Increasing technical dependencies between the engineering disciplines driving the overall design of an aircraft and improving optimization techniques that make use of these interactions blur the lines between distinct disciplines and create demand for a harmonized flight physics model.
In this paper we present considerations on a general framework that allows the representation of the equations and data from various domains in an object-oriented and scalable structure. Emphasis is put on the loads aspect with the distinct fields of gust loads, maneuver loads and ground loads analysis, which are essential for structural design.
A fully generic, grid based data structure is presented, which is suitable for models of different granularity and applicability. All data is represented in this general form independent of its origin and may be transformed in between the different representations using splines. Coordinate transformations are handled automatically. It accepts local sub-models that are combined to form a complete aircraft representation.
This integral model can be applied to all disciplines and their combination if necessary. For example, a nonlinear maneuver can be combined with a linear gust calculation. However, in a fully nonlinear model, several disturbing effects of these nonlinearities in a distinct calculation can be observed as it includes physical limitations from another discipline.
As a work around, a calculation framework is presented that formalizes a very general description of a calculation. Examples for calculations are trim solutions and time or frequency domain simulations on differential equations formulated in different frameworks or programming languages and can be expressed in an implicit or an explicit way. The calculations can be combined in a tree-like database, where each calculation step is followed by an analysis step. If an operation is not suitable for the aircraft model due to a limitation implicitly posed by the nonlinear description, this may be detected and an alternative calculation can be performed fully automatically, such that the considered situation still contributes to the overall result. This is important for uncertainty analysis and multidisciplinary optimization, where a considered result at least needs to be continuous for a wide range of parameters.