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Enhancing model predictability for a scramjet using probabilistic learning on manifolds

Abstract : The computational burden of Large-eddy Simulation for reactive flows is exacerbated in the presence of uncertainty in flow conditions or kinetic variables. A comprehensive statistical analysis, with a sufficiently large number of samples, remains elusive. Statistical learning is an approach that allows for extracting more information using fewer samples. Such procedures, if successful, would greatly enhance the predictability of models in the sense of improving exploration and characterization of uncertainty due to model error and input dependencies, all while being constrained by the size of the associated statistical samples. In this paper, we show how a recently developed procedure for probabilistic learning on manifolds can serve to improve the predictability in a probabilistic framework of a scramjet simulation. The estimates of the probability density functions of the quantities of interest are improved together with estimates of the statistics of their maxima. We also demonstrate how the improved statistical model adds critical insight to the performance of the model.
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Submitted on : Thursday, February 28, 2019 - 5:30:11 PM
Last modification on : Sunday, June 26, 2022 - 2:36:58 AM
Long-term archiving on: : Wednesday, May 29, 2019 - 6:13:48 PM


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Christian Soize, Roger Ghanem, Cosmin Safta, Xun Huan, Zachary P Vane, et al.. Enhancing model predictability for a scramjet using probabilistic learning on manifolds. AIAA Journal, American Institute of Aeronautics and Astronautics, 2019, 57 (1), pp.365-378. ⟨10.2514/1.J057069⟩. ⟨hal-02052839⟩



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