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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Computational Investigation of Aqueous Phase Effects on the Dehydrogenation and Dehydroxylation of Polyols over Pt(111)

Prediction of solvation effects on the kinetics of elementary reactions occurring at metal–water interfaces is of high importance for the rational design of catalysts for the biomass and electrocatalysis communities. A lack of knowledge of the reliability of various computational solvation schemes for processes on metal surfaces is currently a limiting factor. Using a multilevel quantum mechanical/molecular mechanical (QM/MM) description of the potential energy surface, we determined characteristic time and length scales for typical free-energy perturbation (FEP) calculations of bond cleavages in ethylene glycol, a sugar surrogate molecule, over Pt(111). Our approach is based on our explicit solvation model for metal surfaces and the repetition of FEP calculations to estimate confidence intervals. Results indicate that aqueous phase effects on the free energies of elementary processes can be determined with 95% confidence intervals from limited configuration space sampling and the fixed charge approximation used in the QM/MM-FEP methodology of smaller 0.1 eV. Next, we computed the initial O–H, C–H, and C–OH bond cleavages in ethylene glycol over Pt(111) in liquid water utilizing two different metal–water interaction potentials. Our calculations predict that aqueous phase effects are small (<0.1 eV) for the C–H bond cleavage and the activation barrier of the C–OH bond cleavage. In contrast, solvation effects are large (>0.35 eV) for the O–H bond cleavage and the reaction free energy of the C–OH bond scission. While the choice of a different Pt–water force field can lead to differences in predicted solvation effects of up to 0.2 eV, the differences are usually smaller (<0.1 eV), and the trends are always the same. In contrast, implicit solvation methods appear to currently not be able to reliably describe solvation effects originating from hydrogen bonding for metal surfaces even qualitatively.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Determining the tensile strength of fuel surrogate TRISO-coated particle buffer, IPyC, and buffer-IPyC interlayer regions

A novel micro tensile sample fabrication technique for determining the tensile strength of the buffer, IPyC, and buffer -IPyC interlayer regions of surrogate (ZrO2) TRISO fuel particle layers was refined and implemented. Copper micro tensile samples served as baseline materials to verify the methods used. Tensile tests performed in this study, while limited in number, were analyzed using standard and Weibull statistics. As expected, the buffer layer was weakest, with an average ultimate tensile strength of 138.70 MPa, and the IPyC layer samples, were strongest, with an average ultimate tensile strength of 189.74 MPa. In the buffer -IPyC interface samples, all breaks occurred in the buffer region, though the average ultimate tensile strength of the samples, 159.80 MPa, was between the pure buffer and IPyC samples. These results suggest the interlayer region has unique properties, perhaps associated with pyrocarbon infiltration into the buffer layer during particle coating. All interlayer samples fractured within the buffer side; however, the stress strain behavior of some of these samples resembled the behavior of the IPyC layer samples. Here, the buffer and IPyC layer strengths had a normal distribution under Weibull analysis, while the interlayer region had a Rayleigh distribution. Further testing is needed to clarify both the standard and Weibull statistical results

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Predicting interface structure using the minima hopping method

Here, we adapt the minima hopping method (MHM) to the problem of interfacial structure prediction and apply it to study a canonical problem, the tilt grain boundaries in SrTiO 3 . Our method employs a hybrid approach by first exploring the potential energy surface (PES) of different grain boundary samplings with an empirical force field, among which the fifteen candidates with lower energies are then refined using ab initio density functional theory (DFT) calculations. During the exploratory stage, we bias the search using a local order parameter to primarily sample various reconstructions in the vicinity of the interface, while preserving the crystallinity of the bulk regions. We further enhance the search by incorporating initial structures with rigid body displacements to account for translational variations between bulk phases, enabling the MHM to effectively generate both stoichiometric and nonstoichiometric SrTiO 3 Σ⁢3(111)[110] and Σ⁢3(112)[110] grain boundaries. From an algorithmic standpoint, MHM outperforms earlier studies based on genetic algorithms (GA) by identifying more stable interfacial structures of several SrTiO 3 grain boundaries. The performance of the present implementation of the MHM approach is primarily limited by exploring an approximate description of the PES with a rather simple Buckingham potential. This limitation leads to variations in performance when compared to approaches utilizing more advanced surrogate PES models, such as direct DFT-PES sampling or GA with the embedded atom method (EAM). Despite the present limitations, the MHM approach is able to yield interfacial structures with comparable or lower interfacial energies in specific cases, such as Σ⁢3(111)[110] Γ=1, ±0.5 and Σ⁢3(112)[110] Γ= ±1, −2, underscoring the robustness of the MHM approach even with a simple approximation of the DFT PES. The MHM interfacial structure prediction method thus offers an efficient approach to understanding the grain boundaries and heterointerfaces at the atomic scale, providing an important prerequisite for effective materials design.

density functional theory↗
DOE OSTI2025

Applications of Nickelate perovskites for neuromorphic computing from electronic structure and Machine Learning

While the limit of Moore's law is presently being reached with current microelectronic technologies, we need to develop new paradigms that overcome this limitation. In that respect, neuromorphic computing is a concept that emulates the neural behavior and response of the human brain, and it has been recognized as a promising alternative approach. In this research project, we will perform multi-fidelity scale bridging to explore the potential use of materials with metal to insulator transition for neuromorphic applications. In particular, rare earth nickelates are promising for such purposes, as the transition in these materials is quite sensitive to a broad set of different external stimuli. Our multi-fidelity approach will bridge the high-fidelity electronic structure calculations with classical potentials. We will bridge dynamical mean field theory with a classical atomistic representation via a deep learning force field. The neural network is trained with energies, charges, and forces obtained by accurate electronic structure theories based on Dynamical Mean Field Theory. The configurational space is generated from known crystal phases, ab initio molecular dynamics with exchange-correlation functionals corrected with the Hubbard model, disordered phases with different concentrations of oxygen vacancies, and nonsymmetrical positions and induced strain by grain interfaces or contact with a substrate. Strategies to train the model with a reduced number of training examples are obtained from active learning methods, and new structures for improving the learning process are generated by using machine learning autoencoders. This classical potential will be validated through a diversity of electronic structure methods and represents an important step to combine the flexibility and accuracy of first-principles with the speed of classical potentials. The generated multi-fidelity surrogate model will be used to understand the role of strain, oxygen vacancies, proton doping, the variation of the crystal phase, substrate effects, vibrational effects as the octahedral rotation, grain boundaries and defect effects on the response of a Metal to Insulator Transition (MIT) in correlated materials. Long time and large-scale simulations will help understand the role of different stimuli to control the hysteresis of the MIT, as it has been experimentally suggested. Selected configurations will be analyzed with higher-level theories to provide an accurate electronic description and to study how the orbitals and charges are rearranged under different conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Multifidelity methods for uncertainty quantification of a nonlocal model for phase changes in materials

This study is devoted to the construction of a multifidelity Monte Carlo (MFMC) method for the uncertainty quantification of a nonlocal, non-mass-conserving Cahn-Hilliard model for phase transitions with an obstacle potential. Here, we are interested in estimating the expected value of an output of interest (OoI) that depends on the solution of the nonlocal Cahn-Hilliard model. As opposed to its local counterpart, the nonlocal model captures sharp interfaces without the need for significant mesh refinement. However, the computational cost of the nonlocal Cahn-Hilliard model is higher than that of its local counterpart with similar mesh refinement, inhibiting its use for outer-loop applications such as uncertainty quantification. The MFMC method augments the desired high-fidelity, high-cost OoI with a set of lower-fidelity, lower-cost OoIs to alleviate the computational burden associated with nonlocality. Most of the computational budget is allocated to sampling the cheap surrogate models to achieve speedup, whereas the high-fidelity model is sparsely sampled to maintain accuracy. For the non-mass-conserving nonlocal Cahn-Hilliard model, the use of the MFMC method results in, for a given computational budget, about an order of magnitude reduction in the mean-squared error of the expected value of the OoI relative to that of the Monte Carlo method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Effect of irradiation on silicate aggregates’ density and stiffness

We report that when exposed to high-energy radiations, the silicate aggregates used in concrete can exhibit some swelling. This, in turn, can result in some internal stress, which can lead to micro-cracks or de-bonding along the cement–aggregate interface. However, there is presently some uncertainty regarding the long-term effect of such high-energy irradiation on aggregates and the associated risk of cracking and failure. Here, based on atomistic simulations, we investigate the effect of neutron irradiation on eight silicate minerals belonging to three different mineralogical families. We demonstrate that the irradiation-induced alterations do not depend on the silicate family but rather on the inherent structure and composition of each mineral. Interestingly, we show that vitrification can be used as a surrogate to assess the upper limit of irradiation-induced swelling. Further, we observe that this swelling potentially leads to high internal stresses, which can result in the initiation and propagation of cracks. Lastly, these findings can guide the selection of optimal aggregate minerals for which the risk of concrete failure upon irradiation is minimized.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Final Scientific/Technical Report, Multi-Sensor Inspection and Robotic Systems for Dry Storage Casks

Once removed from service, fuel rod assemblies are cooled in a pool and then placed in dry storage until they can be transported to a repository for disposal. The extended duration that used nuclear fuel spends in dry storage makes it prudent to be able to nondestructively inspect the dry storage casks. The degradation mechanisms of most concern are chloride-induced stress corrosion cracking of the stainless steel canister that confines the spent fuel and distributed cracking of the concrete in the overpack structure. Multi-sensor inspection and robotic systems were researched, developed, built, and tested. One system operates completely outside the cask to inspect the concrete. The other system, known as the PRINSE system, for Proactive Robotic Inspection of Nuclear Storage Enclosures, enters the cask through an outlet vent and navigates around the outer surface of the stainless steel canister. The PRINSE system delivers sensors for: (i) real-time characterization of the canister surface in order to assess whether chlorides are present, (ii) detecting the presence of cracks using shear-horizontal ultrasonic guided waves, (iii) measuring the temperature of the canister surface, and (iv) a gamma radiation counter. The four sensing modalities are applicable to any cask containing a stainless steel canister, however the robotic delivery system is designed specifically for Holtec’s HI-STORM family of casks. Horizontal axis casks have exposed concrete surfaces. A fully contactless air-coupled scanning ultrasonic test platform and a frequency-wavenumber domain data analysis method are used for these concrete structures. Analysis, numerical simulation and experimental results demonstrate that distributed cracks in concrete can be detected and cracked regions localized within a scanned region of concrete. Vertical axis casks (like the HI-STORM family) have steel-clad concrete overpacks. In a separate effort, a semi-coupled test platform and a guided wave attenuation-based data analysis method are used for disbond detection. Analysis, numerical simulation and experimental results show that the bonding conditions of steel-concrete interfaces can be quantitatively characterized using the guided wave signal analysis scheme. The PRINSE system is designed for vertical axis dry storage casks, specifically the HI-STORM family. The double pulse laser-induced breakdown spectroscopy (LIBS) system takes advantage of an optical fiber to deliver intense light to the canister surface that enables surface composition characterization in-situ in real time. Chlorine concentrations as low as 10 mg/m2 were detected on stainless steel. Additionally, a simpler laser setup was created to detect sodium as a surrogate for chlorine, and sodium was detectable when the chlorine concentration was as low as 0.5 mg/m2. The electromagnetic acoustic transducers (EMATs) enable detection of stress corrosion cracks. The EMATs generate shear-horizontal waves that propagate around the canister and are reflected by cracks enabling 100% of the canister weld lines to be inspected. The EMAT-based crack detection system was tested at the Pacific Northwest National Laboratory on a canister containing blind defects and was shown to detect the crack sizes it was designed to detect. The PRINSE system is a novel marsupial robot. The command center and winch outside the cask control the delivery arm and train of cars that traverse the outside of the canister from the cask ventilation system. The sensors are housed in the cargo bays of three cars that comprise the train, which is lowered to the bottom of the canister by the winch. Encoders on the winch enable the sensors to be delivered to a prescribed location, e.g., for re-inspection of a problem area. The PRINSE system does not mar the canister surface, can be extracted manually in the case of a power failure, and is designed for an elevated temperature and gamma radiation environment, although not all components of the prototype are made from the final materials. The PRINSE system was tested on a full canister that was placed inside a HI-STORM cask at the Holtec Manufacturing Division. It was also demonstrated on a full-height mockup at Penn State to a live audience.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

Quantifying uncertainty in the process-structure relationship for Al–Cu solidification

Phase field method (PFM) is a simulation tool to predict microstructural evolution during solidification and is helpful to establish the process-structure relationship for alloys. The robustness of the relationship however is affected by model-form and parameter uncertainties in PFM. In this paper, the uncertainty associated with the thermodynamic and process parameters of PFM is studied and quantified. Surrogate modeling is used to interpolate four quantities of interests (QoIs), including dendritic perimeter, area, primary arm length, and solute segregation, as functions of thermodynamic and process parameters. A sparse grid approach is applied to mitigate the curse-of-dimensionality computational burden in uncertainty quantification. Polynomial chaos expansion is employed to obtain the probability density functions of the QoIs. The effect of parameter uncertainty on the Al–Cu dendritic growth during solidification simulation are investigated. Here, the results show that the dendritic morphology varies significantly with respect to the interface mobility and the initial temperature.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Formation of multicomponent alloy particles in doped ceria under I 2+ ion irradiation and thermal annealing

Multicomponent alloys resembling the e-phase particles in nuclear fuel have the potential to immobilize 99 Tc in a stable waste form. Formation of such multicomponent alloys under extreme conditions is studied in a non-activated model system: CeO 2 doped with 2 wt% Mo, 1.5 wt% Ru, 0.75 wt% Pd, 0.5 wt% Re and 0.25 wt% Rh. The doped CeO 2 films were irradiated with I 2+ ions (610°C, 10 16 and 5×10 16 I 2+ /cm 2 ). For selected samples post-irradiation heat treatment was conducted (900°C, 1100°C). Analytical TEM revealed the formation of 5-20 nm precipitates containing Pd, Mo, and Re sometimes associated with cavities. Iodine was found to segregate to grain boundaries and cracks. After heat treatment at 1100°C, the CeO 2 matrix had recrystallized. Precipitates containing Pd, Mo, Re, and Ru were observed near the interface with the polycrystalline YSZ substrate. It follows that Pd is the most mobile element, followed by Mo and Re. Finally, while irradiation promotes precipitation, high-temperature effects are more significant.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Using Flory–Huggins-informed human-in-the-loop Bayesian optimization to map the phase diagram of polymer blends

Mapping the phase diagram of polymer blends is an essential step in controlling the structure–property relationship of polymer-based materials. However, traditional grid-based approaches are inefficient and rely on subjective judgements for terminating the experimental campaign. Artificial intelligence-guided experimentation offers a compelling alternative, especially when data-driven decision-making is interfaced with established polymer thermodynamics to improve efficiency and interpretability. Here, we introduce a physics-informed Bayesian optimization approach to guide the mapping of the phase diagram of a model blend containing poly(methyl methacrylate) and poly(styrene-ran-acrylonitrile). Physical information is derived from a Flory–Huggins representation of the spinodal curve, which is integrated into the Bayesian optimization process as a structured prior mean that acts as a soft constraint. Implemented as a human-in-the-loop workflow, the approach leverages optical imaging of film cloudiness with iterative Gaussian process surrogate modeling and a parameter selection decision policy to identify the composition-temperature conditions for sequential iterations. Convergence of kernel and Flory–Huggins-based hyperparameters provided a stopping criterion, ensuring an objective and interpretable termination of the experimental campaign. The framework recovered the known lower critical solution temperature (∼160 °C), while increasing material efficiency through targeted sampling. This work establishes a proof-of-concept for the application of Bayesian optimization workflows to study polymer blend miscibility.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Phase Field Simulations of Microstructure Evolution in IN718 using a Surrogate Ni–Fe–Nb Alloy during Laser Powder Bed Fusion

The solidification microstructure in IN718 during additive manufacturing was modeled using phase field simulations. The novelty of the research includes the use of a surrogate Ni–Fe–Nb alloy that has the same equilibrium solidification range as IN718 as the model system for phase field simulations, the integration of the model alloy thermodynamics with the phase field simulations, and the use of high-performance computing tools to perform the simulations with a high enough spatial resolution for realistically capturing the dendrite morphology and the level of microsegregation seen under additive manufacturing conditions. Heat transfer and fluid flow models were used to compute the steady state temperature gradient and an average value of the solid-liquid (s-l) interface velocity that were used as input for the phase field simulations. The simulations show that the solidification morphology is sensitive to the spacing between the columnar structures. Spacing narrower than a critical value results in continued growth of a columnar microstructure, while above a critical value the columnar structure evolves into a columnar dendritic structure through the formation of secondary arms. These results are discussed in terms of the existing columnar to dendritic transition (CDT) theories. The measured interdendritic Nb concentration, the primary and secondary arm spacing is in reasonable agreement with experimental measurements performed on the nickel-base superalloy IN718.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Biogeochemical Equation of State for the Sea-Air Interface

We have recently argued that marine interfacial surface tension must have a distinctive biogeography because it is mediated by fresh surfactant macromolecules released locally through the food web. Here we begin the process of quantification for associated climate flux implications. A low dimensionality (planar) equation of state is invoked at the global scale as our main analysis tool. For the reader’s convenience, fundamental surfactant physical chemistry principles are reviewed first, as they pertain to tangential forces that may alter oceanic eddy, ripple, and bubble fields. A model Prandtl (neutral) wind stress regime is defined for demonstration purposes. It is given the usual dependence on roughness, but then in turn on the tension reduction quantity known as surface pressure. This captures the main net influences of biology and detrital organics on global microlayer physics. Based on well-established surrogate species, tangent pressures are related to distributed ecodynamics as reflected by the current marine systems science knowledge base. Reductions to momentum and related heat-vapor exchange plus gas and salt transfer are estimated and placed on a coarse biogeographic grid. High primary production situations appear to strongly control all types of transfer, whether seasonally or regionally. Classic chemical oceanographic data on boundary state composition and behaviors are well reproduced, and there is a high degree of consistency with conventional micrometeorological wisdom. But although our initial best guesses are quite revealing, coordinated laboratory and field experiments will be required to confirm the broad hypotheses even partially. We note that if the concepts have large scale validity, they are super-Gaian. Biological control over key planetary climate-transfer modes may be accomplished through just a single rapidly renewed organic monolayer.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2022

Uncertainty Models for the Hybrid Parametric Variation Method of Uncertainty Quantification; Analysis

There is some level of uncertainty in every finite element model (FEM), which flows to a level of uncertainty in predicted results. The purpose of uncertainty quantification (UQ) is to provide statistical bounds on prediction accuracy based on model uncertainty. This is distinct from model updating, which attempts to modify models to improve their accuracy. UQ does not improve the accuracy of models, but accepts that the models are inherently inaccurate and attempts to quantify the impact of that inaccuracy on predicted results. Previously, an alternate method for UQ, called the Hybrid Parametric Variation (HPV) method, was applied to Space Launch System (SLS) Hurty/Craig-Bampton (HCB) components to predict system-level statistics for launch vehicle attitude control transfer functions and core stage section loads due to buffet. The HPV method combines a parametric variation of the HCB fixed-interface (FI) modal frequencies with a nonparametric variation (NPV) method that randomly varies the HCB mass and stiffness matrices as Wishart random matrix distributions using random matrix theory (RMT). Alternatively, the most common method for modeling uncertainty in the structural dynamics community is a parametric approach, which varies physical parameters in the model. However, there are several disadvantages associated with the parametric method. Determining a reduced set of parameters that have a significant impact on the system response can be time consuming, and the selected parameter probability distributions are rarely reliably known. Therefore, in practice, the parameters are surrogates for the actual errors, and the link to parameter uncertainty is unknown. Another major drawback is that the uncertainty that can be represented is limited to the form of the nominal FEM. It is the experience of the authors that based on numerous aerospace programs, almost all FEM errors are in form rather than parameter values. This hypothesis is supported by the observation of the authors that it is almost never possible to ‘tune’ a FEM to match modal test results by only modifying model parameters. Model-form uncertainty cannot be directly represented by FEM input parameters nor included in a parametric approach. However, model-form uncertainty can be modeled using RMT, where a probability distribution is developed for the matrix ensemble of interest. The major advantage of the NPV method is that it covers errors in model form. The HPV method anchors uncertainty at the HCB component level to component modal test results by matching the HCB and test modes based on mode descriptions or other methods, and then applying differing levels of frequency variation. The specific variations depend on the confidence to which a component FEM has been validated through modal testing. The NPV method is layered on the frequency variation to match modal test self-orthogonality and cross-orthogonality (XO) results. Once the component uncertainty models are identified, they are assembled, and the uncertainty is propagated to the system level using a Monte Carlo (MC) analysis approach that generates statistics for system-level predictions This provides a UQ method that can be traced to test data, which can be updated as additional data and improved correlated models become available. The purpose of this paper is to collect and present all of the theory for HPV that has been previously published in reports and papers and to present examples of its application. Specifically, component uncertainty models based on the dispersion of corresponding mass and stiffness matrices using proposed test/analysis correlation metrics are investigated. The first example is purely academic so that the true answers are known, and the validity of the HPV method and the corresponding uncertainty models can be determined. The purpose of this paper is to collect and present all of the theory for HPV that has been previously published in reports and papers and to present examples of its application. Specifically, component uncertainty models based on the dispersion of corresponding mass and stiffness matrices using proposed test/analysis correlation metrics are investigated. The first example is purely academic so that the true answers are known, and the validity of the HPV method and the corresponding uncertainty models can be determined. The second example is an application to a component that is design specific to the SLS. Based on this work and other assessments, the HPV method provides another tool to the toolset used for complex system UQ analysis. From experience gathered to date using the HPV method, additional design specific applications must be investigated to provide further confidence in the validity of the HPV method of UQ analysis.

Uncertainty quantification↗
DOE OSTI2026

Discovery of Probabilistic Dirichlet-to-Neumann Maps on Graphs

Dirichlet-to-Neumann maps enable the coupling of multiphysics simulations across computational subdomains by ensuring continuity of state variables and fluxes at artificial interfaces. We present a novel method for learning Dirichlet-to-Neumann maps on graphs using Gaussian processes, specifically for problems where the data obey a conservation law arising from an underlying partial differential equation. Our approach combines discrete exterior calculus and nonlinear optimal recovery to infer relationships between vertex and edge values. This framework yields data-driven predictions with uncertainty quantification across the entire graph, even when observations are limited to a subset of vertices and edges. By minimizing the reproducing kernel Hilbert space norm while penalizing kernel complexity through maximum likelihood estimation, our method ensures that the resulting surrogate strictly enforces conservation laws without overfitting. We demonstrate our method on two representative applications: subsurface flow in fracture networks and arterial blood flow. Finally, the results demonstrate that the method maintains high accuracy and well-calibrated uncertainty estimates even under severe data scarcity, highlighting its potential for scientific applications where limited data and reliable uncertainty quantification are critical.

Dirichlet-to-Neumann map↗
DOE OSTI2023

Multifidelity validation of digital surrogates using variable-density turbulent mixing models

In this study, ensembles of experimental data are presented and utilized to compare and validate two models used in the simulation of variable-density (Atwood = 0.22), compressible turbulent mixing. Though models of this kind (Reynolds averaged NavierStokes and large-eddy simulations) have been validated extensively with more canonical flows in previous studies, here the present approach offers novelty in the complexity of the geometry, the ensemble-based validation, and the uniformity of the computational framework on which the models are tested. Moreover, all experimental and computational tasks were completed by the authors which has led to a tightly coupled experimental configuration with its “digital twin.” The experimental divergent-shock-tube facility and its data acquisition methods are described and replicated in simulation space. A 2D Euler model which neglects the turbulent mixing at the interface is optimized to experimental data using a Gaussian process. This model then serves as the basis for both the 2D RANS and 3D LES studies that make comparisons to the mixing-layer data from the experiment. A relatively simple RANS model is shown to produce good agreement with experimental data only at late flow development times. The LES ensembles generally show good agreement with experimental data but display sensitivity to the characterization of initial conditions. Resolution-dependent behavior is also observed for certain higher-order statistics of interest. Overall, the LES model successfully captures the effects of divergent geometry, compressibility, and combined nonlinear instabilities inherent to the problem. The successful prediction of mixing width and its growth rate highlight the existence of three distinct regimes in the development of the instability, each with similarities to previously studied instabilities.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Fuel-Cladding Eutectic Study of Legacy Fast Flux Test Facility (FFTF) MFF HT9/U-10Zr Metallic Fuel

This report presents the first systematic investigation of fuel-cladding eutectic interaction (FCEI) in irradiated HT9/U-10Zr metallic fuel from the Fast Flux Test Facility (FFTF) Materials Fuels Form (MFF) program, using differential scanning calorimetry (DSC) coupled with scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS). Two irradiated fuel cross-sections, MNT07H (9.5 at% burnup, x/L = 0.78) and MNT08H (7.0 at% burnup, x/L = 0.93), were subjected to three successive isothermal annealing rounds (R1–R3) at 820°C for 20 minutes each, yielding a cumulative transient duration of one hour. This study directly addresses a recognized gap in the existing FCEI database, which previously lacked irradiated HT9/U-10Zr data at burnup levels above 8 at%. Two principal findings emerge from the study. First, for both samples, FCEI remained spatially confined within the pre-existing fuel-cladding chemical interaction (FCCI) zone boundaries after R3, with no measurable eutectic penetration into unaffected cladding beyond the original FCCI layer. This self-limiting behavior is consistent with historical Fuel Behavior Test Apparatus (FBTA) results and is attributed to the near-eutectic phase composition of the FCCI zone, which rapidly absorb the available eutectic-forming constituents and then stall penetration once the FCCI zone is consumed. A comparison with unirradiated surrogate data further supports this mechanism: whereas a U–34 at.% Fe sample would be expected to show ~176 µm of iron penetration under comparable conditions, the irradiated samples exhibited only ~20 µm, a discrepancy attributed to irradiation-induced interfacial porosity and pre-existing FCCI composition gradients. Second, for MNT08H, FCEI was observed exclusively on the half of the cladding circumference where pre-existing steady-state FCCI was present, with no detectable FCEI on the opposite half. Three hypotheses are proposed to explain this asymmetry: the inhibiting role of a zirconium-rich rind at the fuel-cladding interface; the chemical sequestration of iron by redistributed zirconium within the fuel matrix; and the persistence of fuel-cladding gaps on the FCEI-free half that preclude direct contact. All three hypotheses require further experimental investigation. The results extend the empirical FCEI database into higher-burnup territory and demonstrate the viability of DSC-based testing as a substitute for the no-longer-available FBTA apparatus. Future work will include additional cross-section testing, compilation of the full FCEI dataset, model evaluation, and DSC testing of ternary fuel compositions to broaden the experimental basis for safety assessment of sodium-cooled fast reactor systems.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

Operator Evaluation of Mobile Robotic Systems for Inventory, Inspection, and Contamination Survey Tasks in D and D Environments - 17314

The Nuclear and Applied Robotics Group (NRG) has developed two mobile systems with simple, intuitive Human-Machine Interfaces (HMIs). Thus, the systems can be operated by operators who are not robotics experts to perform inventory, inspection and routine contamination surveys in areas where Special Nuclear Material (SNM) is stored and handled. This effort presents feedback collected from EM operators using the two systems at the Portsmouth Gaseous Diffusion Plant in Piketon, OH who used the robotic solutions to perform tasks on site and with minimal training. This approach has the potential to address issues related to ALARA, ergonomics, and reliably completing dull, repetitive tasks while maintaining domain-specific experts in the control loop. The two systems were shipped to - and demonstrated in - a DOE designated area and demonstrated completing contamination testing using a combination of real and surrogate sensors. This paper reviews the initial hardware development, demonstration requirements, and the modifications/improvements made to deploy the systems for this effort. It then summarizes the feedback garnered from EM operators who used the systems at the DOE facility. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Helium interactions with alumina formed by atomic layer deposition show potential for mitigating problems with excess helium in spent nuclear fuel

Helium gas accumulation from alpha decay during extended storage of spent fuel has potential to compromise the structural integrity the fuel. We report results obtained with surrogate nickel particles which suggest that alumina formed by atomic layer deposition can serve as a low volume-fraction, uniformly-distributed phase for retention of helium generated in fuel particles such as uranium oxide. Thin alumina layers may also form transport paths for helium in the fuel rod, which would otherwise be impermeable. Micron-scale nickel particles, representative of uranium oxide particles in their low helium solubility and compatibility with the alumina synthesis process, were homogeneously coated with alumina approximately 3–20 nm by particle atomic layer deposition (ALD) using a fluidized bed reactor. Particles were then loaded with helium at 800 °C in a tube furnace. Subsequent helium spectroscopy measurements showed that the alumina phase, or more likely a related nickel/alumina interface structure, retains helium at a density of at least 10 17 atoms/cm 3 . High resolution transmission electron microscopy revealed that the thermal treatment increased the alumina thickness and generated additional porosity. Results from Monte Carlo simulations on amorphous alumina predict the helium retention concentration at room temperature could reach 10 21 atoms/cm 3 at 400 MPa, a pressure predicted by others to be developed in uranium oxide without an alumina secondary phase. This concentration is sufficient to eliminate bubble formation in the nuclear fuel for long-term storage scenarios, for example. Finally, measurements by others of the diffusion coefficient in polycrystalline alumina indicate values several orders of magnitude higher than in uranium oxide, which then can also allow for helium transport out of the spent fuel.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗