Finding the Optimal Position of the Distance Measuring Device with the Aim of Reducing Risk and Uncertainty in the Aircraft Landing Phase

Document Type : Original Article

Authors
1 Ph.D. Student, Department of Aerospace Engineering, University of Tehran Kish International Campus, Tehran, Iran.
2 Associate Professor, Department of Aerospace Engineering, Faculty of New Science and Technology, University of Tehran, Tehran, Iran
3 Tehran university
Abstract
Under non-ideal weather conditions, distance measuring equipment (DME) must be optimally positioned to reduce landing errors. In this context, interval type-2 fuzzy logic is used as an efficient method for determining the optimal position of the DME. With this method, factors such as weather conditions, flight path, and other related variables are evaluated fuzzily to determine the optimal position of the DME. When unequal distances from the normal landing strip exist for each of the DMEs, it is necessary to identify the most suitable location for the secondary DME. To this end, the system is designed dynamically so that after identifying the assumed centerline based on the initial placement of each DME, the optimal location for the secondary DME is ultimately determined. These conditions only apply if both DMEs have unequal distances. The results show that the use of the Grey Wolf Optimization (GWO) algorithm has a significant impact on improving aircraft landing conditions. With this algorithm, the reduction in the impact of turbulence has increased from 20% to 40%. The probability of landing error has decreased from 10% to 5%, and measurement accuracy has increased from 85% to 95%. These results indicate a substantial improvement in landing safety under non-ideal conditions. Given the reduction in the impact of turbulence and the increase in measurement accuracy, the GWO algorithm plays a crucial role in enhancing the safety and efficiency of aircraft landings.
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  • Receive Date 11 May 2024
  • Revise Date 24 July 2024
  • Accept Date 01 October 2024
  • First Publish Date 01 October 2024
  • Publish Date 21 September 2024