MétaCan
Menu
Back to cohort
Record W4404350641 · doi:10.1115/pvp2024-123549

Steps in Application of the Alternative Nozzle Reinforcement Rules for Gasketed Plate Heat Exchangers

2024· article· en· W4404350641 on OpenAlexaff
Milan Nikic, Djordje Srnic

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsCanadian Natural Resources
Fundersnot available
KeywordsNozzleReinforcementHeat exchangerComputer scienceStructural engineeringMaterials scienceMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract This paper is a continuation of the work of previously developed alternative nozzle reinforcement rules for gasketed plate heat exchanger (GPHE) end plates. A staged approach has been adopted in the framework of development of the alternative rules. In the first stage, an analytical approach has been utilized using the basis of ASME BPVC Section VIII, Div. 1, Section UG-39, and the principles and data of a simply supported plate theory. In the second stage, Finite Element Analysis (FEA) has been conducted with an aim to expand and refine the alternative nozzle reinforcement rules for GPHE end plates. The philosophy that has been followed in development of the alternative rules assessed the impact of nozzle size and location on bending stresses in the GPHE end plates with the goal to optimize the design thickness of the GPHE end plates. Additionally, the limits of the design approach have been defined. The objective of this paper is to present application of the alternative nozzle reinforcement rules for GPHE in a concise step-by-step manner. Additionally, the paper will present the geometrical limitations for the application of the rules. Overall, the paper would conclude the work of development of the alternative nozzle reinforcement rules for GPHE by incorporating the results of the previous two stages of the work conducted and published.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.419
Threshold uncertainty score0.141

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 head0.013
GPT teacher head0.235
Teacher spread0.222 · 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 designBench or experimental
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 routes1
Has abstractyes

Explore more

Same topicMetallurgy and Material FormingFrench-language works237,207