Techno-Economic Feasibility Study of Earth Air Heat Exchangers for Gas Turbine Inlet Air Cooling
Bibliographic record
Abstract
In response to pressing energy crises and environmental concerns, enhancing the performance of existing energy conversion systems, such as gas turbines, and tapping into renewable resources have become critical imperatives. Escalating ambient temperatures poses a notable challenge, hampering the efficiency of gas turbines. This study rigorously explores the feasibility of earth-to-air heat exchangers (EAHEs) as a pragmatic solution to cool gas turbine inlet air. The primary objective is an exhaustive techno-economic analysis evaluating the long-term operational viability of EAHE systems. Employing the Taguchi method, the study optimizes the system geometry and pipe arrangement, focusing on four key control factors: pipe diameter, spacing, length, and depth. The pivotal optimization criterion is the levelized cost of energy (LCOE), encompassing thermal performance and economic viability. The net present value (NPV) and Discounted Payback Period provide supplementary insights into the economic prospects. Integral to the study is a novel and intricate hybrid analytical-numerical model that simulates the long-term transient operation of EAHE. Findings from a detailed case study unveil an optimized scenario yielding an LCOE amounting to 0.071 $/kWh, accompanied by a substantial NPV of 4.5 million dollars.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".