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Development of a Diesel Engine Exhaust Gas Heat Recovery System Based on the Thermodynamic Rankine Cycle

2025· article· W7127633010 on OpenAlexaboutno aff
A T Galiakbarov, A V Boldyrev, S. V. Boldyrev, Radik Rakhimov, Lenar I. Fardeev, Danis Israfilov

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicThermodynamic and Exergetic Analyses of Power and Cooling Systems
Canadian institutionsnot available
Fundersnot available
KeywordsOrganic Rankine cycleTurboexpanderExhaust gas recirculationExternal combustion engineThermodynamic cycleGas compressorDiesel engineHeat recovery ventilationWaste heat recovery unitRankine cycle

Abstract

fetched live from OpenAlex

This paper presents the development of a waste heat recovery system for the exhaust gases of a KAMAZ diesel engine, based on an organic Rankine cycle utilizing R11 refrigerant. Using similarity theory and the parameters of a prototype turbine, the flow passages of an axisymmetric Laval nozzle, a rotor wheel, and a diffuser unit of an axial impulse turbine were designed in the Kompas-3D software environment. A mathematical model was constructed and validated in the STAR-CCM+ software, followed by simulations of supersonic compressible gas flow and heat transfer within the designed turbine, using the finite volume method. The calculated theoretical mechanical output power was 4.906 kW with an efficiency of 49.1%. Compared to the prototype, the proposed recovery system demonstrates greater safety for mobile applications due to the non-flammability of the working fluid and operating pressures that are four times lower. Furthermore, the turbine's shaft rotational speed is five times lower, which significantly simplifies the design of the bearing assembly.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.006
GPT teacher head0.201
Teacher spread0.196 · 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 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
Published2025
Admission routes1
Has abstractyes

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