arXiv subjects

Suhas Eswarappa Prameela

Publications and source records attributed to Suhas Eswarappa Prameela.

6 recordsLinked to original sources

arXiv2026

Fine Oxide Dispersoids Modulate Phonon Drag and Dislocation Relaxation in Dynamically Deformed Superalloys

Plastic deformation in metallic alloys is primarily governed by the motion of dislocations, which are atomic scale line defects that move through the crystal lattice under an applied stress. Superalloys containing fine oxide particles withstand extreme temperatures for prolonged durations by resisting dislocation motion. This thermally controlled mechanism conventionally involves dislocations first climbing over the particle and slowly detaching from it, a process that typically occurs on the order of seconds. However, these mechanisms drastically change when dislocation velocities increase, potentially exceeding half the shear wave speed of the material. At such extreme speeds, dislocations can interact with lattice vibrations, leading to pronounced phonon interactions. We leverage a pulsed laser to drive rigid microspheres at controlled velocities towards superalloy substrates containing a dense oxide dispersion. Synchronized high speed imaging allows precise mapping of deformation events, allowing high throughput decoupling and modeling of plasticity contributions. We find that the oxide network produces a dual and counterintuitive effect. Our modeling framework indicates that rapidly moving dislocations bypass oxide particles by bowing rather than climbing, thereby suppressing departure side dislocation relaxation. At the same time, the dense oxide network confines fast moving dislocations within the critical interparticle distance, thereby reducing their interaction with phonons. These findings shed light on new plasticity mechanisms in oxide particle containing superalloys when line defects accelerate and dissipate energy on picosecond timescales.

cond-mat.mtrl-sci↗

arXiv2026

Neural Operator: Is data all you need to model the world? An insight into the paradigm of data-driven scientific ML

Numerical approximations of partial differential equations (PDEs) are routinely employed to formulate the solution of physics, engineering, and mathematical problems involving functions of several variables, such as the propagation of heat or sound, fluid flow, elasticity, electrostatics, electrodynamics, and more. While this has led to solving many complex phenomena, there are some limitations. Conventional approaches such as Finite Element Methods (FEMs) and Finite Difference Methods (FDMs) require considerable time and are computationally expensive. In contrast, data-driven machine learning-based methods, such as neural networks, provide a faster, fairly accurate alternative, and, in particular, focus on neural operators, which have certain advantages such as discretization invariance and resolution invariance. This article aims to provide a comprehensive insight into how data-driven approaches can complement conventional techniques to solve engineering and physics problems, while also noting some of the open problems of machine learning-based approaches. We will note how these new computational approaches can bring immense advantages in tackling many problems in fundamental and applied physics.

cs.AI↗

arXiv2024

Materials Design for Hypersonics

Hypersonic vehicles must withstand extreme conditions during flights that exceed five times the speed of sound. These systems have the potential to facilitate rapid access to space, bolster defense capabilities, and create a new paradigm for transcontinental earth-to-earth travel. However, extreme aerothermal environments create significant challenges for vehicle materials and structures. This work addresses the critical need to develop resilient refractory alloys, composites, and ceramics. We will highlight key design principles for critical vehicle areas such as primary structures, thermal protection, and propulsion systems; the role of theory and computation; and strategies for advancing laboratory-scale materials to flight-ready components.

cond-mat.mtrl-sci↗

arXiv2023

Rapid Quantification of Dynamic and Spall Strength of Metals Across Strain Rates

The response of metals and their microstructures under extreme dynamic conditions can be markedly different from that under quasistatic conditions. Traditionally, high strain rates and shock stresses are measured using cumbersome and expensive methods such as the Kolsky bar or large spall experiments. These methods are low throughput and do not facilitate high-fidelity microstructure-property linkages. In this work, we combine two powerful small-scale testing methods, custom nanoindentation, and laser-driven micro-flyer shock, to measure the dynamic and spall strength of metals. The nanoindentation system is configured to test samples from quasistatic to dynamic strain rate regimes (10$^{-3}$ s$^{-1}$ to 10$^{+4}$ s$^{-1}$). The laser-driven micro-flyer shock system can test samples through impact loading between 10$^{+5}$ s$^{-1}$ to 10$^{+7}$ s$^{-1}$ strain rates, triggering spall failure. The model material used for testing is Magnesium alloys, which are lightweight, possess high-specific strengths and have historically been challenging to design and strengthen due to their mechanical anisotropy. Here, we modulate their microstructure by adding or removing precipitates to demonstrate interesting upticks in strain rate sensitivity and evolution of dynamic strength. At high shock loading rates, we unravel an interesting paradigm where the spall strength of these materials converges, but the failure mechanisms are markedly different. Peak aging, considered to be a standard method to strengthen metallic alloys, causes catastrophic failure, faring much worse than solutionized alloys. Our high throughput testing framework not only quantifies strength but also teases out unexplored failure mechanisms at extreme strain rates, providing valuable insights for the rapid design and improvement of metals for extreme environments.

cond-mat.mtrl-sci↗

arXiv2021

The Interplay Between Solute Atoms and Vacancy Clusters in Magnesium Alloys

Atomic-scale calculations indicate that both stress effects and chemical binding contribute to the redistribution of solute in the presence of vacancy clusters in magnesium alloys. As the size of the vacancy cluster increases, chemical binding becomes more important relative to stress. By affecting the diffusivity of vacancies and vacancy clusters, solute atoms facilitate clustering and stabilize the resulting vacancy clusters, increasing their potential to promote solute segregation and to serve as heterogeneous nucleation sites during precipitation. Experimental observation of solute segregation in simultaneously deformed and aged Mg-Al alloys provides support for this mechanism.

cond-mat.mtrl-sci↗

arXiv2021

Strengthening magnesium by design: integrating alloying and dynamic processing

Magnesium (Mg) has the lowest density of all structural metals and has excellent potential for wide use in structural applications. While pure Mg has inferior mechanical properties; the addition of further elements at various concentrations has produced alloys with enhanced mechanical performance and corrosion resistance. An important consequence of adding such elements is that the saturated Mg matrix can locally decompose to form solute clusters and intermetallic particles, often referred to as precipitates. Controlling the shape, number density, volume fraction, and spatial distribution of solute clusters and precipitates significantly impacts the alloy's plastic response. Conversely, plastic deformation during thermomechanical processing can dramatically impact solute clustering and precipitation. In this paper, we first discuss how solute atoms, solute clusters, and precipitates can improve the mechanical properties of Mg alloys. We do so by primarily comparing three alloy systems: Mg-Al, Mg-Zn, and Mg-Y-based alloys. In the second part, we provide strategies for optimizing such microstructures by controlling nucleation and growth of solute clusters and precipitates during thermomechanical processing. In the third part, we briefly highlight how one can enable inverse design of Mg alloys by a more robust Integrated Computational Materials Design (ICMD) approach.

cond-mat.mtrl-sci↗