Choi Taesu, Lee Dooyoul
Department of Weapon System, Korea National Defense University, Nonsan 33021, Republic of Korea.
Materials (Basel). 2023 Jul 28;16(15):5326. doi: 10.3390/ma16155326.
Atmospheric corrosion is a significant challenge faced by the aviation industry as it considerably affects the structural integrity of an aircraft operated for long periods. Therefore, an appropriate corrosion deterioration model is required to predict corrosion problems. However, practical application of the deterioration model is challenging owing to the limited data available for the parameter estimation. Thus, a high uncertainty in prediction is unavoidable. To address these challenges, a method of integrating a physics-based model and the monitoring data on a Bayesian network (BN) is presented herein. Atmospheric corrosion is modeled using the simulation method, and a BN is constructed using GeNie. Moreover, model calibration is performed using the monitoring data collected from aircraft parking areas. The calibration approach is an improvement over existing models as it incorporates actual environmental data, making it more accurate and applicable to real-world scenarios. In conclusion, our research emphasizes the importance of precise corrosion models for predicting and managing atmospheric corrosion on carbon steel. The study results open new avenues for future research, such as the incorporation of additional data sources to further improve the accuracy of corrosion models.
大气腐蚀是航空业面临的一项重大挑战，因为它会严重影响长期运行的飞机的结构完整性。因此，需要一个合适的腐蚀劣化模型来预测腐蚀问题。然而，由于可用于参数估计的数据有限，劣化模型的实际应用具有挑战性。因此，预测中不可避免地存在高度不确定性。为应对这些挑战，本文提出了一种将基于物理的模型与贝叶斯网络（BN）上的监测数据相结合的方法。使用模拟方法对大气腐蚀进行建模，并使用GeNie构建贝叶斯网络。此外，利用从飞机停放区收集的监测数据进行模型校准。这种校准方法是对现有模型的改进，因为它纳入了实际环境数据，使其更准确且适用于实际场景。总之，我们的研究强调了精确腐蚀模型对于预测和管理碳钢大气腐蚀的重要性。研究结果为未来的研究开辟了新途径，例如纳入更多数据源以进一步提高腐蚀模型的准确性。
