小型喷气发动机蓄水池计算数字孪生

C. J. Wright, N. Biederman, B. Gyovai, D. J. Gauthier, J. P. Wilhelm
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引用次数: 0

摘要

应用机器学习创建了单涡流喷气发动机数值模拟的数字孪生模型。基于该数值模拟研究获得的洞察力的类似模型被用于创建仅使用实验数据的 JetCatP100-RX 喷射发动机的数字孪生模型。发动机数据由一个定制的传感器系统收集,该系统测量推力、排气温度、轴转速、天气条件等参数。通过控制轴速,将发动机置于不同的测试条件下收集数据。机器学习模型是使用下一代存储计算机生成(训练)的,该计算机是用于动态系统的一流机器学习算法。模型训练完成后,就可以用来预测从未见过的行为,与测试数据相比,准确率优于 1.8%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Small jet engine reservoir computing digital twin
Machine learning was applied to create a digital twin of a numerical simulation of a single-scroll jet engine. A similar model based on the insights gained from this numerical study was used to create a digital twin of a JetCat P100-RX jet engine using only experimental data. Engine data was collected from a custom sensor system measuring parameters such as thrust, exhaust gas temperature, shaft speed, weather conditions, etc. Data was gathered while the engine was placed under different test conditions by controlling shaft speed. The machine learning model was generated (trained) using a next-generation reservoir computer, a best-in-class machine learning algorithm for dynamical systems. Once the model was trained, it was used to predict behavior it had never seen with an accuracy of better than 1.8% when compared to the testing data.
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