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05<br />
AIRCRAFT DESIGN, TESTING AND PERFORMANCE<br />
Includes all stages of design of aircraft and aircraft structures and systems. Also includes aircraft testing, performance, and evaluation,<br />
and aircraft and flight simulation technology. For related information see also 18 Spacecraft Design, Testing and Performance; and 39<br />
Structural Mechanics. For land transportation vehicles see 85 Technology Utilization and Surface Transportation.<br />
20030022708 Defence Science and Technology Organisation, Fishermans Bend, Australia<br />
Importance of Reliability Assessment to Helicopter Structural Component Fatigue Life Prediction<br />
Lombardo, D. C.; Fraser, K. F.; June 2002; 22 pp.; In English<br />
Report No.(s): DSTO-TN-0462; DODA-AR-012-522; Copyright; Avail: Other Sources<br />
This paper discusses the need for understanding reliability within the context of helicopter structures and presents a case<br />
for why such understanding is essential to successfully implementing better usage monitoring programs.<br />
Author<br />
Fatigue (Materials); Fatigue Life; Helicopters; Reliability Engineering; Reliability Analysis; Performance Prediction; Risk<br />
20030022714 NASA Ames Research Center, Moffett Field, CA, USA<br />
Aerodynamic Shape Optimization using an Evolutionary Algorithm<br />
Hoist, Terry L.; Pulliam, Thomas H.; January 2003; 32 pp.; In English; No Copyright; Avail: CASI; A03, Hardcopy<br />
A method for aerodynamic shape optimization based on an evolutionary algorithm approach is presented and<br />
demonstrated. Results are presented for a number of model problems to access the effect of algorithm parameters on<br />
convergence efficiency and reliability. A transonic viscous airfoil optimization problem-both single and two-objective<br />
variations is used as the basis for a preliminary comparison with an adjoint-gradient optimizer. The evolutionary algorithm is<br />
coupled with a transonic full potential flow solver and is used to optimize the inviscid flow about transonic wings including<br />
multi-objective and multi-discipline solutions that lead to the generation of pareto fronts. The results indicate that the<br />
evolutionary algorithm approach is easy to implement, flexible in application and extremely reliable.<br />
Author<br />
Algorithms; Shape Optimization; Aerodynamic Configurations; Parameterization<br />
20030025280 NASA Goddard Space Flight Center, Greenbelt, MD, USA<br />
Science Goal Driven Observing: A Step Towards Maximizing Science Returns and Spacecraft Autonomy<br />
Koratkar, Anuradha; Grosvenor, Sandy; Jones, Jeremy; Memarsadeghi, Nargess; Wolf, Karl; [2002]; 12 pp.; In English; SPIE<br />
2002, 22-28 Aug. 2002, Waikoloa, HI, USA; Original contains black and white illustrations; Copyright; Avail: CASI; A03,<br />
Hardcopy<br />
In the coming decade, the drive to increase the scientific returns on capital investment and to reduce costs will force<br />
automation to be implemented in many of the scientific tasks that have traditionally been manually overseen. Thus, spacecraft<br />
autonomy will become an even greater part of mission operations. While recent missions have made great strides in the ability<br />
to autonomously monitor and react to changing health and physical status of spacecraft, little progress has been made in<br />
responding quickly to science driven events. The new generation of space-based telescopes/observatories will see deeper, with<br />
greater clarity, and they will generate data at an unprecedented rate. Yet, while onboard data processing and storage capability<br />
will increase rapidly, bandwidth for downloading data will not increase as fast and can become a significant bottleneck and<br />
cost of a science program. For observations of inherently variable targets and targets of opportunity, the ability to recognize<br />
early if an observation will not meet the science goals of variability or minimum brightness, and react accordingly, can have<br />
a major positive impact on the overall scientific returns of an observatory and on its operational costs. If the observatory can<br />
reprioritize the schedule to focus on alternate targets, discard uninteresting observations prior to downloading, or download<br />
them at a reduced resolution its overall efficiency will be dramatically increased. We are investigating and developing tools<br />
for a science goal monitoring (SGM) system. The SGM will have an interface to help capture higher-level science goals from<br />
scientists and translate them into a flexible observing strategy that SGM can execute and monitor. SGM will then monitor the<br />
incoming data stream and interface with data processing systems to recognize significant events. When an event occurs, the<br />
system will use the science goals given it to reprioritize observations, and react appropriately and/or communicate with ground<br />
systems - both human and machine - for confirmation and/or further high priority analyses.<br />
Author<br />
Telescopes; Proving; Operating Costs; Onboard Data Processing; Data Processing Equipment; Data Flow Analysis; Flight<br />
Vehicles<br />
4