Design and Evaluation of a Conceptual Zero-Emission Truck Model Considering Aerodynamic Efficiency
Bibliographic record
Abstract
Emerging zero-emission-powertrain concepts are providing opportunities to re-shape heavy trucks for improved aerodynamic performance. To investigate the potential for energy savings through aerodynamic improvements, with a goal to inform operators and regulators of such benefits, a multi-phase project was initiated to design and evaluate aerodynamic improvements for Class 8 tractor-trailer combinations. While the focus was battery-electric and hydrogen-fuel-cell powered trucks, improvements for internal-combustion powered trucks were also examined. Previously-reported activities included a scaled-model wind-tunnel test that demonstrated the potential for up to 9% drag reduction from simple shape adaptations, with a follow-up CFD study providing guidance towards further optimization. This paper presents wind-tunnel-test results using a high-fidelity 30%-scale model of a new aerodynamic tractor concept, with comparison to a conventional North American Class 8 tractor with a modern aerodynamic package and identical wheelbase. The study included testing of the new tractor concept along with individual parts such as air dams, under-body panels, grill/cooling-flow configurations, mirrors, wheel fairings, cab extenders, etc. The battery-electric variant of the new tractor provided a 16% drag reduction compared to the conventional truck model when using the same trailer configuration. When paired with a low-drag-trailer concept, the new tractor-trailer combination demonstrated up to 41% drag reduction compared to the conventional tractor with a standard box-van trailer. This configuration is shown to be nearly insensitive to crosswinds with a wind-averaged drag coefficient of 0.34. Examining additional cooling-flow configurations necessary for hydrogen-fuel-cell trucks showed up to 3% increase in drag associated with the extra cooling drag. Additionally, the new tractor-shaping concept applied to a conventional internal-combustion-powertrain arrangement generated 8% drag reduction compared to the conventional tractor.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".