De-risking Pretreatment of Microalgae To Produce Fuels and Chemical Co-products
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In 2015, an incident released approximately 40 Ci of T 2 gas directly into the Tritium Exhaust System. Data from a bubbler system that monitored the stack effluent during the time period encompassing the accident, from 9 days prior through approximately 26 hours following the release, indicated that approximately 0.25% of the total accumulated tritium gas was in the form of tritiated water; however this value does not account for sources of tritium exhaust from other building operations and processes during the 9 days prior to this incident. Further analysis of the bubbler data around this time period considered the 9-day background contributions and shows that the actual fraction of the tritium that was released as tritiated water vapor (during and within 26 hours after the release) was likely lower than 0.1%.
Low-enriched fuel compacts fabricated for the AGR-5/6/7 irradiation experiments did not conform to all fuel specifications. Reasons why the fuel is acceptable for use in the AGR-5/6/7 irradiation are discussed. Revision 1 alters the characterization data for the dispersed uranium and exposed kernel defect fractions. Additional characterization data was obtained for fuel compacts analyzed by Oak Ridge National Laboratory. This enabled statistical outliers to be rejected and to confirm that results obtained from BWX Technologies Nuclear Operations Group were statistically independent of the Oak Ridge results and should be excluded from the analyses. Revision 2 alters the characterization data for the TRISO lot, J52R-16-98005, used in the fabrication of the AGR-5/6/7 fuel. Data from the BWXT J52R-16-98005 Certification package is used to calculate the reported values.
A hot solar heating and hot water system's operational performance from June 1979 through April 1980 is evaluated. Solar energy satisfied 42 percent of the total measure load (hot water plus space heating), which was somewhat higher than the solar fraction of 32 percent. When system losses into the heating space from duct leaks and storage are included, the heating solar fraction increases from 42 to 64 percent. Net electrical energy savings were 5.47 million BTUs.
Experimentally derived applied moments are presented for Space Shuttle crew EVA mission rotations of objects more massive than the human body. These levels appear to be small fractions of physiological limits; horizontal and vertical shoulder strength limits greater than 50 Nm have been established for foot-restrained, pressure-suited subjects in simulated weightlessness. The reduced level in operational EVA may be due to unfamiliarity with manual control in true weightlessness.
The NASA Short term Prediction Research and Transition (SPoRT) Center in Huntsville, AL has been running a real-time configuration of the Noah land surface model within the NASA Land Information System (LIS) since June 2010. The SPoRT LIS version is run as a stand-alone land surface model over a Southeast Continental U.S. domain with 3-km grid spacing. The LIS contains output variables including soil moisture and temperature at various depths, skin temperature, surface heat fluxes, storm surface runoff, and green vegetation fraction (GVF). The GVF represents another real-time SPoRT product, which is derived from the Moderate Resolution Imaging Spectroradiometer instrument aboard NASA's Aqua and Terra satellites. These data have demonstrated operational utility for drought monitoring and hydrologic applications at the National Weather Service (NWS) office in Huntsville, AL since early 2011. The most relevant data for these applications have proven to be the moisture availability (%) in the 0-10 cm and 0-200 cm layers, and the volumetric soil moisture (%) in the 0-10 cm layer. In an effort to better understand their applicability among locations with different terrain, soil and vegetation types, SPoRT is conducting the first formal assessment of these data at NWS offices in Houston, TX, Huntsville, AL and Raleigh, NC during summer 2014. The goal of this assessment is to evaluate the LIS output in the context of assessing flood risk and determining drought designations for the U.S. Drought Monitor. Forecasters will provide formal feedback via a survey question web portal, in addition to the NASA SPoRT blog. In this presentation, the SPoRT LIS and its applications at NWS offices will be presented, along with information about the summer assessment, including training module development and preliminary results.
The scientific balloon program at NASA offers an exciting and open area of opportunity for testing new technologies and for conducting meaningful experimentation at a fraction of the cost of a space mission. This paper outlines a simple thermal model developed and employed for the Primordial Inflation Polarization ExplorER (PIPER). The sub-orbital environment that PIPER operates in hosts an interesting mix of atmospheric and space thermal challenges. The work done was to mitigate thermal loads and passively cool the payload's exterior mounted electronics at an altitude of 36.6 km. This was done by characterizing the thermal environment and then designing solutions for the heat loads through a combined radiation and conduction passive cooling radiator system thermal model. Despite the simplicity and subsequent limitations of the model, as well as some unexpected payload operational events, the model produced results between 0.31% and 11.8% difference between the predicted values and measured average temperatures. From these results, the model was able to successfully provide estimates for the electronics temperatures. Additional flights will be needed to eliminate unknowns encountered in this flight in order to further refine the model for higher accuracy.
The tristructural isotropic (TRISO) fuel assessment and validation database currently available in BISON is based mainly on steady-state irradiation and high-temperature furnace testing. Transient assessment cases are needed to support ongoing U.S. industry efforts to design and deploy commercial reactors that utilize TRISO fuels. Most available historical transient tests involving TRISO fuels were characterized by power densities, temperatures, and energy depositions that were highly conservative with respect to typical high-temperature gas-cooled reactor (HTGR) accident conditions. Nonetheless, assessment of BISON's predictive capabilities, adaptation of material properties toward high-temperature and high-particle-power regimes, and development of a systematic approach to validating BISON for TRISO transient applications now motivate the modeling of historical transient tests. This work describes the development of 1-D thermal models of transient experiments conducted at the Nuclear Safety Research Reactor (NSRR) in Japan, with BISON predictions of energy deposition, UO2 melting onset, and molten volume fractions being compared against experimental measurements. Melting is accounted for by defining an effective specific heat capacity for UO2—one that leverages the material's heat of melting. BISON's predictions of energy deposition and molten volume fraction are in good agreement with the experimental data from the low-energy-deposition tests; however, BISON tends to overpredict melting at higher energy depositions. Though overly conservative compared to the operating conditions expected for near-term TRISO-fueled reactor applications, these simulations effectively exercise BISON TRISO models over a wider range of conditions than those encompassed by the existing assessment database. Potential contributors to the observed discrepancies are noted and additional future developments proposed.
The DUNE Collaboration currently operates an experimental program based at CERN which includes a beam test and an extended cosmic ray run of two large-scale prototypes of the massive Liquid Argon Time Projection Chamber (LArTPC) for the DUNE Far Detector. The volume of data collected by the single-phase prototype (protoDUNE-SP) amounts to 3PB and the sustained rate of data sent to mass storage is O(100) MB/s. Data Quality Monitoring was implemented by directing a fraction of the data stream to the protoDUNE prompt processing system (p3s) which is optimized for continuous low-latency calculation of the vital detector metrics and various graphics including event displays. It served a crucial role throughout the life cycle of the experiment. We present our experience in leveraging the CERN computing environment and operating the system over an extended period of time, while adapting to evolving requirements and computing platform.
Atmospheric pressure plasma jets (APPJs) are used to improve the adhesive and hydrophilic properties of commodity hydrocarbon polymers such as polypropylene, polyethylene, and polystyrene (PS). These improvements largely result from adding oxygen functional groups to the surface. PS functionalization is of interest to produce high value biocompatible well-plates and dishes, which require precise control over surface properties. In this paper, we discuss results from a computational investigation of APPJ functionalization of PS surfaces using He/O 2 /H 2 O gas mixtures. A newly developed surface reaction mechanism for functionalization of PS upon exposure to these plasmas is discussed. A global plasma model operated in plug-flow mode was used to predict plasma-produced species fluxes onto the PS surface. A surface site balance model was used to predict oxygen-functionalization of the PS following exposure to the plasma and ambient air. We found that O-occupancy on the surface strongly correlates with the O-atom flux to the PS, with alcohol groups and cross-linked products making the largest contributors to total oxygen fraction. Free radical sites, such as alkoxy and peroxy, are quickly consumed in the post-plasma exposure to air through passivation and cross-linking. O-atom fluences approaching 10 17 cm -2 saturate the O-occupancy on the PS surface, creating functionality that is not particularly sensitive to moderate changes in operating conditions.
This work presents a mechanistic modeling approach for simulating methane emissions from triethylene glycol (TEG) dehydrators used in oil & gas (O&G) operations. The model was developed as a modular component of the Mechanistic Air Emissions Simulator (MAES) tool, incorporating species-specific absorption and emission dynamics through two-level, second-order polynomial regression (PR) models trained on ProMax simulation data: (1) species-level regression models that track the transfer rates of individual gas species within the dehydrator unit streams, and (2) outlet flow stream regression models that predict the fraction of inlet gas distributed among the outlet streams of the dehydrator unit. These behaviors were characterized over a range of glycol circulation ratios, wet gas pressures, and temperatures. The model was validated using root mean square error (RMSE) analysis. The species-level PR achieved low root mean square error (RMSE) values (<0.03) for light hydrocarbon species across all dehydrator components, ranging from 0.0009 for methane to 0.029 for normal pentane. Similarly, the outlet-level PR yielded RMSE values below 0.002 for the dry gas fraction, 0.001 for the flash tank fraction, and 0.002 for the still vent fraction, demonstrating strong agreement between predicted and reference ProMax values. When deployed at field facilities, the model significantly improved MAES-simulated dehydrator emissions, revealing that gas-assisted glycol pump emissions are the dominant contributors to both dehydrator-level and site-level methane emissions under uncontrolled conditions. Further analysis of the 154 dehydrator units reported by operators under the AMI 2024 project showed that 54 units (31%) used gas-driven glycol pumps, of which 6 units (11%) operated with uncontrolled flash tanks, and 22 units (40.7%) were identified as potentially oversized. Of the six dehydrator units with uncontrolled gas-assisted pumps, pump emissions accounted for 90.25% of total dehydrator emissions and 63.10% of total site-level emissions. These findings highlight substantial opportunities for emissions mitigation through equipment upgrades.
Reductive catalytic fractionation (RCF) is a promising lignin-first biorefining strategy that selectively extracts and depolymerizes lignin from whole biomass. Flow-through (FT)-RCF enables physical separation of the biomass and catalyst, but this process configuration typically operates at high solvent-to-biomass ratios. Here, we demonstrate multi-pass FT-RCF, wherein the lignin-enriched solvent obtained after an initial FT-RCF step is recycled and used in subsequent FT-RCF without intermediate lignin recovery. Multi-pass FT-RCF reduces the solvent-to-biomass ratio from 48 to 1.9 L/kg, which is a lower solvent loading than is accessible in batch reactors, without negative impacts on delignification and monomer yield with up to 12 wt % lignin in the solvent. Overall, this work demonstrates that solvent demand in RCF processes, which is a key cost and energy driver to enable this process at scale, can be reduced by recycling lignin oil between RCF reactions.
This paper presents the results of the systems level thermal modeling for a conceptual Chemical Heat Integrated Power Source (CHIPS). This proposed system offers a combined thermal and electrical power source to support survival of spacecraft operating in extreme low temperature lunar environments, without the use of radioisotope-based sources. A conceptual design study has been completed for this system, that uses heat generated by an exothermic chemical reaction in place of radioisotope or electrical heat sources. The goal of the study was to evaluate the feasibility of such as system through thermodynamic and chemical analysis and thermal modeling, and to identify technology gaps to inform a technology development and maturation plan. The specific technical objectives focused on delivery of 90-100 Wth thermal power and 30-40 We electrical power for 336 hours to a representative Commercial Lunar Payload Services (CLPS) lander, to support lunar surface survival and limited operations through a lunar night. The total system mass was targeted at ≤50 kg. A highly exothermic chemical reaction system is used to generate on-board electrical power for spacecraft systems, and thermal power to maintain critical spacecraft/lander systems within their allowable flight temperature (AFTs). Based on the very high energy content of the chemical reaction system, a much higher amount of heat per unit mass can be delivered to the spacecraft, relative to a rechargeable lithium-ion battery and electrical heater(s). Since radioisotope heaters or generators are not used, the system will be orders of magnitude lower in cost than a radioisotope heating/power unit, without the attendant regulatory complexities. As part of the CHIPS concept, a fraction of the thermal power generated is converted to electrical power via an appropriate thermal-to-electric converter technology (such as a free piston Stirling converter or a thermoelectric generator module), to provide power to critical loads. This approach is ideally suited to support operation of commercial landers (e.g., via the CLPS program). In most cases, these landers are only designed to operate during a portion of the lunar day, with no provision for survival through the lunar night. By supporting lunar night survival, a mission can be extended through the lunar night and at least into another lunar day, thus turning a nominal eight-day mission into a 36-day mission. Therefore, the current system demonstration will focus on scalability to support at least 336 hours (one lunar night) of continuous thermal and electrical power generation. Although initially targeted to support lunar equatorial landings, the technology is extensible to missions at the lunar poles, other extreme environments in the Solar System or even air-independent applications on Earth (e.g., ocean exploration).
This paper presents the results of the systems level thermal modeling for a conceptual Chemical Heat Integrated Power Source (CHIPS). This proposed system offers a combined thermal and electrical power source to support survival of spacecraft operating in extreme low temperature lunar environments, without the use of radioisotope-based sources. A conceptual design study has been completed for this system, that uses heat generated by an exothermic chemical reaction in place of radioisotope or electrical heat sources. The goal of the study was to evaluate the feasibility of such a system through thermodynamic and chemical analysis and thermal modeling, and to identify technology gaps to inform a technology development and maturation plan. The specific technical objectives focused on delivery of 90 to 100 Wth thermal power and 30 to 40 We electrical power for 336 hours to a representative Commercial Lunar Payload Services (CLPS) lander, to support lunar surface survival and limited operations through a lunar night. The total system mass was targeted at ≤50 kg. A highly exothermic chemical reaction system is used to generate on-board electrical power for spacecraft systems, and thermal power to maintain critical spacecraft/lander systems within their allowable flight temperature (AFTs). Based on the very high energy content of the chemical reaction system, a much higher amount of heat per unit mass can be delivered to the spacecraft, relative to a rechargeable lithium-ion battery and electrical heater(s). Since radioisotope heaters or generators are not used, the system will be orders of magnitude lower in cost than a radioisotope heating/power unit, without the attendant regulatory complexities. As part of the CHIPS concept, a fraction of the thermal power generated is converted to electrical power via an appropriate thermal-to-electric converter technology (such as a free piston Stirling converter or a thermoelectric generator module), to provide power to critical loads. This approach is ideally suited to support operation of commercial landers (e.g., via the CLPS program). In most cases, these landers are only designed to operate during a portion of the lunar day, with no provision for survival through the lunar night. By supporting lunar night survival, a mission can be extended through the lunar night and at least into another lunar day, thus turning a nominal eight-day mission into a 36-day mission. Therefore, the current system demonstration will focus on scalability to support at least 336 hours (one lunar night) of continuous thermal and electrical power generation. Although initially targeted to support lunar equatorial landings, the technology is extensible to missions at the lunar poles, other extreme environments in the Solar System or even air-independent applications on Earth (e.g., ocean exploration).
Abstract We show that a system of particles on the lowest Landau level can be coupled to a probe U(1) gauge field$$ \mathcal{A} $$ A μ in such a way that the theory is invariant under a noncommutative U(1) gauge symmetry. While the temporal component$$ \mathcal{A} $$ A 0 of the probe field is coupled to the projected density operator, the spatial components$$ \mathcal{A} $$ A i are best interpreted as quantum displacements, which distort the interaction potential between the particles. We develop a Seiberg-Witten-type map from the noncommutative U(1) gauge symmetry to a simpler version, which we call “baby noncommutative” gauge symmetry, where the Moyal brackets are replaced by the Poisson brackets. The latter symmetry group is isomorphic to the group of volume preserving diffeomorphisms. By using this map, we resolve the apparent contradiction between the noncommutative gauge symmetry, on the one hand, and the particle-hole symmetry of the half-filled Landau level and the presence of the mixed Chern-Simons terms in the effective Lagrangian of the fractional quantum Hall states, on the other hand. We outline the general procedure which can be used to write down effective field theories which respect the noncommutative U(1) symmetry.
The U.S. Department of Energy Bioenergy Technologies Office (BETO) is committed to the development of sustainable, nationwide, commercial biofuel production to displace petroleum-derived fuels, increase domestic energy production, and encourage the creation of a domestic bioenergy and bioproducts industry. Operating a commercial-scale bioenergy operation requires significant technological advancements for determining biomass feedstock quality during the preprocessing stage. Usage of non-food feedstocks- e.g., corn-stover, pine residue, and forest residue - at the biorefineries reduces strain on the food supply chain. But the non-food feedstock heterogeneity- physical size, shape, and chemical composition-poses a significant challenge during milling, conveyance, feeding, and biofuel conversion processes. 3D X-Ray imaging (CT) is capable of distinguishing material features by detecting density and compositional differences. But the inherent complexity introduced during harvesting and bailing makes the reconstruction and interpretation of baled biomass materials from x-ray data time consuming, laborious, and expensive. Feedstocks are low-density materials that produce small contrast differences in the x-ray images. This paper focuses on using the shape and texture properties, using 3D image processing techniques like 3D skeletonization, directional statistics to characterize and extract volumetric content of the different tissue samples in the biomass bales.
One of the most challenging and consequential problems in climate modeling is to provide probabilistic projections of sea level rise. A large part of the uncertainty of sea level projections is due to uncertainty in ice sheet dynamics. At the moment, accurate quantification of the uncertainty is hindered by the cost of ice sheet computational models. In this work we develop a hybrid approach to approximate existing ice sheet models at a fraction of their cost. Our approach consists of replacing the finite element model for the momentum equations for the ice velocity, the most expensive part of an ice sheet model, with a Deep Operator Network, while we retain a classic finite element discretization for the evolution of the ice thickness. We show that the resulting hybrid model is very accurate and it is an order of magnitude faster than the traditional finite element model. Further, a distinctive feature of the proposed model, compared to other neural network approaches, is that it can handle high-dimensional parameter spaces (parameter fields) such as the basal friction at the bed of the glacier and can therefore be used for generating samples for uncertainty quantification. Further, we study the impact of hyper-parameters, number of unknowns and correlation length of the parameter distribution on the training and accuracy of the Deep Operator Network on a synthetic ice sheet model. We then target the evolution of the Humboldt glacier in Greenland and show that our hybrid model can provide accurate statistics of the glacier mass loss and can be effectively used to accelerate the quantification of uncertainty.
Today's energy system uses a variety of fuels and energy carriers in molecular forms, such as coal, oil, and natural gas, each contributing to CO2 emissions (Figure 1). As solar and wind levels grow and the energy system becomes more electrified, fossil molecular fuels will contribute a progressively smaller fraction of overall energy. As shown in Figure 2, hydroelectric storage and molecules, such as coal and natural gas, provide substantial storage and dispatch functionality today that will become increasingly critical for electricity operation and stability. Emerging low-carbon gases, such as hydrogen, can support the efficient integration of renewables in the transition from today's energy system to one that is 100% clean from a carbon emissions perspective.