CFD of the aerodynamics of fog harp water harvesters

Fog water harvesting offers a sustainable solution in arid regions, yet its aerodynamics remain understudied. Most prior work relies on semi-empirical models that focus solely on harvester geometry, omitting key fluid dynamics factors like free-stream velocity. To address this gap, we use CFD simulations and develop an analytical flow model rooted in fluid mechanics to predict fog harvester performance. Our model links aerodynamic forces on individual wires to their Reynolds numbers and reconstructs the full velocity field by accounting for undisturbed inflow, wake formation, blockage, and downstream velocity deficit. We focus on fog harps (i.e., vertical wire arrays), as they outperform conventional meshes while enabling 2D CFD analysis. We evaluate nine cases with flow velocities of 0.1, 1.0, and 10.0 m/s and harp widths of 10, 100, and 1000 wires, using constant wire-to-wire spacing equal to the wire diameter. We quantify flowthrough efficiency (FE) as the ratio of mass flow rate through the harp to that of an unobstructed domain. FE values for 10, 100, and 1000 wires are 10.19%, 15.92%, and 10.09% at 0.1 m/s; 49.95%, 53.48%, and 43.34% at 1.0 m/s; and 63.99%, 74.2%, and 67.16% at 10.0 m/s. From 10 to 100 wires, FE increases by factors of 1.6, 1.1, and 1.2 at 0.1, 1.0, and 10.0 m/s, respectively; increasing to 1000 wires reduces FE by factors of 0.6, 0.8, and 0.9. This non-monotonic trend results from competing effects of increased flow paths and enhanced wake interactions. At constant wire count, increasing velocity significantly improves FE. From 0.1 to 1.0 m/s, FE increases by factors of 5.0, 3.4, and 4.3 for 10, 100, and 1000 wires, respectively; from 1.0 to 10.0 m/s, the corresponding increases are by factors of 1.3, 1.4, and 1.5. These results highlight the dominant role of flow velocity over harp size in enhancing mass flow rate and water collection-a key insight not reported in previous fog harvesting studies. Our analytical model closely matches CFD predictions, offering a physics-based improvement over prior theoretical works.