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Record W2142098279 · doi:10.1115/ht2012-58353

A Feasibility Study of Auxiliary HVAC Systems for Reducing Idling Time of Long Haul Trucks

2012· article· en· W2142098279 on OpenAlexaff
Colin B. Macdonald, R. A. Douglas, Ali Tamayol, Majid Bahrami

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsTruckAutomotive engineeringAlternatorBattery (electricity)Fuel efficiencyHVACEngineeringEnergy storageGas compressorAir conditioningEnergy consumptionWaste managementEnvironmental scienceElectrical engineeringPower (physics)Mechanical engineering

Abstract

fetched live from OpenAlex

The feasibility of several alternatives to long-haul truck idling are investigated. Battery Powered systems (BPS) where batteries are charged off the alternator is considered. Moreover, BPSs with various battery types are compared to determine which one would have the greatest impact on the fuel consumption and overall performance. In addition, applicability of thermal energy storage (TES) is studied as a means of cooling instead of a standard compressor air conditioner. Fuel cell powered systems (FCS) are investigated to replace batteries as a means of energy storage source on the truck. It is concluded that the most feasible method for truck idling reduction is BPS featuring lithium ion batteries while, BPS with lead-acid batteries is the cheapest solution.Copyright © 2012 by ASME

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.043
GPT teacher head0.318
Teacher spread0.275 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2012
Admission routes1
Has abstractyes

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