Fuzzy Based Evaporator Model in Waste Heat Recovery System
Notice bibliographique
Résumé
Extended Abstract Waste heat recovery (WHR) from internal combustion engines using an Organic Rankine Cycle (ORC) has been a growing research area in recent years for reducing the fuel consumption and enhancing the efficiency of the engine. The ORC is a thermodynamic cycle which converts heat into work. The operation of the ORC WHR system at supercritical pressure can improve the thermal efficiency of the cycle by 20% –50% more than a traditional subcritical condition [1]. In ORC WHR system, evaporator is considered to be the most critical component as the effective heat transfer of this device influences the efficiency of the system. Heat transfer in the evaporator under supercritical condition becomes unpredictable as the thermo-physical properties are strongly variable with temperature and the Finite Volume (FV) method is generally used to model the evaporator at this condition [2,3]. Although the FV model can successfully capture those changes in the evaporator, the computation time for this method is high as it consists of many iterative loops. Therefore, the model has a limitation in real time control applications. To reduce the time consumption, a new evaporator model using fuzzy inference technique is developed in this research. The fuzzy evaporator model in this research is built using the fuzzy technique introduced by Mamdani and Assilian [4] and consists of fuzzy logic, membership functions, fuzzy sets, and fuzzy rules representing the nonlinear system of the evaporator by mapping its input variables to the output variables. This model has three inputs (mass flow rate of refrigerant, heat source flow rate and temperature) and two outputs (evaporator power and outlet temperature). The input and output ranges of the evaporator model are divided into different linguistic levels; each of these levels is called a membership function. The collection of membership functions is termed as a fuzzy set. The fuzzy rules of the evaporator are created using fuzzy If-Then statements and are determined and adjusted from intuition and knowledge of characteristics of the evaporator. The simulation of heat transfer at the evaporator using the fuzzy based model was conducted at a supercritical pressure of 6 MPa. The generic heat source with variable mass flow rate and temperature, and a random flow rate of R134a refrigerant were used in the simulation. The outputs of the fuzzy model were compared with that of the FV model. The RMSE and congruency of the fit obtained for the evaporator power was 0.95 and 93.68%; while for the outlet temperature it was 1.48 and 89.16%, respectively. The fuzzy model outputs in the simulation were obtained almost instantly; whereas the simulation time for the FV model was 13,870s. The proposed fuzzy evaporator model can reduce the computation time significantly and therefore it can be used to develop a real time control system for the WHR process.
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.
Comment cette classification a été obtenuedéplier
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, 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 ».