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    <title language="eng">Modeling electrohydrodynamically enhanced drag in channel and pipe flows using one-dimensional turbulence</title>
    <parentTitle language="eng">Conference on Modelling Fluid Flow (CMFF’22)</parentTitle>
    <identifier type="url">https://www-docs.b-tu.de/fg-stroemungsmodellierung/public/Klein_cmff22_abstract_EHDdrag.pdf</identifier>
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    <title language="eng">Modeling electrohydrodynamically enhanced drag in channel and pipe flows using One-Dimensional Turbulenc</title>
    <abstract language="eng">The joint modeling of flow hydrodynamics and electrokinetics is a relatively unexplored area of turbulent flow research. We address a lack of available models for electrohydrodynamic (EHD) turbulent flow utilizing a lower-order approach, the stochastic One-Dimensional Turbulence (ODT) model. ODT is constructed on the principles of the direct energy cascade of Navier–Stokes turbulence, with key emphasis on the accurate resolution of the small molecular transport scales within a notional line-of-sight. We investigate two canonical flow configurations to demonstrate the applicability of the model&#13;
in the simulation of EHD flows. First, we investigate EHD effects in zero-pressure-gradient turbulent boundary layers by two-way coupled model application to plane Couette flow of a dilute electrolyte. Second, we apply the one-way coupled model to EHD-enhanced gas flow through a vertical pipe with an inner concentric electrode, where electric fields are generated by means of a corona discharge and the corresponding effect of a continuum ionic charge density field.</abstract>
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    <title language="eng">Simulating Volatile Wind Energy: Stochastic Forward Modeling and Machine Learning</title>
    <abstract language="eng">The transformation of the energy sector is based on the integration of various renewable sources, such as wind and solar energy. One of the key challenges for the integration of these sources into the existing power grid is their erratic and sometimes discontinuous availability (volatility). Wind energy is one of the most relevant sources of CO2 neutral electric energy, but volatile due to fluctuating wind fields on multiple scales. This has already been realized so that senors provide real-time information on the scale of individual wind turbines. However, fore- casting remains an unresolved problem since numerical weather prediction models cannot provide the necessary level of detail. New modeling strategies are required that integrate turbine-scale and meso-scale information for accurate site-specific short-term prediction. Present and forthcoming research aims to incorporate fluctuations on multiple levels of fidelity, depending on the abstraction layer</abstract>
    <identifier type="url">https://ai-science-atlas.innohub13.de/files/pdf/NSGSimulatingVolatileWindEnergyBTUpdf.pdf</identifier>
    <identifier type="url">https://www-docs.b-tu.de/fg-stroemungsmodellierung/public/Klein_poster_KI-Atlas22.pdf</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="Fprofil">1 Energiewende und Dekarbonisierung / Energy Transition and Decarbonisation</enrichment>
    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <author>
      <firstName>Marten</firstName>
      <lastName>Klein</lastName>
    </author>
    <submitter>
      <firstName>Marten</firstName>
      <lastName>Klein</lastName>
    </submitter>
    <author>
      <firstName>Juan Alí</firstName>
      <lastName>Medina Méndez</lastName>
    </author>
    <author>
      <firstName>Heiko</firstName>
      <lastName>Schmidt</lastName>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>AI transfer</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>AI teaching</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>stochastic modeling</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>wind energy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>volatility modeling and prediction</value>
    </subject>
    <collection role="institutes" number="3504">FG Numerische Strömungs- und Gasdynamik</collection>
    <collection role="institutes" number="7006">Energie-Innovationszentrum / Scientific Computing Lab</collection>
  </doc>
</export-example>
