An Uncertainty Quantification-based Methodology for Resource Allocation toward Technology Maturation
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Pattanayak, Tavish
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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.
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2025-12
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Dissertation (PhD)