Development of the Multifactor Computational Models of the Solid Propellants Combustion by Means of Data Science Methods. Propellant Combustion Genome Conception

Victor Abrukov,Darya Anufrieva,Nichith Chandrasekaran,Alexander Lukin,Charlie Oommen,V R Sanalkumar,M El Ganaoui,R Bennacer,K Ragui,E Sediki

doi:10.1051/matecconf/202033001048

Abstract

The results of usage of data science methods, in particular artificial neural networks, for the creation of new multifactor computational models of the solid propellants (SP) combustion that solve the direct and inverse tasks are presented. The own analytical platform Loginom was used for the models creation. The models of combustion of double based SP with such nano additives as metals, metal oxides, termites were created by means of experimental data published in scientific literature. The goal function of the models were burning rate (direct tasks) as well as propellants composition (inverse tasks). The basis (script) of a creation of Data Warehouse of SP combustion was developed. The Data Warehouse can be supplemented by new experimental data and metadata in automated mode and serve as a basis for creating generalized combustion models of SP and thus the beginning of work in a new direction of combustion science, which the authors propose to call "Propellant Combustion Genome" (by analogy with a very famous Materials Genome Initiative, USA). "Propellant Combustion Genome" opens wide possibilities for accelerate the advanced propellants development Genome" opens wide possibilities for accelerate the advanced propellants development.

Highlights

There is a great deal of experimental data about combustion characteristics of various solid propellant compositions
The artificial neural networks (ANN) structure for solving the direct task – the creation of multifactor computational model revealing the dependence of burning rate of solid propellants (SP) on kind of SP, pressure, additive’s kind - is presented on Figure 2 in Supplementary Materials (SM)
Analysis of the results obtained depicts that ANN have the wide possibilities for solution direct and inverse tasks of SP combustion research

Summary

Introduction

There is a great deal of experimental data about combustion characteristics of various solid propellant compositions. There is no multifactor model that allows the prediction of the burning rate of a new solid propellant mixture for different ranges of pressure and initial temperature (direct problem).

Results

Conclusion