Visual anemometry for physics-informed inference of wind

[1]  C. Peng,et al.  Integrating terrestrial and aquatic ecosystems to constrain estimates of land-atmosphere carbon exchange , 2023, Nature Communications.

[2]  J. Hentati‐Sundberg,et al.  Seabird surveillance: combining CCTV and artificial intelligence for monitoring and research , 2023, Remote Sensing in Ecology and Conservation.

[3]  Peter J. Baddoo,et al.  Physics-informed dynamic mode decomposition , 2023, Proceedings of the Royal Society A.

[4]  M. Lindenbaum,et al.  Efficient machine learning method for spatio-temporal water surface waves reconstruction from polarimetric images , 2023, Measurement Science and Technology.

[5]  H. Frank,et al.  Research challenges and needs for the deployment of wind energy in hilly and mountainous regions , 2022, Wind Energy Science.

[6]  T. Silverman,et al.  Viewing convection as a solar farm phenomenon broadens modern power predictions for solar photovoltaics , 2022, Journal of Renewable and Sustainable Energy.

[7]  T. Silverman,et al.  Row spacing as a controller of solar module temperature and power output in solar farms , 2022, Journal of Renewable and Sustainable Energy.

[8]  Han Liu,et al.  Simulation-based study of turbulent aquatic canopy flows with flexible stems , 2022, Journal of Fluid Mechanics.

[9]  Chunbo Luo,et al.  See the wind: Wind scale estimation with optical flow and VisualWind dataset. , 2022, The Science of the total environment.

[10]  J. Westerweel,et al.  Combined three-dimensional flow field measurements and motion tracking of freely moving spheres in a turbulent boundary layer , 2022, Journal of Fluid Mechanics.

[11]  Kasim Tasdemir,et al.  BLSTM based night-time wildfire detection from video , 2022, PloS one.

[12]  Lup Wai Chew,et al.  Improving thermal model predictions for naturally ventilated buildings using large-eddy simulations , 2022, Building and Environment.

[13]  Negar Tavassolian,et al.  Real-time high-resolution millimeter-wave imaging for in-vivo skin cancer diagnosis , 2022, Scientific Reports.

[14]  C. Gorlé,et al.  Optimal temperature sensor placement in buildings with buoyancy-driven natural ventilation using computational fluid dynamics and uncertainty quantification , 2021, Building and Environment.

[15]  D. Liberzon,et al.  Turbulent jet through porous obstructions under Coriolis effect: an experimental investigation , 2021, Experiments in Fluids.

[16]  Jasenka Rakas,et al.  Designing airspace for urban air mobility: A review of concepts and approaches , 2021 .

[17]  D. Liberzon,et al.  Next generation combined sonic-hotfilm anemometer: wind alignment and automated calibration procedure using deep learning , 2021, Experiments in Fluids.

[18]  D. Liberzon,et al.  Quasi-geostrophic jet-like flow with obstructions , 2021, Journal of Fluid Mechanics.

[19]  Steven L. Brunton,et al.  Kernel learning for robust dynamic mode decomposition: linear and nonlinear disambiguation optimization , 2021, Proceedings of the Royal Society A.

[20]  William Thielicke,et al.  Particle Image Velocimetry for MATLAB: Accuracy and enhanced algorithms in PIVlab , 2021, Journal of Open Research Software.

[21]  I. Kevrekidis,et al.  Physics-informed machine learning , 2021, Nature Reviews Physics.

[22]  S. Alessandrini,et al.  Assessing Boundary Condition and Parametric Uncertainty in Numerical-Weather-Prediction-Modeled, Long-Term Offshore Wind Speed Through Machine Learning and Analog Ensemble , 2021, Wind Energy Science.

[23]  R. Canham,et al.  Sandpipers go with the flow: Correlations between estuarine conditions and shorebird abundance at an important stopover on the Pacific Flyway , 2021, Ecology and evolution.

[24]  M. Debnath,et al.  New methods to improve the vertical extrapolation of near-surface offshore wind speeds , 2021, Wind Energy Science.

[25]  Andr'e Minoro Fusioka,et al.  Active Fire Detection in Landsat-8 Imagery: a Large-Scale Dataset and a Deep-Learning Study , 2021, ISPRS Journal of Photogrammetry and Remote Sensing.

[26]  Abolfazl Razi,et al.  Aerial Imagery Pile burn detection using Deep Learning: the FLAME dataset , 2020, Comput. Networks.

[27]  S. Davis,et al.  Economic footprint of California wildfires in 2018 , 2020, Nature Sustainability.

[28]  Jennifer L. Cardona,et al.  Wind speed inference from environmental flow–structure interactions , 2020, Flow.

[29]  A. Punia Role of temperature, wind, and precipitation in heavy metal contamination at copper mines: a review , 2020, Environmental Science and Pollution Research.

[30]  Sergio Saponara,et al.  Real-time video fire/smoke detection based on CNN in antifire surveillance systems , 2020, J. Real Time Image Process..

[31]  B. Isom,et al.  American WAKE experimeNt (AWAKEN) , 2020 .

[32]  S. Brunton,et al.  SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics , 2020, Proceedings of the Royal Society A.

[33]  Jennifer L. Cardona,et al.  Near-wake structure of full-scale vertical-axis wind turbines , 2019, Journal of Fluid Mechanics.

[34]  J. Peinke,et al.  Grand challenges in the science of wind energy , 2019, Science.

[35]  Ming Wang,et al.  Forest Fire Susceptibility Modeling Using a Convolutional Neural Network for Yunnan Province of China , 2019, International Journal of Disaster Risk Science.

[36]  Ruilong Chen,et al.  Wildlife surveillance using deep learning methods , 2019, Ecology and evolution.

[37]  E. de Langre Plant Vibrations at all Scales: A Review. , 2019, Journal of experimental botany.

[38]  Frederick P. Gosselin,et al.  Mechanics of a Plant in Fluid Flow. , 2019, Journal of experimental botany.

[39]  C. Schär,et al.  Crossing Multiple Gray Zones in the Transition from Mesoscale to Microscale Simulation over Complex Terrain , 2019, Atmosphere.

[40]  L. Chamorro,et al.  Flow-induced motions of flexible plates: fluttering, twisting and orbital modes , 2019, Journal of Fluid Mechanics.

[41]  D. Liberzon,et al.  Obtaining turbulence statistics of thermally driven anabatic flow by sonic-hot-film combo anemometer , 2018, Environmental Fluid Mechanics.

[42]  Manfred Wendisch,et al.  Role of air-mass transformations in exchange between the Arctic and mid-latitudes , 2018, Nature Geoscience.

[43]  M. Turco,et al.  Exacerbated fires in Mediterranean Europe due to anthropogenic warming projected with non-stationary climate-fire models , 2018, Nature Communications.

[44]  M. Calaf,et al.  Pathways for mitigating thermal losses in solar photovoltaics , 2018, Scientific Reports.

[45]  John O. Dabiri,et al.  The Need for Continued Innovation in Solar, Wind, and Energy Storage , 2018, Joule.

[46]  C. Gorlé,et al.  Predictive large eddy simulations for urban flows: Challenges and opportunities , 2018, Building and Environment.

[47]  Jiyun Song,et al.  Natural ventilation in cities: the implications of fluid mechanics , 2018, Building Research & Information.

[48]  C. Halios,et al.  Field measurement of natural ventilation rate in an idealised full-scale building located in a staggered urban array: Comparison between tracer gas and pressure-based methods , 2018 .

[49]  J. Shutler,et al.  Reduced air–sea CO2 exchange in the Atlantic Ocean due to biological surfactants , 2018, Nature Geoscience.

[50]  A. Marquier,et al.  Foliage motion under wind, from leaf flutter to branch buffeting , 2018, Journal of The Royal Society Interface.

[51]  D. Liberzon,et al.  Separation of upslope flow over a plateau , 2018 .

[52]  C. Gorlé,et al.  Improving urban flow predictions through data assimilation , 2018 .

[53]  P. Goymer A trillion trees , 2018, Nature Ecology & Evolution.

[54]  S. E. Haupt,et al.  Short-term wind forecast of a data assimilation/weather forecasting system with wind turbine anemometer measurement assimilation , 2017 .

[55]  J. Dupuy,et al.  Representativeness of wind measurements in fire experiments: Lessons learned from large-eddy simulations in a homogeneous forest , 2017 .

[56]  Barry Gardiner,et al.  Review: Wind impacts on plant growth, mechanics and damage. , 2016, Plant science : an international journal of experimental plant biology.

[57]  Jiaolong Xu,et al.  Deep Convolutional Neural Networks for Forest Fire Detection , 2016 .

[58]  Gorry Fairhurst,et al.  Limitations of recreational camera traps for wildlife management and conservation research: A practitioner’s perspective , 2015, Ambio.

[59]  Peter Bauer,et al.  The quiet revolution of numerical weather prediction , 2015, Nature.

[60]  Marc Saudreau,et al.  Leaf flutter by torsional galloping: Experiments and model , 2015 .

[61]  Bedrich Benes,et al.  Windy trees , 2014, ACM Trans. Graph..

[62]  Ahmad A. A. Alkhatib A Review on Forest Fire Detection Techniques , 2014, Int. J. Distributed Sens. Networks.

[63]  L. Revéret,et al.  A robust videogrametric method for the velocimetry of wind-induced motion in trees , 2014 .

[64]  Steven Verstockt,et al.  Video fire detection - Review , 2013, Digit. Signal Process..

[65]  A. Bertram,et al.  High concentrations of biological aerosol particles , 2013 .

[66]  Fulvio Scarano,et al.  A high-order time-accurate interrogation method for time-resolved PIV , 2013 .

[67]  John-André Henden,et al.  Towards good practice guidance in using camera‐traps in ecology: influence of sampling design on validity of ecological inferences , 2013 .

[68]  C. Shao,et al.  Wind induced deformation and vibration of a Platanus acerifolia leaf , 2012 .

[69]  Bruno Moulia,et al.  The Multimodal Dynamics of a Walnut Tree: Experiments and Models , 2012 .

[70]  Ian N. Harman,et al.  The Wind in the Willows: Flows in Forest Canopies in Complex Terrain , 2012 .

[71]  H. Nepf Flow and Transport in Regions with Aquatic Vegetation , 2012 .

[72]  M. Heck,et al.  Modeling of the nominal operating cell temperature based on outdoor weathering , 2011 .

[73]  D. Liberzon,et al.  Experimental study of the initial stages of wind waves' spatial evolution , 2011, Journal of Fluid Mechanics.

[74]  J. J. Robledo-Arnuncio Wind pollination over mesoscale distances: an investigation with Scots pine. , 2011, The New phytologist.

[75]  Grant Harris,et al.  Automatic Storage and Analysis of Camera Trap Data , 2010 .

[76]  R. Butlin,et al.  Wind-borne insects mediate directional pollen transfer between desert fig trees 160 kilometers apart , 2009, Proceedings of the National Academy of Sciences.

[77]  Charles A. Doswell,et al.  On the implementation of the enhanced Fujita scale in the USA , 2009 .

[78]  Ryan J. Lowe,et al.  Modeling flow in coral communities with and without waves: A synthesis of porous media and canopy flow approaches , 2008 .

[79]  E. D. Langre Effects of Wind on Plants , 2008 .

[80]  R. Petit,et al.  Some Evolutionary Consequences of Being a Tree , 2006 .

[81]  R. Upstill‐Goddard Air–sea gas exchange in the coastal zone , 2006 .

[82]  Bernhard Wieneke,et al.  Tomographic particle image velocimetry , 2006 .

[83]  T. Swetnam,et al.  Warming and Earlier Spring Increase Western U.S. Forest Wildfire Activity , 2006, Science.

[84]  K. Allwine,et al.  Joint Urban 2003: Study Overview And Instrument Locations , 2006 .

[85]  Ran Nathan Long-Distance Dispersal of Plants , 2006, Science.

[86]  Bert Holtslag,et al.  Preface: GEWEX Atmospheric Boundary-layer Study (GABLS) on Stable Boundary Layers , 2006 .

[87]  Joseph Katz,et al.  Instantaneous pressure and material acceleration measurements using a four-exposure PIV system , 2006 .

[88]  Philip Lewis,et al.  3D modelling of forest canopy structure for remote sensing simulations in the optical and microwave domains , 2006 .

[89]  Bruno Moulia,et al.  Measurement of wind-induced motion of crop canopies from digital video images , 2005 .

[90]  Jay R Reichman,et al.  Evidence for landscape-level, pollen-mediated gene flow from genetically modified creeping bentgrass with CP4 EPSPS as a marker. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[91]  Paul Gipe,et al.  “Wind Power” , 2004 .

[92]  Á. Felicísimo,et al.  Wind as a Long-Distance Dispersal Vehicle in the Southern Hemisphere , 2004, Science.

[93]  Ahsan Kareem,et al.  Gust loading factor: past, present and future , 2003 .

[94]  R. Britter,et al.  FLOW AND DISPERSION IN URBAN AREAS , 2003 .

[95]  Jun Zhang,et al.  Drag reduction through self-similar bending of a flexible body , 2002, Nature.

[96]  James K. M. Brown,et al.  Aerial Dispersal of Pathogens on the Global and Continental Scales and Its Impact on Plant Disease , 2002, Science.

[97]  S. Levin,et al.  Mechanisms of long-distance dispersal of seeds by wind , 2002, Nature.

[98]  R. Nathan,et al.  Long-distance dispersal of tree seeds by wind , 2001, Ecological Research.

[99]  P. Linden THE FLUID MECHANICS OF NATURAL VENTILATION , 1999 .

[100]  Robert W. Butler,et al.  Wind assistance: A requirement for migration of shorebirds? , 1997 .

[101]  K. Niklas Differences between Acer saccharum Leaves from Open and Wind-Protected Sites , 1996 .

[102]  U. Siegenthaler,et al.  Atmospheric carbon dioxide and the ocean , 1993, Nature.

[103]  S. Vogel Drag and Reconfiguration of Broad Leaves in High Winds , 1989 .

[104]  Simon A. Levin,et al.  A Theoretical Framework for Data Analysis of Wind Dispersal of Seeds and Pollen , 1989 .

[105]  R. Stull An Introduction to Boundary Layer Meteorology , 1988 .

[106]  Steven Vogel,et al.  Drag and Flexibility in Sessile Organisms , 1984 .

[107]  M. A. R. Koehl,et al.  Effects of Sea Anemones on the Flow Forces They Encounter , 1977 .

[108]  F. Burrows WIND‐BORNE SEED AND FRUIT MOVEMENT , 1975 .

[109]  A. A. Malik,et al.  Wind Forces and the Proximity of Cooling Towers to Each Other , 1966, Nature.

[110]  F. Veron,et al.  The turbulent airflow over wind generated surface waves , 2019, European Journal of Mechanics - B/Fluids.

[111]  O. Dupré,et al.  Thermal Behavior of Photovoltaic Devices , 2017 .

[112]  Daseswara Rao Yendluri,et al.  Fluid-structure Interactions and Flow Induced Vibrations: A Review , 2016 .

[113]  C. Meneveau,et al.  Measurement of unsteady loading and power output variability in a micro wind farm model in a wind tunnel , 2016 .

[114]  K. Davis,et al.  North America ’ s net terrestrial carbon exchange with the atmosphere 1990 – 2009 , 2014 .

[115]  Marc Zebisch,et al.  Wind effect on PV module temperature: Analysis of different techniques for an accurate estimation , 2013 .

[116]  S. Monismith Hydrodynamics of Coral Reefs , 2007 .

[117]  Earl H. Dowell,et al.  Modeling of Fluid-Structure Interaction , 2001 .

[118]  J. Finnigan Turbulence in plant canopies , 2000 .

[119]  D. Aylor,et al.  The Role of Intermittent Wind in the Dispersal of Fungal Pathogens , 1990 .

[120]  V. Drake,et al.  The Influence of Atmospheric Structure and Motions on Insect Migration , 1988 .

[121]  P. Bearman VORTEX SHEDDING FROM OSCILLATING BLUFF BODIES , 1984 .