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Record W4236694707 · doi:10.1115/1.4050363

Experimental and Computational Analysis of Additive Manufactured Augmented Backside Liner Cooling Surfaces for Use in Micro-Gas Turbines

2021· article· en· W4236694707 on OpenAlexaff
Adamos Adamou, Colin Copeland

Bibliographic record

VenueJournal of Turbomachinery · 2021
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsSimon Fraser University
FundersEngineering and Physical Sciences Research Council
KeywordsCombustorMechanical engineeringHeat transferGas turbinesAir coolingTurbineActive coolingNuclear engineeringEnvironmental scienceMaterials scienceAutomotive engineeringProcess engineeringEngineeringMechanicsCombustionChemistry

Abstract

fetched live from OpenAlex

Abstract Augmented backside cooling refers to the enhancement of the backside convection of a combustor liner using extended heat transfer surfaces to fully utilize the cooling air by maximizing the heat transfer to pumping ratio characteristic. Although film cooling has and still is widely used in the gas turbine industry, augmented backside cooling has been in development for decades now. The reason for this is to reduce the amount of air used for liner cooling and to also reduce the emissions caused by using film cooling in the primary zones. In the case of micro-gas turbines, emissions are of even greater importance, since the regulations for such engines will most likely become stricter in the following years due to a global effort to reduce emissions. Furthermore, the liners investigated in this paper are for a 10 kWe micro-turbine, destine for various potential markets, such as combined heat and power for houses, electric vehicle hybrids and even small unmanned aerial vehicles. The majority of these markets require long service intervals, which in turn requires the combustor liners to be under the least amount of thermal stress possible. The desire to also increase combustor inlet temperatures with the use of recuperated exhaust gases, which in turn increase the overall system efficiency, limits the cooling effectiveness of the inlet air. Due to all these reasons, an advanced form of augmented backside cooling would be of substantial significance in such a system. Currently, some very simple designs are used in the form of straight plain fins, transverse strips, or other similar geometries, but the creation of high heat transfer efficiency surfaces in such small sizes becomes very difficult with traditional subtractive manufacturing methods. When using additive manufacturing though these types of surfaces are not an issue. This paper covers the comparison of experimental results with conjugate heat transfer computational fluid dynamics (CFD) models and empirical heat balance models for two different additively manufactured (AM) liner cooling geometries and an AM blank liner. The two cooling fin geometries include a rotating plain fin and an offset strip fin. The liners were tested in an AM-built reverse flow radial swirl stabilized combustion chamber at a variety of operating conditions. During the experiments, the surfaces were compared using a thermal camera to record the outer liner temperature, which was viewed through a quartz outer casing. The experimental results showed that the cooling surfaces were effective at reducing the liner temperatures with minimal pressure losses for multiple operating points. Those results were then compared against the conjugate heat transfer CFD models and the empirical calculations used to design the surfaces initially. From this comparison, it was noticed both the CFD and empirical calculations under-predicted the wall temperatures. This is thought to be due to inaccuracies in the predicted flame temperatures and the assumed emissivity values used to calibrate the thermal imaging camera. Further uncertainties arise from the assumption of a constant air and hot gas temperature and mass flow along the cooling surfaces and the lack of data for the surface roughness of the parts.

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 score0.247
Threshold uncertainty score0.599

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.014
GPT teacher head0.251
Teacher spread0.237 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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