When designing new aircraft, aerospace engineers optimize for cost, fuel efficiency, and reduction in weight and noise. After a lengthy development process, the vehicle must then meet certifications and safety regulations. Miss the mark and you enter the dreaded redesign phase.
Virginia Tech researchers are developing design tools that could help new aviation technologies, such as electrified aircraft, reach commercial service faster. By building those certification and safety requirements into the design process from the start, engineers can reduce the time, cost, and risk of bringing new aviation technologies to market.
To tackle this issue, NASA has awarded Virginia Tech and partners Auburn University and Tuskegee University $5.8 million as part of the agency’s University Leadership Initiative. The three-year project aims to integrate model-based systems engineering, multidisciplinary design optimization, and uncertainty quantification into a unified approach for next-generation aircraft design.
Electrified propulsion technologies, including hybrid-electric and distributed electric propulsion aircraft, have shown promise in reducing fuel use, emissions, noise, and operating costs. However, certifying these aircraft remains a significant hurdle.
“One of the greatest challenges is managing uncertainty early in the design process,” said Darshan Sarojini, assistant professor of aerospace engineering. “As development progresses, redesigning components becomes increasingly expensive and time consuming. This challenge is amplified for emerging technologies because there is limited historical data and regulations continue to evolve. Designers must evaluate hundreds of interconnected variables and understand how aerodynamics, structures, controls, operations, and certification requirements influence one another.”
The NASA project combines advanced computational design methods with experimental validation. Led by Sarojini, researchers will integrate model-based systems engineering and multidisciplinary design optimization approaches that traditionally operate in separate workflows.
This novel approach treats certification as a design driver from the earliest stages rather than a requirement checked after a design is largely complete.
The project brings together experts from across Virginia Tech:
Darshan Sarojini and Rakesh Kapania of the Kevin T. Crofton Department of Aerospace and Ocean Engineering will provide expertise in aircraft design, model-based systems engineering, surrogate model development, and multidisciplinary design optimization.
Ionut Farcas of the Department of Mathematics contributes expertise in uncertainty quantification and multi-fidelity methods.
Nicholas Polys of the Advanced Research Computing unit will support data visualization and large-scale design space exploration.
John Coggin of the Mid-Atlantic Aviation Partnership contributes flight-testing expertise.
A key advantage for the team is access to Virginia Tech’s Mid-Atlantic Aviation Partnership (MAAP), one of the nation’s FAA-designated drone test sites. MAAP, which is housed in the Institute for Critical Technology and Applied Science , provides a unique capability for safely conducting flight tests and collecting flight data. Its authorization and operational procedures for testing aircraft weighing more than 55 pounds allow researchers to evaluate designs under real-world conditions.
Working with Auburn University and Tuskegee University, MAAP will also support a verification and validation effort that compares simulation results with subscale flight-test data. This will help the team better understand how data collected from smaller test aircraft can inform the design of full-scale vehicles.
According to Sarojini, aircraft designers routinely make decisions in the face of incomplete information. Research conducted by NASA has shown that designs optimized without accounting for uncertainty can differ substantially from those developed using uncertainty-aware methods.
“Virginia Tech will develop new approaches that enable uncertainty quantification and multidisciplinary optimization to be performed efficiently at scales relevant to industry,” said Sarojini. “The goal is to help engineers better understand risk and make more informed design decisions earlier in development.”