Potential for integrating entry guidance into the multi-disciplinary entry vehicle optimization environment

The state-of-the-art in Multi-disciplinary, Design, Analysis, and Optimization (MDAO) for entry vehicle shapes decouples trajectory generation from heating predictions for different vehicle shapes explored in the design space. This decoupling occurs because a single representative flight condition and/or trajectory is used to compute the heating conditions for each vehicle shape analyzed in the MDAO process, which may result in an over/under prediction of thermal protection system mass. Typically, the single representative flight condition and/or trajectory is generated using trajectory optimization. Trajectory optimization tools may not: i) model trajectories with feasible control profiles and ii) be tractable for integration into the MDAO process due to lengthy processing time and the potential for non-converging solutions. By contrast, the use of a guidance algorithm for trajectory generation can rapidly provide feasible trajectories with no convergence issues. This work investigates the potential for improved TPS mass estimation by comparing trajectories generated from two different models: i) trajectory optimization and ii) an entry vehicle guidance algorithm. A reference tracking guidance algorithm, Apollo Derived Guidance (ADG), was used to model a Mars entry trajectory for five different mid-L/D vehicle geometries. These geometries were selected from an MDAO vehicle shape optimization study where trajectories were generated using trajectory optimization. These geometries represent aerodynamic dispersions that range from +/-0.5% to +/-16.5% and a single extreme case applies an aerodynamic dispersion of approximately 70% less than the baseline geometry. This study revealed that the ADG generated flight feasible trajectories for dispersions up to 16.5%, but failed for the 70% dispersion case. The results also showed that including flight feasible trajectories for a set of dispersed geometries has the potential to save up to 383 kg of TPS mass as compared to trajectory optimization results.