Drag Force Estimation of a Truck Trailer Model Using Artificial Neural Network

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dc.contributor.authorMustafa Sarıoğlu
dc.contributor.authorMehmet Seyhan
dc.contributor.authorYahya Erkan Akansub
dc.date.accessioned2019-08-01T13:38:39Z
dc.date.available2019-08-01T13:38:39Z
dc.date.issued2016
dc.departmentNiğde ÖHÜ
dc.description.abstractPrediction of the drag forces acting on a truck trailer with/without spoiler is carried out by using artificial neural network (ANN). ANN model data set include the experiments of spoiler positions which have zero level to trailer front corner, -2 mm, -4.5 mm, -9 mm, +4.5 mm and +9 mm and truck trailer without spoiler. The experiments were carried out in the wind tunnel in the range of the free stream velocity between 4.6 m/s and 19.3 m/s, corresponding the Re number range, 1.0×105 -5.0×105. Mean absolute percentage error (MAPE) for training, validation and testing is 2.24%, 3.75% and 4.58% in the prediction of the drag forces, respectively. Prediction performance of the developed ANN model has a very good accuracy. According to the drag coefficients results, Reynolds number independence for truck trailer model is obtained at Reynolds number between 1.97×105 and 4.89×105. For spoiler position cases, while minimum drag coefficient acting on truck trailer with spoiler is seen at - 2mm offset, maximum drag coefficient is seen at -9 mm offset
dc.description.abstractPrediction of the drag forces acting on a truck trailer with/without spoiler is carried out by using artificial neural network (ANN). ANN model data set include the experiments of spoiler positions which have zero level to trailer front corner, -2 mm, -4.5 mm, -9 mm, +4.5 mm and +9 mm and truck trailer without spoiler. The experiments were carried out in the wind tunnel in the range of the free stream velocity between 4.6 m/s and 19.3 m/s, corresponding the Re number range, 1.0×105 -5.0×105. Mean absolute percentage error (MAPE) for training, validation and testing is 2.24%, 3.75% and 4.58% in the prediction of the drag forces, respectively. Prediction performance of the developed ANN model has a very good accuracy. According to the drag coefficients results, Reynolds number independence for truck trailer model is obtained at Reynolds number between 1.97×105 and 4.89×105. For spoiler position cases, while minimum drag coefficient acting on truck trailer with spoiler is seen at - 2mm offset, maximum drag coefficient is seen at -9 mm offset
dc.identifier.endpage175
dc.identifier.issue4
dc.identifier.startpage168
dc.identifier.trdizinid217967
dc.identifier.urihttps://app.trdizin.gov.tr/makale/TWpFM09UWTNOdz09
dc.identifier.urihttps://hdl.handle.net/11480/2302
dc.identifier.volume5
dc.indekslendigikaynakTR-Dizin
dc.institutionauthor[0-Belirlenecek]
dc.language.isoen
dc.relation.ispartofInternational Journal of Automotive Engineering and Technologies
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectMühendislik
dc.subjectMakine
dc.subjectMühendislik
dc.subjectOrtak Disiplinler
dc.titleDrag Force Estimation of a Truck Trailer Model Using Artificial Neural Network
dc.typeArticle

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