This paper presents the uncertainty propagation of turbulent coefficients for the Spalart– Allmaras (SA) turbulence model using projection-based reduced-order models (ROMs). ROMs are used instead of Reynolds-averaged Navier–Stokes (RANS) solvers and stochastic collocation/ Galerkin and Monte Carlo methods because they are computationally inexpensive and tend to offer more accuracy than a polynomial surrogate. The uncertainty propagation is performed on two benchmark RANS cases documented on NASA’s turbulence modeling resource. Uncertainty propagation of the SA turbulent coefficients using a ROMis shown to compare well against uncertainty propagation performed using only RANS and using a Gaussian process regression (GP) model. The ROM is shown to be more robust to the size and spread of the training data compared to a GP model.
The novel Hydromine harvests energy from flowing water with no external moving parts, resulting in a robust system with minimal environmental impact. Here two deployment scenarios are considered: an offshore floating platform configuration to capture energy from relatively steady ocean currents at megawatt-scale, and a river-based system at kilowatt-scale mounted on a pylon. Hydrodynamic and techno-economic models are developed. The hydrodynamic models are used to maximize the efficiency of the power conversion. The techno-economic models optimize the system size and layout and ultimately seek to minimize the levelized-cost-of-electricity produced. Parametric and sensitivity analyses are performed on the models to optimize performance and reduce costs.
This paper presents an investigation into sampling strategies for reducing the computational expense of creating error models for steady hypersonic flow surrogate models. The error model describes the quantity of interest error between a reduced-order model prediction and a full-order model solution. The sampling strategies are separated into three categories: distinct training sets, single training set, and augmented single training set for the reduced-order model and the error model. Using a distinct training set, three sampling strategies are investigated: latin hypercube sampling, latin hypercube sampling with a maximin criterion, and a D-Optimal design. It was found that using a D-Optimal design was the most effective at producing an accurate error model with the fewest number of training points. When using a single training set, the leave-one-out cross validation approach was used on the D-Optimal design training set. This produced an error model with an R2 value of greater than 0.8, but it had some outliers due to high nonlinearities in the space. Augmenting the training points of the error model helped improve its accuracy. Using a D-Optimal design with distinct training sets cut the computational cost of creating the error model by 15% and using the LOOCV approach with the D-Optimal design cut the cost by 64%.
Multivariate designs using three optimization procedures were performed on a low Reynolds number (order 100,000) turbine blade that maximized lift over drag. The turbine blade was created to interface to AeroMINE, a novel wind energy harvester that has no external moving parts. To speed up the optimization process, an interpolation-based procedure using the Proper Orthogonal Decomposition (POD) method was used. This method was used in two ways: by itself (POD-i) and as an initial guess to a full-order model (FOM) solution that is truncated before it reaches full convergence (POD-i with truncated FOM). To compare the result of these methods and their efficiency, optimization using a FOM was also conducted. It was found that there exists a trade off between efficiency and optimal result. The FOM found the highest L/D of 28.87 while POD-i found a L/D of 16.19 and POD-i with truncated FOM found a L/D of 19.11. Nonetheless, POD-i and POD-i with truncated FOM were 32,302 and 697 times faster than the FOM, respectively.