CDS&E: Collaborative Research: Autonomous Systems for Experimental and Computational Data Generation and Data-Driven Modeling of Combustion Kinetics

为了满足社会对更具成本效益和更可持续的能源的迫切需求，未来的内燃机需要更省油，产生更少的排放，并使用包括替代燃料在内的各种燃料。工程师们经常使用燃料燃烧化学的计算机模型来设计性能更好的发动机，并确定某种燃料在发动机中的适用性。在制作供工程师使用的燃烧模型时，科学家通常从创建试验模型开始，然后生成计算和实验数据来测试模型，并根据数据改进和验证模型。后两项任务经常重复，直到得到的模型足够准确，可以可靠地使用为止。目前开发可靠的、经过验证的交通相关燃料模型的技术通常需要结合多个研究小组的努力，需要数年甚至数十年的时间才能获得足够的数据。目前开发燃料燃烧化学模型的方法不足以及时和有效地解决紧迫的能源需求，特别是在许多潜在的现代燃料尚未得到很好表征的情况下。该项目将创建和测试一个新的自主系统的性能，该系统创建试验模型、生成数据并进行模型改进，以快速收敛到一个可靠的、经过验证的燃料化学模型。新型自主系统的成功实施将为燃烧动力学提供先进的模型开发工具，并加速了解许多替代燃料的氧化行为，从而控制它们的生存能力。最后，该项目将吸引本科生和研究生参与研究，并为应用于燃烧动力学的数据科学创建新的教学模块。这些模块将提高年轻一代学生在脚本和数据科学工具方面的熟练程度，这些工具是确保美国具有竞争力的STEM项目所必需的。该项目的技术目标是创建一个自主系统，用于研究燃料氧化化学，并评估其相对于当前时间密集型方法的性能。这个自主系统将使用多物理不确定性量化框架多尺度信息学，以集成自动化动力学模型构建平台、反应机理生成器、适应性自动高通量喷射搅拌反应器实验，以及用于执行自动量子化学、统计热力学和过渡态理论计算(AutoTST)的算法。通过将射流搅拌反应器中实验观测数据和感兴趣量(如发动机点火开始)中的不确定性与动力学模型中有物理意义的参数(如反应的势垒高度)联系起来，计算和实验可以通过优化设计来改进模型--S预测感兴趣量的准确性。本项目旨在(1)创建自主平台，(2)使用它来生成正庚烷的模型，对其性能进行评估，以及(3)将其应用于最近被能源部？S联合优化计划确定的有前途的生物燃料二异丁烯。该项目将为加速燃烧研究创造一种新的数据驱动的方法，有助于对正庚烷和二异丁烯的科学理解，更广泛地说，有助于理解自主科学。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

To meet pressing societal needs for more cost-effective and sustainable energy, future combustion engines need to be more fuel-efficient, produce less emissions, and operate on a variety of fuels, including alternative fuels. Engineers often use computer models of fuel combustion chemistry to design engines with improved performance and determine the suitability of a certain fuel in an engine. In producing combustion models for engineers to use, scientists usually start by creating a trial model, then generate computational and experimental data to test the model, and improve and validate the model against the data. The latter two tasks are often repeated until the resulting model is sufficiently accurate for reliable use. Present techniques for developing reliable, validated models for transportation-relevant fuels typically involve combining the efforts of multiple research groups, taking multiple years or even decades to obtain enough data. The present approach for developing fuel combustion chemistry models is insufficient to address pressing energy needs in a timely and effective manner, particularly as many potential modern fuels have not been well characterized. This project will create and test the performance of a new autonomous system that creates trial models, generates data, and makes model improvements to rapidly converge on a reliable, validated, fuel chemistry model. Successful implementation of the novel autonomous system will provide an advanced model development tool for combustion kinetics and an accelerated means of understanding the oxidation behavior of the many alternative fuels, which governs their viability. Finally, this project will engage undergraduate and graduate students in research and create novel teaching modules for data science applied to combustion kinetics. The modules will enhance proficiency of younger generations of students in the scripting and data science tools necessary to ensuring a competitive STEM program in the U.S.The technical objective of this project is to create an autonomous system for studying fuel oxidation chemistry and evaluate its performance relative to current time-intensive approaches. This autonomous system will use a multi-physics uncertainty quantification framework, MultiScale Informatics, to integrate an automated kinetic model construction platform, Reaction Mechanism Generator, an adaptable automated High-Throughput Jet Stirred Reactor experiment, and an algorithm for performing automated quantum chemistry, statistical thermodynamics, and transition state theory calculations (AutoTST). By linking the uncertainties both in experimental observables in the Jet Stirred Reactor and in Quantities of Interest, such as onset of ignition in an engine, to physically meaningful parameters in the kinetic model, such as barrier heights of a reaction, calculations and experiments can be optimally designed to improve the model?s accuracy for predicting Quantities of Interest. This project seeks to (1) create the autonomous platform, (2) use it to generate a model for n-heptane, for which previous data and models are relatively mature, to assess its performance, and (3) apply it to diisobutylene, a promising biofuel recently identified in the DOE?s Co-Optima program. This project will create a new data-driven approach for combustion research at an accelerated pace, contribute to scientific understanding for n-heptane and diisobutylene, and, more broadly, contribute to understanding of autonomous science.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.