Asset Details
MbrlCatalogueTitleDetail
Do you wish to reserve the book?
Spectral proper orthogonal decomposition using multitaper estimates
by
Schmidt, Oliver T
in
Algorithms
/ Bandwidths
/ Bias
/ Decomposition
/ Eigenvalues
/ Estimates
/ Estimators
/ Fourier transforms
/ Proper Orthogonal Decomposition
/ Resolution
/ Segments
/ Turbulent flow
2022
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
You are currently in the queue to collect this book. You will be notified once it is your turn to collect the book.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Oops! Something went wrong.
While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
Spectral proper orthogonal decomposition using multitaper estimates
by
Schmidt, Oliver T
in
Algorithms
/ Bandwidths
/ Bias
/ Decomposition
/ Eigenvalues
/ Estimates
/ Estimators
/ Fourier transforms
/ Proper Orthogonal Decomposition
/ Resolution
/ Segments
/ Turbulent flow
2022
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Spectral proper orthogonal decomposition using multitaper estimates
Journal Article
Spectral proper orthogonal decomposition using multitaper estimates
2022
Request Book From Autostore
and Choose the Collection Method
Overview
The use of multitaper estimates for spectral proper orthogonal decomposition (SPOD) is explored. Multitaper and multitaper-Welch estimators that use discrete prolate spheroidal sequences (DPSS) as orthogonal data windows are compared to the standard SPOD algorithm that exclusively relies on weighted overlapped segment averaging, or Welch’s method, to estimate the cross-spectral density matrix. Two sets of turbulent flow data, one experimental and the other numerical, are used to discuss the choice of resolution bandwidth and the bias-variance tradeoff. Multitaper-Welch estimators that combine both approaches by applying orthogonal tapers to overlapping segments allow for flexible control of resolution, variance, and bias. At additional computational cost but for the same data, multitaper-Welch estimators provide lower variance estimates at fixed frequency resolution or higher frequency resolution at similar variance compared to the standard algorithm.Graphic abstract
Publisher
Springer Nature B.V
Subject
This website uses cookies to ensure you get the best experience on our website.