CONSTRUCTION AND VALIDATION OF A WIDE-DOMAIN ENGINE FLAMELET COMBUSTION MODEL BASED ON DEEP LEARNING TABLE BUILDING
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摘要: 以新型宽域发动机为动力的未来新一代飞行器的研发迫切需要CFD方法来进行高效高精度的辅助设计. 本文把传统的火焰面/进度变量燃烧模型与深度学习和神经网络方法相结合, 构建了新的智能化改进的燃烧模型并进行了算例测试与验证, 在保证计算效率的同时提高了预测精度. 首先, 给出了人工神经网络的构建方法, 包括数据库划分、数据归一化以及模型的训练等; 然后, 测试分析了不同函数结构对新建模型的影响, 并讨论了基于CPU和GPU的求解器框架下内存占用优化问题; 最后, 把智能化模型耦合到GPU求解器上对飞行马赫数4-12的3个发动机算例进行了数值模拟. 结果表明, 智能化改进的模型可代替传统火焰面/进度变量数据库从而实现高维参数建模及模型改进, 并可以成功运行在GPU上; 智能化改进的模型比传统的模型平均误差减小量均超过了50%, 算例误差最大减小值可达57.2%.
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关键词:
- 火焰面/进度变量模型 /
- 全连接神经网络 /
- 宽域发动机 /
- 燃烧模型 /
- 数值模拟
Abstract: The development of future next-generation aircraft powered by a new wide domain engine urgently requires CFD methods for efficient and high-precision auxiliary design. This article combines the traditional flamelet /progress variable combustion model with deep learning and neural network methods to construct a new intelligent improved combustion model, which is tested and validated by numerical cases. This improves the prediction accuracy while ensuring computational efficiency. Firstly, the construction method of the artificial neural network in this article were proposed in details, including database partitioning, data normalization, and model training; Then, the impact of different function structures on the new model was tested and analyzed, and the optimization of memory usage under the CPU and GPU based solver framework was discussed; Finally, the intelligent model was coupled to a GPU solver for numerical simulation of three engine cases with flight Mach numbers 4-12. The results show that the intelligent improved model can replace the traditional flame surface/progress variable database to achieve high-dimensional parameter modeling and model improvement, and can successfully run on GPU; The intelligent improved model has an average error reduction of over 50% compared to traditional models for all mentioned cases, and the maximum reduction in case error can reach 57.2%. -
图 1 神经元节点数对释热率HRR模型精度的影响
Figure 1. The effect of the number of neuron nodes on the precision of the heat release rate HRR model
图 2 神经元节点数对进度变量源QC模型精度的影响
Figure 2. The effect of the number of neuron nodes on the precision of the source term QC model for progress variable equation
图 3 神经元节点数对模型拟合效果的影响
Figure 3. The effect of the number of neuron nodes on model fitting performance
图 4 不同损失函数对模型精度的影响
Figure 4. The effect of different loss functions on the model accuracy
图 5 损失函数对模型拟合效果的影响 (续)
Figure 5. The effect of loss function on model fitting performance (continued)
图 10 算例1流向中心截面上传统模型(上)与智能化改进模型(下)计算温度云图
Figure 10. Case 1: Temperature contour in the flow direction center section calculated using traditional model (up) and intelligent improved model (down)
图 11 算例1壁面压力沿程分布计算与实验结果对比图(平均误差减小53.2%)
Figure 11. Comparison between calculated and experimental results of wall pressure distribution along the flow path for Case 1 (Average error reduction is 53.2%)
图 12 算例2圆管燃烧室的总体构造和具体尺寸
Figure 12. Case 2: Overall structure and specific dimensions of the circular tube combustor
图 13 算例2流向中心截面传统模型(上)与智能化改进模型(下)计算温度云图
Figure 13. Case 2: Temperature contour in the flow direction center section calculated using traditional model (up) and intelligent improved model (down)
图 14 算例2壁面压力沿程分布计算与实验结果对比图(平均误差减小55.7%)
Figure 14. Comparison between calculated and experimental results of wall pressure distribution along the flow path for Case 2 (Average error reduction is 55.7%)
图 15 算例3氢气凹腔燃烧室构型示意图
Figure 15. Case 3: Schematic diagram of the configuration of the hydrogen-fueled cavity combustion chamber
图 16 算例3流向中心截面传统模型(上)与智能化改进模型(下)计算温度云图
Figure 16. Case 3: Temperature contour in the flow direction center section calculated using traditional model (up) and intelligent improved model (down)
图 17 算例3壁面压力沿程分布计算与实验结果对比图(平均误差减小57.2%)
Figure 17. Comparison between calculated and experimental results of wall pressure distribution along the flow path for Case 3 (Average error reduction is 57.2%)
表 1 M12-02实验超声速来流和燃料射流工况
Table 1. Supersonic inflow and fuel jet conditions for M12-02 Experiment
Ma P/kPa T/K YO2 YN2 YH2 Inflow 6.72 4.0 677 0.2031 0.7969 0.0 Jet 1.0 2100 250 0.0 0.0 1.0 
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表 2 算例1误差减小情况表(飞行Ma12、计算入口Ma6.72、平均误差减小53.2%)
Table 2. Error reduction in Case 1 for intelligent improved combustion model (ANN-FPV) compared with tranditional model (FPV) (with flight Ma12, inlet Ma6.72 and average error reduction 53.2%)
点序列 x/mm FPV模型误差 ANN-FPV模型误差 误差减小值 点序列 x/mm FPV模型误差 ANN-FPV模型误差 误差减小值 1 325 74.3% 74.8% −0.7% 19 1729 18.6% 22.2% −19.3% 2 529 47.5% 47.3% 0.4% 20 1780 31.8% 1.5% 95.2% 3 590 13.7% 13.3% 2.6% 21 1836 26.7% 1.4% 94.7% 4 625 26.3% 26.0% 1.1% 22 1892 31.5% 9.3% 70.6% 5 753 98.5% 100.0% −1.6% 23 1937 38.4% 16.2% 57.8% 6 885 66.2% 11.7% 82.3% 24 1983 36.3% 11.2% 69.1% 7 986 79.3% 75.5% 4.8% 25 2034 27.6% 0.1% 99.7% 8 1078 35.5% 1.8% 95.0% 26 2090 27.2% 2.9% 89.2% 9 1225 28.4% 36.1% −27.1% 27 2131 21.9% 1.7% 92.4% 10 1281 37.4% 12.9% 65.5% 28 2176 17.6% 6.6% 62.4% 11 1332 52.1% 23.0% 55.8% 29 2242 16.5% 9.0% 45.2% 12 1388 33.9% 2.1% 94.0% 30 2278 17.7% 6.8% 61.4% 13 1434 28.2% 3.1% 88.9% 31 2324 17.7% 4.1% 76.9% 14 1475 35.6% 5.9% 83.4% 32 2380 17.9% 0.6% 96.9% 15 1525 34.5% 4.8% 86.0% 33 2441 12.2% 7.3% 39.8% 16 1581 15.5% 14.9% 3.5% 34 2583 45.5% 37.4% 17.8% 17 1622 33.1% 9.5% 71.4% 35 2624 52.6% 41.5% 21.1% 18 1673 29.4% 1.1% 96.3% 36 2685 40.4% 23.1% 42.7%
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表 3 算例2圆管燃烧室工况
Table 3. Case 2: Operating conditions of the circular tube combustor
马赫数 静压/kPa 静温/K YO2 YN2 YC2 H4 来流 4.5 0.55 444 0.233 0.767 0.0 燃料流 1.0 57.1 250 0.0 0.0 1.0
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表 4 算例2误差减小情况表(飞行Ma8、计算入口Ma4.5、平均误差减小55.7%)
Table 4. Error reduction in Case 2 for intelligent improved combustion model (ANN-FPV) compared with tranditional model (FPV) (with flight Ma8.0, inlet Ma4.5 and average error reduction 55.7%)
点序列 x / mm FPV模型误差 ANN-FPV模型误差 误差减小值 1 53 18.0% 18.8% −4.7% 2 97 9.8% 8.4% 14.6% 3 172 16.6% 16.0% 3.7% 4 246 17.2% 15.6% 8.9% 5 346 128.2% 17.5% 86.3% 6 369 50.2% 4.1% 91.8% 7 400 43.7% 19.4% 55.6% 8 417 36.2% 12.1% 66.5% 9 433 45.9% 15.6% 65.9% 10 447 65.1% 6.8% 89.5% 11 510 96.3% 4.2% 95.7% 12 660 69.8% 3.6% 94.9%
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表 5 算例3氢气凹腔燃烧室算例工况
Table 5. Case 3: Operating conditions of the hydrogen-fueled cavity combustion chamber
马赫数 总压/kPa 总温/K YO2 YN2 YH2 O YH2 来流 2.0 820 950 0.21 0.67 0.12 0.0 燃料流 1.0 4000 300 0.0 0.0 0.0 1.0
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表 6 算例3误差减小情况表(飞行Ma4.0、计算入口Ma2.0、平均误差减小57.2%)
Table 6. Error reduction in Case 3 for intelligent improved combustion model (ANN-FPV) compared with tranditional model (FPV) (with flight Ma4.0, inlet Ma2.0 and average error reduction 57.2%)
点序列 x/mm FPV模型误差 ANN-FPV模型误差 误差减小值 点序列 x/mm FPV模型误差 ANN-FPV模型误差 误差减小值 1 11 1.0% 1.0% −0.1% 21 429 16.6% 15.3% 7.9% 2 90 56.8% 0.4% 99.4% 22 449 22.3% 11.4% 49.0% 3 114 73.9% 21.9% 70.3% 23 469 23.5% 11.7% 50.1% 4 131 26.2% 9.8% 62.6% 24 489 23.0% 10.6% 53.7% 5 150 28.3% 6.8% 76.0% 25 510 21.7% 11.0% 49.2% 6 172 29.4% 6.2% 79.0% 26 526 23.8% 13.1% 45.0% 7 189 23.0% 8.5% 63.2% 27 547 24.0% 12.4% 48.3% 8 210 20.7% 10.0% 51.9% 28 569 36.4% 3.0% 91.8% 9 227 20.3% 7.7% 61.9% 29 588 46.3% 6.2% 86.7% 10 251 19.6% 4.7% 76.0% 30 732 24.4% 8.7% 64.5% 11 272 20.6% 0.5% 97.5% 31 762 19.6% 4.3% 78.1% 12 291 3.8% 6.1% −63.2% 32 791 55.2% 12.0% 78.3% 13 309 22.0% 8.3% 62.0% 33 821 134.5% 60.2% 55.2% 14 330 14.7% 12.1% 17.9% 34 848 147.3% 70.6% 52.1% 15 348 13.3% 8.0% 40.0% 35 907 165.3% 77.0% 53.4% 16 370 14.7% 4.6% 68.7% 36 935 145.3% 68.3% 53.0% 17 372 24.3% 3.1% 87.1% 37 965 102.1% 44.4% 56.5% 18 391 14.5% 4.7% 67.5% 38 993 122.0% 65.4% 46.4% 19 402 59.2% 31.8% 46.4% 39 1022 72.6% 37.4% 48.5% 20 407 18.9% 1.1% 94.2% 40 1051 52.4% 19.7% 62.5%
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表 7 三个算例的燃烧模型CFD结果(时间消耗值、内存占用值及精度提升值)
Table 7. CFD results of three cases under two combustion models (time consumption value, memory occupancy value, and accuracy improvement value)
算例1 算例2 算例3 燃烧模型CFD结果 FPV FPV-ANN FPV FPV-ANN FPV FPV-ANN 耗时(s)/100步 61.1 211.1 71.1% 54.2 179.8 69.9% 43.7 138.5 68.4% 内存占用(GB) 16.7 21.1 20.9% 12.9 17.2 25.0% 10.4 14.8 29.8% 精度提高值 53.2% 55.7% 57.2%
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