Savio J Poovathingal | Engineering Papers

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In-situ Nanoscale Ablation

An understanding of the ablation of carbon-based materials is crucial to modeling the behavior of atmospheric entry spacecrafts equipped with thermal protection systems (TPS).Carbon is the backbone of TPS systems such as PICA (phenolic-impregnated carbon ablator).Therefore in the present work we study ablation of highly oriented pyrolytic graphite (HOPG)in oxygen at temperatures up to 1000°C done within a gas reaction cell housed in a scanning transmission electron microscope (STEM). Observation of the HOPG oxidation on specimens sectioned parallel and normal to the carbon basal planes in the presence of oxygen are reported.Pitting caused by residual oxygen and/or platinum particles from the sectioning process was observed before oxygen gas flow was established at 750°C in the specimen sectioned with the carbon basal planes parallel to the beam direction. Introduction of oxygen flow caused rapid oxidation moving in a uniform front that completely consumed the HOPG in approximately 1.5minutes. The specimen sectioned with the carbon basal planes normal to the beam direction did not show the same pitting phenomenon but exhibited rapid oxidation at 1000°C that proceeded in a uniform front and completed in approximately 1 minute. All specimens tested had a husk resembling the original specimen shape left over after oxidation. It is concluded that this husk is most likely ash impurity from the HOPG or impurities from the sectioning process or E-chip. A residual gas analyszer (RGA) was successfully used to monitor gas flows during the experiments but was yet unsuccessful in monitoring oxidation gas products produced during the experiment.Future studies will optimize the RGA setup in order to maximize the potential for detecting these species. These in-situ studies successfully show how this highly ordered carbon ablates on a nano- and micro-scopic length scale and can be used to provide fundamental understanding of carbon ablation that can be used to design the next-generation of TPS systems.

Nanoscale Ablation↗

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A Machine Learning Framework for Error Compensation in Radiative Transfer Calculations

Radiative heat transfer influences the amount of heat flux transferred to the surface of the hypersonic vehicle, which is essential to evaluate the performance of thermal protection systems. The radiative heat flux is found to be computationally prohibitive while accounting for the variation in spatial, angular, and spectral domains. A new methodology has been recently developed to alleviate the cost of computation in the spectral domain by constructing flow-agnostic reduced-order models (ROMs). The developed spectral ROM databases provide grouping strategies that account for non-equilibrium absorption and emission as well as interaction between disparate species due to spectral overlap in associated radiative processes. However, the developed ROMs need to be optimized for a specific combination of interacting gas species and would need to re-calibrated in case individual species are added/omitted. In this work, we use various machine learning (ML) techniques to approximate the radiative intensities determined by a ROM optimized for a specific gas mixture. The ML model relies on the ROM databases developed for a single species which ignores any spectral overlap. Thus, radiation evaluation starts with a simple summation of radiative intensities predicted using these non-calibrated ROMs for the contributing species. The ML framework then provides a correction to account for the interplay in the frequency, i.e., emission of photons by one species and absorption by another, and yields mixture-specific radiation fields. Once trained on the individual ROM databases, the ML framework offers instantaneous corrections that serves as a time/cost effective alternative to the optimization of ROMs for a specific gas mixture. The ML framework is trained on both the high fidelity and ROM evaluated line of sight (LOS) data from Orion, Stardust, and FIRE II cases to obtain a general purpose correction model for earth re-entry scenarios when radiation contributions from both atomic nitrogen and atomic oxygen are considered. A geometric length scale parameter is used in the training process to account for errors introduced in the ROM databases as a consequence of high optical thickness. The efficacy of the ML framework is underscored through extensive analysis of train and test errors with respect to all the re-entry scenarios. The applicability of such an ML framework was further corroborated by embedding it in a state-of-the-art US3D - NERO system for determining the radiative heat flux transferred to the hypersonic vehicle surface.

Radiation↗