PeleLM-FDF Large Eddy Simulator of Turbulent Combustion

Aitzhan, Aidyn; Sammak, Shervin; Givi, Peyman; Nouri, Arash

doi:10.48550/arxiv.2201.00898

Cited by 2 publications

(4 citation statements)

References 79 publications

(95 reference statements)

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“…For demonstration, the canonical configuration of a temporally developing planar CO/H2 jet flame is considered. This flame has been the subject of previous detailed DNS [52], and several subsequent modeling and simulations [51,[53][54][55][56][57]. The flame is rich with strong flame-turbulence interactions resulting in local extinction followed by re-ignition.…”

Section: Flow Configuration and Model Specificationsmentioning

confidence: 96%

“…The conservation equations governing the transport variables are the continuity, momentum, enthalpy (energy) and species mass fraction equations, along with an equation of state in the format detailed in Ref. [51]. The transport equations for composition space (species mass fractions and enthalpy) can be stated in the matrix form as:…”

Section: Formulationmentioning

confidence: 99%

“…The DNS of the base reactive flow is conducted via the PeleLM solver [59] as detailed in Ref. [51]. Simulations are conducted for the duration 0 ≤ t ≤ 60tj, tj = H/U , and the mixture fraction field, denoted by Z(x, t) is constructed from the simulated data [60].…”

Section: Flow Configuration and Model Specificationsmentioning

confidence: 99%

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Reduced Order Modeling of Turbulence-Chemistry Interactions using Dynamically Bi-Orthonormal Decomposition

Aitzhan¹,

Nouri²,

Givi³

et al. 2022

Preprint

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The performance of the dynamically bi-orthogonal (DBO) decomposition for the reduced order modeling of turbulence-chemistry interactions is assessed. DBO is an on-the-fly low-rank approximation technique, in which the instantaneous composition matrix of the reactive flow field is decomposed into a set of orthonormal spatial modes, a set of orthonormal vectors in the composition space, and a factorization of the low-rank correlation matrix. Two factors which distinguish between DBO and the reduced order models (ROMs) based on the principal component analysis (PCA) are: (i) DBO does not require any offline data generation; and (ii) in DBO the lowrank composition subspace is time-dependent as opposed to static subspaces in PCA. Because of these features, DBO can adapt on-the-fly to intrinsic and externally excited transient changes in state of the transport variables. For demonstration, simulations are conducted of a non-premixed CO/H2 flame in a temporally evolving jet. The GRI-Mech 3.0 model with 53 species is used for chemical kinetics modeling. The results are appraised via a posteriori comparisons against data generated via full-rank direct numerical simulation (DNS) of the same flame, and the PCA reduction of the DNS data. The DBO also yields excellent predictions of various statistics of the thermo-chemical variables.

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Section: Flow Configuration and Model Specificationsmentioning

confidence: 96%

Section: Formulationmentioning

confidence: 99%

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Reduced Order Modeling of Turbulence-Chemistry Interactions using Dynamically Bi-Orthonormal Decomposition

Aitzhan¹,

Nouri²,

Givi³

et al. 2022

Preprint

Self Cite

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“…Those finite element computations usually share the same mesh and lead to sparse matrices which share the same sparsity pattern and can be solved independently. Other examples of batched systems can be found in multiphysics or chemical simulations where several degrees of freedom (such as species mass fraction) are associated to each node of the mesh such as in combustion simulation [4].…”

Section: Introductionmentioning

confidence: 99%

Performance Portable Batched Sparse Linear Solvers

Liegeois

Rajamanickam

Berger-Vergiat

2023

IEEE Trans. Parallel Distrib. Syst.

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Solving large number of small linear systems is increasingly becoming a bottleneck in computational science applications. While dense linear solvers for such systems have been studied before, batched sparse linear solvers are just starting to emerge. In this paper, we discuss algorithms for solving batched sparse linear systems and their implementation in the Kokkos Kernels library. The new algorithms are performance portable and map well to the hierarchical parallelism available in modern accelerator architectures. The sparse matrix vector product (SPMV) kernel is the main performance bottleneck of the Krylov solvers we implement in this work. The implementation of the batched SPMV and its performance are therefore discussed thoroughly in this paper. The implemented kernels are tested on different Central Processing Unit (CPU) and Graphic Processing Unit (GPU) architectures. We also develop batched Conjugate Gradient (CG) and batched Generalized Minimum Residual (GMRES) solvers using the batched SPMV. Our proposed solver was able to solve 20,000 sparse linear systems on V100 GPUs with a mean speedup of 76x and 924x compared to using a parallel sparse solver with a block diagonal system with all the small linear systems, and compared to solving the small systems one at a time, respectively. We see mean speedup of 0.51 compared to dense batched solver of cuSOLVER on V100, while using lot less memory. Thorough performance evaluation on three different architectures and analysis of the performance are presented.

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PeleLM-FDF Large Eddy Simulator of Turbulent Combustion

Cited by 2 publications

References 79 publications

Reduced Order Modeling of Turbulence-Chemistry Interactions using Dynamically Bi-Orthonormal Decomposition

Reduced Order Modeling of Turbulence-Chemistry Interactions using Dynamically Bi-Orthonormal Decomposition

Performance Portable Batched Sparse Linear Solvers

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