کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5017423 1466577 2017 19 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Environment identification in flight using sparse approximation of wing strain
ترجمه فارسی عنوان
شناسایی محیط در پرواز با استفاده از تقریب تقریبی کشش بال
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی مهندسی مکانیک
چکیده انگلیسی
This paper addresses the problem of identifying different flow environments from sparse data collected by wing strain sensors. Insects regularly perform this feat using a sparse ensemble of noisy strain sensors on their wing. First, we obtain strain data from numerical simulation of a Manduca sexta hawkmoth wing undergoing different flow environments. Our data-driven method learns low-dimensional strain features originating from different aerodynamic environments using proper orthogonal decomposition (POD) modes in the frequency domain, and leverages sparse approximation to classify a set of strain frequency signatures using a dictionary of POD modes. This bio-inspired machine learning architecture for dictionary learning and sparse classification permits fewer costly physical strain sensors while being simultaneously robust to sensor noise. A measurement selection algorithm identifies frequencies that best discriminate the different aerodynamic environments in low-rank POD feature space. In this manner, sparse and noisy wing strain data can be exploited to robustly identify different aerodynamic environments encountered in flight, providing insight into the stereotyped placement of neurons that act as strain sensors on a Manduca sexta hawkmoth wing.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Fluids and Structures - Volume 70, April 2017, Pages 162-180
نویسندگان
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