We address the problem of loading as much freight as possible in an aircraft while balancing the load in order to minimize fuel consumption and to satisfy stability/safety requirements. Our formulation methodology permits to solve the problem on a PC, within ten min, by off-the-shelf integer linear programming software. This method decides which containers to load (and in which compartment) and which to leave on the ground.
Structural airframe maintenance is a subset of scheduled maintenance, and is performed at regular intervals to detect and repair cracks that would otherwise affect the safety of the airplane. It has been observed that only a fraction of airplanes undergo structural airframe maintenance at earlier scheduled maintenance times. But, intrusive inspection of all panels on the airplanes needs to be performed at the time of scheduled maintenance to ascertain the presence/absence of large cracks critical to the safety of the airplane. Recently, structural health monitoring techniques have been developed. They use on-board sensors and actuators to assess the current damage status of the airplane, and can be used as a tool to skip the structural airframe maintenance whenever deemed unnecessary. Two maintenance philosophies, scheduled structural health monitoring and condition-based maintenance skip, have been developed in this article to skip unnecessary structural airframe maintenances using the on-board structural health monitoring system. A cost model is developed to quantify the savings of these maintenance philosophies over scheduled maintenance.
In this paper, we develop a rigorous new framework for the concepts of forecast and decision horizons. These concepts are conditional in nature and, in turn, enable us to unify the existing concepts of “strong” and “weak” horizons. Moreover, we are able to precisely state the questions of existence of forecast/decision horizons. Finally, we are able to further develop the theory of existence of forecast/decision horizons in the discounted case and procedures to compute these horizons.
Low-fidelity analytical models are often used at the conceptual aircraft design stage. Because of uncertainties on these models and their corresponding input variables, deterministic optimization may achieve under-design or overdesign. Therefore it is important to already consider these uncertainties at the conceptual design stage in order to avoid inefficient design and then costly time over runs due to re-design. This paper presents a procedure for reliable and robust optimization of an aircraft at the conceptual design phase. Uncertainties on model and design variables are taken into account in a probabilistic setting. More precisely, at each point of the optimization process uncertainties are modeled by an adaptive normal law strategy in order to fit the historical aircraft database. The statistical parameters are adjusted depending on the available information at the current point of the optimization process. To improve computational cost, response surface approximations are constructed to represent reliability constraints. The developed methodology is applied to the conceptual design of a short range aircraft. Compared to standard deterministic optimization without design margins, the result shows a modest increase on weight, which allows however to ensure a desired reliability and robustness of the design compared to the unreliable and sensitive deterministic optimum.
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