Sensitivity and Uncertainty Analysis in Computational Thermal Models
Notice bibliographique
Résumé
Computational tools have been extensively applied to predict component temperatures before an actual vehicle is built for testing [1, 2, 3, 4, and 5]. This approach provides an estimate of component temperatures during a specific driving condition. The predicted component temperature is compared against acceptable temperature limits. If violations of the temperature limits are predicted, corrective actions will be applied. These corrective actions may include adding heat shields to the heat source or to the receiving components. Therefore, design changes are implemented based on the simulation results. Sensitivity analysis is the formal technique of determining most influential parameters in a system that affects its performance. Uncertainty analysis is the process of evaluating the deviation of the design from its intended design target. In the case of thermal protection, uncertainty analysis is applied in order to determine the variation of the calculated component temperature around its nominal value. It has been a common understanding that no engineering analysis is complete without conducting uncertainty analysis. Though sensitivity and uncertainty analysis topics have been widely discussed in engineering applications, a very limited number of authors have addressed the need for uncertainty analysis in computational thermal models for automotive applications. The only relevant work [6] focused on the formulation of sensitivity analysis for conjugate heat transfer problems. The purpose of this paper however is to present the uncertainty associated with CFD simulation results when applied to vehicle thermal models. From the user's side, we need to address the effect of uncertainties associated with input data, how they affect the final results and determine most influential input parameters. Therefore, sensitivity and uncertainty analysis should be consistently conducted before results from whenever CFD analysis is implemented for design changes or modifications. Depending on the complexity of the problem being analyzed, two methods are used for this purpose; local sensitivity analysis using Taylor series and a global sensitivity analysis using the Fourier Amplitude Sensitivity Test (FAST). Model uncertainties are expressed as the relative standard deviation of calculated results over the uncertain domain of input parameters. Parametric sensitivities are expressed as the sensitivity coefficient, when Taylor series is applied. Using the FAST method, parametric sensitivity is expressed as the partial variance for each parameter, which measures the contribution of each parameter to the overall uncertainty of predicted component temperatures. In addition to uncertainties associated with CFD calculations, it is critical for the design and release engineers to assess the impact of the calculated temperatures on the component or system durability. This step requires knowledge of component temperatures at various driving conditions, time durations at any given temperature, vehicle duty cycle and the effect of temperature on the performance of components and systems being addressed. In this paper, issues related to the thermal protection process uncertainty are also addressed.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».