This paper reviews data fusion strategies for generating aerodynamic databases
and evaluates their suitability for motorsport aeromaps, with emphasis on the
operational constraints specific to Formula One. A structured survey and
classification of the state of the art is presented, grouping approaches into
(i) surrogate-agnostic methods, (ii) kriging-based methods, and (iii) neural
network–based methods. In addition, the survey explores advanced techniques
currently underutilized in aerodynamic database applications but that show
promise. These methodologies are discussed in the context of addressing
limitations inherent in traditional approaches, such as dependency on nested
sampling plans and linear correlation assumptions between low- and high-fidelity
datasets. The review indicates that, although multi-fidelity data fusion is well
established in aerospace aerodynamic database generation, its direct translation
to motorsport requires additional considerations. In the Formula One context,
the most plausible deployment may involve fusing legacy and current datasets,
rather than combining low- and high-fidelity evaluations of the same geometry.
This shift in premise could increase exposure to negative transfer and therefore
necessitate additional methods to minimize it. This study provides one of the
first motorsport-focused reviews and syntheses of data fusion methods for
aerodynamic database generation. It is intended to guide motorsport engineers
and researchers toward more efficient and effective aeromap generation
strategies. Collectively, the findings establish a foundation for subsequent
phases of a broader project to minimize the number of data points required to
generate an aeromap, with the present survey constituting the first part of that
effort.