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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 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Energetic Study of Helium Cluster Nucleation and Growth in 14YWT through First Principles

First principles calculations have been performed to energetically investigate the helium cluster nucleation, formation and growth behavior in the nano-structured ferritic alloy 14YWT. The helium displays strong affinity to the oxygen:vacancy (O:Vac) pair. By investigating various local environments of the vacancy, we find that the energy cost for He cluster growth increases with the appearance of solutes in the reference unit. He atom tends to join the He cluster in the directions away from the solute atoms. Meanwhile, the He cluster tends to expand in the directions away from the solute atoms. A growth criterion is proposed based on the elastic instability strain of the perfect iron lattice in order to determine the maximum number of He atoms at the vacancy site. We find that up to seven He atoms can be trapped at a single vacancy. However, it is reduced to five if the vacancy is pre-occupied by an oxygen atom. Furthermore, the solute atoms within nanoclusters, such as Ti and Y, will greatly limit the growth of the He cluster. A migration energy barrier study is performed to discuss the reduced mobility of the He atom/He cluster in 14YWT.

Gan, Yingye↗
DOE OSTI2018

Mechanistic Modeling of Departure from Nucleate Boiling under Transient Scenarios

The critical heat flux (CHF) corresponding to the departure from nucleate boiling (DNB) is one of the major limiting factors in the design and operation of pressurized water reactors (PWRs). Various predictive tools have been proposed for steady-state conditions. Empirical correlations and look-up tables yield relatively good agreement with specific experimental datasets and are widely used in subchannel codes for PWR transient simulations. However, experimental studies have revealed that during fast transients, the CHF values can become significantly higher than those in steady-state or slow transient scenarios, causing this modeling approach to result in overly conservative DNB prediction. This paper presents a mechanistic transient CHF model. Based on prior work, two DNB triggering mechanisms prevail in this model—the hydrodynamic thinning process and the thermal thinning process—both of which rely on the generally admitted liquid sublayer dryout theory. These mechanisms evaluate the depletion of the liquid sublayer underneath vapor slugs flowing over the channel. This model is further validated against three sets of power transient experiments at different operating conditions. While it clearly outperforms steady-state approaches and generally agrees closely with measurements, it still remarkably under-estimates CHF for very fast transients at low pressure. Future investigations will address this limitation.

Zhao, X. G.↗
DOE OSTI2019

A ROBUST MECHANISTIC APPROACH TO PREDICTION OF DEPARTURE FROM NUCLEATE BOILING

departure from nucleate boiling (DNB) sets a regulatory limit for licensing pressurized water reactors (PWRs). Under such conditions, the heated surface is permanently blanketed by a vapor film, leading to a sharp deterioration of the heat transfer coefficient at the heater/coolant interface and an abrupt temperature rise. Unfortunately, the path for an accurate, robust prediction of DNB has been elusive due to lack of consensus on its triggering mechanism. This work reviews existing physics-driven modeling tools in the first place. An evolutionary channel-scale mechanistic model that leverages key assumptions in the relatively well-accepted mechanisms of liquid sublayer dryout and near-wall bubble crowding is then proposed. Detailed validation of the proposed model has demonstrated its improved predictive capabilities over previous data/physics-driven models with regard to an extensive DNB-specific CHF test matrix covering a wide range of flow conditions. The unique feature of the proposed model lies in its ability to predict DNB for different heater geometries (including round tube, annulus, and rod bundle), which is essential in deciphering fuel performance metrics from different facilities and reactor types. The proposed model will be further implemented in fuel performance codes to help improve modeling of transient DNB scenarios such as during a reactivity-initiated accident (RIA).

Zhao, Xingang↗
DOE OSTI2022

Machine Learning-based Prediction of Departure from Nucleate Boiling Power for the PSBT Benchmark

Machine Learning (ML) has seen an exponential growth in its applications due to its advanced data driven prediction capabilities. The study presents a data-driven approach as a preliminary attempt to predict the power at which departure from nucleate boiling (DNB) occurs in pressurized water reactors (PWRs) by constructing an advanced ML algorithm that takes outlet pressure, inlet temperature and inlet mass flux as the input features. DNB is a critical heat flux (CHF) phenomenon seen in PWRs. The experimental data from the PWR subchannel and bundle tests (PSBT) benchmark is first used to train an artificial neural network (ANN) to predict the DNB power, which produces a root mean square error (RMSE) of 6.89 kW/m when tested on a blind subset of the PSBT data. Since the PSBT dataset is relatively small to train an accurate ANN, a data augmentation methodology based on generative adversarial networks (GANs) is used to expand the training dataset. By assuming that the real data follows a certain distribution, GANs try to learn that underlying distribution to generate similar synthetic data to augment the database and to improve the predictive capabilities of the ANN. The data generated from GANs are validated using 1-nearest neighbor and kernel maximum mean discrepancy. To further ensure data from GAN is similar to PSBT, the data is tested and filtered out using the sub-channel thermal-hydraulic code CTF. The results indicate that with the addition of 120 data points from GAN the RMSE reduces to 4.84 kW/m showing promising results for future developments.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2012

Nb Films: Substrates, Nucleation & Crystal Growth

Over the years, Nb/Cu technology, despite its shortcomings due to the commonly used magnetron sputtering, has positioned itself as an alternative route for the future of superconducting structures used in accelerators. Recently, significant progress has been made in the development of energetic vacuum deposition techniques, showing promise for the production of thin films tailored for SRF applications. The purpose of using energetic condensation is to improve film structure on low temperature substrates by adding energy to the film during condensation to compensate for the lack of thermally induced growth processes. Energetic condensation is characterized by a number of surface and subsurface processes enabled by the energy of the incoming ions such as desorption of adsorbed molecules, enhanced mobility of surface atoms, and implantation of impinging ions under the surface. All these with the nature of the substrate and its crystallographic mismatch with Nb have an important influence on the nucleation and subsequent growth of the Nb film. This paper will show how the structure and defect density thus electron mean free path (represented by residual resistance ration values) of Nb films can be tailored on various substrates, crystalline and amorphous, by varying the ion energy and thermal energy provided to the substrate.

Valente, Anne-Marie↗
DOE OSTI2015

Navigating Organo-Lead Halide Perovskite Phase Space via Nucleation Kinetics toward a Deeper Understanding of Perovskite Phase Transformations and Structure-Property Relationships

Organo-lead halide perovskite photovoltaics have developed faster than our understanding of the material itself. Using the vast body of work on perovskite processing created in just the past few years, it is possible to create a better picture of this material's complex phase-transformation behavior. This concept article summarizes and correlates the current understanding of structural intermediates, kinetic controls, and structure–property relationships of organo-lead iodide perovskites. To this end, a new way of graphically relating information is developed, allowing the simultaneous mapping of schematic kinetic relationships between all currently prevailing perovskite deposition and growth techniques.

14 SOLAR ENERGY↗
DOE OSTI2022

In-situ imaging of nucleation and growth of superlattices from nanoscale colloidal nanoparticles

Nanoparticle-based superlattices have attracted extensive research efforts due to their versatile composition and packing-dependent optical, magnetic, electronic, catalytic, mechanical properties. Yet the experimental understanding of the thermodynamics and pathways of the crystallization processes for these systems, which govern their ultimate morphology, size, and packing structure, has for long remained underexplored. Here, this minireview highlights recent integration of liquid-phase transmission electron microscopy, which allows for real-space, real-time direct imaging of the crystallization processes at the single-particle level, with simulation and the conceptual framework established by pioneers such as Dr. A. A. Chernov. Different aspects of superlattice formation were revealed by direct imaging for the first time, such as the existence of prenucleation precursor in nonclassical crystallization, surface energy-dependent growth, and coalescence, allowing for the charting of phase coordinates and thermodynamic quantities at the nanoscale. We discuss the similarities and differences of crystal growth behaviors in atomic and nanoparticle systems, as well as engineering opportunities to fashion, shape, and achieve quality control in superlattice formation from nanoparticles, for next-generation optical and mechanical metamaterials.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Solute effects on interfacial dislocation emission in nanomaterials: Nucleation site competition and neutralization

Solutes added to stabilize nano-crystalline metals against grain growth, may segregate to grain boundaries and triple junctions where they can affect the process of the dislocation emission. Here, we demonstrate that this effect can be very complex due to different rates of segregation at different interfaces. Moreover, at large concentrations, when the solutes form clusters, the interfaces between these clusters and the matrix can introduce new dislocation emission sources, which can be activated under lower applied stress. Thus, the strength maximum can occur at a certain solute concentration: adding solutes beyond this optimal concentration can reduce the strength of the material.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Nucleation of Cu n ( n = 1–5) Clusters and Equilibrium Morphology of Cu Particles Supported on CeO 2 Surface: A Density Functional Theory Study

In the present work, the interactions between Cun (n=1~4) clusters and three low-index structural CeO2 (111, 110, 100) surfaces were investigated using density functional theory calculations. The atomic Cu prefers to aggregate into large clusters on the CeO2(111) surface while it is unfavorable on both CeO2(110) and CeO2(100) surfaces. Once the planar Cu4-p cluster is formed, it would convert into the 3D tetrahedral Cu4-t cluster on CeO2(110) and CeO2(111) surfaces, i.e., 2D to 3D transition growth is thermodynamically favorable and kinetically feasible on CeO2(110) and CeO2(111) surfaces. The effects of ceria support structure on the morphology of the large Cu nanoparticle was examined using calculated adhesion and interfacial energies of periodic Cu(khl)/CeO2 model systems. The calculated macroscopic contact angles (? > 100?) of various model systems suggest that the supported Cu nanoparticle is in bad wetting condition. Finally, the morphologies of large Cu nanoparticles on different CeO2 surfaces were predicted using Wulff-Kaichew construction principle.

Cu/CeO2 catalyst, Adsorption and nucleation, DFT c↗