This work describes a deep learning methodology for “emulating” temperature outputs produced by the Fire Dynamics Simulator (FDS), a CFD software. An array of artificial neural networks (ANNs) is trained to predict transient temperatures at specified locations for a transient heat release rate (HRR) input. These locations correspond to the locations of thermocouples used in an experimental burn structure. In order to build the training set, A Gaussian process (GP) framework is used to develop a generative model that produces random viable HRR ramps. Although this procedure may require thousands of FDS runs to build a sufficient training set, the application of transfer learning can reduce the required number of runs by nearly an order of magnitude. This refers to the process of initially training an ANN to predict the output of the Consolidated Model of Fire and Smoke Transport (CFAST) and then transferring its knowledge to an ANN that learns to predict FDS outputs. CFAST is a much faster model than FDS, so a large training set can be generated quickly. The final state of the ANN trained to emulate CFAST is used as the initial state of an ANN that learns to emulate FDS. The result is a model that produces FDS temperature predictions with a mean absolute error (MAE) of less than 2°C and runs over five orders of magnitude faster than FDS. The emulators are also capable of learning inverse mappings; i.e. for a given temperature output, they can predict the HRR ramp that would cause FDS to produce the temperature response. This ability to invert for the HRR profile is exercised on data collected from eight fire experiments with peak HRRs up to 200 kW, including four propane burner fires, two methanol pool fires, and two n-Hexane pool fires. The model inverts for the experimental HRR with a MAE of 5.8 kW-15.4 kW (11.3%–16.7%) for the burner tests and 5.0 kW–25.5 kW (12.1%–28.6%) for the pool fire tests, with a tendency to underestimate the HRR of the pool fires. Finally, the computational speed of the emulators allows for the incorporation of CFD physics in Bayesian parameter inversion. As an example, this is demonstrated to infer the radiative fraction from experimental and synthetic data in conjunction with reported uncertainties from the FDS Validation Guide.

中文翻译：

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用于逆问题和房间尺度量热法的瞬态隔间火灾模拟的深度学习仿真器

这项工作描述了一种深度学习方法，用于“模拟”由 CFD 软件 Fire Dynamics Simulator (FDS) 产生的温度输出。训练一系列人工神经网络 (ANN) 来预测指定位置的瞬态温度，以获取瞬态热释放率 (HRR) 输入。这些位置对应于实验燃烧结构中使用的热电偶的位置。为了构建训练集，使用高斯过程 (GP) 框架来开发生成模型，该模型可生成随机可行的 HRR 斜坡。尽管此过程可能需要数千次 FDS 运行才能构建足够的训练集，但迁移学习的应用可以将所需的运行次数减少近一个数量级。这是指最初训练 ANN 以预测火烟传输综合模型 (CFAST) 的输出，然后将其知识转移到学习预测 FDS 输出的 ANN 的过程。CFAST 是比 FDS 快得多的模型，因此可以快速生成大型训练集。经过训练以模拟 CFAST 的 ANN 的最终状态用作学习模拟 FDS 的 ANN 的初始状态。结果是一个模型可以生成平均绝对误差 (MAE) 小于 2°C 的 FDS 温度预测，并且运行速度比 FDS 快五个数量级。模拟器还能够学习逆映射；即对于给定的温度输出，他们可以预测会导致 FDS 产生温度响应的 HRR 斜坡。这种反转 HRR 曲线的能力是根据从八次峰值 HRR 高达 200 kW 的火灾实验收集的数据进行的，包括四次丙烷燃烧器火灾、两次甲醇池火灾和两次正己烷池火灾。该模型对实验 HRR 进行了反转，燃烧器测试的 MAE 为 5.8 kW-15.4 kW (11.3%–16.7%)，池火测试的 MAE 为 5.0 kW–25.5 kW (12.1%–28.6%)，并且趋向于低估了泳池火灾的 HRR。最后，仿真器的计算速度允许在贝叶斯参数反演中结合 CFD 物理。例如，这被证明可以结合 FDS 验证指南中报告的不确定性从实验和合成数据中推断出辐射分数。两个甲醇池火灾和两个正己烷池火灾。该模型对实验 HRR 进行了反转，燃烧器测试的 MAE 为 5.8 kW-15.4 kW (11.3%–16.7%)，池火测试的 MAE 为 5.0 kW–25.5 kW (12.1%–28.6%)，并且趋向于低估了泳池火灾的 HRR。最后，仿真器的计算速度允许在贝叶斯参数反演中结合 CFD 物理。例如，这被证明可以结合 FDS 验证指南中报告的不确定性从实验和合成数据中推断出辐射分数。两个甲醇池火灾和两个正己烷池火灾。该模型对实验 HRR 进行了反转，燃烧器测试的 MAE 为 5.8 kW-15.4 kW (11.3%–16.7%)，池火测试的 MAE 为 5.0 kW–25.5 kW (12.1%–28.6%)，并且趋向于低估了泳池火灾的 HRR。最后，仿真器的计算速度允许在贝叶斯参数反演中结合 CFD 物理。例如，这被证明可以结合 FDS 验证指南中报告的不确定性从实验和合成数据中推断出辐射分数。仿真器的计算速度允许在贝叶斯参数反演中结合 CFD 物理。例如，这被证明可以结合 FDS 验证指南中报告的不确定性从实验和合成数据中推断出辐射分数。仿真器的计算速度允许在贝叶斯参数反演中加入 CFD 物理。例如，这被证明可以结合 FDS 验证指南中报告的不确定性从实验和合成数据中推断出辐射分数。