Aero-Thermal Optimization of a Hybrid Roadster Tricycle Using Multidisciplinary Design Optimization Tools
Bibliographic record
Abstract
To reach the goal of continuous improvement on automotive vehicles, their overall design strategy needs to be reconsidered. Hence, the future design will have to use global approaches like those developed in the aerospace industry where optimization of all interacting fields is performed jointly. This strategy has been applied to the development and optimization of a hybrid roadster aero-thermal management as part of a major Automotive Partnership Canada (APC) project1. The study presented herein seeks the best compromise between the vehicle aerodynamic drag and the cooling efficiency for the internal combustion engine (ICE) and the electric motor. The optimization of the heat exchanger position is first achieved followed by a multidisciplinary design optimization (MDO) approach with three main steps: first, a design of experiment (DOE) involving parametric CAD model generation, steady state CFD calculations and a heat exchanger optimization loop; secondly, approximations of response surfaces methods; finally, multi-objective optimization on the response surfaces using genetic algorithms and particle swarms. The study is constrained to use the automotive manufacturer’s software and to consider the vehicle environment without bringing significant modifications on non-thermal/aerodynamic parts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".