QuesTek Innovations LLC, Evanston, Illinois, USA, has announced that it has received three new Small Business Innovation Research (SBIR) Phase I Awards from the Office of Naval Research, NASA and the Department of Energy to develop advanced materials and technologies across a diverse range of materials systems, industries and demanding end-use applications.

For the Office of Naval Research, QuesTek intends to develop a software tool leveraging an integrated computational materials engineering (ICME)-based modelling framework to enable optimisation of a nickel alloy for Additive Manufacturing. The software tool will enable alloy composition customisation for conditions observed in AM, enhancing manufacturability, reducing flaws, and improving mechanical properties of additively manufactured Ni components. The software’s mechanistic computational models will be calibrated using experimental techniques to validate predictive models, explaining complex phenomena resulting from AM processing. The developed software tool is expected to improve the understanding of AM technology and harness it to design a new generation of advanced Ni alloys and components for jet engines, industrial gas turbines, and other demanding applications.

In line with the NASA SBIR, QuesTek will computationally design calcia-magnesia-alumina-silicate-(CMAS) resistant multi-layer thermal and environmental barrier coatings for ceramic matrix composite (CMC)-based hot turbine components. While CMCs allow for greater operating temperatures and fuel efficiency compared to Ni superalloys, they are not readily used because current coatings lack long-term protection against molten CMAS attack at high temperature. QuesTek’s new ICME-designed coating system will enable increased reliability and performance of CMCs in aircraft propulsion systems, hypersonic combustor panels, commercial turbo fans and industrial gas turbine plants leading to greater fuel efficiency.

For the Department of Energy, QuesTek intends to apply its ICME tools to develop a machine learning-based, open-source software package enabling reproducible data analysis for multiple electron microscopy systems and data types. This effort specifically addresses the lack of current open-source packages tailored for metallic materials data. The proposed CALPHAD-based thermodynamics and kinetics modelling framework for machine learning models will increase the accuracy for phase identification across all alloy systems of interest. Such a tool will enable the effective analysis of the large and quickly growing pool of electron microscopy data generated at research facilities, universities and companies.

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