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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Classification of bacterial plasmid and chromosome derived sequences using machine learning

Plasmids are important genetic elements that facilitate horizonal gene transfer between bacteria and contribute to the spread of virulence and antimicrobial resistance. Most bacterial genome sequences in the public archives exist in draft form with many contigs, making it difficult to determine if a contig is of chromosomal or plasmid origin. Using a training set of contigs comprising 10,584 chromosomes and 10,654 plasmids from the PATRIC database, we evaluated several machine learning models including random forest, logistic regression, XGBoost, and a neural network for their ability to classify chromosomal and plasmid sequences using nucleotide k-mers as features. Based on the methods tested, a neural network model that used nucleotide 6-mers as features that was trained on randomly selected chromosomal and plasmid subsequences 5kb in length achieved the best performance, outperforming existing out-of-the-box methods, with an average accuracy of 89.38% ± 2.16% over a 10-fold cross validation. The model accuracy can be improved to 92.08% by using a voting strategy when classifying holdout sequences. In both plasmids and chromosomes, subsequences encoding functions involved in horizontal gene transfer—including hypothetical proteins, transporters, phage, mobile elements, and CRISPR elements—were most likely to be misclassified by the model. This study provides a straightforward approach for identifying plasmid-encoding sequences in short read assemblies without the need for sequence alignment-based tools.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Building Trustworthy Machine Learning Models for Astronomy

Astronomy is entering an era of data-driven discovery, due in part to modern machine learning (ML) techniques enabling powerful new ways to interpret observations. This shift in our scientific approach requires us to consider whether we can trust the black box. Here, we overview methods for an often-overlooked step in the development of ML models: building community trust in the algorithms. Trust is an essential ingredient not just for creating more robust data analysis techniques, but also for building confidence within the astronomy community to embrace machine learning methods and results.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Large-scale molecular dynamics simulations of bubble collapse in water: Effects of system size, water model, and nitrogen

Molecular dynamics simulations in the microcanonical ensemble are performed to study the collapse of a bubble in liquid water using the single-site mW and the four-site TIP4P/2005 water models. To study system size effects, simulations for pure water systems are performed using periodically replicated simulation boxes with linear dimensions, L, ranging from 32 to 512 nm with the largest systems containing 8.7 × 10 6 and 4.5 × 10 9 molecules for the TIP4P/2005 and mW water models, respectively. The computationally more efficient mW water model allows us to reach converging behavior when the bubble dynamics results are plotted in reduced units, and the limiting behavior can be obtained through linear extrapolation in L –1 . Qualitative differences are observed between simulations with the mW and TIP4P/2005 water models, but they can be explained by the models’ differences in predicted viscosity and surface tension. Although bubble collapse occurs on time scales of only hundreds of picoseconds, the system sizes used here are sufficiently large to obtain bubble dynamics consistent with the Rayleigh–Plesset equation when using the models’ thermophysical properties as input. For the conditions explored here, extreme heating of the interfacial water molecules near the time of collapse is observed for the larger mW water systems (but the model underpredicts the viscosity), whereas heating is less pronounced for the TIP4P/2005 water systems because its larger viscosity contribution slows the collapse dynamics. The presence of nitrogen within the bubble only starts to affect bubble dynamics near the very end of the initial collapse, leading to an incomplete collapse and strong rebound for the mW water model. Although nitrogen is non-condensable at 300 K, it becomes highly compressed and reaches a liquid-like density near the collapse point. We find that the dissolution of nitrogen is much slower than the movement of the collapsing water front, and the re-expansion of the dense nitrogen droplet gives rise to bubble rebound. The incompatibility of the collapse and dissolution time scales should be considered for continuum-scale modeling of bubble dynamics. Finally, we also confirm that the diffusion coefficient for dissolved nitrogen is insensitive to pressure as the liquid transitions from a compressed to a stretched state.

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

Accurate predictions from small boxes: variance suppression via the Zel'dovich approximation

Simulations have become an indispensable tool for accurate modelling of observables measured in galaxy surveys, but can be expensive if very large dynamic range in scale is required. We describe how to combine Lagrangian perturbation theory models with N-body simulations to reduce the effects of finite computational volume in the prediction of ensemble average properties in the simulations within the context of control variates. In particular we use the fact that Zel'dovich displacements, computed during initial condition generation for any simulation, correlate strongly with the final density field. Since all the correlators of biased tracers can be computed with arbitrary precision for these displacements, pairing the Zel'dovich `simulation' with the N-body realization allows hundredfold reductions in sample variance for power spectrum or correlation function estimation. Additionally, Zel'dovich control variates can accurately extend matter or tracer field emulators to larger scales than previously possible, as well as improving measurements of statistics in simulations which are inherently limited to small volumes, such as hydrodynamical simulations of galaxy formation and reionization.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Quantifying the Impact of Vertical Resolution on the Representation of Marine Boundary Layer Physics for Global-Scale Models

Abstract While GCM horizontal resolution has received the majority of scale improvements in recent years, ample evidence suggests that a model’s vertical resolution exerts a strong control on its ability to accurately simulate the physics of the marine boundary layer. Here we show that, regardless of parameter tuning, the ability of a single-column model (SCM) to simulate the subtropical marine boundary layer improves when its vertical resolution is improved. We introduce a novel objective tuning technique to optimize the parameters of an SCM against profiles of temperature and moisture and their turbulent fluxes, horizontal winds, cloud water, and rainwater from large-eddy simulations (LES). We use this method to identify optimal parameters for simulating marine stratocumulus and shallow cumulus. The novel tuning method utilizes an objective performance metric that accounts for the uncertainty in the LES output, including the covariability between model variables. Optimization is performed independently for different vertical grid spacings and value of time step, ranging from coarse scales often used in current global models (120 m, 180 s) to fine scales often used in parameterization development and large-eddy simulations (10 m, 15 s). Uncertainty-weighted disagreement between the SCM and LES decreases by a factor of ∼5 when vertical grid spacing is improved from 120 to 10 m, with time step reductions being of secondary importance. Model performance is shown to converge at a vertical grid spacing of 20 m, with further refinements to 10 m leading to little further improvement. Significance Statement In successive generations of computer models that simulate Earth’s atmosphere, improvements have been mainly accomplished by reducing the horizontal sizes of discretized grid boxes, while the vertical grid spacing has seen comparatively lesser refinements. Here we advocate for additional attention to be paid to the number of vertical layers in these models, especially in the model layers closest to Earth’s surface where climatologically important marine stratocumulus and shallow cumulus clouds reside. Our experiments show that the ability of a one-dimensional model to represent physical processes important to these clouds is strongly dependent on the model’s vertical grid spacing.

Meteorology & Atmospheric Sciences↗
DOE OSTI2022

Development of a simplified calibrated building simulation model of a supermarket for proposed ECMs and control strategies impact evaluation

Calibrated building energy simulation is an important pathway to more energy-efficient buildings, but the information requirements of some approaches to this problem are significant. This is particularly true for supermarkets and other so-called “big-box” retail stores. Another characteristic of supermarkets is the significant interaction between Heating Ventilating and Air Conditioning (HVAC) and refrigeration systems in these buildings. These buildings could contain a wide variety of systems and a degree of load diversity that makes calibrated modeling a challenge. This paper describes a simplified approach that uses OpenStudio and EnergyPlus to combine known building parameters with “typical” parameters, resulting in a simplified building that is amenable to calibration. This approach was applied to a big-box store located in Nashville, Tennessee, and a calibrated model was obtained that was used to study potential energy conservation measures. Further, the paper also explores the capabilities of whole-building energy modeling tools, such as EnergyPlus, for modeling the HVAC controls and sequences and their impact evaluation. Although some measures are precluded by the model simplicity, several measures were found to improve the efficiency of the model and demonstrate that the simplified modeling approach is effective. Practical Application: This paper introduces a hybrid approach of building energy model calibration using limited information available from the actual building in combination with characteristics of a “typical” building of the same type. This hybrid approach would also be applicable for other building types than discussed in this paper to calibrate the building energy model using limited information from the actual building.

42 ENGINEERING↗
DOE OSTI2023

Multi-objective Bayesian alloy design using multi-task Gaussian processes

In design applications, correlations among material properties (such as the tendency for stronger materials to be less ductile) are often neglected. This approach is echoed in multi-objective optimization techniques which treat each performance characteristic as an independent objective, aiming to optimize scalar functions and find optimal Pareto fronts. However, this overlooks the statistical relationships between performance characteristics inherent in a material system. To address this, we propose the use of Bayesian optimization, a highly efficient black-box optimization algorithm known for constructing Gaussian processes (GPs) – uncorrelated surrogates - to model objective functions. Rather than evaluating multiple GPs for each objective function separately, we argue for a shift towards jointly modeling these objective functions, considering their statistical correlations. This integrated approach utilizes naturally occurring relationships among material properties, providing additional information to enhance the performance of the design framework. This requires the replacement of multiple independent GPs with a single multi-task GP, employing a correlation matrix to construct a multi-task kernel function, wherein each task corresponds to a single objective function. Here, we anticipate this refined methodology will better leverage material correlations, improving design optimization results.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Scoping Thermal Response Calculations of RNS Waste During Transport to and Disposal at the WIPP

Sandia National Laboratories (SNL) was contracted by the United States Department of Energy Environmental Management (DOE-EM), Los Alamos Field Office to perform mechanical and thermal scoping calculations as part of a study seeking to understand the ignitability risk of the Remediated Nitrate Salts (RNS) waste drums during transportation from the Waste Control Specialists (WCS) facility to Waste Isolation Pilot Plant (WIPP) and permanent disposal of the waste at WIPP. The scoping thermal simulations described in this report pertain to thermal calculations performed with a packaging system consisting of one Standard Waste Box (SWB) loaded with drums placed inside a Standard Large Box 2 (SLB2). During transportation, the SLB2 is inside Transuranic Package Transporter Model III (TRUPACT-III), which provides the third layer of the packaging. Once at the WIPP, it is assumed the SLB2 is extracted from the TRUPACT-III and maintained above ground, and then subsequently placed underground for permanent disposal. In these proposed configurations, the space between the SLB2 and the SWB is always filled by a layer of insulation consisting of air-filled glass microbubbles except for the bottom which rests directly on the SLB2. The thermal scoping calculations described in this report specifically address whether the introduction of external heat inputs, combined with the contributions from the internally generated radiolytic decay heat and chemical reactions, lead to an unstable thermal state during the time of its movement and placement in the permanent disposal location. The external heat inputs are of two forms: 1) ambient thermal irradiation (e.g., solar and ambient storage/disposal temperatures) and 2) accident-induced fire. Three scoping calculation scenarios were derived as representative, conservative scenarios: 1A) TRUPACT-III transient transportation, 1B) SLB2 48-hour outdoor storage with solar radiation, and 2) fully-engulfing fire during SLB2 handling or emplacement following a steady-state analysis in a 38 °C environment. All the simulated scenarios are conservative relative to the operational conditions expected for handling the waste package during transportation and placement in the WIPP underground disposal unit. The predictions obtained from simulating the three exposure scenarios revealed that adding the SLB2 and the air-filled glass microbubbles to the transport and storage/disposal configurations provides additional thermal protection of the drums beyond what the SWB provides alone, both during long-term above ground insolation and underground during a fire accident. Under the current transportation/storage/disposal concepts, the degree of protection provided by the packaging concept is sufficient to prevent the waste from being ignitable. The simulation results demonstrate that there is adequate margin to safely transport and place the RNS waste from WCS to the WIPP under the current operational concept.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Canonical and noncanonical Hamiltonian operator inference

Here, a method for the nonintrusive and structure-preserving model reduction of canonical and noncanonical Hamiltonian systems is presented. Based on the idea of operator inference, this technique is provably convergent and reduces to a straightforward linear solve given snapshot data and gray-box knowledge of the system Hamiltonian. Examples involving several hyperbolic partial differential equations show that the proposed method yields reduced models which, in addition to being accurate and stable with respect to the addition of basis modes, preserve conserved quantities well outside the range of their training data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

KGML-ag: a modeling framework of knowledge-guided machine learning to simulate agroecosystems: a case study of estimating N<sub>2</sub>O emission using data from mesocosm experiments

Abstract. Agricultural nitrous oxide (N2O) emission accounts for a non-trivial fraction of global greenhouse gas (GHG) budget. To date, estimating N2O fluxes from cropland remains a challenging task because the related microbial processes (e.g., nitrification and denitrification) are controlled by complex interactions among climate, soil, plant and human activities. Existing approaches such as process-based (PB) models have well-known limitations due to insufficient representations of the processes or uncertainties of model parameters, and due to leverage recent advances in machine learning (ML) a new method is needed to unlock the “black box” to overcome its limitations such as low interpretability, out-of-sample failure and massive data demand. In this study, we developed a first-of-its-kind knowledge-guided machine learning model for agroecosystems (KGML-ag) by incorporating biogeophysical and chemical domain knowledge from an advanced PB model, ecosys, and tested it by comparing simulating daily N2O fluxes with real observed data from mesocosm experiments. The gated recurrent unit (GRU) was used as the basis to build the model structure. To optimize the model performance, we have investigated a range of ideas, including (1) using initial values of intermediate variables (IMVs) instead of time series as model input to reduce data demand; (2) building hierarchical structures to explicitly estimate IMVs for further N2O prediction; (3) using multi-task learning to balance the simultaneous training on multiple variables; and (4) pre-training with millions of synthetic data generated from ecosys and fine-tuning with mesocosm observations. Six other pure ML models were developed using the same mesocosm data to serve as the benchmark for the KGML-ag model. Results show that KGML-ag did an excellent job in reproducing the mesocosm N2O fluxes (overall r2=0.81, and RMSE=3.6 mgNm-2d-1 from cross validation). Importantly, KGML-ag always outperforms the PB model and ML models in predicting N2O fluxes, especially for complex temporal dynamics and emission peaks. Besides, KGML-ag goes beyond the pure ML models by providing more interpretable predictions as well as pinpointing desired new knowledge and data to further empower the current KGML-ag. We believe the KGML-ag development in this study will stimulate a new body of research on interpretable ML for biogeochemistry and other related geoscience processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Solving sparse finite element problems on neuromorphic hardware

The finite element method (FEM) is one of the most important and ubiquitous numerical methods for solving partial differential equations (PDEs) on computers for scientific and engineering discovery. Applying the FEM to larger and more detailed scientific models has driven advances in high-performance computing for decades. Here we demonstrate that scalable spiking neuromorphic hardware can directly implement the FEM by constructing a spiking neural network that solves the large, sparse, linear systems of equations at the core of the FEM. We show that for the Poisson equation, a fundamental PDE in science and engineering, our neural circuit achieves meaningful levels of numerical accuracy and close to ideal scaling on modern, inherently parallel and energy-efficient neuromorphic hardware, specifically Intel’s Loihi 2 neuromorphic platform. We illustrate extensions to irregular mesh geometries in both two and three dimensions as well as other PDEs such as linear elasticity. Our spiking neural network is constructed from a recurrent network model of the brain’s motor cortex and, in contrast to black-box deep artificial neural network-based methods for PDEs, directly translates the well-understood and trusted mathematics of the FEM to a natively spiking neuromorphic algorithm.

Applied mathematics↗
DOE OSTI2020

Editorial: Plant-Soil Interactions Under Changing Climate

The health and well-being of plants and soil is crucial for all life on Earth. It is well-known that vegetation cover follows climatic zones, and plants respond to climatic drivers such as temperature and precipitation. It is also well-known that plant health depends on the properties and health of the soil, and that strong interactions among biota above and belowground dictate the functioning of both realms. Yet, soils and the processes occurring belowground are often considered a “black box,” and are treated very simplistically in our efforts to understand, quantify, and model the future of the planet. Our understanding of the interactions between plants and soils is also far from complete and offers some of the most important research frontiers in community ecology, biogeochemistry, and global change science.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

A Hybrid Gradient Method to Designing Bayesian Experiments for Implicit Models

Bayesian experimental design (BED) aims at designing an experiment to maximize the information gathering from the collected data. The optimal design is usually achieved by maximizing the mutual information (MI) between the data and the model parameters. When the analytical expression of the MI is unavailable, e.g.,having implicit models with intractable data distributions, a neural network-based lower bound of the MI was recently proposed and a gradient ascent method was used to maximize the lower bound [1]. However, the approach in [1] requires a pathwise sampling path to compute the gradient of the MI lower bound with respect to the design variables, and such a pathwise sampling path is usually inaccessible for implicit models. In this work, we propose a hybrid gradient approach that leverages recent advances in variational MI estimator and evolution strategies (ES)combined with black-box stochastic gradient ascent (SGA) to maximize the MI lower bound. This allows the design process to be achieved through a unified scalable procedure for implicit models without sampling path gradients. Several experiments demonstrate that our approach significantly improves the scalability of BED for implicit models in high-dimensional design space.

Zhang, Jiaxin↗
DOE OSTI2024

Time Dilated Bundt Cake Analysis of PV Output [Poster]

We present a novel method for modeling time-dependent statistics in the power signal generated by a photovoltaic (PV) system. Our white-box machine learning method is interpretable and auditable, based on principles of multiperiodic basis functions and convex optimization. Our proposed method of time dilating the daily signal to remove night time values results in a novel representation of PV power signals, evocative of a ‘Bundt cake’. The proposed model describes the marginal distribution of power output as a function of date and time. The resulting probabilistic model of a PV system can be used to perform a variety of tasks, and here, we demonstrate the application of clear sky detection.

14 SOLAR ENERGY↗
DOE OSTI2026

Adaptive Computing and Multi-Fidelity Learning

We describe our ongoing research in adaptive computing. Our goal is to use a combination of low- and high-fidelity simulation models to enable computationally efficient optimization and uncertainty quantification. We develop optimization formulations that take into account the compute resources currently available, which act as a constraint with regards to the fidelity level simulation we can run while maximizing information gain. We will discuss a few application examples that can benefit from this approach, especially when considering challenges arising in scaling up experiments and simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Deep Generative Models that Solve PDEs: Distributed Computing for Training Large Data-Free Models

Recent progress in scientific machine learning (SciML) has opened up the possibility of training novel neural network architectures that solve complex partial differential equations (PDEs). Several (nearly data free) approaches have been recently reported that successfully solve PDEs, with examples including deep feed forward networks, generative networks, and deep encoder-decoder networks. However, practical adoption of these approaches is limited by the difficulty in training these models, especially to make predictions at large output resolutions (≥1024×1024). Here we report on a software framework for data parallel distributed deep learning that resolves the twin challenges of training these large SciML models - training in reasonable time as well as distributing the storage requirements. Our framework provides several out of the box functionality including (a) loss integrity independent of number of processes, (b) synchronized batch normalization, and (c) distributed higher-order optimization methods. We show excellent scalability of this framework on both cloud as well as HPC clusters, and report on the interplay between bandwidth, network topology and bare metal vs cloud. We deploy this approach to train generative models of sizes hitherto not possible, showing that neural PDE solvers can be viably trained for practical applications. We also demonstrate that distributed higher-order optimization methods are 2-3× faster than stochastic gradient-based methods and provide minimal convergence drift with higher batch-size.

PDEs↗
DOE OSTI2023

ReaxFF reactive force field model enables accurate prediction of physiochemical and mechanical properties of crystalline and amorphous shape‐memory polyurethane

Abstract We investigate reactive force field (ReaxFF) prediction of physiochemical and mechanical properties, and stimuli response behavior of a shape‐memory polyurethane (SMPU) rigid segment. We used SMPUs as a platform to link the rigid/hard segment domain crystallinity with properties that can also be experimentally measured. Specifically, we developed models for crystalline and amorphous forms of a common rigid segment of SMPUs, for example, 4,4′‐diphenylmethane diisocyanate (MDI) with n‐butanediol (BDO), denoted as MDI‐BDO. ReaxFF molecular dynamics simulations with equilibrium dynamics are performed by controlling mass, temperature, and pressure or volume to predict the structural and physiochemical properties of crystalline and amorphous rigid segments of SMPU systems, followed by simulating their XRD and FTIR patterns, respectively. Non‐equilibrium ReaxFF simulations are implemented by using uniaxial box deformation technique to study response‐behavior of crystalline SMPU under tensile loading and during stress‐relaxation. The model and simulation scheme presented in this study demonstrates a robust theoretical pathway toward application‐dictated, on‐demand design of high‐performance SMPUs in applications such as soft robotics.

Afroz, Mohammad M.↗
DOE OSTI2024

Effect of Anoxic Iron Corrosion on WIPP Brine Geochemistry FY23 Final Report (U)

A 280-day study was completed to evaluate the effect of zero-valent iron (Fe 0 ) on the Waste Isolation Pilot Plant (WIPP) brine geochemistry under anticipated reducing conditions. Hydrogen (H 2 ) gas is expected to be present in the repository after closure due to the anoxic corrosion of a vast quantity of iron contained in the waste forms disposed at WIPP; therefore, a background argon atmosphere containing H 2 was chosen for this study. WIPP groundwater brine pH and E h will impact the mobility and fate of plutonium within the repository. Modeling and laboratory results for Castile WIPP brine indicate that equilibrium fa values relative to the standard hydrogen electrode (SHE) are 40 mV more reducing (i.e., more negative) than those for Salado WIPP brine (-480 mV vs. -440 mV, respectively) because of the higher pH of the Castile brine (pH 9 .3 for Castile vs. pH 8.8 for Salado). The E h and pH data were corrected for the effects of high ionic strength. The experimental results for both brines are consistent with thermodynamic predictions using OLI Systems' Mixed Solvent Electrolyte chemical equilibrium model. The measured and corrected pH and E h data from this study are provided in Table ES-I and Table ES-2, respectively. The experimental study, with four test conditions in triplicate, was performed in a dual glovebox with a nominally 3 vol.% H 2 in argon atmosphere (target H 2 range: 3 ± I vol.%). Simulants containing MgO only ( experimental control) and MgO+Fe 0 (WIPP base case) were prepared for both the Salado and Castile brines. MgO was included in all simulants to account for the use of bulk magnesium oxide in the WIPP repository. Fe 0 was included in some simulants to incorporate the effects of the anoxic corrosion of iron and in-situ hydrogen generation in the study. The brine compositions were developed by Sandia National Laboratory (SNL; Xiong, 2008) and have been used in previous WIPP evaluations. The test method (agitation, etc.) is partially based on ASTM D3987-12. Twelve rounds of periodic measurements of pH and E h were performed over the course of the study. Chemical analysis results for liquids and solids (ICP-MS, ICP-ES, IC Anion, TIC, SEM-EDX) are consistent with the pH, E h , and thermodynamic modeling results. This study included the following conditions that deviate from anticipated post-closure conditions following brine intrusion, but were selected to facilitate bench-scale testing to validate modeling of pH and E h for the post-closure WIP P repository: an anoxic glove box atmosphere containing ≤ 4 vol. % H 2 vs. substantially higher H 2 gas concentrations assumed in the WIPP Performance Assessment (PA); a significantly higher liquid-to-solid test ratio compared to the much lower phase ratio anticipated in the WIP P repository; agitation of the simulant bottles to maximize mass transfer; and finally the use of Fe 0 reagents having a much greater surface area than expected in the WIP P repository. Non-representative conditions were chosen for various reasons such as: to provide bounding conservative results, to provide a margin of safety for testing, or to facilitate simulant sub-sampling and analysis. In a parallel effort, aqueous electrolyte thermodynamic models were developed for the synthetic Salado and Castile brines to inform the experimental design, facilitate laboratory data interpretation, and allow extension of evaluations beyond the parameters tested. Thermodynamic modeling simulations including the MgO and Fe 0 additives that are directly relevant to the experimental measurements (e.g., pH calibration curve, ORP corrections) are included in this report. The measured fa of the simulants was close to the OLI model predictions for both brines and was largely controlled by the background H 2 partial pressure in the vapor phase as well as H 2 generated in situ in the aqueous phase by the Fe 0 corrosion. The H 2 gas-phase concentration tested and thermodynamically evaluated was much lower than is assumed in the WIPP PA; however, H 2 (g) concentrations significantly below this level are still predicted to result in very reducing conditions. In conclusion: • The experimental results are consistent with thermodynamic model predictions for fa, pH, and the effects of high ionic strength. • Evidence to date suggests that the H2 concentration in the glovebox atmosphere ultimately determined the final E h values of the simulants and resulted in highly reducing conditions. As a result, little difference was observed between the control simulants containing only MgO and the WIPP base-case simulants that contained MgO and Fe 0 . • This test methodology is recommended for future studies evaluating WIPP repository conditions. The methodology includes: (1) background H 2 in argon with agitation ( or could alternatively include in-situ-generated H 2 in sealed bottles); (2) carefully measured and corrected ORP data ( with much effort focused on allowing the probes to fully stabilize); and (3) ionic-strength-corrected pH data. Other best practices, such as simulant sparging/handling, ORP probe replacement, etc., should also be considered. • The coupling of experimental studies and thermodynamic modeling is also highly recommended because these methods inform and direct one another leading to greater confidence in and understanding of the results.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗