Turbulent drag reduction by fibrous permeable substrates
Abstract
navigation or timing. Multi-point observations that are inhomogeneous in space and often irregular in time are now central to the monitoring of space weather. SuperMAG collates 100+ ground based magnetometer observations which monitor ground magnetic field perturbations during geomagnetic storms and substorms, and the pulsations on closed field lines (Pc waves) excited at storm onset and by energetic ions injected into the inner magnetosphere. Total Electron Content (TEC) estimates electron column-integrated density along the ray path between GPS satellites and the ground. Incoherent scatter radar chains such as SuperDARN rely on the backscatter from ionospheric irregularities to monitor ionospheric convection. The raw observations are at seconds-to-minutes time resolution, at of order 100 observing points, with diurnal and seasonal biases in time and geographical biases in space. The challenge is to reduce the full dataset to obtain operationally relevant information. Traditionally this has been a trade-off between (i) working with a small subset of data from a few individual stations-observing points and (ii) assimilating the full set of irregular multi-point observations onto a time-varying spatially regular grid or map. We propose new approaches to quantitatively parameterize these large-scale datasets- (i) networks and (ii) statistics of bursts and extremes. A successful parameterization would capture the spatio-temporal dynamics of (i) the full spatial correlation pattern and (ii) the far-from Gaussian nature of turbulent fluctuations, their extremes and bursty dynamics. The underlying physics is inherently non-linear, and our goal is to identify relevant parameters that quantify- (i) how this non-linear behaviour is ordered by the state of the solar wind and magnetosphere and (ii) on what spatial and temporal scales there is ‘memory’ and hence predictability.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 04, 2023
- Source ID
- FA86552217062
Entities
People
- Ricardo Garcia-Mayoral
Organizations
- Air Force Office of Scientific Research
- United States Air Force
- University of Cambridge