Precision in launch dynamics and trajectory optimisation is critical for enhancing mission success, fuel efficiency, and payload capacity in modern aerospace operations. This study aims to develop and validate a high-fidelity, simulation-based framework for optimising rocket ascent trajectories, providing a cost-effective, scalable alternative to extensive physical testing. The methodology integrates ANSYS Fluent for aerodynamic and thermal load modelling, MATLAB/Simulink for dynamic control system simulation, and General Mission Analysis Tool (GMAT) for orbital trajectory planning, enabling a multi-domain approach that accounts for thrust vectoring, aerodynamic drag, stage separation, and environmental disturbances. Optimisation algorithms, the Genetic Algorithm (GA) and Particle Swarm Optimisation (PSO), were applied to refine thrust profiles, gravity-turn timing, and staging parameters. Simulation results demonstrate an apogee prediction within ± 1.5% of the 98.5 km target and a final velocity accuracy of ± 2% when benchmarked against real-world launch data. Fuel efficiency improved from approximately 70% to over 90%, apogee accuracy increased by 0.2 km, and payload delivery precision improved by 0.08 km compared to non-optimised trajectories. The framework also reduced trajectory deviations caused by crosswinds by up to 85% through adaptive correction manoeuvres. The novelty of this research lies in its integrated, closed-loop simulation architecture, which supports rapid iterative design cycles and links aerodynamic modelling, control optimisation, and orbital planning within a single environment. This approach not only accelerates the design-to-validation process but also enhances accuracy and operational reliability. The study concludes that such simulation-based optimisation provides a robust foundation for current suborbital missions and has high potential for future reusable launch systems and interplanetary mission planning.
Zulkifli et al. (2026) studied this question.