先进机身结构和材料设计

由于大型商用飞机机身结构的复杂性,其碳纤维复合材料部件的设计、制造和取证都不能借用现有的制造维修基本经验.碳纤维复合材料机身壁板超越了现代化金属技术,对应力应变的保守限制要求必须保证将具有损伤容限的机身结构交付到航空公司.这样,碳纤维复合材料壁板技术的应用至少限制了新飞机家族第一代成员的重量.     

The Genesis Solar Wind Concentrator

The primary goal of the Genesis Mission is to collect solar wind ions and, from their analysis, establish key isotopic ratios that will help constrain models of solar nebula formation and evolution. The ratios of primary interest include ¹⁷O/¹⁶O and ¹⁸O/¹⁶O to ±0.1%, ¹⁵N/¹⁴N to ±1%, and the Li, Be, and B elemental and isotopic abundances. The required accuracies in N and O ratios cannot be achieved without concentrating the solar wind and implanting it into low-background target materials that are returned to Earth for analysis. The Genesis Concentrator is designed to concentrate the heavy ion flux from the solar wind by an average factor of at least 20 and implant it into a target of ultra-pure, well-characterized materials. High-transparency grids held at high voltages are used near the aperture to reject >90% of the protons, avoiding damage to the target. Another set of grids and applied voltages are used to accelerate and focus the remaining ions to implant into the target. The design uses an energy-independent parabolic ion mirror to focus ions onto a 6.2 cm diameter target of materials selected to contain levels of O and other elements of interest established and documented to be below 10% of the levels expected from the concentrated solar wind. To optimize the concentration of the ions, voltages are constantly adjusted based on real-time solar wind speed and temperature measurements from the Genesis ion monitor. Construction of the Concentrator required new developments in ion optics; materials; and instrument testing and handling. This revised version was published online in August 2006 with corrections to the Cover Date.     

Crater detection via genetic search methods to reduce image features

Recent approaches to crater detection have been inspired by face detection’s use of gray-scale texture features. Using gray-scale texture features for supervised machine learning crater detection algorithms provides better classification of craters in planetary images than previous methods. When using Haar features it is typical to generate thousands of numerical values from each candidate crater image. This magnitude of image features to extract and consider can spell disaster when the application is an entire planetary surface. One solution is to reduce the number of features extracted and considered in order to increase accuracy as well as speed. Feature subset selection provides the operational classifiers with a concise and denoised set of features by reducing irrelevant and redundant features. Feature subset selection is known to be NP-hard. To provide an efficient suboptimal solution, four genetic algorithms are proposed to use greedy selection, weighted random selection, and simulated annealing to distinguish discriminate features from indiscriminate features. Inspired by analysis regarding the relationship between subset size and accuracy, a squeezing algorithm is presented to shrink the genetic algorithm’s chromosome cardinality during the genetic iterations. A significant increase in the classification performance of a Bayesian classifier in crater detection using image texture features is observed.