[Crib-list] SPEAKER: Christiane Adcock (Stanford) - Friday, October 7, 2022 - 12:00 PM - 1:00 PM via "ZOOM" (fwd)

[Shirley Entzminger]

    REMINDER...


          COMPUTATIONAL RESEARCH in BOSTON and BEYOND Seminar
 			      (CRIBB)


   ZOOM meeting info...

 	https://mit.zoom.us/j/96155042770

 	Meeting ID: 961 5504 2770


====================================

DATE:	Friday, October 7, 2022

TIME:	12:00 PM - 1:00 PM


TITLE:	Hybrid Modeling for Energy System Simulation and Control


SPEAKER:  CHRISTIANE ADCOCK (Stanford)

ABSTRACT:

Simulating and controlling energy systems, such as buildings and wind farms,
often requires a tradeoff between accuracy and computational speed. I explore
how hybrid methods can combine the best attributes of each constituent method
and thereby achieve both accuracy and speed. First, I consider high-fidelity
simulation of a wind farm. Typically, simulations accurately represent only a
subset of the following: blade boundary layer dynamics, wake-atmospheric
boundary layer (ABL) interactions, and turbine-turbine interactions. I develop a
hybrid between two fluid models, one Reynolds-Averaged Navier Stokes (RANS) and
one large eddy simulation (LES), to simultaneously capture all three effects. I
implement this work in a massively parallel flow solver, Nalu-Wind, and run
simulations on a supercomputer, Eagle. Second, I compare a range of model-based,
learning-based, and hybrid methods for real-time control of a grid-interactive
building. The model-based approaches are accurate but require slow online
optimization. In contrast, the learning-based approaches are rapid online but
require lengthy offline training. I show that hybrid methods can achieve high
accuracy while running quickly online and offline. Finally, I apply the most
promising method from building control, differentiable predictive control (DPC),
to wind farm control, specifically wake steering for power maximization.
Typically wake steering methods develop a lookup table that maps wind farm
conditions, such as incoming wind speed and direction, to the yaw angle for each
turbine in the farm which maximizes the total wind farm power. If any turbines
in the farm shutdown, the lookup table leads to sub-optimal control; extending
the table to account for shutdown would be computationally prohibitive.  I show
that  DPC can accomplish wake steering under turbine shutdown while maintaining
low online and offline computational cost.

=======================================

For information about the "Computational Research in Boston and Beyond Seminar"
(CRIBB), please visit:

 		https://math.mit.edu/sites/crib/


=================

Shirley A. Entzminger
Administrative Assistant II
Department of Mathematics
Massachusetts Institute of Technology
77 Massachusetts Avenue
Building 2, Room 350A
Cambridge, MA 02139
PHONE: 	(617) 253-4994
E-mail:	daisymae at math.mit.edu
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