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Scanning Doppler lidar measurements of drag force on a solitary tree

Published online by Cambridge University Press:  28 April 2021

Nikolas Angelou Open the ORCID record for Nikolas Angelou [Opens in a new window] ,
Jakob Mann Open the ORCID record for Jakob Mann [Opens in a new window]  and
Ebba Dellwik Open the ORCID record for Ebba Dellwik [Opens in a new window]
Nikolas Angelou*
Affiliation:
Department of Wind Energy, Technical University of Denmark, Roskilde, 4000, Denmark
Jakob Mann
Affiliation:
Department of Wind Energy, Technical University of Denmark, Roskilde, 4000, Denmark
Ebba Dellwik
Affiliation:
Department of Wind Energy, Technical University of Denmark, Roskilde, 4000, Denmark
*
†Email address for correspondence: nang@dtu.dk
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Abstract

Trees are known to reduce the wind momentum efficiently. Yet, firm quantitative estimates of their contribution to the land surface drag have remained elusive, partly because trees have complex shapes that consist of elastic multi-scale elements. This structural complexity makes trees inherently difficult to scale for wind tunnel studies. Here, we test a new method for quantifying the drag force on a solitary mature tree in its natural environment. The method is based on the application of mass and momentum conservation over a control volume that encloses the tree. For this control volume, the drag force is estimated through the momentum deficit in the wake. For the characterisation of the heterogeneous and high-gradient wind field in the wake, spatially distributed measurements of the wind vector were acquired using three synchronously scanning wind lidar instruments in a vertical plane encompassing the wake. The resulting drag force estimate is compared to a reference measurement from a tree-mounted sensor at the base of the stem. We find that the drag force in both methods shows a dependence on the wind speed raised to an exponent of 1.8 and that the drag force, based on the momentum deficit method, is consistently underestimated by 1 %–10 %. Potential reasons for this bias are discussed in light of the accuracy of both methods. The relatively close agreement between the two methods indicates that scanning Doppler lidar measurements can be used to determine the drag force on complex objects in their natural environment, such as trees.

JFM classification

Aerodynamics: Flow-structure interactions Wakes/Jets: Wakes Geophysical and Geological Flows: Atmospheric flows
Type
JFM Papers
Information
Journal of Fluid Mechanics , Volume 917 , 25 June 2021 , A30
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Copyright
© The Author(s), 2021. Published by Cambridge University Press

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References

REFERENCES

Angelou, N. & Dellwik, E. 2020 Single tree project: the short-range windscanner experiment. Tech. Rep. DTU Wind Energy E-0207. Technical University of Denmark.Google Scholar
Angelou, N., Dellwik, E. & Mann, J. 2019 Wind load estimation on an open-grown European oak tree. Forestry 92 (4), 381–392.CrossRefGoogle Scholar
Angelou, N., Mann, J., Sjöholm, M. & Courtney, M. 2012 Direct measurement of the spectral transfer function of a laser based anemometer. Rev. Sci. Instrum. 83 (3), 033111.Google ScholarPubMed
Bai, K., Meneveau, C. & Katz, J. 2012 Near-wake turbulent flow structure and mixing length downstream of a fractal tree. Boundary-Layer Meteorol. 143 (2), 285–308.Google Scholar
Bechmann, A., Berg, J., Courtney, M.S., Jørgensen, H.E., Mann, J. & Sørensensen, N.N. 2009 The bolund experiment: overview and background. Tech. Rep. Risø-R-1658(EN). Risø DTU – National Laboratory for Sustainable Energy.Google Scholar
Blackburn, G.R.A. 1997 The growth and mechanical response of trees to wind loading. PhD thesis, University of Manchester.Google Scholar
Chan, W.L., et al. 2020 Experimental study of wind load on tree using scaled fractal tree model. In International Journal of Modern Physics B 34, 2040087.Google Scholar
Dellwik, E. & Jensen, N.O. 2000 Internal equilibrium layer growth over forest. Theor. Appl. Climatol. 66, 173–184.CrossRefGoogle Scholar
Dellwik, E., van der Laan, M.P., Angelou, N., Mann, J. & Sogachev, A. 2019 Observed and modeled near-wake flow behind a solitary tree. Agric. Forest Meteorol. 265 (October 2018), 78–87.Google Scholar
Dong, Z., Mu, Q., Luo, W., Qinan, G., Lu, P. & Wang, H. 2008 An analysis of drag force and moment for upright porous wind fences. J. Geophys. Res.: Atmos. 113 (4), 1–8.Google Scholar
Gardiner, B.A. 1992 Mathematical modelling of the static and dynamic characteristics of plantation trees. In Mathematical Modelling of Forest Ecosystems (ed. J. Franke & A. Roeder), pp. 40–61. Sauerländer.Google Scholar
Gardiner, B.A., Stagey, G.R., Belcher, R.E. & Wood, C.J. 1997 Field and wind tunnel assessments of the implications of respacing and thinning for tree stability. Forestry 70 (3), 233–252.Google Scholar
Garrat, J.R. 1990 The internal boundary layer – a review. Boundary-Layer Meteorol. 50, 171–203.CrossRefGoogle Scholar
Gosselin, F.P. 2019 Mechanics of a plant in fluid flow. J. Expl Bot. 70 (14), 3533–3548.Google Scholar
Gromke, C. & Ruck, B. 2008 Aerodynamic modelling of trees for small-scale wind tunnel studies. Forestry 81 (3), 243–258.CrossRefGoogle Scholar
Hagen, L.J. & Skidmore, E.L. 1971 Windbreak drag as influenced by porosity. Trans. ASAE 14 (3), 464–465.Google Scholar
Hasager, C.B., Nielsen, N.W., Jensen, N.O., Bøgh, E., Christensen, J.H., Dellwik, E. & Søgaard, G. 2003 Effective roughness calculated from satellite-derived land cover maps and hedge-information used in a weather forecasting model. Boundary-Layer Meteorol. 109 (3), 227–254.Google Scholar
Högström, U. 1988 Non-dimensional wind and temperature profiles in the atmospheric surface layer: a re-evaluation. Boundary-Layer Meteorol. 42 (1–2), 55–78.Google Scholar
Hopwood, W.P. 1996 Observations and parametrization of momentum transfer in heterogeneous terrain consisting of regularly spaced obstacles. Boundary-Layer Meteorol. 81 (3–4), 217–243.Google Scholar
Kane, B. & Smiley, E.T. 2006 Drag coefficients and crown area estimation of red maple. Can. J. Forest Res. 36 (8), 1951–1958.CrossRefGoogle Scholar
Koizumi, A., Motoyama, J., Sawata, K., Sasaki, Y. & Hirai, T. 2010 Evaluation of drag coefficients of poplar-tree crowns by a field test method. J. Wood Sci. 56 (3), 189–193.Google Scholar
Kundu, P., Cohen, I. & Dowling, D. 2012 Fluid Mechanics. Elsevier Inc.Google Scholar
de Langre, E. 2008 Effects of wind on plants. Annu. Rev. Fluid Mech. 40 (1), 141–168.Google Scholar
Lee, J.P., Lee, E.J. & Lee, S.J. 2014 Shelter effect of a fir tree with different porosities. J. Mech. Sci. Technol. 28 (2), 565–572.Google Scholar
Lv, P., Dong, Z., Mu, Q., Luo, W. & Qian, G. 2014 An analysis of drag force on wind of shrubs simulated in a wind tunnel. Environ. Earth Sci. 71 (1), 125–131.CrossRefGoogle Scholar
Manickathan, L., Defraeye, T., Allegrini, J., Derome, D. & Carmeliet, J. 2018 Comparative study of flow field and drag coefficient of model and small natural trees in a wind tunnel. Urban For. Urban Gree. 35, 230–239.CrossRefGoogle Scholar
Mayhead, G.J. 1973 Some drag coefficients for british forest trees derived from wind tunnel studies. Agr. Meteorol. 12 (C), 123–130.Google Scholar
Mikkelsen, T., Sjöholm, M., Angelou, N. & Mann, J. 2017 3D Windscanner lidar measurements of wind and turbulence around wind turbines, buildings and bridges. IOP Conf. Ser.: Mater. Sci. 276, 012004.Google Scholar
Miller, D.R., Rosenberg, N.J. & Bagley, W.T. 1974 Wind reduction by a highly permeable tree shelter-belt. Agr. Meteorol. 14 (1–2), 321–333.CrossRefGoogle Scholar
Moore, J.R. & Maguire, D.A. 2005 Natural sway frequencies and damping ratios of trees: influence of crown structure. Trees 19 (4), 363–373.CrossRefGoogle Scholar
Peña, A., Dellwik, E. & Mann, J. 2019 A method to assess the accuracy of sonic anemometer measurements. Atmos. Meas. Tech. 12 (1), 237–252.CrossRefGoogle Scholar
Raupach, M.R. 1992 Drag and drag partition on rough surfaces. Boundary-Layer Meteorol. 60 (4), 375–395.CrossRefGoogle Scholar
Rudnicki, M., Mitchell, S.J. & Novak, M.D. 2004 Wind tunnel measurements of crown streamlining and drag relationships for three conifer species. Can. J. Forest Res. 34 (3), 666–676.CrossRefGoogle Scholar
Seginer, I. 1972 Windbreak drag calculated from the horizontal velocity field. Boundary-Layer Meteorol. 3 (1), 87–97.CrossRefGoogle Scholar
Seginer, I. & Sagi, R. 1971 Drag on a windbreak in two-dimensional flow. Agr. Meteorol. 9, 323–333.CrossRefGoogle Scholar
Sjöholm, M., Angelou, N., Courtney, M., Dellwik, E., Mann, J., Mikkelsen, T.K. & Pedersen, A.T. 2018 Synchronized agile beam scanning of coherent continuous-wave Doppler lidars for high-resolution wind field characterization. In Proceedings of the 19th Coherent Laser Radar Conference (pp. 70–73), vol. 19. Cooperative Institute for Research in Environmental Sciences.Google Scholar
Spoelstra, A., de Martino Norante, L., Terra, W., Sciacchitano, A. & Scarano, F. 2019 On-site cycling drag analysis with the Ring of Fire. Exp. Fluids 60 (6), 90.CrossRefGoogle Scholar
Tadrist, L., Saudreau, M., Hemon, P., Amandolese, X., Marquier, A., Leclercq, T. & De Langre, E. 2018 Foliage motion under wind, from leaf flutter to branch buffeting. J. R. Soc. Interface 15 (142), 9.CrossRefGoogle ScholarPubMed
Terra, W., Sciacchitano, A., Scarano, F. & van Oudheusden, B.W. 2018 Drag resolution of a PIV wake rake for transiting models. Exp. Fluids 59 (7), 120.CrossRefGoogle Scholar
Terra, W., Sciacchitano, A. & Shah, Y.H. 2019 Aerodynamic drag determination of a full-scale cyclist mannequin from large-scale PTV measurements. Exp. Fluids 60 (2), 29.CrossRefGoogle Scholar
Thom, A.S. 1971 Momentum absorption by vegetation. Q. J. R. Meteorol. Soc. 97 (414), 414–428.CrossRefGoogle Scholar
Vogel, S. 1984 Drag and flexiility in sessile organisms. Am. Zool. 24, 37–44.CrossRefGoogle Scholar
Vollsinger, S., Mitchell, S.J, Byrne, K.E., Novak, M.D. & Rudnicki, M. 2005 Wind tunnel measurements of crown streamlining and drag relationships for several hardwood species. Can. J. Forest Res. 35 (5), 1238–1249.CrossRefGoogle Scholar
Whittaker, P., Wilson, C., Aberle, J., Rauch, H.P. & Xavier, P. 2013 A drag force model to incorporate the reconfiguration of full-scale riparian trees under hydrodynamic loading. J. Hydraul Res. 51 (5), 569–580.CrossRefGoogle Scholar
Woodruff, N.P., Fryrear, D.W. & Lyles, L. 1963 Engineering similitude and momentum transfer principles applied to shelterbelt studies. Trans. ASAE 6 (1), 41–47.CrossRefGoogle Scholar
Wyngaard, J.C. 2010 Turbulence in the Atmosphere. Cambridge University Press.CrossRefGoogle Scholar