This research studies the fluid-structure interaction (FSI) of compliant surfaces in air flows, with an objective of finding possible turbulent viscous drag reduction. A compliant surface is a thin layer of viscoelastic material drawing inspiration from dolphin epidermis, which was thought to have drag-reducing capabilities by Gray (1936). Research into the drag reduction capabilities of compliant surfaces has been long going for more than five decades, starting with Kramer (1960), yet no firm conclusion has been reached. In addition, most of the experimental research has been focused on water flows, with air flows regarded as incapable of inertially forcing a compliant surface to deform. This research attempts to disprove this assumption by applying the proper inertial scaling to the FSI between the compliant surface and air flows. Compliant surfaces are characterised by the stiffness and thickness, which are presumed to be of first-order influence on the FSI. A parameter sweep of these compliant surface properties was conducted by drag delta measurements and flow visualisation by planar particle image velocimetry (PIV) in the M-Tunnel at the Low Speed Laboratory (LSL) Drag delta results confirm the possibility of turbulent viscous drag reduction by compliant surfaces, with a measured drag delta of -3.43 %. This result is further supported by the decrease in 1D turbulence intensity at the test plate's trailing edge from hot-wire measurements, and a smaller decay of the shape factor H from PIV. Quadrant analysis of the PIV data found evidence of a reduction in combined Q2 and Q4 events, further supporting the drag delta measured. Correlation between the compliant surface's viscoelastic properties and the drag delta found a negative correlation between the magnitude of complex shear modulus and a positive correlation between the loss tangent and drag reduction. The high loss tangent for the drag-reducing compliant surface and its subtle positive correlation with the drag reduction indicates that the viscoelasticity might have a greater influence on the FSI than expected.

In this paper, we present a new computational pipeline for designing and fabricating 4D garments as knitwear that considers comfort during body movement. This is achieved by careful control of elasticity distribution to reduce uncomfortable pressure and unwanted sliding caused by body motion. We exploit the ability to knit patterns in different elastic levels by single-jersey jacquard (SJJ) with two yarns. We design the distribution of elasticity for a garment by physics-based computation, the optimized elasticity on the garment is then converted into instructions for a digital knitting machine by two algorithms proposed in this paper. Specifically, a graph-based algorithm is proposed to generate knittable stitch meshes that can accurately capture the 3D shape of a garment, and a tiling algorithm is employed to assign SJJ patterns on the stitch mesh to realize the designed distribution of elasticity. The effectiveness of our approach is verified on simulation results and on specimens physically fabricated by knitting machines.