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NASA NTRS1997

Will Global Change Effect Primary Productivity in Coastal Ecosystems?

Algae are the base of coastal food webs because they provide the source of organic carbon for the remaining members of the community. Thus, the rate that they produce organic carbon to a large extent controls the productivity of the entire ecosystem. Factors that control algal productivity range from the physical (e.g., temperature, light), chemical (e.g., nutrient levels) to the biological (e.g., grazing). Currently, levels of atmospheric carbon dioxide surficial fluxes of ultraviolet radiation are rising. Both of these environmental variables can have a profound effect on algal productivity. Atmospheric carbon dioxide may increase surficial levels of dissolved inorganic carbon. Our laboratory and field studies of algal mats and phytoplankton cultures under ambient and elevated levels of pCO2 show that elevated levels of inorganic carbon can cause an increase in photosynthetic rates. In some cases, this increase will cause an increase in phytoplankton numbers. There may be an increase in the excretion of fixed carbon, which in turn may enhance bacterial productivity. Alternatively, in analogy with studies on the effect of elevated pCO2 on plants, the phytoplankton could change their carbon to nitrogen ratios, which will effect the feeding of the planktonic grazers. The seasonal depletion of stratospheric ozone has resulted in elevated fluxes of UVB radiation superimposed on the normal seasonal variation. Present surface UV fluxes have a significant impact on phytoplankton physiology, including the inhibition of the light and dark reactions of photosynthesis, inhibition of nitrogenase activity, inhibition of heterocyst formation, reduction in motility, increased synthesis of the UV-screening pigment scytonemin, and mutation. After reviewing these issues, recent work in our lab on measuring the effect of UV radiation on phytoplankton in the San Francisco Bay Estuary will be presented.

Rothschild, Lynn J.↗

NASA NTRS2010

Analysis of Photospheric Convection Cells with SDO/HMI

Supergranulation is a component of solar convection that assists in the outward transportation of internal energy. Supergranule cells are approximately 35 Mm across, have lifetimes on the order of a day and have divergent horizontal velocities of around 300 m/s, a factor of 10 higher than their central radial components. While they have been observed using Doppler methods for around half a century, their existence is also observed in other datasets such as magnetograms and Ca II K images. These datasets clearly show the influence of supergranulation on solar magnetism and how the local field is organized by the flows of supergranule cells. The Heliospheric and Magnetic Imager (HMI) aboard SDO is making fresh observations of convection phenomena at a higher cadence and a higher resolution that should make granular features visible. Granulation and supergranulation characteristics can now be compared within the same datasets, which may lead to further understanding of any mutual influences. The temporal and spatial enhancements of HMI will also reduce the noise level within studies of convection so that more detailed studies of their characteristics may be made. We present analyses of SDO/HMI Dopplergrams that provide new estimates of convection cell sizes, lifetimes, and velocity flows, as well as the rotation rates of the convection patterns across the solar disk. We make comparisons with previous data produced by MDI, as well as from data simulations.

Williams, Peter E.↗

NASA NTRS2017

Application of IRTAM to Support ISS Program Safety

The International Space Station (ISS) orbits near the F-peak of the ionosphere (approximately 400 km altitude). Generally, satellites orbiting at this altitude would have a floating potential (FP) of approximately -1 V due to the electron temperature (Te). However, the ISS has 8 large negatively grounded 160 V solar array wings (SAW) that collect a significant electron current from the ionosphere. This current drives the ISS FP much more negative during insolation and is highly dependent on the electron density (Ne). Also, due to the size of the ISS, magnetic inductance caused by the geomagnetic field produces a delta potential up to 40 V across the truss, possibly producing positive potentials. During Extravehicular Activity (EVA) the negative FP can lead to an arcing hazard when it exceeds -45.5 V, and the positive FP can produce a DC current high enough to stimulate the astronaut's muscles and also cause a hazard. Data collected from the Floating Potential Monitoring Unit (FPMU) have shown that the probability of either of these hazards occurring during times with quiet to moderately disturbed geomagnetic activity is low enough to no longer be considered a risk. However, a study of the ionosphere Ne during severe geomagnetic storm activity has shown that the Ne can be enhanced by a factor of 6 in the ISS orbit. As a result, the ISS Safety Review Panel (SRP) requires that ionospheric conditions be monitored using the FPMU in conjunction with the ISS Plasma Interaction Model (PIM) to determine if a severe geomagnetic storm could result in a plasma environment that could produce a hazard. A 'Real-Time' plasma hazard assessment process was developed to support ISS Program real-time decision making providing constraint relief information for EVAs planning and operations. This process incorporates 'real time' ionospheric conditions, ISS solar arrays' orientation, ISS flight attitude, and where the EVA will be performed on the ISS. This assessment requires real time data that is presently provided by the FPMU including ISS floating potential, along with ionospheric Ne and Te, in order to determine the present environment. Once the present environment conditions are known to be either above, below, or near the current IRI values, the IRI is used to forecast what the environment could become in the event of a severe geomagnetic storm. If the FPMU should fail, the Space Environments team needs another source of data which is utilized to support a short-term forecast for EVAs. The IRI Real-Time Assimilative Mapping (IRTAM) model is an ionospheric model that uses real time measurements from approximately 70 digisondes to produce foF2 and hmF2 global maps in 15 minute cadence. The Boeing Space Environments team has used the IRI coefficients produced in IRTAM to calculate the Ne along the ISS orbital track. The results of the IRTAM model have been compared to FPMU measurements and show excellent agreement (figure 1). IRTAM has been identified as a potential FPMU back-up system will be used as a backup for the FPMU to support the ISS Program following completion of an FPMU/IRTAM validation campaign.

Hartman, William A.↗

DOE OSTI2025

Deep Learning Super-Resolution X-Ray Computed Tomography Algorithms for Additive Manufacturing

Industrial X-ray computed tomography (XCT) is a nondestructive method for inspection and characterization of additively manufactured (AM) materials and parts. In practice, the resolution of XCT can be limited by factors such as detector binning, restricted field of view for large-scale objects, system blur, motion during scanning, and acquisition settings. These limitations can reduce the detectability of critical flaws such as pores, cracks, and lack of fusion. Super-resolution (SR) techniques offer a promising solution for improving the effective resolution and image quality of XCT reconstructions without the need for expensive hardware upgrades or laborious, time-consuming scans. In particular, deep learning-based SR methods have garnered attention in recent years as powerful tools for reconstructing high-resolution volumes from low-resolution inputs. In this work, a novel deep learning-based SR method is proposed for XCT scans of AM parts, and compared against several existing state-of-the-art (SOTA) methods. The proposed method, Simurgh-SR, is built on the pre-existing Simurgh framework and consists of a 2.5D U-Net trained to map low-quality inputs containing noise and artifacts to high-quality reconstructions characterized by higher flaw contrast, better noise texture, and reduced artifacts. The experimental results demonstrate superior performance of Simurgh-SR in performing 4× SR on real industrial XCT scans of thick 316L components, enhancing the structural similarity score and peak signal-to-noise ratio (>7dB) compared to the LR counterpart while improving the F1-score for flaw detection by more than 2.3× when compared to alternative SOTA SR methods. This improvement enables more accurate and significantly faster characterization of metal AM components. Additionally, Simurgh-SR was trained for both 2X and 4X SR and performs effectively at both levels, enabling the use of a single model for various SR factors.

Rahman, Obaid [ORNL] (ORCID:0000000277810840)↗

DOE OSTI2026

Increasing Power Generation at the Patua Nevada Geothermal Field through Targeted and Adaptive EGS

In 2020, Patua Acquisition Company LLC, submitted the proposal “Increasing Power Generation at the Patua Nevada Geothermal Field through Targeted and Adaptive EGS”, in response to the DOE’s Geothermal Wells of Opportunity Funding Opportunity Announcement. The primary goal of the project was to convert an idle well, Patua 16-29, to an active producer and generate at least an additional 5 MWe at the Patua plant. The project was intended to provide an example of a reproducible methodology for well stimulation in Enhanced Geothermal Systems The project was proposed to be performed in three phases over a period of performance of four years. A first project go/no-go decision was made at the end of Phase 1 based on feasibility of the wellbore, site, and ability to realize a 5 MWe benefit from the project. Several factors were recognized that led to a no-go decision at this phase: • Electrical needs for placing the stimulated well on production were not considered in the initial scoping. Costs of pipeline costs were also underestimated in the original cost share. Easements and rights-of-way to establish a geothermal pipeline underneath or across an interstate highway were also found to be intractable on the time scale of the project. • The plant is injection limited, and no design or cost estimate was in place to handle the additional production needed to realize a 5 Mwe benefit. A new injection well would need to be drilled in addition to the other facility improvements to bring in the stimulated well. Patau Acquisition Company attempted to locate additional wells of opportunity that could realize the SOPOs goals, but was unsuccessful. As a result, this project is being terminated at the end of Phase 1.

15 GEOTHERMAL ENERGY↗

DOE OSTI2023

Time dependent model for non-inductive ECH X-I current ramp-up for SHPD tokamak facility

Non-inductive (NI) plasma current start-up and ramp-up is an important research topic for spherical tokamak (ST) based reactors and fusion pilot plant (FPP). For a compact FPP, the OH flux availability is highly restricted due to its compact geometry. Efficient fundamental extraordinary mode (X-I) electron cyclotron heating (ECH) current start-up and ramp-up regime was identified for a reactor-like high toroidal magnetic field range which has more than a hundred times higher current drive efficiency compared to more conventional ECH methods for the relevant start-up temperature range. High current drive efficiency is possible due to the strong X-I fundamental ECH interaction only with unidirectional passing electrons constrained by the wave accessibility conditions. Here, we extend the X-I electron cyclotron current drive (ECCD) investigation to a time dependent model to simulate the non-inductive current ramp-up to 10 MA for the Sustained High-Power Density (SHPD) facility. As the X-I ECCD driven current I EC rises, due to the back EMF driven negative current, the net plasma current Ip rises more slowly with the current resistive time scale. For tokamak confinement time (both L-mode and H-Mode) which tends to rise with I p , a positive feedback results and even with constant applied ECH power, T e0 , I EC , and I p can continue to rise to very high values. However, in a realistic situation, the T e0 rise should saturate due to a number of factors such as enhanced core radiation and increased power loss at high temperature. To simulate this effect, we adopt a maximum T e0 model which would limit the temperature rise to certain T e0 . With this model, we investigated the current ramp-up for various ECH power levels and the maximum T e0 of 15, 20, and 25 keV. We find that while the power required is reduced with increasing T e0 limit due to increased current drive efficiency, the time to reach 10 MA tends to go up due to the reduced plasma resistivity for the higher T e0 limit. We also find for a given Te0 limit, the time to reach 10 MA tends to be reduced by increasing the applied ECH power by over driving the current ramp-up where I EC is driven at significantly higher level than 10 MA. While the current ramp-up time may not be an issue for the steady-state reactor systems, if it is desirable to minimize the current ramp-up time, it is prudent to have a sufficient ECH power for current over-drive and have some T e0 limiting tools such as impurity seeding for enhanced radiation. In conclusion, a well-controlled NI ECH start-up also has a potential of improving the tokamak start-up reliability and avoid run-away electrons while the NI off-axis current drive could enhance MHD stability and plasma performance improvements.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

Electropolishing Study on Nitrogen-doped Niobium Surface

The nitrogen doping (N-doping) process is applied to niobium (Nb) superconducting cavities to enhance their quality factors. The N-doping is followed by an electropolishing process that provides the final surface of the cavities. A controlled EP process is necessary to get the benefit of N-doping and achieve a high accelerating gradient. We have performed electropolishing of N-doped Nb surface under various conditions to understand their impact on the surface. A modified EP process was developed to obtain a smooth pit-free surface.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Effect of Molecular Weight and Layer Thickness on Dielectric Breakdown Strength of Neat and Homopolymer Swollen Lamellar Block Copolymer Films

Designing next-generation lightweight pulsed power devices hinges on understanding the factors influencing the energy storage performance of dielectric materials. Polymer dielectric films have a quadratic dependence of energy storage on the voltage breakdown strength and strategies to enhance the breakdown strength are expected to yield a path toward high energy storage densities. Highly stratified lamellar block copolymer (L-BCP) films of model polystyrene-b-polymethylmethacrylate (PS-b-PMMA) exhibited as much as ~50% enhancement in breakdown voltage (E BD ), (225 % increase in stored energy density, U~ E BD 2 ), compared to unordered as-cast L-BCP films. Such an energy density using amorphous polymer is on par with industry-standard semi-crystalline biaxially oriented polypropylene (BOPP), and as such a notable development in the field. This work develops a deeper understanding of the molecular mechanisms of E BD enhancement in L-BCP films, due to combination of interface formation and spatial isolation of the chain ends into segregated zones. We further confirm the role of chain ends in the breakdown process blending a low M n L-BCP with matched M n homopolymers to attain same layer spacing as neat L-BCP of higher M n . E BD shows a significant decrease at low homopolymer fractions due to increased net chain end density within swollen ordered L-BCP domains in wet-brush regime, followed by increased E BD due to layer thickness increase via segregated “interphase layer” formation by excess homopolymers. Notably, E BD of homopolymer swollen L-BCPs is always lower than neat L-BCP of the same domain spacing, due to overall adverse chain ends contribution from homopolymers. These findings provide important selection rules for L-BCPs for designing next-generation flexible electronics with high energy density solid-state BCP film capacitors.

36 MATERIALS SCIENCE↗

DOE OSTI2024

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are exploring hidden geothermal resources in the U.S.A. and designing profitable enhanced geothermal systems (EGS). Many processes and parameters control geothermal exploration and energy production from geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize subsurface geothermal conditions. Sparse and multi-scale characteristics of these datasets prohibit properly leveraging these datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) promise to resolve these issues. The tremendous challenges and risks of geothermal exploration and production bring the demand for novel ML methods and tools that can (1) analyze large field datasets, (2) assimilate model simulations (large inputs and outputs), (3) process sparse datasets, (4) perform transfer learning (between sites with different exploratory levels), (5) extract hidden geothermal signatures in the field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. To address these necessities, ML-based geothermal resources exploration and enhanced geothermal systems (EGS) design tools have been developed. The exploration tool is called GeoThermalCloud and EGS design tool is called GeoDT-ML. GeoThermalCloud (https://github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. Also, it enables the identification of critical measurements needed to identify geothermal resource signatures. Alternatively, GeoDT-ML (https://github.com/SmartTensors/GeoThermalCloud.jl/tree/master/EGS) is an ML-based alternative to GeoDT (https://github.com/GeoDesignTool/GeoDT.git), a fast, simplified multi-physics solver to evaluate EGS project designs in uncertain geologic systems. GeoDT-ML leverages recent advances in deep learning and high-performance computing. It is a faster and simpler version of GeoDT. To make this project a success, we used capabilities of LANL, PNNL, Google, Stanford, and Julia Computing. We analyzed eight datasets of the U.S.A. using GeothermalCloud and demonstrated potential highly prospective geothermal resources and identified key factors defining highly prospective sites. The first data set includes 44 locations in southwest New Mexico and 18 geological, hydrogeological, geophysical, geothermal, geochemical attributes. We defined low- and medium-temperature hydrothermal systems and discovered a new highly prospective site. The second data set analyzed 18 shallow water chemistry attributes at 14,342 locations in the Great Basin. It demarcated modestly, moderately, and highly prospective sites including key attributes for each type of prospectivity. The third data set analyzed Utah FORGE data including satellite (InSAR), geophysical (gravity, seismic), geochemical, and geothermal attributes. Here, we performed prospectivity analysis to identify future drilling locations using geological, geochemical, and geophysical attributes. Maps of temperature at depth and heat flow are constructed based on the available data. Prospectivity maps were generated, and drilling locations were proposed for future geothermal field exploration. The fourth data set analyzed 21 attributes at 120 locations in Tularosa Basin, New Mexico; data comes from past play fairway analyses in this region. ML analyses identified geothermal signatures associated with modestly, moderately, and highly hydrothermal systems. We also defined dominant attributes and spatial distribution of the geothermal signatures. The fifth, sixth, seventh, and eighth datasets include Tohatchi Springs, New Mexico, Hawaii, Brady site, Nevada, and EGS Collab, respectively. Moreover, we coupled GeothermalCloud and magnetotellurics data to pinpoint drilling locations for developing geothermal projects in the Tularosa Basin, New Mexico. GeothermalCloud found potential prospective locations for geothermal resources near White Sands Missile Range and McGregor Range at Fort Bliss. Magnetotellurics data determined the potential depth (~1800m) of geothermal prospects at McGregor Range based on apparent resistivity structures/layers in the subsurface. The McGregor Range consists of three resistivity layers and two resistivity structures. Magnetotellurics data also helps identify that the western portion of the McGregor Range has thick and low-resistivity earth materials. The low resistivity to the west is most likely for a fault system. Assuming temperature is consistent with a geothermal reservoir, the west-central part of the McGregor Range has the highest geothermal potential because of the increase in porosity and associated permeability attributed to the interpreted fault system. Also, we devised a coupling strategy between a process model and GeothermalCloud to characterize hydrogeological conditions and geothermal conditions, respectively. The process model characterizes hydrogeological and geothermal conditions on highly prospective geothermal sites provided by GeothermalCloud. We developed a physics-informed neural network (PINN) version of the Burns equation that can be easily coupled with GeothermalCloud. Furthermore, we performed an optimal design decision maximizing the economic value of an EGS power plant. This study optimized the range of well spacing between injection and production wells maximizing net present value in dollars (NPV). For this task, we used the GeoDT to simulate the Utah FORGE EGS development cycle from the initial well design to the end of production. Next, we accomplished another crucial task, which is predicting permeability of geothermal reservoirs. Predicting permeability of geothermal reservoirs is a non-trivial task because of huge computational runtime of simulation and lack of measurements. To avoid these limitations, we used easy-to-measure chemical concentrations in the subsurface as measurement data and convolutional neural network based ML model of a high-fidelity model. Next, we predicted permeability using Markov chain Monte Carlo simulation. We found that Markov chain Monte Carlo simulation predicts permeability with a high certainty if the prediction zone in the simulation area has chemical concentration data. Finally, we analyzed the DOE funded INGENIOUS and GeoDAWN projects data. For discovering hidden geothermal systems in the Great Basin, the INGENIOUS project accumulated old data, collected new data, and released them in 2022. The dataset includes a total of 24 geological, geophysical, and geochemical attributes. Data resolution and scale significantly vary prohibiting an appropriate usage. To avoid such limitations, we brought all data in the same resolution and scale by applying the inverse distance weighting interpolation technique for predicting data in unsampled locations. Subsequently, we analyzed LiDAR data of the GeoDAWN project. We received data in tiles format. The DOE’s overarching goal is to use ML on LiDAR data for finding favorable geological structures (e.g., step up faults in Brady, Nevada). To serve the purpose, we need to label favorable geologic structures that correspond to LiDAR data. We wrote an algorithm to label the LiDAR data with the favorable geologic structures.

15 GEOTHERMAL ENERGY↗

DOE OSTI2023

Theory-Enabled High-Throughput Screening of Ion Dissociation Explains Conductivity Enhancements in Diluted Ionic Liquid Mixtures

The growing demand for room-temperature ionic liquids (RTILs) for energy applications necessitates the development of an efficient screening platform. Here, in this study, we successfully developed a fully automated high-throughput RTIL screening platform specifically designed for assessing ionic conductivity. By utilizing the 96 wells of a microtiter plate as individual electrolysis cells, we measured the ionic conductivity of 22 different RTILs, encompassing various combinations of cations and anions, and benchmarked the values with existing literature. We also employed the screening platform to investigate the conductivities of RTIL mixtures with a nonaqueous solvent, ethylene glycol (EG). Specific combinations of RTILs with EG result in approximately 200% enhancement in the conductivity values compared to the pure RTILs. To understand the underlying mechanisms responsible for this enhancement, we developed a theoretical framework for ionic conductivity that considers factors such as the degree of dissociation, viscous forces, and molal volume of the RTIL-EG mixtures. The optimized electrolyte mixture was then employed in the migration-assisted moisture gradient (MAMG) CO 2 capture process to study the effects of improved ionic conductivity on the energy efficiency of the process. Notably, the enhanced conductivity of the RTIL-EG mixture led to a nearly 50% reduction in energy consumption for capturing CO 2 . These outcomes highlight the effectiveness of our strategy in screening RTILs and improving existing processes. Moreover, this fully automated high-throughput setup, combined with the developed theoretical framework, provides a comprehensive platform for screening and studying RTIL mixtures with different solvents, enabling their application in various fields.

25 ENERGY STORAGE↗

NASA NTRSDate not supplied

Detecting Volcanic Co2 Emissions Hidden in Tropical Volcanic Forests Using Fixed-Wing sUAS

CO2 emissions are among the earliest indicators of subsurface state change and reactivation of volcanic systems and these deep signals reflect in faint diffuse emissions variations on volcanic flanks. Monitoring vast areas of rainforests covering tropical volcanoes is challenging, and space-borne sensors like OCO-2 are not sensitive enough. Fortunately, trees exposed to mild enhancements of CO2 may experience fertilization and build excess CO2 into excess biomass (Cawse-Nicholson, et al. 2018, Biogeosciences). They also process excess CO2 through photosynthesis, measurable by fluorescence (Bogue et al., 2019, Biogeosciences). Furthermore, spaceborne remote sensing instruments have demonstrated increasing NDVI months to years before eruptions, unexplained by other factors (e.g., Houlie et al. 2006, EPSL; Seiler et al. 2017, PLoS One). By providing a means to map large areas of above-canopy CO2 variations over tropical rainforest on the flanks of actively degassing volcanoes, we enable to interpret the signals plants provide in response to excess CO2 with airborne and spaceborne hyperspectral remote sensing methods, including a possible future Surface Biology and Geology (SBG) NASA satellite mission.As a critical step to mature and test this concept, we deployed small unmanned aerial systems (sUAS) using a fixed-wing sUAS designed for autonomous operations and long endurance in extreme environments, without causing significant disturbance of the sampled air. We integrated an in-situ sensor to record the CO2 enhancement field above the forest canopy. Test flight results on the flanks of Turrialba volcano in Costa Rica above forest canopies covering known moderate gas seeps demonstrate the sUAS-borne detection and mapping capabilities of above-canopy elevated CO2 gradients. The strong detection capabilities and high detector signal stability resulted from key system design elements including RF shielding, mechanical stabilization, and calibration procedures. This highly robust system is readily applied for diffuse volcanic CO2 emission studies on active volcanoes covered by dense vegetation.

Volcanic↗

DOE OSTI2022

A laser parameter study on enhancing proton generation from microtube foil targets

Abstract The interaction of an intense laser with a solid foil target can drive $$\sim$$ ∼ TV/m electric fields, accelerating ions to MeV energies. In this study, we experimentally observe that structured targets can dramatically enhance proton acceleration in the target normal sheath acceleration regime. At the Texas Petawatt Laser facility, we compared proton acceleration from a $$1\, {\upmu }\hbox {m}$$ 1 μ m flat Ag foil, to a fixed microtube structure 3D printed on the front side of the same foil type. A pulse length (140–450 fs) and intensity ((4–10) $$\times 10^{20}$$ × 10 20 W/cm $$^2$$ 2 ) study found an optimum laser configuration (140 fs, 4 $$\times 10^{20}$$ × 10 20 W/cm $$^2$$ 2 ), in which microtube targets increase the proton cutoff energy by 50% and the yield of highly energetic protons ( $$>10$$ > 10 MeV) by a factor of 8 $$\times$$ × . When the laser intensity reaches $$10^{21}$$ 10 21 W/cm $$^2$$ 2 , the prepulse shutters the microtubes with an overcritical plasma, damping their performance. 2D particle-in-cell simulations are performed, with and without the preplasma profile imported, to better understand the coupling of laser energy to the microtube targets. The simulations are in qualitative agreement with the experimental results, and show that the prepulse is necessary to account for when the laser intensity is sufficiently high.

43 PARTICLE ACCELERATORS↗

NASA NTRS1984

Report of the Panel on Propulsion

Propulsion, while conventionally included on the list of important aeronautical disciplines along with aerodynamics, structures, etc., is in itself a systems endeavor, analogous to the engineering of the entire vehicle; indeed propulsion encompasses important aspects of all the other disciplines. In recognition of this fact, the panel focused its discussion on those aspects of the key disciplines that are especially or uniquely important to propulsion. From the initial development of the airplane, the propulsion system has been recognized as one of the pacing technologies. It is perhaps because of the technological disparity between the reciprocating engine and the primitive airframe that the two remained relatively and separate, were developed somewhat independently, usually by different organizations. In recent years, the maturing of the gas turbine power plant and the advance in high-speed airframes have rendered this separation somewhat artificial. The power plant and the airframe now share common structural and aerodynamic elements; as the flight Mach number rises, the degree of interaction increases. By the year 2000, this interdependence will have increased in many respects to a point where independent design may not be practical or possible. During the period since the initiation of the aircraft gas turbine, the solid propellant rocket and the liquid propellant rocket, a vast array of other novel engines have been studied, covering the full spectrum of flight conditions from low subsonic to hypersonic and transatmospheric flight. In each instance, performance limits have been investigated under the assumption that current technology or reasonably foreseeable technology would be available for their development. Among the extensive list of advanced, high-performance concepts and cycles examined are the hypersonic ramjet, the variable cycle, runway-to-orbit airbreathing engine, the ram rocket (airbreathing and rich solid propellant rocket), and the air turborocket. At various times, these systems have come relatively close to meriting development and application. In many instances, limitations of materials and technologies curtailed development. As important and with almost equal frequency, the lack of commercial or military utility of the concept precluded the necessary funding. It is instructive to note that two former items on this list, the turbofan (bypass engine) and the high-speed turboprop, are respectively a mainstay engine and a promising development. In the case of the turbofan, its full potential could not be realized until turbine cooling technology had been developed and new materials developed to permit the construction of transonic fans. In the case of the highspeed turbopropeller engine, not only were the material and turbine technologies needed, but, in addition, the rise in fuel costs provided the impetus to take advantage of its favorable fuel consumption characteristic. As the basic technologies progress and as new missions become attractive, the engines in the foregoing list become candidates for new feasibility studies and further technology development. At the present time, the ram rocket is the prime contender to augment the range of small missiles. Of interest also is the hypersonic ram jet and its logical extension, the runway-to-orbit airbreathing engine. Much of this report deals with the development of current or near-future power plant concepts. First, the motivating factors for aeronautical propulsion research are reviewed as a reminder of the importance of continued effort in a field that has often been characterized as mature. Next, technical areas are discussed in which the panel feels additional research effort is warranted and would lead to the realization of the technological potentials between now and the year 2000. Under these guidelines, new cycles (e.g., isothermal energy exchange) were not considered by the panel. Finally, although facility requirements were not a prime consideration in the current projections, the panel believes that the increasing complexity of propulsion systems; the need for more refined interaction between propulsion system, airframe, and controls; and increasing operation in adverse weather will require test capabilities beyond those now available (see appendix). Enhanced test capability is needed in the areas of propulsion airframe integration and in largescale icing research with proper concurrent treatment of altitude, temperature, and speed.

Kerrebrock, Jack L.↗

DOE OSTI2025

Annual carbon dioxide flux over seasonal sea ice in the Canadian Arctic

Continuous measurements of carbon dioxide (CO 2 ) flux were collected from a 10 m eddy covariance tower in a coastal-marine environment in the Canadian Arctic Archipelago over the course of a 17-month period. The extended length of data collection resulted in a unique dataset that includes measurements from two spring melt and summer seasons and one autumn freeze-up. These field observations were used to verify findings from previous theoretical and laboratory experiments investigating air-sea gas exchange in connection with sea ice. The results corroborated previous findings showing that thick ice cover under winter conditions acts as a barrier to gas exchange. In the spring, CO 2 fluxes were downward (uptake) in both the presence of melt ponds and during ice break-up. However, diurnal cycles were present throughout the early spring melt period, corresponding to the opposing influences of freezing and melting at the ice surface. Fluxes measured during melt periods confirmed previous laboratory tank measurements that showed a gas transfer coefficient of melting ice of 0.4 mol m −2 d −1 atm −1 . Open water CO 2 fluxes showed outgassing in early summer and uptake in mid-to-late summer, tied closely to trends in surface water temperature and its effect on the partial pressure of CO 2 in the water. The autumn period of the field campaign represents the first eddy covariance CO 2 fluxes measured over naturally forming sea ice. Our measurements showed mean upward fluxes (outgassing) of 1.1±1.5 mmol m −2 d −1 associated with the freezing of ice – the same order of magnitude found by previous laboratory tank experiments. However, peak flux periods during ice formation had measured fluxes that were a factor of 3 higher than the tank experiments, suggesting the importance of natural conditions (e.g., wind) on air-ice gas exchange. Conducting an Arctic-wide extrapolation we estimate CO 2 outgassing from the freezing period to be a counterbalance equivalent to 5 to 15 % of the magnitude of the estimated Arctic CO 2 sink. Overall, there was no evidence of dramatically enhanced gas exchange in marginal ice conditions as proposed by previous studies. Although the different seasons showed active CO 2 exchange, there was a balance between upward and downward fluxes at this specific location, resulting in a small net CO 2 uptake over the annual cycle of −0.3 g C m −2 .

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Enhanced critical axial tensile strain limit of CORC ® wires: FEM and analytical modeling

Abstract Conductor on Round Core (CORC ® ) cables and wires are composed of spiraled high-temperature superconducting (HTS) rare-earth barium copper oxide (REBCO) tapes, wound in multiple layers, and can carry very high currents in background magnetic fields of more than 20 T. They combine isotropic flexibility and high resilience to electromagnetic and thermal loads. The brittle nature of HTS tapes limits the maximum allowable axial tensile strain in superconducting cables. An intrinsic tensile strain above about 0.45% will introduce cracks in the REBCO layer of straight HTS tapes resulting in irreversible damage. The helical fashion at which the REBCO tapes are wound around the central core allows tapes to experience only a fraction of the total axial tensile strain applied to the CORC ® wire. As a result, the critical strain limit of CORC ® wires can be increased by a factor of more than 10 that of REBCO tapes. Finite element (FE) and analytical models are developed to predict the performance of CORC ® wires under axial tensile strain. A parametric analysis is carried out by varying the winding angle, the Poisson’s ratio of the CORC ® wire core, the core diameter, and the tape width. The results show that a small variation in winding angle can have a significant impact on the cable’s axial tensile strain tolerance. While the radial contraction of the helically wound tapes in a CORC ® wire under axial tensile strain depends on its winding angle, it is mostly driven by the Poisson’s ratio of the central core, affecting the tape strain state and thus its performance. Contact pressure from multiple layers within the CORC ® wire also affects the CORC ® wire performance. The FE model can be used to optimize the cable design for specific application conditions, resulting in an irreversible strain limit of CORC ® cables and wires as high as 7%.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

NASA NTRS1989

Helicopter flights with night-vision goggles: Human factors aspects

Night-vision goggles (NVGs) and, in particular, the advanced, helmet-mounted Aviators Night-Vision-Imaging System (ANVIS) allows helicopter pilots to perform low-level flight at night. It consists of light intensifier tubes which amplify low-intensity ambient illumination (star and moon light) and an optical system which together produce a bright image of the scene. However, these NVGs do not turn night into day, and, while they may often provide significant advantages over unaided night flight, they may also result in visual fatigue, high workload, and safety hazards. These problems reflect both system limitations and human-factors issues. A brief description of the technical characteristics of NVGs and of human night-vision capabilities is followed by a description and analysis of specific perceptual problems which occur with the use of NVGs in flight. Some of the issues addressed include: limitations imposed by a restricted field of view; problems related to binocular rivalry; the consequences of inappropriate focusing of the eye; the effects of ambient illumination levels and of various types of terrain on image quality; difficulties in distance and slope estimation; effects of dazzling; and visual fatigue and superimposed symbology. These issues are described and analyzed in terms of their possible consequences on helicopter pilot performance. The additional influence of individual differences among pilots is emphasized. Thermal imaging systems (forward looking infrared (FLIR)) are described briefly and compared to light intensifier systems (NVGs). Many of the phenomena which are described are not readily understood. More research is required to better understand the human-factors problems created by the use of NVGs and other night-vision aids, to enhance system design, and to improve training methods and simulation techniques.

Brickner, Michael S.↗

NASA NTRS2001

Development of Numerical Models for Performance Predictions of Single-Photon Avalanche Photodetectors (SPAP) for the 2-Micron Regime

Field dependent drift velocity results are presented for electron transport in bulk Indium Arsenide (InAs) material based on a Monte Carlo model, which includes an analytical treatment of band-to-band impact ionization. Avalanche multiplication and related excess noise factor (F) are computed as a function of device length and applied voltage. A decrease in F with increases in device length is obtained. The results suggest an inherent utility for InAs-based single-photon avalanche detectors, particularly around the 2 microns region of interest for atmospheric remote sensing applications. The dark current response was also evaluated. The role of the various components has been analyzed. For shorter devices, the tunneling component is shown to dominate at low temperatures. Finally, possible structures for enhanced photodetection are proposed for future research.

Joshi, Ravindra P.↗

DOE OSTI2020

A spatial-statistical investigation of surface expressions associated with cyclic steaming in the Midway-Sunset Oil Field, California

In the Midway Sunset Oil Field in Central California, operators inject steam into the shallow diatomite formation to enhance heavy oil recovery through imbibition, wettability alteration, and viscosity reduction, among other mechanisms. The injected steam, however, does not always remain in the reservoir or return through the wells. In two zones in the study area, the steam comes out at the surface, creating sinkholes, seeps, and steam outlets. These phenomena, called “surface expressions,” pose safety and environmental hazards. Even though these surface expressions are a widespread problem in Central California, they are not well documented and understood. Possible causes of the surface expressions include: high injection pressure, structurally controlled flow patterns, leakage of steam through old improperly abandoned wells, high injection volumes, or flow along naturally occurring faults, among other possible factors. This work examines attributes of the zones with surface expressions in order to determine factors that may contribute to their occurrence. Spatial statistical analysis using logistic regression, random forests, and classification trees is used to explore the relationship between the surface expressions and geological and production-related attributes. The results point to a significant spatial correlation between the surface expressions and two predictors: concentration of plugged wells and geologic seal thickness. The results guide follow-up studies to further investigate the role of well abandonment and seal thickness in the occurrence of surface expressions.

02 PETROLEUM↗