system demand impacted system wholesale prices and the overall grid operating costs. This cost structure determined the revenue needed to be collected from customers by each rate design. Participating customers on the DE and DE+C rates (located in one of the modeled DSOs) saw reductions in average annual electricity bills of 11-17% with average increases in monthly bill variation of no more than 13%. At such high participation levels, TOU customers saw 10% higher average annual bills (due to system-wide rebound effects) and average increased monthly bill variation of 16%. Residential owners of large flexible loads (such as electric vehicles) saw larger bill savings (17-20%) when on a fully dynamic rate. The presence of on-site generation (such as rooftop solar) did not appear to appreciably change customer outcomes. Customers on the Block and Swing rate did see 6% lower monthly bill variation (as intended) than the flat rate case, but at the expense of appreciable bill savings, which were only 3%, comparable to the savings seen by non-participants. Given this finding we recommend that additional research be conducted into how best various bill protection mechanisms can balance minimizing customer bill variation with providing financial incentives commensurate with the flexibility customers provide. We also recommend that customer outcomes be explored across a range of regions using current actual customer and system cost data.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRSDate not supplied

ISRU Pilot Excavator: Bucket Drum Scaling Experimental Results

NASA’s Space Technology Mission Directorate (STMD) is funding the development of a robotic excavator called the “ISRU Pilot Excavator” (IPEx) which will be a technology demonstration of excavating and transporting 10 metric tons of lunar regolith on the surface of the moon with a 30kg-class robotic excavator. IPEx will be the next generation of robotic excavators to use bucket drums as excavation tools. This is an evolution of the Regolith Advanced Surface Systems Operations Robot (RASSOR) developed at NASA’s Kennedy Space Center (KSC). Bucket drums are hollow cylinders with regularly spaced scoops around the perimeter. The drums rotate in one direction to collect regolith with the scoops. The regolith slides down an internal baffling system inside the drum which prevents the regolith from falling back out of the scoops (see Figure 1). The captured regolith can then be transported while held in the drum and then deposited by rotating the drum in the opposite direction allowing the regolith to slide back down the baffling and out of the excavation scoops. Bucket drums were developed by Lockheed Martin in 2008 and used on multiple robotic excavator prototypes ever since. However, the forces on a bucket drum and considerations for scaling have not been measured in detail. Bucket drums are challenging to model using classical blade\bucket equations because of their unique geometry. Therefore, this experiment was performed to measure the forces on three bucket drums of the same geometry at different scales. Small: 9.4” (239mm) dia. x 8.1” (206mm) width, Medium: 11.6” (294mm) dia. x 10” (254mm) width, and Large: 17” (432mm) dia. x 14.1” (358mm) width. The test stand consisted of an actuated gantry with controlled motion in the vertical (Z) and horizontal (X) axes and a single rotation axis (R). The bucket drums were individually mounted to the rotary axis of the test stand and translated across a prepared bed of BP-1 lunar regolith simulant at a specified linear speed and cutting depth. The test stand was outfitted with a torque sensor in line with the rotation of the drum (R) and a 3 axis (X, Y, and Z) load cell. In addition to the three sizes of bucket drums the linear excavation speed and cutting depth were test variables. The results of these experiments show the relationship between the three scales of bucket drums for factors such as: excavation force, torque due to regolith rotation inside the drum, excavation energy, time to fill, etc. and will be discussed in detail in this paper. This fundamental data will be used in the design of IPEx and can inform the design of future bucket drum excavators.

RASSOR↗
DOE OSTI2025

Measurement of directed flow in Au + Au collisions at $\sqrt{s{NN}}$ = 19.6 and 27 GeV with the STAR event plane detector

In heavy-ion collision experiments, the global collectivity of final-state particles can be quantified by anisotropic flow coefficients (𝑣 𝑛 ) . The first-order flow coefficient, also referred to as the directed flow (𝑣 1 ), describes the collective sideward motion of produced particles and nuclear fragments in heavy-ion collisions. It carries information on the very early stage of the collision, especially at large pseudorapidity (𝜂), where it is believed to be generated during the nuclear passage time. Directed flow therefore probes the onset of bulk collective dynamics during thermalization, providing valuable experimental guidance to models of the pre-equilibrium stage. In 2018, the Event Plane Detector (EPD) was installed in STAR and used for the Beam Energy Scan phase-II (BES-II) data taking. The combination of EPD (2.1 < |𝜂| < 5.1) and high-statistics BES-II data enables us to extend the 𝑣 1 measurement to the forward and backward 𝜂 regions. In this paper, we present the measurement of 𝑣 1 over a wide 𝜂 range in Au + Au collisions at $\sqrt{s_{NN}}$ = 19.6 and 27 GeV using the STAR EPD. The results of the analysis at $\sqrt{s_{NN}}$ = 19.6 GeV exhibit excellent consistency with the previous PHOBOS measurement, while elevating the precision of the overall measurement. The increased precision of the measurement also revealed finer structures in heavy-ion collisions, including a potential observation of the first-order event-plane decorrelation. Multiple physics models were compared to the experimental results. Only a transport model and a three-fluid hybrid model can reproduce a sizable 𝑣 1 at large 𝜂 as was observed experimentally. The model comparison also indicates 𝑣 1 at large 𝜂 might be sensitive to the QGP phase transition.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRSDate not supplied

Machine Learning based Aircraft Performance Model Estimation for Trajectory Prediction

The accurate prediction of aircraft trajectory by ground-based decision support tools is a critical component of air traffic management in the US National Airspace System (NAS). Accurate predictions of where the aircraft will be in the future or when they will arrive at specific locations (e.g., fixes) is a key enabler for sequencing and efficient arrival management of flights. Traditional physics based aircraft trajectory prediction relies on a simplified point-mass total energy model whose parameters are referred to as Aircraft Performance Model (APM) parameters. Even though the performance coefficients and weight of an aircraft are a vital part of the aircraft performance model’s predictions and accuracy, these coefficients are proprietary in nature and therefore, unavailable to decision-support tools. Current approaches freeze some coefficients to default base of aircraft data (BADA) values and optimize others. However, the APM parameters are highly coupled by the flight dynamics and prioritizing one parameter over others leads to bias and skewed predictions. To alleviate this problem, we provide a combined optimization framework to predict all the critical (thrust, drag and weight) APM parameters. This paper is focused on training Machine Learning (ML) models that map historical flights to optimized APM parameters that provide the best fit (in terms of prediction error). Our dataset obtained from NASA’s Sherlock data warehouse is comprised of thousands of historical flights and includes weather and track data collected from 2019. Using different subsets of relevant features (e.g., aircraft type), we trained several ML models to estimate the aircraft’s take off weight, drag polar coefficients (both parasitic and lift induced), and thrust settings (multiplier applied to the maximum engine thrust). The chosen flights are from three of the most common aircraft types (B738, B737, and A320) arriving at four airports (LAX, DEN, MSP, and DFW). Our ML approach is comprised of two different solutions: 1- using a subset of features that are known prior to the flight departure and do not change during flight (such as engine type, current temperature at departure & destination airports, aircraft type) and 2 - using a subset of temporal features of the flight trajectory (such as cruise altitude, Mach, airspeed, and rate of climb) in addition to the pre-departure features from the first solution. The labels or target variables are the APM parameters that were obtained by an optimized ordinary differential equations (ODE) fitting process (applied to individual flights). The ODE-fitting is very time intensive and is therefore performed offline. Thus, training an ML model to learn the relationship between the flight features and ODE-generated labels enables faster estimation of the APM parameters and is therefore amenable to real-time prediction. Various ML models including linear regression, random forest, XGBoost, and neural network were trained, and the results are compared. After model validation and hyperparameter-tuning, we observed that the Random Forest model outperformed the other three models by the overall mean square error (MSE) of 2% for the first solution and 1.5% for the second solution. Finally, the ML-derived parameters are compared against default BADA APM parameters using NASA’s Autonomy Development toolkit (ADK) simulation software. The simulation results for one of each aircraft type is shown and discussed.

Aida Sharif Rohani↗
DOE OSTI2021

Resampling and data augmentation for short-term PV output prediction based on an imbalanced sky images dataset using convolutional neural networks

Integrating photovoltaics (PV) into electricity grids is challenged by potentially large fluctuations in power generation. In recent years, sky image-based PV output prediction using convolutional neural networks (CNNs) has emerged as a promising approach to forecasting fluctuations. A key challenge is imbalanced sky image datasets: because of the geography of solar PV system installations, sky image datasets are often rich in sunny condition data but deficient in cloudy condition data. This imbalance contrasts with the fact that model errors are dominated by cloudy condition performance. In this study, we attempt to remedy this by exploring the enrichment and augmentation of an imbalanced sky images dataset for two PV output prediction tasks: nowcasting (predicting concurrent PV output) and forecasting (predicting 15-minute-ahead future PV output). We empirically examine the efficacy of using different resampling and data augmentation approaches to create a rebalanced dataset for model development. A three-stage greedy search is used to determine the optimal resampling approach, data augmentation techniques and over-sampling rate. The results show that for the nowcast problem, resampling and data augmentation can effectively enhance the model performance, reducing overall root mean squared error (RMSE) by an average of 4%, or a 15 std. (standard deviation) of improvement compared to the variability of the baseline model. In contrast, the treatment RMSE for the forecast problem nearly always overlaps the baseline performance at the ± 2 std. level. The optimal resampling approach expands on the original dataset by over-sampling the minority cloudy data, with the best results from large over-sampling rate (e.g., 4 ~ 6 times over-sampling of cloudy images).

14 SOLAR ENERGY↗
DOE OSTI2020

Measurements of Density and Sound Speed in Mixtures Relevant to Supercritical CO2 Cycles

Abstract The objective of this research is to validate properties of mixtures relevant to supercritical carbon dioxide (sCO2) power cycles. Direct-fired sCO2 cycles are a promising technology for the future power generation systems. The working fluid of sCO2 cycles will be near and above critical point of CO2. One of the challenges is that the simulation of mixtures should consider real gas behavior. Expected operating conditions of Allam cycles reach up to 300 bar and 1000 °C. Characterizing the mixtures at the extreme conditions is an important issue in current researches and industrial applications. Thermophysical properties of mixtures may be beyond the valid range of the widely used database such as NIST REFPROP. Experimental data of mixture properties in the literature are limited which is necessary to develop high-fidelity design tools for sCO2 power cycles. We measured the density and sound speed of several multi-component mixtures. A temperature-controlled high-pressure test cell was used for the density measurements. Sound speed was measured by resonant frequency detection using an external speaker and a piezoelectric pressure sensor. Mixtures studied in this work include carbon dioxide, methane, oxygen, and water vapor. Properties of pure CO2 were measured to show the validity of our technique. Compositions were selected to be close to frozen mixtures at the inlet, mid-progress, and exhaust conditions of a model sCO2 combustor in the previous numerical simulation work. Corresponding reaction progress variables (RPV) were RPV = 0, 0.5, and 1. Temperature and pressure conditions of experiments are 310–450 K and 0–150 bar. In our study, density and sound speed from the NIST REFPROP database agree with experimental measurements within the range of our measurement uncertainties.

Energy & Fuels↗