The increasing number of rocket launches has intensified concerns about the environmental effects of spaceflight. Quantifying rocket emissions at different altitudes and locations within the atmosphere is essential to assess these impacts. This requires realistic launcher models, but much of the publicly available technical data is incomplete or inconsistent. This research aimed to develop a semi-automated framework for launcher remodelling that can handle uncertain or missing input parameters while producing reliable and efficient results.
The framework was developed at the German Aerospace Center (DLR) and integrates a flexible input data structure, statistical estimation methods, and trajectory optimisation tools. Three statistical techniques — Monte Carlo, Latin Hypercube, and Approximate Bayesian Computation — were evaluated to estimate unknown parameters and define their valid ranges. Their performance was tested using several expendable launch vehicles with liquid propulsion.

Results showed that Latin Hypercube sampling achieved the best balance between accuracy and computational cost. When applied to real rockets, the framework produced configurations with payload estimates within 2% of reference values, even when up to five input parameters were uncertain.
Overall, the developed framework demonstrates that launcher remodelling can be automated while effectively handling uncertainty. It facilitates the generation of realistic launcher models and supports ongoing efforts to quantify the environmental impact of rocket emissions