Aboveground compressed air energy storage systems: Experimental and numerical approach
Bibliographic record
Abstract
• Instrumented experimental workbench for aboveground CAES is studied and modeled. • Calibrated model is presented having every mean absolute percentage error <4 %. • Addition of a TES system to recover waste heat is proven to be relevant. • Round trip efficiency of 16% is achieved through triple stage expansion simulation. The transition towards renewable energy sources necessitates reliable energy storage solutions to address the intermittency of solar and wind power. Among these solutions, compressed air energy storage technology holds promise, particularly in aboveground installations. While underground compressed air energy storage systems have shown potential at the grid scale, the focus on smaller aboveground installations is increasing due to their flexibility and higher energy density, yet they remain less mature and require further investigation. This research presents a comprehensive analysis of an aboveground system using both experimental data and numerical simulations, develops numerical model with real air properties and employs a quasi-steady-state approach. Experimental data calibration ensured model accuracy with a mean absolute percentage error below 4.0%, and parametric analysis revealed significant variations in round-trip efficiency, notably improving from 4.5% to 16.0% by increasing turbine stages from one to three with preheating. Further analysis confirmed the feasibility and relevance of integrating thermal energy storage into the system, aligning with the adiabatic concept, where compression heat is stored for subsequent expansion preheating, thereby enabling fully heated expansion using a three-stage turbine.
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) | 0.000 | 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) | 0.002 | 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".