Performance Prediction of a Practical Low-Pressure-Ratio Highly Efficient Split-Cycle Recuperated Engine
Bibliographic record
Abstract
Split-cycle recuperated engines are promising candidates to compete with hydrogen-based fuel cells for high-duty cycles. They can potentially achieve similar, or even higher, efficiencies at the cost of historically cheap piston engines. However, existing approaches are either limited in efficiency or difficult to develop, mainly because of the challenges around the high-temperature expansion piston. This article presents a practical architecture of a low-pressure-ratio, recuperated split-cycle engine with a contact-free expansion piston using labyrinth seals supported by thermodynamics and numerical modeling. The engine operates under a regenerative dual Brayton cycle to combine the benefits of constant pressure heat recuperation and near-constant volume combustion. Thermodynamics results reveal pre-compressing the residual mass in the expansion cylinder before intake is crucial. A 0D transient model integrating main losses is implemented to explore the design space and maximize efficiency through a numerical design of experiments. The blowby in the expansion cylinder is the main loss but remains acceptable for relatively tight clearances. An indicated efficiency of 60% is predicted for a cycle pressure of 20 bar and an expansion piston exhaust temperature of 1250 K. The predicted indicated power density of 6.5 kW/L is relatively low but in the range of micro-combined-heat-power diesel engines.
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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".