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Record W4405573643 · doi:10.3390/en17246397

A New Approach for Measuring and Comparing the Energy Performances in Hydraulic Systems

2024· article· en· W4405573643 on OpenAlexafffund
Gustavo Koury Costa, Nariman Sepehri

Bibliographic record

VenueEnergies · 2024
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEfficient energy useEnergy (signal processing)Variety (cybernetics)Computer scienceElectronic circuitMeasure (data warehouse)Hydraulic machineryReliability engineeringPower (physics)Industrial engineeringEnergy managementControl engineeringEngineeringMechanical engineeringElectrical engineeringMathematicsData miningArtificial intelligence

Abstract

fetched live from OpenAlex

The need for “green” energy management has sparked discussions on developing hydraulically actuated systems that are more efficient, consume less power, and are consequently more environmentally friendly. Numerous scientific papers and extensive research have been dedicated to this important topic. However, due to the variety of designs and different modes of operation, there is still no unified method to compare different systems with respect to energy management. In fact, terms such as “efficiency” and “energy regeneration” are often loosely defined and need to be revisited periodically. In this paper, we propose a new, physically meaningful indicator called the “Cyclic Performance Ratio” to measure the energy performance of hydraulic circuits. The goal is to establish a universal method that can be reliably used to compare industrial hydraulically actuated machines with respect to their energy efficiencies. Specifically, we aim to (a) precisely define the three possible modes of operation of hydraulic circuits, (b) establish the correct conditions under which the usual definition of efficiency can be applied in hydraulic circuits, (c) demonstrate that the current concept of efficiency cannot be used for operations where load energy is recovered, and (d) argue that the newly defined performance indicator correctly accounts for energy load recovery. Some examples are provided to show how the new indicator can be used with confidence in various applications.

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 score0.368
Threshold uncertainty score0.323

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 head0.028
GPT teacher head0.211
Teacher spread0.182 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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