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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A single-point modeling approach for the intercomparison and evaluation of ozone dry deposition across chemical transport models (Activity 2 of AQMEII4)

A primary sink of air pollutants and their precursors is dry deposition. Dry deposition estimates differ across chemical transport models, yet an understanding of the model spread is incomplete. Here, we introduce Activity 2 of the Air Quality Model Evaluation International Initiative Phase 4 (AQMEII4). We examine 18 dry deposition schemes from regional and global chemical transport models as well as standalone models used for impact assessments or process understanding. We configure the schemes as single-point models at eight Northern Hemisphere locations with observed ozone fluxes. Single-point models are driven by a common set of site-specific meteorological and environmental conditions. Five of eight sites have at least 3 years and up to 12 years of ozone fluxes. The interquartile range across models in multiyear mean ozone deposition velocities ranges from a factor of 1.2 to 1.9 annually across sites and tends to be highest during winter compared with summer. No model is within 50 % of observed multiyear averages across all sites and seasons, but some models perform well for some sites and seasons. For the first time, we demonstrate how contributions from depositional pathways vary across models. Models can disagree with respect to relative contributions from the pathways, even when they predict similar deposition velocities, or agree with respect to the relative contributions but predict different deposition velocities. Both stomatal and nonstomatal uptake contribute to the large model spread across sites. Our findings are the beginning of results from AQMEII4 Activity 2, which brings scientists who model air quality and dry deposition together with scientists who measure ozone fluxes to evaluate and improve dry deposition schemes in the chemical transport models used for research, planning, and regulatory purposes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Evaluating the Hybrid Modelling Competition: A Step Towards Developing Good Modelling Practice

Hybrid modelling, a combination of mechanistic and data-driven modelling, is a promis¬ing approach to advance current mathematical models towards improved deci¬sion support tools for today's water-related challenges. Researchers have been develop¬ing guidelines or references for good modelling practices in the water field for mecha¬nistic (Rieger et al., 2012) and data-driven (Zhu et al., 2023) modelling, respectively. However, good modelling practices for hybrid modelling are currently missing (Schneider et al., 2022). Therefore, the International Water Association’s (IWA) hybrid modelling working group initiated the first competition on a data science competition platform (i.e. Kaggle) for water resource recovery modelling at the Watermatex con¬ference in September 2023 in Quebec. The main objective of this competition was to gain insights and experience to create good modelling practices. Further goals were to motivate students, researchers, and practitioners model, foster a vibrant and engaged community, and evaluate the efficacy of com¬petitions in solving modelling challenges within the water domain. Our next goal is that facilities will measure and gather relevant data for future competitions to solve their challenges from a modeller’s perspective.

Schneider, Mariane↗
DOE OSTI2019

Physics based Reliability Models for Supercritical CO 2 Turbomachinery Components

CSP plants using sCO 2 power cycle can potentially achieve high thermal conversion efficiency at low capital cost due to compact turbo-machinery and other components. An sCO 2 expander and improved heat exchanger is expected to provide a major stepping stone for achieving CSP power at 0.06/kW-hr LCOE, energy conversion efficiency > 50 % ,and total power block cost< $1,200/kW installed. Hybrid gas bearings (HGB) and Dry gas seals (DGS) are two key components that are critical for robust and efficient operation of future sCO 2 power cycles. From a system perspective, HGBs can provide substantial benefits (~3 to 5%) to the modular CSP operation, while use of DGSs limits the leakage losses as well as the windage losses in the generator and can increase overall generator efficiency by ~ 8%. The life limiting mechanisms of HGBs and DGSs in high pressure, high temperature sCO 2 environment were not well understood and hence this program aims at developing physics-based performance prediction and life prediction models for these two components. The output of the performance models serves as input to the life models. The performance models were validated using experimental data and using data from literature. DGS and HGB models have shed light on some risks associated with its operation in sCO 2 expander and will guide more robust designs tailored towards sCO 2 cycle applications. A DGS was tested in 1MWe sCO 2 turbo expander to study its performance and validate the performance models. To understand the effect of high pressures, high temperatures and sCO 2 environment on crack initiation, crack propagation and LCF/HCF life of these components, a novel experimental setup was developed. The LCF test data was used to calibrate and validate the life prediction models. The effect of sCO 2 corrosion on the life of Ni-based super-alloys is quantified using long duration experiments. Advanced microstructure and spectroscopic analyses conducted shed light on some key differences between IN718 and IN617 in terms of oxidation morphology, chemical species diffusion and trapping, the formation of protective corrosion resistant layers and changes in surface properties. A novel crack initiation model was developed which takes into account strain distribution, temperature, hold times and activation energy at the gas-metal interface and predicts the number of cycles to initiate the crack in the Inconel alloy. An advanced 3D fracture mechanics-based crack propagation model was also developed which takes into account the transient loading mission of the component and predict how fast the crack will grow and what will be its final shape and size. High energy X-Ray tomography measurements were conducted to study and validate 3D morphology of crack growth inside the specimen. These two models along with chemical kinetics models obtained from corrosion tests can be combined to predict the total life of the component under given long term operating conditions. A Bayesian Hybrid Modeling (BHM) probabilistic framework was developed to quantify uncertainty in physics-based performance models and in crack initiation and crack propagation models, to calibrate the models and to validate the models with statistical confidence. Advanced coupled physics multi-phase flow models were developed to study condensation, boiling and phase change behavior of CO 2 under various sCO 2 turbomachinery and heat exchanger conditions. A novel experimental setup was also designed to observe phase change of CO 2 during its transition across liquid-gas-supercritical state lines.

14 SOLAR ENERGY↗
DOE OSTI2019

Parametric Controls on Vegetation Responses to Biogeochemical Forcing in the CLM5

Future projections of land carbon uptake in Earth System Models are affected by land surface model responses to both CO 2 and nitrogen fertilization. The Community Land Model, Version 5 (CLM5), contains a suite of modifications to carbon and nitrogen cycle representation. Globally, the CLM5 has a larger CO 2 response and smaller nitrogen response than its predecessors. To improve our understanding of the controls over the fertilization responses of the new model, we assess sensitivity to eight parameters pertinent to the cycling of carbon and nitrogen by vegetation, both under present-day conditions and with CO 2 and nitrogen fertilization. The impact of fertilization varies with both model parameters and with the balance of limiting factors (water, temperature, nutrients, and light) in the pre-fertilization model state. The model parameters that impact the pre-fertilization state are in general not the same as those that control fertilization responses, meaning that goodness of fit to present-day conditions does not necessarily imply a constraint on future transient projections. Where pre-fertilization state has low leaf area, fertilization-induced increases in leaf production amplify the model response to the initial fertilization via further increases in photosynthesis. Model responses to CO 2 and N fertilization are strongly impacted by how much plant communities can increase their rates of nitrogen fixation and also directly affected by costs of N extraction from soil and stoichiometric flexibility. Illustration of how parametric flexibility impacts model outputs should help inform the interpretation of carbon-climate feedbacks estimated by in fully coupled Earth system model simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Radiation-induced Swelling and Microcracking in SiC Cladding for LWRs

After the Fukushima accident in 2011, SiC has attracted considerable attention as a potential material for fuel cladding that can offer increased accident tolerance in light water reactors (LWRs) compared to zircaloy cladding. Existing measurements on nuclear grade SiC/SiC composites made of high-purity, stoichiometric SiC have already shown very good radiation tolerance in various harsh environments. However, there are a number of challenges that need to be addressed before SiC composites can be successfully used in LWR technologies. One of them is irradiation-induced swelling, which can lead to development of significant stresses in the matrix, followed by microcracking and subsequent release of fission products. Models of irradiation-induced swelling had been largely empirical and therefore they cannot be used to predict the microstructural dependence of microcracking. Existing models are also limited by the lack of understanding of defects that form due to irradiation in the range of temperatures relevant to fuel cladding in LWRs (<1000°C). Many of the defects in this regime of temperature are too small to be detected with traditional transmission electron microscopy (TEM) techniques. In fact the defects observed in traditional TEM account only for 10-45% of the swelling measured in irradiated SiC. Here, we developed a multi-scale simulation methodology combined with state of the art experimental imaging techniques based on aberration corrected scanning transmission electron microscopy (STEM) to provide a scientific basis for predictive models of radiation swelling and microcracking in SiC. Specifically, we have developed a phase field fracture model that predicts microcrack initiation and growth in polycrystalline SiC. The model accounts for single crystal anisotropy, including anisotropy in the elastic constants and anisotropic surface energy. It also considers the reduced fracture strength of grain boundaries. The model was implemented using the MARMOT mesoscale nuclear materials tool. We have parameterized the model for SiC using molecular dynamics simulation results. The model has been verified using single crystal, bicrystal, and polycrystal examples. Swelling model has also been developed based on cluster dynamics simulations of defect evolution in SiC. We found that point defects and small defect clusters have a significant contribution to swelling. This finding is important because point defects and very small defect clusters are not visible in experiments (e.g., in TEM). Combined modeling and experiments can bring critical insights into how swelling depends on irradiation conditions and the microstructure. In situ nanoindentation tests and ex situ nanoindentation tests were performed on assynthesized 3C-SiC and 3C-SiC irradiated under different radiation conditions. Without radiation, bend contour movement and residual bend contours after indentation demonstrate some room temperature plastic flow and residual plastic deformation. Plastic deformation recovery is also observed on unirradiated samples during unloading. Radiation-induced embrittlement is demonstrated by differences in crack geometry in in situ tests, and is consistent with ex situ test results. Failure by single, straight, cracks indicates decreased fracture toughness due to irradiation in 3C-SiC. Future work includes coupling of the swelling and fracture models and development of a master equation that can be used in the BISON code.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Thermal and Deposition Modeling of the Canister Deposition Field Demonstration

This report provides initial thermal and deposition modeling results of the spent nuclear fuel (SNF) storage system planned for use in the Canister Deposition Field Demonstration (CDFD). The goal of the CDFD testing is to collect deposition measurements on the surface of the dry shielded canister (DSC) to aid in chloride-induced stress corrosion crack (CISCC) research. Ideally this testing will occur in a marine coastal environment but at the time of this report no official site has been selected. The CDFD will use the NUHOMS ® Advanced Horizontal Storage Module – High Seismic (AHSM-HS) with a 32PTH2 canister. The SNF assemblies will be replaced with electrical heaters. Canisters are currently being outfitted with these heaters and tested at Sandia National Laboratory (SNL). Thermal modeling was conducted to ensure the electrical heaters were representative of the temperature distribution found within a storage system with SNF. Deposition models were built to evaluate contaminant deposition on SNF canisters. The CDFD testing will help to validate these models, which will then be used to plan and inform on-site test programs and predictive models for the timing and occurrence of canister CISCC.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

A comparison of model validation approaches for echo state networks using climate model replicates

As global temperatures continue to rise, climate mitigation strategies such as stratospheric aerosol injections (SAI) are increasingly discussed, but the downstream effects of these strategies are not well understood. As such, there is interest in developing statistical methods to quantify the evolution of climate variable relationships during the time period surrounding an SAI. Feature importance applied to echo state network (ESN) models has been proposed as a way to understand the effects of SAI using a data-driven model. This approach depends on the ESN fitting the data well. If not, the feature importance may place importance on features that are not representative of the underlying relationships. Typically, time series prediction models such as ESNs are assessed using out-of-sample performance metrics that divide the times series into separate training and testing sets. However, this model assessment approach is geared towards forecasting applications and not scenarios such as the motivating SAI example where the objective is using a data driven model to capture variable relationships. Here, in this paper, we demonstrate a novel use of climate model replicates to investigate the applicability of the commonly used repeated hold-out model assessment approach for the SAI application. Simulations of an SAI are generated using a simplified climate model, and different initialization conditions are used to provide independent training and testing sets containing the same SAI event. The climate model replicates enable out-of-sample measures of model performance, which are compared to the single time series hold-out validation approach. For our case study, it is found that the repeated hold-out sample performance is comparable, but conservative, to the replicate out-of-sample performance when the training set contains enough time after the aerosol injection.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A Hybrid Biophysical‐Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing

Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy ( LE ) and sensible heat ( H ) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R 2 = 0.81–0.94) and H (R 2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

evapotranspiration↗
DOE OSTI2026

Modeling the Cosmological Lyman-𝛼 Forest at the Field Level

The distribution of absorption lines in the spectra of distant quasars, called the Lyman-𝛼 (Ly-𝛼) forest, is a unique probe of cosmology and the intergalactic medium at high redshifts and small scales. The statistical power of ongoing redshift surveys demands precise theoretical tools to model the Ly-𝛼 forest. We address this challenge by developing an analytic, perturbative forward model to predict the Ly-𝛼 forest at the field level for a given set of cosmological initial conditions. Our model shows a remarkable performance when compared with the Sherwood hydrodynamic simulations: it reproduces the Ly-𝛼 forest flux power spectrum, its cross-correlation with dark matter halos, and the one-point probability distribution function of both fields at the percent level down to scales of a few Mpc. Our work provides crucial tools that bridge analytic modeling on large scales with simulations on small scales, enabling field-level inference from Ly-𝛼 forest data and simulation-based priors for cosmological analyses. Furthermore, this is especially timely for realizing the full scientific potential of the Ly-𝛼 forest measurements by the dark energy spectroscopic instrument.

Cosmological parameters↗
DOE OSTI2020

Multiscale Thermomechanical Damage Model with Internal State Variables for Ceramic Matrix Composites

A length scale-dependent matrix damage model has been developed to simulate the response of ceramic matrix composites (CMCs). This model captures damage initiation and propagation in the brittle matrix material by employing internal state variable (ISV) theory within a multiscale modeling framework to obtain damaged matrix stress-strain constitutive relationships at each length scale. Material degradation due to matrix cracking is captured by a damage variable determined using fracture mechanics and the self-consistent scheme. An additional state variable is introduced to capture the nucleation and growth of matrix porosity, a key mechanism contributing to matrix nonlinearity. Porosity occurs as a result of material diffusion around grain boundaries and the growth of the porosity state variable is related to the material entropy dissipation and the volumetric strain. The nonlinear predictive capabilities of the material model are demonstrated for monolithic silicon carbide, unidirectional (UD) carbon fiber/silicon carbide matrix (C/SiC) CMC, and five-harness satin (5HS) woven C/SiC CMC.

Skinner, Travis D.↗
DOE OSTI2026

Data for: A hybrid biophysical-machine learning framework for diurnal surface energy flux estimation using proximal sensing

Thermal-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal datasets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for specific surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of an ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81-0.94) and H (R2 = 0.46-0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical – machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

Agricultural Sciences↗
DOE OSTI2024

A Machine Learning Initializer for Newton-Raphson AC Power Flow Convergence

Power flow computations are fundamental to many power system studies. Obtaining a converged power flow case is not a trivial task especially in large power grids due to the non-linear nature of the power flow equations. One key challenge is that the widely used Newton based power flow methods are sensitive to the initial voltage magnitude and angle estimates, and a bad initial estimate would lead to non-convergence. This paper addresses this challenge by developing a random-forest (RF) machine learning model to provide better initial voltage magnitude and angle estimates towards achieving power flow convergence. This method was implemented on a real ERCOT 6102 bus system under various operating conditions. By providing better Newton-Raphson initialization, the RF model precipitated the solution of 2,106 cases out of 3,899 non-converging dispatches. These cases could not be solved from flat start or by initialization with the voltage solution of a reference case. Finally, results obtained from the RF initializer performed better when compared with DC power flow initialization, Linear regression, and Decision Trees.

random forest↗
DOE OSTI2023

The June 2012 North American Derecho: A Testbed for Evaluating Regional and Global Climate Modeling Systems at Cloud‐Resolving Scales

Abstract In this paper, we introduce a testbed for evaluating and comparing climate modeling systems at cloud resolving scales using hindcasts of the June 2012 North American derecho. To demonstrate its utility for model intercomparison, the testbed is applied to two models: the regionally‐refined Simple Cloud‐Resolving E3SM Atmosphere Model (SCREAM) at 6.5, 3.25 and 1.625 km grid spacing and the Weather Research and Forecasting (WRF) model with 3.2 and 1.6 km grid spacing. We find the simulation results to be highly sensitive to the initial conditions (ICs), initialization time, and model configurations, with ICs from the Rapid Refresh producing the best simulation. Significant improvement is identified in both models as horizontal grid spacing is refined. While a propagation delay of approximately 2 hr is found in both models, SCREAM at 1.625 km simulates the observed bow echo structure of the derecho well and predicts strong surface gusts that exceed 30 m/s. In comparison, WRF has difficulty producing surface wind over 25 m/s, with wind gusts in WRF 42%–46% lower than in SCREAM. However, WRF has a lower bias in simulating cloud top temperature and extent, but overestimates precipitation intensity. Both models reproduce the observed outgoing longwave radiation spatial patterns well (Pearson correlation >0.88), but, compared with NEXRAD observations, simulate generally larger areas of composite radar reflectivity >40 dBZ and underestimate the precipitating area by ∼47%.

2012 North American derecho↗
DOE OSTI2025

Development of an ERT‐Based Framework for Bentonite Buffers Monitoring From Laboratory Tests: 2. Quantitative Moisture Dynamics Estimation Model

Abstract The long‐term containment of high‐level radioactive waste in geological disposal repositories relies on Engineered Barrier Systems (EBS), with bentonite clay emerging as a candidate material due to its unique properties. Understanding moisture dynamics within bentonite buffers is crucial for EBS performance, as it directly influences the material's swelling capacity, thermal and hydraulic conductivity, mechanical properties, and long‐term evolution under complex thermal‐hydrological‐mechanical (THM) processes. This study develops an advanced Electrical Resistivity Tomography (ERT)‐based framework to quantitatively monitor moisture dynamics under THM conditions. Our framework extends the Waxman‐Smits model to incorporate the coupled effects of temperature, water content, fluid chemistry, and mechanical changes on bentonite's electrical properties. Utilizing HotBENT‐Lab data from our companion paper, which includes electrical conductivity, CT density, and thermocouple measurements, this study offers a novel methodological framework bridging different scales of the model. Our results show that the extended model can estimate water content from ERT data, capturing spatial and temporal variations in moisture distribution within bentonite columns. However, the model tends to overestimate water content compared to CT density‐derived measurements. We address this discrepancy by incorporating a simplified swelling effect model, which improves agreement between ERT and CT density‐based water content estimates. We also discuss model limitations, including simplified treatment of swelling and micropore effects, and propose a conceptual framework for transitioning from laboratory to field applications, addressing challenges such as parameter scalability, field validation methods, and integration of diverse data sources. This ERT‐based framework can potentially advance real‐world moisture monitoring of bentonite‐based EBS in nuclear waste repositories. Plain Language Summary Safely containing high‐level radioactive waste depends on barriers made from materials like bentonite clay, which is effective because it swells and seals in the waste. To ensure these barriers work well over time, it's important to understand how moisture moves through the clay. Our study developed a new method using ERT to monitor moisture levels in bentonite under conditions that mimic those in actual storage sites, including changes in temperature, water content, and mechanical stress. This study improved an existing model to better account for how these factors affect the clay, allowing us to create more accurate moisture maps. Initially, the proposed model overestimated the amount of water in the clay, but its accuracy was improved by factoring in how the clay swells when wet. This study also identified some limitations of the model and suggested ways to adapt it for use in real‐world waste storage sites. This new approach could lead to better monitoring and safety checks for nuclear waste storage systems, helping to ensure long‐term containment. Key Points This work develops an ERT‐based framework extending the Waxman‐Smits model to monitor bentonite moisture dynamics during coupled THM processes The extended model accurately estimates water content from Electrical Resistivity Tomography data, incorporating swelling effects to improve precision This work proposes a conceptual framework for transitioning from laboratory to field applications, advancing EBS monitoring in nuclear waste repositories

Chen, Hang↗
DOE OSTI2022

A Note on the Probability of Initiation Problem

The purpose of this note is to draw attention to Pál’s derivation of the Pál Bell equation which is used by LLNL and LANL to model Probability of Initiation (POI) problems. Both laboratories credit the equation to Bell and Lee. Although the equation formulated by Bell and Lee and the equation derived by Pál appear to be the same, the derivation by Bell and Lee is flawed. Scattering, which was absent in Bell’s original formulation of a POI problem, was later introduced by Bell and Lee by simply replacing a fission term with a scattering term. Such a replacement, however, leads to an inaccurate average number v̅ of neutrons that is produced by a fission event. By approaching a POI problem from probabilistic point of view, Pál formulates the problem as a branching process that determines v̅ correctly. There are six reasons for writing this report. The first is to clarify the failing in Bell and Lee’s derivation. The second is to establish the origin of the equation used by the laboratories. The third is to elucidate the fixed point technique formulated by Mokhtar-Kharroubi for solving the Pál-Bell equation. The fourth is to derive the α eigenvalue that is associated with a time dependent Pál-Bell equation. The fifth is to show in Appendix A that the non-linear fission operator of the Pál-Bell equation is descended from the operator of the Galton-Watson process of stochastic theory. The sixth is to describe in Appendix B a second order positivity preserving technique that accelerates the convergence of the first order fixed point method developed by Mokhtar-Kharroubi.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Neutrino energy reconstruction from semi-inclusive samples

We study neutrino-nucleus charged-current reactions on finite nuclei for the situation in which an outgoing muon and a proton are detected in coincidence, {\it i.e.,} we focus on semi-inclusive cross sections. We limit our attention to one-body current interactions (quasielastic scattering) and assess the impact of different nuclear effects in the determination of the neutrino energy. We identify the regions in phase space where the neutrino energy can be reconstructed relatively well, and study whether the cross section in those regions is significant. Our results indicate that it is possible to filter more than 50\% of all events according to the muon and proton kinematics, so that for the DUNE and T2K fluxes the neutrino energy can be determined with an uncertainty of less than 1\% and 3\%, respectively. Furthermore, we find that the reconstructed neutrino energy does not depend strongly on how one treats the final-state interactions and is not much affected by the description of the initial state. On the other hand, the estimations of the uncertainty on the neutrino energy show important sensitivity to the modeling of the initial state.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Pahute Mesa-Oasis Valley Hydrostratigraphic Framework Model for Corrective Action Units 101 and 102: Central and Western Pahute Mesa, Nye County, Nevada (Rev. 0)

A new, revised three-dimensional (3-D) hydrostratigraphic framework model (HFM) for the Central and Western Pahute Mesa Corrective Action Units 101 and 102 was completed in 2019. The initial (Phase I) model was completed in 2002 and was documented in A Hydrostratigraphic Model and Alternatives for the Groundwater Flow and Contaminant Transport Model of Corrective Action Units 101 and 102: Central and Western Pahute Mesa, Nye County, Nevada (BN, 2002). Subsequent flow and transport modeling revealed the need to collect additional data to reduce uncertainties. The Phase II data collection and characterization effort included extensive well drilling and testing activities, and rebuilding of the Phase I 3-D HFM, resulting in the Phase II HFM (NSTec, 2014). Further drilling (Well ER-20-12), data collection, and interpretive work (e.g., Thirsty Canyon Lineament [Wurtz and Day, 2018]) were incorporated into the Pahute Mesa-Oasis Valley (PM-OV) HFM. The PM-OV HFM incorporates the area of interest from the Phase II HFM and adds additional areas, primarily to the north and west with minor additions to the south and east. The new, expanded area matches the PM-OV groundwater basin as defined in Fenelon et al. (2016) and includes Pahute Mesa, a former nuclear testing area at the Nevada National Security Site; and Oasis Valley, a groundwater discharge area downgradient from contaminant source areas on Pahute Mesa. The data and information necessary to build the PM-OV HFM are provided in Navarro (2019). The PM-OV HFM will be used by hydrologic modelers who are tasked with developing a model to determine how contaminants are transported by groundwater flow in an area of complex geology. The model area is large (more than 6,250 square kilometers) and geologically complex, including Paleozoic- to Mesozoic-age structures (e.g., thrust faults, Thirsty Canyon Lineament), at least seven Tertiary-age calderas, several intrusive bodies, and many relatively recent basin-and-range normal faults. Investigators from the U.S. Geological Survey; National Security Technologies, LLC; National Laboratories; and U.S. Department of Energy contractors have organized the more than 300 volcanic units and other rocks in the model area into 77 hydrostratigraphic units (HSUs). The volcanic rocks are subdivided into 42 aquifers, 11 confining units, and 9 composite units (containing both aquifer and confining unit rocks). The underlying pre-Tertiary rocks were divided into 5 HSUs, comprising 2 aquifers and 3 confining units. The model also includes 1 alluvial aquifer unit, 8 intrusive confining units, and 1 granite confining unit. The model depicts the thickness, extent, and geometric relationships of these HSUs (“layers” in the model) along with all the major structural features that control them, including calderas and faults. The most substantive differences between the Phase II HFM and the PM-OVHFM include (1) addition of the new data and resulting revised interpretations from the additional area encompassed by the PM-OV groundwater basin; (2) incorporation of 16 new faults; (3) 12 boreholes from the northern extension; (4) 2 additional caldera-collapse collars; and (5) correlation of the simulated faulting and hydrostratigraphy in the eastern part of the model domain with the delineations represented in the neighboring Rainier Mesa-Shoshone Mountain HFM-Prime and Yucca Flat-Climax Mine HFM. All PM-OV HFM delineations were based on published and unpublished data from geophysical investigations, surface geologic maps, and drill-hole records. Phase II approached model uncertainty using the model-building software to test model configurations and hydrologic consequences using a range of values for parameters such as fault offset amounts and HSU elevations. The PM-OV HFM expands on this approach to encompass the entire PM-OV groundwater basin.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Anomalous Transport in Three-Dimensional Discrete Fracture Networks: Interplay Between Aperture Heterogeneity and Injection Modes

In this work, we study how the interplay between fracture aperture heterogeneity and tracer injection mode controls fluid flow and tracer transport in three-dimensional (3D) discrete fracture networks (DFNs). The direct 3-D DFN simulations show that tracer injection mode has substantial effects on tracer spreading across all levels of aperture heterogeneity. The key controlling factor for effective transport is the initial Lagrangian velocity distribution, which is determined by the interplay between injection mode and aperture heterogeneity. The fundamental difference between initial Lagrangian velocity distribution and domain-scale Eulerian velocity distribution plays a vital role in determining anomalous transport. We effectively capture the observed anomalous transport using an upscaled transport model that incorporates initial velocity distribution, stationary velocity distribution, velocity correlation length, and average advective tortuosity. With the upscaled transport model, we accurately capture the evolution of Lagrangian velocity distribution and predict longitudinal spreading in 3-D DFN.

58 GEOSCIENCES↗