Using electrical resistance tomography (ERT) and computational fluid dynamics (CFD) to study the mixing of pseudoplastic fluids with a SCABA 6SRGT impeller
Bibliographic record
Abstract
The objective of this work is to use electrical resistance tomography (ERT) and computational fluid dynamic (CFD) modeling to investigate the flow field generated by a Scaba 6SRGT impeller in the agitation of the xanthan solution, as a pseudoplastic fluid with yield stress. ERT provides a non-destructive technique to measure, in three dimensions, the concentration fields inside the mixing tanks. Using ERT, the impeller flow pattern, the dimensions of the cavern formed and the mixing time in the agitation of xanthan solutions were evaluated. The sizes of cavern measured using ERT were in good agreement with that calculated using Elson's model (cylindrical model). ERT provides both overall mixing time using 1264 probes (316 probes for each plan) and local mixing time using 4 selected probes or pixels. The dimensionless mixing times obtained from ERT were correlated well with the Moo-Young correlation, confirming that increased impeller speeds decreases the mixing times. The 3D flow field generated by a Scaba 6SRGT impeller and tracer homogenization in the agitation of xanthan gum were also simulated using the commercial CFD package (FLUENT). The experimental torque measurements were used to validate the numerical simulations. The validated CFD model provided useful information regarding the impeller pumping capacity and flow pattern, the velocity profiles, the formation of cavern around the impeller, and the mixing time. CFD results show good qualitative as well as quantitative agreement with the experimental results and theory. The sizes of cavern measure using CFD were in good agreement with that calculated using Elson's model. The local mixing times predicted from CFD simulations agreed well with literature in a way that mixing times varied inversely with the cube root of the power consumed per unit volume of the solution. CFD under predicted the local mixing times measured using ERT by 11-47%.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".