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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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DOE OSTI2022

Extended Modeling of DOE Sealed Canisters with Updated Chemistry Models

Road-ready and final disposition packaging configurations for the advanced test reactor (ATR) fuel currently specifies storage within helium backfilled DOE sealed standard canisters. The aluminum cladding of the ATR fuel contains an oxyhydroxide layer of boehmite/bayerite that generates hydrogen when subjected to irradiation. Understanding the effect of this hydrogen buildup over time to important for long term storage considerations. Previous modeling efforts have built a coupled CFD-chemical model to simulate the temperature gas phase concentrations within the DOE sealed standard canisters. These models have coupled the temperature conditions to both the gas phase radiolysis chemistry and the surface chemistry associated with the oxyhydroxide layer. The previous iteration of the model utilized constant G-values for the hydrogen generation a 50-year period. This new iteration of the model utilizes new experimental data to update the hydrogen generation rate, as well as increase the simulated time to a 200-year period. Given the half-life assumed for the primary Cs-137 isotope responsible for gamma radiation in the ATR spent fuel, the 200-year period decreases the decay heat and dose rate of the spent fuel by a factor of 100, which combined with the updated chemistry substantially decreases new hydrogen generation. Continued experimental work has identified trends for aluminum surrogate samples with oxyhydroxide layers for tests done at higher dose rates. At low initial doses a fast generation rate of hydrogen occurs which starts to roll over to a lower generation as the total dose applied increase. A small-scale chemical model was built to replicate as mini-canister surrogate system at SRNL as well as for the smaller capsule tests performed at INL. A variety of chemical models to capture this effect were tested, and a back reaction of H radical absorbing onto the surface, or inhibition of the reaction by significant H 2 cover gas were both able to fit both the mini-canister and the small capsule test data. Both kinetic fits were used to generate data from the new 200-year simulation. As additional experiments continue, the kinetic fits may be adjusted to adapt to new data. However, updated data shows that hydrogen atmosphere has little effect on actual generation data, so the model was reverted to use a star-stepped G-value for low-dose and high-dose regions. For the undried fuel case, the three models differ significantly with an end concentration of 1% for back reaction, 4.2% for inhibition, and 13.1% for constant G-value for the nominal scenario. For the dried fuel case, the nominal cases showed end concentrations of 0.23% for back reaction, 1.9% for inhibition, and 4.2% for const G-value for the nominal scenario. For the constant G-value case that is less conservative than the other two, the pressure for undried fuel increases to 1.94 atm over 200 years for the nominal case and 2.11 atm for the high decay heat case. The total hydrogen concentration after 200 years is 4.92% for the low decay heat case and 27.4% for the high decay heat case for undried fuel and is 1.4% and 9.6% for low and high decay heat fuel for the dried fuel case. If small amount of residual air is present, the potential for nitric acid formation of 595, 1568, and 2816 ppm for the lower, nominal, and upper fuel decay heat can occur. At long-term timeframes no significant shift in major species present occurs, so no appreciable amount of oxygen is present in the system. The continued rate of hydrogen generation in the canister occurs at an increase of 0.04% by mole over the final 10 years. Increasing the model range out to 1000 years continues to drop the hydrogen generation rate through decreasing dose rate.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Predicting nucleon-nucleus scattering observables using nuclear structure theory

Developing a predictive capability for inelastic scattering will find applications in multiple areas. Experimental data for neutron-nucleus inelastic scattering is limited and thus one needs a robust theoretical framework to complement it. Charged-particle inelastic scattering can be used as a surrogate for ( n, γ ) reactions to predict capture cross sections for unstable nuclei. Our work uses microscopic nuclear structure calculations for spherical nuclei to obtain nucleon-nucleus scattering potentials and calculate cross sections for these processes. We implement the Jeukenne, Lejeune, Mahaux (JLM) semi-microscopic folding approach, where the medium effects on nuclear interaction are parameterized in nuclear matter to obtain the nucleon-nucleon ( NN ) interaction in a medium at positive energies. We solve for the nuclear ground state using the Hartree-Fock-Bogliubov (HFB) many-body method, assuming the nucleons within the nucleus interact via the Gogny-D1M potential. The vibrational excited states of the target nucleus are calculated using the quasi-particle random phase approximation (QRPA). We demonstrate our approach for spherical nuclei in the medium-mass region, showing scattering results for the 90 Zr nucleus.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Quantifying the Impact of Lagged Hydrological Responses on the Effectiveness of Groundwater Conservation

Many irrigated agricultural areas seek to prolong the lifetime of their groundwater resources by reducing pumping. However, it is unclear how lagged responses, such as reduced groundwater recharge caused by more efficient irrigation, may impact the long-term effectiveness of conservation initiatives. Here, we use a variably saturated, simplified surrogate groundwater model to: 1) analyze aquifer responses to pumping reductions, 2) quantify time lags between reductions and groundwater level responses, and 3) identify the physical controls on lagged responses. We explore a range of plausible model parameters for an area of the High Plains Aquifer (USA) where stakeholder-driven conservation has slowed groundwater depletion. We identify two types of lagged responses that reduce the long-term effectiveness of groundwater conservation, recharge-dominated and lateral-flow-dominated, with vertical hydraulic conductivity (K Z ) the major controlling variable. When high K z allows percolation to reach the aquifer, more efficient irrigation reduces groundwater recharge. By contrast, when low K z impedes vertical flow, short term changes in recharge are negligible, but pumping reductions alter the lateral flow between the groundwater conservation area and the surrounding regions (lateral-flow-dominated response). For the modeled area, we found that a pumping reduction of 30% resulted in median usable lifetime extensions of 20 or 25 years, depending on the dominant lagged response mechanism (recharge- vs. lateral-flow-dominated). These estimates are far shorter than estimates that do not account for lagged responses. To conclude, results indicate that conservation-based pumping reductions can extend aquifer lifetimes, but lagged responses can create a sizable difference between the initially perceived and actual long-term effectiveness.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Energy-Dispersive X-ray Spectroscopy and Atom-probe Tomography Data Quantifying Component-Ratios of Multicomponent Nano-Precipitates in Ion-Irradiated Ceria

Samples of ~1 µm films of CeO2 doped with 2 wt% Mo, 1.5 wt% Ru, 0.75 wt% Pd, 0.5 wt% Re and 0.25 wt% Rh grown with pulsed laser deposition were irradiated with I2+ ions (610°C and 730°C, 1016 and 5×1016 I2+/cm2). For selected samples post-irradiation heat treatment was conducted (900°C, 1100°C). The specimens were sectioned with focussed ion beam milling and characterized in a transmission electron microscope with energy dispesive x-ray spectroscopy, and with atom-probe tomography. Energy dispersive x-ray spectroscopy was used to obtain elemental maps showing the distribution of dopants in the specimen after exposure. Some of these maps are discussed in detail in our companion article “Formation of multicomponent alloy particles in doped ceria under I2+ ion irradiation and thermal annealing” in the Journal of Nuclear Materials [REF]. Advanced computational analysis could be used to more accurately quantify local compositions. Data is provided for additional regions of interest and one additional irradiation condition. The doped Ceria film that was heat treated at 1100 °C delaminated from the substrate in most places. Samples were extracted from the underside of a delaminated piece and analyzed with atom-probe tomography. The resulting data show Ceria and a Mo-rich particle and demonstrate that this approach is feasable in principle to study local compositions in a sample exposed to such extreme conditions.

multicomponent alloys, epsilon phase, EDS, atom pr↗
DOE OSTI2021

Sanitary Waste Landfill Effects on an Invasive Wild Pig Population

Being opportunistic omnivores, wild pigs (Sus scrofa) readily feed on edible garbage. Given the presence of substantial volumes of edible food waste, large multi-county and regional municipal sanitary waste landfills constitute attractive forage resources for pigs, providing a year-round anthropogenic source of potentially high-quality forage. Our objective was to assess the effects that a large regional landfill has on the local pigs foraging in that facility's waste disposal cells. The landfill, located on the United States Department of Energy's Savannah River Site (SRS) in South Carolina, USA, became operational in 1998 and pigs began foraging there in 2001. By 2009 >100 pigs/night were observed foraging in the landfill, suggesting landfill establishment may have important consequences for population dynamics, public safety, and disease transmission. We evaluated changes in body mass, fetal litter size, numbers of pigs removed, and wild pig-vehicle collisions (WPVCs) before (1980–2000) and after (2001–2019) pigs began foraging in the landfill on SRS. Body mass during the after period increased to a greater extent for pigs in the vicinity of the landfill compared to pigs on the rest of SRS. Fetal litter size increased for pigs in the vicinity of the landfill, whereas it remained unchanged on the rest of SRS. Our density surrogate (number of pigs harvested) increased around the landfill during the after period by 2.9 times, whereas on the rest of the site it only increased by 53%. No WPVCs occurred adjacent to the landfill before 2001, but WPVCs increased along the 2 major roads bordering the landfill after 2001. Effects of sanitary waste landfills on wild pig populations scavenging there can present unique challenges to population management, control, public safety, and disease transmission. Potential approaches to address these challenges could be exclusion fencing to prevent access to the landfill's waste disposal cells or enhanced placement of waste cell covers to reduce access.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2021

Validation Assessment for the Soil Moisture Active Passive (SMAP) Level 4 Carbon (L4_C) Data Product Version 5

The post-launch Cal/Val phase of the SMAP mission is guided by two primary objectives for each science product team: 1) to calibrate, verify, and improve the performance of the science algorithms, and 2) validate accuracies of the science data products as specified in the SMAP Level-1 mission science requirements. Algorithm science and product maintenance activities during the SMAP extended mission phase have also involved periodic algorithm calibration and product refinements to maintain or enhance product consistency and performance as well as science utility. This report provides an assessment of the latest (Version 5) SMAP Level 4 Carbon (L4_C) product. The L4_C Version 5 (v5) global record now spans more than six years (March 2015 – present) of SMAP operations and has benefited from five major reprocessing updates to the operational product. These reprocessing events and L4_C product release updates have incorporated various algorithm refinements and calibration adjustments to account for similar refinements to the upstream GEOS land model assimilation system, SMAP brightness temperatures, and MODIS vegetation inputs used for L4_C processing. The SMAP L4_C algorithms utilize a terrestrial carbon flux model informed by daily surface and root zone soil moisture information contributed from the SMAP Level 4 Soil Moisture (L4_SM) product along with optical remote sensing-based (e.g. MODIS-based) land cover and canopy fractional photosynthetic active radiation (fPAR), and other ancillary biophysical data. The carbon flux model estimates global daily net ecosystem CO2 exchange (NEE) and the component carbon fluxes, namely, vegetation gross primary production (GPP) and soil heterotrophic respiration (Rh). Other L4_C product elements include surface (~0-5 cm depth) soil organic carbon (SOC) stocks and associated environmental constraints to these processes, including soil moisture-related controls on GPP and ecosystem respiration (Kimball et al. 2014, Jones et al. 2017). The L4_C product addresses SMAP carbon cycle science objectives by: 1) providing a direct link between terrestrial carbon fluxes and underlying freeze/thaw and soil moisture-related constraints to these processes, 2) documenting primary connections between terrestrial water, energy and carbon cycles, and 3) improving understanding of terrestrial carbon sink activity. The SMAP L4_C algorithms and operational product are mature and at a CEOS Validation Stage 4 level (Jackson et al. 2012) based on extensive validation of the multi-year record against a diverse array of independent benchmarks, well characterized global performance, and systematic refinements gained from five major reprocessing events. There are no Level-1 mission science requirements for the L4_C product; however, self-imposed requirements have been established focusing on NEE as the primary product field for validation, and on demonstrating L4_C accuracy and success in meeting product science requirements (Jackson et al. 2012). The other L4_C product fields also have strong utility for carbon science applications (e.g., Liu et al. 2019, Endsley et al. 2020); however, analysis of these other fields is considered secondary relative to primary validation activities focusing on NEE. The L4_C targeted accuracy requirements are to meet or exceed a mean unbiased root-mean-square error (ubRMSE, or standard deviation of the error) for NEE of 1.6 g C m-2 d-1 and 30 g C m-2 yr-1, emphasizing northern (≥45°N) boreal and arctic ecosystems; this accuracy is similar to that of tower eddy covariance measurement-based observations (Baldocchi 2008). Methods used for the latest v5 L4_C product performance and validation assessment have been established from the SMAP Cal/Val plan and previous studies (Jackson et al. 2012, Jones et al. 2017) and include: 1) consistency evaluations of the product fields against earlier product releases (version 4 or earlier); 2) comparisons of daily carbon flux estimates with independent tower eddy 4covariance measurement-based daily carbon (CO2) flux observations from core tower validation sites (CVS); and 3) consistency checks against other global carbon products, including soil carbon inventory records, global GPP records derived from tower observation upscaling methods, and satellite-based observations of canopy solar induced chlorophyll fluorescence (SIF) as a surrogate for GPP. Metrics used to evaluate relative agreement between L4_C product fields and observational benchmarks include correlation (r-value), RMSE differences, bias and model sensitivity diagnostics. Following these validation criteria, the present report provides a validation assessment of the latest L4_C product release (v5). Detailed descriptions of the L4_C algorithm and additional global product accuracy and performance results are given elsewhere (Jones et al. 2017, Endsley et al. 2020). The v5 L4_C product replaces earlier product versions and continues to show: (i) accuracy and performance levels meeting or exceeding SMAP L4_C science requirements; (ii) improvement over the previous product version (version 4); and (iii) suitability for a diversity of science applications. Example L4_C applications from the recent literature include clarifying environmental trends and controls on the northern terrestrial carbon sink (Liu et al. 2019), diagnosing drought-related impacts on ecosystem productivity (Li et al. 2020), and regional monitoring of cropland conditions for projecting annual yields (Wurster et al. 2020). 2EXPECTED L4_C ALGORITHM AND PRODUCTPERFORMANCE The L4_C algorithm performance, including variance and uncertainty estimates of model outputs, was determined during the mission pre-launch phase through spatially explicit model sensitivity studies using available model inputs similar to those currently being used for operational production and evaluating the resulting model simulations over the observed range of northern (≥45 °N) and global conditions (Kimball et al. 2012, Entekhabi et al. 2014). The L4_C algorithm options were also evaluated during the mission prelaunch phase, including deriving canopy fPAR from lower order NDVI (Normalized Difference Vegetation Index) inputs in lieu of using MODIS (MOD15) fPAR; and including an explicit model representation of boreal fire disturbance recovery impacts. These results indicated that the L4_C accuracy requirements (i.e., NEE ubRMSE ≤ 30 g C m-2 yr-1or ≤ 1.6 g C m-2 d-1) could be met from the baseline algorithms over more than 82% and 89% of global and northern vegetated land areas, respectively (Yi et al. 2013, Kimball et al. 2014). The global L4_C algorithm error budget for NEE derived during the mission prelaunch phase indicated that the estimated NEE ubRMSE uncertainty is proportional to GPP and is therefore larger in higher biomass productivity areas, including forests and croplands (Kimball et al. 2014). Likewise, NEE ubRMSE uncertainty is expected to be lower in less-productive areas, including grasslands and shrublands. Expected model NEE ubRMSE levels were also generally within targeted accuracy levels for characteristically less-productive boreal and Arctic biomes, even though relative model error as a proportion of total productivity (NEE RMSE / GPP) may be large in these areas. The estimated NEE uncertainty was lower than expected in some warmer tropical high biomass productivity areas (e.g. Amazon rainforest) because of reduced low temperature and moisture constraints to the L4_C respiration calculations so that the bulk of model uncertainty is contributed by GPP in these areas. Model NEE uncertainty in the African Congo was estimated to

SMAP↗
DOE OSTI2020

Density functional theory calculation of helium segregation and decohesion effect in W110/Ni111 interphase boundary

Density functional theory (DFT) is employed to study the preferential distribution and decohesion effect of He in a W-NiFe composite consisting of W particles embedded in an Ni-based solid solution matrix. A slab containing {110}<100>W//{111}<110>Ni interface is employed as a surrogate model for the W-NiFe system. Firstly, the fracture energy of the W/Ni interphase boundary (IB) (4.37 J/m 2 ) is higher than the cleavage energy of Ni{111} (3.82 J/m^2) and lower than the cleavage energy of W{110} (6.60 J/m 2 ). The comparison shows that the cohesion of the IB is stronger than the Ni{111} planes that are away from the IB. However, the cohesion between the Ni{111} planes adjacent to the IB is found to be the weakest in this system, with a cleavage energy of 3.11 J/m 2 . Subsequently, formation energy of He is calculated in the Ni slab, W slab, and various interstitial sites in the IB. The DFT calculations show that He is significantly more stable in Ni than in W, by about 1.75 eV. Interestingly, He does not prefer to segregate at the IB as compared to bulk Ni. Nevertheless, it prefers to segregate to the region between the Ni{111} planes adjacent to the IB, and decreases the cohesion of the already weakest region. Based on an estimated amount of He gas production in 5 years under first wall neutron irradiation (neutron flux of 1.04x10 15 n/cm 2 /s), the He will decrease the cleavage energy of the weakest region by 21.2%, 15.4%, and 12.2% at 800, 1000, and 1200 C, respectively.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2003

Global Lightning Activity

Our knowledge of the global distribution of lightning has improved dramatically since the 1995 launch of the Optical Transient Detector (OTD) followed in 1997 by the launch of the Lightning Imaging Sensor (LIS). Together, these instruments have generated a continuous seven-year record of global lightning activity. These lightning observations have provided a new global perspective on total lightning activity. For the first time, total lightning activity (CG and IC) has been observed over large regions with high detection efficiencies and accurate geographic location. This has produced new insights into lightning distributions, times of occurrence and variability. It has produced a revised global flash rate estimate (46 flashes per second) and has lead to a new realization of the significance of total lightning activity in severe weather. Accurate flash rate estimates are now available for large areas of the earth (+/- 72deg latitude) Ocean-land contrasts as a function of season are clearly revealed, as are orographic effects and seasonal and interannual variability. The data set indicates that air mass thunderstorms, not large storm systems dominate global activity. The ability of LIS and OTD to detect total lightning has lead to improved insight into the correlation between lightning and storm development. The relationship between updraft development and lightning activity is now well established and presents an opportunity for providing a new mechanism for remotely monitoring storm development. In this concept, lightning would serve as a surrogate for updraft velocity. It is anticipated hat this capability could lead to significantly improved severe weather warning times and reduced false warning rates.

Christian, Hugh↗
NASA NTRS2004

Total Lightning Activity as Observed from Space

Our knowledge of the global distribution of lightning has improved dramatically since the 1995 launch of the Optical Transient Detector (OTD), followed in 1997 by the launch of the Lightning Imaging Sensor (LIS). Together, these instruments have generated a continuous seven-year record of global lightning activity. These lightning observations have provided a new global perspective on total lightning activity. For the first time, total lightning activity (CG and IC) has been observed over large regions with high detection efficiencies and accurate geographic location. This has produced new insights into lightning distributions, times of occurrence and variability. It has produced a revised global flash rate estimate (44 flashes per second) and has lead to a new realization of the significance of total ligh&g activity in severe weather. Accurate flash rate estimates are now available for areas of the earth (+/- 72 deg. latitude). Ocean-land contrasts as a function of season are clearly reveled, as are orographic effects and seasonal and interannual variability. The data set indicates that air mass thunderstorms, not large storm system dominate global activity. The ability of LIS and OTD to detect total lightning has lead to improved insight into the correlation between lightning and storm development. The relationship between updraft development and lightning activity is now well established and presents an opportunity for providing a new mechanism for remotely monitoring storm development. In this concept, lightning would serve as a surrogate for updraft velocity. It is anticipated that this capability could lead to significantly improved severe weather warning times and reduced false warning rates.

Christian, Hugh J.↗
NASA NTRS2004

Total Lightning Activity as Observed from Space

ABSTRACT: Our knowledge of the global distribution of lightning has improved dramatically since the 1995 launch of the Optical Transient Detector (OTD), followed in 1997 by the launch of the Lightning Imaging Sensor (LIS). Together, these instruments have generated a continuous seven-year record of global lightning activity. These lightning observations have provided a new global perspective on total lightning activity. For the first time, total lightning activity (CG and IC) has been observed over large regions with high detection efficiencies and accurate geographic location. This has produced new insights into lightning distributions, times of occurrence and variability. It has produced a revised global flash rate estimate (44 flashes per second) and has lead to a new realization of the significance of total ligh&g activity in severe weather. Accurate flash rate estimates are now available for areas of the earth (+/- 72 deg. latitude). Ocean-land contrasts as a function of season are clearly reveled, as are orographic effects and seasonal and interannual variability. The data set indicates that air mass thunderstorms, not large storm system dominate global activity. The ability of LIS and OTD to detect total lightning has lead to improved insight into the correlation between lightning and storm development. The relationship between updraft development and lightning activity is now well established and presents an opportunity for providing a new mechanism for remotely monitoring storm development. In this concept, lightning would serve as a surrogate for updraft velocity. It is anticipated that this capability could lead to significantly improved severe weather warning times and reduced false warning rates.

Christian, Hugh J.↗
DOE OSTI2020

Evaluation of a New Chemical Mechanism for 2-Amino-2-methyl-1-propanol in a Reactive Environment from CSIRO Smog Chamber Experiments

Amines are considered as an emerging class of atmospheric pollutants that are of great importance to atmospheric chemistry and new particle formation. As a typical amine, 2-amino-2-methyl-1-propanol (AMP) is one of the proposed solvents for capturing CO 2 from flue gas streams in amine-based post-combustion CO 2 capture plants, and it is expected to result in AMP emission and secondary products formation in the atmosphere. However, the current knowledge of its atmospheric chemistry and kinetics is poorly understood, particularly in a reactive environment. As such, in this work we used the CSIRO smog chamber to study the photo-oxidation of AMP in the presence of VOCs–NOx surrogate mixtures over a range of initial amine concentrations. O 3 formation was significantly inhibited when AMP was added to the surrogate VOCs–NOx mixtures, implying that AMP could alter known atmospheric chemical reaction pathways and the prevailing reactivity. Simultaneously, a large amount of AMP-derived secondary aerosol was formed, with a considerably high aerosol mass yield (i.e. ratio of aerosol formed to reacted AMP) of 1.06±0.20. Based on updated knowledge of its kinetics, oxidation pathways and product yields, we have developed a new mechanism (designated as CSIAMP-19) and integrated into the Carbon Bond 6 (CB6) chemical mechanism, and evaluated it against available smog chamber data. Compared with the existing AMP mechanism (designated as CarterAMP-08), the modified CB6 with CSIAMP-19 mechanism improves prediction against AMP–VOCs–NOx experiments across a range of initial AMP concentrations, within ±10% model error for gross ozone production. Our results contribute to scientific understanding of AMP photochemistry and to the development of the chemical mechanism of other amines. The updated AMP chemical reactions scheme can be further embedded into the chemical transport model for regional modelling scenarios where AMP-related emissions are of concern.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2004

Global Lightning Activity

Our knowledge of the global distribution of lightning has improved dramatically since the advent of spacebased lightning observations. Of major importance was the 1995 launch of the Optical Transient Detector (OTD), followed in 1997 by the launch of the Lightning Imaging Sensor (LIS). Together, these instruments have generated a continuous eight-year record of global lightning activity. These lightning observations have provided a new global perspective on total lightning activity. For the first time, total lightning activity (cloud-to-ground and intra-cloud) has been observed over large regions with high detection efficiency and accurate geographic location. This has produced new insights into lightning distributions, times of occurrence and variability. It has produced a revised global flash rate estimate (44 flashes per second) and has lead to a new realization of the significance of total lightning activity in severe weather. Accurate flash rate estimates are now available over large areas of the earth (+/- 72 deg. latitude). Ocean-land contrasts as a function of season are clearly reveled, as are orographic effects and seasonal and interannual variability. The space-based observations indicate that air mass thunderstorms, not large storm system dominate global activity. The ability of LIS and OTD to detect total lightning has lead to improved insight into the correlation between lightning and storm development. The relationship between updraft development and lightning activity is now well established and presents an opportunity for providing a new mechanism for remotely monitoring storm development. In this concept, lightning would serve as a surrogate for updraft velocity. It is anticipated that this capability could lead to significantly improved severe weather warning times and reduced false warning rates. This talk will summarize our space-based lightning measurements, will discuss how lightning observations can be used to monitor severe weather, and present a concept for continuous geostationary-based lightning observations.

Christian, Hugh J.↗
DOE OSTI2020

Influence of pilot-fuel mixing on the spatio-temporal progression of two-stage autoignition of diesel-sprays in low-reactivity ambient fuel-air mixture

The spatial and temporal locations of autoignition for direct-injection compression-ignition engines depend on fuel chemistry, temperature, pressure, and mixing trajectories in the fuel jets. Dual-fuel systems can provide insight into both fuel-chemistry and physical effects by varying fuel reactivities and engine operating conditions. In this context, the spatial and temporal progression of two-stage autoignition of a diesel-fuel surrogate, n-heptane, in a lean-premixed charge of synthetic natural-gas (NG) and air is imaged in an optically accessible heavy-duty diesel engine. The lean-premixed charge of NG is prepared by fumigation upstream of the engine intake manifold. Optical diagnostics include high-speed (15kfps) cool-flame chemiluminescence-imaging as an indicator of low-temperature heat-release (LTHR) and OH* chemiluminescence-imaging as an indicator high-temperature heat-release (HTHR). NG prolongs the ignition delay of the pilot fuel and increases the combustion duration. Zero-dimensional chemical-kinetics simulations provide further understanding by replicating a Lagrangian perspective for mixtures evolving along streamlines originating either at the fuel nozzle or in the ambient gas, for which the pilot-fuel concentration is either decreasing or increasing, respectively. The zero-dimensional simulations predict that LTHR initiates most likely on the air streamlines before transitioning to HTHR, either on fuel-streamlines or on air-streamlines in regions of near-constant φ. Due to the relatively short pilot-fuel injection-durations, the transient increase in entrainment near the end of injection (entrainment wave) is important for quickly creating auto-ignitable mixtures. To achieve desired combustion characteristics, e.g., multiple ignition-kernels and favorable combustion phasing and location (e.g., for reducing wall heat-transfer or optimizing charge stratification), adjusting injection parameters could tailor mixing trajectories to offset changes in fuel ignition chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2004

CoSSIR: A New Instrument for Exploring the Utility of Submillimeter-wave Radiometry for Earth Observation

The Conical Scanning Submillimeter-wave Imaging Radiometer (CoSSIR) has been developed to study the application of submillimeter-wave radiometry for remote sensing of cirrus clouds and humidity sounding. Measurements of the global distribution of ice cloud mass and particle size are important for understanding the Earth s energy budget and for evaluating global climate models. The spatial variability and the wide variety of cloud particle shapes and sizes make ice clouds particularly difficult to measure. Ice clouds are essentially undetectable at microwave frequencies due to the low dielectric of ice and small size of the particles relative to wavelength. However, submillimeter wavelengths demonstrate significant response to the presence of ice clouds thus this frequency regime is applicable to measuring ice clouds. Another potentially viable application for submillimeter-wave radiometry is humidity and temperature sounding. The principle of sounding at submillimeter wavelengths is similar to that at microwavelengths. Submillimeter-wave radiometry has the advantage of achieving finer spatial resolution using a smaller antenna aperture which is an important consideration for spaceborne observatories. Submillimeter-wave radiometry also offers the potential of sounding over land and as a surrogate measurement for precipitation. CoSSIR is a new instrument to explore these applications. The CoSSIR is designed to fly aboard the ER-2 aircraft and its modest size (approximately 100 kg) permits it to be configured for other aircraft. A dual-axes gimbals mechanism provides conical, across-track, and along-track scanning capability. In its present configuration CoSSIR has fifteen channels between 183 GHz and 640 GHz. Three channels are centered about the 183 GHz water vapor absorption line, four channels are centered about the 380 GHz water vapor absorption line, and three dual-polarized channels are centered about the 487 GHz oxygen absorption line. Two channels are located in atmospheric windows at 220 GHz and 640 GHz. All channels are single-linear polarized with the exception of those near 487 GHz. Calibration is achieved by periodically observing two blackbody radiators; one blackbody is heated to approximately 325 K and the other is approximately 250 K during flight. Details of the instrument design as well as measurements from the Cirrus Regional Study of Tropical Anvils and Cirrus Layers - Florida Area Cirrus Experiment will be presented.

Racette, P. E.↗
DOE OSTI2020

A Novel Active Optimization Approach for Rapid and Efficient Design Space Exploration Using Ensemble Machine Learning

In this work, a novel design optimization technique based on active learning, which involves dynamic exploration and exploitation of the design space of interest using an ensemble of machine learning algorithms, is presented. In this approach, a hybrid methodology incorporating an explorative weak learner (regularized basis function model) that fits high-level information about the response surface and an exploitative strong learner (based on committee machine) that fits finer details around promising regions identified by the weak learner is employed. For each design iteration, an aristocratic approach is used to select a set of nominees, where points that meet a threshold merit value as predicted by the weak learner are selected for evaluation. In addition to these points, the global optimum as predicted by the strong learner is also evaluated to enable rapid convergence to the actual global optimum once the most promising region has been identified by the optimizer. Additionally, this methodology is first tested by applying it to the optimization of a two-dimensional multi-modal surface and, subsequently, to a complex internal combustion (IC) engine combustion optimization case with nine control parameters related to fuel injection, initial thermodynamic conditions, and in-cylinder flow. It is found that the new approach significantly lowers the number of function evaluations that are needed to reach the optimum design configuration (by up to 80%) when compared to conventional optimization techniques, such as particle swarm and genetic algorithm-based optimization techniques.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Isotherm Modeling and Techno-Economic Analysis of Contactor Technologies for New Tetraamine-Appended MOF for NGCC Applications

Recently, a family of tetraamine-functionalized metal-organic frameworks (MOF) has been reported as promising sorbent materials for capturing CO2 from flue gas conditions relevant to natural gas combined cycle (NGCC) applications. The main advantages of these materials are their two-step cooperative CO2 adsorption, which gives rise to unusual two step-shaped CO2 adsorption profiles and their high thermal stability. This work presents the modelling of the two-transition isotherm of the tetraamine-appended MOF, N,N'-bis(3-aminopropyl)-1,4-diaminobutane (3-4-3)-appended Mg2(dobpdc), and the techno-economic analysis (TEA) of carbon capture processes utilizing this sorbent. Due to the unusual isotherm shapes of the experimental CO2 adsorption data for tetraamine-appended Mg2(dobpdc) and the strong nonlinearity of CO2 loading with respect to temperature and pressure, we tested two different models which use logistic functions for representing the different isotherm behaviors in the different pressure ranges. The first model uses the quadratic isotherm model in the low-pressure region, the Langmuir isotherm model in the middle pressure range, and the dual site Langmuir isotherm model in the high-pressure range. To model the transition between regions we used the arctangent functions independent of temperature and the thermal effect was accounted by using the Clausius-Clapeyron relation]. The second model is an extension of the weighted dual-site Langmuir isotherm model presented by Hughes et al.. In this extended model, the dual-site Langmuir isotherm is employed in the three transition regions, using temperature-dependent logistic functions to activate or deactivate the isotherm model in the low, middle, and high-pressure ranges. Both models fit the experimental data quite well with root mean squared errors (RMSE’s) of 0.41 and 0.17 for model 1 and model 2, respectively. Since model 2 resulted in a lower RMSE, it was leveraged for the development of the gas/solid contactor models used by the TEA. Specifically, two different contactor models, an axial-flow fixed bed and moving bed contactor, were developed as part of this work. These models are dynamic, pressure-driven, and consist of mass, energy, and momentum conservation equations. A kinetic model was also developed by performing parameter estimation using experimental fixed bed breakthrough data. These models are then used to simulate CO2 capture processes from the flue gas generated from a ~600 gross MW NGCC power plant. A cost model was developed which considers the capital cost of the reactors and the significant operating costs such as steam and electricity. Using NETL’s Framework for the Optimization and Quantification of Uncertainty of Uncertainty and Surrogates tool (FOQUS), which has the capability of linking models built using numerous modelling platforms with derivative-free optimization solvers, a techno-economic optimization of the carbon capture processes was performed which minimizes the cost of capture.

Caballero, Daison↗
DOE OSTI2021

A physics-informed and hierarchically regularized data-driven model for predicting fluid flow through porous media

This paper presents a new deep learning data-driven model for predicting structure dependent pore-fluid velocity fields in rock. The model is based on a Convolutional Auto-Encoder (CAE) artificial neural network capable of learning from image data generated by direct numerical simulations of fluid flow through pore-structures, such as by Lattice Boltzmann or molecular dynamics methods. The main novelty of the model in comparison to previous CAE-based data-driven approaches consists of three parts. The first is a methodology for decomposing the full-domain of the porous media into sub-regions, or “sub-domains”, in order to reduce the overall size of the CAE, batch process the sub-domains in parallel, and enable the CAE to learn local and generalizable nonlinear mappings of pore-fluid velocities. The second consists of embedding the finite difference solutions of the incompressible Navier-Stokes and continuity equations into convolutional layers prior to the CAE in order to provide the CAE with knowledge of fluid dynamics physics (PhyFlow). The third main novelty is that the training of the CAE is regularized with a hierarchical loss function that encourages the learning of fluid flow patterns (in a way similar to ranked modes in principal component analysis), ranking from most to least important. This is shown to increase the stability in learning, reduce over-fitting, and promote interpretability of the CAE neural network layers (HierCAE). The comprehensive new data-driven model, which we call the PhyFlow-HierCAE model, is shown to exhibit improved accuracy and generalizability of flow field predictions over conventional CAE models, attributable to the embedded physical knowledge and the hierarchical regularization, as well as realize orders of magnitude speed-ups in computation times as a surrogate for the direct numerical simulations. Examples of training and forward predictions on unseen pore-structures are provided and evaluated for data from Lattice Boltzmann and molecular dynamics simulations of pore-fluid flow. The model is shown to be a fast and accurate emulator (or “surrogate”) for predicting effective permeability of unseen pore-structures based on learning from relatively small direct numerical simulation datasets.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

MPEX AI Digital Twins

All magnetically confined plasma fusion power plant concepts (Tokamak, Spherical Tokamak, Stellarator, Mirror, ...) must exhaust the heat and plasma from the core confinement region to the material walls. The primary channel for this exhaust is through a plasma divertor which directs plasma along open magnetic field lines to a material target. The Material Plasma Exposure eXperiment (MPEX) illustrated in Figure 1, is a high-power, steady-state linear plasma device designed to produce the plasma material interaction (PMI) conditions of the divertor of future magnetic confinement fusion power plants: energy flux 20MW/m 2 , ion fluence 1031/m 2 , pulse duration 106 sec. These goals of plasma exposure in MPEX are well beyond those achieved in magnetic fusion experimental devices. Successfully achieving these high power steady state conditions for long pulses requires operational control of the heating and particle sources and the plasma flux to the walls and target. The MPEX AI Hot Spot Controller, proposed in this project, will help achieve the operational milestones of MPEX. The MPEX device will begin commissioning at the end of FY26. A smaller proto-MPEX was operated for 14,666 plasma discharges and will resume operation in September of 2025 as proto-MPEX-lite, with reduced capability, to test a new window for the Helicon plasma source. The proto-MPEX data has undergone surrogate modeling with machine learning methods (R. Archibald, 2022 IEEE International Conference on Big Data). This proto-MPEX data will be used to begin development of the AI digital twins described in this white paper. The scientific mission of MPEX is to qualify materials of different composition for use in the high energy and plasma flux conditions of a fusion power plant. The materials exposed in MPEX will in some cases be exposed to high neutron fluxes at other ORNL facilities to measure the changes to their PMI properties. The targets exposed in MPEX will be transported under vacuum to a Surface Analysis Station (SAS). The SAS will be equipped with the following diagnostics: Focused Ion Beam (FIB) for trench milling, 100-400 angstrom resolution scanning electron microscope (SEM), surface mapping x-ray spectrometer, high resolution camera, and a future upgrade to a laser induced breakdown spectroscopy quadruple mass spectrometer (LIBS-QMS). The MPEX experiments will generate diverse pre- and post-exposure measurement data of detailed material properties down to the crystal grain level in 3D for post-exposure assessment of PMI damage (e.g. cracking, melting, erosion and redeposition of the material). Physics models for the PMI, and how the material composition and manufacturing impact its performance under high energy plasma exposure, need to be validated with MPEX data to guide the selection of new candidate materials. Our vision for the MPEX AI Digital Twins project is to supply experimental and physics model simulation data to train Artificial Intelligence (AI) models for data processing, analysis, operational control, PMI and materials simulation to maximize the scientific output of the MPEX device. Ultimately, an AI digital twin of MPEX material assessment metrics for tested and synthetic material types with simulated PMI will be trained by the AI Modeling Teams on the experimental and physics simulation data submitted to the American Science Cloud by this project. A purely empirical search for the best material is inefficient given the finite number of samples that can be tested on MPEX. In order to expand the material properties database for training the MPEX Material Assessment AI Digital Twin, and to gain physics understanding of the PMI processes, physics models of the material properties and PMI processes are required. The physics simulations provide detailed simulation data, like impact angles for plasma ions, sputtering yields, transport of the ionized sputtered target material in the plasma, and redeposition locations. This simulation data expands the measurement data for deeper physics understanding. The experimental data is essential to validate the PMI and material structure simulation models. The validated models can then be used to generate new simulation data of MPEX material assessments for synthetic material compositions that have not been exposed in MPEX. These predictive simulations, plus the whole experimental dataset, will be used to train the MPEX Material Assessment AI Digital Twin allowing a rapid generative AI search for new materials with reduced PMI damage by interpolating the domain of the training set. These new optimum materials can be simulated with the physics codes and/or tested in MPEX. The ability of AI neural networks to interpolate multi-dimensional parameter spaces and generate virtual data is exploited for a more efficient search for optimum materials. The advent of the Transformational AI Models Consortium (TAIMC) is an opportunity to engage with state of the art private and public AI developers to achieve the goals of the AI digital twins and AI accelerated physics models proposed in this project. Our partners at ORNL from the Advance Scientific Computing Research (ASCR) organization will collaborate in accelerating the integrated plasma material interaction simulation framework. This simulation framework will provide a platform for generating simulation data across a range of physical fidelities, including hybrid methods that produce multi-fidelity results. This data will be leveraged for AI model development, both for generation of surrogates and the automation of simulation campaigns. A part of the research below will include collaborative efforts with the TAIMC to (i) adapt data storage approaches to ensure AI-readiness, (ii) provide a protypical exemplar to inform and exercise constructed workflows, and (iii) generate and share data, using the TAIMC unified AI data standard, for foundational models that will be trained from multiple sources across the DOE complex. We will also collaborate with the TAIMC, as well as the planned AI modeling teams, to develop approaches for reducing the cost of data generation. These include tailored multi-fidelity approaches as well as fine-tuning strategies to augment general, large-scale foundational models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗