A Perspective on the Process and Turbomachinery Design of Compressed Air Energy Storage Systems
Bibliographic record
Abstract
Abstract The present paper will describe the Baker Hughes experience in the development of the turbomachinery equipment for Hydrostor’s Advanced Compressed Air Energy Storage A-CAES system. At the core of a CAES plant there is an air compressing system, followed by an air expander used to recover the stored energy. To achieve a reliable and effective solution, the expander is obtained from the architecture of Baker Hughes steam turbines, which was adapted to match the specific process needs. The criticalities that were addressed and solved to derive the expanders from the original steam turbine are presented. Specifically, the paper describes the activities performed to optimize the inlet and exhaust sections of each segment, the development of the blades for high atmospheric volume flow, and the implications that thermal transients have on the machine reliability. The inlet and exhaust sections were arranged according to the layout constraints, which were set to mitigate the effects of the thermal stresses and to reduce the weight to facilitate the machine transportation, while maintaining high aero-performance. As for the expander, a single-body configuration was selected to optimize capital expenditures and reduce leakage to atmosphere. A new set of blades derived from Baker Hughes’s Steam Turbine stages were developed. This new stage is characterized by rotating blades with high radius ratio; therefore, an optimization strategy was adopted to include the mechanical constraints from the beginning of the design cycle and obtain a final geometry that can be used with different flow path and operating conditions. Finally, the 3D full Navier Stokes Computational fluid dynamics analysis was used to assess the performance of the new stages in nominal and off-design conditions. A detailed analysis of the thermal transient of the expander parts with FEA methods was performed to assess the life expectations of the equipment. The FEA results are discussed in the paper to show the capability of the machine to sustain an extremely fast start up sequence. Compressor train is characterized by a multiple body configuration. A detailed optimization was performed to improve the efficiency and the availability of the selected solution. The low-pressure axial compressor discharge section has been optimized to reduce losses and a detailed study of rotor has been performed to evaluate the capability to withstand a high number of start and stop cycles.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".