Ph.D. Defense

Tavish Pattanayak

(Prof. Dimitri Mavris)

An Uncertainty Quantification-based Methodology for Resource Allocation towards Technology Maturation

Tues., Nov. 4

10:00 a.m.

Weber, CoVE

Abstract:

To address the aviation industry's need to decarbonize amid rising travel demand, this dissertation proposes a comprehensive, data-driven methodology to optimize testing strategies for novel technologies, such as hybrid-electric propulsion (HEP). The research is structured in three parts: first, it quantifies how component-level uncertainties impact system performance, identifying battery cell-specific energy as the primary driver of variability through sensitivity analysis. Second, it develops a multi-attribute decision-making framework that holistically prioritizes component testing based on risk and uncertainty, consistently ranking the battery as the highest priority. Third, it translates these priorities into a practical, adaptive test plan that dynamically reallocates resources in response to new information. By integrating these areas, this work provides a cohesive, end-to-end framework for technology maturation that overcomes the limitations of traditional, static methods. The resulting systematic and technology-agnostic approach offers a versatile tool for managing complex engineering development in aerospace and other industries.

Committee:
Dr. Dimitri Mavris (advisor), School of Aerospace Engineering
Prof. Daniel Schrage, School of Aerospace Engineering
Prof. Grame J. Kennedy, School of Aerospace Engineering
Dr. Raphael Gautier, School of Aerospace Engineering
Dr. Andrew Meade, NASA