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Takeoff Obstacle Clearance Procedures: The Feasibility of Extended Second Segment Climb

Description

To ensure safety is not precluded in the event of an engine failure, the FAA has

established climb gradient minimums enforced through Federal Regulations.

Furthermore, to ensure aircraft do

To ensure safety is not precluded in the event of an engine failure, the FAA has

established climb gradient minimums enforced through Federal Regulations.

Furthermore, to ensure aircraft do not accidentally impact an obstacle on takeoff due to

insufficient climb performance, standard instrument departure procedures have their own

set of climb gradient minimums which are typically more than those set by Federal

Regulation. This inconsistency between climb gradient expectations creates an obstacle

clearance problem: while the aircraft has enough climb gradient in the engine inoperative

condition so that basic flight safety is not precluded, this climb gradient is often not

strong enough to overfly real obstacles; this implies that the pilot must abort the takeoff

flight path and reverse course back to the departure airport to perform an emergency

landing. One solution to this is to reduce the dispatch weight to ensure that the aircraft

retains enough climb performance in the engine inoperative condition, but this comes at

the cost of reduced per-flight profits.

An alternative solution to this problem is the extended second segment (E2S)

climb. Proposed by Bays & Halpin, they found that a C-130H gained additional obstacle

clearance performance through this simple operational change. A thorough investigation

into this technique was performed to see if this technique can be applied to commercial

aviation by using a model A320 and simulating multiple takeoff flight paths in either a

calm or constant wind condition. A comparison of takeoff flight profiles against real

world departure procedures shows that the E2S climb technique offers a clear obstacle

clearance advantage which a scheduled four-segment flight profile cannot provide.

Contributors

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Created

Date Created
  • 2017

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Deriving an obstacle-avoiding shortest path in continuous space: a spatial approach

Description

The shortest path between two locations is important for spatial analysis, location modeling, and wayfinding tasks. Depending on permissible movement and availability of data, the shortest path is either derived

The shortest path between two locations is important for spatial analysis, location modeling, and wayfinding tasks. Depending on permissible movement and availability of data, the shortest path is either derived from a pre-defined transportation network or constructed in continuous space. However, continuous space movement adds substantial complexity to identifying the shortest path as the influence of obstacles has to be considered to avoid errors and biases in a derived path. This obstacle-avoiding shortest path in continuous space has been referred to as Euclidean shortest path (ESP), and attracted the attention of many researchers. It has been proven that constructing a graph is an effective approach to limit infinite search options associated with continuous space, reducing the problem to a finite set of potential paths. To date, various methods have been developed for ESP derivation. However, their computational efficiency is limited due to fundamental limitations in graph construction. In this research, a novel algorithm is developed for efficient identification of a graph guaranteed to contain the ESP. This new approach is referred to as the convexpath algorithm, and exploits spatial knowledge and GIS functionality to efficiently construct a graph. The convexpath algorithm utilizes the notion of a convex hull to simultaneously identify relevant obstacles and construct the graph. Additionally, a spatial filtering technique based on intermediate shortest path is enhances intelligent identification of relevant obstacles. Empirical applications show that the convexpath algorithm is able to construct a graph and derive the ESP with significantly improved efficiency compared to visibility and local visibility graph approaches. Furthermore, to boost the performance of convexpath in big data environments, a parallelization approach is proposed and applied to exploit computationally intensive spatial operations of convexpath. Multicore CPU parallelization demonstrates noticeable efficiency gain over the sequential convexpath. Finally, spatial representation and approximation issues associated with raster-based approximation of the ESP are assessed. This dissertation provides a comprehensive treatment of the ESP, and details an important approach for deriving an optimal ESP in real time.

Contributors

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Created

Date Created
  • 2015