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An integrated method of flammable cloud size prediction for offshore platforms
International Journal of Naval Architecture and Ocean Engineering ( IF 2.2 ) Pub Date : 2021-05-14 , DOI: 10.1016/j.ijnaoe.2021.03.003
Bin Zhang , Jinnan Zhang , Jiahang Yu , Boqiao Wang , Zhuoran Li , Yuanchen Xia , Li Chen

Response Surface Method (RSM) has been widely used for flammable cloud size prediction as it can reduce computational intensity for further Explosion Risk Analysis (ERA) especially during the early design phase of offshore platforms. However, RSM encounters the overfitting problem under very limited simulations. In order to overcome the disadvantage of RSM, Bayesian Regularization Artificial Neural (BRANN)-based model has been recently developed and its robustness and efficiency have been widely verified. However, for ERA during the early design phase, there seems to be room to further reduce the computational intensity while ensuring the model's acceptable accuracy. This study aims to develop an integrated method, namely the combination of Center Composite Design (CCD) method with Bayesian Regularization Artificial Neural Network (BRANN), for flammable cloud size prediction. A case study with constant and transient leakages is conducted to illustrate the feasibility and advantage of this hybrid method. Additionally, the performance of CCD-BRANN is compared with that of RSM. It is concluded that the newly developed hybrid method is more robust and computational efficient for ERAs during early design phase.



中文翻译:

海上平台易燃云团尺寸预测的综合方法

响应面法(RSM)已被广泛用于可燃云的大小预测,因为它可以减少用于进一步爆炸风险分析(ERA)的计算强度,尤其是在海上平台的早期设计阶段。但是,RSM在非常有限的模拟下遇到了过拟合问题。为了克服RSM的缺点,最近开发了基于贝叶斯正则化人工神经(BRANN)的模型,并且其鲁棒性和效率得到了广泛验证。但是,对于早期设计阶段的ERA,似乎仍有空间进一步降低计算强度,同时确保模型的可接受精度。本研究旨在开发一种集成方法,即将中心复合设计(CCD)方法与贝叶斯正则化人工神经网络(BRANN)相结合,用于易燃云的大小预测。进行了具有恒定和瞬态泄漏的案例研究,以说明此混合方法的可行性和优势。此外,将CCD-BRANN的性能与RSM的性能进行了比较。结论是,在早期设计阶段,新开发的混合方法对于ERA具有更强的鲁棒性和计算效率。

更新日期:2021-05-14
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