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  • 1.
    Haugen, Nils E.L.
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science. Nordita, KTH Royal Institute of Technology and Stockholm University, Hannes Alfvéns väg 12, SE-10691 Stockholm, Sweden; SINTEF Energi A.S., Sem Saelands vei 11, 7034 Trondheim, Norway.
    Brandenburg, Axel
    Nordita, KTH Royal Institute of Technology and Stockholm University, Hannes Alfvéns väg 12, SE-10691 Stockholm, Sweden; The Oskar Klein Centre, Department of Astronomy, Stockholm University, AlbaNova, SE-10691 Stockholm, Sweden.
    Sandin, Christer
    Nordita, KTH Royal Institute of Technology and Stockholm University, Hannes Alfvéns väg 12, SE-10691 Stockholm, Sweden.
    Mattsson, Lars
    Nordita, KTH Royal Institute of Technology and Stockholm University, Hannes Alfvéns väg 12, SE-10691 Stockholm, Sweden.
    Spectral characterisation of inertial particle clustering in turbulence2022In: Journal of Fluid Mechanics, ISSN 0022-1120, E-ISSN 1469-7645, Vol. 934, article id A37Article in journal (Refereed)
    Abstract [en]

    Clustering of inertial particles is important for many types of astrophysical and geophysical turbulence, but it has been studied predominately for incompressible flows. Here, we study compressible flows and compare clustering in both compressively (irrotationally) and vortically (solenoidally) forced turbulence. Vortically and compressively forced flows are driven stochastically either by solenoidal waves or by circular expansion waves, respectively. For compressively forced flows, the power spectrum of the density of inertial particles is a useful tool for displaying particle clustering relative to the fluid density enhancement. Power spectra are shown to be particularly sensitive for studying large-scale particle clustering, while conventional tools such as radial distribution functions are more suitable for studying small-scale clustering. Our primary finding is that particle clustering through shock interaction is particularly prominent in turbulence driven by spherical expansion waves. It manifests itself through a double-peaked distribution of spectral power as a function of Stokes number. The two peaks are associated with two distinct clustering mechanisms; shock interaction for smaller Stokes numbers and the centrifugal sling effect for larger values. The clustering of inertial particles is associated with the formation of caustics. Such caustics can only be captured in the Lagrangian description, which allows us to assess the relative importance of caustics in vortically and compressively forced turbulence. We show that the statistical noise resulting from the limited number of particles in the Lagrangian description can be removed from the particle power spectra, allowing us a more detailed comparison of the residual spectra. We focus on the Epstein drag law relevant for rarefied gases, but show that our findings apply also to the usual Stokes drag.

  • 2.
    Jayawickrama, Thamali Rajika
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Chishty, Muhammad Aqib
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Haugen, Nils Erland L.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science. Department of Thermal Energy, SINTEF Energy Research, Kolbjørn Hejes vei 1 A, 7491 Trondheim, Norway.
    Babler, Matthaus U.
    Department of Chemical Engineering, KTH Royal Institute of Technology, SE-10044 Stockholm, Sweden.
    Umeki, Kentaro
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    The effects of Stefan flow on the flow surrounding two closely spaced particles2023In: International Journal of Multiphase Flow, ISSN 0301-9322, E-ISSN 1879-3533, Vol. 166, article id 104499Article in journal (Refereed)
    Abstract [en]

    The aim of the work was to study the effects of neighboring particles with uniform Stefan flow in particle–fluid flows. Particle-resolved numerical simulations were carried out for particles emitting a uniform Stefan flow into the bulk fluid. The bulk fluid was uniform and isothermal. The Stefan flow volume emitted from the two particles is equal, such that it represents idealized conditions of reacting particles. Particles were located in tandem arrangement and particle distances were varied between 1.1 and 10 particle diameters (). Three particle Reynolds numbers were considered during the simulations ( and 14), which is similar to our previous studies. Three Stefan flow velocities were also considered during simulations to represent inward, outward, and no Stefan flow. The drag coefficient of the particles without Stefan flow showed that the results fit with previous studies on neighbor particle effects. When the particle distance is greater than 2.5 diameters (), the effects of Stefan flow and neighboring particles are independent of each other. I.e. an outward Stefan flow decreases the drag coefficient () while an inward Stefan flow increases it and the upstream particle experience a higher  than the downstream particle. When , the effect of Stefan flow is dominant, such that equal and opposite pressure forces act on the particles, resulting in a repelling force between the two neighboring particles. The pressure force showed a large increase compared to the viscous force at these distances. The effect of Stefan flow is weakened at higher Reynolds numbers. A model was developed for the calculation of the drag coefficient. The model, which reproduce the results from the numerical simulations presented above, is a product of independent models that describe the effects of both neighboring particles and two distinguished effects of the Stefan flow.

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  • 3.
    Jayawickrama, Thamali Rajika
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.
    Haugen, Nils Erland L.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science. Department of Thermal Energy, SINTEF Energy Research, Kolbjørn Hejes vei 1 A, 7491 Trondheim, Norway.
    Umeki, Kentaro
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science. Technical University of Munich, Chair of Energy Systems, Boltzmannstr. 15, 85748 Garching b. München, Germany.
    On the inaccuracies of point-particle approach for char conversion modeling2024In: Fuel, ISSN 0016-2361, E-ISSN 1873-7153, Vol. 370, article id 131743Article in journal (Refereed)
    Abstract [en]

    Char conversion is a complex phenomenon that involves not only heterogeneous reactions but also external and internal heat and mass transfer. Reactor-scale simulations often use a point-particle approach (PP approach) as sub-models for char conversion because of its low computational cost. Despite a number of simplifications involved in the PP approach, there are very few studies that systematically investigate the inaccuracies of the PP approach. This study aims to compare and identify when and why the PP approach deviates from resolved-particle simulations (RP approach). Simulations have been carried out for CO2 gasification of a char particle under zone II conditions (i.e., pore diffusion control) using both PP and RP approaches. Results showed significant deviations between the two approaches for the effectiveness factor, gas compositions, particle temperature, and particle diameter. The most significant sources of inaccuracies in the PP approach are negligence of the non-uniform temperature inside the particle and the inability to accurately model external heat transfer. Under the conditions with low effectiveness factors, the errors of intra-particle processes were dominant while the errors of external processes became dominant when effectiveness factors were close to unity. Because it assumes uniform internal temperature, the models applying the PP approach always predict higher effectiveness factors than the RP approach, despite its accurate estimation of intra-particle mass diffusion effects. As a consequence, the PP approach failed to predict the particle size changes accurately. Meanwhile, no conventional term for external heat transfer could explain the inaccuracy, indicating the importance of other sources of errors such as 2D/3D asymmetry or penetration of external flows inside the particles.

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  • 4.
    Li, Xiang-Yu
    et al.
    Department of Meteorology and Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden; Nordita, KTH Royal Institute of Technology and Stockholm University, 10691 Stockholm, Sweden; Swedish e-Science Research Centre, www.e-science.se, Stockholm, Sweden; JILA and Laboratory for Atmospheric and Space Physics, University of Colorado, Boulder, CO 80303, USA.
    Mehlig, Bernhard
    Department of Physics, Gothenburg University, 41296 Gothenburg, Sweden.
    Svensson, Gunilla
    Department of Meteorology and Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden; Swedish e-Science Research Centre, www.e-science.se, Stockholm, Sweden.
    Brandenburg, Axel
    Nordita, KTH Royal Institute of Technology and Stockholm University, 10691 Stockholm, Sweden; JILA and Laboratory for Atmospheric and Space Physics, University of Colorado, Boulder, CO 80303, USA; The Oskar Klein Centre, Department of Astronomy, Stockholm University, AlbaNova, SE-10691 Stockholm, Sweden.
    Haugen, Nils Erland L.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science. SINTEF Energy Research, 7465 Trondheim, Norway.
    Collision fluctuations of lucky droplets with superdroplets2022In: Journal of the Atmospheric Sciences, ISSN 0022-4928, E-ISSN 1520-0469, Vol. 79, no 6, p. 1821-1835Article in journal (Refereed)
    Abstract [en]

    It was previously shown that the superdroplet algorithm for modeling the collision–coalescence process can faithfully represent mean droplet growth in turbulent clouds. An open question is how accurately the superdroplet algorithm accounts for fluctuations in the collisional aggregation process. Such fluctuations are particularly important in dilute suspensions. Even in the absence of turbulence, Poisson fluctuations of collision times in dilute suspensions may result in substantial variations in the growth process, resulting in a broad distribution of growth times to reach a certain droplet size. We quantify the accuracy of the superdroplet algorithm in describing the fluctuating growth history of a larger droplet that settles under the effect of gravity in a quiescent fluid and collides with a dilute suspension of smaller droplets that were initially randomly distributed in space (“lucky droplet model”). We assess the effect of fluctuations upon the growth history of the lucky droplet and compute the distribution of cumulative collision times. The latter is shown to be sensitive enough to detect the subtle increase of fluctuations associated with collisions between multiple lucky droplets. The superdroplet algorithm incorporates fluctuations in two distinct ways: through the random spatial distribution of superdroplets and through the Monte Carlo collision algorithm involved. Using specifically designed numerical experiments, we show that both on their own give an accurate representation of fluctuations. We conclude that the superdroplet algorithm can faithfully represent fluctuations in the coagulation of droplets driven by gravity.

  • 5.
    Wartha, Eva-Maria
    et al.
    TU Wien, Institute of Chemical, Environmental and Bioscience Engineering, Getreidemarkt 9/166, 1060 Vienna, Austria.
    Haugen, Nils Erland
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science. SINTEF Energi A.S., Sem Saelands vei 11, 7034 Trondheim, Norway.
    Karchniwy, Ewa
    SINTEF Energi A.S., Sem Saelands vei 11, 7034 Trondheim, Norway.
    Bösenhofer, Markus
    TU Wien, Institute of Chemical, Environmental and Bioscience Engineering, Getreidemarkt 9/166, 1060 Vienna, Austria; K1-Met GmbH, Area 4 - Simulation and Analyses, Stahlstrasse 14, BG 88, 4020 Linz, Austria.
    Harasek, Michael
    TU Wien, Institute of Chemical, Environmental and Bioscience Engineering, Getreidemarkt 9/166, 1060 Vienna, Austria.
    Løvås, Terese
    Department of Energy and Process Engineering, Norwegian University of Science and Technology, Kolbjørn Hejes vei 1B, 7034 Trondheim, Norway.
    The effect of turbulence on the conversion of coal under blast furnace raceway conditions2023In: Fuel, ISSN 0016-2361, E-ISSN 1873-7153, Vol. 331, no Part 2, article id 125840Article in journal (Refereed)
    Abstract [en]

    The main production route for steel in Europe is still via the blast furnace. Computational fluid dynamics (CFD) can be used to analyze the process virtually and thus improve its performance. Different reducing agents can be used to (partially) substitute the coke and consequently reduce overall emissions. To analyze different reducing agents effectively using CFD, their conversion process has to be modeled accurately. Under certain conditions, coal particles can cluster as the result of turbulence effects, which further reduces the mass transfer to the coal surface and consequently the conversion rate. We analyze the effect of turbulence under blast furnace raceway conditions on the conversion of coal particles and on the overall burnout. The model is applied in RANS to polydisperse particle systems and this is then compared to the simplified monodisperse assumption. Additionally, the model is extended by adding gasification reactions. Overall, we find that the turbulent effects on coal conversion are significant under blast furnace raceway conditions and should be considered in further simulations. Furthermore, we show that an a-priori assessment is difficult because the analysis via averaged quantities is impractical due to a strong variation of conditions in the furnace. Therefore, the effects of turbulence need to be correlated to the regions of conversion.

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