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R.S. Ul Haq
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Sustainability is a key commitment for future innovation and improvement of the aerospace industry and to realise active research is invested towards advanced and new aircraft designs such as the Flying V. The Flying V is a flying wing design for commercial aviation, promising higher efficiency against conventional tube-and-wing aircraft. The Flight Control System (FCS) has to be designed to prove the airworthiness of the aircraft. In this work, a fault-tolerant FCS is designed that includes an adaptive incremental dynamic inversion inner loop rate control law with an outer loop that consists of longitudinal C* control law and Rate Control Attitude Hold roll control laws for lateral control. Research and activities has led to an updated geometry design with aerodynamic data from RANS simulation, which requires tuning of its outer loop flight controls to be within level 1 handling quality. To investigate the fault tolerance of the aircraft with a structural fault case that results in a loss of effectiveness. It is shown that the adaptation allows the aircraft to cope with the faults and maintain satisfactory tracking performance.
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Sustainability is a key commitment for future innovation and improvement of the aerospace industry and to realise active research is invested towards advanced and new aircraft designs such as the Flying V. The Flying V is a flying wing design for commercial aviation, promising higher efficiency against conventional tube-and-wing aircraft. The Flight Control System (FCS) has to be designed to prove the airworthiness of the aircraft. In this work, a fault-tolerant FCS is designed that includes an adaptive incremental dynamic inversion inner loop rate control law with an outer loop that consists of longitudinal C* control law and Rate Control Attitude Hold roll control laws for lateral control. Research and activities has led to an updated geometry design with aerodynamic data from RANS simulation, which requires tuning of its outer loop flight controls to be within level 1 handling quality. To investigate the fault tolerance of the aircraft with a structural fault case that results in a loss of effectiveness. It is shown that the adaptation allows the aircraft to cope with the faults and maintain satisfactory tracking performance.
Convection-dominated flow problems are well-known to have non-physical oscillations near steep gradients or discontinuities in the solution when solved with standard numerical methods, such as finite elements or finite difference methods. To overcome this limitation, algebraic flux correction (AFC) can be used, which is a stabilization method. However, AFC contains time-consuming computations, therefore, alternative approaches are explored. The rapidly rising field of machine learning in the mathematical world, so called scientific machine learning, has successful applications in solving partial differential equations. In this work, the focus is on convection-dominated flow problems, in particular the steady state convection-diffusion equation in one-dimension. To solve this, two alternative approaches based on neural network-learning have been developed that are able to mimic the AFC limiter with a certain accuracy and performance. In some cases, the neural network-based limiter is outperforming the AFC limiter.
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Convection-dominated flow problems are well-known to have non-physical oscillations near steep gradients or discontinuities in the solution when solved with standard numerical methods, such as finite elements or finite difference methods. To overcome this limitation, algebraic flux correction (AFC) can be used, which is a stabilization method. However, AFC contains time-consuming computations, therefore, alternative approaches are explored. The rapidly rising field of machine learning in the mathematical world, so called scientific machine learning, has successful applications in solving partial differential equations. In this work, the focus is on convection-dominated flow problems, in particular the steady state convection-diffusion equation in one-dimension. To solve this, two alternative approaches based on neural network-learning have been developed that are able to mimic the AFC limiter with a certain accuracy and performance. In some cases, the neural network-based limiter is outperforming the AFC limiter.