The prevalent use of organic materials in manufacturing is a fire safety concern, and motivates the need for predictive thermal decomposition models. A critical component of predictive modeling is numerical inference of kinetic parameters from bench scale data. Currently, an active area of computational pyrolysis research focuses on identifying efficient, robust methods for optimization. This paper demonstrates that kinetic parameter calibration problems can successfully be solved using classical gradient-based optimization. We explore calibration examples that exhibit characteristics of concern: high nonlinearity, high dimensionality, complicated schemes, overlapping reactions, noisy data, and poor initial guesses. The examples demonstrate that a simple, non-invasive change to the problem formulation can simultaneously avoid local minima, avoid computation of derivative matrices, achieve a computational efficiency speedup of 10x, and make optimization robust to perturbations of parameter components. Techniques from the mathematical optimization and inverse problem communities are employed. By re-examining gradient-based algorithms, we highlight opportunities to develop kinetic parameter calibration methods that should outperform current methods.
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.
Power production of the turbines at the Department of Energy/Sandia National Laboratories Scaled Wind Farm Technology (SWiFT) facility located at the Texas Tech University’s National Wind Institute Research Center was measured experimentally and simulated for neutral atmospheric boundary layer operating conditions. Two V27 wind turbines were aligned in series with the dominant wind direction, and the upwind turbine was yawed to investigate the impact of wake steering on the downwind turbine. Two conditions were investigated, including that of the leading turbine operating alone and both turbines operating in series. The field measurements include meteorological evaluation tower (MET) data and light detection and ranging (lidar) data. Computations were performed by coupling large eddy simulations (LES) in the three-dimensional, transient code Nalu-Wind with engineering actuator line models of the turbines from OpenFAST. The simulations consist of a coarse precursor without the turbines to set up an atmospheric boundary layer inflow followed by a simulation with refinement near the turbines. Good agreement between simulations and field data are shown. These results demonstrate that Nalu-Wind holds the promise for the prediction of wind plant power and loads for a range of yaw conditions.
