Technological watch

We created a subsurface drip irrigation (SDI)-specific variable flow emitter (VFE) that switches working stages automatically based on the inlet pressure (H) to achieve a step change in the flow rate. At working stage I (H = 0.1 MPa), namely the conventional water supply stage, the VFE provided a normal flow rate (qI) of 1–2 L/h for crop irrigation. At working stage II (H > 0.1 MPa; exceeding the design pressure), VFE delivered a larger flow rate (qII). The larger qII facilitated water movement upward from the underground to the surface seedbed during the crop planting, thus ameliorating crop germination issues under SDI. We focused on the impacts of four structural parameters of the flow channel: tooth height (E), tooth spacing (B), tooth angle (A), and flow channel depth (D) on the qI and VFE-flow index (x) at working stage I. Computational fluid dynamic (CFD) simulations were conducted along with a physical laboratory test to develop VFE using computerized numerical control (CNC) technology (accuracy = 0.05 mm). Nine VFEs were designed using an L9(34) orthogonal test. The combination of tetrahedral meshing with a six-layer boundary layer and the realizable k–ε turbulence model was found suitable for CFD simulations. The standard root-mean-square error (nRMSE) of the measured and simulated qIs was a minimum of 7.4%. The four parameters influenced qIs as D > B > E > A, and the four factors influenced the xs as B > E > D > A. Based on the numerical simulation data, multiple linear regression models were constructed for the qIs and xs with four parameters when H = 0.1 MPa. Aiming for the minimum x, the optimal combination of the flow channel structural parameters corresponding to different qIs was determined by the ergodic optimization algorithm. When qI was 1.5 L/h, the optimal structural combinations were E = 1.2 mm, B = 1.8 mm, A = 42°, and D = 1 mm. The VFE with a qI of 1.5 L/h was created by CNC technology. The relative errors of the measured and predicted qIs using the regression model were −0.19–6.31%, and their nRMSE was 6.76%. Thus, optimizing the flow channel structural parameters based on a multiple linear regression model and the ergodic optimization algorithm is a highly precise theoretical base for VFE development.