Cruise Efficient, Short Take-Off And Landing Research Articles

Method to Explore the Design Space of a Turbo-Electric Distributed Propulsion System

AbstractMeeting future goals for aircraft and air traffic system performance will require a fundamental shift in approach to aircraft and engine design. In 2005, the National Aeronautics and Space Administration (NASA) released plans of a next generation commercial airplane for 2030 combining the blended wing body (BWB) and a superconducting distributed propulsion system. The BWB concept adapts NASA’s cruise-efficient short take-off and landing (CESTOL) airframe. The propulsion system employs distributed electric fans, which are embedded on the upper surface of the airframe, driven by superconducting motors with power provided by two wing-tip mounted turboelectric generators. This paper describes a method to design a turboelectric distributed propulsion (TeDP) system on the hybrid wing body airframe, including a way to obtain the propulsor number and its weight, a method to simulate boundary layer ingestion, and a method to calculate electric system performances and its weight. An examination of the syste...

Robustness Analysis of an Aircraft Design for Short Takeoff and Landing

As part of the Collaborative Research Center 880, preliminary aircraft design activities are carried out for a new class of low-noise cruise-efficient short takeoff and landing (CESTOL) transport aircraft. A corresponding aircraft is quite different from a state-of-the-art commercial aircraft because of the use of a high-lift system with active flow control. The fact that new technologies are not sufficiently understood yet in combination with the assumption of common design data and the use of classical calculation methods expresses itself in uncertainties that are of epistemic character. The robustness of a deterministic CESTOL aircraft design toward parameters such as the necessary engine thrust, direct operating costs, and the maximum takeoff and landing distances is investigated here concerning the mentioned uncertainties. For this purpose, a stochastic description of parameter variations of the design is formulated. Stochastic quantities are computed by Monte Carlo sampling to rate the robustness. A distributed component-based software implementation is used to perform the Monte Carlo sampling. The software system is installed on a Linux cluster with several multi-CPU computers; a deterministic sample is simulated through the design program PrADO.