Facilitating New Technology Introduction through Monitoring and Controlling System Constraints
Bibliographic record
Abstract
High value systems are required to be able to adapt and cope with change, as the requirements for flexibility in system design mean that not only do systems need to be capable of adapting to evolving design environments, but also evolving design requirements. Current complex engineering systems are being designed for increasingly longer design lifetimes, driven by (and often justified by) economic analysis. While this has short term benefits, it overlooks the issues surrounding obsolescence before the anticipated retirement date due to rapid technology advancement. In many cases, the initial circumstances from which the original system requirements were derived have changed or been modified, and as such flexibility is a key property which should be embedded in so-called ‘high-value’ assets. When considering modifications to system components to respond to these changing needs, it is critical to have a clear and complete understanding of the implications of such modifications not only on the local system performance, but on all the systems which reference it through data exchange. The current work proposes a methodology by which the design space may be investigated in order to determine instances in which no viable design solution exists, and facilitates a method by which constraints may be relaxed and/or shifted in order to expand the allowable design space. This is of specific interest to cases in which new technology is introduced into an existing system, and this will be specifically addressed with regards as to how ascertain the feasibility of incorporating advanced technology into existing system architectures. The paper presents the manner in which the implications of aircraft system upgrading can be assessed, and how the identification of those linkages which are impacted by the introduction of a new system may be used in order to ensure that new systems will operate seamlessly with existing system components, or facilitate a shift in the design space to enable new operating regimes.
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) | 0.000 | 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 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".