SBIR/STTR Award attributes
Wind power plays an increasingly important role in satisfying the power needs of the United States. With increased market penetration, failures from unanticipated unsteady loads, installation related reductions in power generation and significant maintenance costs have underscored the need to predict the unsteady fluid structure interactions related to turbine layout and off-design wind conditions. Contemporary turbine design tools fail to account for such loadings, and thus, the research community has started utilizing HPC Computational Fluid Dynamics (CFD) solvers to investigate these phenomena. Unfortunately, such HPC tools are complicated to use and too expensive for routine industrial use. This problem is exacerbated further when including unsteady aeroelastic coupling between blade motion and flexibility, wake aerodynamics, and the interaction with the turbulent atmosphere. In an ongoing DOE SBIR Phase IIB an advanced forensic level HPC method for predicting wind turbine fatigue and wind farm aeromechanics is being developed. While this effort is demonstrating improved analysis capabilities and garnering interest from industry, it has also highlighted the need for an analysis method that provides easier, faster and more reliable setup to resolve the flow near to the turbine for routine design, analysis and test support. Current tools employ either low-order models that lack fidelity or body-fitted CFD grids that are time consuming and require significant expertise to create and interface with the accompanying structural model. It is generally recognized that CAD cleanup/idealization and creating body-fitted meshes are the largest impediments to practical adoption of CFD by industry and the generation of reliable results. In aeroelastic analysis, this challenge is further compounded by the task of conservatively coupling the flow solution to the structural analysis in a seamless and automated manner. It is exactly these hurdles that the proposed effort seeks to address by integrating and enhancing emerging CFD methods into a robust and easy-to-setup CFD-based aeroelastic analysis system with automated mesh generation and fluid-structure coupling procedures targeted to wind turbines and wind farms, as well as broader applicability throughout the energy, engineering and manufacturing industries. CDI recently demonstrated a high Reynolds number adaptive cut-cell Cartesian grid CFD solver generalized to accommodate imperfect surface geometries, typical of those automatically generated by CAD, and obtain high quality aerodynamics predictions autonomously. Phase I saw the development of aeroelastic analysis capabilities in this solver and proof-of-concept application to wind turbines. In this effort we plan to integrate these capabilities with the far-field flow solver being developed in ongoing ASCR work, and create a new and hardened analysis ideally suited to routine wind turbines analysis by engineers that are experts in wind energy but not CFD and CAD. A successful effort would produce a robust, validated multidisciplinary computational tool for integrated wind turbine design and analysis, along with individual solver libraries for more general application. This tool directly addresses the limitations of current CFD techniques for predicting fluid structure interaction problems such as unsteady turbine blade loading and wind farm situational interactions. Broad applicability of easy to use CFD-based analysis software to the wider energy and manufacturing industries significantly increases the commercial potential for this technology, and is evidenced by the included letter of support. Based upon a modest market entry, combined sales, and associated service work, could generate more than $33M in sales over several years, with major cost savings for customers attributed to improved predictions and lower maintenance costs.
