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Record W2232233059 · doi:10.1115/1.4031510

Thermodynamic Analysis of Hydraulic Braking Energy Recovery Systems for a Vehicle

2015· article· en· W2232233059 on OpenAlexaff
Satyam Panchal, İbrahim Dinçer, Martin Agelin‐Chaab

Bibliographic record

VenueJournal of Energy Resources Technology · 2015
Typearticle
Languageen
FieldEngineering
TopicElectric and Hybrid Vehicle Technologies
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsAccumulator (cryptography)Hydraulic pumpHydraulic machineryHydraulic motorEnergy recoveryHydraulic accumulatorVariable displacement pumpEnvironmental scienceHydraulicsAutomotive engineeringControl theory (sociology)EngineeringEnergy (signal processing)Mechanical engineeringComputer sciencePhysics

Abstract

fetched live from OpenAlex

In this study, a thermodynamic analysis of a hydraulic braking energy recovery system used in vehicles is performed for newly developed systems. The present system is related to the field of energy efficiency in vehicles. The energy recovery system comprises a first pump, a hydraulic accumulator, and a hydraulic motor. The first pump is a variable displacement hydraulic pump (VDP). The hydraulic accumulator is connected to the first pump which operates to store hydraulic fluid under pressure. The hydraulic motor is hydraulically connected to the accumulator to receive hydraulic fluid. The motor is adapted to drive a second hydraulic pump, which is hydraulically connected to the auxiliary system, using hydraulic energy stored in the accumulator. The overall charging and discharging efficiencies, and the overall system efficiency is calculated and presented in this paper. For the purpose of the analysis, EES (engineering equation solver) is used. In addition, parametric studies are performed to observe the effects of different substantial parameters, namely, the inlet pressure and temperature of the accumulator, and the reference environment temperature, in order to investigate the variations in the system performance in terms of the efficiencies. Two systems are developed and it is found that the charging and discharging efficiencies for one system are 83.81% and 87.73%, while for the other system the charging and discharging efficiencies are 81.84% and 85.67%, respectively.

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 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 score0.148
Threshold uncertainty score0.666

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.010
GPT teacher head0.209
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations24
Published2015
Admission routes1
Has abstractyes

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