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Record W2927253809 · doi:10.4271/2019-01-0916

Filter Element Robustness Strategy for Mud Ingestion

2019· article· en· W2927253809 on OpenAlex
John L. Emley, Venkatesan Shrevatsan, J. M. Nichols

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2019
Typearticle
Languageen
FieldEngineering
TopicBauxite Residue and Utilization
Canadian institutionsnot available
Fundersnot available
KeywordsRobustness (evolution)Computer scienceFilter (signal processing)Computer visionChemistry

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">Air filter elements have been around since the dawn of automotive development. The function of an air induction system and the filter element in particular is to remove particulates such as dust, soot, and relatively minor contaminants from the air flow. This protects the engine, turbocharger, and other components from wear. However, sometimes severe duty cycles may cause large amounts of dust, mud, and water to enter the air induction system (AIS). This can cause filter degradation and even rupture or deformation, leading to highly increased engine and turbocharger wear. One example of this extreme loading is the tar sands region of Alberta, Canada, where trucks can accumulate over 1000 pounds of mud on a vehicle during normal usage over a few weeks’ time. Significant amounts of this mud also get ingested into the AIS.</div><div class="htmlview paragraph">This study attempts to analyze different aspects of filter design to increase robustness to severe usage, particularly mud. Different aspects studied are filter element structure, filter element media, inlet location, and inlet blocking. Traditional ISO 5011 tests would not replicate the mud aspect that was sometimes seen in the field. To get a repeatable laboratory measurement, the authors developed a new mud cycle for testing that alternates a water spray and normal ISO 5011dust injection to accumulate mud on filter elements until rupture or deformation, causing a bypass. This test showed similar results of deformation as was seen in Alberta. Using this testing process, various filter elements with varying design attributes such as media type, filter element sizes etc. are tested and compared. This study compares different filter elements and comes up with a relation between the different filter design attributes and mud testing performance. Knowing the design factors that play a significant role in affecting the performance would help to design better, mud enduring filter elements in the future. Concurrently, virtual simulations are performed on a couple of filter elements with significantly different design and inlet area to help compare the flow dynamics of mud and water particles. Flow simulation studies also validate the obtained testing results and aid in providing more design recommendations.</div></div>

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.957
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.246
Teacher spread0.229 · 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