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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Neural lumped parameter differential equations with application in friction-stir processing

Lumped parameter methods aim to simplify the evolution of spatially-extended or continuous physical systems to that of a “lumped” element representative of the physical scales of the modeled system. For systems where the definition of a lumped element or its associated physics may be unknown, modeling tasks may be restricted to full-fidelity physics simulations. Here, in this work, we consider data-driven modeling tasks with limited point-wise measurements of otherwise continuous systems. We build upon the notion of the Universal Differential Equation (UDE) to construct data-driven models for reducing dynamics to that of a lumped parameter and inferring its properties. The flexibility of UDEs allow for composing various known physical priors suitable for application-specific modeling tasks, including lumped parameter methods. The motivating example for this work is the plunge and dwell stages for friction-stir welding; specifically, (i) mapping power input into the tool to a point-measurement of temperature and (ii) using this learned mapping for process control.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

A Tensor Network-Based Quantum Algorithm for the Nonlinear 1D Burgers' Equation

In this work, we implement a tensor network-based quantum algorithm to solve unsteady, nonlinear partial differential equations (PDEs). The challenge lies in how to effectively represent, encode, process, and evolve the nonlinear system of PDEs on quantum computers. We will discuss the new techniques using the compressible 1-dimensional (1D) Burgers' equation as an example, because it represents the fundamental nonlinear feature and yet removes certain complexity in physics, allowing us to focus on the design of quantum algorithms. Previous attempts to solve nonlinear PDEs in quantum computation have often involved storing multiple copies of solutions or employing linearizations. Neither is practical due to exponential scaling with evolution time or insufficient solution accuracy. Our framework is based on matrix product states (MPSs) and matrix product operators (MPOs). For example, the velocity field is represented by MPS, whereas the linear and nonlinear spatial differential terms of the velocity field are processed by MPOs. Our primary focus herein is to verify and validate the various tensor network components of the algorithm using solutions obtained by the classical algorithms on high performance computing (HPC) architectures. We use a classical time marching method to demonstrate the functionality of the tensor network operations to model the PDE and their robustness with the time evolution of the system. Our classical simulation results demonstrate the utility of tensor network-based operations in modeling nonlinear PDEs and highlight the necessity as well as potential advantages of using quantum simulations for these techniques.

Gopalakrishnan Meena, Murali [ORNL] (ORCID:0000000↗
DOE OSTI2025

Advancing the Understanding of Snow Accumulation, Melting, and Associated Thermal Insulation Using Spatially Dense Snow Depth and Temperature Time Series

Snow thermal insulation is a critical factor influencing ground thermal dynamics and associated biogeochemical processes. We analyzed the spatiotemporal variability of snow accumulation, melting, and thermal insulation dynamics using spatially dense, collocated snow depth and ground interface temperature time series over two consecutive years. We demonstrated that considering late‐winter snow depth alone was insufficient to fully capture the complexity in snow and insulation dynamics. The influence of vegetation and topography on snow depth distribution varied over the season, across sites and years. We found that deep snow with a long melting period had a substantial impact on thawing n‐factors. To better predict snow insulation effects, we proposed a new weighted snow depth metric that integrates mean daily snow depth and air temperature throughout the cold season. Our results provide insights for developing space‐time remote sensing products and evaluating the representation of snow and permafrost processes in Earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Uncovering Uranium Isotopic Heterogeneity of Fuel Pellets from the Fifth Collaborative Materials Exercise of the Nuclear Forensics International Technical Working Group

In 2017, the Nuclear Forensics International Technical Working Group (ITWG) organized their fifth 37 Collaborative Materials Exercise (CMX-5). The exercise samples were two uranium dioxide fuel pellets 38 manufactured from the same starting materials by different processes to have similar bulk isotopic 39 composition, but different spatial uranium isotopic distributions. Sets of identical materials were sent to 40 all participating laboratories, who then utilized their existing nuclear forensic capabilities to 41 independently analyse fuel pellets and identify similarities and differences of the materials’ 42 characteristics. The analytical methods used to probe the fuel pellets included ex situ, such as sectioning 43 or breaking up the pellets and analyzing dissolved pieces using inductively coupled plasma mass 44 spectrometry (ICP-MS), analyzing particles collected from intact or fragmented pellets by secondary ion 45 mass spectrometry (SIMS), as well as in situ methods, such as laser ablation coupled with ICP-MS, 46 autoradiography and nanoSIMS. In this paper we present the results of these independent analyses and 47 compare the capabilities of those nuclear forensic analytical methods to uncover details of the isotopic 48 heterogeneity of uranium fuel pellets.

Nuclear Forensic Analysis of Uranium Fuel Pellets,↗
DOE OSTI2020

Projecting spatiotemporally explicit effects of climate change on stream temperature: A model comparison and implications for coldwater fishes

Conservation planners and resource managers seek information about how the availability and locations of cold-water habitats will change in the future and how these predictions vary among models. In this work, we used a physical process-based model to demonstrate the implications of climate change for streamflow and water temperature in two watersheds with distinctive flow regimes: the Snoqualmie watershed (WA) and Siletz watershed (OR), USA. Our model incorporated a downscaled ensemble of global climate model outputs and was calibrated with in situ and remotely sensed water temperatures. Furthermore, we compared predictions from our processed-based model to those from a publicly available and widely used statistical model. The process-based model projected greater changes in summer maximum water temperatures for the mixed-rain-snow Snoqualmie watershed than for the rain-dominated Siletz watershed as a result of the near-complete loss of winter snowpack and significant reduction in summer flow in the Snoqualmie watershed expected by the 2080s. Both models projected generally similar future spatial patterns of maximum water temperature in the two rivers, with cool reaches distributed farther upstream and fewer in number. However, the process-based model projected higher spatial heterogeneity in water temperature due to our spatially explicit simulation of streamflow and because we calibrated the model with spatially continuous remotely sensed water temperature data. We used stream temperature projections to assess the vulnerability of Pacific salmon and trout to changes in the spatial distribution of cold-water habitats during August by the 2080s. Results suggest that salmonids may have fewer summertime cold-water habitats in both watersheds. Projected stream warming may further limit particular species and life stages, especially in the Snoqualmie watershed. Our comparison of models highlights the importance of considering what might be gained by using a process-based model for evaluating and prioritizing management actions that mitigate climate impacts on cold-water habitats for stream fishes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Subsurface Redox Interactions Regulate Ebullitive Methane Flux in Heterogeneous Mississippi River Deltaic Wetland

Abstract As interfaces connecting terrestrial and ocean ecosystems, coastal wetlands develop temporally and spatially complex redox conditions, which drive uncertainties in greenhouse gas emission as well as the total carbon budget of the coastal ecosystem. To evaluate the role of complex redox reactions in methane emission from coastal wetlands, a coupled reactive‐transport model was configured to represent subsurface biogeochemical cycles of carbon, nitrogen, and sulfur, along with production and transport of multiple gas species through diffusion and ebullition. This model study was conducted at multiple sites along a salinity gradient in the Barataria Basin at the Mississippi River Deltaic Plain. Over a freshwater to saline gradient, simulated total flux of methane was primarily controlled by its subsurface production and consumption, which were determined by redox reactions directly (e.g., methanogenesis, methanotrophy) and indirectly (e.g., competition with sulfate reduction) under aerobic and/or anaerobic conditions. At fine spatiotemporal scales, surface methane fluxes were also strongly dependent on transport processes, with episodic ebullitive fluxes leading to higher spatial and temporal variability compared to the gradient‐driven diffusion flux. Ebullitive methane fluxes were determined by methane fraction in total ebullitive gas and the frequency of ebullitive events, both of which varied with subsurface methane concentrations and other gas species. Although ebullition thresholds are constrained by local physical factors, this study indicates that redox interactions not only determine gas composition in ebullitive fluxes but can also regulate ebullition frequency through gas production.

03 NATURAL GAS↗
DOE OSTI2020

Connecting Femtosecond Transient Absorption Microscopy with Spatially Coregistered Time Averaged Optical Imaging Modalities

Multimodal all-optical imaging involving coregistered femtosecond transient absorption microscopy (TAM), time-integrated photoluminescence (PL), and steady-state modalities such as confocal reflectance and transmission offers an appealing approach to gain a comprehensive understanding of complex electronic excited-state phenomena in spatially heterogeneous systems. A unique combination of these modalities allows us to unravel not only the competing electronic excited-state dynamical processes but also the underlying morphological information with simultaneous high temporal and spatial resolution. However, correlating the various images obtained from time-resolved and time-independent modalities is generally nontrivial and particularly challenging when the electronic dynamics under study evolve in both time and space. Here, we demonstrate a new approach for rationally correlating time-resolved microscopy with coregistered time-integrated or steady-state modalities. Specifically, our approach involves an extended global lifetime analysis of the time-resolved microscopic data set to separate distinct dynamical processes taking place on commensurate time scales, and the resulting decay-associated amplitude maps (DAAMs) were applied to explore correlations with the images acquired using time-independent modalities. The feasibility of our approach was validated through analyzing a multimodal data set acquired from a thin film of chloride-containing mixed lead halide perovskites (CH 3 NH 3 PbI 3–x Cl x ) using femtosecond transient absorption, time-integrated PL, and confocal reflectance microscopies. Analysis of the results obtained enable us to gain new insight into the complex ultrafast relaxation dynamics in this highly heterogeneous system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Automated Classification of Vehicle Movements at Signalized Intersections Using Vehicle Trajectories

Accurate vehicle movement classification through signalized intersections is of paramount importance to the analysis of intersection performance and the optimization of traffic control strategies. Conventional techniques for tracking vehicle turning movements depend on infrastructure-based strategies like human counts, loop detectors, and video analytics, all of which are costly, prone to errors, and spatially constrained. High-frequency trajectory data can be utilized to determine vehicle movement patterns in a scalable and infrastructure-independent method due to the adoption of connected vehicles (CVs). In recent years, several studies have utilized connected vehicle data to generate performance measures. Most of the trajectory-based performance measures approaches, however, require map matching-i.e., extracting geospatial references from maps to identify the movements that individual vehicles make at a signalized intersection. These approaches are often time-consuming and hinder scalability since geographic features need to be provided for an analysis to be conducted. Map matching methods are prone to errors as different map versions change these geographic features. This research presents a novel automatic classification pipeline that uses CV trajectory data to classify vehicle movements at signalized crossings, specifically pass-through left-turn and right-turn maneuvers. The process starts by filtering trips that cross a spatial bounding box that has been defined at the target intersection. Approach and departure headings for each trajectory crossing the boundary are computed and are clustered together to identify dominant movements. The proposed algorithm is used to classify the movement of vehicles at 10 intersections in the state of California, and the results indicate that the algorithm can classify movements at these intersections with varying traffic volumes and road network configurations, all in a map-less framework with no need for conflation of vehicle trajectories to a digital base map.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Simulation-based characterization of the variability of earthquake risk to buildings in the near-field

Recent advancements in high performance computing platforms and computational workflow for regional-scale simulations are enabling unprecedented modeling of fault-to-structure earthquake processes. Regional simulations resolving ground motions at frequencies relevant to engineered systems are becoming computationally viable and provide a new capability to improve understanding of the geographical distribution and intensity of risk to buildings and critical infrastructure. As computational capabilities advance, it is essential to move beyond illustrative single rupture realizations for scenario earthquake events towards the development of a full suite of rupture realizations that appropriately characterize the range of risk to building systems. The work described in this article investigates the application of a suite of fault rupture realizations with the objective of assessing near-fault, site-specific seismic demand variability for building structures. A representative high-performance regional-scale computational model is utilized to execute ground motion and building response simulations based on 18 kinematic rupture realizations of an M7 strike-slip scenario earthquake. The fault rupture models for the scenario earthquake are created by systematically perturbing the hypocenter location and stochastically generating rupture parameters (slip, rise time, rake angle) to represent a breadth of ground motion intensities resulting from the spatial and temporal variabilities of an earthquake rupture process. The resulting seismic demand variability for three-story (short period) and forty-story (long period) steel moment-resisting frame buildings is characterized in terms of the median and distribution of peak inter-story drift ratio for a range of near-fault sites. The full suite of 18 fault rupture realizations and approximately 280,000 nonlinear dynamic building simulations indicate that the three-story building undergoes higher median seismic demand and significantly greater variability of demand at a given site than the forty-story building, which has important implications for the level of certainty in predicting building performance during an earthquake. The simulations performed provide deeper insight into the relationship between fault rupture parameterization and building response, which is essential information for developing a representative suite of rupture realizations for specific earthquake scenarios.

58 GEOSCIENCES↗
DOE OSTI2024

A multiphysics coupling framework for exascale simulation of fracture evolution in subsurface energy applications

Predicting the evolution of fractured media is challenging due to coupled thermal, hydrological, chemical and mechanical processes that occur over a broad range of spatial scales, from the microscopic pore scale to field scale. We present a software framework and scientific workflow that couples the pore scale flow and reactive transport simulator Chombo-Crunch with the field scale geomechanics solver in GEOS to simulate fracture evolution in subsurface fluid-rock systems. This new multiphysics coupling capability comprises several novel features. An HDF5 data schema for coupling fracture positions between the two codes is employed and leverages the coarse resolution of the GEOS mechanics solver which limits the size of data coupled, and is, thus, not taxed by data resulting from the high resolution pore scale Chombo-Crunch solver. The coupling framework requires tracking of both before and after coarse nodal positions in GEOS as well as the resolved embedded boundary in Chombo-Crunch. We accomplished this by developing an approach to geometry generation that tracks the fracture interface between the two different methodologies. The GEOS quadrilateral mesh is converted to triangles which are organized into bins and an accessible tree structure; the nodes are then mapped to the Chombo representation using a continuous signed distance function that determines locations inside, on and outside of the fracture boundary. The GEOS positions are retained in memory on the Chombo-Crunch side of the coupling. The time stepping cadence for coupled multiphysics processes of flow, transport, reactions and mechanics is stable and demonstrates temporal reach to experimental time scales. The approach is validated by demonstration of 9 days of simulated time of a core flood experiment with fracture aperture evolution due to invasion of carbonated brine in wellbore-cement and sandstone. We also demonstrate usage of exascale computing resources by simulating a high resolution version of the validation problem on OLCF Frontier.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Challenges in studying water fluxes within the soil-plant-atmosphere continuum: A tracer-based perspective on pathways to progress

Tracing and quantifying water fluxes in the hydrological cycle is crucial for understanding the current state of ecohydrological systems and their vulnerability to environmental change. Especially the interface between ecosystems and the atmosphere that is strongly mediated by plants is important to meaningfully describe ecohydrological system functioning. Many of the dynamic interactions generated by water fluxes between soil, plant and the atmosphere are not well understood, which is partly due to a lack of interdisciplinary research. This opinion paper reflects the outcome of a discussion among hydrologists, plant ecophysiologists and soil scientists on open questions and new opportunities for collaborative research on the topic "water fluxes in the soil-plant-atmosphere continuum" especially focusing on environmental and artificial tracers. We emphasize the need for a multi-scale experimental approach, where a hypothesis is tested at multiple spatial scales and under diverse environmental conditions to better describe the small-scale processes (i.e., causes) that lead to large-scale patterns of ecosystem functioning (i.e., consequences). Novel in-situ, high-frequency measurement techniques offer the opportunity to sample data at a high spatial and temporal resolution needed to understand the underlying processes. Here we advocate for a combination of long-term natural abundance measurements and event-based approaches. Multiple environmental and artificial tracers, such as stable isotopes, and a suite of experimental and analytical approaches should be combined to complement information gained by different methods. Virtual experiments using process-based models should be used to inform sampling campaigns and field experiments, e.g., to improve experimental designs and to simulate experimental outcomes. On the other hand, experimental data are a pre-requisite to improve our currently incomplete models. Interdisciplinary collaboration will help to overcome research gaps that overlap across different earth system science fields and help to generate a more holistic view of water fluxes between soil, plant and atmosphere in diverse ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Utilizing computer vision and artificial intelligence algorithms to predict and design the mechanical compression response of direct ink write 3D printed foam replacement structures

Additive Manufacturing (AM) of porous polymeric materials, such as foams, recently became a topic of intensive research due their unique combination of low density, impressive mechanical properties, and stress dissipation capabilities. Conventional methods for fabricating foams rely on complex and stochastic processes, making it challenging to achieve precise architectural control of structured porosity. In contrast, AM provides access to a wide range of printable materials, where precise spatial control over structured porosity can be modulated during the fabrication process enabling the production of foam replacement structures (FRS). Current approaches for designing FRS are based on intuitive understanding of their properties or an extensive number of finite element method (FEM) simulations. These approaches, however, are computationally expensive and time consuming. As such, in this work, we present a novel methodology for determining the mechanical compression response of direct ink write (DIW) 3D printed FRS using a simple cross-sectional image. By obtaining measurement data for a relatively small number of samples, an artificial neural network (ANN) was trained, and a computer vision algorithm was used to make inferences about foam compression characteristics from a single cross-sectional image. Finally, a genetic algorithm (GA) was used to solve the inverse design problem, generating the AM printing parameters that an engineer should use to achieve a desired compression response from a DIW printed FRS. The methods developed herein present an avenue for entirely autonomous design and analysis of additively manufactured structures using artificial intelligence.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Application of Modified Meshgraphnets for Subsurface Prediction during CO2 Sequestration

In the face of the increasingly dire consequences of anthropogenic climate change, capturing and storing carbon dioxide is paramount. However, several impediments exist to the safe and effective subsurface storage of CO2, such as cost of transport, identification of suitable sites for subsurface storage, and assessment of long-term risk from storage in subsurface aquifers. Accurate subsurface modeling is necessary to ensure that CO2 storage is both safe and effective. Still, such modeling has traditionally required either substantial time and computational power (numerical simulation) or a substantial amount of pre-existing data for training (machine learning models). Additionally, these models lack flexibility in dealing with both changes in discretization of the input data and generalizability beyond the data on which they are trained. In order to address these issues, this research applies graph neural networks (GNNs) to predict subsurface saturation and pressure during CO₂ injection in a model of the Illinois Basin-Decatur Project (IBDP). GNNs provide a flexible, intuitive method for representing and manipulating complex unstructured data, which is often found in many practical domain problems such as fluid flow and subsurface characterization. These unstructured grids are easily represented in GNNs by representing spatially-localized features such as permeability, porosity, saturation, and pressure as nodes in a graph and relationships between these properties as edges connecting these nodes. This research applies a specific GNN model called MeshGraphNets (MGN) to model the change in CO2 saturation and pressure over a 50-month time period (36 months of injection, 14 months post-injection). The MGN model leverages a message passing process that allows the network to learn both the spatial and temporal dynamics of this system simultaneously. Additionally, training on a limited dataset (64 realizations, 20 time points each) resulted in a high degree of accuracy in saturation prediction both within the same timeframe as the training (20 months, 0.039 average RMSE) and when projecting out to the end of injection (36 months, 0.053 average RMSE). Temporal predictions such as those generated by MGNs and other similar models are prone to accumulated error over time; in order to address this, a multi-step rollout (MSR) training process was applied to calculate training loss. This method mimics the forward prediction during inference by “rolling out” multiple time points in a single training step using the previous prediction as input to the MGN model. By calculating the loss several time steps forward from the current prediction, the model is forced to find a more stable state over time. Application of MSR to the MGN model resulted in an average 15% reduction in inference error over time during forward prediction. This study showcases the immense potential of GNNs as a game-changing methodology for predicting pressure and saturation evolution in CCS projects, ultimately paving the way for more sustainable and effective carbon storage solutions. Presentation prepared for the 2024 AiChE Annual Meeting, October 27 to November 1 2024, San Diego, CA.

Holcomb, Paul↗
DOE OSTI2021

Control of quantum electrodynamical processes by shaping electron wavepackets

Fundamental quantum electrodynamical (QED) processes, such as spontaneous emission and electron-photon scattering, encompass phenomena that underlie much of modern science and technology. Conventionally, calculations in QED and other field theories treat incoming particles as single-momentum states, omitting the possibility that coherent superposition states, i.e., shaped wavepackets, can alter fundamental scattering processes. Here, we show that free electron waveshaping can be used to design interferences between two or more pathways in a QED process, enabling precise control over the rate of that process. As an example, we show that free electron waveshaping modifies both spatial and spectral characteristics of bremsstrahlung emission, leading for instance to enhancements in directionality and monochromaticity. The ability to tailor general QED processes opens up additional avenues of control in phenomena ranging from optical excitation (e.g., plasmon and phonon emission) in electron microscopy to free electron lasing in the quantum regime.

42 ENGINEERING↗
DOE OSTI2024

The MOSAiC Distributed Network: Observing the coupled Arctic system with multidisciplinary, coordinated platforms

Central Arctic properties and processes are important to the regional and global coupled climate system. The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Distributed Network (DN) of autonomous ice-tethered systems aimed to bridge gaps in our understanding of temporal and spatial scales, in particular with respect to the resolution of Earth system models. By characterizing variability around local measurements made at a Central Observatory, the DN covers both the coupled system interactions involving the ocean-ice-atmosphere interfaces as well as three-dimensional processes in the ocean, sea ice, and atmosphere. The more than 200 autonomous instruments (“buoys”) were of varying complexity and set up at different sites mostly within 50 km of the Central Observatory. During an exemplary midwinter month, the DN observations captured the spatial variability of atmospheric processes on sub-monthly time scales, but less so for monthly means. They show significant variability in snow depth and ice thickness, and provide a temporally and spatially resolved characterization of ice motion and deformation, showing coherency at the DN scale but less at smaller spatial scales. Ocean data show the background gradient across the DN as well as spatially dependent time variability due to local mixed layer sub-mesoscale and mesoscale processes, influenced by a variable ice cover. The second case (May–June 2020) illustrates the utility of the DN during the absence of manually obtained data by providing continuity of physical and biological observations during this key transitional period. We show examples of synergies between the extensive MOSAiC remote sensing observations and numerical modeling, such as estimating the skill of ice drift forecasts and evaluating coupled system modeling. The MOSAiC DN has been proven to enable analysis of local to mesoscale processes in the coupled atmosphere-ice-ocean system and has the potential to improve model parameterizations of important, unresolved processes in the future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

LLNL Kimberlina 1.2 NUFT Simulations June 2018 (v2)

This dataset contains the output 6,000, 3-dimensional reactive multi-phase flow and transport aquifer simulations of brine and CO2 leakage into a protective aquiver in California’s San Joaquin Valley and input data files detailing the geologic mesh, aquifer physical properties and CO2 and brine injection rates. This data set was generated as an ongoing effort with the US DOE National Risk Assessment Partnership (NRAP) to evaluate the effectiveness of monitoring techniques to detect brine and CO2 leakage from legacy wells into underground sources of drinking water overlaying a CO2 storage reservoir. Each simulation contains a unique set of input parameters, generated stochastically. The outputs consist of these upper three geologic layers (from top): the Etchegoin, Macoma-Chanac, Santa Margarita-McLure formations. These simulations span the several distances (1, 3 and 6 km or wells W31-0.2, W31-0.5 and W31-1.0, respectively) from the CO2 injector, initiated from bottom hole pressure and saturation to calculate wellbore leakage from the storage reservoir, with low and high regional groundwater gradients and wellbore leakage into 5 leaky nodes. The dataset includes 1,000 unique simulations for each distance, which each contain a unique aquifer heterogeneity, aquifer and caprock permeability, and two model generations are included with a high permeability (prod07) and hybrid permeability (prod09). The range of permeability distributions is listed in Table 1. Each model generation consists of 3,000 simulations. Included in the dataset are the leakage rates determined from 2D wellbore models which utilize the pressure and CO2 saturation from LBL's reservoir simulations, NUFT mesh files with distributed lithology, NUFT rocktab files which describe the material properties for the geologic layers and the NUFT input files and post-processed output 'ntab' files. Each ntab file contains spatial (rows) and temporal (columns) model output tables for each model cell, the locations (x,y,z) and dimensions for each cells (dx, dy, dz). Table 1. Permeability distribution ranges for prod07 and prod09 model generations Geologic Layer: Permeability Range (log10 m^2) prod07 prod09 Etchegoin -12.92 to -10.92 -13.70 to -11.44 Macoma-Chanac -12.72 to -10.72 -13.50 to -11.24 Santa Margarita-McLure -12.70 to -10.70 -13.48 to -11.22 The input files used to generate the model include which are included in the dataset are: Time series of CO2 leakage input into the model (ex: Q_brn.W31-0.2.sim1000.layers123.tab) Time series of CO2 leakage input into the model (ex: Q_CO2.W31-0.2.sim1000.layers123.tab) Physical properties of the aquifer materials detailing the aquifer porosity, solid density, partitioning coefficients, permeabilities and van-Genuchten parameters detailed in a NUFT rocktab file: (ex: sim1000.usnt.rocktab) Numerical mesh and geologic data assigned to each model cell detailed in a NUFT genmsh format (ex: sim1000.mesh_k16.prod07.trans.genmsh) The primary output parameters are: pH (use absolute value) Change in TDS (mg/kg) Change in Pressure (Pa) Change CO2 gas saturation (fraction range 0.0-1.0) for example, the directory /p/lscratchh/mansoor1/nrap/kimberlina/prod09/mainfiles/sim1000/W31- 0.2 contains: sim1000.W31-0.2.trans.pH.red.ntab sim1000.W31-0.2.no_bg.trans.TDS.red.ntab sim1000.W31-0.2.usnt.P.deltabg.red.ntab sim1000.W31-0.2.usnt.CO2_sat.deltabg.red.ntab Each row in the NTAB files consist of model output per numerical grid cell. Each output file contains 33 columns (variables), including the information of numerical records, geologic location and sizes and the simulated parameter values over time. The first 13 variables are about numerical records and relative geologic information for a simulation grid: 1. index: simulation index 2. i: the ith grid of x-axis 3. j: the ith grid of y-axis 4. k: the ith grid of z-axis 5. element_ref: element reference 6. nuft_ind: nuft index 7. x: grid location in the x axis direction 8. y: grid location in the y axis direction 9. z: grid location in the z axis direction 10. dx: grid length in the x axis direction 11. dy: grid length in the y axis direction 12. dz: grid length in the z axis direction 13. volume: volume of the simulation grid The remainder (14, 15, 16...) variables are the simulated parameter values over time, take Pressure as an example, are: 14. 0.0y: initial pressure per cell. 15. 10.0y: simulated pressure at the end of the 10th year. 16. 20.0y: simulated pressure at the end of the 20th year. ... (repeated for every 10 years until 200 years)... The model extends 10,000 m, 5,000 m and 1,411 m in the x,y and z dimensions, respectively. The mesh consists of 164,832 cells with mesh dimensions of 101 x 51 x 32 (nx, ny, nz), with cell dimensions ranging from 100 m laterally (along x and y-axis) and model layers are as designated in the z-axis: Layer 1: atmosphere (1e-30 m thick) Layer 2: upper caprock (10 m thick) Layers 3-13: Etchegoin (536.23 m thck) Layers 14-27: Macoma-Chanac (679.04 m thick) Layers 28-32: Santa Margarita-McLure (185.94 m thick) The wellbore is placed along node i=51, j=26, and extends vertically along 5 nodes from the top to the bottom of the model. Special instructions when extracting files: Each Gzip archive (ex: prod07.sim1000-sim00099.tar.gz) contains 100 simulations. Gzip archives should be transferred into base directories (ie. In Linux: mkdir prod07; mv prod07.*.tar.gz prod07/.) before extracting, or files will be overwritten. Each sub-simulation tree should have the following file structure pattern (using the linux 'tree' command): |-- prod07 | |-- sim0001 | |-- W31-0.2 | | |-- Q_brn.W31-0.2.sim0001.layers123.tab | | |-- Q_co2.W31-0.2.sim0001.layers123.tab | | |-- sim0001.W31-0.2.no_bg.trans.TDS.red.ntab | | |-- sim0001.W31-0.2.trans.pH.red.ntab | | |-- sim0001.W31-0.2.usnt.CO2_sat.deltabg.red.ntab | | |-- sim0001.W31-0.2.usnt.P.deltabg.red.ntab | |-- W31-0.5 | | |-- Q_brn.W31-0.5.sim0001.layers123.tab | | |-- Q_co2.W31-0.5.sim0001.layers123.tab | | |-- sim0001.W31-0.5.no_bg.trans.TDS.red.ntab | | |-- sim0001.W31-0.5.trans.pH.red.ntab | | |-- sim0001.W31-0.5.usnt.CO2_sat.deltabg.red.ntab | | |-- sim0001.W31-0.5.usnt.P.deltabg.red.ntab | |-- W31-1.0 | | |-- Q_brn.W31-1.0.sim0001.layers123.tab | | |-- Q_co2.W31-1.0.sim0001.layers123.tab | | |-- sim0001.W31-1.0.no_bg.trans.TDS.red.ntab | | |-- sim0001.W31-1.0.trans.pH.red.ntab | | |-- sim0001.W31-1.0.usnt.CO2_sat.deltabg.red.ntab | | |-- sim0001.W31-1.0.usnt.P.deltabg.red.ntab | |-- sim0001.mesh_k16.prod07.trans.genmsh Disclaimer This document was prepared as an account of work sponsored by an agency of the United States government. Neither the United States government nor Lawrence Livermore National Security, LLC, nor any of their employees makes any warranty, expressed or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States government or Lawrence Livermore National Security, LLC. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States government or Lawrence Livermore National Security, LLC, and shall not be used for advertising or product endorsement purposes. Lawrence Livermore National Laboratory is operated by Lawrence Livermore National Security, LLC, for the U.S. Department of Energy, National Nuclear Security Administration under Contract DE-AC52-07NA27344. This report was reviewed and released as LLNL-MI-753464.

aquifer↗
DOE OSTI2022

Increased signal-to-noise ratios within experimental field trials by regressing spatially distributed soil properties as principal components

Environmental variability poses a major challenge to any field study. Researchers attempt to mitigate this challenge through replication. Thus, the ability to detect experimental signals is determined by the degree of replication and the amount of environmental variation, noise, within the experimental system. A major source of noise in field studies comes from the natural heterogeneity of soil properties which create microtreatments throughout the field. In addition, the variation within different soil properties is often nonrandomly distributed across a field. We explore this challenge through a sorghum field trial dataset with accompanying plant, microbiome, and soil property data. Diverse sorghum genotypes and two watering regimes were applied in a split-plot design. We describe a process of identifying, estimating, and controlling for the effects of spatially distributed soil properties on plant traits and microbial communities using minimal degrees of freedom. Importantly, this process provides a method with which sources of environmental variation in field data can be identified and adjusted, improving our ability to resolve effects of interest and to quantify subtle phenotypes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Imaging and spatially resolved mass spectrometry applications in nephrology

The application of spatially resolved mass spectrometry (MS) and MS imaging approaches for studying biomolecular processes in the kidney is rapidly growing. These powerful methods, which enable label-free and multiplexed detection of many molecular classes across omics domains (including metabolites, drugs, proteins and protein post-translational modifications), are beginning to reveal new molecular insights related to kidney health and disease. Further, the complexity of the kidney often necessitates multiple scales of analysis for interrogating biofluids, whole organs, functional tissue units, single cells and subcellular compartments. Various MS methods can generate omics data across these spatial domains and facilitate both basic science and pathological assessment of the kidney. Optimal processes related to sample preparation and handling for different MS applications are rapidly evolving. Emerging technology and methods, improvement of spatial resolution, broader molecular characterization, multimodal and multiomics approaches and the use of machine learning and artificial intelligence approaches promise to make these applications even more valuable in the field of nephology. Overall, spatially resolved MS and MS imaging methods have the potential to fill much of the omics gap in systems biology analysis of the kidney and provide functional outputs that cannot be obtained using genomics and transcriptomic methods.

60 APPLIED LIFE SCIENCES↗