Investigation of Burst Pressures in PWR Primary Pressure Boundary Components

Namgung, Ihn; Giang, Nguyen Hoang

doi:10.1016/j.net.2015.11.001

Cited by 9 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, references [9] - [13] all used FE modeling to assess the burst pressure of corroded pipelines. References [14] and [15] used FE models to assess the burst strength of irregular geometries. To determine the burst pressure most FE © 2023 by ASME codes use a RIKS type analysis that assumes the von Mises flow theory of plasticity.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks for Predicting Burst Strength of Thick and Thin-Walled Pressure Vessels

Johnson,

Zhu,

Sindelar

et al. 2023

Volume 5: Materials &Amp; Fabrication

View full text Add to dashboard Cite

The common use of pressure vessels (PVs) and pipelines and the high cost of their failure or overdesign in the oil and gas industry makes predicting their burst pressure critical. However, experimental tests of burst pressure are expensive and finite element analysis (FEA) is time consuming. Artificial neural networks (ANNs) hold the promise of rapid prediction of burst pressure for a wide range of PV materials and geometries, including pipelines with defects such as corrosion. This paper demonstrates ANNs designed and trained to accurately predict the burst pressure of both thick and thin-walled PVs for various strain hardening carbon steels. To accomplish this, we trained single and double hidden layer ANNs on experimental data, augmented with FEA, data of predicted burst pressure to create a larger database. A statistical study of hundreds of models exploring the key hyperparameters provided statistically optimal hyperparameters, resulting in highly accurate ANNs. The results, two ANNs with comparably low prediction error across geometries ranging from 3 < Do/t < 120, demonstrate the first use of ANNs to address both thick and thin-walled PV burst pressure within a single network. This is an important step towards designing ANNs for predicting the burst pressure of PVs and pipelines with defects.

show abstract

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks for Predicting Burst Strength of Thick and Thin-Walled Pressure Vessels

Johnson,

Zhu,

Sindelar

et al. 2023

Volume 5: Materials &Amp; Fabrication

View full text Add to dashboard Cite

show abstract

“…Some models include Faupel's formulae, validated using experimental test data [16], and Svennson's approximate formulae [6]. Ihn and Nguyen evaluated these and other formulae against FEA models of pressurized water reactor cooling components and found that their burst strength predictions could vary by as much as 45% [17]. Recently, in reference [18], the Zhu-Leis theory using the average shear stress to predict pressure vessels was extended to thick-walled PV's as well, providing the first analytical theory capable of accurately predicting the burst pressure for both thin and thick-walled PVs.…”

Section: Introductionmentioning

confidence: 99%

Determining Burst Strength of Thin and Thick-Walled Pressure Vessels Through Parametric Finite Element Analysis

Johnson,

Zhu,

Wiersma

et al. 2023

Volume 2: Computer Technology &Amp; Bolted Joints; Design &Amp; Analysis

View full text Add to dashboard Cite

Previous work on pressure vessel (PV) burst strength established a numerical technique for determining the more accurate Zhu-Leis burst strength from the pressure-Mises stress curve provided by finite element analyses (FEA). That work was not applied to capture the burst strength for thick-walled pipes. Recent analytical developments have resulted in a thick-walled Zhu-Leis burst strength solution. This work examines this solution using FEA calculations. To do so, the FEA stress was measured at multiple points through the wall thickness of the PV and compared to the analytical thick-walled solution to determine the best measurement location through the thickness. To further examine the burst pressure for different vessel sizes and materials, we developed a parametric code, using the Abaqus Python application programming interface, to rapidly iterate over 60 different combinations of geometries and material properties. This code also extracted stress data from the FEA results and analyzed the numerical pressure-Mises stress curves to find the critical point corresponding to the Zhu-Leis burst pressure. A linear regression model was developed which can very accurately predict the PV burst pressure. This regression model applies over a range of pipeline steels from grade B to X80, and diameter-to-thickness (D/t) ratios from 6 to 120.

show abstract

Development and verification of the coupled thermal–hydraulic code – TRACE/SCF based on the ICoCo interface and the SALOME platform

Zhang

Muñoz

2021

Annals of Nuclear Energy

View full text Add to dashboard Cite

Investigation of Burst Pressures in PWR Primary Pressure Boundary Components

Cited by 9 publications

References 7 publications

Artificial Neural Networks for Predicting Burst Strength of Thick and Thin-Walled Pressure Vessels

Artificial Neural Networks for Predicting Burst Strength of Thick and Thin-Walled Pressure Vessels

Determining Burst Strength of Thin and Thick-Walled Pressure Vessels Through Parametric Finite Element Analysis

Development and verification of the coupled thermal–hydraulic code – TRACE/SCF based on the ICoCo interface and the SALOME platform

Contact Info

Product

Resources

About