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Record W4205877971 · doi:10.1504/ijvd.2021.10044412

Design optimisation of a hybrid electric vehicle cooling system considering performance and packaging

2021· article· en· W4205877971 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

VenueInternational Journal of Vehicle Design · 2021
Typearticle
Languageen
FieldEngineering
TopicElectric and Hybrid Vehicle Technologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsEngineeringProcess (computing)Automotive engineeringConceptual designSoftwareSystems designElectric vehicleSystems engineeringMechanical engineeringComputer sciencePower (physics)

Abstract

fetched live from OpenAlex

Optimal system design at the conceptual functional level, i.e., before the embodiment of the functions is determined in detail, focuses primarily on performance. Embodiment determines the geometry and position of subsystems and components, which must be packaged usually within strict spatial envelopes to achieve compactness or other external requirements such as styling. Packaging objectives and constraints may therefore compete with performance ones, leading to redesign and costly delays if these conflicts are not addressed early in the design process. This paper presents a design optimisation framework for coupled performance and packaging problems. Using the cooling system for a heavy duty tracked series hybrid electric vehicle (SHEV) as an example, we demonstrate the framework combining commercial CAD software with optimisation tools and including pipe routing which is a basic requirement in many mechanical systems.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.235
Threshold uncertainty score0.550

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.020
GPT teacher head0.219
Teacher spread0.199 · 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