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Record W7023919042

Rekuperaattorin optimaalinen mitoitus ja sopivan ripa- ja putkigeometrian tutkimus

2025· other· en· W7023919042 on OpenAlexaboutno aff

Bibliographic record

VenueLUTPub (LUT University) · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
FundersEuropean Commission
KeywordsDimensioningRecuperatorGas turbinesPressure dropHeat transferTurbineRenewable energyCogeneration
DOInot available

Abstract

fetched live from OpenAlex

This thesis is conducted in collaboration with Alfa Laval Aalborg Oy and the EU’s MARPOWER project. The project’s goal is to develop an innovative gas turbine energy conversion system to aid in the decarbonization of marine transportation. The aim of this thesis is to design, dimension, and optimize a recuperator for the gas turbine system. Five different fin and tube geometry options are considered for the recuperator’s configuration. To achieve the thesis goals, the LMTD method and appropriate correlations from the literature are utilized for heat transfer performance and pressure drop estimation for each of the five options. The thesis presents detailed dimensioning calculations for each option, including suitable material selections for the recuperator. The optimal option features continuous smooth fins and circular tubes in staggered arrangement, offering moderate heat transfer effectiveness, low pressure drops, compact overall dimensions, and cost efficiency compared to the other alternatives. Additionally, the study introduces the EU’s goals for the decarbonization of shipping and presents the MARPOWER project’s aims to contribute to these goals. It examines sustainable fuels such as hydrogen, renewable methane, methanol, and ammonia, which are intended for use in the developed gas turbine system. The fuel production methods, combustion properties related to emissions, and necessary modifications to the gas turbine system are also investigated.

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 categoriesMeta-epidemiology (narrow), Bibliometrics, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.031
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0140.009
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0080.010

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 head0.008
GPT teacher head0.202
Teacher spread0.194 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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