Nichith Chandrasekaran

A closed-form analytical model for predicting 3D boundary layer displacement thickness for the validation of viscous flow solvers

A closed-form analytical model is developed for estimating the 3D boundary-layer-displacement thickness of an internal flow system at the Sanal flow choking condition for adiabatic flows obeying the physics of compressible viscous fluids. At this unique condition the boundary-layer blockage induced fluid-throat choking and the adiabatic wall-friction persuaded flow choking occur at a single sonic-fluid-throat location. The beauty and novelty of this model is that without missing the flow physics we could predict the exact boundary-layer blockage of both 2D and 3D cases at the sonic-fluid-throat from the known values of the inlet Mach number, the adiabatic index of the gas and the inlet port diameter of the internal flow system. We found that the 3D blockage factor is 47.33 % lower than the 2D blockage factor with air as the working fluid. We concluded that the exact prediction of the boundary-layer-displacement thickness at the sonic-fluid-throat provides a means to correctly pinpoint the causes of errors of the viscous flow solvers. The methodology presented herein with state-of-the-art will play pivotal roles in future physical and biological sciences for a credible verification, calibration and validation of various viscous flow solvers for high-fidelity 2D/3D numerical simulations of real-world flows. Furthermore, our closed-form analytical model will be useful for the solid and hybrid rocket designers for the grain-port-geometry optimization of new generation single-stage-to-orbit dual-thrust-motors with the highest promising propellant loading density within the given envelope without manifestation of the Sanal flow choking leading to possible shock waves causing catastrophic failures

Development of the Multifactorial Computational Models of the Solid Propellants Combustion by Means of Data Science Methods - Phase I

In this paper, we present the results of usage of data science methods, in particular artificial neural networks, for the creation of new multifactor computational models for prediction of burn rate of the solid propellants (SP). The analytical system PolyAnalyst and analytical platform Loginom were used for the model creation. The particular model developed was for burn rate prediction of double base propellants with thermite additives, both nano and micro by means of training the ANN using experimental data published in scientific literature. The basis (script) of a creation of Data Wharehouse of SP combustion was developed. The Data Wharehouse can be supplemented by new data in automated mode and serve as a basis for creating new 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 (MGI)). Propellant Combustion Genome opens possibilities for accelerating the advanced propellants development.