Use of Triz SU-Field Models in the Process of Improving the Injector of an Internal Combustion Engine
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it