GM(1,N) method for the prediction of critical failure pressure of type III tank in fire scenarios

Abstract

The determination of the pressure-bearing performance of hydrogen storage tanks (HST) is integral to enhancing their operational safety and risk management capabilities. In this study, an inaugural development of low-cost critical failure pressure (BPc) prediction model for small samples was proposed, which was based on a combination of the fuzzy grey relational analysis (FGRA) and GM(1,N). Specifically, a total of 15 of BPc-related factors were proposed for analysis and projections based on the data from bonfire tests. Results from the FGRA indicated burst pressure was most closely linked to the initial filling pressure (Rij1 = 0.939) of tanks under fire scenario. The normal working pressure (Rij2 = 0.924) and the elasticity modulus (Rij3 = 0.871) also demonstrating significant impacts. Furthermore, three GM(1,N) prediction models which could estimate BPc with multi-factor coupling were developed. Increasing the number of input feature factors could enhance the predictive capability of GM model. The GM(1,16) had optimal prediction performance among the models, achieving a prediction accuracy of 99.8 %. As well, the mean absolute error (MAE) was 0.799 MPa while the mean absolute percentage error (MAPE) was 1.56 %. This paper offered a novel option for establishing the HST critical failure criterion safely and efficiently.