Prediction of Lightning Currents on Fastening Assemblies of an Aircraft Fuel Tank With Machine Learning Methods

Abstract

An important challenge for the aircraft industry consists to predict the currents on the fastening assemblies in order to avoid sparking, which can lead to accident, especially for fuel tank fasteners. In the literature, it has been demonstrated that the contact resistance plays a major role in the current path on fasteners. Nevertheless, these contact resistances cannot be well determined and vary greatly. As a result, the prediction of current must be done in a statistical way. Usually, it requires several aircraft simulations with several set of contact resistances, which represents a significant computational cost. This article proposes a machine learning model, which allows us to predict the currents in the fastening assemblies of an aircraft fuel tank in a few seconds. This model is built from a database of FDTD simulations of the aircraft fuel tank in the lightning frequency range 100 Hz to 1 MHz. The FDTD modeling is depicted in detail in this article based on previous work. From this database, several machine leaning approaches are explored ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -nearest neighbors, support vector regression, XGBoost, and a neural network). As a result of this study, XGBoost presents the best performances. Further investigations using XGBoost highlights the ability of the model to predict well the current for most fasteners and frequencies, even with a small amount of simulations as training data. Moreover, the proposed model allows us to perform a parametric analysis, which underline the ability of the model to provide results in agreement with the physical effects of the issue (current paths, resistive effects, inductive effects, etc.). The results presented are promising for the use of the proposed methodology in the aeronautical industry.