Finite Element Analysis and Machine Learning‐Based Prediction of Oil Tank Behavior Under Diverse Operating Conditions
ABSTRACT Ensuring the structural integrity and operational reliability of oil storage tanks is critical to preventing catastrophic failures, including environmental pollution and economic losses. This study integrates Finite Element Analysis (FEA) and Machine Learning (ML) to predict the behavior (s...
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| Format: | Article |
| Language: | English |
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Wiley
2025-05-01
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| Series: | Engineering Reports |
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| Online Access: | https://doi.org/10.1002/eng2.70173 |
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| author | Themba Mashiyane Lagouge Tartibu Smith Salifu |
| author_facet | Themba Mashiyane Lagouge Tartibu Smith Salifu |
| author_sort | Themba Mashiyane |
| collection | DOAJ |
| description | ABSTRACT Ensuring the structural integrity and operational reliability of oil storage tanks is critical to preventing catastrophic failures, including environmental pollution and economic losses. This study integrates Finite Element Analysis (FEA) and Machine Learning (ML) to predict the behavior (structural) and useful life of oil tanks under diverse operating conditions. The methodology involves applying FEA simulation (using Abaqus) to model the strain, stress, and buckling behavior of the oil storage tank. Thereafter, fe‐safe postprocessing software was used to post‐process the FEA results to estimate the useful life of the tank. These FEA and fe‐safe outputs were trained using Artificial Neural Networks (ANN) and Adaptive Neuro‐Fuzzy Inference Systems (ANFIS) to predict unknown tank operating scenarios. The study revealed that the filled tanks experienced higher stress (485.4 MPa) and reduced life expectancy (1429 h) compared to half‐filled tanks (388.7 MPa and 3551 h). For the ML, ANFIS excelled in predicting stress and strain with R2 values of 0.999, while ANN proved superior for useful life predictions with R2 values of 0.998. This hybrid FEA‐ML approach enables efficient and precise analysis, thereby facilitating design optimization and maintenance strategies for industrial applications. |
| format | Article |
| id | doaj-art-43a9dc71aba443978301ba03890bbbfe |
| institution | Kabale University |
| issn | 2577-8196 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Engineering Reports |
| spelling | doaj-art-43a9dc71aba443978301ba03890bbbfe2025-08-20T03:48:27ZengWileyEngineering Reports2577-81962025-05-0175n/an/a10.1002/eng2.70173Finite Element Analysis and Machine Learning‐Based Prediction of Oil Tank Behavior Under Diverse Operating ConditionsThemba Mashiyane0Lagouge Tartibu1Smith Salifu2Department of Mechanical and Industrial Engineering Technology University of Johannesburg Johannesburg South AfricaDepartment of Mechanical and Industrial Engineering Technology University of Johannesburg Johannesburg South AfricaCentre for Nanoengineering and Advanced Materials University of Johannesburg Johannesburg South AfricaABSTRACT Ensuring the structural integrity and operational reliability of oil storage tanks is critical to preventing catastrophic failures, including environmental pollution and economic losses. This study integrates Finite Element Analysis (FEA) and Machine Learning (ML) to predict the behavior (structural) and useful life of oil tanks under diverse operating conditions. The methodology involves applying FEA simulation (using Abaqus) to model the strain, stress, and buckling behavior of the oil storage tank. Thereafter, fe‐safe postprocessing software was used to post‐process the FEA results to estimate the useful life of the tank. These FEA and fe‐safe outputs were trained using Artificial Neural Networks (ANN) and Adaptive Neuro‐Fuzzy Inference Systems (ANFIS) to predict unknown tank operating scenarios. The study revealed that the filled tanks experienced higher stress (485.4 MPa) and reduced life expectancy (1429 h) compared to half‐filled tanks (388.7 MPa and 3551 h). For the ML, ANFIS excelled in predicting stress and strain with R2 values of 0.999, while ANN proved superior for useful life predictions with R2 values of 0.998. This hybrid FEA‐ML approach enables efficient and precise analysis, thereby facilitating design optimization and maintenance strategies for industrial applications.https://doi.org/10.1002/eng2.70173bucklingmachine learningoil storage tankstructural analysisuseful life |
| spellingShingle | Themba Mashiyane Lagouge Tartibu Smith Salifu Finite Element Analysis and Machine Learning‐Based Prediction of Oil Tank Behavior Under Diverse Operating Conditions Engineering Reports buckling machine learning oil storage tank structural analysis useful life |
| title | Finite Element Analysis and Machine Learning‐Based Prediction of Oil Tank Behavior Under Diverse Operating Conditions |
| title_full | Finite Element Analysis and Machine Learning‐Based Prediction of Oil Tank Behavior Under Diverse Operating Conditions |
| title_fullStr | Finite Element Analysis and Machine Learning‐Based Prediction of Oil Tank Behavior Under Diverse Operating Conditions |
| title_full_unstemmed | Finite Element Analysis and Machine Learning‐Based Prediction of Oil Tank Behavior Under Diverse Operating Conditions |
| title_short | Finite Element Analysis and Machine Learning‐Based Prediction of Oil Tank Behavior Under Diverse Operating Conditions |
| title_sort | finite element analysis and machine learning based prediction of oil tank behavior under diverse operating conditions |
| topic | buckling machine learning oil storage tank structural analysis useful life |
| url | https://doi.org/10.1002/eng2.70173 |
| work_keys_str_mv | AT thembamashiyane finiteelementanalysisandmachinelearningbasedpredictionofoiltankbehaviorunderdiverseoperatingconditions AT lagougetartibu finiteelementanalysisandmachinelearningbasedpredictionofoiltankbehaviorunderdiverseoperatingconditions AT smithsalifu finiteelementanalysisandmachinelearningbasedpredictionofoiltankbehaviorunderdiverseoperatingconditions |