Use of Triz SU-Field Models in the Process of Improving the Injector of an Internal Combustion Engine
Bibliographic record
Abstract
Abstract The article describes a method for analyzing and solving problem situations with the use of Su-Field models and 76 inventive standards. These tools are part of the “Theory of Inventive Problem Solving”. The author has presented the basic concepts of Su-Field models, including in the compilation of the most commonly used substances their fields and types of interactions in Su-Field models. The inventive standards have also been presented and grouped. Attempts have been made to solve two undesirable situations that occur during the operation of a complex technical system, which is the fuel injector of the self-ignition engine. Problem situations related to insufficient impact were modelled - too low tightening of the injector spring, and negative (harmful) interaction - erosive wear of the holes in the atomizer nozzle. Using the inventive standards of Class-1 and Class-2, general solutions to these problems have been found. After the transformation, exemplary detailed ways of solving the aforementioned problems have been presented in order to improve the design of the injector for these models. A summary and comments on the applicability of the presented methodology, regarding such complex technical systems, have also been presented.
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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 | 0.002 | 0.002 |
| 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 | 0.001 |
| 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".