Physics Informed Machine Learning Model for Local Creep Prediction in Turbine Blade
Bibliographic record
Abstract
Abstract Gas turbine blades are an essential part of modern power generation systems, where they operate under high temperature, pressure and centrifugal load for prolonged period. Over time, these operating conditions lead to creep deformation, which can cause significant damage to the turbine. Conventionally, Creep assessment is carried out using Finite Element Method (FEM) which is a time-consuming process. Various material models namely strain hardening, time hardening, Norton Bailey Law, Garofalo Law etc. are used to describe the creep response of a given material, particularly metals and crystalline solids. The motivation behind this work is to reduce the time to run creep assessment. This paper focuses on exploring hybrid machine learning techniques to predict the local creep strain in turbine blades. This hybrid machine learning model can prove to be effective for quick sensitivity checks, decision making on service request and to study manufacturing related deviations in design. Multiple approaches were evaluated such as neural network, physics informed neural network, stress prediction using machine learning followed by creep prediction using physics-based formulation, ensemble neural network, generalized linear model (GLM) and time series method. Data driven approach significantly reduced the creep run time from few days to minutes and therefore provide flexibility in terms of analysing multiple models with different boundary conditions, material cases etc.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".