In the design of practical combustion systems, ensuring safety and reliability is an important requirement. For instance, reliably avoiding lean blowout, flame flashback or inlet unstart is critical for ensuring safe operation. Currently, the science of predicting such events is based on prior experience, limited modeling or diagnostic tools and purely statistical approaches. Even though computational and experimental tools for studying combustion devices have vastly advanced in the last three decades, the analysis of such failure events has not been pursued widely. While the use of data for model development and calibration is being widely accepted, the extension to failure events introduces numerous challenges. In particular, the focus here is on so-called data-poor problems, where the cost of generating data is extremely high and is not easily amenable to existing computational and experimental approaches. Data-poor problems are particularly relevant when related to extreme events (also called anomalous events) that can lead to catastrophic failure of the system. It is argued that transient events that describe such failure can have different causal mechanisms. To develop the scientific inference process, a classification of such problems is used to determine specific modeling paths as well as computational tools needed. Research opportunities in the emerging field of extreme event prediction are highlighted in order to identify critical and immediate needs.

在实际燃烧系统的设计中，保证安全性和可靠性是一个重要的要求。例如，可靠地避免稀燃、火焰回火或入口未启动对于确保安全运行至关重要。目前，预测此类事件的科学是基于先前的经验、有限的建模或诊断工具以及纯粹的统计方法。尽管用于研究燃烧装置的计算和实验工具在过去三年中取得了巨大进步，但对此类故障事件的分析并没有得到广泛的研究。虽然将数据用于模型开发和校准已被广泛接受，但故障事件的扩展带来了许多挑战。特别是，这里的重点是所谓的数据贫乏问题，其中生成数据的成本非常高，并且不容易适应现有的计算和实验方法。当与可能导致系统灾难性故障的极端事件（也称为异常事件）相关时，数据贫乏问题尤其重要。有人认为，描述此类故障的瞬态事件可能具有不同的因果机制。为了开发科学推理过程，此类问题的分类用于确定特定的建模路径以及所需的计算工具。强调了极端事件预测新兴领域的研究机会，以确定关键和紧迫的需求。当与可能导致系统灾难性故障的极端事件（也称为异常事件）相关时，数据贫乏问题尤其重要。有人认为，描述此类故障的瞬态事件可能具有不同的因果机制。为了开发科学推理过程，此类问题的分类用于确定特定的建模路径以及所需的计算工具。强调了极端事件预测新兴领域的研究机会，以确定关键和紧迫的需求。当与可能导致系统灾难性故障的极端事件（也称为异常事件）相关时，数据贫乏问题尤其重要。有人认为，描述此类故障的瞬态事件可能具有不同的因果机制。为了开发科学推理过程，此类问题的分类用于确定特定的建模路径以及所需的计算工具。强调了极端事件预测新兴领域的研究机会，以确定关键和紧迫的需求。此类问题的分类用于确定特定的建模路径以及所需的计算工具。强调了极端事件预测新兴领域的研究机会，以确定关键和紧迫的需求。此类问题的分类用于确定特定的建模路径以及所需的计算工具。强调了极端事件预测新兴领域的研究机会，以确定关键和紧迫的需求。