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Record W4413218306 · doi:10.1115/gt2025-151619

Sensitivity Analysis of Phase Change Modelling for Non-Ideal Fluids in Turbomachinery Applications

2025· article· en· W4413218306 on OpenAlexaboutno aff
Katharina Tegethoff, Sebastian Schuster

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicnanoparticles nucleation surface interactions
Canadian institutionsnot available
Fundersnot available
KeywordsSensitivity (control systems)NucleationTurbomachineryCondensationMechanicsStatistical physicsPhase changeThermodynamicsNozzleTwo-fluid modelRange (aeronautics)Supersonic speedMaterials sciencePhysicsEngineering

Abstract

fetched live from OpenAlex

Abstract Investigating phase change phenomena with numerical methods reveals a strong dependence of the results on the selected model equations. The phase change from gaseous to liquid, referred to as condensation, is primarily determined by the physical processes of nucleation and droplet growth. A large number of different model equations for both processes can be found in the literature. These are derived based on the law of ideal gases and empirical factors, whose range of definition was determined for the medium water for historical reasons. Despite this fluid-specific adjustment, the results show a strong model sensitivity even for water. This effect is amplified for other fluids, such as CO2, which are additionally characterised by a significant non-ideality of their thermophysical properties. To ensure suitable modelling for these fluids, it seems indispensable to first quantify which model parameters show a dominant influence on the results. The present study contributes to this by systematically varying different model approaches and parameters. For each of the fluids, water, and CO2, a test case of a supersonic flow through a Laval nozzle geometry is chosen. The sensitivity study includes a variation of the calculation approaches for the critical energy barrier, which is decisive for nucleation, the droplet growth rate, and various types of modelling of the droplet size distribution. An additional comparison of two fundamentally different numerical schemes also makes it possible to relate the variation of the results due to different physical model approaches to the general numerical variance. The results for CO2 show a sensitivity comparable to that of water concerning the type of droplet size distribution. For the phase change modelling, however, a broadening of the range of results compared to water is apparent. This also shows a dependence on the non-ideality of the fluid states. The results presented in this paper allow a systematic estimation of the uncertainties associated with modelling the phase change when designing turbomachinery operated with CO2. The analysis also indicates the need for experimental validation of the condensation modelling for non-ideal fluids.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score0.906

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.037
GPT teacher head0.311
Teacher spread0.275 · 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 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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