Sensitivity and Uncertainty Analysis in Computational Thermal Models
Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
<div class="section abstract"><div class="htmlview paragraph">Computational tools have been extensively applied to predict component temperatures before an actual vehicle is built for testing [<span class="xref">1</span>, <span class="xref">2</span>, <span class="xref">3</span>, <span class="xref">4</span>, and <span class="xref">5</span>]. 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.</div><div class="htmlview paragraph">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 [<span class="xref">6</span>] 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.</div><div class="htmlview paragraph">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.</div></div>
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 découle