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The computational methodology combines solution technologies in a multi-mesh, multi-solver paradigm through a dynamic overset framework. The coupling of a finite volume solver and a high-order, hp-adaptive finite element solver is utilized. Additional technologies including in-situ visualization and atmospheric microscale modeling are incorporated into the analysis environment. Validation of the computational framework is performed on the National Renewable Energy Laboratory (NREL) 5MW baseline wind turbine, the unsteady aerodynamics experimental NREL Phase VI turbine, and the Siemens SWT-2.3-93 wind turbine. The power and thrust results of all single turbine simulations agree well with low-fidelity model simulation results and field experiments when available. Scalability of the computational framework is demonstrated using 6, 12, 24, 48, and 96 wind turbine setups including the 48 turbine wind plant known as Lillgrund. The largest case consisting of 96 wind turbines and a total of 385 overset grids are run on 44,928 cores at a weak scaling efficiency of 86%. Demonstration of the coupling of atmospheric microscale and Computational Fluid Dynamics (CFD) solvers is presented using the National Center for Atmospheric Research (NCAR) Weather Research and Forecasting Model (WRF) solver and the NREL Simulator fOr Wind Farm Applications (SOWFA) solver. <\/jats:p>","DOI":"10.1177\/1094342019832960","type":"journal-article","created":{"date-parts":[[2019,3,12]],"date-time":"2019-03-12T03:46:00Z","timestamp":1552362360000},"page":"897-923","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":25,"title":["Wind farm simulations using an overset <i>hp<\/i>-adaptive approach with blade-resolved turbine models"],"prefix":"10.1177","volume":"33","author":[{"given":"Andrew C","family":"Kirby","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, University of Wyoming, Laramie, WY, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9610-6624","authenticated-orcid":false,"given":"Michael J","family":"Brazell","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, University of Wyoming, Laramie, WY, USA"}]},{"given":"Zhi","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, University of Wyoming, Laramie, WY, USA"}]},{"given":"Rajib","family":"Roy","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, University of Wyoming, Laramie, WY, USA"}]},{"given":"Behzad R","family":"Ahrabi","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, University of Wyoming, Laramie, WY, USA"}]},{"given":"Michael K","family":"Stoellinger","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, University of Wyoming, Laramie, WY, USA"}]},{"given":"Jay","family":"Sitaraman","sequence":"additional","affiliation":[{"name":"Parallel Geometric Algorithms, LLC, Sunnyvale, CA, USA"}]},{"given":"Dimitri J","family":"Mavriplis","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, University of Wyoming, Laramie, WY, USA"}]}],"member":"179","published-online":{"date-parts":[[2019,3,11]]},"reference":[{"key":"bibr1-1094342019832960","doi-asserted-by":"publisher","DOI":"10.1002\/fld.2400"},{"key":"bibr2-1094342019832960","doi-asserted-by":"publisher","DOI":"10.1002\/fld.2454"},{"key":"bibr3-1094342019832960","unstructured":"Bergstr\u00f6m H (2009) Meteorological Conditions at Lillgrund. 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