A comparative study of heat absorption and chemical reaction on MHD flow with fractional derivatives
Bibliographic record
Abstract
In this article, Caputo and Prabhakar fractional derivatives are used to analyze the influence of heat flux on fractionalized second grade flow. The fluid model is generalized by Fick’s and Fourier’s laws. Moreover, radiation and slip effects are also taken into account additionally. Fractional governing models are solved semi-analytically by using Caputo and Prabhakar fractional derivatives. The method of Laplace method is applied to solve the dimensional model for temperature, velocity, and concentration profiles. The results are contrasted visually. A variety of graphs are used to illustrate the impacts of several parameters, including the heat absorption Q, fractional parameter, magnetic parameter M, and chemical reaction R. It is evident from the figure that the velocity distribution is affected less by chemical and magnetic field, while the fluid velocity is affected more by diffusion-thermodynamics and mass Grashoff number. Furthermore, comparisons among classical and fractional fluid models are made to check the validity of the result. It is noted that the classical approach is less convenient as compared to the fractional approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".