A data fusion technique for merging multiple data sources with differing fidelity and resolution was developed to support the production of aerodynamic lineload databases for the Liftoff and Transition flight phase of the Space Launch System. The technique uses a reduced order model based on a high-fidelity lineload dataset from Computational Fluid Dynamics (CFD) to predict solutions for a much larger solution space. Even higher-fidelity force and moment information (from wind-tunnel tests) is then used to adjust the model. The adjustment uses constrained optimization through the method of Lagrange multipliers in order to minimize the deviation of the lineload distribution from the spatially-dense CFD solution, while ensuring that the integrated force and moment values match those measured in physical wind tunnel experiments. Though the wind-tunnel data are operationally-dense (available at many flow conditions), they are spatially coarse (as only the overall forces and moments are available). Conversely, CFD for such complex configurations is expensive, and thus operationally sparse. Data fusion techniques are employed to merge the two datasets to deliver accurate lineloads within time and resource constraints.
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