Faghihi receives NSF CAREER award

by Jane Stoyle Welch

Even though newer insulation materials have been developed in research laboratories that are safer and more efficient, they have not yet found widespread application in practice.

Mechanical and aerospace engineer Danial Faghihi aims to change this situation. He recently received a National Science Foundation CAREER award to design superinsulation materials systems for use in buildings via data-driven predictive computational tools.

“Buildings and their associated materials are the cause of almost 40% of the total amount of energy consumption and greenhouse gas emissions in the U.S.” says Faghihi. “Transforming conventional building insulation using innovative and eco-friendly materials systems is now critically needed to dramatically reduce the negative environmental impacts of buildings.”

“This project aims at establishing possibly the first rigorous mathematical theories and computational models that can accurately predict the future state of engineering systems using limited and uncertain observational data. Leveraging these predictive tools for the design of high-performance insulation systems can close the gap between laboratory discovery and deployment of novel building materials,” he added.

Faghihi, an assistant professor in the Department of Mechanical and Aerospace Engineering, received a $595,593 award for his project, Reliable Superinsulated Building Envelopes via Predictive Multiphysics Modeling, from NSF’s Division of Civil, Mechanical and Manufacturing Innovation.

“Danial’s research has the potential to significantly reduce energy consumption and carbon dioxide emission of next-generation buildings, leading to extensive environmental benefits, improved human health and welfare, and enhanced national economic competitiveness,” says Francine Battaglia, professor and chair of the Department of Mechanical and Aerospace Engineering.

The prestigious award supports early-career faculty who have the potential to serve as academic role models in research and education and to lead advances in the mission of their department or organization.

Faghihi will develop a mathematical and computational framework to predict the future performance of additively manufactured insulation components in a wide range of buildings. The framework will integrate material models with scientific machine learning methods to discover modern superinsulation components, despite the uncertainties induced by the manufacturing process, limited experimental data and imperfect material models.

“This project will support my long-term vision of implementing novel predictive computational tools at the intersection of multiscale modeling, machine learning, and scientific computing to discover next-generation engineering systems of great significance to modern society,” says Faghihi.

Faghihi’s research interests focus on predictive multiscale computational modeling of complex materials and biological systems. In particular, he develops modern algorithms to exploit large-scale data to enhance the predictive ability of computational models. His current research consists of predictive modeling applications to advanced manufacturing of materials and personalized cancer treatment. He has published 32 journal articles in the area of computational and applied mechanics.

Faghihi joined the University at Buffalo in 2019 from the Oden Institute for Computational Engineering and Sciences at the University of Texas at Austin, where he was a research scientist. He received his PhD in structural engineering and mechanics from Louisiana State University.