Dynamic soaring is a non-powered flight mode that enables extremely high speeds by extracting energy from thin shear wind layers. Trajectory optimization is applied to construct solutions of the maximum speed achievable with dynamic soaring and to determine characteristic properties of that flight mode, using appropriate models of the vehicle dynamics and the shear wind layer. Furthermore, an energy-based flight mechanics model of high-speed dynamic soaring is developed, with reference made to trajectory optimization. With this model, analytic solutions for high-speed dynamic soaring are derived. The key factors for the maximum speed performance are identified and their effects are determined. Furthermore, analytic solutions for other, non-performance quantities of significance for high-speed dynamic soaring are derived. The analytic solutions virtually agree with the results achieved with the trajectory optimization using the vehicle dynamics model. This is considered a validation of the energy-based model yielding analytic solutions. The analytical solutions are also valid for the high subsonic Mach number region involving significant compressibility effects. This is of importance for future developments in high-speed dynamic soaring, as modern gliders are now capable of reaching that Mach number region.

To satisfy an increasing demand to reconstruct an athlete’s motion for performance analysis, this paper proposes a new method for reconstructing the position and velocity in the context of ski jumping trajectories. Therefore, state-of-the-art wearable sensors, including an inertial measurement unit, a magnetometer, and a GPS logger are used. The method employs an extended Rauch-Tung-Striebel smoother with state constraints to estimate state information offline from recorded raw measurements. In comparison to the classic inertial navigation system and GPS integration solution, the proposed method includes additional geometric shape information of the ski jumping hill, which are modeled as soft constraints and embedded into the estimation framework to improve the position and velocity estimation accuracy. Results for both simulated measurement data and real measurement data demonstrate the effectiveness of the proposed method. Moreover, a comparison between jump lengths obtained from the proposed method and video recordings shows the relative root-mean-square error of the reconstructed jump length is below 1.5 m depicting the accuracy of the algorithm.

Dynamic soaring is a flight mode that uniquely enables high speeds without an engine. This is possible in a horizontal shear wind that comprises a thin layer and a large wind speed. It is shown that the speeds reachable by modern gliders approach the upper subsonic Mach number region where compressibility effects become significant, with the result that the compressibility-related drag rise yields a limitation for the achievable maximum speed. To overcome this limitation, wing sweep is considered an appropriate means. The effect of wing sweep on the relevant aerodynamic characteristics for glider type wings is addressed. A 3-degrees-of-freedom dynamics model and an energy-based model of the vehicle are developed in order to solve the maximum-speed problem with regard to the effect of the compressibility-related drag rise. Analytic solutions are derived so that generally valid results are achieved concerning the effects of wing sweep on the speed performance. Thus, it is shown that the maximum speed achievable with swept wing configurations can be increased. The improvement is small for sweep angles up to around 15 deg and shows a progressive increase thereafter. As a result, wing sweep has potential for enhancing the maximum-speed performance in high-speed dynamic soaring.

Fuel efficient coordination of aircraft operations in the Terminal Maneuvering Area of large airports can contribute to the reduction of the environmental impact of air traffic. To exploit the full potential of the air traffic system, coordinated routing in the Terminal Maneuvering Area, runway assignment and scheduling need to be optimized considering detailed models of the aircraft dynamics and performance. Due to the inhomogeneous nature and level of detail of these combined discrete and continuous decision problems, the optimization of the overall operations poses significant challenges. As part of an integrated approach to the solution of this hybrid problem, this work explores the generation of surrogate models for the fuel consumption of individual aircraft using optimal control methods to exploit the physical capability of the aircraft within an operationally permissible envelope. The surrogate models approximate the predicted fuel consumption of a given point-mass aircraft model on short generic trajectory segments. The trade-off between flight duration and fuel consumption on such segments is analyzed, focusing on the influences of initial aircraft mass, altitude, distance, mean climb angle, along-track wind velocity and linear wind shear. An extensive description of the optimal control-based data generation and surrogate modeling methodology is followed by a discussion of the effects of parameter variation. Based on an illustrative case study, the applicability of the approach is critically analyzed.