Journal of Aerospace Engineering

Lack of complete knowledge of process characteristics, particularly uncertainties associated with milling processes and spindle behavior, severely limits the reliability of high speed spindles to support high speed machining. Attempts to regulate high speed milling processes based on sensing spindle power and controlling feed rate to prevent spindle stalls have not been successful, mainly due to the fact that power is often not the limiting factor. Instead, problems such as chatter, cutter and/or part deflection, milling forces, and its effects on the rapid wear of the taper of the tool holder and the spindle bearings can easily lead to scrapped parts and/or spindle breakdowns unless extreme care is taken. This paper addresses the stability analysis and design of a novel, robust, nonlinear milling controller implemented at Boeing Co. This controller is aimed at driving an end mill at the highest possible feedrate without damaging the cutter and the spindle. The successful implementation of this controller at Boeing has significantly reduced spindle failures in their extremely demanding high speed machining production lines.

Current remote sensing satellites with multispectral sensors capture high-resolution images and produce vast quantities of data. The size and volume of this information has dramatically increased in the last decade as sensor resolution and capabilities have significantly improved, without a similar improvement on the satellite system capacity to accommodate these changes. Remote sensing satellites currently operate on a “store and forward” paradigm, where data is stored on the satellite until the satellite is in view of the ground station. Low Earth orbit satellites may only see a ground station for a 10–15 min window per pass, in which time all the collected information must be telemetered to the ground. This process requires large and expensive onboard storage resources and places tremendous stress on communication channels. Hence, a complete image may not be successfully telemetered in one pass causing a significant delay between capture and analysis and limiting the benefits of these images. Smart satellites are more technologically advanced, require less ground station support and data storage, and are capable of transmitting required information quickly and easily to ground stations. With onboard reconfigurable data processing, these satellites have faster data product turnaround, less communication requirements, and provide more useful information. The high performance computing (HPC-I) payload on board the Australian satellite FedSat, launched in December 2002, is a demonstration device of the feasibility of reconfigurable computing technology in space. This device is small in size, requires low power, and has the processing capacity to handle large data volumes. Using this device in conjunction with a high-resolution imaging sensor, such as the bispectral infrared detection (BIRD) sensor, smart dedicated satellites become a feasible and cost effective solution to remote sensing needs. This paper elaborates on the system level design of a real-time fire observation system in the context of a smart satellite mission for detecting and monitoring natural disasters. The proposed system is built upon flight tested field programmable gate arrays based HPC-I technology, and would be capable of producing useful information about natural disasters directly broadcasted to interested parties within rapid timeframes. The algorithms for onboard real-time detection of direction, intensity, and location of fires are discussed, and reliable algorithms for detecting and verifying these fires using smoke plume detection are presented. Further work is described including fire-front analysis and the tracking of fire movement.

This study utilizes remotely sensed pre- and post-disaster images in order to detect any change specifically associated with structural and major regional damage caused by natural disasters such as a strong earthquake. Currently, postdisaster reconnaissance is conducted using mostly ground-truth teams. Although by using this method high-resolution assessment can be provided, it is very time consuming, costly and sometimes not practical. This study investigates an unsupervised near near-time method of reconnaissance using a pair of coregistered remotely sensed images of the same scene acquired at different times as input. The output is an image (mostly binary) in which “changed” pixels are separated from “not-changed” ones. The approach is fully deterministic using principal component analysis, normalization and further setting a threshold to classify pixels. For verification, a set of multispectral images from Golcuk-Turkey 1999 earthquake was used. It is concluded the program generates very similar results as obtained by ground-truth teams.

This paper investigates optimal tiltrotor flight trajectories and performance in vertical takeoff operations from a city-center heliport considering the possibility of one engine failure. A two-dimensional longitudinal rigid body model of a tiltrotor aircraft is used. Tiltrotor flights after engine failure in both continued takeoff and rejected takeoff are formulated as nonlinear optimal control problems that minimize the heliport size, subject to various constraints from safety considerations and tiltrotor performance limitations. These problems are parametrized via collocation into parameter optimization for numerical solutions. Extensive numerical solutions are obtained, and sensitivity analyses are conducted to examine effects of model parameter uncertainties. Optimization results indicate that the maximum gross weight capability of a tiltrotor in a vertical operation is determined by the need for safe landing in the event of a single engine failure. The height at which the decision for either continuing or rejecting the takeoff is made depends on the need to maintain sufficient height during the continued takeoff flight after an engine failure.

The paper presents a general framework for analyzing uncertainties in material and geometrical properties of a microforce gauge. Since fabrication of the microgauge is surface micromachining of microelectromechanical systems (MEMS), it has inherent variations that lead to both material and geometrical uncertainties. A convex model is used as a tool for addressing uncertainties in microforce gauge analysis. Mathematical derivations of convex models are presented and advantages of the convex approach over the standard statistical and probability approach in analyzing uncertainty in MEMS devices is highlighted.