{"686193":{"#nid":"686193","#data":{"type":"event","title":"PhD Defense by Tavish Pattanayak","body":[{"value":"\u003Cp\u003EPattanayak, Tavish has requested to schedule their PhD Thesis Final Examination (Defense). This request has been approved by their faculty advisor, the AE Associate Chair for Graduate Programs, and the AE Communications Office. Please proceed to post the annoucement on the OGE website. The details are as follows:\u003Cbr\u003E\u003Cbr\u003EStudent Name: Tavish Pattanayak\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EAdvisor: Dr. Dimitri Mavris\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EMilestone: PhD Thesis Final Examination (Defense)\u003Cbr\u003E\u003Cbr\u003EDegree Program: Aerospace Engineering\u003Cbr\u003E\u003Cbr\u003ETitle: An Uncertainty Quantification-based Methodology for Resource Allocation towards Technology Maturation\u003Cbr\u003E\u003Cbr\u003EAbstract: To address the aviation industry\u0027s 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.\u003Cbr\u003E\u003Cbr\u003EDate and time: 2025-11-04, 10 am\u003Cbr\u003E\u003Cbr\u003ELocation: CoVE, Weber Building\u003Cbr\u003E\u003Cbr\u003ECommittee:\u003Cbr\u003EDr. Dimitri Mavris (advisor), School of Aerospace Engineering\u003Cbr\u003EProf. Daniel Schrage, School of Aerospace Engineering\u003Cbr\u003EProf. Grame J. Kennedy, School of Aerospace Engineering\u003Cbr\u003EDr. Raphael Gautier, School of Aerospace Engineering\u003Cbr\u003EDr. Andrew Meade, NASA\u003Cbr\u003E,\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EAn Uncertainty Quantification-based Methodology for Resource Allocation towards Technology Maturation\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"An Uncertainty Quantification-based Methodology for Resource Allocation towards Technology Maturation"}],"uid":"27707","created_gmt":"2025-11-04 20:44:55","changed_gmt":"2025-11-04 20:46:31","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-11-14T10:00:00-05:00","event_time_end":"2025-11-14T12:00:00-05:00","event_time_end_last":"2025-11-14T12:00:00-05:00","gmt_time_start":"2025-11-14 15:00:00","gmt_time_end":"2025-11-14 17:00:00","gmt_time_end_last":"2025-11-14 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"CoVE, Weber Building","extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}