A filter-dependent granular temperature model from large-scale CFD-DEM data
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908539285405696 |
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| author | Rosenberg, Lee Fullmer, William Beetham, Sarah |
| author_facet | Rosenberg, Lee Fullmer, William Beetham, Sarah |
| contents | The computational study of strongly-coupled, gas-solid flows at scales relevant to most environmental and engineering applications requires the use of `coarse-grained' methodologies such as the two-fluid model, particle-in-cell approach or the multiphase Reynolds Averaged Navier-Stokes equations. While these strategies enable computations at desirable length- and time-scales, they rely heavily on models to capture important flow physics that occur at scales smaller than the mesh. To date, the models that do exist are based on a limited set of flow conditions, such as very dilute particle phase. To this end, we leverage the most large-scale repository of CFD-DEM data to date to develop a filter-size dependent model for granular temperature -- a key quantity for accurately predicting gas-solid flows. This filter-size dependence then translates directly to grid-size dependence in the context of coarse-grained approaches. In addition, we leverage the CFD-DEM dataset to propose an improved model for the mean variance in particle volume fraction, a quantity that characterizes the degree of clustering in the flow. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_10728 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A filter-dependent granular temperature model from large-scale CFD-DEM data Rosenberg, Lee Fullmer, William Beetham, Sarah Fluid Dynamics The computational study of strongly-coupled, gas-solid flows at scales relevant to most environmental and engineering applications requires the use of `coarse-grained' methodologies such as the two-fluid model, particle-in-cell approach or the multiphase Reynolds Averaged Navier-Stokes equations. While these strategies enable computations at desirable length- and time-scales, they rely heavily on models to capture important flow physics that occur at scales smaller than the mesh. To date, the models that do exist are based on a limited set of flow conditions, such as very dilute particle phase. To this end, we leverage the most large-scale repository of CFD-DEM data to date to develop a filter-size dependent model for granular temperature -- a key quantity for accurately predicting gas-solid flows. This filter-size dependence then translates directly to grid-size dependence in the context of coarse-grained approaches. In addition, we leverage the CFD-DEM dataset to propose an improved model for the mean variance in particle volume fraction, a quantity that characterizes the degree of clustering in the flow. |
| title | A filter-dependent granular temperature model from large-scale CFD-DEM data |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2509.10728 |