Engineering topics

Bieniawski, Stefan R.

Publications and source records attributed to Bieniawski, Stefan R..

4 records

NASA NTRS2008

Miniature Trailing Edge Effector for Aerodynamic Control

Improved miniature trailing edge effectors for aerodynamic control are provided. Three types of devices having aerodynamic housings integrated to the trailing edge of an aerodynamic shape are presented, which vary in details of how the control surface can move. A bucket type device has a control surface which is the back part of a C-shaped member having two arms connected by the back section. The C-shaped section is attached to a housing at the ends of the arms, and is rotatable about an axis parallel to the wing trailing edge to provide up, down and neutral states. A flip-up type device has a control surface which rotates about an axis parallel to the wing trailing edge to provide up, down, neutral and brake states. A rotating type device has a control surface which rotates about an axis parallel to the chord line to provide up, down and neutral states.

Lee, Hak-Tae↗

NASA NTRS2004

Fleet Assignment Using Collective Intelligence

Airline fleet assignment involves the allocation of aircraft to a set of flights legs in order to meet passenger demand, while satisfying a variety of constraints. Over the course of the day, the routing of each aircraft is determined in order to minimize the number of required flights for a given fleet. The associated flow continuity and aircraft count constraints have led researchers to focus on obtaining quasi-optimal solutions, especially at larger scales. In this paper, the authors propose the application of an agent-based integer optimization algorithm to a "cold start" fleet assignment problem. Results show that the optimizer can successfully solve such highly- constrained problems (129 variables, 184 constraints).

Antoine, Nicolas E.↗

NASA NTRS2004

Fleet Assignment Using Collective Intelligence

Product distribution theory is a new collective intelligence-based framework for analyzing and controlling distributed systems. Its usefulness in distributed stochastic optimization is illustrated here through an airline fleet assignment problem. This problem involves the allocation of aircraft to a set of flights legs in order to meet passenger demand, while satisfying a variety of linear and non-linear constraints. Over the course of the day, the routing of each aircraft is determined in order to minimize the number of required flights for a given fleet. The associated flow continuity and aircraft count constraints have led researchers to focus on obtaining quasi-optimal solutions, especially at larger scales. In this paper, the authors propose the application of this new stochastic optimization algorithm to a non-linear objective cold start fleet assignment problem. Results show that the optimizer can successfully solve such highly-constrained problems (130 variables, 184 constraints).

Antoine, Nicolas E.↗

NASA NTRS2004

Product Distributions for Distributed Optimization

With connections to bounded rational game theory, information theory and statistical mechanics, Product Distribution (PD) theory provides a new framework for performing distributed optimization. Furthermore, PD theory extends and formalizes Collective Intelligence, thus connecting distributed optimization to distributed Reinforcement Learning (FU). This paper provides an overview of PD theory and details an algorithm for performing optimization derived from it. The approach is demonstrated on two unconstrained optimization problems, one with discrete variables and one with continuous variables. To highlight the connections between PD theory and distributed FU, the results are compared with those obtained using distributed reinforcement learning inspired optimization approaches. The inter-relationship of the techniques is discussed.

Bieniawski, Stefan R.↗