Enhancing the ability to predict airflow around the Vertical Tail Plane (VTP) of an aircraft is vital in the aviation industry. The size of the VTP is driven by a particular flight condition - loss of an engine during take-off and low speed climb. Nowadays, Computational Fluid Dynamics (CFD) is the main tool used by engineers to assess VTP flows. However, due to uncertainties in the prediction of VTP effectiveness, aircraft designers keep to a conservative approach, which risks oversizing of the tail plane, adding more drag. Uncertainties emerge from difficulties in predicting the massive separation that occurs on the swept tail when it is approached by a flow at high incidence. Furthermore, the deployment of the control surface (the rudder) over the tail plane and the skewed flow along the span increase the CFD challenges. Improved predictive capabilities of the flow around VTPs would enable a more optimal design approach with potential drag saving. The correct prediction of flow separation is the essence of this study. Currently, the industry uses steady Reynolds-Averaged Navier-Stokes (RANS) simulations to analyse VTPs flow. In order to assess RANS performance, the study of airflow detaching from a backward rounded ramp is performed and the results are compared to Large-Eddy Simulations (LES). The analysis shows that, even though RANS may predict the onset of flow separation correctly, they completely miss the location of flow reattachment over the ramp, and this affects the whole flow solution. Moreover, the flow features a strong anisotropy at the onset of separation, difficult to be captured by RANS. The analysis shows that RANS cannot predict production of turbulent kinetic energy in the detached flow region correctly, discouraging flow mixing, and delaying flow reattachment. A hybrid RANS/LES carried out on the same test case shows the benefits of using eddy-resolving simulations for detached flows. The prediction of the locations of the separation and reattachment points differs by only 1% from the highly-resolved simulation. The VTP investigation carried out in this thesis uses a wind tunnel model tested at Airbus. The study starts with steady RANS approaches for different turbulence models. RANS simulations produce acceptable results for the flow at low incidence levels. On the contrary, at high incidence, when flow separation occurs, RANS methods fail. The second step of the research consists of using unsteady RANS (URANS) simulations for VTP flows at high sideslip angles. The introduction of time-accuracy brings important benefits. Nevertheless, the results still show some inaccuracies (around 20% error). Finally, restarting from the flow solutions obtained by URANS simulations, higher fidelity hybrid RANS/LES techniques in the form of Delayed Detached-Eddy Simulations (DDES) are used to assess the characteristics of the separated flow around the tail plane. Results show a remarkable improvement of the flow solution. The pressure distribution matches experimental results favourably, and this translates into an improved prediction of the aerodynamic loads over the VTP. This leads towards a new strategy for the assessment of the flow over aircraft VTPs, amounting to an important contribution to the design of future aircraft.

An artificial Neural Network (ANN) system has been developed that can analyse aircraft flight data to provide a reconstruction of the aerodynamic loads experienced by the aircraft during flight, including manoeuvre, buffet and distributed loading. For this research data was taken from the International Follow-On Structural Test Project (IFOSTP) F/A-18 fatigue test conducted by the Royal Australian Air Force and Canadian Forces. This fatigue test involved the simultaneous application of both manouevre and buffet loads using airbag actuators and shakers. The applied loads were representative of the actual loads experienced by an FA/18 during flight tests. Following an evaluation of different ANN types an Ellman network with three linear layers was selected. The Elman back-propagation network was tested with various parameters and structures. The network was trained using the MATLAB 'traingdx' function with is a gradient descent with momentum and adaptive learning rate back-propagation algorithm. The ANN was able to provide a good approximation of the actual manoeuvre or buffet loads at the location where the training loads data were recorded even for input values which differ from the training input values. In further tests the ability to estimate distributed loading at locations not included in the training data was also demonstrated. The ANN was then modified to incorporate various methods for the calculation and prediction of output error and reliability Used in combination and in appropriate circumstances, the addition of these capabilities significantly increase the reliability, accuracy and therefore usefulness of the ANN system's ability to estimate aircraft loading.To demonstrate the ANN system's usefulness as a fatigue monitoring tool it was combined with a formulae for crack growth analysis. Results inficate the ANN system may be a useful fatigue monitoring tool enabling real time monitoring of aircraft critical components using existing strain gauge sensors.