Construction of predictive uncertainty quantification framework to the extension of TPS plasma wind tunnel experiment data to flight conditions

Rostkowski, Przemyslaw

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https://hdl.handle.net/2142/116187 Copy

Description

Title

Construction of predictive uncertainty quantification framework to the extension of TPS plasma wind tunnel experiment data to flight conditions

Author(s)

Rostkowski, Przemyslaw

Issue Date

2022-07-07

Director of Research (if dissertation) or Advisor (if thesis)

Panesi, Marco

Doctoral Committee Chair(s)

Panesi, Marco

Committee Member(s)

• Smith, Ralph C
• Panerai, Francesco
• Dutton, J. Craig
• Doostan, Alireza

Department of Study

Aerospace Engineering

Discipline

Aerospace Engineering

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Hypersonics
• Uncertainty Quantification
• Bayesian Inference
• Thermal Protection Systems
• Surrogate Modeling
• Sensitivity Analysis
• Charring Ablator
• Atmospheric Entry
• Ablation
• TPS
• Arcjet
• Plasma Wind Tunnel
• Mars
• Material Response
• PATO
• TACOT

Abstract

Space exploration and travel to other stellar bodies in the solar system often include hypersonic entries into planetary atmospheres. During these maneuvers, an enormous amount of heat is generated and conveyed to the entry vehicle surface as its kinetic energy is dissipated through high-speed collisions with atmospheric particles. Engineers utilize thermal protection system materials to construct heat shields that dissipate the deposited thermal energy through various phenomena. In particular, charring ablators are used in many high-profile missions beyond lower earth orbit to attenuate conducted heat to the vehicle's substructure by internal decomposition and surface ablation phenomena. High-fidelity computational models are vital in predicting the performance of these materials and are a critical component in the thickness-sizing stages of the vehicle design process. The validation of these tools and implemented theoretical models is done with material performance data obtained inside ground facility complexes. However, predicting the behavior of charring ablators under high heating conditions entails accurate simulation of numerous complex, interacting phenomena. Conceptual models often prescribe several assumptions to make the prediction problem more tractable. These potential sources of model inadequacy and those stemming from a complete lack of knowledge thus introduce uncertainty for obtained model outputs regarding their accuracy. Still, it is standard practice to employ deterministic calibration and validation procedures that do not provide adequate mechanisms for considering various sources of uncertainty. This problem is further compounded by the inability to completely replicate the high-enthalpy flow environments that characterize high-speed entries into dense, planetary atmospheres at any facility alone. Models are thus validated with data that do not accurately correspond to the scenario of interest and whose values can be further affected by experimental uncertainty. This thesis utilizes statistical methodologies and their novel applications in the field to analyze the material response prediction problem of charring ablators from a non-deterministic standpoint. The current effort aims to set the foundation for using detailed statistical methodologies in future endeavors and increase uncertainty quantification capabilities for predictive scenarios based on the knowledge gained from testing campaigns. These tasks rely on Bayesian methods that naturally incorporate a probabilistic description of uncertainty when conducting calibration and subsequent prediction exercises. The introductory contents of this work are thus dedicated to a brief review of conceptual models regarding the response of charring ablators and discussions concerning employed Bayesian methodologies and other statistical treatments. These approaches are then utilized to study the various aspects of the prediction exercise. Effects of various simplifications commonly utilized in the literature on results of statistical treatments are investigated in the present context, where several anticipated and some unexpected behaviors are observed. A general procedure for the sensitivity analysis of time-dependent field quantities is also outlined as part of this task. Subsequent discussion focuses on formulating a general Bayesian inference framework for the calibration and uncertainty quantification of charring ablator material response frameworks given captured data. In addition, preliminary analysis consisting of developed sensitivity analysis aspects in the preceding effort and surrogate modeling are carried out to reduce the computational complexity of the inversion procedure. The aspects are then deployed to infer material properties given retrieved Mars Science Lab thermocouple temperature profiles. The results of the preceding investigations are finally leveraged in the construction of a probabilistic framework for extension of performance data obtained inside plasma wind tunnel facilities to flight conditions. The developed novel approach that is the capstone effort of this work considers model inadequacy for conditions outside the testing envelope and accounts for the applicability of ground facility data to the flight prediction scenario. This functionality is achieved in tandem by model discrepancy emulator and Bayesian model averaging approaches, along with the methodologies utilized as a part of preceding tasks, that capture the additional degree of uncertainty due to the limited ground testing capabilities and general model inadequacy. The constructed procedure is applied with data retrieved from an arc jet facility test applied to a Mars entry scenario, where a significant improvement in predictive uncertainty quantification capabilities is observed compared to standard naive approaches. The obtained prediction intervals consistently capture those obtained directly with flight data without significant overshoot.

Graduation Semester

2022-08

Type of Resource

Thesis

Copyright and License Information

Copyright 2022 Przemyslaw Rostkowski

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