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Mean and Unsteady Flow Reconstruction Using Data-Assimilation and Resolvent Analysis

Symon, Sean and Sipp, Denis and Schmid, Peter J. and McKeon, Beverley J. (2020) Mean and Unsteady Flow Reconstruction Using Data-Assimilation and Resolvent Analysis. AIAA Journal, 58 (2). pp. 575-588. ISSN 0001-1452. https://resolver.caltech.edu/CaltechAUTHORS:20190820-132839798

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Abstract

A methodology is presented that exploits both data-assimilation techniques and resolvent analysis for reconstructing turbulent flows, containing organized structures, with an efficient set of measurements. The mean (time-averaged) flow is obtained using variational data-assimilation that minimizes the discrepancy between a limited set of flow measurements, generally from an experiment, and a numerical simulation of the Navier–Stokes equations. The fluctuations are educed from resolvent analysis and time-resolved data at a single point in the flow. Resolvent analysis also guides where measurements of the mean and fluctuating quantities are needed for efficient reconstruction of a simple example case study: flow around a circular cylinder at a Reynolds number of Re=100. For this flow, resolvent analysis reveals that the leading singular value, most amplified modes, and the mean profile for 47<Re<320 scale with the shedding frequency and length of the recirculation bubble. A relationship between these two parameters reinforces the notion that a wave maker, for which the length scales with the recirculation bubble, determines the frequency and region where an instability mechanism is active. The procedure offers a way to choose sensor locations that capture the main coherent structures of a flow and a method for computing mean pressure by using correctly weighted resolvent modes.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.2514/1.J057889DOIArticle
ORCID:
AuthorORCID
Symon, Sean0000-0001-9085-0778
Sipp, Denis0000-0002-2808-3886
Schmid, Peter J.0000-0002-6585-8871
McKeon, Beverley J.0000-0003-4220-1583
Additional Information:© 2019 by the American Institute of Aeronautics and Astronautics, Inc. Received 6 September 2018; revision received 20 February 2019; accepted for publication 2 April 2019; published online 24 May 2019. The authors would like to acknowledge financial support from U.S. Air Force Office of Scientific Research grant number FA 9550-16-1-0361, Army Research Office grant number W911NF-17-1-0306, and Office of Naval Research grant number N0014-17-1-3022.
Group:GALCIT
Funders:
Funding AgencyGrant Number
Air Force Office of Scientific Research (AFOSR)FA 9550-16-1-0361
Army Research Office (ARO)W911NF-17-1-0306
Office of Naval Research (ONR)N0014-17-1-3022
Issue or Number:2
Record Number:CaltechAUTHORS:20190820-132839798
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190820-132839798
Official Citation:Mean and Unsteady Flow Reconstruction Using Data-Assimilation and Resolvent Analysis. Sean Symon, Denis Sipp, Peter J. Schmid, and Beverley J. McKeon. AIAA Journal 2020 58:2, 575-588; doi: 10.2514/1.J057889
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:98042
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:20 Aug 2019 21:06
Last Modified:09 Mar 2020 13:19

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