A multi-fidelity system of computer codes for the analysis and design of vehicles having extensive areas of laminar flow is under development at the NASA Langley Research Center. The overall approach consists of the loose coupling of a flow solver, a transition prediction method and a design module using shell scripts, along with interface modules to prepare the input for each method. This approach allows the user to select the flow solver and transition prediction module, as well as run mode for each code, based on the fidelity most compatible with the problem and available resources. The design module can be any method that designs to a specified target pressure distribution. In addition to the interface modules, two new components have been developed: 1) an efficient, empirical transition prediction module (MATTC) that provides n-factor growth distributions without requiring boundary layer information; and 2) an automated target pressure generation code (ATPG) that develops a target pressure distribution that meets a variety of flow and geometry constraints. The ATPG code also includes empirical estimates of several drag components to allow the optimization of the target pressure distribution. The current system has been developed for the design of subsonic and transonic airfoils and wings, but may be extendable to other speed ranges and components. Several analysis and design examples are included to demonstrate the current capabilities of the system.
Power plant water usage is a coupling of the energy–water nexus; this research investigates water droplet motion, with implications for water recovery in cooling towers. Simulations of a 2.6 mm-diameter droplet motion on a hydrophobic, vertical surface were conducted in xflow using the lattice Boltzmann method (LBM). Results were compared to two experimental cases; in the first case, experimental and simulated droplets experienced 30 Hz vibrations (i.e., ±0.1 mm x-direction amplitude, ±0.2 mm y-direction amplitude) and the droplet ratcheted down the surface. In the second case, 100 Hz vibrations (i.e., ±0.8 mm x-direction amplitude, ±0.2 mm y-direction amplitude) caused droplet ejection. Simulations were then conducted for a wide range of frequencies (i.e., 10–100 Hz) and amplitudes (i.e., ±0.018–50 mm), resulting in maximum accelerations of 0.197–1970 m/s2. Under low maximum accelerations (e.g., <7 m/s2), droplets rocked upward and downward in rocking mode, but did not overcome the contact angle hysteresis and, therefore, did not move. As acceleration increased, droplets overcame the contact angle hysteresis and entered ratcheting mode. For vibrations that prompted droplet motion, droplet velocities varied between 10–1000 mm/s. At capillary numbers above approximately 0.0044 and Weber numbers above 3.6, liquid breakup was observed in ratcheting droplets (e.g., the formation of smaller child droplets from the parent droplet). It was noted that both x- and y-direction vibrations were required for droplet ejection.
This study proposes a numerical model to collect and analyze relationships between flow structures and drag forces on a microfin enhanced surface. We utilized a large eddy simulation (LES) with a localized, dynamic kinetic energy, subgrid-scale model (LDKM) to predict turbulent flow structures. The accuracy of the numerical model was verified by a telescopic particle image velocimetry (PIV) system. Of special note was the strong match of PIV flow structures with numerical flow structures simulated with LES. To detect two main flow structures, lateral and longitudinal, a new method based on the correlation coefficient of velocity fluctuation was developed. Two main types of drag, form, and skin-friction, were discussed and analyzed as occurring on complex near-surface engineered enhancements. Several problems about the relationships were discussed and solved. First, the study determined which drag force dominated the pressure drop (Δp) with different Reynolds numbers. Second, the study analyzed how turbulent flow structures affected form drag and friction drag, respectively. Third, the study explained why the microfins in the paper designed by Webb et al. were better suited for the high Reynold number cases (Reynolds number ≈ 28,000). The goal of the paper was not to find a new Reynolds number-based correlation but to find flow structures responsible for pressure drop and understand the mechanisms causing it.
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