Physics-based controller design to reduce data dependencies and engine calibration workload
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Model-based controls development has the potential to reduce cost and development time and helps to improve quality of the control design. It helps creating an infrastructure more robust to unforeseen changes to the development cycle plan. More upfront effort is required, including physical parameter characterization, model development, and “potentially” increased demands for processing power and memory, but the longer term benefits outweigh these costs. On top of that, the language of physics can be used to characterize a system by reducing its behavior to a set of differential equations. The main benefit is a potentially significant reduction in calibration effort, since there will be less calibratable parameters, tables and logic operators. Calibrations are reduced to dynamic system parameterization, thus reducing dependencies on empirical data during the control design. This also allows for future fidelity improvements without tearing up the architecture. These benefits find a roadblock in hardware constraints on embedded applications, where increased complexity of the dynamic equations might affect real time capabilities. This paper explores these benefits in the development of a software module comparing the calibration effort on both the physics based and the empirical data based applications. The particular system under investigation is the embedded physics model for boil-off phenomenon that occur in internal combustion engines (ICEs) in flex-fuel vehicles (FFV) applications, used during the compensation of injected fuel. The ethanol vaporization from the oil sump was parametrized using physics modeling, facilitating its development and understanding. The model for fuel drainage to the sump is out of the scope for this paper.</div></div>
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".