A novel Artificial Neural Network-based streamline tracing strategy applied to hypersonic waverider design

TL;DR

This paper introduces an ANN-based streamline tracing strategy for hypersonic waverider design, significantly reducing computational cost while maintaining high accuracy compared to traditional methods.

Contribution

The paper presents a novel ANN-based approach to predict streamline coefficients, enabling faster and accurate waverider shape optimization in hypersonic flow design.

Findings

ANN method achieves 0.68% coordinate accuracy.

The approach is 20 times faster than conventional methods.

The derived waverider shows no severe flow spillage in RANS simulations.

Abstract

Streamline tracing in conical hypersonic flows is essential for designing high-performance waverider and intake. Conventionally, the streamline equations are solved after obtaining the velocity field from the solution of the axisymmetric conical flow field. The hypersonic waverider shape is generated from the base conical flow field by repeatedly applying the streamline tracing approach along several planes. When exploring the design space for optimization of the waverider, streamline tracing can be computationally expensive. We provide a novel strategy where first the Taylor-Maccoll equations for the inviscid axisymmetric conical flowfield and the streamlines from the shock are solved for a wide range of cone angle and Mach number conditions resulting in an extensive database. The streamlines are parametrized by a third-order polynomial, and an Artificial Neural Network (ANN) is trained to predict the coefficients of the polynomial for arbitrary inputs of Mach number, cone angle, and streamline originating location on the shock . We apply this strategy to design a cone derived waverider and compare the geometry obtained with the standard conical waverider design method and the simplified waverider design method. The ANN technique is highly accurate, with a difference of 0.68% with the standard in the coordinates of the waverider. RANS computations show that the ANN derived waverider does not indicate severe flow spillage at the leading edge, which is observed in the waverider generated from the simplified method. The new ANN-based approach is 20 times faster than the conventional method.