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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Theoretical investigation of intersystem crossing in the cyanonitrene molecule, 1 NCN → 3 NCN

The NCN diradical is an important intermediate of prompt nitric oxide formation in flames. The mechanism of intersystem crossing (ISC) in the NCN molecule formed via pyrolysis or photolysis of NCN 3 is of relevance to the interpretation of experiments that utilize NCN 3 as a precursor for laboratory studies of NCN kinetics. This mechanism has been investigated by means of multi-reference configuration interaction calculations. From the potential energy surfaces for NCN 3 dissociation, it was inferred that both thermal and photo-chemical decomposition initially lead to NCN in its lowest singlet state, $\tilde{a}^{-1}$$Δ_g$, with a possible contribution from the $\tilde{b}^{-1}\Sigma_g^+$ state at low photolysis wavelengths. Direct formation of the triplet ground state $\tilde{X}^{-3}\Sigma_g^-$ is also feasible for the photolytic pathway. Ananalysis of surface crossings between $\tilde{a}$ or $\tilde{b}$ and the triplet ground state $\tilde{X}^{-3}\Sigma_g^-$ in the absence and presence of a helium atom revealed an ISC channel 1 NCN ($\tilde{a}$) → 3 NCN($\tilde{X}$) via a strongly bent structure. However, its barrier of 38 kcal mol -1 relative to the singlet minimum turned out to be much too high to explain the fast ISC observed in experiments. A rigid-bender model including Renner-Teller interactions was used to examine the occurrence of mixed-multiplicity rovibrational states-so-called gateway states-that could enhance collision-induced ISC. The results of this study indicate that a gateway mechanism is probably not operative in the case of the $\tilde{a}$/$\tilde{X}$pair of states in NCN.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

NeuroSEM: A hybrid framework for simulating multiphysics problems by coupling PINNs and spectral elements

Multiphysics problems that are characterized by complex interactions among fluid dynamics, heat transfer, structural mechanics, and electromagnetics, are inherently challenging due to their coupled nature. While experimental data on certain state variables may be available, integrating these data with numerical solvers remains a significant challenge. Physics-informed neural networks (PINNs) have shown promising results in various engineering disciplines, particularly in handling noisy data and solving inverse problems in partial differential equations (PDEs). However, their effectiveness in forecasting nonlinear phenomena in multiphysics regimes, particularly involving turbulence, is yet to be fully established. Here, this study introduces NeuroSEM, a hybrid framework integrating PINNs with the highfidelity Spectral Element Method (SEM) solver, Nektar++. NeuroSEM leverages the strengths of both PINNs and SEM, providing robust solutions for multiphysics problems. PINNs are trained to assimilate data and model physical phenomena in specific subdomains, which are then integrated into the Nektar++ solver. We demonstrate the efficiency and accuracy of NeuroSEM for thermal convection in cavity flow and flow past a cylinder. The framework effectively handles data assimilation by addressing those subdomains and state variables where the data is available. We applied NeuroSEM to the Rayleigh-B´enard convection system, including cases with missing thermal boundary conditions and noisy datasets. Finally, we applied the proposed NeuroSEM framework to real particle image velocimetry (PIV) data to capture flow patterns characterized by horseshoe vortical structures. Our results indicate that NeuroSEM accurately models the physical phenomena and assimilates the data within the specified subdomains. The framework’s plug-and-play nature facilitates its extension to other multiphysics or multiscale problems. Furthermore, NeuroSEM is optimized for efficient execution on emerging integrated GPU-CPU architectures. This hybrid approach enhances the accuracy and efficiency of simulations, making it a powerful tool for tackling complex engineering challenges in various scientific domains.

42 ENGINEERING↗
DOE OSTI2015

Phase partitioning and volatility of secondary organic aerosol components formed from α-pinene ozonolysis and OH oxidation: the importance of accretion products and other low volatility compounds

We measured a large suite of gas- and particle-phase multi-functional organic compounds with a Filter Inlet for Gases and AEROsols (FIGAERO) coupled to a high-resolution time-of-flight chemical ionization mass spectrometer (HR-ToF-CIMS) developed at the University of Washington. The instrument was deployed on environmental simulation chambers to study monoterpene oxidation as a secondary organic aerosol (SOA) source. We focus here on results from experiments utilizing an ionization method most selective towards acids (acetate negative ion proton transfer), but our conclusions are based on more general physical and chemical properties of the SOA. Hundreds of compounds were observed in both gas and particle phases, the latter being detected by temperature-programmed thermal desorption of collected particles. Particulate organic compounds detected by the FIGAERO–HR-ToF-CIMS are highly correlated with, and explain at least 25–50 % of, the organic aerosol mass measured by an Aerodyne aerosol mass spectrometer (AMS). Reproducible multi-modal structures in the thermograms for individual compounds of a given elemental composition reveal a significant SOA mass contribution from high molecular weight organics and/or oligomers (i.e., multi-phase accretion reaction products). Approximately 50 % of the HR-ToF-CIMS particle-phase mass is associated with compounds having effective vapor pressures 4 or more orders of magnitude lower than commonly measured monoterpene oxidation products. The relative importance of these accretion-type and other extremely low volatility products appears to vary with photochemical conditions. We present a desorption-temperature-based framework for apportionment of thermogram signals into volatility bins. The volatility-based apportionment greatly improves agreement between measured and modeled gas-particle partitioning for select major and minor components of the SOA, consistent with thermal decomposition during desorption causing the conversion of lower volatility components into the detected higher volatility compounds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Tailoring the Electronic Structures and Spectral Properties of ZnO with Irradiation Defects Generated Under Intense Electronic Excitation: A Combined Experimental and DFT Approach

Here, the relationship between the composition of the internal defect states, spectral properties, and correlated electronic structures of wurtzite zinc oxide (ZnO) crystals under 645 MeV Xe 35+ irradiation is systematically investigated, employing experimental characterizations combined with first-principle calculations. Based on the ion irradiation-induced thermal expansion and relaxation processes, the high concentration of vacancy/interstitial defects produced from the transient disordered phase in molten track states trigger photoelectric changes, as follows: i) the generation of internal defect states effectively reduces the intrinsic bandgap (3.25 eV → 2.66 eV); ii) a large number of defective active sites inhibits the recombination between electron–hole pairs, causing dark conductance and photoconductance to increase with increasing damage levels until optimal fluence is achieved. Based on the density functional theory (DFT) with the GGA + U (GGA = generalized gradient approximation) method, the defective models associated with the different electronic structures, density of states, formation energy, and the nature of the chemical bonding are established. The narrowing of the bandgap observed experimentally and the enhancement of carrier concentration originating from the internal electron defect states are qualitatively verified, therefore laying the foundation for designing future nanoscale photoelectronic devices and microelectronics applications.

36 MATERIALS SCIENCE↗
DOE OSTI2017

FIVE YEARS OF SYNTHESIS OF SOLAR SPECTRAL IRRADIANCE FROM SDID/SISA AND SDO /AIA IMAGES

In this paper we describe the synthetic solar spectral irradiance (SSI) calculated from 2010 to 2015 using data from the Atmospheric Imaging Assembly (AIA) instrument, on board the Solar Dynamics Observatory spacecraft. We used the algorithms for solar disk image decomposition (SDID) and the spectral irradiance synthesis algorithm (SISA) that we had developed over several years. The SDID algorithm decomposes the images of the solar disk into areas occupied by nine types of chromospheric and 5 types of coronal physical structures. With this decomposition and a set of pre-computed angle-dependent spectra for each of the features, the SISA algorithm is used to calculate the SSI. We discuss the application of the basic SDID/SISA algorithm to a subset of the AIA images and the observed variation occurring in the 2010–2015 period of the relative areas of the solar disk covered by the various solar surface features. Our results consist of the SSI and total solar irradiance variations over the 2010–2015 period. The SSI results include soft X-ray, ultraviolet, visible, infrared, and far-infrared observations and can be used for studies of the solar radiative forcing of the Earth’s atmosphere. These SSI estimates were used to drive a thermosphere–ionosphere physical simulation model. Predictions of neutral mass density at low Earth orbit altitudes in the thermosphere and peak plasma densities at mid-latitudes are in reasonable agreement with the observations. The correlation between the simulation results and the observations was consistently better when fluxes computed by SDID/SISA procedures were used.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Bond Dissociation Energies of C 10 and C 18 Methyl Esters from Local Multireference Averaged-Coupled Pair Functional Theory

We previously developed a fast, local, reduced scaling Cholesky-decomposed multireference averaged-coupled pair functional (CD-LMRACPF2) method, which takes advantage of the locality of dynamic correlation and numerical approximations such as Cholesky decomposition and integral screening. Motivated by the desire to study large biodiesel methyl ester molecules, here we validate CD-LMRACPF2 for the computation of bond dissociation energies (BDEs) in a suite of oxygenated molecules, and show that the low-cost method is very accurate compared to the conventional variant. We then demonstrate the power of CD-LMRACPF2 for fast and accurate computation of energies of molecules containing up to 13 second-row atoms within a polarized triple-ζ (cc-pVTZ) basis set. We use biodiesel methyl esters as a chemically interesting model system and furnish BDEs of C 10 and C 18 methyl esters, with the latter performed within a cc-pVDZ basis set. Here, we describe trends in the BDEs and explain how structural (isomeric) differences affect BDEs, as well as discuss implications of BDE trends for biodiesel physical and chemical properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Population Level Analysis of Evolved Mutations Underlying Improvements in Plant Hemicellulose and Cellulose Fermentation by Clostridium phytofermentans

The complexity of plant cell walls creates many challenges for microbial decomposition. Clostridium phytofermentans, an anaerobic bacterium isolated from forest soil, directly breaks down and utilizes many plant cell wall carbohydrates. The objective of this research is to understand constraints on rates of plant decomposition by Clostridium phytofermentans and identify molecular mechanisms that may overcome these limitations. Experimental evolution via repeated serial transfers during exponential growth was used to select for C. phytofermentans genotypes that grow more rapidly on cellobiose, cellulose and xylan. To identify the underlying mutations an average of 13,600,000 paired-end reads were generated per population resulting in ~300 fold coverage of each site in the genome. Mutations with allele frequencies of 5% or greater could be identified with statistical confidence. Many mutations are in carbohydrate-related genes including the promoter regions of glycoside hydrolases and amino acid substitutions in ABC transport proteins involved in carbohydrate uptake, signal transduction sensors that detect specific carbohydrates, proteins that affect the export of extracellular enzymes, and regulators of unknown specificity. Structural modeling of the ABC transporter complex proteins suggests that mutations in these genes may alter the recognition of carbohydrates by substrate-binding proteins and communication between the intercellular face of the transmembrane and the ATPase binding proteins. Experimental evolution was effective in identifying molecular constraints on the rate of hemicellulose and cellulose fermentation and selected for putative gain of function mutations that do not typically appear in traditional molecular genetic screens. The results reveal new strategies for evolving and engineering microorganisms for faster growth on plant carbohydrates.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Vibrational Dynamics and Dissociation of Ground- and Excited-State Clusters (Final Report)

Experimental studies of the dissociation of selectively excited molecules probe fundamental as-pects of chemical reactivity and molecular decomposition, and understanding excited-state decomposition dynamics presents both experimental and theoretical challenges. Photoexcitation of molecules from selec-tively prepared vibrational states is a proven means of studying electronically excited molecules and, in favorable cases, of controlling their dissociation pathways. This double resonance scheme, vibrationally mediated photodissociation, has uncovered new vibrational spectroscopy, determined bond strengths with high accuracy, altered excited state dissociation pathways, and revealed properties and couplings in elec-tronically excited states. Many practically important processes, such as excited state decomposition and bimolecular reaction, involve the intersection of two electronic states along one or more coordinates, and the resulting interaction often creates a conical intersection between the two states. These structures are both intriguing and significant because the evolution of molecules through conical intersections deter-mines the disposal of energy into dissociation fragments and the branching between different reaction pathways. This project is a systematic study of the photodissociation of vibrationally excited molecules and of their complexes with various adducts. This research uses vibrational and electronic excitation to deter-mine the influence of specific excitation on decomposition pathways. Previous studies of the dissociation of ammonia and phenol have shown that vibrational excitation can influence the formation of excited-state and ground-state products. The studies described here systematically study the photodissociation of vibrationally excited molecules and the behavior of their complexes in order to identify dissociation pathways, discover the factors that control the course of decompositions, and test theoretical models of the decomposition of energized molecules. The major accomplishments are a series of measurements on the ammonia dimer, the ammonia trimer, and the ammonia-aminophenol complex that reveal their vibra-tional predissociation dynamics and determine their binding energies. In addition, the research investi-gates the photodissociation of molecules in which multiple potentially energy surfaces play a role.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2000

The Dynamical Evolution of A Tubular Leonid Persistent Train

The dynamical evolution of the persistent train of a bright Leonid meteor was examined for evidence of the source of the luminosity and the physical conditions in the meteor path. The train consisted of two parallel somewhat diffuse luminous tracks, interpreted as the walls of a tube. A general lack of wind shear along the trail allowed these structures to remain intact for nearly 200 s, from which it was possible to determine that the tubular structure expanded at a near constant 10.5 m/s, independent of altitude between 86 and 97 km. An initial fast decrease of train intensity below 90 km was followed by an increase in intensity and then a gradual decrease at longer times, whereas at high attitudes the integrated intensity was nearly constant with time. These results are compared to a model that describes the dynamical evolution of the train by diffusion, following an initial rapid expansion of the hot gaseous trail behind the meteoroid. The train luminosity is produced by O ((sup 1)S) emission at 557 nm, driven by elevated atomic O levels produced by the meteor impact, as well as chemiluminescent reactions of the ablated metals Na and Fe with O3. Ozone is rapidly removed within the train, both by thermal decomposition and catalytic destruction by the metallic species. Hence, the brightest emission occurs at the edge of the train between outwardly diffusing metallic species and inwardly diffusing O3. Although the model is able to account plausibly for a number of characteristic features of the train evolution, significant discrepancies remain that cannot easily be resolved.

Jenniskens, Peter↗
NASA NTRS1995

Emerging CFD technologies and aerospace vehicle design

With the recent focus on the needs of design and applications CFD, research groups have begun to address the traditional bottlenecks of grid generation and surface modeling. Now, a host of emerging technologies promise to shortcut or dramatically simplify the simulation process. This paper discusses the current status of these emerging technologies. It will argue that some tools are already available which can have positive impact on portions of the design cycle. However, in most cases, these tools need to be integrated into specific engineering systems and process cycles to be used effectively. The rapidly maturing status of unstructured and Cartesian approaches for inviscid simulations makes suggests the possibility of highly automated Euler-boundary layer simulations with application to loads estimation and even preliminary design. Similarly, technology is available to link block structured mesh generation algorithms with topology libraries to avoid tedious re-meshing of topologically similar configurations. Work in algorithmic based auto-blocking suggests that domain decomposition and point placement operations in multi-block mesh generation may be properly posed as problems in Computational Geometry, and following this approach may lead to robust algorithmic processes for automatic mesh generation.

Aftosmis, Michael J.↗
DOE OSTI2023

Enhancing SRF cavity stability and minimizing detuning with data-driven resonance control based on dynamic mode decomposition

Effective resonance control of superconducting radio frequency (SRF) cavities is critical for large machines like LCLS-II, as failure to achieve proper control can result in increased RF power consumption, higher cryogenic heat loads, and increased costs. To address this challenge, we have developed a machine learning (ML) model based on the dynamic mode decomposition method to represent the forced cavity dynamics. Using this model, we designed a model predictive controller (MPC) and demonstrated through simulation that the MPC can effectively stabilize the amplitude and phase of SRF cavities using only a frequency actuator, even in the presence of multiple mechanical modes. The lightweight and explicit ML model makes the controller suitable for direct implementation on field-programmable gate arrays, unlocking the full potential of SRF linacs like LCLS-II, enabling higher beam power and energy, and also serving as an advanced motion controller for various applications, such as photon beamlines and storage rings.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Learning the structure of wind: A data-driven nonlocal turbulence model for the atmospheric boundary layer

In this work, we develop a novel data-driven approach to modeling the atmospheric boundary layer. This approach leads to a nonlocal, anisotropic synthetic turbulence model which we refer to as the deep rapid distortion (DRD) model. Our approach relies on an operator regression problem that characterizes the best fitting candidate in a general family of nonlocal covariance kernels parameterized in part by a neural network. This family of covariance kernels is expressed in Fourier space and is obtained from approximate solutions to the Navier–Stokes equations at very high Reynolds numbers. Each member of the family incorporates important physical properties such as mass conservation and a realistic energy cascade. The DRD model can be calibrated with noisy data from field experiments. After calibration, the model can be used to generate synthetic turbulent velocity fields. To this end, we provide a new numerical method based on domain decomposition which delivers scalable, memory-efficient turbulence generation with the DRD model as well as others. We demonstrate the robustness of our approach with both filtered and noisy data coming from the 1968 Air Force Cambridge Research Laboratory Kansas experiments. Using these data, we witness exceptional accuracy with the DRD model, especially when compared to the International Electrotechnical Commission standard.

17 WIND ENERGY↗
NASA NTRS2015

Thermochemical Ablation Analysis of the Orion Heatshield

The Orion Multi-Purpose Crew Vehicle will one day carry astronauts to the Moon and beyond, and Orion's heatshield is a critical component in ensuring their safe return to Earth. The Orion heatshield is the structural component responsible for absorbing the intense heating environment caused by re-entry to Earth's atmosphere. The heatshield is primarily composed of Avcoat, an ablative material that is consumed during the re-entry process. Ablation is primarily characterized by two processes: pyrolysis and recession. The decomposition of in-depth virgin material is known as pyrolysis. Recession occurs when the exposed surface of the heatshield reacts with the surrounding flow. The Orion heatshield design was changed from an individually filled Avcoat honeycomb to a molded block Avcoat design. The molded block Avcoat heatshield relies on an adhesive bond to keep it attached to the capsule. In some locations on the heatshield, the integrity of the adhesive bond cannot be verified. For these locations, a mechanical retention device was proposed. Avcoat ablation was modelled in CHAR and the in-depth virgin material temperatures were used in a Thermal Desktop model of the mechanical retention device. The retention device was analyzed and shown to cause a large increase in the maximum bondline temperature. In order to study the impact of individual ablation modelling parameters on the heatshield sizing process, a Monte Carlo simulation of the sizing process was proposed. The simulation will give the sensitivity of the ablation model to each of its input parameters. As part of the Monte Carlo simulation, statistical uncertainties on material properties were required for Avcoat. Several properties were difficult to acquire uncertainties for: the pyrolysis gas enthalpy, non-dimensional mass loss rate (B´c), and Arrhenius equation parameters. Variability in the elemental composition of Avcoat was used as the basis for determining the statistical uncertainty in pyrolysis gas enthalpy and B´c. A MATLAB program was developed to allow for faster, more accurate and automated computation of Arrhenius reaction parameters. These parameters are required for a material model to be used in the CHAR ablation analysis program. This MATLAB program, along with thermogravimetric analysis (TGA) data, was used to generate uncertainties on the Arrhenius parameters for Avcoat. In addition, the TGA fitting program was developed to provide Arrhenius parameters for the ablation model of the gap filler material, RTV silicone.

Sixel, William↗
DOE OSTI2014

Flow Partitioning in Fully Saturated Soil Aggregates

Microbes play an important role in facilitating organic matter decomposition in soils, which is a major component of the global carbon cycle. Microbial dynamics are intimately coupled to environmental transport processes, which control access to labile organic matter and other nutrients that are needed for the growth and maintenance of microorganisms. Transport of soluble nutrients in the soil system is arguably most strongly impacted by preferential flow pathways in the soil. Since the physical structure of soils can be characterized as being formed from constituent micro aggregates which contain internal porosity, one pressing question is the partitioning of the flow among the “inter-aggregate” and “intra-aggregate” pores and how this may impact overall solute transport within heterogeneous soil structures. The answer to this question is particularly important in evaluating assumptions to be used in developing upscaled simulations based on highly-resolved mechanistic models. We constructed a number of diverse multi-aggregate structures with different packing ratios by stacking micro-aggregates containing internal pores and varying the size and shape of inter-aggregate pore spacing between them. We then performed pore-scale flow simulations using computational fluid dynamics methods to determine the flow patterns in these aggregate-of-aggregates structures and computed the partitioning of the flow through intra- and inter-aggregate pores as a function of the spacing between the aggregates. The results of these numerical experiments demonstrate that soluble nutrients are largely transported via flows through inter-aggregate pores. Although this result is consistent with intuition, we have also been able to quantify the relative flow capacity of the two domains under various conditions. For example, in our simulations, the flow capacity through the aggregates (intra-aggregate flow) was less than 2% of the total flow when the spacing between the aggregates was larger than 18 micron. Inter-aggregate pores continued to be the dominant flow pathways even at much smaller spacing; intra-aggregate flow was less than 10% of the total flow when the inter- and intra-aggregate pore sizes were comparable. Such studies are making it possible to identify which model upscaling assumptions are realistic and what computational methods are required for detailed numerical investigation of microbial carbon cycling dynamics in soil systems.

computational fluid dynamics↗
DOE OSTI2021

Accelerating phase-field-based microstructure evolution predictions via surrogate models trained by machine learning methods

Abstract The phase-field method is a powerful and versatile computational approach for modeling the evolution of microstructures and associated properties for a wide variety of physical, chemical, and biological systems. However, existing high-fidelity phase-field models are inherently computationally expensive, requiring high-performance computing resources and sophisticated numerical integration schemes to achieve a useful degree of accuracy. In this paper, we present a computationally inexpensive, accurate, data-driven surrogate model that directly learns the microstructural evolution of targeted systems by combining phase-field and history-dependent machine-learning techniques. We integrate a statistically representative, low-dimensional description of the microstructure, obtained directly from phase-field simulations, with either a time-series multivariate adaptive regression splines autoregressive algorithm or a long short-term memory neural network. The neural-network-trained surrogate model shows the best performance and accurately predicts the nonlinear microstructure evolution of a two-phase mixture during spinodal decomposition in seconds, without the need for “on-the-fly” solutions of the phase-field equations of motion. We also show that the predictions from our machine-learned surrogate model can be fed directly as an input into a classical high-fidelity phase-field model in order to accelerate the high-fidelity phase-field simulations by leaping in time. Such machine-learned phase-field framework opens a promising path forward to use accelerated phase-field simulations for discovering, understanding, and predicting processing–microstructure–performance relationships.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Computational study of the electrostatic potential and charges of multivalent ionic liquid molecules

The electrostatic interactions in ionic liquids (ILs) can be difficult to quantify, but this information is very valuable for improving intermolecular potentials for performing molecular-level simulations. Although polarizable forcefields can provide improved accuracy, the computational efficiency of fixed charge molecular models is often preferred for larger systems. Here, we propose a multi-scale screening approach for analyzing the charge scaling behavior and other electrostatic properties of ILs, which is more attainable than previous analysis methods that rely on the IL crystalline structures. Based on isolated molecules and small clusters, we study the electrostatic potential properties and charges of multivalent IL molecules using density-functional theory calculations and ab initio molecular dynamics simulations. The charges of the IL molecules are calculated and compared using several different charge estimation techniques. Although there are some differences among the estimated partial charges on the atoms, the total anion and cation charges are very similar. In conclusion, the charge scaling factors are substantial, and they are only slightly affected by the IL cluster size model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Vibrational Properties of Anhydrous and Partially Hydrated Uranyl Fluoride

Uranyl fluoride (UO 2 F 2 ) is a hygroscopic powder with two main structural phases: an anhydrous crystal and a partially hydrated crystal of the same R¯3m symmetry. The formally closed-shell electron structure of anhydrous UO 2 F 2 is amenable to density functional theory calculations. We use density functional perturbation theory (DFPT) to calculate the vibrational frequencies of the anhydrous crystal structure and employ complementary inelastic neutron scattering and temperature-dependent Raman scattering to validate those frequencies. As a model closed-shell actinide, we investigated the effect of LDA, GGA, and non-local vdW functionals as well as the spherically-averaged Hubbard +U correction on vibrational frequencies, electronic structure, and geometry of anhydrous UO 2 F 2 . A particular choice of U eff = 5.5 eV yields the correct U Oyl bond distance and vibrational frequencies for the characteristic Eg and A1g modes that are within the resolution of experiment. Inelastic neutron scattering and Raman scattering suggest a degree of water coupling to the lattice vibrations in the more experimentally accessible partially hydrated UO 2 F 2 system, with the symmetric O-U-O stretching vibration shifted approximately 47 cm -1 lower in energy compared to the anhydrous structure. Evidence of water interaction with the uranyl ion is present from a two-peak decomposition of the uranyl stretching vibration in the Raman spectra and anion hydrogen stretching vibrations in the inelastic neutron scattering spectra. A first-order dehydration phase transition temperature is definitively identified to be 125 °C using temperature-dependent Raman scattering.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗