Order of Magnitude Scaling: A Systematic Approach to Approximation and Asymptotic Scaling of Equations in Engineering
Bibliographic record
Abstract
This work introduces the “order of magnitude scaling” (OMS) technique, which permits for the first time a simple computer implementation of the scaling (or “ordering”) procedure extensively used in engineering. The methodology presented aims at overcoming the limitations of the current scaling approach, in which dominant terms are manually selected and tested for consistency. The manual approach cannot explore all combinations of potential dominant terms in problems represented by many coupled differential equations, thus requiring much judgment and experience and occasionally being unreliable. The research presented here introduces a linear algebra approach that enables unassisted exhaustive searches for scaling laws and checks for their self-consistency. The approach introduced is valid even if the governing equations are nonlinear, and is applicable to continuum mechanics problems in areas such as transport phenomena, dynamics, and solid mechanics. The outcome of OMS is a set of power laws that estimates the characteristic values of the unknowns in a problem (e.g., maximum velocity or maximum temperature variation). The significance of this contribution is that it extends the range of applicability of scaling techniques to large systems of coupled equations and brings objectivity to the selection of small terms, leading to simplifications. The methodology proposed is demonstrated using a linear oscillator and thermocapillary flows in welding.
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 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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".