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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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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Sensitivity and history match analysis of a carbon dioxide “huff-and-puff” injection test in a horizontal shale gas well in Tennessee

Due to improvements in well development, shale gas production has gained importance recently, especially in the United States. To improve gas production and develop a better understanding of shale reservoirs, researchers are conducting field tests to monitor CO 2 storage and enhanced gas recovery. Reservoir simulations can then utilize these field results for sensitivity studies, uncertainty analysis, history matching, gas production forecasts, and CO 2 storage capacity estimations. One such field test was performed for the Chattanooga Shale formation in Morgan County, Tennessee. Approximately 463 tonnes (510 tons) of CO 2 was successfully injected into a hydraulically fractured horizontal well over a thirteen-day period in March 2014. The injection test was achieved in four steps: pre-injection, injection, soaking, and flowback. In this paper, those steps were modeled with a reservoir simulator to match the historic production and injection rates with gas component compositions and forecast the production for five years. The reservoir fluid was modeled as a multi-component gas, including CH 4 , C 2 H 6 , C 3 H 8 , N 2 , and CO 2 . Langmuir constants, reservoir pressure, and fracture network volume were adjusted to match simulation results with observed production rates and gas composition. Results showed that, under the same reservoir conditions, each gas component behaves differently by way of compositional changes in production. CH4 behaves like N2, while CO 2 behaves like heavier hydrocarbons such as C 2 H 6 and C 3 H 8 . CO 2 plume results showed that, after injection, the produced CO 2 is mostly derived from fractured limestone (i.e., Fort Payne Formation) because the injected CO 2 is not adsorbed in limestone but rather by shale formations.

03 NATURAL GAS↗
DOE OSTI2022

Alternative stable states of the forest mycobiome are maintained through positive feedbacks

Most trees on Earth form a symbiosis with either arbuscular mycorrhizal or ectomycorrhizal fungi. By forming common mycorrhizal networks, actively modifying the soil environment and other ecological mechanisms, these contrasting symbioses may generate positive feedbacks that favour their own mycorrhizal strategy (that is, the con-mycorrhizal strategy) at the expense of the alternative strategy. Positive con-mycorrhizal feedbacks set the stage for alternative stable states of forests and their fungi, where the presence of different forest mycorrhizal strategies is determined not only by external environmental conditions but also mycorrhiza-mediated feedbacks embedded within the forest ecosystem. Here, in this work, we test this hypothesis using thousands of US forest inventory sites to show that arbuscular and ectomycorrhizal tree recruitment and survival exhibit positive con-mycorrhizal density dependence. Data-driven simulations show that these positive feedbacks are sufficient in magnitude to generate and maintain alternative stable states of the forest mycobiome. Given the links between forest mycorrhizal strategy and carbon sequestration potential, the presence of mycorrhizal-mediated alternative stable states affects how we forecast forest composition, carbon sequestration and terrestrial climate feedbacks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Integration of Biorelevant Pediatric Dissolution Methodology into PBPK Modeling to Predict In Vivo Performance and Bioequivalence of Generic Drugs in Pediatric Populations: a Carbamazepine Case Study

This study investigated the impact of gastro-intestinal fluid volume and bile salt (BS) concentration on the dissolution of carbamazepine (CBZ) immediate release (IR) 100 mg tablets and to integrate these in vitro biorelevant dissolution profiles into physiologically based pharmacokinetic modelling (PBPK) in pediatric and adult populations to determine the biopredictive dissolution profile. Dissolution profiles of CBZ IR tablets (100 mg) were generated in 50–900 mL biorelevant adult fasted state simulated gastric and intestinal fluid (Ad-FaSSGF and Ad-FaSSIF), also in three alternative compositions of biorelevant pediatric FaSSGF and FaSSIF medias at 200 mL. This study found that CBZ dissolution was poorly sensitive to changes in the composition of the biorelevant media, where dissimilar dissolution (F2 = 46.2) was only observed when the BS concentration was changed from 3000 to 89 μM (Ad-FaSSIF vs Ped-FaSSIF 50% 14 BS). PBPK modeling demonstrated the most predictive dissolution volume and media composition to forecast the PK was 500 mL of Ad-FaSSGF/Ad-FaSSIF media for adults and 200 mL Ped-FaSSGF/FaSSIF media for pediatrics. A virtual bioequivalence simulation was conducted by using Ad-FaSSGF and/or Ad-FaSSIF 500 mL or Ped-FaSSGF and/or Ped-FaSSIF 200 mL dissolution data for CBZ 100 mg (reference and generic test) IR product. The CBZ PBPK models showed bioequivalence of the product. This study demonstrates that the integration of biorelevant dissolution data can predict the PK profile of a poorly soluble drug in both populations. Further work using more pediatric drug products is needed to verify biorelevant dissolution data to predict the in vivo performance in pediatrics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Cosmology before noon with multiple galaxy populations

Near-future facilities observing the high-redshift universe (2 < z < 5) will have an opportunity to take advantage of "multi-tracer" cosmology by observing multiple tracers of the matter density field: Lyman alpha emitters (LAE), Lyman break galaxies (LBG), and CMB lensing κ. Here, in this work we use Fisher forecasts to investigate the effect of multi-tracers on next-generation facilities. In agreement with previous work, we show that multiple tracers improve constraints primarily from degeneracy breaking, instead of the traditional intuition of sample variance cancellation. Then, we forecast that for both BBN and CMB primary priors, the addition of lensing and LAEs onto a LBG-only sample will gain 25% or more in many parameters, with the largest gains being factor of ~ 10 improvement for f EDE . We include a preliminary approach towards modelling the impact of radiative transfer (RT) on forecasts involving LAEs by introducing a simplified model at linear theory level. Our results, albeit preliminary, show that while RT influences LAE-only forecasts strongly, its effect on composite multi-tracer forecasts is limited.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Global anthropogenic emissions (CAMS-GLOB-ANT) for the Copernicus Atmosphere Monitoring Service simulations of air quality forecasts and reanalyses

Anthropogenic emissions are the result of many different economic sectors, including transportation, power generation, industrial, residential and commercial activities, waste treatment and agricultural practices. Air quality models are used to forecast the atmospheric composition, analyze observations and reconstruct the chemical composition of the atmosphere during the previous decades. In order to drive these models, gridded emissions of all compounds need to be provided. This paper describes a new global inventory of emissions called CAMS-GLOB-ANT, developed as part of the Copernicus Atmosphere Monitoring Service (CAMS; https://doi.org/10.24380/eets-qd81, Soulie et al., 2023). The inventory provides monthly averages of the global emissions of 36 compounds, including the main air pollutants and greenhouse gases, at a spatial resolution of 0.1° × 0.1° in latitude and longitude, for 17 emission sectors. The methodology to generate the emissions for the 2000–2023 period is explained, and the datasets are analyzed and compared with publicly available global and regional inventories for selected world regions. Depending on the species and regions, good agreements as well as significant differences are highlighted, which can be further explained through an analysis of different sectors as shown in the figures in the Supplement.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Autonomous Ion Mass Spectrometer Sentry (AIMSS)

Satellite measurements of plasma density and temperature help predict the intensity of geomagnetic storms, which can impact satellites, communications, and power systems. Accurate forecasting requires knowledge of plasma composition. However, typical orbital mass spectrometers have difficulty distinguishing between nitrogen (N+) and oxygen (O+) ions due to their similar masses. This limitation makes it difficult to validate modern space weather models, which depend on accurate knowledge of ion composition in near-Earth space. N+ and O+ ions behave differently in space due to differences in their origin, transport, and loss mechanisms. Improving our ability to distinguish between these ions is critical for understanding and predicting space weather.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Disentangling the effects of sulfate and other seawater ions on microbial communities and greenhouse gas emissions in a coastal forested wetland

Seawater intrusion into freshwater wetlands causes changes in microbial communities and biogeochemistry, but the exact mechanisms driving these changes remain unclear. Here we use a manipulative laboratory microcosm experiment, combined with DNA sequencing and biogeochemical measurements, to tease apart the effects of sulfate from other seawater ions. We examined changes in microbial taxonomy and function as well as emissions of carbon dioxide, methane, and nitrous oxide in response to changes in ion concentrations. Greenhouse gas emissions and microbial richness and composition were altered by artificial seawater regardless of whether sulfate was present, whereas sulfate alone did not alter emissions or communities. Surprisingly, addition of sulfate alone did not lead to increases in the abundance of sulfate reducing bacteria or sulfur cycling genes. Similarly, genes involved in carbon, nitrogen, and phosphorus cycling responded more strongly to artificial seawater than to sulfate. These results suggest that other ions present in seawater, not sulfate, drive ecological and biogeochemical responses to seawater intrusion and may be drivers of increased methane emissions in soils that received artificial seawater addition. A better understanding of how the different components of salt water alter microbial community composition and function is necessary to forecast the consequences of coastal wetland salinization.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Tropical Cyclone Wind Shear-Relative Asymmetry in Reanalyses

Abstract While tropical cyclones (TCs) are axisymmetric vortices to the first order, they often exhibit noteworthy structural asymmetries. These often result from environmental vertical wind shear, which tilts the vortex and induces a wavenumber 1 pattern in the circulation and precipitation fields. Reanalyses and climate models have improved in representing the TC structure and climatology, but their relatively coarse resolution and dependence on parameterized physics cast doubt on their ability to capture the asymmetric TC structure. We perform the most comprehensive process-oriented assessment of TC asymmetry to date in reanalyses. Specifically, we analyze the composite shear-relative TC structure in ERA5 and Climate Forecast System Reanalysis (CFSR), which vary in their resolutions, physical parameterization suites, and data assimilation techniques. These structures are compared with aircraft reconnaissance radar observations. In agreement with the observations, the strongest tangential winds are usually found left-of-shear, while inner core rainfall, ascent, vortex tilt, and low-level inflow are favored directly downshear or in the downshear-left quadrant. Outer rainband convection generally peaks in the downshear-right quadrant. Thermodynamic asymmetries are also apparent, with anomalous low-level moisture right-of-shear, midlevel warmth in the upshear-right quadrant (uptilt), and cloud properties suggestive of a realistic precipitation life cycle from growth to fallout. We also decompose rainfall contributions from the convective parameterization and large-scale cloud schemes and highlight the roles of vorticity advection, buoyancy advection, and diabatic processes in driving asymmetric vertical motions in the inner core and outer rainband regions. Our results suggest that process-level studies of TC asymmetry and TC–wind shear interaction under future warming are viable using climate models. Significance Statement Asymmetries are common in tropical cyclones (TCs), influencing their intensity, track, and hazards. Vertical wind shear often plays a leading-order role in causing these asymmetries. It is uncertain how well asymmetric structures and processes are captured in reanalyses and global climate models (GCMs) with grid spacings of 0.25° and coarser. In this study, we first evaluate TC asymmetry in reanalyses, which have the benefit of being forced by observations. This helps to assess whether the resolutions associated with GCMs sufficiently capture asymmetric structures and processes and motivates upcoming work with free-running GCMs to study how TC asymmetry may change in a warming climate.

Carstens, Jacob D.↗
DOE OSTI2023

Genomic materials design: CALculation of PHAse Dynamics

The CALPHAD system of fundamental phase-level databases, now known as the Materials Genome, has enabled a mature technology of computational materials design and qualification that has already met the acceleration goals of the national Materials Genome Initiative. Here, as first commercialized by QuesTek Innovations, the methodology combines efficient genomic-level parametric design of new material composition and process specifications with multidisciplinary simulation-based forecasting of manufacturing variation, integrating efficient uncertainty management. Recent projects demonstrated under the multi-institutional CHiMaD Design Center notably include novel alloys designed specifically for additive manufacturing. With the proven success of the CALPHAD-based Materials Genome technology, current university research emphasizes new methodologies for affordable accelerated expansion of more accurate CALPHAD databases. Rapid adoption of these new capabilities by US apex corporations has compressed the materials design and development cycle to under 2 years, enabling a new “materials concurrency” integrated into a new level of concurrent engineering supporting an unprecedented level of manufacturing innovation.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Energy Efficient Thermoplastic Composite Manufacturing

The objective of the project was to establish an effective and affordable method to lay-up and consolidate/join large thermoplastic composite aerospace structure with cycle times measured in minutes rather than hours. Composite airplane designs have proven efficient and effective however future potential product production rates are challenging what the current systems can efficiently produce due to material lay-down constraints and extended thermal cycle times. The ability to lay-up then rapidly heat, consolidate, and cool large complex composite structures plus very accurately tool them (i.e. matching CTE of composite materials) along with very precise thermal control is a difficult challenge. Current systems such as autoclave processing of thermoset materials require long cycle times due to method of heating and the large associated thermal masses. These extended cycle times inhibit the ability to meet higher rate production scenarios due to the need for multiple sets of equipment and tools. Thermoplastic composite materials were used to facilitate more rapid cycle times via the elimination of a need for a cure dwell at temperature. Also, utilized induction heating along with smart susceptors to enable the quick cycle times needed while providing precise intrinsic thermal control. The intrinsic control of the induction heating process via the smart susceptors along with the use of laser assisted fiber placement were the key innovations areas developed. The ability to rapidly heat and then precisely control the temperature of the consolidation component was paramount to the success of this project. It is this unique processing attribute coupled with the thermoplastic material characteristics along with rapid lay-up methods that are novel and advantageous. While many requirements and influencing factors decide the materials and processes utilized for future airplane construction, the forecast of accelerated production rates and the recent performance successes of composites in airplane construction provide an opportunity for this processing technology to have significant influence. During the course of this project a number of key accomplishments were completed. They are as follows: The needed laser assisted fiber placement capabilities have been developed and validated on a component of meaningful scale and complexity. Subsequently, a thermoplastic preform of the part scale-up part design was laser assisted fiber placed and made availalbe for consolidation. An induction consolidation system was sized, designed, and fabricated resulting in the full capability to consolidate the scale-up component. The tool for induction consolidating of the scale-up component was designed and fabricated. An aluminum pressure bladder was welded and formed for use as the pressurization membrane to perform the needed application of pressure on the part at temperature to achieve consolidation. The induction consolidation system consisting of the restraint, induction power supply, induction tool with integrated induction coils and smart susceptor liners, along with the aluminum bladder were successfully used to consolidate a large thermoplastic skin. The capability of accomplishing rapid heat-up rates and cool down rates for consolidation of large thermoplastic skins with precise thermal control and even pressure application at the consolidation temperature was validated. The general conclusion is that the use of induction consolidation with smart susceptors is an effective method to rapidly and efficiently consolidate large thermoplastic composite components and enable affordable high rate manufacturing of composites. More specific conclusions generated by this project are as follows: A restraint type induction consolidation system provides an affordable option over the standard press when using the induction consolidation processing method. Aluminum bladders can be formed to the complex shapes needed and supply uniform pressure for consolidation and co-consolidation of thermoplastic composite materials. Straightforward modifications can be made to existing fiber placement robotic systems to enable their use for thermoplastic lay-up applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Emerging opportunities for hybrid perovskite solar cells using machine learning

While there are several bottlenecks in hybrid organic–inorganic perovskite (HOIP) solar cell production steps, including composition screening, fabrication, material stability, and device performance, machine learning approaches have begun to tackle each of these issues in recent years. Different algorithms have successfully been adopted to solve the unique problems at each step of HOIP development. Specifically, high-throughput experimentation produces vast amount of training data required to effectively implement machine learning methods. Here, we present an overview of machine learning models, including linear regression, neural networks, deep learning, and statistical forecasting. Experimental examples from the literature, where machine learning is applied to HOIP composition screening, thin film fabrication, thin film characterization, and full device testing, are discussed. These paradigms give insights into the future of HOIP solar cell research. As databases expand and computational power improves, increasingly accurate predictions of the HOIP behavior are becoming possible.

Hering, Abigail R. (ORCID:0000000270806953)↗
DOE OSTI2020

Improving collaborations between empiricists and modelers to advance grassland community dynamics in ecosystem models

Climate change, increasing atmospheric CO 2 , and land use change have altered biogeochemical and hydrologic cycles world-wide, with grassland systems being particularly vulnerable to resulting vegetation shifts (Komatsu et al., 2019). Therefore, incorporating plant community dynamics into ecosystem models is critical for accurate forecasting of ecosystem responses to global change (Levine, 2016). Process-based ecosystem models, which simulate the biogeochemical transfers of mass and energy among biota, the subsurface, and atmosphere, require representation of dynamic composition of organisms within ecosystems. For example, these models simulate leaf and plant-level characteristics, such as electron transport rate and allometry of carbon (C) allocation, to predict how net primary productivity and other ecosystem processes respond to abiotic drivers. These models are particularly useful in scaling from organismal to ecosystem levels but are still underdeveloped in their ability to capture community change, especially in grassland ecosystems. To represent compositional changes, these models must simulate competition, mortality, establishment, and reproduction of plant populations within communities. Yet, current ecosystem modeling approaches to forecast plant community change have derived from studies of forested systems and are either too coarse to capture fine-scale community dynamics (e.g. dynamic global vegetation models (DGVMs)) or too complex to be used at large spatial scales (e.g. forest gap models).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A New Window into the Baryon Cycle at Cosmic Noon with Line Intensity Mapping: Forecasts for auto- and cross-correlations in [CII]-158$μ$m, HI 21 cm, CO$_{J+1\rightarrow J}$, and H$α$ galaxies

Across the peak of cosmic star formation at $z\sim1-2$, inflow, processing, and feedback drive rapid changes in the spatial distribution and chemical composition of baryons in galaxies and surrounding reservoirs; this baryon cycle can be tomographically mapped by line intensity mapping (LIM) of atomic hydrogen, ionized carbon, and carbon monoxide. We present a simulation-based forecasting framework for detecting auto- and cross-power spectra between spectroscopic surveys of four such tracers at $z\sim0.5-1.7$ mapping the same deep field - TIM, EoRSpec/FYST, MeerKAT, & Euclid. We forward-model 3-D distributions for these tracers from magnetohydrodynamic simulations, directly capturing the two-halo, one-halo, and shot statistics without relying on analytical decompositions. We further detail a signal-to-noise formalism, tailored to LIM surveys with highly anisotropic geometries and Fourier-space coverage. We demonstrate that galaxy cross-correlations will be the dominant discovery channel for current-generation surveys. These instruments will detect the auto-spectra for CO and HI 21 cm and the CO $\times$ 21 cm cross-spectrum at modest S/N $\sim 1-10$, while placing upper limits on the [CII]-158$μ$m signals. [CII], CO, and HI LIM will be $\sim3-30\times$ ($0.5-1.5$ dex) more sensitive to cross-correlation with the Euclid survey, however, than their respective auto-correlations, constraining all three models of line emission at high significance (S/N $\sim 10-40$) within this decade. Finally, we formulate a staged instrumental trajectory with planned or reasonable improvements, including the as-proposed SKA-Mid. We forecast advancing the per-$k$-mode sensitivities of each auto-, galaxy-line, and line-line spectrum by several orders of magnitude, enabling new percent- and sub-percent level constraints on cosmology and the redshift evolution of star formation and the baryon cycle.

Agrawal, Shubh [Pennsylvania U., Dept. Math.]↗
DOE OSTI2023

Modeling the Impact of the Organic Aerosol Phase State on Multiphase OH Reactive Uptake Kinetics and the Resultant Heterogeneous Oxidation Timescale of Organic Aerosol in the Amazon Rainforest

Accurate predictions of chemical lifetime, i.e., oxidation timescale and change in the mass of organic aerosols (OAs) in atmospheric models, are critical to quantify the impacts of OA on aerosol–cloud interactions, radiative forcing, and air quality. The heterogeneous oxidation of OA by hydroxyl (OH) radicals is a key process governing OA mass changes during their oxidation. Recently, laboratory observations of the OH uptake coefficient (γ OH ) and heterogeneous reaction rate (k het,OH ) for different OA systems with varying phase states have been combined to develop a new parameterization of γ OH and k het,OH as a function of the OA phase state, i.e., viscosity (η OA ). Here, in this work, we use a recently published viscosity prediction framework to analyze the new γ OH and k het,OH parameterization with a box model. Subsequently, we implement this box model to predict γ OH and k het,OH over the Amazon rainforest in the dry-to-wet transition season with both significant biomass burning and biogenic influences. Relevant parameters within our box model are specified based on detailed regional model simulations over the Amazon using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). η OA is predicted as a function of species volatility, OA composition (including water uptake) along with ambient relative humidity (RH), and temperature. Based on ambient conditions simulated by WRF-Chem over the entire atmospheric column, we use the box model to predict the upper bounds of the heterogeneous oxidation timescale of OA. Based on previous laboratory measurements, we assume that this heterogeneous OH oxidation causes fragmentation (carbon loss) of OA, followed by the evaporation of the fragmented molecules that results in the exponential decay of OA. We predict that the oxidation timescale of OA is ~1 month near the Earth’s surface in the pristine Amazonian background because of low OH concentrations. But OA is oxidized more rapidly within urban and wildfire plumes near the Earth’s surface with an order of magnitude higher OH concentrations compared to the pristine background, causing the simulated oxidation timescale of OA to be much shorter, ~3–4 days. At 3–5 km altitudes where biomass burning OA is predicted to be semisolid, the heterogeneous oxidation timescale is estimated as ~6 days to 3 weeks and decreases with increasing OH concentrations within plumes. We show that the simulated mass loss of OA is strongly size-dependent, where smaller particles oxidize more rapidly compared to larger particles due to their greater per-particle surface area-to-volume ratio. Increasing urbanization and deforestation in the Amazon in the future might increase OH concentrations in the background Amazon, causing faster oxidation of OA. At higher altitudes above liquid clouds, especially in the upper troposphere where temperatures approach ~250 K, future measurements are needed to reduce the uncertainties related to the mass loss of OA at colder temperatures and low relative humidity conditions due to its heterogeneous OH oxidation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Predicting oxidation damage in ultra high-temperature borides: A machine learning approach

Ultra-high temperature (UHT) borides are ceramics materials with melting points above 3000 °C for structural applications in extreme environments. However, at temperatures exceeding 1600 °C and under oxidizing conditions, the material suffers from detrimental degradation. Optimized design and performance of diboride materials under such extreme conditions requires filling the missing composition-microstructure-oxidation gap. This study proposes a computational data-driven framework to connect the processing and microstructure of Ultra-high temperature borides with the oxidation damage. Random Forest Regressor (RFR) model is adopted to forecast the oxide scale thickness developed after oxidation testing based on processing variables and microstructural features. The model trained on a dataset consisting of 107 samples of experimental data extracted from the literature aims to predict oxidation damage. With proper data manipulation and fine model tuning, the predictor could forecast the oxide scale thickness of UHT diborides with a Mean Absolute Error of 37.45 μm and an R-square of 0.83. This model could be used as a high-throughput scheme to design and test new UHT diborides materials computationally. Furthermore, a model with larger composition capabilities could also be developed in the future as more experimental data become available.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Distribution of Infiltration in the 216-U-10 and 216-B-3 Pond Systems 1944-1997

A modeling system to simulate fate and transport of contaminant plumes in groundwater beneath the Central Plateau to support the Cumulative Impact Evaluation (CIE) and Composite Analysis (CA) is being developed. The CIE will evaluate the cumulative effect of multiple Central Plateau radiological and chemical sources on groundwater quality (DOE/RL-2018-69, Cumulative Impact Evaluation Technical Approach Document ). This will enable cleanup decisions to be evaluated in the context of all Central Plateau waste sites and existing groundwater conditions. The CA evaluates the cumulative effect of all sources of radioactive materials on radiological dose to future members of the public (DOE/RL-2018-60, Annual Status Report (FY 2018): Composite Analysis of Low Level Waste Disposal in the Central Plateau of the Hanford Site ). Fate and transport modeling will be used to forecast future groundwater concentrations for both the CIE and CA. The CIE/CA subsurface modeling system is divided into source (inventory), vadose zone, and saturated zone facets. Source inventory is a necessary input to the vadose zone facet, and releases from the vadose zone to groundwater are inputs to the saturated zone facet. The vadose zone facet simulates historical and future releases from the vadose zone to groundwater for both liquid waste and solid waste disposal sites. Effluent release volumes and radionuclide inventory estimates for liquid waste sites are contained in ECFHANFORD- 17-0079, Hanford Soil Inventory Model (SIM-v2) Calculated Radionuclide Inventory of Direct Liquid Discharges to Soil in the Hanford Site’s 200 Areas , herein referred to as SIM-v2. The liquid waste sites in SIM-v2 include the 216-B-3 Pond near 200 East Area and the 216-U-10 Pond in 200 West Area. The 216-B-3 Pond system consisted of a main pond and 3 expansion lobes that were operational during overlapping time periods. It also included influent ditches, but these are not listed as liquid waste sources in SIM-v2 and are not included in the CIE/CA modeling. The total effluent volume released to the pond system was measured, but the partitioning of this volume to the various expansion lobes was not determined and is not accounted for in SIM-v2. Regarding the 216-U-10 Pond, it received water from several influent ditches. The pond and the influent ditches are listed as liquid waste sites in SIM-v2 and are included as sources in the CIE/CA modeling. Because water in the ditches flowed into the pond, only a portion of the effluent volumes and contaminant inventories assigned to the ditches in SIM-v2 infiltrated from the ditches. Thus, partitioning of infiltration and contaminant inventories between the ditches and the pond is not fully accounted for in SIM-v2. Further, some of the water in the 216-U-10 Pond entered overflow ditches and the amount of this overflow was not recorded and is not accounted for in SIM-v2. Thus, the purpose of this Environmental Calculation File (ECF) is to partition the infiltration of wastewater and contaminant inventory between the main pond and each expansion lobe of the 216-B-3 Pond system, and to partition the infiltration of wastewater and contaminant inventory between the ditches and pond of the 216-U-10 Pond system.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Elucidating Processes Controlling Arctic Atmospheric Aerosol Sources, Aging, and Mixing States (Final Report)

Atmospheric aerosols play critical roles in the Earth’s energy budget, directly by scattering or absorbing solar and terrestrial radiation and indirectly by serving as seeds (nuclei) for cloud droplet and ice crystal formation and by depositing on snow and ice surface, thereby changing the surface albedo. These effects are dependent on aerosol particle size and chemical composition and impact the hydrological cycle as well. This project provided single-particle size and chemical composition measurements across the entire annual cycle in the high Arctic and in the Alaskan Arctic during fall – winter, addressing the most significant gaps in Arctic aerosol observational data. These needs were based on recent rapid sea ice loss across the entire Arctic, as well as the major annual delays in sea ice freeze-up during fall in the Chukchi Sea and increased wintertime sea ice fracturing in the Beaufort Sea, both off the North Slope of Alaska. Two DOE Atmospheric Radiation Measurement (ARM) field campaigns were conducted for atmospheric aerosol sampling. The Aerosols during the Polar Utqiagvik Night (APUN – ‘snow on ground’ in Iñupiaq) ARM field campaign at Utqagivik, Alaska was conducted from Oct. 28 – Dec. 22, 2018. Aerosol sizing instrumentation and a single-particle mass spectrometer were successfully deployed for size-resolved number concentration measurements and measurements of individual particle size and chemical composition, respectively. These results show the influence of locally-produced sea spray aerosol, with high cloud-forming potential, due to delayed sea ice freeze-up in the fall. During the 2019‐2020 international Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, daily atmospheric aerosol particles were collected aboard the German icebreaker Polarstern in the Central Arctic from Nov. 2019 – Oct. 2020. Sea salt aerosol and marine organics were observed year-round during MOSAiC with varying morphologies and sources. These findings are important because most Arctic models do not include a sea spray aerosol source, despite this source increasing with declining sea ice extent. In addition to collecting new samples and data, this project also conducted further analysis of previously collected single-particle chemical composition measurements within the North Slope of Alaska oil fields and at Utqiaġvik, AK, during Aug. – Sep. 2015 and 2016 field campaigns. This work resulted in the discovery of chemical reactions of oil field combustion emissions occurring within fog droplets across the North Slope of Alaska and forming secondary aerosol, showing the impact of Arctic oil field emissions beyond black carbon aerosol and greenhouse gases. In addition, the distribution of chemical species across the aerosol population within the oil fields was quantified, using these data and a previously development framework. We also presented the first ambient evidence of the collision of two atmospheric particles resulting in formation of an organic-coated ammonium sulfate particle of marine origin, which has implications for cloud formation with declining sea ice extent. Overall, this project has elucidated connections between seawater biogeochemistry, resource extraction activities, atmospheric composition, clouds, and the energy budget of the Arctic region. The results of this project are expected to improve weather and sea ice forecasting for security and development in the Arctic and beyond.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Optimizing resource allocation in Miscanthus breeding via sparse testing designs for genomic prediction

Phenotyping high-biomass perennial crops is laborious and the rate of genetic gain in conventional perennial crop breeding programs is typically low. So, it is especially important to identify methods that produce efficiency gains in the breeding process. Miscanthus is a C4 perennial grass with favorable characteristics for producing biomass as a feedstock for biofuels and diverse bio-based products. Increasing biomass yield will increase profitability and environmental benefits, so it is a key target for Miscanthus breeding. In addition, the identification of well-adapted genotypes across a wide range of environmental conditions requires the establishment of multi-environment trials (METs). Sparse testing is a genomic prediction-based strategy that reduces the phenotyping costs in METs by selecting a subset of genotypes to evaluate in a subset of environments and then predicts the performance of the unobserved genotype-environment combinations. A Miscanthus sacchariflorus (MSA) population comprising 336 genotypes observed across three environments was analyzed implementing sparse testing designs. Three prediction models considering main effects (environments, genotypes, genomic) and interaction effects (genotype-by-environment; G×E interaction) were implemented for forecasting dry biomass yield (YDY), total culm (TCM), average internode length (AIL), and culm node number (CNN). Multiple calibration sets based on different compositions and sizes were considered to evaluate performance in terms of the predictive ability (PA) and the mean square error (MSE) for a fixed testing set size. The training set size ranged from 52 to 112 to predict a fixed set of 224 unobserved genotypes across all three environments. The results showed that the model accounting for G×E interaction consistently presented the highest PA and the lowest MSE: for CNN (PA: ~0.77, MSE: ~0.5) and YDY (PA: ~0.70, MSE: ~1.3) while for TCM and AIL these ranged from ~0.28 to 0.41 and ~1.3 to 4.3, respectively. Overall, varying training sets and allocation strategies did not affect PA and MSE, with 52 non-overlapping and 0 overlapping genotypes per environment as the optimal cost-effective allocation framework. This suggests that implementing sparse testing designs could significantly reduce phenotyping costs by fivefold, without compromising PA in breeding programs for perennial crops such as Miscanthus.

Miscanthus sacchariflorus (MSA)↗