A multi-objective optimizer-based model for predicting composite material properties

Lu, Hongfang; Behbahani, Saleh; Ma, Xin; Iseley, Tom

doi:10.1016/j.conbuildmat.2021.122746

Cited by 24 publications

(6 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the intelligent model, how to determine the input variables is a very critical issue [60,61]. Therefore, in the remaining strength and remaining life prediction model, it is necessary to consider which factors are used as input variables fully.…”

Section: Input Variable and Output Valuementioning

confidence: 99%

Residual Strength Assessment and Residual Life Prediction of Corroded Pipelines: A Decade Review

Huang

Zeng³

et al. 2022

Energies

View full text Add to dashboard Cite

Prediction of residual strength and residual life of corrosion pipelines is the key to ensuring pipeline safety. Accurate assessment and prediction make it possible to prevent unnecessary accidents and casualties, and avoid the waste of resources caused by the large-scale replacement of pipelines. However, due to many factors affecting pipeline corrosion, it is difficult to achieve accurate predictions. This paper reviews the research on residual strength and residual life of pipelines in the past decade. Through careful reading, this paper compared several traditional evaluation methods horizontally, extracted 71 intelligent models, discussed the publishing time, the evaluation accuracy of traditional models, and the prediction accuracy of intelligent models, input variables, and output value. This paper’s main contributions and findings are as follows: (1) Comparing several traditional evaluation methods, PCORRC and DNV-RP-F101 perform well in evaluating low-strength pipelines, and DNV-RP-F101 has a better performance in evaluating medium–high strength pipelines. (2) In intelligent models, the most frequently used error indicators are mean square error, goodness of fit, mean absolute percentage error, root mean square error, and mean absolute error. Among them, mean absolute percentage error was in the range of 0.0123–0.1499. Goodness of fit was in the range of 0.619–0.999. (3) The size of the data set of different models and the data division ratio was counted. The proportion of the test data set was between 0.015 and 0.4. (4) The input variables and output value of predictions were summarized.

show abstract

Section: Input Variable and Output Valuementioning

confidence: 99%

Residual Strength Assessment and Residual Life Prediction of Corroded Pipelines: A Decade Review

Huang

Zeng³

et al. 2022

Energies

View full text Add to dashboard Cite

show abstract

“…The total length of the pipeline is 198.5 km and the pipe diameter is 400 mm. As shown in Figure 1 a considerable threat to the safety of the pipeline's operation [8,9]. Hence, the study of wax deposition management is significant in determining pipeline transportation process formulation and realizing an economical and safe operation.…”

Section: The Basic Situation Of the Shunbei Crude Oil Pipelinementioning

confidence: 99%

“…In addition, the increase in the thickness of the wax layer at the pipe wall hinders the restart process during shutdown conditions. It may even fail to start in severe cases, which poses a considerable Processes 2021, 9, 1572 2 of 11 threat to the safety of the pipeline's operation [8,9]. Hence, the study of wax deposition management is significant in determining pipeline transportation process formulation and realizing an economical and safe operation.…”

Section: Introductionmentioning

confidence: 99%

Safety Study on Wax Deposition in Crude Oil Pipeline

et al. 2021

View full text Add to dashboard Cite

The Shunbei crude oil pipeline is prepared to use the unheated transportation process to transport waxy crudes. However, the wax formation in the pipeline is unknown. In order to predict the wax deposition of the pipeline, the physical property experiment of Shunbei crude oil was carried out through field sampling. The density, freezing point, hydrocarbon composition, and viscosity–temperature characteristics of crude oil are obtained. The cloud point and wax precipitation characteristics of the crude oil were obtained using the differential scanning calorimetry (DSC) thermal analysis method. Then, the wax deposition rate of the pipeline was predicted by two methods: OLGA software and wax deposition kinetic model. Finally, the optimal pigging cycle of the pipeline was calculated on this basis. The results show that: Shunbei crude oil is a light crude oil with low wax content, a low freezing point, and a high cloud point. Comparing the OLGA simulation results with the calculation results of the Huang Qiyu model, the development trend of wax deposition along the pipeline was the same under different working conditions. The relative error of the maximum wax layer thickness was 6%, proving that it is feasible for OLGA to simulate wax deposition in long-distance crude oil pipelines. Affected by the wax precipitation characteristics of Shunbei crude oil, there was a peak of wax precipitation between the pipeline section where crude oil temperature was 9.31–13.31 °C and the recommended pigging cycle at the lowest throughput was 34 days in winter and 51 days in spring and autumn.

show abstract

“…Topology optimization (TOP) presented a greater strength geometry; overall, GA suggested a better strength-to-weight ratio solution. Lu et al [20] used machine learning to introduce a model, a multi-objective gray wolf optimizer combined with a support vector machine, for predicting the properties of composite materials. The model used six datasets to infer properties from available data.…”

Section: Introductionmentioning

confidence: 99%

Application of Particle Swarm Optimization for Improvement of Peel Strength in a Laminated Double-Lap Composite Joint

et al. 2022

View full text Add to dashboard Cite

A double lap joint is commonly used in thin structures under low running loads. Peel and inter-laminar stresses are among the major limitations of this type of joint, which may cause delamination failure. These stresses should be determined for designing a stronger structure. This study presents a method based on particle swarm optimization to find the best layup for a classic double lap joint under horizontal constant tensile forces. The peel stress equation is analytically obtained and utilized as the objective function for the algorithm. The method’s accuracy is explored by assessing the algorithm’s ability. This helps to find the best arrangement with the highest strength delamination against considering four initial layups. The results show that the optimized layups, on average, can reduce peel stress by about 96%. Additionally, the effects of different parameters on joint strength are investigated.

show abstract

A multi-objective optimizer-based model for predicting composite material properties

Cited by 24 publications

References 34 publications

Residual Strength Assessment and Residual Life Prediction of Corroded Pipelines: A Decade Review

Residual Strength Assessment and Residual Life Prediction of Corroded Pipelines: A Decade Review

Safety Study on Wax Deposition in Crude Oil Pipeline

Application of Particle Swarm Optimization for Improvement of Peel Strength in a Laminated Double-Lap Composite Joint

Contact Info

Product

Resources

About