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How many times have you found that your institution has access to a digital version of a book you need only to discover that it comes in 15 different PDF files?
Rushdi, M., Yoshida, S., & Dief, T. N. (2018). Simulation of a Tether of a Kite Power System Using a Lumped Mass Model.
In this paper, we presented a detailed mathematical analysis of the kite power system followed by typical 2-D simulation results.
Dief, T. N., Rushdi, M. A., Halawa, A., & Yoshida, S. (2020). Hardware-in-the-Loop (HIL) and Experimental Findings for the 7 kW Pumping Kite Power System. In AIAA Scitech 2020 Forum (p. 1244).
In this paper, we showed the flight test experiments of KPS.
Rushdi, M., Hussein, A., Dief, T. N., Yoshida, S., & Schmehl, R. (2020). Simulation of the transition phase for an optimally-controlled tethered vtol rigid aircraft for airbornewind energy generation. In AIAA Scitech 2020 Forum (p. 1243).
In this paper, we formulate the Transition phase (for an AWE VTOL aircraft) as an optimal control problem, so as to determine the optimal control inputs which constitute the control surface deflections and the thrust force; which steers the aircraft from hovering with its nose upwards to forward flight. Subsequently, we simulate the trajectory for two cases of optimality; (a) minimizing the power consumption and (b) minimizing the endurance, during this phase.
Dief, T. N., Fechner, U., Schmehl, R., Yoshida, S., & Rushdi, M. A. (2020). Adaptive flight path control of airborne wind energy systems. Energies, 13(3), 667.
In this paper, we applied a system identification algorithm and an adaptive controller to a simple kite system model to simulate crosswind flight maneuvers for airborne wind energy harvesting.
Rushdi, M. A., Rushdi, A. A., Dief, T. N., Halawa, A. M., Yoshida, S., & Schmehl, R. (2020). Power prediction of airborne wind energy systems using multivariate machine learning. Energies, 13(9), 2367.
In this work, we present the kite system of Kyushu University and demonstrate how experimental data can be used to train machine learning regression models.
Rushdi, M. A., Dief, T. N., Halawa, A. M., & Yoshida, S. (2020). System identification of a 6 m2 kite power system in fixed-tether length operation. International Review of Aerospace Engineering, 13(4), 150-158.
A system identification algorithm was applied to evaluate the correlation between the tension force and the kite’s rolling angle over four tests, then come up with a transfer function that describes the system. Thereby enabling study the kite behavior as a preliminary step for the achievement of autonomous flight.
Rushdi, M. A., Dief, T. N., Yoshida, S., & Schmehl, R. (2020). Towing test data set of the kyushu university kite system. Data, 5(3), 69.
In this paper, we present measurement data from seven individual tow tests with the kite system developed by Kyushu University.
Rushdi, M. A., Yoshida, S., Watanabe, K., & Ohya, Y. (2021). Machine learning approaches for thermal updraft prediction in wind solar tower systems. Renewable Energy, 177, 1001-1013.
In this paper, we present the modeling of thermal updraft using machine learning for a wind-solar-tower developed by Kyushu University.
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.