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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Detection efficiency measurement and operational tests of the X-Arapuca for the first module of DUNE far detector

The Deep Underground Neutrino Experiment (DUNE) is a dual-site experiment for long-baseline neutrino oscillation studies, able to resolve the neutrino mass hierarchy and measure δ$_{CP}$. DUNE will also have sensitivity to supernova neutrinos and to processes beyond the Standard Model, such as nucleon decay searches. The Far Detector (FD) will consist of four liquid argon TPC (17.5 kt total mass) with systems for the detection of charge and scintillation light produced by an ionization event. The charge detection system permits both calorimetry and position determination. In addition, the photon-detection system (PDS) enhances the detector capabilities for all DUNE physics drivers. The PDS of the first FD module consists of light collector modules placed in the inactive space between the innermost wire planes of the TPC anode. The light collectors, the so-called X-ARAPUCAS, are functionally a light trap that captures wavelength-shifted photons inside boxes with highly reflective internal surfaces where they are guided to Silicon Photo-multipliers (SiPM) by wavelength-shifting (WLS) bars. Functionality and operational tests of the X-ARAPUCAS to be installed in ProtoDUNE-SP phase II (FD DUNE prototype at the scale 1:20), as well as the measurement of their absolute detection efficiency is reported in this publication.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

AERO-MAP: a data compilation and modeling approach to understand spatial variability in fine- and coarse-mode aerosol composition

Abstract. Aerosol particles are an important part of the Earth climate system, and their concentrations are spatially and temporally heterogeneous, as well as being variable in size and composition. Particles can interact with incoming solar radiation and outgoing longwave radiation, change cloud properties, affect photochemistry, impact surface air quality, change the albedo of snow and ice, and modulate carbon dioxide uptake by the land and ocean. High particulate matter concentrations at the surface represent an important public health hazard. There are substantial data sets describing aerosol particles in the literature or in public health databases, but they have not been compiled for easy use by the climate and air quality modeling community. Here, we present a new compilation of PM2.5 and PM10 surface observations, including measurements of aerosol composition, focusing on the spatial variability across different observational stations. Climate modelers are constantly looking for multiple independent lines of evidence to verify their models, and in situ surface concentration measurements, taken at the level of human settlement, present a valuable source of information about aerosols and their human impacts complementarily to the column averages or integrals often retrieved from satellites. We demonstrate a method for comparing the data sets to outputs from global climate models that are the basis for projections of future climate and large-scale aerosol transport patterns that influence local air quality. Annual trends and seasonal cycles are discussed briefly and are included in the compilation. Overall, most of the planet or even the land fraction does not have sufficient observations of surface concentrations – and, especially, particle composition – to characterize and understand the current distribution of particles. Climate models without ammonium nitrate aerosols omit ∼ 10 % of the globally averaged surface concentration of aerosol particles in both PM2.5 and PM10 size fractions, with up to 50 % of the surface concentrations not being included in some regions. In these regions, climate model aerosol forcing projections are likely to be incorrect as they do not include important trends in short-lived climate forcers.

Mahowald, Natalie M. (ORCID:000000022873997X)↗
DOE OSTI2023

AToM: Advanced Tokamak Modeling Environment

Stability constraints play an important role in integrated modeling. The global plasma stability and macro-instabilities such as internal kink modes, neoclassical tearing modes, and edge localized perturbations limit the plasma performance and can result in large-scale transient events and plasma disruptions. These macro-instabilities can be also beneficial to the plasma performance. For example, the peeling models often lead to the edge localized modes (ELMs). However, the formation of stationary edge harmonic oscillations (EHOs) due to nonlinear interaction of peeling modes can result in a transition to the Quiescent H-mode (QH-mode) without ELMs. The profiles predicted with transport models need to be refined using the MHD stability constraints. This is important especially for transient and nonlinear stages of discharges such as ramp-up access to hybrid and steady state operation, and L- to H-mode transition as well as transition to the QH-mode. Several stability and MHD codes are already included in the OMFIT framework. These codes include BALOO, ELITE, GATO, M3DC1, MARS, and NIMROD. However, the verifications of stability constraints are currently mostly excluded from transport modeling workflows. The only exception is the EPED module which includes the ELITE predictions to limit the pedestal height. Here, we improved the OMFIT workflow to include stability calculations. Including the stability conditions to the integrated modeling workflow improved the robustness of the predictive modeling discharges. Being implemented in the workflow, the stability conditions can be also used for the stability analysis of experimental data. This improved the physics understanding of various discharge scenarios and can be used in experiment planning.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Developing the Science Basis for Understanding Polymer Encapsulant Degradation Mechanisms: DuraMAT 2.0 Final Project Report

Polymeric encapsulants are essential materials in photovoltaic modules, protecting sensitive electronics from the environment while providing mechanical integrity to the multilayered assembly. However, these polymeric materials are susceptible to degradation processes driven by the ingress of environmental species, ultraviolet radiation, thermal stresses, and mechanical loading. In this study, we employ a combined atomistic simulation and accelerated aging experimental approach to study the molecular-scale mechanisms of encapsulant degradation. Classical molecular dynamics simulations quantify the diffusion of environmental and degradation species through the polymer matrix, producing composition-specific diffusion coefficients. Reactive simulations characterize activation energy barriers and reaction rate constants for key chemical pathways. In parallel, thermal-desorption analyses coupled with mass spectrometry monitor the emergence and concentration profiles of degradation products under controlled stressor conditions. By integrating simulation and experiment, we establish quantitative correlations between polymer composition, species diffusivity, and chemical reactivity. We anticipate that these relations and quantitative values could serve as high-fidelity inputs to reaction-diffusion models, enabling physics-informed lifetime predictions and guiding the design of more durable encapsulant materials for solar energy applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Understanding collective human movement dynamics during large-scale events using big geosocial data analytics

Conventional approaches for modeling human mobility pattern often focus on human activity and movement dynamics in their regular daily lives and cannot capture changes in human movement dynamics in response to large-scale events. With the rapid advancement of information and communication technologies, many researchers have adopted alternative data sources (e.g., cell phone records, GPS trajectory data) from private data vendors to study human movement dynamics in response to large-scale natural or societal events. Big geosocial data such as georeferenced tweets are publicly available and dynamically evolving as real-world events are happening, making it more likely to capture the real-time sentiments and responses of populations. However, precisely-geolocated geosocial data is scarce and biased toward urban population centers. In this research, we developed a big geosocial data analytical framework for extracting human movement dynamics in response to large-scale events from publicly available georeferenced tweets. The framework includes a two-stage data collection module that collects data in a more targeted fashion in order to mitigate the data scarcity issue of georeferenced tweets; in addition, a variable bandwidth kernel density estimation(VB-KDE) approach was adopted to fuse georeference information at different spatial scales, further augmenting the signals of human movement dynamics contained in georeferenced tweets. To correct for the sampling bias of georeferenced tweets, we adjusted the number of tweets for different spatial units (e.g., county, state) by population. To demonstrate the performance of the proposed analytic framework, we chose an astronomical event that occurred nationwide across the United States, i.e., the 2017 Great American Eclipse, as an example event and studied the human movement dynamics in response to this event. Finally, this analytic framework can easily be applied to other types of large-scale events such as hurricanes or earthquakes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

An Integrative Model for Soil Biogeochemistry and Methane Processes: I. Model Structure and Sensitivity Analysis

Abstract Environmental changes are anticipated to generate substantial impacts on carbon cycling in peatlands, affecting terrestrial‐climate feedbacks. Understanding how peatland methane (CH 4 ) fluxes respond to these changing environments is critical for predicting the magnitude of feedbacks from peatlands to global climate change. To improve predictions of CH 4 fluxes in response to changes such as elevated atmospheric CO 2 concentrations and warming, it is essential for Earth system models to include increased realism to simulate CH 4 processes in a more mechanistic way. To address this need, we incorporated a new microbial‐functional group‐based CH 4 module into the Energy Exascale Earth System land model (ELM) and tested it with multiple observational data sets at an ombrotrophic peatland bog in northern Minnesota. The model is able to simulate observed land surface CH 4 fluxes and fundamental mechanisms contributing to these throughout the soil profile. The model reproduced the observed vertical distributions of dissolved organic carbon and acetate concentrations. The seasonality of acetoclastic and hydrogenotrophic methanogenesis—two key processes for CH 4 production—and CH 4 concentration along the soil profile were accurately simulated. Meanwhile, the model estimated that plant‐mediated transport, diffusion, and ebullition contributed to ∼23.5%, 15.0%, and 61.5% of CH 4 transport, respectively. A parameter sensitivity analysis showed that CH 4 substrate and CH 4 production were the most critical mechanisms regulating temporal patterns of surface CH 4 fluxes both under ambient conditions and warming treatments. This knowledge will be used to improve Earth system model predictions of these high‐carbon ecosystems from plot to regional scales.

58 GEOSCIENCES↗
DOE OSTI2022

Detecting the Presence of Intrusive Drilling in Secure Transport Containers Using Non-Contact Millimeter-Wave Radar

We employ a 77–81 GHz frequency-modulated continuous-wave (FMCW) millimeter-wave radar to sense anomalous vibrations during vehicle transport at highway speeds for the first time. Secure metallic containers can be breached during transport by means of drilling into their sidewalls but detecting a drilling signature is difficult because the large vibrations of transport drown out the small vibrations of drilling. For the first time, we demonstrate that it is possible to use a non-contact millimeter-wave radar sensor to detect this micron-scale intrusive drilling while highway-speed vehicle movement shakes the container. With the millimeter-wave radar monitoring the microdoppler signature of the container’s vibrating walls, we create a novel signal-processing pipeline consisting of range–angle tracking, time–frequency analysis, horizontal stripe image convolution, and principal component analysis to create a robust and powerful detection statistic to alarm if drilling is present. To support this pipeline, we develop a statistical model combining the vibrating container and the random vibrations induced by vehicle movement to explore the robustness of the sensor’s detection capabilities. The presented results strongly support the inclusion of a millimeter-wave radar vibration sensor into a transport security system.

Wagner, Samuel↗
DOE OSTI2019

Building a free-energy functional from atomically resolved imaging: Atomic-scale phenomena in La-doped BiFe O 3

Scanning transmission electron microscopy (STEM) has enabled mapping of atomic structures of solids with sub-picometer precision, providing insight to the physics of ferroic phenomena and chemical expansion. Yet, only a subset of information is available, due to projective nature of imaging in the beam direction. Correspondingly, the analysis often relies on the postulated form of macroscopic Landau-Ginzburg energy for the ferroic long-range order parameter, and some predefined relationship between experimentally determined atomic coordinates and the order-parameter field. Here, we propose an approach for exploring the structure of ferroics using reduced order-parameter models constructed based on experimental data only. We develop a four-sublattice model (FSM) for the analytical description of A -cation displacement in (anti)ferroelectric-antiferrodistortive perovskites of ABO 3 type. The model describes the displacements of cation A in four neighboring unit cells and determines the conditions of different structural phases’ appearance and stability in ABO 3 . We show that FSM explains the coexistence of rhombohedral, orthorhombic, and spatially modulated phases, observed by atomic-resolution STEM in La-doped BiFeO 3 . Using this method, we atomically resolve and theoretically model the sublattice asymmetry inherent to the case of the A -site La/Bi cation sublattice in La x Bi 1- x FeO 3 polymorphs. This approach allows the exploration of ferroic behaviors from experimental data only, without additional assumptions on the nature of the order parameter.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Growth-induced Donnan exclusion influences swelling kinetics in highly charged dynamic polymerization hydrogels

Polymeric gels crosslinked by DNA sequences can exploit DNA strand-displacement reactions to promote swelling through dynamic polymerization. The degree of swelling and the rate of swelling must be directly tunable to achieve the promise of programmable soft matter. Though the kinetics of the strand-displacement reaction provide insertion rates up to 10 4 /Molar/second as measured in bulk solution, DNA hydrogel swelling can take upwards of 30 h to complete. Computational modeling of the reaction-induced swelling of these gels with our recently-developed reactive electrochemomechanical theory (Zimmerman et al., 2024) suggests that their extraordinarily slow swelling is partly due to a scaling mismatch between the addition of charge and the addition of fluid volume, leading to a large transient increase in the fixed charge density. The significant increase in the gel’s fixed charge density, due to the binding of negatively charged DNA, sharply restricts the concentration of mobile hairpins through the phenomenon of Donnan charge exclusion, an effect commonly exploited in nanofiltration applications using polymeric membranes. The scaling problem is overcome when the mean additional swelling provided to the hydrogel by addition of a crosslink is above a critical value, thus the swelling outpaces the charge accumulation, leading the fixed charge density to drop and significantly accelerating the swelling process. This study shows that Donnan exclusion can explain the kinetics of DNA hydrogel swelling, and studies ways to modulate the reaction speed by either modifying the salt concentration or increasing or decreasing the number of base pairs in each DNA sequence.

42 ENGINEERING↗
DOE OSTI2023

Thermal energy storage in concrete utilizing a thermosiphon heat exchanger

The performance of a lab-scale concrete thermal energy storage (TES) module with a 2-kWh thermal capacity is evaluated at temperatures up to 400 °C. The TES module uses conventional normal weight concrete with thermal and mechanical properties that are tailored for use as a solid thermal energy storage media. A thermosiphon heat exchanger is used to provide rapid heat transfer into the concrete module, which is reinforced with a steel pipe outer shell. The charging and discharging cycles are examined to assess the thermal performance of the concrete and thermosiphon. Based on the results of testing, the thermosiphon is able to repeatably charge the concrete module from 300 to 400 °C over a 4-h period. After 2250 total hours of charge and discharge cycles, there was no observed delamination between the concrete and the thermosiphon or the steel jacket reinforcement. Non-destructive measurements of the concrete indicated no significant degradation of the solid storage media. The experimental results are then used to validate a numerical model of the concrete TES module for parametric study. Finally, varying insulation conditions were analyzed using this model to determine practical configurations for regular operation of the proposed concrete TES module.

25 ENERGY STORAGE↗
DOE OSTI2022

Solar Receiver with Integrated Thermal Storage for a Supercritical Carbon Dioxide Power Cycle

This project endeavors to design and demonstrate an integrated CSP system that meets the Department of Energy’s 6¢/kW-hr LCOE goal [1]. This design is based on the 10 MWe Supercritical Transformational Electric Power (STEP) engine specifications, but adaptable to any sCO 2 power module up to 10 MW e . The enabling technology to be tested is a new class of metal hydride based thermal energy storage materials which can store thermal energy in the hydrogen bonds between the metal hydride and hydrogen. This thermochemical energy storage solution provides a much greater energy density than sensible and latent energy storage enabling an integrated system design that closely linked the metal hydride to the power block. This allows the entire system to be mounted up tower to provide cost reductions. A novel metal hydride, capable of operating above 760 °C, was developed during the course of this project and thoroughly tested at a lab scale. The material’s hydrogen capacity, reaction enthalpy, thermal conductivity, and cyclic stability were measured. A demonstration test article was built that can store 2kWh of thermal energy and was coupled to Brayton Energy’s CO 2 loop where the performance of the metal hydride under commercial conditions was measured. An integrated model was created that uses the inputs from all the different sub-models in order to return an overall system LCOE using SAM. This project greatly advanced metal hydride based thermochemical energy storage technology and demonstrated its use. Previously there were no metal hydrides which were capable of operating at the proposed temperature limits (760°C), there is now a high temperature metal hydride with documented performance/stability and a path for low-cost production. Additionally, the metal hydride was tested at a scale >650x larger than what had previously been done in the lab. While not at commercial scale, it was an excellent demonstration of the technology and provided knowledge that can be leveraged for other applications.

08 HYDROGEN↗
DOE OSTI2020

The effect of dynamic near-wake modulation on utility-scale wind turbine wake development

High-resolution field-scale experiments using flow visualization with natural snowfall and high-fidelity large eddy simulations are combined to investigate the effect of dynamic turbine operation and atmospheric conditions on wind turbine wake mixing and recovery in the wake of a 2.5 MW wind turbine. Instantaneous near-wake expansion and deflection in response to changes in blade pitch and wind direction, termed dynamic wake modulation, is quantified using both techniques, demonstrating excellent agreement. The simulations are used to extend these results by calculating the energy flux into the wake 7 rotor diameters downstream, showing that dynamic turbine-atmospheric interactions enhance mixing in the far-wake. This finding is exhibited under both uniform and turbulent inflow conditions. Under turbulent flow, a synergistic relationship is also observed between dynamic wake modulation and wake meandering, as wake recovery can be further accelerated when the two phenomena occur together. The results of this study have implications for the development of more realistic far-wake models that include the significant impact of dynamic wake modulation on wake mixing and development. Additionally, the findings from the current study can be used to develop advanced control algorithms to speed up wake breakdown and recovery, further improving wind farm efficiency.

17 WIND ENERGY↗
DOE OSTI2019

Multiscale Solar Water Heating (MSWH) v1.0

The main purpose of the software is to model energy use for individual and community scale solar water heating projects in California. Usage The user provides a climate zone for a project, an occupancy for each household and whether any of the occupants stay at home during the day. The software can then load a set of example California specific hourly domestic hot water end-use load profiles from a database, size and locate the systems. The user can now simulate the hourly system performance over a period of one representative year, visualize and explore the simulation results using time-series plots for temperature profiles, heat and power rates, or look at annual summaries. Similarly the user can model individual household solar water heating projects and base case conventional gas tank water heater systems, such that the results can be compared between the individual, community and base case systems. This functionality is readily available through a Jupyter notebook and a Django web framework, depending on what level of detail the user would like to access. Features - Python module to calculate solar irradiation on a tilted surface - Python module with simplified component models for Converter (solar collectors, electric resistance heater, gas burner, photovoltaic panels, heat pump), Storage (solar thermal tank, heat pump thermal tank, conventional gas tank water heater), and Distribution (distribution and solar pump, piping losses) components - Python module with preconfigured system simulation models for: base case gas tank water heaters, solar thermal water heaters (solar collector feeding a storage tank, with a tankeless gas water heater backup in a new installation cases and a base case gas tank water heater in a retrofit case) and solar electric water heaters (heat pump storage tank with an electric resistance backup) - Database with component performance parameters, California specific weather data and domestic hot water end-use load profiles - Django web framework to configure project, parametrize components and run simulation from a web browser The package contains functional and unit tests and it is structured so that it can be extended with further technologies, applications and locations.

Grahovac, Milica↗
DOE OSTI2018

Battery Safety Testing

Abuse tests are designed to determine the safe operating limits of HEV\PHEV energy storage devices. Testing is intended to achieve certain worst-case scenarios to yield quantitative data on cell\module\pack response, allowing for failure mode determination and guiding developers toward improved materials and designs. Standard abuse tests with defined start and end conditions are performed on all devices to provide comparison between technologies. New tests and protocols are developed and evaluated to more closely simulate real world failure conditions. While robust mechanical models for vehicles and vehicle components exist, there is a gap for mechanical modeling of EV batteries. The challenge with developing a mechanical model for a battery is the heterogeneous nature of the materials and components (polymers, metals, metal oxides, liquids). Our work will provide empirical data on the mechanical behavior of batteries under compressive load to understand how a battery may behave in a vehicle crash scenario. This work is performed in collaboration with the U.S. Council for Automotive Research (USCAR) and Computer Aided Engineering of Batteries (CAEBAT). These programs have supported the design and development of a drop tower testing apparatus to close the gap between cell/string level testing and full scale crash testing with true dynamic rate effects.

25 ENERGY STORAGE↗
DOE OSTI2023

Spatio-Temporal Denoising Graph Autoencoders with Data Augmentation for Missing Photovoltaic Data Imputation

The integration of the global Photovoltaic (PV) market with real time data-loggers has enabled large scale PV data analytical pipelines for power forecasting and long-term reliability assessment of PV fleets. Nevertheless, the performance of PV data analysis heavily depends on the quality of PV timeseries data. This paper proposes a novel Spatio-Temporal Denoising Graph Autoencoder (STD-GAE) framework to impute missing PV Power Data. STDGAE exploits temporal correlation, spatial coherence, and value dependencies from domain knowledge to recover missing data. It is empowered by two modules. (1) To cope with sparse yet various scenarios of missing data, STD-GAE incorporates a domain-knowledge aware data augmentation module that creates plausible variations of missing data patterns. This generalizes STD-GAE to robust imputation over different seasons and environment. (2) STD-GAE nontrivially integrates spatiotemporal graph convolution layers (to recover local missing data by observed “neighboring” PV plants) and denoising autoencoder (to recover corrupted data from augmented counterpart) to improve the accuracy of imputation accuracy at PV fleet level. We have evaluated our proposed model on two realworld PV datasets. Experimental results show that STD-GAE can achieve a gain of 43.14% in imputation accuracy and remains less sensitive to missing rate, different seasons, and missing scenarios, compared with state-of-the-art data imputation methods such as MIDA and LRTC-TNN.

Fan, Yangxin↗
DOE OSTI2024

Variations in Bedrock and Vegetation Cover Modulate Subsurface Water Flow Dynamics of a Mountainous Hillslope

Abstract Predicting the hydrological response of watersheds to climate disturbances requires a detailed understanding of the processes connecting hillslopes and streams. Using a network of soil moisture and temperature sensors, electrical resistivity tomography monitoring, and a weather station we assess the above and below‐ground processes driving the hydrological response of a hillslope during snowmelt and summer monsoon. The transect covers bedrock and vegetation gradients, with a steep upper part characterized by shallow bedrock, and gentle lower part underlain by colluvium. The main vegetation cover is conifers on the upper, and grass and veratrum on the lower part. Combined with a simplified hydrological model, we show that the thin soil layer of the steep slope acts as a preferential flow path, leading to mostly shallow lateral flow, interrupted by vertical flow, mostly at tree locations, and likely facilitated by flow along fractures and roots. Vertical flow and upstream‐driven groundwater dynamics are prevailing at the colluvium, presenting a very different hydrological behavior compared to the upper part. These results show that subsurface structure and features have a strong control on the hydrological response of a hillslope and that those can create considerably varying hydrological dynamics across small spatial scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Large-Signal Impedance Modeling of Three-Phase Voltage Source Converters

Resonance problems have become a major hurdle in the large-scale grid integration of renewable energy. It is important to be able to predict the amplitude of resonance-induced distortions for the protection and control design of power electronic converters. We recently presented an impedance-based approach for the prediction of resonance amplitude. It was shown that the impedance response of a converter starts changing with the amplitude of resonance; this blocks the amplitude growth beyond a certain point where the converter forms a limit cycle mode of sustained oscillations. The large-signal impedance of a converter captures the change in its impedance response with the resonance amplitude, and it can be used to predict the resonance amplitude under different operation conditions. This paper presents large-signal sequence impedance modeling for three-phase voltage source converters (VSC) with dq-current control and PLL. Modeling challenges encountered in the absence of the small-signal approximation are addressed by a) leveraging the dominating influence of hard nonlinearities such as pulse-width modulation saturation and limiters over soft nonlinearities in shaping the converter large-signal behavior and b) modeling the large-signal gain of hard nonlinearities using appropriate describing functions. Developed large-signal impedance models are validated using numerical simulations. Measurements of the large-signal impedance responses of a commercial 1-MW VSC-based inverter are also presented to experimentally demonstrate the effects of the injected perturbation amplitude on impedance response as predicted by the developed model.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Verification Testing of OLI Systems Mixed Solvent Electrolyte Model for the Na-K-Mg-Ca-H-Cl-SO 4 -OH-HCO 3 -CO 3 -CO 2 -H 2 ) System to High Ionic Strength at 25°C

This technical report summarizes model verification results and summary statistics for 41 evaporite mineral solubility cases evaluated by Savannah River National Laboratory using OLI Systems’ aqueous electrolyte thermodynamic modeling software. The 41 verification cases containing a total of 60 solubility curves comprise mineral solubility data from low to high ionic strength at 25°C for the eight-component system Na-K-Mg-Ca-H-Cl-SO 4 -OH-HCO 3 -CO 3 -CO 2 -H 2 O as reported by Harvie et al. (1984). Thermodynamic calculations were executed using OLI Systems’ Stream Analyzer computation module within the OLI Studio software platform (Ver. 11.0, Rev. 11.0.1.9). The Mixed Solvent Electrolyte (MSE) thermodynamic framework was chosen for this investigation because of its superiority in modeling high ionic-strength inorganic salt solutions and actinide redox chemistry and solubility, both of which are relevant to the geological repository conditions at the Waste Isolation Pilot Plant in Carlsbad, New Mexico. Mineral solubility data in various inorganic salt solutions were digitized and extracted from figures generated by Harvie et al. (1984). For each of the 60 solubility curves, a case-specific chemistry model and input file were generated in OLI Studio using OLI Stream Analyzer and the MSE (H 3 O + ion) public databank provided by OLI Systems. Model simulation results were exported to Microsoft Excel to calculate summary statistics and to generate graphs comparing the OLI model predictions to the solubility data. Summary statistics include residuals (model – data) and concordance (accuracy × precision, where precision is indicated by the Pearson correlation coefficient and accuracy accounts for bias and scale differential). Private databanks were not developed, and activity coefficient model regressions were not performed to improve OLI model fits to the data. Of the 41 model verification plots, 83% have a mean of the percent residuals less than or equal to 25%. Similarly, 75% display a concordance greater than or equal to 0.75. Only seven of the 41 verification plots fail to show good agreement between the model and data. Of these seven, three are relevant to the WIPP repository because they involve the Mg-OH-Cl-SO 4 -CO 3 aqueous system. The remaining four address salt solubilities at the pH extremes (strong acid and strong base). It should be noted that in two of the three Mg-OH-Cl-SO 4 -CO 3 system cases, the regressed Harvie et al. (1984) solubility curve also deviated from the data. Lack of agreement between the OLI model-predicted solubility curves and the data is attributable to one or more of the following: specific solid species are not included in the OLI MSE databank; there is significant variation among the different solubility datasets chosen by Harvie et al. (1984); the OLI MSE model’s thermodynamic parameters were determined using different solubility datasets; and the activity coefficient parameters for certain relevant ion-ion and ion-molecule pairs have not been optimized via data regression. Two recommendations for future work are to (1) evaluate solubility data for the Mg-OH-Cl-SO 4 -CO 3 system at high ionic strength and, if necessary, develop a private OLI MSE database that includes missing species and, where necessary, regressed standard state properties and interaction parameters; (2) perform similar verification testing of the OLI model for actinide solubility data.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗