Automotive Underhood Thermal Management Analysis Using 3-D Coupled Thermal-Hydrodynamic Computer Models: Thermal Radiation Modeling
Bibliographic record
Abstract
The goal of the radiation modeling effort was to develop and implement a radiation algorithm that is fast and accurate for the underhood environment. As part of this CRADA, a net-radiation model was chosen to simulate radiative heat transfer in an underhood of a car. The assumptions (diffuse-gray and uniform radiative properties in each element) reduce the problem tremendously and all the view factors for radiation thermal calculations can be calculated once and for all at the beginning of the simulation. The cost for online integration of heat exchanges due to radiation is found to be less than 15% of the baseline CHAD code and thus very manageable. The off-line view factor calculation is constructed to be very modular and has been completely integrated to read CHAD grid files and the output from this code can be read into the latest version of CHAD. Further integration has to be performed to accomplish the same with STAR-CD. The main outcome of this effort is to obtain a highly scalable and portable simulation capability to model view factors for underhood environment (for e.g. a view factor calculation which took 14 hours on a single processor only took 14 minutes on 64 processors). The code has also been validated using a simple test case where analytical solutions are available. This simulation capability gives underhood designers in the automotive companies the ability to account for thermal radiation - which usually is critical in the underhood environment and also turns out to be one of the most computationally expensive components of underhood simulations. This report starts off with the original work plan as elucidated in the proposal in section B. This is followed by Technical work plan to accomplish the goals of the project in section C. In section D, background to the current work is provided with references to the previous efforts this project leverages on. The results are discussed in section 1E. This report ends with conclusions and future scope of work in section F.
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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 | 0.000 | 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) | 0.002 | 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".