Numerical analysis on MHD heat transfer of Casson hybrid nanofluid (Cu-Al<sub>2</sub>O<sub>3</sub>/H<sub>2</sub>O) flow across an inclined moving plate in presence of radiation
Bibliographic record
Abstract
The ability of hybrid nanomaterials to enhance heat transmission has attracted many academics to study the working fluid. This work examines the impressions of radiation efficiency on the convective Casson flow of hybrid Nano liquid across an angled moving plate with a magnetic impression using a mathematical solution. The main aim of this study is to increase the thermal efficiency of the fluid by using different categories of Cu and Al2O3 nanoparticles are combined with the base fluid water. A nonlinear partial differential equation system is created while keeping in mind some reasonable presumptions. Using the similarity transformation, the partial differential equations are changed into nonlinear ordinary differential equations. And it is then mathematically simplified with the bvp4c technique. Consequences of an exclusivity group of unique impacts on motion characteristics, shear stress, thermal field impressions, and heat transport are described clearly. The motion rised with increasing Casson fluid for stretching and shrinking surfaces. For radiation impression, an energy upsurge profile is visible. With an inclined plate, the shear rate decreases, and the buoyancy impression causes it to rise. This work provides new information about the behavior of heat plumes under magnetic fields, thermal radiation and flow, and has potential applications in enhancing cooling systems in industrial applications, modelling of oil reservoirs, and nuclear waste storage. Nanoparticles are used for cooling processors, cancer therapy, medicine, metal strips, automobile engines, welding equipment, fusion reactions, chemical reactions, and for cooling heat exchange mechanisms in various engineering devices due to their superior thermophysical properties.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".