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At least 199 records · Page 11

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NASA NTRS2014

Modeling the Deterioration of Engine and Low Pressure Compressor Performance During a Roll Back Event Due to Ice Accretion

The main focus of this study is to apply a computational tool for the flow analysis of the engine that has been tested with ice crystal ingestion in the Propulsion Systems Laboratory (PSL) of NASA Glenn Research Center. A data point was selected for analysis during which the engine experienced a full roll back event due to the ice accretion on the blades and flow path of the low pressure compressor. The computational tool consists of the Numerical Propulsion System Simulation (NPSS) engine system thermodynamic cycle code, and an Euler-based compressor flow analysis code, that has an ice particle melt estimation code with the capability of determining the rate of sublimation, melting, and evaporation through the compressor blade rows. Decreasing the performance characteristics of the low pressure compressor (LPC) within the NPSS cycle analysis resulted in matching the overall engine performance parameters measured during testing at data points in short time intervals through the progression of the roll back event. Detailed analysis of the fan-core and LPC with the compressor flow analysis code simulated the effects of ice accretion by increasing the aerodynamic blockage and pressure losses through the low pressure compressor until achieving a match with the NPSS cycle analysis results, at each scan. With the additional blockages and losses in the LPC, the compressor flow analysis code results were able to numerically reproduce the performance that was determined by the NPSS cycle analysis, which was in agreement with the PSL engine test data. The compressor flow analysis indicated that the blockage due to ice accretion in the LPC exit guide vane stators caused the exit guide vane (EGV) to be nearly choked, significantly reducing the air flow rate into the core. This caused the LPC to eventually be in stall due to increasing levels of diffusion in the rotors and high incidence angles in the inlet guide vane (IGV) and EGV stators. The flow analysis indicating compressor stall is substantiated by the video images of the IGV taken during the PSL test, which showed water on the surface of the IGV flowing upstream out of the engine, indicating flow reversal, which is characteristic of a stalled compressor.

Veres, Joseph P.↗

NASA NTRS2014

Modeling the Deterioration of Engine and Low Pressure Compressor Performance During a Roll Back Event Due to Ice Accretion

Jorgenson, Philip C. E.↗

DOE OSTI2024

RASPA3

RASPA3, a molecular simulation code for computing adsorption and diffusion in nanoporous materials and thermodynamic and transport properties of fluids. It implements force field based classical Monte Carlo/molecular dynamics in various ensembles. RASPA3 is rewritten from the ground up in C++23 with speed and code readability in mind. Transition-matrix Monte Carlo is added to compute the density of states and free energies. The Monte Carlo code for rigid molecules is based on quaternions, and the atomic positions needed in the energy evaluation are recreated from the center of mass position and quaternion orientation. The expanded ensemble methodology for fractional molecules, with a scaling parameter λ between 0 and 1, now also keeps track of analytic expressions of dU/dλ, allowing independent verification of the chemical potential using thermodynamic integration. The source code is freely available under the MIT license on GitHub.

Dubbeldam, David↗

DOE OSTI2026

Dynamic compression and spallation behavior of NbTaTiVZr high-entropy alloy

High-entropy alloys are a new class of materials with promising properties for aerospace and defense applications. The unique behavior of these materials is driven by the complex interactions between dissimilar atoms in a crystal and requires combined theoretical and experimental efforts to unlock their full potential. Here, we evaluate the relationship between the microstructure and the dynamic response of the equiatomic NbTaTiVZr alloy. Specifically, the shock Hugoniot, or equation of state, was measured up to a particle velocity of 0.5 mm μs −1 using gas gun plate impacts. Shock wave profile and incipient spallation experiments were used to characterize wave propagation and damage formation with post-mortem recovery experiments. Molecular dynamics simulations confirm the experimental findings and extend them up to 2.1 mm μs −1 particle velocity. Computational thermodynamic calculation of phase diagram simulations explain details of the material microstructure, which explains the measured strength and experimental damage patterns. Overall, this work provides detailed high-strain-rate characterization of a refractory high-entropy alloy, and more importantly, a framework and demonstration of the utility and necessity of a combined theoretical and experimental approach, outlining the importance of considering processing and manufacturing conditions when evaluating the performance of new materials.

36 MATERIALS SCIENCE↗

DOE OSTI2021

On the competitive antagonism effect in combined chloride-sulfate attack: A numerical exploration

Highlights: • A chemo-physical-mechanical method based on thermodynamic modelling is implemented. • The contradicting findings from reported experiments are numerically explored. • The competitive antagonism effect is time-dependent rather than a conclusive term. • The competitive antagonism effect is quantifiable with a novel assessment criterion. It is of tremendous difficulty to draw a conclusive verdict on the complex process of mutual interference regarding the combined chloride-sulfate attack on concrete, where many have explored the topic, but contradicting results have been reported. In this paper, by taking advantages of the chemo-physical-mechanical method, based on the robust technique of thermodynamic modelling, the competitive antagonism effect in combined chloride-sulfate attack is investigated. Firstly, a range of reported tests are numerically modelled to demonstrate the effectiveness of the employed method in interpreting those opposing and diverse experimental outcomes. More importantly, a fresh conjecture incorporating a novel assessment criterion for the competitive antagonism effect is proposed and supported through a carefully designed illustrative example. Based on the numerical exploration, unique findings and implications for future engineering practices are revealed.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Investigation of aluminum nitrate as a set retarder of magnesium potassium phosphate cement: Mechanisms involved in diluted suspension

In this study, the influence of aluminum nitrate on the hydration process of magnesium potassium phosphate (MKP) cement was investigated experimentally and through thermodynamic modelling using diluted systems with Mg/PO{sub 4} molar ratio and water-to-cement ratio of 1 and 100 respectively. Three processes contributed to retard cement hydration: the acidic nature of aluminum nitrate which reduced the initial pH of the solution by two units, the early precipitation of a taranakite-like amorphous potassium aluminophosphate, which reduced the aqueous phosphate and potassium concentrations, and a salt effect due to the presence of soluble nitrate. Precipitation of potassium-containing magnesium phosphate hydrates, like Mg{sub 2}KH(PO{sub 4}){sub 2}⋅15H{sub 2}O and K-struvite, was prohibited, whereas precipitation of newberyite and cattiite was delayed. Furthermore, the modelled phase development with ongoing cement hydration agreed rather well with the experimental findings.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Probing the thermodynamics and kinetics of ethylene carbonate reduction at the electrode–electrolyte interface with molecular simulations

Understanding the formation of the solid-electrolyte interphase (SEI) in lithium-ion batteries (LIBs) is an ongoing area of research due to its high degree of complexity and the difficulties encountered by experimental studies. Herein, we investigate the initial stage of SEI growth, the reduction reaction of ethylene carbonate (EC), from both a thermodynamic and kinetic approach with theory and molecular simulation. We employed both the potential distribution theorem (PDT) and the SMD implicit solvent model to EC solvation for the estimation of reduction potentials of Li$^+$, EC, and Li$^+$-solvating EC (s-EC), as well as reduction rate constants of EC and s-EC. We find that solvation effects greatly influence these quantities of interest, particularly the Li$^+$/Li reference electrode potential in EC solvent. Further, we also compute the inner- and outer-sphere reorganization energies for both EC and s-EC at the interface of liquid EC and a hydroxyl-terminated graphite surface, where total reorganization energies are predicted to be 76.6 and 88.9 kcal/mol, respectively. With the computed reorganization energies, we estimate reduction rate constants across a range of overpotentials and show that EC has a larger electron transfer rate constant than s-EC at equilibrium, despite s-EC being more thermodynamically favorable. Furthermore, this manuscript demonstrates how ion solvation effects largely govern the prediction of reduction potentials and electron transfer rate constants at the electrode-electrolyte interface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Hydrogen Peroxide‐Induced Overoxidation of Fe−N−C Catalysts: Implications for ORR Activity

Fe−N−C (iron-nitrogen-carbon) electrocatalysts have emerged as promising alternatives to precious metals for the oxygen reduction reaction (ORR), but they remain insufficiently stable for widespread adoption in fuel cell technologies. One plausible mechanism to explain this lack of stability, and the associated catalyst degradation, is oxidative attack on the catalyst surface by hydrogen peroxide, a non-selective byproduct of the ORR. In this work, we perform a detailed analysis of this degradation mechanism, using a combination of periodic Density Functional Theory (DFT) calculations and ab-initio molecular dynamics (AIMD) simulations to probe the thermodynamics and kinetics of hydrogen peroxide activation on a series of candidate active sites for the Fe−N−C catalyst. The results demonstrate that carbon atoms neighbouring FeN 4 active sites can be strongly over-oxidized via formation of hydroxyl or epoxy groups when hydrogen peroxide is present in the electrolyte. In most cases, the interaction between the over-oxidizing groups and the ORR reaction intermediates reduces the ORR activity, and we further propose that the over-oxidized sites are likely precursors to irreversible carbon corrosion and further catalyst deactivation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Explicitly determined sea ice emissivity and emission temperature over the Arctic for surface–sensitive microwave channels

Data assimilation of satellite microwave measurements is one of the important keys to improving weather forecasting over the Arctic region. However, the use of surface-sensitive microwave-sounding channel measurements for data assimilation or retrieval has been limited, especially during winter, due to the poorly constrained sea ice emissivity. In this study, aiming at more use of those channel measurements in the data assimilation, we propose an explicit method for specifying the surface radiative boundary conditions (namely emissivity and emitting layer temperature of snow and ice). These were explicitly determined with a radiative transfer model for snow and ice and with snow/ice physical parameters (i.e., snow/ice depths and vertical distributions of temperature, density, salinity, and grain size) simulated from the thermodynamically-driven snow/ice growth model. Here we conducted 1-D Var experiments in order to examine whether this approach can help to use the surface-sensitive microwave temperature channel measurements over the Arctic sea ice region for data assimilation. Results show that (1) the surface-sensitive microwave channels can be used in the 1-D Var retrieval, and (2) the specification of the radiative boundary condition at the surface using the snow/sea ice emission model can significantly improve the atmospheric temperature retrieval, especially in the lower troposphere (500 hPa to surface). The successful retrieval suggests that useful information can be extracted from surface-sensitive microwave-sounding channel radiances over sea ice surfaces through the explicit determination of snow/ice emissivity and emitting layer temperature.

1-D Var retrieval↗

DOE OSTI2021

Effect of temperature curing on properties and hydration of wollastonite blended magnesium potassium phosphate cements

K-struvite, the main hydrate of magnesium potassium phosphate (MKP) cements, dehydrates at ~50 °C, thus elevated temperatures at service conditions could affect cement properties and durability. In this study, properties and hydration of MKP cement without and with wollastonite were investigated at 20 and 50 °C. In hydrated pure MKP cement K-struvite decomposes progressively over time to MgKPO{sub 4}·H{sub 2}O at 50 °C, which leads to a strong reduction of solid volume and severe strength loss. The presence of wollastonite significantly slows down the decomposition rate of K-struvite, which is still observed after 393 days at 50 °C. K-struvite together with amorphous hydroxyapatite, M-(C)-S-H and CaK{sub 3}H(PO{sub 4}){sub 2} from the wollastonite reaction result in a cement with good short and long-term strength at both 20 and 50 °C.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Phase field modeling of hydrogen release in nitrile-butadiene rubber composites after high-pressure hydrogen exposure

Experimental data showed that a simple diffusion model doesn’t suffice to explain the hydrogen (H 2 ) release kinetics in nitrile butadiene rubber (NBR) composites (one with no filler or plasticizer, one with plasticizer only, one with fillers and plasticizer, and one with fillers only) after exposed to hydrogen gas at a pressure of 27.6 MPa. In this work, we developed a phase field model that considers the effect of H 2 gas bubble evolution on H 2 release kinetics. With the model, we simulated the effect of thermodynamic and kinetics properties on the nucleation, growth, and shrinkage of gas bubbles and H 2 release kinetics during and after decompression. Here, the results demonstrated that 1) the model built upon certain thermodynamic and kinetics properties of a given material can well describe its H 2 release kinetics measured in experiments, and 2) the model can predict the gas bubble evolution, which is associated with the material property degradation and failures, with accurate thermodynamic and kinetics properties or H 2 release data from experiments.

08 HYDROGEN↗

DOE OSTI2024

Material analysis of metal hydrides for bulk hydrogen storage

The United States Department of Energy (DOE) Hydrogen Storage Engineering Center of Excellence (HSECoE), 2009–2014, was established to evaluate the viability of material-based hydrogen storage for light duty vehicles. The HSECoE concluded that there were no metal hydrides that simultaneously met the DOE targets for gravimetric and volumetric capacity, and rate of onboard refueling. However, it was noted by the DOE that potential applications existed for stationary hydrogen storage systems. An application of particular interest is using a metal-hydride to store a hydrogen supply for fuel cell backup power to a 5 MWe data center. A generalized numerical model that coupled mass and energy conservation, chemical kinetics and thermodynamics was developed to simulate the discharge process for a given metal-hydride. The model was applied to an interstitial and a complex metal-hydride. In conclusion, it was found that the interstitial metal-hydride, which is discharged by a pressure swing is preferrable in a practical sense to the complex metal-hydride, which requires a temperature swing discharge process.

08 HYDROGEN↗

DOE OSTI2022

Cantor-derived medium-entropy alloys: bridging the gap between traditional metallic and high-entropy alloys

The year 2004 marked the beginning of a new era in the design of metallic materials, as the concept of multiple principal component alloys, commonly known as High-Entropy Alloys (HEAs), was proposed by Cantor and Yeh. The unexpected single-phase microstructure, instead of the expected brittle intermetallic compounds, was attributed to the large entropy of mixing and immediately caught the attention of the scientific community. Today, HEAs are considered important advanced materials and a broad range of alloys using nominally the same design principle have been investigated. Despite that, the CrMnFeCoNi (Cantor) alloy stands out as the most successful HEA due to its outstanding mechanical properties and microstructure. In this scenario, variants of the Cantor alloy, named medium-entropy alloys (MEAs), are gaining significant interest as they display a better industrial potential than both HEAs and traditional alloys. These variants of the Cantor alloy with only three or four main elements result in 15 possible combinations. The microstructure of these alloys is discussed in terms of advanced characterization as well as thermodynamic parameters and computational simulation. Their phase stability is addressed over a wide range of temperatures and strain rates. The mechanical properties, especially the fracture toughness, of the CrFeCoNi and CrCoNi alloys have been reported to be even superior to those of the Cantor alloy and most modern engineering alloys. This is associated with the formation of a continuous sequence of strengthening mechanisms, including hierarchical twin networks, which serve to prolong the strain hardening. The present article reviews and critically assesses, for the first time, recent advances in these Cantor-derived MEAs.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Adsorption Equilibrium, Kinetics, and Column Breakthrough Data for Aqueous Solutions of Binary-Acid and Ternary-Acid Mixtures of Acetic Acid, Butyric Acid, and Lactic Acid on IRN-78 Ion-Exchange Resin at Initial pH Levels of ∼3–7 and at 25–55 °C

This work is part of an effort to develop thermophysical property data and models supporting adsorptive process development for organic acid separation from a dilute aqueous solution of fermentation broth. It presents systematic experimental measurements for aqueous-phase adsorption equilibrium, kinetics, and column breakthrough for three binary-acid aqueous mixtures (acetic acid + lactic acid, butyric acid + lactic acid, and acetic acid + butyric acid) and one ternary-acid aqueous mixture (acetic acid + butyric acid + lactic acid) on Amberlite IRN-78 ion-exchange resin. The equilibrium measurements covered broad ranges of the initial acid concentration (100–400 mmol/L), initial pH (∼3–7), and temperature (25–55 °C). The equilibrium data for the binary-acid and ternary-acid aqueous mixtures indicate selective adsorption of lactic acid at initial pH levels of ∼3 and 4, while acetic acid and butyric acid are selectively adsorbed at initial pH levels of 5, 6, and 7. The subsequent kinetics and column breakthrough experiments were performed at 200 mmol/L with equimolar ratios, an initial pH of 6, and 25 °C. In conclusion, the measurements provide essential data sets for rigorous thermodynamic modeling and process simulation of adsorptive separation processes for organic acid separation from fermentation broth.

Adsorption↗

DOE OSTI2025

A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials

Atomic disorder can strongly influence material properties such as charge transport, optical response, and catalytic activity. However, efficiently modeling these disorder effects remains challenging for first-principles methods due to the cost of sampling large configurational spaces and computing complex physical quantities. Recent advances of machine learning techniques, particularly graph neural networks (GNNs), has enabled the efficient and accurate predictions of complex material properties, offering promising tools for studying disordered systems. In this work, we present a general machine-learning-assisted computational framework that integrates equivariant GNNs with Monte Carlo simulations to compute the thermodynamic and ensemble-averaged functional properties of disordered materials. Using the surface-termination-disordered MXene monolayer Ti 3 C 2 T 2–x as a representative system, we find that electrical conductivity exhibits an emergent peak near the order–disorder phase transition temperature due to the interplay between electron scattering and doping. In contrast, optical conductivity remains largely insensitive to local atomic disorder and reflects the global surface chemical composition. These results highlight the role of atomic disorder in affecting material properties and demonstrate the potential of our approach for statistically modeling disorder effects in a wide range of materials such as high-entropy alloys and spin liquids.

MXene↗

DOE OSTI2025

Simulating Atmospheric Processes in Earth System Models and Quantifying Uncertainties With Deep Learning Multi‐Member and Stochastic Parameterizations

Abstract Deep learning is a powerful tool to represent subgrid processes in climate models, but many application cases have so far used idealized settings and deterministic approaches. Here, we develop stochastic parameterizations with calibrated uncertainty quantification to learn subgrid convective and turbulent processes and surface radiative fluxes of a superparameterization embedded in an Earth System Model (ESM). We explore three methods to construct stochastic parameterizations: (a) a single Deep Neural Network (DNN) with Monte Carlo Dropout; (b) a multi‐member parameterization; and (c) a Variational Encoder Decoder with latent space perturbation. We show that the multi‐member parameterization improves the representation of convective processes, especially in the planetary boundary layer, compared to individual DNNs. The respective uncertainty quantification illustrates that methods (b) and (c) are advantageous compared to a dropout‐based DNN parameterization regarding the spread of convective processes. Hybrid simulations with our best‐performing multi‐member parameterizations remained challenging and crash within the first days. Therefore, we develop a pragmatic partial coupling strategy relying on the superparameterization for condensate emulation. Partial coupling reduces the computational efficiency of hybrid Earth‐like simulations but enables model stability over 5 months with our multi‐member parameterizations. However, our hybrid simulations exhibit biases in thermodynamic fields and differences in precipitation patterns. Despite this, the multi‐member parameterizations enable improvements in reproducing tropical extreme precipitation compared to a traditional convection parameterization. Despite these challenges, our results indicate the potential of a new generation of multi‐member machine learning parameterizations leveraging uncertainty quantification to improve the representation of stochasticity of subgrid effects.

Behrens, Gunnar [Deutsches Zentrum für Luft‐ und R↗

DOE OSTI2025

The Role of Wind‐Moisture Characteristics in Shaping Atmospheric River Flood Hazards

Atmospheric rivers (ARs) are key drivers of extreme precipitation in the Western U.S. Using regionally downscaled thermodynamic global warming (TGW) simulations, we examine how ARs with varying wind and moisture characteristics respond to warming. We classified 812 historical AR events into Gusty-Wet, Gusty-Dry, Calm-Wet, and Calm-Dry groups to evaluate differences in precipitation behavior. ARs with stronger winds and higher moisture content exhibit higher precipitation efficiency (PE) and greater integrated water vapor (IWV). Regionally, Calm ARs show higher IWV accumulation due to slower inland transport and reduced PE. Projections indicate increases in storm-total (sub-Clausius-Clapeyron (CC) scaling) and maximum 3-hourly precipitation (super-CC scaling) across all groups, with the most pronounced changes in Gusty-Wet and Calm-Wet ARs. Spatial differences in surface runoff, PE, and inland reach highlight the importance of AR subtype in shaping future flood hazards. These results offer insights into event-level and regional-scale precipitation changes under evolving environmental conditions.

Zhou, Yang [Lawrence Berkeley National Laboratory ↗

DOE OSTI2024

Low-index mesoscopic surface reconstructions of Au surfaces using Bayesian force fields

Metal surfaces have long been known to reconstruct, significantly influencing their structural and catalytic properties. Many key mechanistic aspects of these subtle transformations remain poorly understood due to limitations of previous simulation approaches. Using active learning of Bayesian machine-learned force fields trained from ab initio calculations, we enable large-scale molecular dynamics simulations to describe the thermodynamics and time evolution of the low-index mesoscopic surface reconstructions of Au (e.g., the Au(111)-‘Herringbone,’ Au(110)-(1 × 2)-‘Missing-Row,’ and Au(100)-‘Quasi-Hexagonal’ reconstructions). This capability yields direct atomistic understanding of the dynamic emergence of these surface states from their initial facets, providing previously inaccessible information such as nucleation kinetics and a complete mechanistic interpretation of reconstruction under the effects of strain and local deviations from the original stoichiometry. We successfully reproduce previous experimental observations of reconstructions on pristine surfaces and provide quantitative predictions of the emergence of spinodal decomposition and localized reconstruction in response to strain at non-ideal stoichiometries. A unified mechanistic explanation is presented of the kinetic and thermodynamic factors driving surface reconstruction. Furthermore, we study surface reconstructions on Au nanoparticles, where characteristic (111) and (100) reconstructions spontaneously appear on a variety of high-symmetry particle morphologies.

36 MATERIALS SCIENCE↗