Neural Network-Based Preconditioning of Adaptive Tabulation for Reactive Flow Applications

基于神经网络的反应流应用自适应表格预处理

• 批准号: 2154446
• 负责人: Pavel Popov
• 金额: $ 29.46万
• 依托单位: San Diego State University Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2154446&HistoricalAwards=false
• 关键词: Neural; Network; Based; Preconditioning; Adaptive

Combustion is the prevalent source of energy in today’s world, and is projected to remain so until the year 2050. Moreover, it is the only viable option for many engineering applications (for example, space launch vehicles). Therefore, the continued study of combustion physics and development of new, more efficient, and cleaner combustion devices is a high priority in today’s engineering world. Over the past five decades, combustion simulations have been invaluable to this effort, and yet they still pose a multitude of challenges. This project aims to tackle one such challenge, specifically how to quickly evaluate chemical properties. The approach proposed here is to combine machine learning methods, which can give a rough estimate of the property of interest, with previously developed tabulation methods that work by bridging the gaps between a relatively small number of evaluations. This combined approach will allow for combustion simulations that are faster, use more complex chemical models, and are more accurate. The project will involve graduate and undergraduate students and provide them with mentoring and experience that will be valuable for their future careers in academia and industry. This research aims to improve the computational efficiency of chemical property evaluations, which comprise most of the computational cost in reactive flow simulations with detailed chemistry. The proposed approach is to use a combination of neural networks (NN) and in situ adaptive tabulation (ISAT), combining the strengths of both. Whereas NN can provide a function approximation at low memory cost, their accuracy cannot be improved without eventually overfitting the data. In contrast, ISAT can achieve any desired level of accuracy, but the size of the resulting table, and hence the computational cost, increases as the maximum allowable error is decreased. The ISAT table size scales with the Hessian of the function being approximated, and so reduction of this Hessian will also lead to a smaller table and lower computational cost. Such reduction will be achieved by using ISAT to tabulate not the full function of interest, but rather the difference between it and an NN function which approximates its Hessian. Two approaches for Hessian approximation will be developed and evaluated. In the first, a simple NN implementation will be trained to approximate the chemical function itself, and will thus approximate the Hessian only implicitly. For the second, an explicit Hessian approximation will be implemented via custom NN loss definitions based on finite differences between adjacent points. The effectiveness of the NN+ISAT combination will be tested on both partially-stirred reactor (PaSR) test cases and complex geometry reacting flow simulations. Success of the proposed methods can lead to significant speedup in reactive flow simulations, enabling the use of larger and more accurate chemical mechanisms.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

燃烧是当今世界普遍的能源，预计到2050年仍将如此。此外，它是许多工程应用（例如，空间运载火箭）的唯一可行选择。因此，继续研究燃烧物理和开发新的，更有效的，更清洁的燃烧装置是当今工程界的一个高度优先事项。在过去的五十年里，燃烧模拟对这一努力非常宝贵，但它们仍然带来了许多挑战。该项目旨在解决这样一个挑战，特别是如何快速评估化学性质。这里提出的方法是将联合收割机机器学习方法与以前开发的制表方法相结合，机器学习方法可以粗略估计感兴趣的属性，制表方法通过弥合相对较少数量的评估之间的差距来工作。这种组合方法将允许更快的燃烧模拟，使用更复杂的化学模型，并且更准确。该项目将涉及研究生和本科生，并为他们提供指导和经验，这将是他们未来在学术界和工业界的职业生涯有价值。本研究的目的是提高化学性质评估的计算效率，其中包括大部分的计算成本与详细的化学反应流模拟。所提出的方法是使用神经网络（NN）和原位自适应制表（ISAT）的组合，结合两者的优势。虽然NN可以以低内存成本提供函数近似，但如果最终不过度拟合数据，则无法提高其精度。相比之下，ISAT可以实现任何所需的精度水平，但所得到的表的大小，因此计算成本，随着最大允许误差的减少而增加。ISAT表的大小与被近似的函数的Hessian成比例，因此该Hessian的减少也将导致更小的表和更低的计算成本。这种减少将通过使用ISAT来制表而不是完整的感兴趣的函数，而是它与近似其Hessian的NN函数之间的差异来实现。两种方法海森近似将开发和评估。首先，将训练一个简单的NN实现来逼近化学函数本身，从而仅隐式地逼近Hessian函数。对于第二种情况，将通过基于相邻点之间的有限差异的自定义NN损失定义来实现显式Hessian近似。NN+ISAT组合的有效性将在部分搅拌反应器（PaSR）测试用例和复杂几何反应流模拟上进行测试。所提出的方法的成功可以导致显着加速反应流模拟，使使用更大，更准确的化学mechanism.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。