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Solvation thermodynamics in molten salt is accurately and efficiently predicted by combining ab initio molecular dynamics (AIMD) simulations, deep neural network interatomic potentials (NNIP), and quasichemical theory (QCT).
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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.
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Density functional theory-based thermodynamic modeling was performed to determine the effect of humidity and H 2 /O 2 gas pressure on the defect chemistry and transport properties of proton conducting oxide BaZr 1-x Y x O 3-δ (x ≤ 0.1) in the temperature range of 800–1200K, relevant for solid oxide fuel/electrolysis cell applications. The first-principles charge defect analysis was carried out to obtain the defect energetic ensembles as a function of Fermi level as well as the dependence of the hydration and oxidation reaction energies on complex defect-dopant configurations. It is shown that oxygen vacancies introduced to compensate for the extra Ba vacancies may cause a slight increase in the concentration of hydroxyl and proton species upon hydration of Ba deficient BaZr 1-x Y x O 3-δ , while the overall proton diffusivity is predicted to decrease with increased proton hopping barriers upon trapping near the Ba vacancies. The developed defect model is further demonstrated to be able to describe the bulk defect chemistry and transport properties of BaZr 0.9 Y 0.1 O 3-δ under the solid oxide cell operating conditions. Finally, the theoretical framework developed in this work further allows the inclusion of configurational, energetic and electronic characteristics of the defects that can be used as a complementary tool to experimental measurements for testing various mechanistic pathways or to provide essential mechanistic data.
To address the need for physics-based models to predict corrosion behavior of materials in molten salts, here we propose potential methods to aid in selection of optimum materials for structural components in molten salt powered technologies. In the present work, the role of alloy thermodynamics and kinetics on governing corrosion rates of Ni-based alloys will be discussed combining experimental and computational methods. A few strategies are presented to quantify corrosion rates of Ni-based materials isothermally exposed in purified KCl-MgCl 2 salts at 600° C-800° C. The influence of capsule material, potential corrosion products and role of alloy composition on the observed corrosion rates was discussed with coupled thermodynamic-kinetic models. Larger depths of corrosion attack were observed in alloy 230 specimens compared to other alloys under similar conditions was attributed to the much higher chemical activity of Cr in the alloy that results in a larger Cr chemical potential gradient.
Geologic hydrogen (GeoH 2 ) is emerging as a viable clean energy source. It is largely produced through serpentinization, a geological process in which ultramafic rocks react with water under suitable temperature and pressure. Here, this review synthesizes the current understanding of H 2 generation by serpentinization, with an emphasis on reaction mechanisms, kinetics, and thermodynamics, as well as on modeling flow and transport of reacting fluids in geological formations. We describe the role of mineral assemblages, such as olivine and pyroxene, fluid-rock interactions, and catalytic surfaces, in influencing GeoH 2 yield and reaction rates. By integrating models of reaction kinetics, subsurface reactive flow and transport, and the serpentinization process, and by accounting for the thermodynamic state of the system, this review aims to guide future GeoH 2 research and to evaluate the potential of natural hydrogen as a sustainable clean energy source.
In this study, we leverage the massive amount of multi-modal on-machine metrology data generated from Laser Powder Directed Energy Deposition (LP-DED) to construct a comprehensive surrogate model of the 3D printing process. By employing Dynamic Mode Decomposition with Control (DMDc), a data-driven technique, we capture the complex physics inherent in this extensive dataset. This physics-based surrogate model emphasizes thermodynamically significant quantities, enabling us to accurately predict key process outcomes. The model ingests 21 process parameters, including laser power, scan rate, and position, while providing outputs such as melt pool temperature, melt pool size, and other essential observables. Furthermore, it incorporates uncertainty quantification to provide bounds on these predictions, enhancing reliability and confidence in the results. We then deploy the surrogate model on a new, unseen part and monitor the printing process as validation of the method. Our experimental results demonstrate that the predictions align with actual measurements with high accuracy, confirming the effectiveness of our approach. Furthermore, this methodology not only facilitates real-time predictions but also operates at process-relevant speeds, establishing a basis for implementing feedback control in LP-DED.
Understanding the governing principles behind organisms’ metabolism and growth underpins their effective deployment as bioproduction chassis. A central objective of metabolic modeling is predicting how metabolism and growth are affected by both external environmental factors and internal genotypic perturbations. The fundamental concepts of reaction stoichiometry, thermodynamics, and mass action kinetics have emerged as the foundational principles of many modeling frameworks designed to describe how and why organisms allocate resources towards both growth and bioproduction. Furthermore, this review focuses on the latest algorithmic advancements that have integrated these foundational principles into increasingly sophisticated quantitative frameworks.
U-Nb’s discontinuous precipitation, $\gamma^{bcc}_{matrix}$, $\alpha^{orth}_{cellular}$, $\gamma\prime^{bcc}_{cellular}$, is intriguing in the sense that it allows formation and growth of the metastable $\gamma\prime$ phase during the course of its occurrence. Previous attempts to explain the thermodynamic origin of U-Nb’s discontinuous precipitation hypothesized that the energy of $\alpha$ forms an intermediate common tangent with the first potential of the double-well energy of $\gamma$ at the $\gamma\prime$ composition. In this work, we examine different possible mechanisms by which the discontinuous precipitation product in the U-Nb system can be stabilized. We put forward a mechanism by which the bulk free energy of the $\gamma$ can develop a non-monotonic dependency with composition. Additionally we posit that local contributions due to lattice mismatch between the $\alpha$ and $\gamma$ phases may be responsible for the generation of metastable states that may stabilized by thermodynamics as well as by kinetics. Our work suggests that local misfit strain tends to play a crucial role in the growth of the discontinuous precipitation product. Depending on the magnitude of strain developed at the $\alpha$/$\gamma\prime$ interfaces, either an increasing $\gamma\prime$ composition or a random distribution of $\gamma\prime$ compositions around the equiatomic value with respect to increasing temperature could be expected. Moreover, we show how it is possible to stabilize the discontinuous precipitation front through highly anisotropic and fast interface diffusion.
In U.S. district heating (DH) systems, steam is the most common heat transport medium. Industry demand for new advanced modeling capabilities of complete steam DH systems is increasing; however, the existing models for water/steam thermodynamics are too slow for large system simulations because of computationally expensive algebraic loops that require the solution to nonlinear systems of equations. For practical applications, this work presents a novel split-medium approach that implements numerically efficient liquid water models alongside various water/steam models, breaking costly algebraic loops by decoupling mass and energy balance equations. New component models for steam DH systems are also presented. We implemented the models in the equation based Modelica language and evaluated accuracy and computing speed across multiple scales: from fundamental thermodynamic properties to complete districts featuring 10 to 200 buildings. Compared to district models with the IF97 water/steam model and equipment models from the Modelica Standard Library, the new implementation improves the scaling rate for large districts from cubic to quadratic with negligible compromise to accuracy. Additionally, for an annual simulation with 180 buildings, this translates to a computing time reduction from 33 to 1-1.5 h. These results are critically important for industry practitioners to simulate steam DH systems at large scales.
In this paper we present a non-intrusive data-driven approach for model reduction of nonlinear systems. The approach considers the particular case of nonlinear partial differential equations (PDEs) that form systems of partial differential–algebraic equations (PDAEs) when lifted to polynomial form. Such systems arise, for example, when the governing equations include Arrhenius reaction terms (e.g., in reacting flow models) and thermodynamic terms (e.g., the Helmholtz free energy terms in a phase-field solidification model). Using the known structured form of the lifted algebraic equations, the approach computes the reduced operators for the algebraic equations explicitly, using straightforward linear algebra operations on the basis matrices. The reduced operators for the differential equations are inferred from lifted snapshot data using operator inference, which solves a linear least squares regression problem. The approach is illustrated for the nonlinear model of solidification of a pure material. The lifting transformations reformulate the solidification PDEs as a system of PDAEs that have cubic structure. The operators of the lifted system for this solidification example have affine dependence on key process parameters, permitting us to learn a parametric reduced model with operator inference. Numerical experiments show the effectiveness of the resulting reduced models in capturing key aspects of the solidification dynamics.
Three models to compute the refractive index of gaseous mixtures at real gas conditions are presented with the purpose to improve the accuracy of state relationships between refractive index and thermodynamic properties. Models are compared with experimental data to determine one that is applicable to high pressure mixtures with non-ideal thermodynamic behavior near or above a fluids’ critical point. The optimal model is applied to analyze adiabatic thermal mixing of fuel and air at typical diesel engine conditions. Our results show that the ideal gas mixture law is appropriate in the vapor region, assuming it is known, for example, from experimental measurements. Finally, the model is applied for binary fuel-air mixing at supercritical conditions to demonstrate its full potential.
Increasing peak cylinder pressures and operating temperatures of turbo-charged internal combustion engines (ICEs) engines present new challenges for existing materials employed in engine exhaust valves. Oxidation induced degradation of current Ni based alloys for these components will be a life-limiting mechanism while strict cost margins in the automotive transportation industry further limit the choice of suitable candidate materials. Metallic diffusion aluminide coatings can provide a cost-effective way to improve the oxidation resistance of underlying materials. In this study, the high temperature oxidation behavior of diffusion nickel aluminide coatings on two Ni based alloys (commercially available alloy 31V and a developmental alloy) during cyclic oxidation behavior in air+10% H 2 O at 900 °C was investigated. A complete depletion of the beneficial Al-rich β-(NiFe)Al phase was observed in the coating on alloy 31V while a significant fraction of this phase was retained in the coating on the second alloy. A coupled thermodynamic-kinetic model showed that the disappearance of the Al-rich phase in the coating on 31V was mainly due to the combined effect of steeper chemical potential gradients of Al and Ti between the coating and the substrate and their faster diffusivities compared to the coated developmental alloy. Finally, a higher content of Fe was shown to support the retention of β-(NiFe)Al phase in the coating on the developmental alloy while the presence of Ti in the alloy was shown to be detrimental for long-term coating performance.
Here, we introduce a generative modeling framework for thermodynamic computing, in which structured data are synthesized from noise by the natural time evolution of a physical system governed by Langevin dynamics. While conventional diffusion models use neural networks to perform denoising, here the information needed to generate structure from noise is encoded by the dynamics of a thermodynamic system. Training proceeds by maximizing the probability with which the computer generates the reverse of a noising trajectory, which ensures that the computer generates data with minimal heat emission. We demonstrate this framework within a digital simulation of a thermodynamic computer. If realized in analog hardware, such a system would function as a generative model that produces structured samples without the need for artificially injected noise or active control of denoising.
Abstract Classical Monte Carlo simulation of the Heisenberg model poorly describes many thermodynamic phenomena due to its neglect of the quantum nature of spins. Alternatively, we discuss how to semiclassically approach the quantum problem and demonstrate a simple method for introducing a locally approximate form of spin quantization. While the procedure underestimates magnetic short-range order, our results suggest a simple correction for recovering realistic spin–spin correlations above the critical temperature. Moreover, ensemble fluctuations are found to provide reasonably accurate thermodynamics, largely reproducing quantum mechanically calculated heat capacities and experimental magnetometry for ferromagnetic Fe and antiferromagnetic RbMnF 3 . Extensions of the method are proposed to address remaining inaccuracies.
The Geothermal Operational Optimization with Machine Learning (GOOML) project has developed a generic and extensible component-based system modeling framework to study complex geothermal fields using a data-driven approach. Through building a digital twin of a geothermal steam field with the GOOML modeling framework, operators can analyze historical and forecasted power production, explore possible steam field configurations, and optimize real world operations, all in a cost-effective digital environment. The GOOML modeling software is based on a historical data-assimilation framework that uses first-principal thermodynamics to model steam field components using historical data, and a forecast framework that uses machine-learning-driven models of steam field components to predict future operations. This modeling framework creates countless new opportunities for digital exploration of steam field design and operations. To date, digital twins have been developed for several steam fields in New Zealand and the United States. These digital twins have been validated by comparing hindcast predictions against historical production data. Field design and operations have been explored using genetic optimization and reinforcement learning. Initial results show compelling and often surprising opportunities for improved design and operation of fields with 2 to 5 percent improvements in annual energy production. GOOML is driving a step-change in geothermal operations by applying state-of-the-art machine learning algorithms, comprehensive data analytics, and a first-of-its-kind intelligent geothermal systems model.
Aerosol pH is a fundamental property of aerosols in terms of atmospheric chemistry and its impact on air quality, climate, and health. Precise estimation of aerosol pH in chemical transport models (CTMs) is critical for aerosol modeling and thus influences policy development that partially relies on results from model simulations. We report the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem) simulated PM 2.5 pH over China during a period with heavy haze episodes in Beijing, and explore the sensitivity of the modeled aerosol pH to factors including emissions of nonvolatile cations (NVCs) and NH 3 , aerosol phase state assumption, and heterogeneous production of sulfate. We find that default WRF-Chem could predict spatial patterns of PM 2.5 pH over China similar to other CTMs, but with generally lower pH values, largely due to the underestimation of alkaline species (NVCs and NH 3 ) and the difference in thermodynamic treatments between different models. Increasing NH 3 emissions in the model would improve the modeled pH in comparison with offline thermodynamic model calculations of pH constrained by observations. In addition, we find that the aerosol phase state assumption and heterogeneous sulfate production are important in aerosol pH predictions for regions with low relative humidity (RH) and high anthropogenic SO 2 emissions, respectively. These factors should be better constrained in model simulations of aerosol pH in the future. Analysis of the modeled temporal trend of PM 2.5 pH in Beijing over a haze episode reveals a clear decrease in pH from 5.2 ± 0.9 in a clean period to 3.6 ± 0.5 in a heavily polluted period. The increased acidity under more polluted conditions is largely due to the formation and accumulation of secondary species including sulfuric acid and nitric acid, even though being modified by alkaline species (NVCs, NH 3 ). Our result suggests that NO 2 oxidation is unlikely to be important for heterogeneous sulfate production during the Beijing haze as the effective pH for NO 2 oxidation of S(IV) is at a higher pH of ~6.