Multipoint high-fidelity CFD-based aerodynamic shape optimization of a 10 MW wind turbine

Abstract

Abstract. The wind energy industry relies heavily on computational fluid dynamics (CFD) to analyze new turbine designs. To utilize CFD earlier in the design process, where lower-fidelity methods such as blade element momentum (BEM) are more common, requires the development of new tools. Tools that utilize numerical optimization are particularly valuable because they reduce the reliance on design by trial and error. We present the first comprehensive 3-D CFD adjoint-based shape optimization of a modern 10 MW offshore wind turbine. The optimization problem is aligned with a case study from International Energy Agency (IEA) Wind Task 37, making it possible to compare our findings with the BEM results from this case study and therefore allowing us to determine the value of design optimization based on high-fidelity models. The comparison shows that the overall design trends suggested by the two models do agree, and that it is particularly valuable to consult the high-fidelity model in areas such as root and tip where BEM is inaccurate. In addition, we compare two different CFD solvers to quantify the effect of modeling compressibility and to estimate the accuracy of the chosen grid resolution and order of convergence of the solver. Meshes up to 14×106 cells are used in the optimization whereby flow details are resolved. The present work shows that it is now possible to successfully optimize modern wind turbines aerodynamically under normal operating conditions using Reynolds-averaged Navier–Stokes (RANS) models. The key benefit of a 3-D RANS approach is that it is possible to optimize the blade planform and cross-sectional shape simultaneously, thus tailoring the shape to the actual 3-D flow over the rotor. This work does not address evaluation of extreme loads used for structural sizing, where BEM-based methods have proven very accurate, and therefore will likely remain the method of choice.

CFD-based curved tip shape design for wind turbine blades

“…With respect to the gained improvement in mechanical power it can be said that the optimization results from the finer grid levels (SPL2e3cb, SPL1e3cb, and SPL1e3hb) agree very well on 0.42 − 0.44% improvement where the result from L3 is much lower. This observation agrees with earlier findings (Madsen et al, 2019) stating that very coarse mesh levels should be used with care in shape optimizations. Furthermore, it should be pointed out that the SPL3e3c improvement percentage from Tab.…”

“…However, it is difficult from the reported final optimality (Dhert et al, 2017, Tab. 2) to learn how many orders of magnitude it has been converged making it difficult to compare with the presented timings in this study. Madsen et al (2019) report CPU timings (Madsen et al, 2019, Tab. 6) for adjoint-based high-fidelity shape optimizations using a single design variable of close to 60 hours for a mesh with resolution equal to L1 in Tab.…”

“…While the presented CFD-based approach using the finite difference method is well-functioning, it is not feasible to undertake a full simultaneous design of airfoils and blade planform. For this, the associated cost of computing the gradient is too high since 3-D shape optimizations of full rotor configurations involve hundreds of design variables (Nielsen and Diskin, 2012;Madsen et al, 2019). To carry out a design optimization study of a full rotor configuration using finite-difference based gradients, one could apply a more conventional approach where a sequential procedure in two steps should work (Barrett and Ning, 2018):…”

pyOptSparse: A Python framework for large-scale constrained nonlinear optimization of sparse systems

“…pyOptSparse has been used extensively in engineering applications, particularly in multidisciplinary design optimization. Researchers have used it to perform aerodynamic shape optimization of aircraft wings (Secco & Martins, 2019), wind turbines (Madsen, Zahle, Sørensen, & Martins, 2019), and aerostructural optimization of an entire aircraft (Brooks, Kenway, & Martins, 2018). pyOptSparse is also supported by OpenMDAO (Gray, Hwang, Martins, Moore, & Naylor, 2019), a popular Python framework for multidisciplinary analysis and optimization.…”

Laminar-turbulent transition characteristics of a 3-D wind turbine rotor blade based on experiments and computations

“…where the subscript "tr" is the transition onset, ν is the kinematic viscosity, σ is here the spot propagation rate, and n is the nondimensional spot formation rate, n = n • ν 2 /U 3 e,Tr (Mayle, 1998). The intermittency factor is calculated on the surface and then solved for the entire boundary layer and wake within the transport equation (Michelsen, 2002).…”