Big-Volume SliceGAN for Improving a Synthetic 3D Microstructure Image of Additive-Manufactured TYPE 316L Steel.

A modified SliceGAN architecture was proposed to generate a high-quality synthetic three-dimensional (3D) microstructure image of TYPE 316L material manufactured through additive methods. The quality of the resulting 3D image was evaluated using an auto-correlation function, and it was discovered that maintaining a high resolution while doubling the training image size was crucial in creating a more realistic synthetic 3D image. To meet this requirement, modified 3D image generator and critic architecture was developed within the SliceGAN framework.

Phase-field modeling for pH-dependent general and pitting corrosion of iron.

This study proposes a new phase-field (PF) model to simulate the pH-dependent corrosion of iron. The model is formulated based on Bockris's iron dissolution mechanism to describe the pH dependence of the corrosion rate. We also propose a simulation methodology to incorporate the thermodynamic database of the electrolyte solutions into the PF model. We show the applications of the proposed PF model for simulating two corrosion problems: general corrosion and pitting corrosion in pure iron immersed in an acid solution. The simulation results of general corrosion demonstrate that the incorporation of the anodic and cathodic current densities calculated by a Corrosion Analyzer software allows the PF model to simulate the migration of the corroded iron surface, the variation of ion concentrations in the electrolyte, and the electrostatic potential at various pH levels and temperatures. The simulation of the pitting corrosion indicates that the proposed PF model successfully captures the anisotropic propagation of a pit that is affected by the local pH of the electrolyte solution and the aggregation of Cl- ions in the pit.

Data assimilation for massive autonomous systems based on a second-order adjoint method.

Data assimilation (DA) is a fundamental computational technique that integrates numerical simulation models and observation data on the basis of Bayesian statistics. Originally developed for meteorology, especially weather forecasting, DA is now an accepted technique in various scientific fields. One key issue that remains controversial is the implementation of DA in massive simulation models under the constraints of limited computation time and resources. In this paper, we propose an adjoint-based DA method for massive autonomous models that produces optimum estimates and their uncertainties within reasonable computation time and resource constraints. The uncertainties are given as several diagonal elements of an inverse Hessian matrix, which is the covariance matrix of a normal distribution that approximates the target posterior probability density function in the neighborhood of the optimum. Conventional algorithms for deriving the inverse Hessian matrix require O(CN^{2}+N^{3}) computations and O(N^{2}) memory, where N is the number of degrees of freedom of a given autonomous system and C is the number of computations needed to simulate time series of suitable length. The proposed method using a second-order adjoint method allows us to directly evaluate the diagonal elements of the inverse Hessian matrix without computing all of its elements. This drastically reduces the number of computations to O(C) and the amount of memory to O(N) for each diagonal element. The proposed method is validated through numerical tests using a massive two-dimensional Kobayashi phase-field model. We confirm that the proposed method correctly reproduces the parameter and initial state assumed in advance, and successfully evaluates the uncertainty of the parameter. Such information regarding uncertainty is valuable, as it can be used to optimize the design of experiments.

Fabrication of three-dimensional ordered nanodot array structures by a thermal dewetting method.

A new fabrication method for three-dimensional nanodot arrays with low cost and high throughput is developed in this paper. In this process, firstly a 2D nanodot array is fabricated by combination of top-down and bottom-up approaches. A nanoplastic forming technique is utilized as the top-down approach to fabricate a groove grid pattern on an Au layer deposited on a substrate, and self-organization by thermal dewetting is employed as the bottom-up approach. On the first-layer nanodot array, SiO(2) is deposited as a spacer layer. Au is then deposited on the spacer layer and thermal dewetting is conducted to fabricate a second-layer nanodot array. The effective parameters influencing dot formation on the second layer, including Au layer thickness and SiO(2) layer thickness, are studied. It is demonstrated that a 3D nanodot array of good vertical alignment is obtained by repeating the SiO(2) deposition, Au deposition and thermal dewetting. The mechanism of the dot agglomeration process is studied based on geometrical models. The effects of the spacer layer thickness and Au layer thickness on the morphology and alignment of the second-layer dots are discussed.