NASA awards Virginia Tech $5.8 million to advance aircraft design and certification
The project's goal is faster and more efficient aircraft design that incorporates certification requirements earlier, reducing costly redesigns later in development.
Virginia Tech
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.
New technologies, new uncertainties
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.”
Preserving cross-gap differences improves GNSS noise analysis with missing values
image:
Proportion of GNSS coordinate time series containing non-stationary noise in the East, North and Up components obtained at 60 global GNSS sites.
view moreCredit: Satellite Navigation
A new study presents two information-preserving maximum likelihood methods for analyzing Global Navigation Satellite System (GNSS) coordinate time series when colored noise is non-stationary and observations are missing. Standard first-order differencing can stabilize non-stationary noise, but it discards differenced observations involving missing epochs, losing valid information and biasing noise parameters and velocity uncertainty. The proposed methods retain cross-gap differences through either a linear transformation matrix or marginalization of missing values in the original series. Tests on simulated and real GNSS data show consistent, more accurate noise parameter and velocity uncertainty estimates, with computational efficiency depending on the percentage of missing values.
High-precision Global Navigation Satellite System (GNSS) coordinate time series underpin studies of plate motion, earthquakes, terrestrial water storage, sea level change, and landslides. Their noise is often temporally correlated colored noise, and power-law (PL) noise with spectral index alpha ≤ -1 is non-stationary, making covariance matrices non-Toeplitz. First-order differencing can transform such noise into stationary processes, but missing values cause extra loss: one missing epoch removes two adjacent first differences. This loss can bias noise model parameters and velocity uncertainty, especially when non-stationary noise and many gaps coexist. Given these challenges, in-depth research is needed on information-preserving noise analysis for GNSS coordinate time series with missing values in the first-differenced domain.
Researchers from the China University of Mining and Technology, the State Key Laboratory of Spatial Datum, the Jiangxi University of Science and Technology, and the University of Beira Interior published (DOI: 10.1186/s43020-026-00220-0) their study in Satellite Navigation on 05 October 2026. The work develops two modified first-differenced maximum likelihood estimation (MLE) algorithms for GNSS coordinate time series with missing values. One uses a linear transformation matrix; the other uses a marginal likelihood function. Both aim to preserve valid differenced information that standard first-differenced MLE discards.
The first algorithm constructs free differenced observations from consecutive available epochs, even across missing segments, using a linear transformation matrix. The second treats missing original observations as latent variables and integrates them out through a marginal likelihood, while preserving the Toeplitz structure of the complete first-differenced covariance matrix. The authors evaluated the methods using 10,800 simulated GNSS coordinate time series covering stationary, weakly non-stationary, and strongly non-stationary power-law noise. Missing values percentages ranged from 0% to 50% in the main comparative experiments and were extended to 65% for the computational efficiency analysis. The evaluation also included 180 real GNSS coordinate time series from 60 global GNSS sites. The two modified algorithms produced highly consistent estimates, with Pearson correlation coefficients of 0.9999 for key noise parameters and velocity uncertainty and near-zero mean differences. Their main difference was speed. The marginal likelihood algorithm was generally faster when missing values were below about 60%, whereas the linear transformation algorithm became more efficient at higher missing percentages. In real data, 12% of series favored the linear transformation approach, close to the share with more than 60% missing values. Both modified algorithms outperformed normal MLE and standard first-differenced MLE under non-stationary noise and high missingness.
The authors said the key advance is not filling gaps but keeping valid differences that span them. They said that instead of treating every difference near a missing epoch as unusable, their methods preserve the information already present in available observations. They explained that the two algorithms are statistically equivalent in parameter estimates but differ in speed: the marginal likelihood approach is often faster with low-to-moderate missing data, while the linear transformation approach gains an edge when gaps are extensive. They added that this makes the framework practical for large global GNSS archives.
More reliable GNSS velocity uncertainty can improve interpretations of plate motion, earthquake deformation, sea level change, water storage, and landslide monitoring. The framework also offers a complementary first-differenced extension to existing fast Toeplitz-based MLE tools, rather than replacing them. Likelihood values from the proposed algorithms can support model selection criteria such as the Akaike information criterion (AIC) or Bayesian information criterion (BIC). The authors caution that applying the methods to other geophysical time series may require adapting the functional model, sampling interval, and covariance construction, especially for irregular sampling, non-Gaussian errors, or noise processes different from white noise plus power-law (WN+PL).
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References
DOI
Original Source URL
https://doi.org/10.1186/s43020-026-00220-0
Funding information
This work is supported by the National Natural Science Foundation of China (42364002); Outstanding Youth Fund of Jiangxi Natural Science Foundation (20252BAC220015); the Graduate Innovation Program of China University of Mining and Technology (2026WLKXJ086); the Postgraduate Research & Practice Innovation Program of Jiangsu Province (26CXJH4871).
About Satellite Navigation
Satellite Navigation (ISSN: 2662-1363; ISSN: 2662-9291) Satellite Navigation is the official journal of the Aerospace Information Research Institute. The journal aims to report innovative ideas, new results, and progress in the theories, techniques, and applications of satellite navigation. The journal welcomes original articles, reviews and commentaries.
Journal
Satellite Navigation
Subject of Research
Not applicable
Article Title
Information-preserving maximum likelihood noise analysis of GNSS coordinate time series with missing values in the first-differenced domain
Article Publication Date
15-Oct-2026
