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可持续能源的迫切需求，未来的内燃机需要提高燃油效率，产生更少的排放，并使用多种燃料，包括替代燃料。工程师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。在生产供工程师使用的燃烧模型时，科学家通常首先创建一个试验模型，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精确，可以可靠地使用。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涉及多个研究小组的努力，需要数年甚至数十年才能获得足够的数据。目前发展燃料燃烧化学模型的方法不足以及时有效地解决紧迫的能源需求，特别是因为许多潜在的现代燃料尚未得到很好的表征。该项目将创建并测试一个新的自主系统的性能，该系统可以创建试验模型，生成数据，并对模型进行改进，以快速收敛于可靠的、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，并加速理解许多替代燃料的氧化行为，这决定了它们的可行性。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颖的教学模块。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，以确保美国的STEM项目具有竞争力。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耗时的方法的性能。该自主系统将使用多物理场不确定性量化框架，多尺度信息学，集成自动化动力学模型构建平台，反应机制生成器，适应性自动化高通量射流搅拌反应器实验，以及执行自动化量子化学，统计热力学和过渡态理论计算（AutoTST）的算法。通过将喷射搅拌反应器的实验观测值和感兴趣的量（如发动机的点火时间）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，可以优化设计计算和实验以改进模型。预测兴趣数量的准确性。该项目旨在(1)创建自主平台，(2)使用它来生成正庚烷的模型，之前的数据和模型相对成熟，以评估其性能，(3)将其应用于二异丁烯，一种最近在美国能源部确定的有前途的生物燃料。s Co-Optima项目。该项目将以更快的速度为燃烧研究创造一种新的数据驱动方法，有助于对正庚烷和二异丁烯的科学理解，更广泛地说，有助于对自主科学的理解。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.