Power Optimization of Wind Turbines Subject to Navier-Stokes Equations

In this thesis, we first develop a second-order corrected-explicit-implicit domain decomposition scheme (SCEIDD) for the parallel approximation of convection-diffusion equations over multi-block sub-domains. The stability and convergence properties of the SCEIDD scheme is analyzed, and it is proved that this scheme is unconditionally stable. Moreover, it is proved that the SCEIDD scheme is second-order accurate in time and space. Furthermore, three different numerical experiments are performed to verify the theoretical results. In all the experiments the SCEIDD scheme is compared with the EIPCMU2D scheme which is first-order in time. Then, we focus on the application of numerical PDEs in wind farm power optimization. We develop a model for wind farm power optimization while considering the wake interaction among wind turbines. The proposed model is a PDE-constrained optimization model with the objective of maximizing the total power of the wind turbines where the three-dimensional Navier-Stokes equations are among the constraints. Moreover, we develop an efficient numerical algorithm to solve the model. This numerical algorithm is based on the pattern search method, the actuator line method and a numerical scheme which is used to solve the Navier-Stokes equations. Furthermore, the proposed numerical algorithm is used to investigate the wake structures. Numerical results are consistent with the field-tested results. Moreover, we find that by optimizing the turbines operation while considering the wake effect, we can gain an additional 8% in the total power. Finally, we relax the deterministic assumption for the incoming wind speed. The developed model is ultimately a PDE-constrained stochastic optimization model. Moreover, we develop an efficient numerical algorithm to solve this model. This numerical algorithm is based on the Monte Carlo simulation method, the pattern search method, the actuator line method and the corrected-explicit-implicit domain decomposition scheme which we develop for the parallel approximation of three-dimensional Navier-Stokes equations. The developed numerical algorithm, the parallel scheme, and the model are validated by a benchmark used in the literature and the experimental data. We find that by optimizing the turbines operation and considering the randomness of incoming wind speed, we can gain an additional 9% in total power

Convergence study and optimal weight functions of an explicit particle method for the incompressible Navier--Stokes equations

To increase the reliability of simulations by particle methods for incompressible viscous flow problems, convergence studies and improvements of accuracy are considered for a fully explicit particle method for incompressible Navier--Stokes equations. The explicit particle method is based on a penalty problem, which converges theoretically to the incompressible Navier--Stokes equations, and is discretized in space by generalized approximate operators defined as a wider class of approximate operators than those of the smoothed particle hydrodynamics (SPH) and moving particle semi-implicit (MPS) methods. By considering an analytical derivation of the explicit particle method and truncation error estimates of the generalized approximate operators, sufficient conditions of convergence are conjectured.Under these conditions, the convergence of the explicit particle method is confirmed by numerically comparing errors between exact and approximate solutions. Moreover, by focusing on the truncation errors of the generalized approximate operators, an optimal weight function is derived by reducing the truncation errors over general particle distributions. The effectiveness of the generalized approximate operators with the optimal weight functions is confirmed using numerical results of truncation errors and driven cavity flow. As an application for flow problems with free surface effects, the explicit particle method is applied to a dam break flow.Comment: 27 pages, 13 figure

Solving optimal control problems governed by random Navier-Stokes equations using low-rank methods

Many problems in computational science and engineering are simultaneously characterized by the following challenging issues: uncertainty, nonlinearity, nonstationarity and high dimensionality. Existing numerical techniques for such models would typically require considerable computational and storage resources. This is the case, for instance, for an optimization problem governed by time-dependent Navier-Stokes equations with uncertain inputs. In particular, the stochastic Galerkin finite element method often leads to a prohibitively high dimensional saddle-point system with tensor product structure. In this paper, we approximate the solution by the low-rank Tensor Train decomposition, and present a numerically efficient algorithm to solve the optimality equations directly in the low-rank representation. We show that the solution of the vorticity minimization problem with a distributed control admits a representation with ranks that depend modestly on model and discretization parameters even for high Reynolds numbers. For lower Reynolds numbers this is also the case for a boundary control. This opens the way for a reduced-order modeling of the stochastic optimal flow control with a moderate cost at all stages.Comment: 29 page

RNS Applications for Interacting Sub- and Supersonic Flows

A solution based grid adaptation method that combines elements of the multigrid method for solution acceleration and the domain decomposition philosophy for grid optimization is described. Unlike other solution based adaptive gridding schemes, wherein the overhead of recomputing the grid and re-evaluating the solution on the adapted grid leads to higher computational costs compared to a non-adapted calculation, the present methodology reduces the computational time required to obtain the solution. The computational effort involved in the present calculation is significantly lower than a non-adapted calculation that utilizes the multigrid method purely as a convergence acceleration tool. In addition to convergence acceleration, the multigrid framework provides a mechanism of information transfer from regions wherein grid refinement is specified to unrefined coarse grid regions. The basis for domain decomposition in the current procedure is the variation in grid refinement requirements for each coordinate direction in different portions of the flow field. The method is demonstrated herein on an efficient set of governing equations termed the reduced Navier Stokes equations, applied in conjunction with a set of physical boundary conditions. The governing equations are discretized through a pressure based flux splitting procedure that is uniformly applicable from incompressible to supersonic Mach numbers

Proper general decomposition (PGD) for the resolution of Navier–Stokes equations

In this work, the PGD method will be considered for solving some problems of fluid mechanics by looking for the solution as a sum of tensor product functions. In the first stage, the equations of Stokes and Burgers will be solved. Then, we will solve the Navier–Stokes problem in the case of the lid-driven cavity for different Reynolds numbers (Re = 100, 1000 and 10,000). Finally, the PGD method will be compared to the standard resolution technique, both in terms of CPU time and accuracy.Région Poitou-Charente

Achieving High Speed CFD simulations: Optimization, Parallelization, and FPGA Acceleration for the unstructured DLR TAU Code

Today, large scale parallel simulations are fundamental tools to handle complex problems. The number of processors in current computation platforms has been recently increased and therefore it is necessary to optimize the application performance and to enhance the scalability of massively-parallel systems. In addition, new heterogeneous architectures, combining conventional processors with specific hardware, like FPGAs, to accelerate the most time consuming functions are considered as a strong alternative to boost the performance. In this paper, the performance of the DLR TAU code is analyzed and optimized. The improvement of the code efficiency is addressed through three key activities: Optimization, parallelization and hardware acceleration. At first, a profiling analysis of the most time-consuming processes of the Reynolds Averaged Navier Stokes flow solver on a three-dimensional unstructured mesh is performed. Then, a study of the code scalability with new partitioning algorithms are tested to show the most suitable partitioning algorithms for the selected applications. Finally, a feasibility study on the application of FPGAs and GPUs for the hardware acceleration of CFD simulations is presented