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arXiv 2302.12645physics.chem-phcs.LG
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使用扩展的深度神经算子学习刚性化学动力学

Learning stiff chemical kinetics using extended deep neural operators

Brown University(布朗大学);University of Pittsburgh(匹兹堡大学);Applied Research Associates, Inc.(应用研究联合公司)

Somdatta Goswami, Ameya D. Jagtap, Hessam Babaee, Bryan T. Susi, George Em Karniadakis

摘要:

我们利用神经操作符来学习具有挑战性的化学动力学方程的解传播子。具体来说,我们应用深度运营商网络(DeepONet)及其扩展,例如基于自动编码器的DeepONet和新提出的单位分割(PoU-)DeepONet,以研究一系列示例,包括具有三个物种的ROBERS问题,具有25个物种的POLLU问题,用于$CO/H_2$燃烧的合成气骨架模型的纯动力学,其包含11个物种和21个反应,最后,使用相同的合成气机理的暂时发展的平面$CO/H_2$射流火焰(湍流火焰)。我们已经证明了所提出的方法的优点,通过这些数值例子。具体而言,为了训练DeepONet用于合成气模型,我们针对不同的初始条件求解骨架动力学模型。在第一种情况下,我们根据当量比和初始温度值参数化初始条件。在第二种情况下,我们进行了一个二维的时间发展$CO/H_2$射流火焰的直接数值模拟。然后,我们初始化的动力学模型的热化学状态访问的一个子集的网格点在不同的时间快照。用传统的刚性求解器求解刚性问题在计算上是昂贵的。因此,这项工作的目的是开发一个基于神经算子的代理模型来解决刚性化学动力学。操作员,一旦离线训练,可以准确地整合热化学状态任意大的时间推进,导致显着的计算增益相比,僵硬的集成方案。

英文摘要:

We utilize neural operators to learn the solution propagator for the challenging chemical kinetics equation. Specifically, we apply the deep operator network (DeepONet) along with its extensions, such as the autoencoder-based DeepONet and the newly proposed Partition-of-Unity (PoU-) DeepONet to study a range of examples, including the ROBERS problem with three species, the POLLU problem with 25 species, pure kinetics of the syngas skeletal model for $CO/H_2$ burning, which contains 11 species and 21 reactions and finally, a temporally developing planar $CO/H_2$ jet flame (turbulent flame) using the same syngas mechanism. We have demonstrated the advantages of the proposed approach through these numerical examples. Specifically, to train the DeepONet for the syngas model, we solve the skeletal kinetic model for different initial conditions. In the first case, we parametrize the initial conditions based on equivalence ratios and initial temperature values. In the second case, we perform a direct numerical simulation of a two-dimensional temporally developing $CO/H_2$ jet flame. Then, we initialize the kinetic model by the thermochemical states visited by a subset of grid points at different time snapshots. Stiff problems are computationally expensive to solve with traditional stiff solvers. Thus, this work aims to develop a neural operator-based surrogate model to solve stiff chemical kinetics. The operator, once trained offline, can accurately integrate the thermochemical state for arbitrarily large time advancements, leading to significant computational gains compared to stiff integration schemes.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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