On the Modular Design Application for the Gas Turbine Sector: A Performance Optimization Approach in the Context of Industry 4.0
Bibliographic record
Abstract
Production changes enabled by Industry 4.0 (I4.0) allow industries to respond to customer needs in a much more precise and agile manner. It also permits companies to focus on the development of sustainable and more efficient solutions. The energy sector is still lacking progress in this context, however, and the implementation of I4.0 and modularity could help to solve such issues. The present research study contributes to addressing the research gap in I4.0 implementation in the Gas Turbine (GT) sector by developing a design application for modular GT configuration. The main objective of the developed modular design application (MDA) is to facilitate the relationship between customer and engineer by providing an accessible application (program), including pre-designed heat cycles (HCs), that proposes optimized modular solutions, according to customer requirements, using simulation. Indeed, this study presents the functioning of the novel application, the different deployed components and their variables, such as the compressor efficiency, heat exchangers, or turbine stages, and the decision variable, e.g., the costs of generated energy. Simulations and comparisons using reported HCs in the literature have been performed to validate the accuracy of the simulation processes. Finally, a study case is presented, placing the MDA in an industrial context to illustrate its benefits and to provide solutions for GT modularity. It is concluded that the developed MDA correctly simulates HCs and enables a first step towards modular HC design. Indeed, the proposed MDA architecture allows for continuous improvement and expansion, e.g., by the addition of HC-related components or the integration of different entry variables, such as the company’s financial scope, world location, desired power, and available components.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".