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Record W2149669086 · doi:10.2514/6.2000-563

A study of optimal cooling strategies in thermal processes

2000· article· en· W2149669086 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.

Bibliographic record

Venue38th Aerospace Sciences Meeting and Exhibit · 2000
Typearticle
Languageen
FieldEngineering
TopicAdvanced Numerical Methods in Computational Mathematics
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsCoolantSensitivity (control systems)Broyden–Fletcher–Goldfarb–Shanno algorithmBlock (permutation group theory)Finite element methodComputer scienceFlow (mathematics)ThermalMathematical optimizationMechanical engineeringMathematicsMechanicsThermodynamicsEngineeringElectronic engineeringPhysicsGeometry

Abstract

fetched live from OpenAlex

Convection is often used as a means for cooling parts in thermal processes. In this paper. we present . a continuous sensitivity equation (CSE) method to assess strategies for enhancing the cooling of a block submersed in a channel of coolant (fluid). A strat- e,gy studied involves introducing a plate into the flow to deflect cooler fluid towards the block. Thus, op- timal design techniques are used at the conceptual design level in order to see if this is a feasible design strate3. Such optimization problems are solved using this CSE coupled with a BFGS/trust-region optimiza- tion algorithm. The CSE. which describes the influ- ence of the design (shape) parameters on the flow: leads to an efficient method for calculating the gra- dient. However. in order to be an effective tool: n-e need to find an estimate of how accurate the func- tion and gradient calculations are. To achier-e this, the coupled flow and sensitivity equations are solved using an adaptive finite element method. Our study includes a numerical verification

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score0.356

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.027
GPT teacher head0.306
Teacher spread0.279 · 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