Optimum waste heat recovery from diesel engines: Thermo-economic assessment of nanofluid-based systems using a robust evolutionary approach
Bibliographic record
Abstract
Nearly 30% of the input energy to a diesel engine is wasted through the exhaust gas; thus, considerable attention has been directed toward developing efficient heat recovery systems for these engines. Given the demonstrated ability of nanofluids to boost the heat transfer rate of heat exchangers, these heat transfer fluids merit consideration for use in diesel exhaust heat recovery systems. In this study, the effects of employing nanofluids on the optimum design of these systems are investigated. An existing heat diesel engine exhaust heat recovery system is modeled to work with Al 2 O 3 /water and a modified imperialist competitive algorithm is employed for the optimization. Seven variables consisting of five heat exchanger geometric characteristics together with nanoparticle volume fraction and coolant mass flow rate are considered as design variables. The heat exchanger cost and charging rate of the storage tank are optimization objectives, while the greenhouse gas savings of the heat recovery system are assessed for measuring the environmental impact of the energy recovery. The results indicate that the proposed approach can overcome the challenge of finding the near-optimal design of this complex system and using nanofluids enhances the performance of the heat recovery heat exchanger.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".