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CATEGORIES:Conferences/Seminars/Lectures
DESCRIPTION:Speaker: Adrián Lozano-Durán (MIT)\n\nTitle: Machine-learnin
g building-block model for computational fluid dynamics\n\nAbstract:\n\nOne
of the primary factors hindering the adoption of transformative low-emissi
ons aircraft designs is the time-consuming (taking years) and costly (costi
ng billions of dollars) experimental campaigns required during the design c
ycle. Computational fluid dynamics (CFD) might accelerate the process and a
lleviate the cost. However\, current turbulence models do not meet the stri
ngent accuracy requirements demanded by the industry. Here\, we have devise
d a new closure model for CFD to bridge the gap between our current predict
ive capabilities and those required by the aerospace industry. This model\,
referred to as the building-block-flow model\, conceives the flow as a col
lection of simple units that contain the essential flow physics necessary t
o predict complex flows. The approach is implemented using two artificial n
eural networks: a classifier that identifies the contribution of each build
ing block in the flow\, and a predictor that estimates the effect of missin
g scales through a combination of the building-block units. The training da
ta are directly obtained from CFD with exact modeling for mean quantities t
o ensure consistency with the numerical discretization. The model's output
is accompanied by confidence in the prediction\, which is used for uncertai
nty quantification. The model is validated in realistic aircraft configurat
ions.\n\nShort bio: Adrian Lozano-Duran is the Boeing Assistant Professor
at MIT AeroAstro. He is also a faculty at the MIT Center for Computational
Science and Engineering. He received his Ph.D. in Aerospace Engineering fro
m the Technical University of Madrid in 2015. From 2016 to 2020\, he was a
Postdoctoral Research Fellow at the Center for Turbulence Research at Stanf
ord University. His research is focused on computational fluid mechanics an
d physics of turbulence. His work includes turbulence theory using graph th
eory and information theory\, and reduced-order modeling for computational
fluids by artificial intelligence.
DTEND:20230927T213000Z
DTSTAMP:20240304T161321Z
DTSTART:20230927T203000Z
LOCATION:MIT - Math Dept.\, Building 2\, Room 449
SEQUENCE:0
SUMMARY:Seminar: Numerical Methods for Partial Differential Equations
UID:tag:localist.com\,2008:EventInstance_44321932869556
URL:https://calendar.mit.edu/event/seminar_numerical_methods_for_partial_di
fferential_equations_7310
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