Development of the multifactorial computational models of the solid propellants combustion by means of data science methods

Abrukov, VS and Lukin, AN and Oommen, C and Sanalkumar, VR and Chandrasekaran, N and Sankar, V (2017) Development of the multifactorial computational models of the solid propellants combustion by means of data science methods. In: 53rd AIAA/SAE/ASEE Joint Propulsion Conference, 2017, 10 July 2017 - 12 July 2017, Atlanta.

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Abstract

Modeling of the propellant burning characteristics with using of data science methods can find wide application for current and future combustion researches. In the present paper we demonstrate possibilities of the data science methods application in the generalization of the connections between the variables of combustion experiments as well as in forecasting of "new experimental results" with using the artificial neural networks (ANN), that are one of the most promising methods of data science. A review has been carried out for the previous modeling efforts. The ANN allows modeling temperature profiles in propellant combustion waves, predicting burning rate of various propellant mixtures for different ranges of pressure and initial temperature, determining propellant mixture providing a necessary burning rate for various pressures and temperatures. The preliminary results, earlier obtained by our research team depict that ANN can be considered as a good approximation tool for experimental functions of several variables for the study of combustion behaviors, as a more affordable way to receive additional novel experimental results, and as a good tool to present to scientific community the experimental results obtained with more predictive capabilities on the starting transient flow features of dualthrust rockets.

Item Type:	Conference Paper
Publication:	53rd AIAA/SAE/ASEE Joint Propulsion Conference, 2017
Publisher:	American Institute of Aeronautics and Astronautics Inc, AIAA
Additional Information:	The copyright for this article belongs to American Institute of Aeronautics and Astronautics Inc, AIAA.
Keywords:	Mixtures; Neural networks; Propellants; Propulsion; Rockets; Solid propellants, Burning characteristics; Combustion behavior; Combustion experiments; Computational model; Functions of several variables; Initial temperatures; Predictive capabilities; Scientific community, Combustion
Department/Centre:	Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Date Deposited:	27 Jul 2022 10:14
Last Modified:	27 Jul 2022 10:14
URI:	https://eprints.iisc.ac.in/id/eprint/74754

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