Numerical simulations have played a vital role in the design of modern combustion systems. Over the last two decades, the focus of research has been on the development of the large eddy simulation (LES) approach, which leveraged the vast increase in computing power to dramatically improve predictive accuracy. Even with the anticipated increase in supercomputing capabilities, the use of LES in design is limited by its high computational cost. Moreover, to aid decision making, such LES computations have to be augmented to estimate underlying uncertainties in simulation components. At the same time, other changes are happening across industries that build or use combustion devices. While efficiency and emissions reduction are still the primary design objectives, reducing cost of operation by optimizing maintenance and repair is becoming an important segment of the enterprise. This latter quest is aided by the digitization of combustors, which allows collection and storage of operational data from a host of sensors over a fleet of devices. Moreover, several levels of computing including low-power hardware present on board the combustion systems are becoming available. Such large data sets create unique opportunities for design and maintenance if appropriate numerical tools are made available. As LES revolutionized computing-guided design by leveraging supercomputing, a new generation of numerical approaches is needed to utilize this vast amount of data and the varied nature of computing hardware. In this article, a review of emerging computational approaches for this heterogeneous data-driven environment is provided. A case is made that new but unconventional opportunities for physics-based combustion modeling exist in this realm.

数值模拟在现代燃烧系统的设计中起着至关重要的作用。在过去的二十年中，研究重点一直放在大型涡流仿真（LES）方法的开发上，该方法利用了计算能力的巨大提高来显着提高预测准确性。即使预期超级计算能力会增加，但在设计中使用LES仍然受到其高计算成本的限制。而且，为了帮助决策，必须增加这种LES计算，以估计仿真组件中潜在的不确定性。同时，在制造或使用燃烧装置的行业中，其他变化也在发生。尽管效率和减排仍然是主要的设计目标，通过优化维护和修理来降低运营成本已成为企业的重要组成部分。燃烧器的数字化辅助了后一个任务，燃烧器的数字化允许从一组设备上的大量传感器收集和存储操作数据。而且，包括燃烧系统上存在的低功率硬件在内的几种计算水平变得可用。如果提供了适当的数值工具，那么如此大的数据集将为设计和维护创造独特的机会。随着LES通过利用超级计算彻底改变了计算指导的设计，需要新一代的数值方法来利用大量数据和计算硬件的各种特性。在本文中，本文提供了针对这种异构数据驱动环境的新兴计算方法的综述。有一个案例表明，在这个领域中存在基于物理的燃烧建模的新的但非常规的机会。