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A temporal fluid-parcel backwards-tracing method for Direct-Numerical and Large-Eddy Simulation employing Lagrangian particles

Author

Listed:
  • Engelmann, L.
  • Welch, C.
  • Schmidt, M.
  • Meller, D.
  • Wollny, P.
  • Böhm, B.
  • Dreizler, A.
  • Kempf, A.

Abstract

In this study, a method which allows for causal backtracing of turbulent flow phenomena is suggested. Key events in technical processes based on turbulent mixing can thereby be studied by their detailed temporal and spatial convective history. As a demonstration case for this method, a study of cyclic variations in internal combustion engines is used. Since it is assumed that major influences on cyclic variations are transported convectively, a set of Lagrangian tracer particles is generated at the start of a simulation run. The simulation is performed until the point where an arbitrary phenomenon of interest is observed and the simulation run is cancelled. The method then identifies particles in the neighboring region and saves their identification. Subsequently, the simulation is restarted using the same exact initial flow conditions for artificial turbulence generation and recreates the previous run. However, in the second run, only the identified particles are simulated and their spatial and temporal trajectories are logged and observed in detail. The trajectory provides information on pressure, temperature, velocities and mixture composition to reconstruct the causes of the phenomenon of interest. This approach offers an efficient work-around of computational memory and input/output problems, which occur in modern computational fluid dynamics while also saving temporal and spatial information of small parts in the computational domain. The presented method can be of fundamental value in the research of cyclical processes with complex flows.

Suggested Citation

  • Engelmann, L. & Welch, C. & Schmidt, M. & Meller, D. & Wollny, P. & Böhm, B. & Dreizler, A. & Kempf, A., 2023. "A temporal fluid-parcel backwards-tracing method for Direct-Numerical and Large-Eddy Simulation employing Lagrangian particles," Applied Energy, Elsevier, vol. 342(C).
    • Handle: RePEc:eee:appene:v:342:y:2023:i:c:s0306261923004580
    DOI: 10.1016/j.apenergy.2023.121094
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    References listed on IDEAS

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    1. Giacomo Falcucci & Giorgio Amati & Pierluigi Fanelli & Vesselin K. Krastev & Giovanni Polverino & Maurizio Porfiri & Sauro Succi, 2021. "Extreme flow simulations reveal skeletal adaptations of deep-sea sponges," Nature, Nature, vol. 595(7868), pages 537-541, July.
    2. d'Adamo, Alessandro & Breda, Sebastiano & Fontanesi, Stefano & Irimescu, Adrian & Merola, Simona Silvia & Tornatore, Cinzia, 2017. "A RANS knock model to predict the statistical occurrence of engine knock," Applied Energy, Elsevier, vol. 191(C), pages 251-263.
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