International Journal of Naval Architecture and Ocean Engineering

The Society of Naval Architects of Korea (대한조선학회)
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An integrated method of flammable cloud size prediction for offshore platforms

  • Zhang, Bin (Marine Engineering College, Dalian Maritime University) ;
  • Zhang, Jinnan (Marine Engineering College, Dalian Maritime University) ;
  • Yu, Jiahang (Marine Engineering College, Dalian Maritime University) ;
  • Wang, Boqiao (Marine Engineering College, Dalian Maritime University) ;
  • Li, Zhuoran (Marine Engineering College, Dalian Maritime University) ;
  • Xia, Yuanchen (Marine Engineering College, Dalian Maritime University) ;
  • Chen, Li (Marine Engineering College, Dalian Maritime University)
  • Received : 2020.11.26
  • Accepted : 2021.03.14
  • Published : 2021.11.30
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Abstract

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.

Keywords

Acknowledgement

This study was supported by the Liaoning Provincial Natural Science Foundation of China [grant numbers 2020JH2/10300107], the National Natural Science Foundation of China [grant numbers 51306026] and Fundamental Research Funds for the Central Universities [grant numbers 3132019038, 3132019339].

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