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Record W4255128606 · doi:10.3384/lic.diva-99382

Design Automation of Complex Hydromechanical Transmissions

2013· book· en· W4255128606 on OpenAlexaff
Karl Pettersson

Bibliographic record

VenueLinköping University Electronic Press eBooks · 2013
Typebook
Languageen
FieldEngineering
TopicElectric and Hybrid Vehicle Technologies
Canadian institutionsEngineering Link (Canada)
Fundersnot available
KeywordsAutomationEfficient energy useEngineeringAutomatic transmissionTransmission (telecommunications)Process (computing)Energy consumptionPower transmissionEnergy modelingTorqueComputer scienceControl engineeringAutomotive engineeringPower (physics)Mechanical engineeringElectrical engineering

Abstract

fetched live from OpenAlex

This thesis proposes an automated methodology for the design of complex multiple-mode hydromechanical transmissions.High fuel prices and strict emission regulations are today drivers of the development of new fuel-efficient drive transmissions for construction machinery.Hydromechanical transmissions have high energy efficiency and a wide torque/speed conversion range.They are today strong candidates to replace the fuel-thirsty torque converters conventionally used in heavy construction machines.The trend towards more complex transmission architectures increases the need for more sophisticated product development methods.Complex multiple-mode transmissions are difficult to design and prototype and can be realised in a great number of different architectures.This increases the need for reliable concept evaluation in early design stages.The design of the transmission is also strongly coupled to its energy consumption and for a fair comparison between transmission concepts optimal designs are necessary.Design automation and optimisation with detailed simulation models can support the industrial engineer in the design task and increase the available knowledge early in the design process.The proposed methodology uses simulation-based optimisation to design the transmission for a specific vehicle application.Various aspects of the transmission's characteristics may be targeted, although energy efficiency is in great focus in this work.To evaluate the energy efficiency, the transmission designs are simulated using backward-facing simulations with detailed power loss models.The methodology is applicable for designing the drive transmissions of construction machines and other mobile working vehicles such as agricultural machines, forest machines and mobile mining equipment.i w Weight factor [-] xii Little work has been done on the design process of HMTs.In his dissertation, Erkkilä [2] proposed a design methodology where a preliminary design is iteratively redesigned by the engineer until the technical requirements are fulfilled.Volpe et al.[3] suggest an optimisation-based design process for power-split transmissions using kinematic models of the transmission.The objective was to avoid recirculative energy at the most frequently used operating points of the vehicle's working cycle.In this work, power loss models are not used; instead it is assumed that the operating range with the most recirculating power has the worst efficiency.Macor and Rosetti [4] also applied optimisation on the design of basic hydromechanical power-split concepts.The proposed design methodology uses more detailed simulation models of the components.The single objective was to minimise the consumed energy from a zero to a maximum speed acceleration and does not take into account the operating behaviour of the vehicle.It is highlighted that optimisation

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.025
GPT teacher head0.185
Teacher spread0.160 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations8
Published2013
Admission routes1
Has abstractyes

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