Drag reduction study of a microfiber-coated cylinder.

Drag reduction for a bluff body is imperative in a time of increasing awareness of the environmental impact and sustainability of air travel. Microfiber coating has demonstrated its ability to reduce drag on a bluff body. This was done by applying strips of the coating to a cylinder. To widen the application range of the microfiber coating, a fully microfiber-coated cylinder is studied as it has no directionality relative to incoming flow. It is hypothesized that a large coating coverage will cause a reduction in drag dependent on the Reynolds number Re. The fully microfiber-coated cylinder is studied in a wind tunnel and the drag coefficient is determined at a range of Re in the subcritical-flow regime. It is found that the drag coefficient of the microfiber-coated cylinder is a function of Re, and the critical Reynolds number, where the maximum drag reduction occurs, is lower for a microfiber-coated cylinder compared to that of a conventional smooth-surface cylinder.

Predicting Pressure Sensitivity to Luminophore Content and Paint Thickness of Pressure-Sensitive Paint Using Artificial Neural Network.

An artificial neural network (ANN) was constructed and trained for predicting pressure sensitivity using an experimental dataset consisting of luminophore content and paint thickness as chemical and physical inputs. A data augmentation technique was used to increase the number of data points based on the limited experimental observations. The prediction accuracy of the trained ANN was evaluated by using a metric, mean absolute percentage error. The ANN predicted pressure sensitivity to luminophore content and to paint thickness, within confidence intervals based on experimental errors. The present approach of applying ANN and the data augmentation has the potential to predict pressure-sensitive paint (PSP) characterizations that improve the performance of PSP for global surface pressure measurements.