Vaughn College of Aeronautics and Technology

About: Vaughn College of Aeronautics and Technology is a education organization based out in New York, New York, United States. It is known for research contribution in the topics: Gravitational microlensing & Planetary system. The organization has 727 authors who have published 708 publications receiving 14082 citations. The organization is also known as: College of Aeronautics.

Experimental and Numerical Simulation Analysis of Typical Carbon Woven Fabric/Epoxy Laminates Subjected to Lightning Strike

Abstract: To clarify the evolution of damage for typical carbon woven fabric/epoxy laminates exposed to lightning strike, artificial lightning testing on carbon woven fabric/epoxy laminates were conducted, damage was assessed using visual inspection and damage peeling approaches. Relationships between damage size and action integral were also elucidated. Results showed that damage appearance of carbon woven fabric/epoxy laminate presents circular distribution, and center of the circle located at the lightning attachment point approximately, there exist no damage projected area dislocations for different layers, visual damage territory represents maximum damage scope; visible damage can be categorized into five modes: resin ablation, fiber fracture and sublimation, delamination, ablation scallops and block-shaped ply-lift; delamination damage due to resin pyrolysis and internal pressure exist obvious distinguish; project area of total damage is linear with action integral for the same type specimens, that of resin ablation damage is linear with action integral, but no correlation with specimen type, for all specimens, damage depth is linear with logarithm of action integral. The coupled thermal–electrical model constructed is capable to simulate the ablation damage for carbon woven fabric/epoxy laminates exposed to simulated lightning current through experimental verification.

The Shear Stiffness of Flat-sided Corrugated Webs

Abstract: A method of analysis is presented for the shear stiffness of a flat-sided corrugated web, attached at a series of discrete points to flanges either on one side or on both sides of the web. Simple expressions are obtained for the shear stiffness of both types of web, and it is shown that there may be a considerable loss of stiffness due to the method of attachment. A comparison is made with experimental results for corrugated webs with both types of attachment.

A Novel Hybrid PSO-K-Means Clustering Algorithm Using Gaussian Estimation of Distribution Method and Lévy Flight

Abstract: Clustering is an important data analysis technique, which has been applied to many practical scenarios. However, many partitioning based clustering algorithms are sensitive to the initial state of cluster centroids, may get trapped in a local optimum, and have poor robustness. In recent years, particle swarm optimization (PSO) has been regarded as an effective solution to the problem. However, it has the possibility of converging to a local optimum, especially when solving complex problems. In this paper, we propose a hybrid PSO-K-means algorithm, which uses the Gaussian estimation of distribution method (GEDM) to assist PSO in updating the population information and adopts Levy flight to escape from the local optimum. The proposed algorithm is named a GEDM and Levy flight based PSO-K-means (GLPSOK) clustering algorithm. Firstly, during initialization, a few particles are initialized using the cluster centroids generated by K-means, while other particles are randomly initialized in the search space. Secondly, GEDM and PSO are selected with different probability to update the population information at different optimization stages. Thirdly, Levy flight is adopted to help the search escape from the local optimum. Finally, the greedy strategy is carried out to select the promising particles from the parents and the newly generated candidates. Experimental results on both synthetic data sets and real-world data sets show that the proposed algorithm can produce better clustering results and is more robust than existing classic or state-of-the-art clustering algorithms.