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Temperature assimilation for convective flows by convolutional neural networks

Mommert, Michael and Bauer, Christian and Wagner, Claus (2023) Temperature assimilation for convective flows by convolutional neural networks. In: iTi X Conference on Turbulence 2023. iTi X Conference on Turbulence 2023, 2023-07-24 - 2023-07-26, Bertinoro, Italien.

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Official URL: https://www.fdy.tu-darmstadt.de/iti/itihome_2.en.jsp

Abstract

The transport of heat in convective flows plays an important role in nature and in many technical applications. Precise predictions of such convective flows can be made in Direct Numerical Simulations (DNS), which require a substantial amount computing time and storage space. Thus, DNSs can only be used for predictions over comparatively short time periods and for flow problems, which can usually also be investigated in the laboratory due to their dimensions. A canonical laboratory experiment that is well suited for basic investigations of turbulent, thermal convection flows and the development of models is the so-called turbulent Rayleigh-B ́enard convection, which occurs as a result of buoyancy forces in cells heated from below and cooled from above with adiabatic side walls. Compared to the DNS, measurements can capture the velocity field over long periods of time. However, in order to be able to determine the heat transport in convective flows, additional spatial temperature measurements are typically carried out. Respective combined measurements are also very laborious and therefore only applied scarcely. In order to provide an estimated temperature distribution based on precise velocity field measurements, the assimilation of the temperature field from the velocity vector field is pursued. So far, the approach of extracting temperature fields based on the conservation laws has been explored [1]. At the same time, machine learning provides promising tools for regression tasks such as theone at hand [2]

Item URL in elib:https://elib.dlr.de/194433/
Document Type:Conference or Workshop Item (Poster)
Title:Temperature assimilation for convective flows by convolutional neural networks
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Mommert, MichaelUNSPECIFIEDhttps://orcid.org/0000-0002-7817-3388141158103
Bauer, ChristianUNSPECIFIEDhttps://orcid.org/0000-0003-1838-6194UNSPECIFIED
Wagner, ClausUNSPECIFIEDhttps://orcid.org/0000-0003-2273-0568UNSPECIFIED
Date:24 July 2023
Journal or Publication Title:iTi X Conference on Turbulence 2023
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:convection, assimilation, convolutional neural networks
Event Title:iTi X Conference on Turbulence 2023
Event Location:Bertinoro, Italien
Event Type:international Conference
Event Start Date:24 July 2023
Event End Date:26 July 2023
Organizer:TU Darmstadt
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Rail Transport
DLR - Research area:Transport
DLR - Program:V SC Schienenverkehr
DLR - Research theme (Project):V - RoSto - Rolling Stock
Location: Göttingen
Institutes and Institutions:Institute for Aerodynamics and Flow Technology > Ground Vehicles
Deposited By: Mommert, Michael
Deposited On:25 Aug 2023 15:57
Last Modified:24 Apr 2024 20:55

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