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Record W2968256635 · doi:10.1177/0306419019868801

A systematic approach for modeling multi-physics systems

2019· article· en· W2968256635 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Journal of Mechanical Engineering Education · 2019
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDomain (mathematical analysis)Domain modelComputer scienceContext (archaeology)Focus (optics)Physical systemRealization (probability)Domain engineeringArtificial intelligenceDomain knowledgePhysicsMathematicsSoftware

Abstract

fetched live from OpenAlex

An engineering system may consist of several different types of components, belonging to such physical “domains” as mechanical, electrical, fluid, and thermal. It is termed a multi-domain (or multi-physics) system. In developing an analytical model of a multi-physics system, it is advantageous to use “unified” and “integrated” procedures for formulating different physical domains while including inter-domain dynamic interactions, in a systematic manner that will lead to a “unique” (single) model having physically meaningful variables. Such a model formulation is the focus of the present paper. In this context, a generalized method to incorporate impedance (e.g., the analogous use of mobility in the mechanical domain), exporting the modeling procedures from one domain into a different domain, conversion of a system in one domain into another domain, and realization of an equivalent single-domain model for a multi-domain system are addressed. This knowledge is useful in education, research, and application of multi-physics models of engineering dynamic systems. Illustrative examples are provided to clarify the presented approaches. This paper assumes a knowledge in linear graphs and some background material, as presented in the prior work of the author. The relevant nomenclature is listed at the end of the paper.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.864
Threshold uncertainty score0.659

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.257
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it