Abstract

Thick plate bending process (warm bending and tempering) has a profound impact on the material strength distribution (MSD) in hydrogenation reactor shells. To date, few studies have studied the thick plate bending process. In this work, an artificial neural network (ANN) combined with finite element analysis (FEA) was utilized to investigate the impact of thick plate bending on the MSD of reactor shells. First, tensile tests of 0-10% pre-strained 2.25Cr-1Mo-0.25 V specimens were subjected to 390 to 510 °C. The results obtained from this experiment were used to develop ANN with two inputs (temperature and plastic strain) to predict the strength of pre-deformed steel. Subsequently, the plastic strain distribution of reactor shells after warm bending was obtained via FEA. We then inputted the FEA results into well-established ANN to predict the MSD of un-tempered reactor shells. The MSD of an actual tempered reactor shell was measured to study the synergic effect of warm bending and tempering on MSD variation. Results showed that the average absolute relative errors between the proposed ANN and tensile test results were below 4%. The absolute relative errors of the proposed prediction method varied from 0.24 to 7.88%. The proposed method is therefore reliable in the lightweight design of the hydrogenation reactor.

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