[Faculty] Fwd: [CSRC.COLLOQUIUM] "Adjoint-based data assimilation and Hessian analysis of turbulent flows "

[Jose Castillo]

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DATE:
*Friday, October 15, 2021*


TITLE:
*Adjoint-based data assimilation and Hessian analysis of turbulent flows   *


TIME:
*3:30-4:30PM*



LOCATION:
*In Person - GMCS 314*
or
Join Zoom Meeting -   https://SDSU.zoom.us/j/86808277973
<https://sdsu.zoom.us/j/86808277973>


SPEAKER/BIO:
*Qi Wang, Aerospace Engineering, San Diego State University *


ABSTRACT:

Turbulence affects fluid flows across scales and application domains. The
capabilities to accurately model and estimate information for turbulence is
essential for understanding, predicting and controlling these chaotic
flows. Two traditional approaches: laboratory experiments and numerical
simulation can be combined to overcome their individual deficiencies. An
approach that optimally infuses measurements from experiments into
computational models is demonstrated, the realism and fidelity of
computational models are increased and uncertainties mitigated. This data
assimilation approach can be formulated as optimization problems,
constrained by the nonlinear governing equations, and guided by gradients
from their discrete adjoint. We demonstrate this adjoint-based optimization
in two canonical scenarios: scalar source reconstruction and flow state
estimation from limited measurements in a turbulent channel flow. Scalar
source reconstruction focuses on finding the location of the source of
passive pollutant transported in turbulent environments, which is an
ill-posed problem with broad applications. Current study uses direct
numerical simulation of both forward and adjoint scalar transport equations
to identify the location and size of a source in turbulent channel flow
based on sensor measurements downstream. We can reconstruct the spatial
distribution of a steady source by doing forward and adjoint simulation
repeatedly. The other application involves estimating the initial state of
turbulent channel flow from surface measurements. The discrete adjoint
operator is applied to perform a domain-of-dependence analysis of surface
measurements. The analysis relies on evaluation of the Hessian matrix of
the cost function, in the vicinity of the true solution, and analysis of
the Hessian eigen-spectra. The leading eigenmodes correspond to the highest
sensitivity of measurements to the flow; these modes are concentrated in
the near-wall region upstream of the sensor location. Their structure
explains the fundamental difficulty of estimating the flow state from wall
measurements.


Host:
*Parag Katira*

Note: Videos of previous colloquium talks can be seen on the CSRC website
in the colloquium archive section or on the CSRC YouTube page here
<https://www.youtube.com/channel/UCN0ZEztlmyDqG2pm-Rle_Eg/feed>.


Jose E. Castillo, Ph.D.
Director/Professor
Computational Science Research Center
San Diego State University
5500 Campanile Dr
San Diego CA 92182-1245
www.csrc.sdsu.edu

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