Performance prediction of ion-engineered water injection (EWI) in chalk reservoirs using Response Surface Methodology (RSM)

Mosallanezhad, Abdolali; Kalantariasl, Azim

doi:10.1016/j.egyr.2021.05.012

Cited by 12 publications

(2 citation statements)

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“…Particle swarm optimization, genetic algorithms, and desirability optimization are the primary types of optimization techniques now in use. [44][45][46][47] Recently, the non-dominated sorting genetic algorithm-II (NSGA-II) has grown into a widely used optimization approach. Excellent individuals can be kept around while the approach's computer costs are kept to a low level.…”

Section: Introductionmentioning

confidence: 99%

“…To get the ideal structural parameters, an appropriate optimization procedure must be established. Particle swarm optimization, genetic algorithms, and desirability optimization are the primary types of optimization techniques now in use 44–47 . Recently, the non‐dominated sorting genetic algorithm‐II (NSGA‐II) has grown into a widely used optimization approach.…”

Section: Introductionmentioning

confidence: 99%

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Bi‐stable cured shape of CFRP variant structures with asymmetric layup: Rapid prediction and inverse design

Sun,

Liu,

Zhao

et al. 2024

Polymer Composites

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This study aims to produce variant composite structures that can precisely maintain equilibrium after changing stable states. A rapid and precise prediction method for the cure‐induced bi‐stable warping was developed, and the inverse design for the required part's structural and material parameters was investigated. Firstly, the mechanism causing the warping was investigated. Then, in order to acquire basic data for a data‐driven model to achieve rapid and accurate warping prediction, a finite element analysis (FEA) was developed, with most of the prediction deviations being under 10%. Additionally, deformation forms of variant parts with various layups and shapes were studied to offer guidance for the different shape requirements. The structural and material parameters were analyzed using the response surface methodology and sensitivity analysis. After removing parameters like all rubbery properties with low influence, basic data from the parameterized FEA was utilized to build a precise proxy model. Finally, for the variant structures requiring reasonable shape forms, the inverse design of the material, layup, and sizes can be achieved using the proxy model and the non‐dominated sorting genetic algorithm II. All optimization results were validated using FEA or experiments. The deformation form in the two steady states of each case meets the specifications. The shape difference between the produced part and the required shape is less than 5 mm, and the design deviation for the maximum deformation is less than 10%. Consequently, the rapid prediction and inverse design methods for the bi‐stable shape of variant composite parts were effective and accurate.Highlights All the rubbery properties are almost uncorrelated to the bi‐stable warping. The proxy model can rapidly and precisely predict the bi‐stable shape. The inverse design can be achieved for a part requiring proper bi‐stable shapes. The design part can precisely maintain equilibrium with the required shapes.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%