Microstructured materials that can selectively control the optical properties are crucial for the development of thermal management systems in aerospace and space applications. However, due to the vast design space available for microstructures with varying material, wavelength, and temperature conditions relevant to thermal radiation, the microstructure design optimization becomes a very time-intensive process and with results for specific and limited conditions. Here, we develop a deep neural network to emulate the outputs of finite-difference time-domain simulations (FDTD). The network we show is the foundation of a machine learning based approach to microstructure design optimization for thermal radiation control. Our neural network differentiates materials using discrete inputs derived from the materials’ complex refractive index, enabling the model to build relationships between the microtexture’s geometry, wavelength, and material. Thus, material selection does not constrain our network and it is capable of accurately extrapolating optical properties for microstructures of materials not included in the training process. Our surrogate deep neural network can synthetically simulate over 1,000,000 distinct combinations of geometry, wavelength, temperature, and material in less than a minute, representing a speed increase of over 8 orders of magnitude compared to typical FDTD simulations. This speed enables us to perform sweeping thermal-optical optimizations rapidly to design advanced passive cooling or heating systems. The deep learning-based approach enables complex thermal and optical studies that would be impossible with conventional simulations and our network design can be used to effectively replace optical simulations for other microstructures.

No abstract

No abstract

Flow exiting the combustor is highly turbulent and contains significant spatial gradients of pressure and temperature. The high pressure turbine nozzle vanes operating in this environment redistribute these spatial gradients and impact the inflow characteristics of the turbine rotor blades. The present study investigates the redistribution of total temperature through a turbine nozzle vane. Numerical investigation was performed using three-dimensional RANS analysis. Simulations were conducted using the Wilcox k–ω turbulence model and Shear Stress Transport (SST) with and without γ–Reθ transition model. Experimental measurements were obtained in an annular nozzle cascade facility. Two sets of inlet conditions were considered. The first was a nominally uniform total temperature. The second had a span-wise variation of total temperature. Both sets of inlet conditions had nominally the same inlet total pressure and inlet Mach number. Span-wise redistribution was evaluated using the circum-ferentially averaged total temperature profile at a plane downstream of the nozzle. Physical arguments about the influence of nozzle secondary flows on this redistribution are presented.

A spacecraft cabin ventilation fan suitable for aerodynamic and acoustic ground tests was designed and two copies of the fan assembly were fabricated - designated as Quiet Space Fan. Both fans were tested for aerodynamic performance and acoustic levels in the NASA Glenn Research Center Acoustical Testing Laboratory. A new test rig for small axial flow fans was designed to accommodate the instrumentation and back-pressure adjustments. Measurements acquired were - static pressures for measuring performance, a 72-channel in-duct microphone array, external microphone measurements for acoustics, and inter-stage hot wire measurements of the fan wake. This report documents the wake velocity and turbulence measurements as part of a series of reports.