In this project we considered the initial stages of helium bubble nucleation via the proposed mechanism of self-interstitial atom nucleation. By calculating the energy barrier to self-interstitial atom nucleation in a range of Fe-Ni-Cr alloys we identified the most important energetic contributions to the phenomenon: the Frenkel-pair energy barrier in the absence of helium and the difference of insertion energy for a He cluster into a perfect lattice and vacancy. From this observation, we developed a simple model of helium-assisted self-interstitial atom nucleation.
We describe a data-driven, multiscale technique to model reactive wetting of a silver–aluminum alloy on a Kovar™ (Fe-Ni-Co alloy) surface. We employ molecular dynamics simulations to elucidate the dependence of surface tension and wetting angle on the drop's composition and temperature. A design of computational experiments is used to efficiently generate training data of surface tension and wetting angle from a limited number of molecular dynamics simulations. The simulation results are used to parameterize models of the material's wetting properties and compute the uncertainty in the models due to limited data. The data-driven models are incorporated into an engineering-scale (continuum) model of a silver–aluminum sessile drop on a Kovar™ substrate. Model predictions of the wetting angle are compared with experiments of pure silver spreading on Kovar™ to quantify the model-form errors introduced by the limited training data versus the simplifications inherent in the molecular dynamics simulations. The paper presents innovations in the determination of “convergence” of noisy MD simulations before they are used to extract the wetting angle and surface tension, and the construction of their models which approximate physio-chemical processes that are left unresolved by the engineering-scale model. Together, these constitute a multiscale approach that integrates molecular-scale information into continuum scale models.
We present a physics-driven modeling framework for early-time detonation soot formation, integrating hydrodynamic flow simulations and particle growth simulations to predict particle dynamics. Validated against SAXS data, our model supports diffusion-limited growth. Molecular dynamics simulations provide diffusion rates to keep particle models species-informed. The methodology is tested on a gram-scale colliding-wave explosive geometry to explore sensitivity of particle fusion to temperature and initial size. This modeling framework, decoupled from empirical methods, enhances predictive capabilities in explosive soot modeling.
Molybdenum disulfide (MoS2) is a lamellar solid lubricant often used in aerospace applications because of its extremely low friction coefficient (∼0.01) in inert environments. The lubrication performance of MoS2 is significantly impaired by exposure to even small amounts of water and oxygen, and the mechanisms behind this remain poorly understood. We use density functional theory calculations to study the binding of water on MoS2 sheets with and without defects. In general, we find that pristine MoS2 is slightly hydrophilic but that defects greatly increase the binding affinity for water. Intercalated water disrupts the crystal structure of bulk MoS2 due to the limited space between lamellae (∼3.4 Å), and this leads to generally unfavorable adsorption, except in the cases where water molecules are located on the sites of sulfur vacancies. We also find that water adsorption is more favorable directly below a surface layer of MoS2 compared to in the bulk.
