In this Letter we present a detailed mechanism of drag reduction by dimples on a sphere such as golf-ball dimples by measuring the streamwise velocity above the dimpled surface. Dimples cause local flow separation and trigger the shear layer instability along the separating shear layer, resulting in the generation of large turbulence intensity. With this increased turbulence, the flow reattaches to the sphere surface with a high momentum near the wall and overcomes a strong adverse pressure gradient formed in the rear sphere surface. As a result, dimples delay the main separation and reduce drag significantly. The present study suggests that generation of a separation bubble, i.e., a closed-loop streamline consisting of separation and reattachment, on a body surface is an important flow-control strategy for drag reduction on a bluff body such as the sphere and cylinder.

In this paper, we present a new passive control device for form-drag reduction in flow over a two-dimensional bluff body with a blunt trailing edge. The device consists of small tabs attached to the upper and lower trailing edges of a bluff body to effectively perturb a two-dimensional wake. Both a wind-tunnel experiment and large-eddy simulation are carried out to examine its drag-reduction performance. Extensive parametric studies are performed experimentally by varying the height and width of the tab and the spanwise spacing between the adjacent tabs at three Reynolds numbers of $\hbox{\it Re}\,{=}\,u_\infty h/\nu\,{=}\,20\,000$, 40 000 and 80 000, where $u_\infty$ is the free-stream velocity and $h$ is the body height. For a wide parameter range, the base pressure increases (i.e. drag reduces) at all three Reynolds numbers. Furthermore, a significant increase in the base pressure by more than 30% is obtained for the optimum tab configuration. Numerical simulations are performed at much lower Reynolds numbers of $\hbox{\it Re}\,{=}\,320$ and 4200 to investigate the mechanism responsible for the base-pressure increase by the tab. Results from the velocity measurement and numerical simulations show that the tab introduces the spanwise mismatch in the vortex-shedding process, resulting in a substantial reduction of the vortical strength in the wake and significant increases in the vortex formation length and wake width.

Polydimethylsiloxane (PDMS) has been widely used in fabricating microfluidic devices for prototyping and proof-of-concept experiments. Due to several material limitations, PDMS has not been widely adopted for commercial applications that require large-scale production. This paper describes a novel injection-molded plastic array 3D culture (IMPACT) platform that incorporates a microfluidic design to integrate patterned 3D cell cultures within a single 96-well (diameter = 9 mm) plate. Cell containing gels can be sequentially patterned by capillary-guided flow along the corner and narrow gaps designed within the 96-well form factor. Compared to PDMS-based hydrophobic burst valve designs, this work utilizes hydrophilic liquid guides to obtain rapid and reproducible patterned gels for co-cultures. When a liquid droplet (i.e. cell containing fibrin or collagen gel) is placed on a corner, spontaneous patterning is achieved within 1 second. Optimal dimensionless parameters required for successful capillary loading have been determined. To demonstrate the utility of the platform for 3D co-culture, angiogenesis experiments were performed by patterning HUVEC (human umbilical endothelial cells) and LF (lung fibroblasts) embedded in 3D fibrin gels. The angiogenic sprouts (with open lumen tip cells expressing junctional proteins) are comparable to those observed in PDMS based devices. The IMPACT device has the potential to provide a robust high-throughput experimental platform for vascularized microphysiological systems.

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The white blood cell differential count of the bone marrow provides information concerning the distribution of immature and mature cells within maturation stages. The results of such examinations are important for the diagnosis of various diseases and for follow-up care after chemotherapy. However, manual, labor-intensive methods to determine the differential count lead to inter- and intra-variations among the results obtained by hematologists. Therefore, an automated system to conduct the white blood cell differential count is highly desirable, but several difficulties hinder progress. There are variations in the white blood cells of each maturation stage, small inter-class differences within each stage, and variations in images because of the different acquisition and staining processes. Moreover, a large number of classes need to be classified for bone marrow smear analysis, and the high density of touching cells in bone marrow smears renders difficult the segmentation of single cells, which is crucial to traditional image processing and machine learning. Few studies have attempted to discriminate bone marrow cells, and even these have either discriminated only a few classes or yielded insufficient performance. In this study, we propose an automated white blood cell differential counting system from bone marrow smear images using a dual-stage convolutional neural network (CNN). A total of 2,174 patch images were collected for training and testing. The dual-stage CNN classified images into 10 classes of the myeloid and erythroid maturation series, and achieved an accuracy of 97.06%, a precision of 97.13%, a recall of 97.06%, and an F-1 score of 97.1%. The proposed method not only showed high classification performance, but also successfully classified raw images without single cell segmentation and manual feature extraction by implementing CNN. Moreover, it demonstrated rotation and location invariance. These results highlight the promise of the proposed method as an automated white blood cell differential count system.