Heat transfer analysis of unsteady graphene oxide nanofluid flow using a fuzzy identifier evolved by genetically encoded mutable smart bee algorithm
Bibliographic record
Abstract
In the current research, the unsteady two dimensional Graphene Oxide water based nanofluid heat transfer between two moving parallel plates is analyzed using an intelligent black-box identifier. The developed intelligent tool is known as evolvable evolutionary fuzzy inference system (EE-FIS) which is based on the integration of low-level fuzzy programming and hyper-level evolutionary computing concepts. Here, the authors propose the use of a modified evolutionary algorithm (EA) which is called hybrid genetic mutable smart bee algorithm (HGMSBA). The proposed HGMSBA is used to evolve both antecedent and consequent parts of fuzzy rule base. Besides, it tries to prune the rule base of fuzzy inference system (FIS) to decrease its computational complexity and increase its interpretability. By considering the prediction error of the fuzzy identifier as the objective function of HGMSBA, an automatic soft interpolation machine is developed which can intuitively increase the robustness and accuracy of the final model. Here, HGMSBA-FIS is used to provide a nonlinear map between inputs, i.e. nanoparticles solid volume fraction ( ϕ ) , Eckert number (Ec) and a moving parameter which describes the movements of plates ( S ), and output, i.e. Nusselt number (Nu). Prior to proceeding with the modeling process, a comprehensive numerical comparative study is performed to investigate the potentials of the proposed model for nonlinear system identification. After demonstrating the efficacy of HGMSBA for training the FIS, the system is applied to the considered problem. Based on the obtained results, it can be inferred that the developed HGMSBA-FIS black-box identifier can be used as a very authentic tool with respect to accuracy and robustness. Besides, as the proposed black-box is not a physics-based identifier, it frees experts from the cumbersome mathematical formulations, and can be used for advanced real-time applications such as model-based control. The simulations indicate that the gradient of Nu has a direct nonlinear relation with the values of ϕ and Ec. It is also observed that an increase in the value of S decreases the value of Nu.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".