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At least 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Mauka Energy FEVER Tool Dataset

Mauka Energy’s dataset, developed under the Forestry Electric Vehicle Energy Routing (FEVER) project and funded by the U.S. Department of Energy’s Small Business Innovation Research program, is a high-resolution geospatial resource designed to support energy modeling for electric log trucks in complex forestry environments. The dataset integrates detailed spatial and road network data to enable accurate simulation of vehicle performance across varied terrain. At its core, the dataset incorporates lidar-derived elevation models, road alignments, and surface classifications from Oregon State University’s McDonald-Dunn Research Forest. These data capture fine-scale variations in slope, curvature, and surface conditions across forest road systems, allowing for vehicle-level analysis of energy consumption and recovery. The dataset also includes data collected on the surrounding public and private road networks in Benton County, Oregon, used in real-world haul routes. These connecting segments provide critical context for modeling transitions between forest operations and regional transportation infrastructure, incorporating attributes such as grade profiles, elevation change, and speed constraints. This combined dataset underpins the development of Mauka Energy’s rolldown tool, which quantifies energy use and regenerative braking potential on downhill and variable-grade segments. By leveraging high-resolution terrain and road data, the FEVER project enables more accurate assessment of electric vehicle feasibility and performance in forestry applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Validating the Funnel, Filter Paper, and Drainage Method for Maximum Water Holding Capacity Over a Wide Range of Soil Textures

The data provided includes the results of maximum water holding capacity using the funnel, filter paper, and drainage (FFPD) method, as well as soil water retention curves generated from the water desorption method (via pressure cells) for a full range of soil textures. It will also include data from sensitivity trials to test readily available materials and varying conditions the FFPD method could be expanded to use for more accessibility to community scientists. The format will be in data tables.

Nelson, Jessica↗
DOE OSTI2021

Injection data analysis using material balance time for CO 2 storage capacity estimation in deep closed saline aquifers

Estimating the ultimate storage capacity of deep saline aquifers is important to address the formation potential to store the envisioned large volumes of CO 2 . Injection data (i.e. injection rate, bottomhole pressure, and cumulative injected volume of CO 2 ) are routinely recorded during storage operations. These data contain valuable information on the subsurface (e.g. the reservoir pore volume and the formation storage capacity) that can be extracted. In this paper, we present a two-step graphical technique to infer the pore volume and the ultimate storage capacity of closed saline aquifers by analyzing the available injection data. First, the pore volume is inferred through adapting the concept of the material balance time. Material balance time is an approximate superposition time function developed to interpret production data from oil and gas wells operating at variable pressure/rate conditions during the boundary-dominated flow period. Using material balance techniques, the ultimate storage capacity is then estimated through linear extrapolation of the average pressure trend to the maximum allowable pressure the formation can withstand. The average pressure is not available in practice, but is can be obtained from the injection data. Two approaches are presented in this study to calculate the average pressure; namely the rigorous and the approximate approaches. Unlike the rigorous approach, the approximate approach does not require a prior knowledge of some reservoir properties (e.g. relative permeability, absolute permeability, formation porosity and thickness) to calculate the average pressure. To investigate its potential and reliability in analyzing CO 2 injection data, the proposed technique is applied to four synthetic cases representing different well operating conditions. Results indicate that the approximate approach consistently overestimates the actual (simulated) storage capacity as compared to the rigorous approach. The agreement - between the inferred and the simulated reservoir pore volume, and between the analytical and numerical estimates of storage capacity - validates the potential application of the technique to CO 2 storage in closed saline aquifers. The technique is further substantiated through application to a field data set utilized from a commercial-scale geological storage (CGS) project. Finally, field data interpretation shows that the proposed technique can be utilized to identify the degree of hydraulic continuity and reservoir compartmentalization within a target formation by interpreting the corresponding pressure and rate responses.

02 PETROLEUM↗
DOE OSTI2022

Comparison of planetary boundary layer height from ceilometer with ARM radiosonde data

Abstract. Ceilometer measurements of aerosol backscatter profiles have been widely used to provide continuous planetary boundary layer height (PBLHT) estimations. To investigate the robustness of ceilometer-estimated PBLHT under different atmospheric conditions, we compared ceilometer- and radiosonde-estimated PBLHTs using multiple years of U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) ceilometer and balloon-borne sounding data at ARM fixed-location atmospheric observatories and from ARM mobile facilities deployed around the world for various field campaigns. These observatories cover from the tropics to the polar regions and over both ocean and land surfaces. Statistical comparisons of ceilometer-estimated PBLHTs from the Vaisala CL31 ceilometer data with radiosonde-estimated PBLHTs from the ARM PBLHT-SONDE Value-added Product (VAP) are performed under different atmospheric conditions including stable and unstable atmospheric boundary layer, low-level cloud-free conditions, and cloudy conditions at these ARM observatories. Under unstable conditions, good comparisons are found between ceilometer- and radiosonde-estimated PBLHTs at ARM low- and mid-latitude land observatories. However, it is still challenging to obtain reliable PBLHT estimations over ocean surfaces even using radiosonde data. Under stable conditions, ceilometer- and radiosonde-estimated PBLHTs have weak correlations. We compare different PBLHT estimations utilizing the Heffter, the Liu–Liang, and the bulk Richardson number methods applied to radiosonde data with ceilometer-estimated PBLHT. We find that ceilometer-estimated PBLHT compares better with the Liu–Liang method under unstable conditions and compares better with the bulk Richardson number method under stable conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Photonic Doppler Velocimetry for Valve Analysis and Surveillance

This study is an experimental evaluation to measure and surveil the velocity of stainless steel (Carpenter 455) pistons within a forged 21-6-9 stainless steel valve body. The velocity of the piston “stroke” will be identified using Photonic Doppler Velocimetry (PDV). The piston velocity will be the measured variable of interest. The outcome of this testing series provides further understanding to whether the extracted piston velocity can provide enough information to determine valve integrity. PDV has been identified as a diagnostic of interest as it can detect surface motion at high speeds with reliable accuracy. Through preliminary studies conducted with velocity interferometer system for any reflector (VISAR), the velocity of the piston is expected to stroke with a speed of 135 m/s. The PDV technique is much more reliable and repeatable than the VISAR technique because of its high-speed precision and accuracy, so it will not only validate the preliminary data, but also provide a testing technique that is capable of being used in future valve qualification efforts. The initial testing was completed using valves that were assembled per qualified procedures in a pristine condition. These initial tests were conducted to provide baseline data. This baseline data was then compared to data collected from valves where man-made defects were introduced. These defects are discussed in later sections. Lastly, actuators used to operate the valves were loaded with varied amounts of high explosives to identify if velocity is a function of the amount of explosive in an actuator. These parameters were adjusted to conclusively determine and identify if PDV can be used as a method to assess valve integrity. It was concluded through the testing discussed in this paper that in fact, PDV is capable of measuring the differences in piston velocities based on varied parameters. As far as understanding the PDV accuracy of the measured differences, this is ongoing and will have to be assessed in continued testing series.

42 ENGINEERING↗
DOE OSTI2021

Hydropower Biological Evaluation Toolset Best Practice Guide

Field studies using live fish are necessary for the evaluation of turbine biological performance, but they cannot determine the specific hydraulic conditions or physical stresses experienced by the fish, the locations where deleterious conditions occur, or the specific causes of the biological response. Using the Sensor Fish (SF) sensing technology, this deficiency can be overcome because the SF can be released independently or concurrently with live fish directly into operating infrastructure, and it takes high-frequency measurements of hydraulic conditions such as pressure, acceleration, and rotation acting on a body in situ during downstream passage. The Hydropower Biological Evaluation Tools (HBET; Hou et al. 2018) software package, developed by Pacific Northwest National Laboratory (PNNL), is designed to assemble, organize, and process data collected by the PNNL-developed SF and by live fish. HBET was developed specifically to design SF field studies, process the raw data, and analyze the processed data efficiently and scientifically. Its objectives are to facilitate SF studies focused on characterizing hydraulic conditions and to apply SF data for evaluating the impacts on fish from passage through hydro-structures. HBET allows users to design new studies, analyze data, perform statistical analyses, and evaluate predicted biological responses. It can be used by researchers, turbine designers, hydropower operators, and regulators to evaluate hydro-structures to enhance environmental sustainability in a cost-effective manner.

13 HYDRO ENERGY↗
DOE OSTI2023

The Amylolytic Regulator AmyR of Aspergillus niger Is Involved in Sucrose and Inulin Utilization in a Culture-Condition-Dependent Manner

Filamentous fungi degrade complex plant material to its monomeric building blocks, which have many biotechnological applications. Transcription factors play a key role in plant biomass degradation, but little is known about their interactions in the regulation of polysaccharide degradation. Here, we deepened the knowledge about the storage polysaccharide regulators AmyR and InuR in Aspergillus niger. AmyR controls starch degradation, while InuR is involved in sucrose and inulin utilization. In our study, the phenotypes of A. niger parental, ΔamyR, ΔinuR and ΔamyRΔinuR strains were assessed in both solid and liquid media containing sucrose or inulin as carbon source to evaluate the roles of AmyR and InuR and the effect of culture conditions on their functions. In correlation with previous studies, our data showed that AmyR has a minor contribution to sucrose and inulin utilization when InuR is active. In contrast, growth profiles and transcriptomic data showed that the deletion of amyR in the ΔinuR background strain resulted in more pronounced growth reduction on both substrates, mainly evidenced by data originating from solid cultures. Overall, our results show that submerged cultures do not always reflect the role of transcription factors in the natural growth condition, which is better represented on solid substrates. Importance: The type of growth has critical implications in enzyme production by filamentous fungi, a process that is controlled by transcription factors. Submerged cultures are the preferred setups in laboratory and industry and are often used for studying the physiology of fungi. In this study, we showed that the genetic response of A. niger to starch and inulin was highly affected by the culture condition, since the transcriptomic response obtained in a liquid environment did not fully match the behavior of the fungus in a solid environment. These results have direct implications in enzyme production and would help industry choose the best approaches to produce specific CAZymes for industrial purposes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Benchmark of interfacial area concentration approaches for the two-fluid model in gas-dispersed condensing flow

Accurate predictions of interfacial area concentration (IAC) are required for closure of the condensation phase change term. The interfacial area transport equation (IATE) can dynamically predict interfacial area concentration and is increasingly being used in the benchmarking of phase change flows over static based correlations which require a flow regime map. For higher flow regimes, the two-group two-fluid IATE model can separately model the transport of spherical bubbles and cap/slug bubbles. Recently, a modified formulation of the one-group and two-group two-fluid model with interfacial area transport equation was proposed for gas-dispersed condensing flows. Three datasets have been used for validation consisting of condensing two-phase flow data in the unheated section of a vertical annulus with conditions spanning a range of pressures, inlet subcoolings and mass fluxes for benchmarking the various IAC approaches. For bubbly flows, the coupled void transport - IATE simulation results in good agreement in the predictions of the void fraction and the interfacial area concentration under nearly all sixty conditions. Considering the proposed two-group two-fluid IATE model formulation, the group-2 Nusselt number correlation is investigated to account for the appropriate heat transfer length scale for group-2 bubbles. Validation of the proposed two-group two-fluid model against the new dataset shows good agreement with experimental data with significant improvement over the one-group IATE model for conditions with substantial group-2 void fraction throughout the simulation domain.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Modular Autonomous Experimentation for Biological Applications

The Modular Autonomous Research System (MARS) was created to address a key challenge in scientific discovery: experiments are often slow, require significant manual labor, and generate data that is not easily integrated across different tools. This limits how quickly scientists can explore new materials, processes, and chemical reactions. Our motivation was to design a system that makes research faster, more reliable, and adaptable by combining automation with artificial intelligence. By doing so, we aimed to reduce human error, accelerate discovery, and allow researchers to quickly test many possibilities that would otherwise take months or years. Our approach was to build a flexible platform that connects laboratory robots, measurement instruments, and a central data system, all guided by artificial intelligence. MARS integrates liquid handling robots, robotic arms, and plate readers with an intelligent decision-making system that chooses the most informative experiments to run next. This creates a closed loop where experiments are performed automatically, the data is analyzed in real time, and new conditions are immediately tested. Through this work, we demonstrated that MARS can carry out multiple experiments with little or no human intervention, adapt to different scientific problems, and handle uncertain or noisy measurements in a robust way. The results show that modular and intelligent automation can significantly accelerate the pace of discovery, providing a model for future self-driving laboratories. This approach addresses the growing scientific need for adaptable, data-driven research platforms that can keep up with the complexity and scale of modern science.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Automated classification of big X-ray diffraction data using deep learning models

Abstract In current in situ X-ray diffraction (XRD) techniques, data generation surpasses human analytical capabilities, potentially leading to the loss of insights. Automated techniques require human intervention, and lack the performance and adaptability required for material exploration. Given the critical need for high-throughput automated XRD pattern analysis, we present a generalized deep learning model to classify a diverse set of materials’ crystal systems and space groups. In our approach, we generate training data with a holistic representation of patterns that emerge from varying experimental conditions and crystal properties. We also employ an expedited learning technique to refine our model’s expertise to experimental conditions. In addition, we optimize model architecture to elicit classification based on Bragg’s Law and use evaluation data to interpret our model’s decision-making. We evaluate our models using experimental data, materials unseen in training, and altered cubic crystals, where we observe state-of-the-art performance and even greater advances in space group classification.

Chemistry↗
DOE OSTI2025

A New Vehicle-to-Vehicle Communication System: Visual-Enhanced Cooperative Traffic Operations

The advent of Connected and Autonomous Vehicles (CAVs) has highlighted the necessity for robust communication systems between vehicles and their environment. This study introduces a novel vehicle-to-vehicle (V2V) communication system, termed the Visual-Enhanced Cooperative Traffic Operations (VECTOR) system. The VECTOR system addresses the need for robust communication by converting dynamic data (including velocity and yaw angle data) into binary code, which is displayed on an LED panel mounted on the top of the vehicle. Following vehicles detect this panel and decode the information using a camera, implementing a visual-based communication method. VECTOR system employs a comprehensive five-module process. Initially, polynomial fitting techniques are applied to velocity data over fixed time intervals using third-degree polynomials, with validation via R² and MSE metrics. The second module converts velocity and yaw angle data into binary form, thereby enhancing detection and processing efficiency. The third module focuses on improving detection stability across various environmental conditions to enhance traffic safety. The fourth module decodes the binary data back into trajectory information, ensuring the fidelity of velocity and yaw angles. The final module integrates eco-control through the VECTOR system, employing advanced control algorithms to minimize energy consumption in CAVs. Experimental evaluations conducted using a modified CAV test platform based on the Lincoln MKZ demonstrate the feasibility and efficiency of the VECTOR system, achieving a 75% R-squared accuracy rate in replicating original velocity data. This methodology not only highlights potential applications but also underscores significant implications for advancing CAV technology.

Ma, Ke↗
DOE OSTI2025

Characterizing IHE Response to Multiple Shock Loading

The response of high explosives to shock loading is traditionally measured with a steady loading pressure. In many accident scenarios involving fragment impact, however, a loading duration that is shorter than the build up to detonation may occur. Fragments passing through multiple materials before reaching a high explosive charge may produce loading that is comprised of more than one shock wave. Additionally, the build up to detonation in high explosive corner turning loads the explosive a short duration pressure pulse, since rarefactions can often rapidly overtake the reactive wave. For these reasons, we have studied the response of the insensitive high explosive (IHE) materials PBX 9502 and LX-17 to complex loadings of varied intensity and duration. We refer to a single loading of limited duration as a “thin pulse”, whereas more complex scenarios were studied with an impactor that produces a double shock in the explosive. The following report presents experimental data and analyses of thin pulse shock initiation and double shock experiments designed to guide development of models of Insensitive High Explosives (IHEs) under controlled one-dimensional conditions relevant to accident scenarios and corner turning. Thin pulse shock initiation data on PBX 9502 and LX-17 were obtained under varied pulse duration, pressed density, and temperature conditions in order to probe various parameters essential for the development of a physics-based Cheetah reactive flow hotspot model. In situ pressure gauges provide insight into the degree of reaction in the explosive that are not obtainable with optical PDV measurements or distance measurements such as run to detonation. Double shock data was obtained to inform a Composition Aware Cheetah model which can be applied to any TATB-based IHEs. This model supports efforts to find a new IHE formulation and potentially incorporate new binders into IHE formulations. Simulations of each experiment are included to demonstrate the utility of these focused experiments to developing models of HE behavior. One-dimensional gas gun experiments are essential for characterizing shocked HE behavior and informing HE models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

SATS Transient Fission Gas Release Test with Irradiated Fuel under Loss of Coolant Accident Conditions

The transient fission gas release (tFGR) during the temperature ramp associated with a loss-of-coolant accident (LOCA) in light-water reactors (LWRs) is likely a significant contribution to the total pressure in a fuel rod and may cause an unexpected rod burst. The lack of data related to tFGR continues to be a key gap in understanding LWR cladding burst behavior under LOCA conditions. To fully characterize this behavior, tFGR data must be collected from several different systems that can capture all relevant testing conditions. Oak Ridge National Laboratory (ORNL) has developed a system to measure the integral tFGR from irradiated fuel segments. This system was designed to integrate with the existing Severe Accident Test Station (SATS) and to build upon decades of experience capturing fission gas to characterize fuel behavior. The tFGR system consists of a sweep gas system to transport gases from the in-cell SATS apparatus to an out-of-cell fission gas detection system composed of a series of cold traps to capture the off-gas from the heating tests and a gamma spectrometry system to detect and measure 85 Kr. Initial system testing operations were completed during which 85 Kr collection and measurement were verified along with the ability to detect stable inert gases. A tFGR test with a high-burnup fuel specimen was successfully conducted by the in-cell SATS-tFGR system at ORNL. The posttest examination is under way, the result of which will be reported in FY24.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Evaluating the pressure dependence of PZT structures using a virtual reality environment

Pb–Zr–Ti–O (PZT) perovskites span a large solid-solution range and have found widespread use due to their piezoelectric and ferroelectric properties that also span a large range. Crystal structure analysis via Rietveld refinement facilitates materials analysis via the extraction of the structural parameters. These parameters, often obtained as a function of an additional dimension (e.g., pressure), can help to diagnose materials response within a use environment. Often referred to as “ in-situ ” studies, these experiments provide an abundance of data. Viewing structural changes due to applied pressure conditions can give much-needed insight into materials performance. However, challenges exist for viewing/presenting results when the details are inherently three-dimensional (3D) in nature. For PZT perovskites, the use of polyhedra (e.g., Zr/Ti–O 6 octahedra) to view bonding/connectivity is beneficial; however, the visualization of the octahedra behavior with pressure dependence is less easily demonstrated due to the complexity of the added pressure dimension. We present a more intuitive visualization by projecting structural data into virtual reality (VR). We employ previously published structural data for Pb 0.99 (Zr 0.95 Ti 0.05 ) 0.98 Nb 0.02 O 3 as an exemplar for VR visualization of the PZT R3c crystal structure between ambient and 0.62 GPa pressure. This is accomplished via our in-house CAD2VR™ software platform and the new CrystalVR plugin. The use of the VR environment enables a more intuitive viewing experience, while enabling on-the-fly evaluation of crystal data, to form a detailed and comprehensive understanding of in-situ datasets. Discussion of methodology and tools for viewing are given, along with how recording results in video form can enable the viewing experience.

Rodriguez, Mark A. (ORCID:0000000269405044)↗
DOE OSTI2023

Integration of sensors through additive manufacturing leading to increased efficiencies of gas turbines for power generation and propulsion

To realize the full capability of additively manufactured components in complex energy systems, it is imperative to minimize early component failures during development phases and during operation. Traditional field feedback timelines and offline inspection protocols significantly reduce the design-manufacturing iteration times. To address this specific question, the project developed and demonstrated a method for the integration of sensors into complex components through additive manufacturing. The team used gas turbine engines as a platform, which meets the need of both power generation and propulsion and offer opportunities for cost reductions and efficiency increases. The innovation of this intelligent integration of sensors into complex components uniquely customized to address questions of integrity and durability for additively manufactured components. With real-time sensing data from additively manufactured components, turbine manufacturers will realize higher efficiencies, reduced component failures, and a 30-50% acceleration in product deployment of high efficiency gas turbine components due to a faster reduction in component risk assessment under actual operating conditions. This is a transformative shift towards a data-driven design and qualification of additively manufactured gas turbine components. To directly integrate sensors into additively manufactured components with all the complexities of actual hardware, powder bed fusion (direct metal laser sintering) and laser metal deposition technologies was developed. Validation took take place in two university laboratories both of which contain actual engine hardware and closely simulate a gas turbine prior to demonstrating the technology in a turbine development test. Indeed, two major technologies from this research cold impact turbine systems in the near future: (1) higher efficiency materials and designs enabled by additive manufacturing with 50% faster design to manufacturing cycle time, to enable faster time-to-market targets; and (2) integration of sensors into additively manufactured components enabling broad health and condition based prognostics for faster component and engine risk reduction.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Improved Particle Heat Transfer by way of Bimodal Particle Distributions for High Temperature Solar Thermal Energy

High temperature solar thermal facilities are looking to increase operating temperatures through novel heat transfer media, one such being solid particles. These particles operating at high temperatures will require transferring their thermal energy into another working fluid like supercritical carbon dioxide which can be used in advanced power cycles. Achieving high heat transfer between the particles and supercritical carbon dioxide is essential to high efficiency and low-cost operation. Therefore, optimizing the thermal conductivity of these particles is one potential way to ensure high performance. Traditionally, unimodal particle distributions have been employed in high temperature particle solar power plants. However, ambient temperature testing of bimodal particle distributions has revealed a superior thermal conductivity when compared to its unimodal counterpart at the same temperature. This data was obtained by certified, off-the-shelf instruments that can effectively simulate the conditions a particle would be exposed to in a high temperature solar thermal system. Data obtained in this way suggests that the increased thermal conductively imputed by a bimodal particle distribution is significant at working temperatures in solar facilities. Furthermore, the thermal conductivity of these bimodal particle distributions peaks when the best combination of large and small particles is applied. At high temperatures, binary particle distributions are compared to monodispersed distributions of larger particles where heat transfer is more prolific due to the increased surface radiation. Various thermal conductivity, porosity and heat exchanger models are explored in conjunction with data acquired up to 700 C.

Stout, Dallin (ORCID:0009000294586091)↗
DOE OSTI2021

RouteE: A Vehicle Energy Consumption Prediction Engine

The emergence of connected and automated vehicles and smart cities technologies create the opportunity for new mobility modes and routing decision tools, among many others. To achieve maximum mobility and minimum energy consumption, it is critical to understand the energy cost of decisions and optimize accordingly. The Route Energy prediction model (RouteE) enables accurate estimation of energy consumption for a variety of vehicle types over trips or sub-trips where detailed drive cycle data are unavailable. Applications include vehicle route selection, energy accounting and optimization in transportation simulation, and corridor energy analyses, among others. The software is a Python package that includes a variety of pre-trained models from the National Renewable Energy Laboratory (NREL). However, RouteE also enables users to train custom models using their own data sets, making it a robust and valuable tool for both fast calculations and rigorous, data-rich research efforts. The pre-trained RouteE models are established using NREL's Future Automotive Systems Technology Simulator paired with approximately 1 million miles of drive cycle data from the Transportation Secure Data Center, resulting in energy consumption behavior estimates over a representative sample of driving conditions for the United States. Validations have been performed using on-road fuel consumption data for conventional and electrified vehicle powertrains. Transferring the results of the on-road validation to a larger set of real-world origin-destination pairs, it is estimated that implementing the present methodology in a green-routing application would accurately select the route that consumes the least fuel 90% of the time. The novel machine learning techniques used in RouteE make it a flexible and robust tool for a variety of transportation applications.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

2D reactive transport model of shale chemical weathering and biogeochemical fluxes along a mountainous hillslope, East River Watershed, Colorado: Input files and simulation results

This data package contains input files and simulation results for a two-dimensional (2D) reactive transport model used to quantitatively analyze the coupled hydrological and biogeochemical processes governing shale weathering and associated biogeochemical fluxes under realistic environmental conditions in the high-elevation East River Watershed. These data support the conclusions presented in Stolze et al. (Water Resources Research, under review), "Model-based interpretation of solute exports and carbon partitioning during shale weathering in a mountainous hillslope". The model simulates atmospheric-subsurface gas exchange, subsurface water flow, and shale weathering processes under dynamic, year-scale conditions along a shale-underlain hillslope located in the East River watershed. The simulations were performed using the PFLOTRAN flow and reactive transport code and executed on the Perlmutter supercomputer to leverage its large-scale parallel computing capabilities. The data package contains two zipped folders, "model_input_files" and "simulation_results", and one readme.txt file. "model_input_files" contains the necessary input files to run the calibrated base-base model presented in Stolze et al. (Water Resources Research, under review). "simulation_results" contains a single hdf5 file ("Output_2D_hillslope_model.h5") which includes the results of simulation performed using the base-case model. This file can be opened with HDFView 3.1.4, Python, or MATLAB. "readme.txt" contains relevant information about the base-case model and provides guidelines on how to run the associated input files provided in the folder "model_input_files". Furthermore, readme.txt provides information regarding the model results provided in "Output_2D_hillslope_model.h5" such as matrix dimensionality and output units. Field datasets used to evaluate model performance were collected at three monitoring wells located along a hillslope transect (PLM1, PLM2, and PLM3). Dissolved ion concentration data were collected from November 2016 to October 2021 for Ca, Mg, DIC, Na, K, SO4 (Dong et al., 2025 - dic_npoc_data_2014_2024.zip - DOI:10.15485/1660459; Williams et al., 2025 - anion_data_2014_2024.zip - DOI:10.15485/1668054; Dong et al., 2025 - cation_data_2014_2024.zip - DOI:10.15485/1668055). Note that we used the files named er_PLM1_xx_yy, er_PLM2_xx_yy, and er_PLM3_xx_yy where xx stands for the name of the aqueous species and yy stands for the depth where the measurements were performed. Soil water content ([0 - 1] m) and water table depth were collected from November 2016 to October 2021 (Wan et al., 2024 - Dynamic_water_table__depthsFig2b.csv and Soil_water_content_Fig4e.csv - DOI:10.15485/2322567). Gaseous CO2 concentration were collected from October 2020 to December 2021(Wan et al., 2024 - Soil_CO2_concentrations_Fig4h.csv - DOI:10.15485/2322567) Gaseous CO2 flux from the subsurface to the atmosphere were collected in the vicinity of PLM2 from October 2019 to May 2022 (Wu et al., 2025). Soil microbial biomass concentration was measured from August 2016 to June 2017 (Sorensen et al., 2019 - 2017_East_River_Pumphouse_Microbial_Biomass__1_.csv - DOI:10.15485/1577267) All field data are published as CSV files compatible with Microsoft Excel, MATLAB, and Python, or as text files. The coordinates of the monitoring wells and the CO2(g) flux sensor in the coordinate system WGS84 are: -PLM1: [38.9197710 ; -106.9492750] -PLM2: [38.9201580 ; -106.9487170] -PLM3: [38.9207843 ; -106.9483668] -PLM4: 38.9210060 ; -106.9479528] -CO2(g) flux sensor: [38.9199180 ; -106.9489906] ------------------------------------------------------------------------------------------- This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a Department of Energy User Facility using NERSC award BER-ERCAP 23980, BER-ERCAP 28550, and BER-ERCAP 33789.

54 ENVIRONMENTAL SCIENCES↗