A Neural Network based Approach to Computationally Intense Distributed Aerospace Simulations

P. Stewart, R. Harrison, M. Sdralis, P.J. Fleming, and S.A. MacKenzie (UK)


Distributed Simulation, Neural Networks, Radial Basis Functions, Computational Fluid Dynamics.


Extremely complex dynamic aerospace systems can be represented by distributed simulation via a real time architecture. Although inter-component communication channels exist to facilitate high band width data transfers, computationally intensive models compromise the overall simulation performance. Modelling techniques such as Finite Element (FE) analysis and Computational Fluid Dynamics (CFD) require large computational resources, and depending on the complexity of the modelled system, require long periods of time to converge to a solution. In this paper, Radial Basis Function (RBF) networks are applied to the CFD analysis of an air craft wing in a transonic airflow. It is desired that the analysis form a part of a distributed aircraft simulation environment. The paper investigates to what extent these systems can be approximated by neural networks.

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