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Placid Ferreira

Publications and source records attributed to Placid Ferreira.

3 recordsLinked to original sources

Revealing the origin of ionic conduction in silver-iodide-doped silver phosphate glass

Fast ionic transport is a defining feature of many solid electrolytes, yet its microscopic origin is not fully understood. In the absence of microscopic insights, the development of next-generation solid-state batteries remains largely empirical. Most existing measurements access either the low-frequency transport response or the high-frequency bound polarization, yet the intermediate mesoscopic frequency regime is where ionic transport emerges. By varying the $\mathrm{AgI}$ concentration ($x$) and performing time-domain terahertz spectroscopy (TDTS) in a prototypical glassy electrolyte $\left(\mathrm{AgI}\right)_{x}\left(\mathrm{AgPO_3}\right)_{(1-x)}$, we reveal this intermediate frequency regime and identify a crossover from bound-current-dominated conduction to conductivity arising from short-range dispersive ionic transport. We find that bound polarization associated with the bond-bending motion of the $\mathrm{P{-}O^- -Ag^+}$ motif is present across compositions but is insufficient to produce ionic transport on its own. Transport emerges only when this polarization is embedded in a sufficiently soft $\mathrm{AgPO_3}$ glassy matrix and accompanied by a high carrier density. These ingredients together take the system from a vibrationally bound response to short-range dispersive motion.

cond-mat.mtrl-sci↗

Optimization of Solidification in Die Casting using Numerical Simulations and Machine Learning

In this paper, we demonstrate the combination of machine learning and three dimensional numerical simulations for multi-objective optimization of low pressure die casting. The cooling of molten metal inside the mold is achieved typically by passing water through the cooling lines in the die. Depending on the cooling line location, coolant flow rate and die geometry, nonuniform temperatures are imposed on the molten metal at the mold wall. This boundary condition along with the initial molten metal temperature affect the product quality quantified in terms of micro-structure parameters and yield strength. A finite volume based numerical solver is used to determine the temperature-time history and correlate the inputs to outputs. The objective of this research is to develop and demonstrate a procedure to obtain the initial and wall temperatures so as to optimize the product quality. The non-dominated sorting genetic algorithm (NSGA-II) is used for multi-objective optimization in this work. The number of function evaluations required for NSGA-II can be of the order of millions and hence, the finite volume solver cannot be used directly for optimization. Therefore, a multilayer perceptron feed-forward neural network is first trained using the results from the numerical solution of the fluid flow and energy equations and is subsequently used as a surrogate model. As an assessment, simplified versions of the actual problem are designed to first verify results of the genetic algorithm. An innovative local sensitivity based approach is then used to rank the final Pareto optimal solutions and select a single best design.

cs.CE↗

Finite Volume Simulation Framework for Die Casting with Uncertainty Quantification

The present paper describes the development of a novel and comprehensive computational framework to simulate solidification problems in materials processing, specifically casting processes. Heat transfer, solidification and fluid flow due to natural convection are modeled. Empirical relations are used to estimate the microstructure parameters and mechanical properties. The fractional step algorithm is modified to deal with the numerical aspects of solidification by suitably altering the coefficients in the discretized equation to simulate selectively only in the liquid and mushy zones. This brings significant computational speed up as the simulation proceeds. Complex domains are represented by unstructured hexahedral elements. The algebraic multigrid method, blended with a Krylov subspace solver is used to accelerate convergence. State of the art uncertainty quantification technique is included in the framework to incorporate the effects of stochastic variations in the input parameters. Rigorous validation is presented using published experimental results of a solidification problem.

math.NA↗