This document describes the architecture and algorithms of a high fidelity fixed wing flight simulator intended to test and validate novel guidance, navigation, and control (GNC) algorithms for autonomous aircraft. It aims to replicate the influence of as many factors as possible on the aircraft performances, the Earth model, the physics of flight and the associated equations of motion, and in particular the behavior of the onboard sensors, limiting the assumptions to the bare minimum, and including multiple relatively minor effects not usually considered in simulation that may play a role in the GNC algorithms not performing as intended. The author releases the flight simulator C ++ implementation as open-source software. The simulator modular design enables the replacement of the standard GNC algorithms with the objective of evaluating their performances when subject to specific missions and meteorological conditions (atmospheric properties, wind field, air turbulence). The testing and evaluation is performed by means of Monte Carlo simulations, as most simulation modules (such as the aircraft mission, the meteorological conditions, the errors introduced by the sensors, and the initial conditions) are defined stochastically and hence vary in a pseudo-random way from one execution to the next according to certain user-defined input parameters, ensuring that the results are valid for a wide range of conditions. In addition to modeling the outputs of all sensors usually present onboard a fixed wing platform, such as accelerometers, gyroscopes, magnetometers, Pitot tube, air vanes, and a Global Navigation Satellite System (GNCC) receiver, the simulator is also capable of generating realistic images of the Earth surface that resemble what an onboard camera would record if following the resulting trajectory, enabling the use and evaluation of visual and visual inertial navigation systems.