The influence of gas heating on the calculated supersonic parameters of gas-powder flow at slag blower in the converter. Message 1
Bibliographic record
Abstract
The article shows that one of the priority tasks in steel production technology is to increase the durability of the lining of oxygen converters. It is shown that the slag blower is a radical way to increase the durability of the lining of oxygen converters. When blowing the melt with oxygen, the MgO content in the slag is 6-8%. To increase the chemical affinity of the slag and the lining, it is proposed to blow the converter slag with jets of nitrogen or powder gas. To achieve optimal chemical affinity of the lining and slag, it is recommended to modify the latter by increasing the content of magnesium oxide in it to 12-14%. Increased durability of the lining makes it possible to solve the problem of replacing wasteful wastes of coal slag and changing environmental concerns on the excess media. It is shown that an attempt to increase the efficiency of modeling very complex processes of interaction of supersonic jets with converter slag has not yet been solved a. A critical analysis of existing methods for constructing mathematical models based on the use of known gas-dynamic laws of interaction of free turbulent jets with the melt is carried out. It has been established that only supersonic jets with their characteristic shock-wave structure always enter the slag melt. An integral method for calculating the parameters of a gas-powder flow in a supersonic nozzle taking into account the powder concentration is presented. It is determined how heating of the gas suspension affects the required pressure in front of the nozzle block, the speed and flow density in the outlet section of the supersonic nozzle. The gas dynamics system is connected to the heat exchange of the flowing gas-dispersed mixture in the Laval expansion nozzles to establish the heating effect in front of the nozzle block of the two-phase gas suspension to a temperature that will ensure The increase in the fluidity of the gas flow is doubled and its kinetic energy is increased threefold. The method of expanding the parameters of the gas-powder flow between the Laval expansion nozzle involves the infusion of 10 physical effects into 14 parameters of the gas-powder flow. It is shown that numerical expansions allow the tuyere body to be used as a heat exchanger for heating the gas suspension, as well as for increasing the pressure of the gas-powder jet that flows from the nozzles
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".