Performance of Five Different Natural Gas and Hydrogen Blending Mixer Designs via CFD
Bibliographic record
Abstract
Abstract The aspiration for blending hydrogen (H2) into natural gas (NG) in gas transmission systems is high and is happening globally. However, the mechanics and details of blending the two streams are not well developed or perfected. There is a need to arrive at the best technique and approach to achieve perfect blending to minimize the potential adverse impact on the operation of downstream facilities as well as on the end-users. The challenge is primarily driven by the fact that NG and H2 have vastly different properties, principally densities, that may lead to possible stratification, short circuiting, and pockets of undesirable high concentration of H2 in the blended stream. The paper documents Computational Fluid Dynamics (CFD) simulation results conducted on five different concepts of mixer/blending designs. These mixer designs are: i) single or multiple side entries, ii) dual spiral ribbon (DSR) type mixer, iii) venturi mixer, iv) hybrid mixer of DSR inside a venturi, and v) NC5 perforated tube bundle type mixer. An example of an NPS 12 (DN300) ultrasonic meter run with an NPS 20 (DN500) header was assumed throughout the analysis. It was found that the venturi mixing concept with a single side entry is the optimum design due to its simplicity, cost effectiveness, and relatively low pressure drop. With this simple design, 99% mixing efficiency is achieved within 13D at maximum flow, where D is the main header diameter downstream of the mixing station. The pressure drop coefficient for this design is estimated to be approx. 3.1, which amounts to ∼6 kPa at maximum flow, which is relatively low. However, mixing will halt at coefficient of variance = 0.2 (80% mixing efficiency) at very low flow rate of a turndown ratio of 20:1. Final selection of a mixer design from the five designs investigated depends on the tradeoff between mixing efficiency, pressure drop and cost.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".