Uncertainty quantification using Auto-tuned Surrogates of CFD model Simulating Supersonic flow over tactical missile body

Miriyala, Srinivas Soumitri and Banerjee, Raja and Mitra, Kishalay (2020) Uncertainty quantification using Auto-tuned Surrogates of CFD model Simulating Supersonic flow over tactical missile body. In: IEEE Symposium Series on Computational Intelligence, SSCI 2020, 1-4 December 2020, Canberra; Australia.

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In contemporary practices, Computational Fluid Dynamics (CFD) based tools are increasingly applied to build high fidelity First Principles based Models (FPMs) for designing tactical missile systems. However, optimization, sensitivity analysis and uncertainty quantification using such models still remain to be extremely tedious and, hence, are performed offline. Artificial Neural Networks (ANNs) are known to be efficient machine learning based models capable of modelling large amount of nonlinearities in the data. However, heuristics involved in their modelling prevent their application as surrogate models to computationally intensive FPMs. In this work, a novel algorithm, aimed at simultaneous optimal estimation of architecture (number of hidden layers and nodes in each layer), training sample size and activation function in ANNs is proposed. The proposed algorithm classifies as a generic multi-objective evolutionary neural architecture search strategy to design ANNs. It is solved using the population based evolutionary optimization algorithm called Nondominated Sorting Genetic Algorithm-II. The high fidelity data to train the ANNs are obtained from CFD model for simulating supersonic flow over a tactical missile body in ANSYS Fluent .

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IITH Creators:
IITH CreatorsORCiD
Banerjee, Rajahttp://orcid.org/0000-0002-7163-1470
Mitra, Kishalayhttp://orcid.org/0000-0001-5660-6878
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: ANOVA, CFD based Epistemic Uncertainties, Missile design, Neural Architecture Search, NSGA-II,Surrogate models, Uncertainty quantification
Subjects: Physics > Mechanical and aerospace
Physics > Pneumatics
Physics > Modern physics
Divisions: Department of Mechanical & Aerospace Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 28 May 2021 07:30
Last Modified: 28 May 2021 07:30
URI: http://raiith.iith.ac.in/id/eprint/7862
Publisher URL: http://doi.org/10.1109/SSCI47803.2020.9308325
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