Opportunity: Predicting the Thermoelastic Response of Gradient Composition Infrared Windows

This project seeks to understand the influence of gradient composition in the deformation of infrared (IR) windows when subjected to extreme environments. To optimize sensor performance (e.g., accuracy when tracking a target), targeting error must be minimized. However, these windows can undergo bowing due to thermal gradients as one side of the the material expands more than the other. This results in refraction of incoming light, thus reducing the accuracy of target tracking. Introducing gradient compositions would allow for more control over the temperature gradient and the thermal expansion, thus improving performance.

The participant will utilized developed computational capabilities, primarily finite element models (FEniCS), to evaluate the effect of both through thickness and radial gradients on the evolution of the temperature, stress, and deformation of potential materials. High performance computing resources will be utilized for creating FEniCS meshes for gradient designs and performing coupled thermomechanical simulations. The intern will develop workflows to a systematic study of how the magnitude and rate of gradation affect the resultant curvature of the window.