Parametric Importance Analysis and Design Optimization of a Torque Converter Model Using Sensitivity Information
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Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Torque converters are used as coupling devices in automobile powertrains involving automatic transmissions. Efficient modeling of torque converters capturing various modes of operation is important for powertrain design and simulation, (<span class="xref">Hroval and Tobler 1</span>, <span class="xref">Ishihara and Emori 2</span>) optimization and control applications. Models of torque converters are available in various commercial simulation packages, <span class="xref">Hadi et. al. 3</span>. The information about the effect of model parameters on torque converter performance is valuable for any design operation. In this paper, a symbolic sensitivity analysis of a torque converter model will be presented. Direct differentiation (<span class="xref">Serban and Freeman 4</span>) is used to generate the sensitivity equations which results in equations in symbolic form. By solving the sensitivity equations, the effect of a perturbation of the model parameters on the behavior of the system is determined. A parametric importance analysis is performed on the model: the model parameters are arranged according to their effect on the amount loss of energy during the operation of the torque converter. The radii of the pump, turbine and stator, the density of the hydraulic fluid and the exit angle of the vanes of the stator were found to have the most significant effects on the model. Using the sensitivity information, a design optimization problem is defined and solved to obtain a set of parameter values that minimizes the energy lost during the torque converter operation.</div></div>
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it