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Record W4388874110 · doi:10.1115/detc2023-114957

Frequency Response Based Optimization of an Aircraft Hydraulic Pump Support Structure

2023· article· en· W4388874110 on OpenAlexaff
Simon Kersten, Chris K. Mechefske

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicProbabilistic and Robust Engineering Design
Canadian institutionsQueen's University
Fundersnot available
KeywordsVibrationBandwidth (computing)Frequency responseReduction (mathematics)Finite element methodOptimal designFrame (networking)Structural engineeringComputer scienceControl theory (sociology)AcousticsEngineeringMathematicsMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

Abstract Free-size optimization is an effective method for determining the optimal thickness of the elements in a finite element model given a set of constraints and objectives. This work aims to show that size optimization can be used as a tool to reduce the structural vibrations transmitted from non-structural components on-board an aircraft into the cabin. A test structure of a hydraulic pump support frame is analyzed using a frequency response analysis. The output velocity is measured for three independent dynamic input loads across a 20–4000 Hz bandwidth. The maximum frequency response velocity for each input load was used to define the upper bounds of the velocity for the optimized model. The design space had a minimum thickness equal to the original thickness and a maximum thickness of 1.5 times the original thickness. The objective was to determine the optimal locations for new material to be added, so that the original design was not significantly changed. This was done to ensure the structural integrity of the frame and increase the ease of implementing the design while still reducing the transmission of structural vibrations. The optimized model showed an average reduction of 6% in the maximum frequency response velocity for the three loading directions, while increasing the mass by 9%.

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)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.738
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.054
GPT teacher head0.320
Teacher spread0.267 · 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.

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
Published2023
Admission routes1
Has abstractyes

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