Reliable Prediction of Under Platform Damping for Turbine Blade at Variable Operating Condition
Bibliographic record
Abstract
Abstract Gas turbines (GT) are one of the prime sources of power generation because of its high efficiency, reliability and availability. GT need to operate at higher temperature and pressure to achieve the efficiency and power requirement for 50/60 Hz grid. Turbine blades are one of the most critical components of gas turbine because of its severe operating conditions such as high temperature, pressure and centrifugal load. Because of these loads along with the operational requirement, turbine blades are prone to low cycle fatigue (LCF), creep, oxidation and high cycle fatigue (HCF). The HCF safe operation is one of the critical requirements for rotating turbine blades. For low engine orders (EO) of flow excitation this is achieved by optimum design of a under-platform damper (UPD) and accurate prediction of damping behavior at various operating conditions. UPD works on the principal of frictional dissipation and is actively used across many heavy-duty, industrial and aero gas turbines for HCF safe operation of turbine blades. In general, non-linear dynamic simulations of the rotating turbine blade with a damper require a numerical tool which combines the contact physical data and forced blade and damper vibrations while linearizing non-linear frictional forces with Harmonic Balance Method. In this work, accurate prediction of the damping behavior and its correlation with the field and test observation is compared for some of the heavy-duty GT frames. It was observed that the damped frequency and resonance response predictions align well with the experimental data during engine tests for both front and rear stage turbine blades. This was validated not just for lower order modes (LOM) but also for higher order modes (HOM) regarding the design strategies of resonance-proof and resonance-free vibrations, respectively. The higher accuracy in analytical prediction results in reliable tuning of the turbine blade at various operating conditions, such as uploading from FSNL (Full Speed No Load) to FSFL (Full Speed Full Load) engine operations. The study concludes the effectiveness of Siemens Energy tools and methods in accurate prediction of non-linear blade and UPD dynamic behavior, resulting in design of gas turbines with high reliability and availability of interest of end users.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".