Wall‐modeled large‐eddy simulation in a finite element framework

Owen, Herbert; Chrysokentis, Georgios; Ávila, Matías; Mira, Daniel; Houzeaux, Guillaume; Borrell, R.; Cajas, Juan Carlos; Lehmkuhl, O.

doi:10.1002/fld.4770

Cited by 30 publications

(22 citation statements)

References 38 publications

(90 reference statements)

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“…The output from the model is u + , i.e., u/u τ , from which τ ω can be easily computed. τ ω is the output from a typical EQWM, which acts as a boundary condition in WMLES [33]. However, choosing an input is not straight-forward.…”

Section: Model Inputs and Outputmentioning

confidence: 99%

“…[44]. The performance of the model is also evaluated by comparing the results with that of the EQWM [33].…”

Section: Wall-mounted Humpmentioning

confidence: 99%

“…In this study, a wall model with XGBoost [32] is presented. The performance of the model is evaluated on two cases, one of which is significantly different from the trained data and the results are compared with that of an algebraic equilibrium wall model(EQWM [33]).…”

Section: Introductionmentioning

confidence: 99%

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A Data-Driven Wall-Shear Stress Model for LES Using Gradient Boosted Decision Trees

Radhakrishnan

Gyamfi

Miró

et al. 2021

Lecture Notes in Computer Science

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With the recent advances in Machine Learning, strategies based on data could be used to augment wall modeling in Large Eddy Simulation(LES). In this work, a wall model based on gradient boosted decision trees is presented. The model is trained to learn the boundary layer of a turbulent channel flow so that it can be used to make predictions for significantly different flows where the equilibrium assumptions are valid.The methodology of building the model is presented in detail. The experiments conducted to choose the data for training the model, as well as to choose the model input features are described. The trained model is tested a posteriori on a Turbulent channel flow and the flow over a wall mounted hump. The results from the tests are compared with that of an algebraic equilibrium wall model and the performance is evaluated. The results show that the model has succeeded in learning the boundary layer and performs as good as an algebraic wall stress model.

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Section: Model Inputs and Outputmentioning

confidence: 99%

“…[44]. The performance of the model is also evaluated by comparing the results with that of the EQWM [33].…”

Section: Wall-mounted Humpmentioning

confidence: 99%

See 1 more Smart Citation

A Data-Driven Wall-Shear Stress Model for LES Using Gradient Boosted Decision Trees

Radhakrishnan

Gyamfi

Miró

et al. 2021

Lecture Notes in Computer Science

Self Cite

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“…At both the inlet and the boundaries that are far from the region of interest, an homogeneous velocity is prescribed. For the aircraft walls an equilibrium wall model is used in [21]. For the initial condition, a uniform velocity is used in the whole domain except on the aircraft walls where the velocity is modified such that its normal component is zero.…”

Section: Test Case: Airplane Simulationmentioning

confidence: 99%

Heterogeneous CPU/GPU co-execution of CFD simulations on the POWER9 architecture: Application to airplane aerodynamics

Borrell

Dosimont

García-Gasulla

et al. 2020

Future Generation Computer Systems

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High fidelity Computational Fluid Dynamics simulations are generally associated with large computing requirements, which are progressively acute with each new generation of supercomputers. However, significant research efforts are required to unlock the computing power of leadingedge systems, currently referred to as pre-Exascale systems, based on increasingly complex architectures. In this paper, we present the approach implemented in the computational mechanics code Alya. We describe in detail the parallelization strategy implemented to fully exploit the different levels of parallelism, together with a novel co-execution method for the efficient utilization of heterogeneous CPU/GPU architectures. The latter is based on a multi-code co-execution approach with a dynamic load balancing mechanism. The assessment of the performance of all the proposed strategies has been carried out for airplane simulations on the POWER9 architecture accelerated with NVIDIA Volta V100 GPUs. * c 2020 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ https://doi.

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“…To model the subgrid scale stress tensor, Vreman LES model is used [29]. Wall modeling is based on a law of the wall, extensively described by Owen et al in [19].…”

Section: Test Case: Full Airplane Simulationmentioning

confidence: 99%

Benchmarking of state-of-the-art HPC Clusters with a Production CFD Code

Banchelli

García-Gasulla

Houzeaux

et al. 2020

Proceedings of the Platform for Advanced Scientific Computing Conference

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Computing technologies populating high-performance computing (HPC) clusters are getting more and more diverse, offering a wide range of architectural features. As a consequence, efficient programming of such platforms becomes a complex task. In this paper we provide a micro-benchmarking of three HPC clusters based on different CPU architectures, predominant in the Top500 ranking: x86, Armv8 and IBM Power9. On these platforms we study a production fluid-dynamics application leveraging different compiler technologies and micro-architectural features. We finally provide a scalability study on state-of-the-art HPC clusters. The two most relevant conclusions of our study are: i) Compiler development is critical for squeezing performance out of most recent technologies; ii) Micro-architectural features such as Single Instruction Multiple Data (SIMD) units and Simultaneous Multi-Threading (SMT) can impact the overall performance. However, a closer look shows that while SIMD is improving the performance of compute bound regions, SMT does not show a clear benefit on HPC workloads. CCS CONCEPTS• Applied computing → Physics; • Computing methodologies → Parallel computing methodologies; • General and reference → Performance; • Computer systems organization → Multicore architectures.

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Wall‐modeled large‐eddy simulation in a finite element framework

Cited by 30 publications

References 38 publications

A Data-Driven Wall-Shear Stress Model for LES Using Gradient Boosted Decision Trees

A Data-Driven Wall-Shear Stress Model for LES Using Gradient Boosted Decision Trees

Heterogeneous CPU/GPU co-execution of CFD simulations on the POWER9 architecture: Application to airplane aerodynamics

Benchmarking of state-of-the-art HPC Clusters with a Production CFD Code

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