Burst Pressure Prediction of Cylindrical Vessels Using Artificial Neural Network

Zolfaghari, Alireza; Izadi, Moein

doi:10.1115/1.4045729

Cited by 17 publications

(6 citation statements)

References 24 publications

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“…Each layer consists of a set of neurons and is trained with an algorithm of back-propagation. It is one of the most extensively utilized algorithms for supervised training of multilayered neural networks [65][66][67]. It works by approximating the non-linear relation between the input and the response by varying the weight values internally.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Improving the Resolution of GRACE Data for Spatio-Temporal Groundwater Storage Assessment

Ali

Liu

et al. 2021

Remote Sensing

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Groundwater has a significant contribution to water storage and is considered to be one of the sources for agricultural irrigation; industrial; and domestic water use. The Gravity Recovery and Climate Experiment (GRACE) satellite provides a unique opportunity to evaluate terrestrial water storage (TWS) and groundwater storage (GWS) at a large spatial scale. However; the coarse resolution of GRACE limits its ability to investigate the water storage change at a small scale. It is; therefore; needed to improve the resolution of GRACE data at a spatial scale applicable for regional-level studies. In this study; a machine-learning-based downscaling random forest model (RFM) and artificial neural network (ANN) model were developed to downscale GRACE data (TWS and GWS) from 1° to a higher resolution (0.25°). The spatial maps of downscaled TWS and GWS were generated over the Indus basin irrigation system (IBIS). Variations in TWS of GRACE in combination with geospatial variables; including digital elevation model (DEM), slope; aspect; and hydrological variables; including soil moisture; evapotranspiration; rainfall; surface runoff; canopy water; and temperature; were used. The geospatial and hydrological variables could potentially contribute to; or correlate with; GRACE TWS. The RFM outperformed the ANN model and results show Pearson correlation coefficient (R) (0.97), root mean square error (RMSE) (11.83 mm), mean absolute error (MAE) (7.71 mm), and Nash–Sutcliffe efficiency (NSE) (0.94) while comparing with the training dataset from 2003 to 2016. These results indicate the suitability of RFM to downscale GRACE data at a regional scale. The downscaled GWS data were analyzed; and we observed that the region has lost GWS of about −9.54 ± 1.27 km3 at the rate of −0.68 ± 0.09 km3/year from 2003 to 2016. The validation results showed that R between downscaled GWS and observational wells GWS are 0.67 and 0.77 at seasonal and annual scales with a confidence level of 95%, respectively. It can; therefore; be concluded that the RFM has the potential to downscale GRACE data at a spatial scale suitable to predict GWS at regional scales.

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Improving the Resolution of GRACE Data for Spatio-Temporal Groundwater Storage Assessment

Ali

Liu

et al. 2021

Remote Sensing

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“…Backpropagation is one of the most extensively used algorithms for supervised training of multilayered neural networks [76][77][78]. Backpropagation works by approximating the non-linear relationship between the input and the target by altering the weight values internally.…”

Section: Annmentioning

confidence: 99%

Statistical Applications to Downscale GRACE-Derived Terrestrial Water Storage Data and to Fill Temporal Gaps

et al. 2020

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The Gravity Recovery and Climate Experiment (GRACE) has been successfully used to monitor variations in terrestrial water storage (GRACETWS) and groundwater storage (GRACEGWS) across the globe, yet such applications are hindered on local scales by the limited spatial resolution of GRACE data. Using the Lower Peninsula of Michigan as a test site, we developed optimum procedures to downscale GRACE Release-06 monthly mascon solutions. A four-fold exercise was conducted. Cluster analysis was performed to identify the optimum number and distribution of clusters (areas) of contiguous pixels of similar geophysical signals (GRACETWS time series); three clusters were identified (cluster 1: 13,700 km2; cluster 2: 59,200 km2; cluster 3: 33,100 km2; Step I). Variables (total precipitation, normalized difference vegetation index (NDVI), snow cover, streamflow, Lake Michigan level, Lake Huron level, land surface temperature, soil moisture, air temperature, and evapotranspiration (ET)), which could potentially contribute to, or correlate with, GRACETWS over the test site were identified, and the dataset was randomly partitioned into training (80%) and testing (20%) datasets (Step II). Multivariate regression, artificial neural network, and extreme gradient boosting techniques were applied on the training dataset for each of the identified clusters to extract relationships between the identified hydro-climatic variables and GRACETWS solutions on a coarser scale (13,700–33,100 km2), and were used to estimate GRACETWS at a spatial resolution matching that of the fine-scale (0.125° × 0.125° or 120 km2) inputs. The statistical models were evaluated by comparing the observed and modeled GRACETWS values using the R-squared, the Nash–Sutcliffe model efficiency coefficient (NSE), and the normalized root-mean-square error (NRMSE; Step III). Lastly, temporal variations in GRACEGWS were extracted using outputs of land surface models and those of the optimum downscaling methodology (downscaled GRACETWS) (Step IV). Findings demonstrate that (1) consideration should be given to the cluster-based extreme gradient boosting technique in downscaling GRACETWS for local applications given their apparent enhanced performance (average value: R-squared: 0.86; NRMSE 0.37; NSE 0.86) over the multivariate regression (R-squared: 0.74; NRMSE 0.56; NSE 0.64) and artificial neural network (R-squared: 0.76; NRMSE 0.5; NSE 0.37) methods; and (2) identifying local hydrologic variables and the optimum downscaling approach for individual clusters is critical to implementing this method. The adopted method could potentially be used for groundwater management purposes on local scales in the study area and in similar settings elsewhere.

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“…Early work on applying ANN's to the PV burst problem looked at predicting the effect of interacting defects on pipe burst pressure [18]. Other work has also looked at using ANNs to predict the burst pressure of defect free pipes [19], the burst pressure of dented pipelines [20], and the use of an adaptive neuro-fuzzy inference system (ANFIS) to predict PV burst pressure from experimental data. ANFIS, a type of ANN that uses fuzzy logic to reduce noise in data, resulted in a high accuracy prediction of PV burst strength [21].…”

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

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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.

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Burst Pressure Prediction of Cylindrical Vessels Using Artificial Neural Network

Cited by 17 publications

References 24 publications

Improving the Resolution of GRACE Data for Spatio-Temporal Groundwater Storage Assessment

Improving the Resolution of GRACE Data for Spatio-Temporal Groundwater Storage Assessment

Statistical Applications to Downscale GRACE-Derived Terrestrial Water Storage Data and to Fill Temporal Gaps

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

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