ExaWind: Open‐source CFD for hybrid‐RANS/LES geometry‐resolved wind turbine simulations in atmospheric flows

Predictive high-fidelity modeling of wind turbines with computational fluid dynamics, wherein turbine geometry is resolved in an atmospheric boundary layer, is important to understanding complex flow accounting for design strategies and operational phenomena such as blade erosion, pitch-control, stall/vortex-induced vibrations, and aftermarket add-ons. The biggest challenge with high-fidelity modeling is the realization of numerical algorithms that can capture the relevant physics in detail through effective use of high-performance computing. For modern supercomputers, that means relying on GPUs for acceleration. In this paper, we present ExaWind, a GPU-enabled open-source incompressible-flow hybrid-computational fluid dynamics framework, comprising the near-body unstructured grid solver Nalu-Wind, and the off-body block-structured-grid solver AMR-Wind, which are coupled using the Topology Independent Overset Grid Assembler. Turbine simulations employ either a pure Reynolds-averaged Navier–Stokes turbulence model or hybrid turbulence modeling wherein Reynolds-averaged Navier–Stokes is used for near-body flow and large eddy simulation is used for off-body flow. Being two-way coupled through overset grids, the two solvers enable simulation of flows across a huge range of length scales, for example, 10 orders of magnitude going from O(μm) boundary layers along the blades to O(10 km) across a wind farm. In this paper, we describe the numerical algorithms for geometry-resolved turbine simulations in atmospheric boundary layers using ExaWind. We present verification studies using canonical flow problems. Validation studies are presented using megawatt-scale turbines established in literature. Additionally presented are demonstration simulations of a small wind farm under atmospheric inflow with different stability states

ExaWind: Open‐source CFD for hybrid‐RANS/LES geometry‐resolved wind turbine simulations in atmospheric flows

Abstract Predictive high‐fidelity modeling of wind turbines with computational fluid dynamics, wherein turbine geometry is resolved in an atmospheric boundary layer, is important to understanding complex flow accounting for design strategies and operational phenomena such as blade erosion, pitch‐control, stall/vortex‐induced vibrations, and aftermarket add‐ons. The biggest challenge with high‐fidelity modeling is the realization of numerical algorithms that can capture the relevant physics in detail through effective use of high‐performance computing. For modern supercomputers, that means relying on GPUs for acceleration. In this paper, we present ExaWind, a GPU‐enabled open‐source incompressible‐flow hybrid‐computational fluid dynamics framework, comprising the near‐body unstructured grid solver Nalu‐Wind, and the off‐body block‐structured‐grid solver AMR‐Wind, which are coupled using the Topology Independent Overset Grid Assembler. Turbine simulations employ either a pure Reynolds‐averaged Navier–Stokes turbulence model or hybrid turbulence modeling wherein Reynolds‐averaged Navier–Stokes is used for near‐body flow and large eddy simulation is used for off‐body flow. Being two‐way coupled through overset grids, the two solvers enable simulation of flows across a huge range of length scales, for example, 10 orders of magnitude going from O(μm) boundary layers along the blades to O(10 km) across a wind farm. In this paper, we describe the numerical algorithms for geometry‐resolved turbine simulations in atmospheric boundary layers using ExaWind. We present verification studies using canonical flow problems. Validation studies are presented using megawatt‐scale turbines established in literature. Additionally presented are demonstration simulations of a small wind farm under atmospheric inflow with different stability states

Science goals and mission architecture of the Europa Lander mission concept

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Hand, K., Phillips, C., Murray, A., Garvin, J., Maize, E., Gibbs, R., Reeves, G., San Martin, A., Tan-Wang, G., Krajewski, J., Hurst, K., Crum, R., Kennedy, B., McElrath, T., Gallon, J., Sabahi, D., Thurman, S., Goldstein, B., Estabrook, P., Lee, S. W., Dooley, J. A., Brinckerhoff, W. B., Edgett, K. S., German, C. R., Hoehler, T. M., Hörst, S. M., Lunine, J. I., Paranicas, C., Nealson, K., Smith, D. E., Templeton, A. S., Russell, M. J., Schmidt, B., Christner, B., Ehlmann, B., Hayes, A., Rhoden, A., Willis, P., Yingst, R. A., Craft, K., Cameron, M. E., Nordheim, T., Pitesky, J., Scully, J., Hofgartner, J., Sell, S. W., Barltrop, K. J., Izraelevitz, J., Brandon, E. J., Seong, J., Jones, J.-P., Pasalic, J., Billings, K. J., Ruiz, J. P., Bugga, R. V., Graham, D., Arenas, L. A., Takeyama, D., Drummond, M., Aghazarian, H., Andersen, A. J., Andersen, K. B., Anderson, E. W., Babuscia, A., Backes, P. G., Bailey, E. S., Balentine, D., Ballard, C. G., Berisford, D. F., Bhandari, P., Blackwood, K., Bolotin, G. S., Bovre, E. A., Bowkett, J., Boykins, K. T., Bramble, M. S., Brice, T. M., Briggs, P., Brinkman, A. P., Brooks, S. M., Buffington, B. B., Burns, B., Cable, M. L., Campagnola, S., Cangahuala, L. A., Carr, G. A., Casani, J. R., Chahat, N. E., Chamberlain-Simon, B. K., Cheng, Y., Chien, S. A., Cook, B. T., Cooper, M., DiNicola, M., Clement, B., Dean, Z., Cullimore, E. A., Curtis, A. G., Croix, J-P. de la, Pasquale, P. Di, Dodd, E. M., Dubord, L. A., Edlund, J. A., Ellyin, R., Emanuel, B., Foster, J. T., Ganino, A. J., Garner, G. J., Gibson, M. T., Gildner, M., Glazebrook, K. J., Greco, M. E., Green, W. M., Hatch, S. J., Hetzel, M. M., Hoey, W. A., Hofmann, A. E., Ionasescu, R., Jain, A., Jasper, J. D., Johannesen, J. R., Johnson, G. K., Jun, I., Katake, A. B., Kim-Castet, S. Y., Kim, D. I., Kim, W., Klonicki, E. F., Kobeissi, B., Kobie, B. D., Kochocki, J., Kokorowski, M., Kosberg, J. A., Kriechbaum, K., Kulkarni, T. P., Lam, R. L., Landau, D. F., Lattimore, M. A., Laubach, S. L., Lawler, C. R., Lim, G., Lin, J. Y., Litwin, T. E., Lo, M. W., Logan, C. A., Maghasoudi, E., Mandrake, L., Marchetti, Y., Marteau, E., Maxwell, K. A., Namee, J. B. Mc, Mcintyre, O., Meacham, M., Melko, J. P., Mueller, J., Muliere, D. A., Mysore, A., Nash, J., Ono, H., Parker, J. M., Perkins, R. C., Petropoulos, A. E., Gaut, A., Gomez, M. Y. Piette, Casillas, R. P., Preudhomme, M., Pyrzak, G., Rapinchuk, J., Ratliff, J. M., Ray, T. L., Roberts, E. T., Roffo, K., Roth, D. C., Russino, J. A., Schmidt, T. M., Schoppers, M. J., Senent, J. S., Serricchio, F., Sheldon, D. J., Shiraishi, L. R., Shirvanian, J., Siegel, K. J., Singh, G., Sirota, A. R., Skulsky, E. D., Stehly, J. S., Strange, N. J., Stevens, S. U., Sunada, E. T., Tepsuporn, S. P., Tosi, L. P. C., Trawny, N., Uchenik, I., Verma, V., Volpe, R. A., Wagner, C. T., Wang, D., Willson, R. G., Wolff, J. L., Wong, A. T., Zimmer, A. K., Sukhatme, K. G., Bago, K. A., Chen, Y., Deardorff, A. M., Kuch, R. S., Lim, C., Syvertson, M. L., Arakaki, G. A., Avila, A., DeBruin, K. J., Frick, A., Harris, J. R., Heverly, M. C., Kawata, J. M., Kim, S.-K., Kipp, D. M., Murphy, J., Smith, M. W., Spaulding, M. D., Thakker, R., Warner, N. Z., Yahnker, C. R., Young, M. E., Magner, T., Adams, D., Bedini, P., Mehr, L., Sheldon, C., Vernon, S., Bailey, V., Briere, M., Butler, M., Davis, A., Ensor, S., Gannon, M., Haapala-Chalk, A., Hartka, T., Holdridge, M., Hong, A., Hunt, J., Iskow, J., Kahler, F., Murray, K., Napolillo, D., Norkus, M., Pfisterer, R., Porter, J., Roth, D., Schwartz, P., Wolfarth, L., Cardiff, E. H., Davis, A., Grob, E. W., Adam, J. R., Betts, E., Norwood, J., Heller, M. M., Voskuilen, T., Sakievich, P., Gray, L., Hansen, D. J., Irick, K. W., Hewson, J. C., Lamb, J., Stacy, S. C., Brotherton, C. M., Tappan, A. S., Benally, D., Thigpen, H., Ortiz, E., Sandoval, D., Ison, A. M., Warren, M., Stromberg, P. G., Thelen, P. M., Blasy, B., Nandy, P., Haddad, A. W., Trujillo, L. B., Wiseley, T. H., Bell, S. A., Teske, N. P., Post, C., Torres-Castro, L., Grosso, C. Wasiolek, M. Science goals and mission architecture of the Europa Lander mission concept. The Planetary Science Journal, 3(1), (2022): 22, https://doi.org/10.3847/psj/ac4493.Europa is a premier target for advancing both planetary science and astrobiology, as well as for opening a new window into the burgeoning field of comparative oceanography. The potentially habitable subsurface ocean of Europa may harbor life, and the globally young and comparatively thin ice shell of Europa may contain biosignatures that are readily accessible to a surface lander. Europa's icy shell also offers the opportunity to study tectonics and geologic cycles across a range of mechanisms and compositions. Here we detail the goals and mission architecture of the Europa Lander mission concept, as developed from 2015 through 2020. The science was developed by the 2016 Europa Lander Science Definition Team (SDT), and the mission architecture was developed by the preproject engineering team, in close collaboration with the SDT. In 2017 and 2018, the mission concept passed its mission concept review and delta-mission concept review, respectively. Since that time, the preproject has been advancing the technologies, and developing the hardware and software, needed to retire risks associated with technology, science, cost, and schedule.K.P.H., C.B.P., E.M., and all authors affiliated with the Jet Propulsion Laboratory carried out this research at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (grant No. 80NM0018D0004). J.I.L. was the David Baltimore Distinguished Visiting Scientist during the preparation of the SDT report. JPL/Caltech2021