Synthetic turbulence, fractal interpolation, and large-eddy simulation

Basu, Sukanta; Foufoula‐Georgiou, Efi; Porté‐Agel, Fernando

doi:10.1103/physreve.70.026310

Cited by 53 publications

(47 citation statements)

References 49 publications

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“…Scale-invariant patterns have been observed in physical systems far from equilibrium states and shown to be caused by nonlinear interactions between multiple control components affecting the overall system at different time scales (Mandelbrot, 1982;Bak et al, 1987;Basu et al, 2004). Similar complexity of multi-scale control may be operating in numerous neurophysiological systems via networks of coupled feedback loops (Ivanov et al, 1998;Ashkenazy et al, 2002).…”

Section: Physiological Origin Of Scale Invariancementioning

confidence: 99%

“…Simulations indicated that these scale-invariant patterns are unlikely to be caused by simple superposition of independent controlling factors , hence interactions among controlling factors are required to explain such scale invariant patterns. Indeed, mathematical models of physical systems reveal that such scale-invariant patterns can be explained by interactions between multiple control system components-control nodes-that affect the overall system at different time scales (Mandelbrot, 1982;Bak et al, 1987;Basu et al, 2004). Similar complexity may be operating in numerous neurophysiological systems via networks of control nodes coupled by feedback loops (Ivanov et al, 1998;Ashkenazy et al, 2002).…”

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confidence: 99%

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The suprachiasmatic nucleus functions beyond circadian rhythm generation

et al. 2007

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We recently discovered that human activity possesses a complex temporal organization characterized by scale-invariant/self-similar fluctuations from seconds to ~4 hour-statistical properties of fluctuations remain the same at different time scales. Here, we show that scale-invariant activity patterns are essentially identical in humans and rats, and exist for up to ~24 hour: six-times longer than previously documented. Theoretically, such scale-invariant patterns can be produced by a neural network of interacting control nodes-system components with feedback loops-operating at different time scales. However such control nodes have not yet been identified in any neurophysiological model of scale invariance/self-similarity in mammals. Here we demonstrate that the endogenous circadian pacemaker (suprachiasmastic nucleus; SCN), known to modulate locomotor activity with a periodicity of ~24 hour, also acts as a major neural control node responsible for the generation of scale-invariant locomotor patterns over a broad range of time scales from minutes to at least 24 hour (rather than solely at ~24 hour). Remarkably, we found that SCN lesion in rats completely abolished the scale-invariant locomotor pattern between 4 and 24 hour and significantly altered the patterns at time scales <4 hour. Identification of the control nodes of a neural network responsible for scale invariance is the critical first step in understanding the neurophysiological origin of scale invariance/self-similarity. KeywordsActivity; Scale-invariance; Suprachiasmatic nucleus lesion; Human; Rat; Network The suprachiasmastic nucleus (SCN) in mammals generates self-sustained oscillations that orchestrate a wide range of neurophysiologic functions-from cellular processes to overt behaviors-with a time period of ~24 hour (Weaver, 1998). One of the fundamental processes influenced by the SCN is motor activity, which displays clear ~24 hour rhythms even under

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Section: Physiological Origin Of Scale Invariancementioning

confidence: 99%

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confidence: 99%

The suprachiasmatic nucleus functions beyond circadian rhythm generation

et al. 2007

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“…Because of this limitation, instead of performing computationally expensive high-resolution large-eddy simulations, we consider the possibility of enhancing the high-frequency content of coarse-resolution LES data by using the so-called fractal interpolation technique (FIT). FIT is an iterative affine mapping procedure that may be used to construct synthetic deterministic small-scale fields from a few given large-scale interpolating points [28]. FIT is computationally very inexpensive and, more importantly, it preserves the higher-order moments and non-Gaussian probability density function of the velocity increments [28].…”

Section: Fractal Interpolation Of Les-generated Time Seriesmentioning

confidence: 99%

On Space-Time Resolution of Inflow Representations for Wind Turbine Loads Analysis

2012

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Efficient spatial and temporal resolution of simulated inflow wind fields is important in order to represent wind turbine dynamics and derive load statistics for design. Using Fourier-based stochastic simulation of inflow turbulence, we first investigate loads for a utility-scale turbine in the neutral atmospheric boundary layer. Load statistics, spectra, and wavelet analysis representations for different space and time resolutions are compared. Next, large-eddy simulation (LES) is employed with space-time resolutions, justified on the basis of the earlier stochastic simulations, to again derive turbine loads. Extreme and fatigue loads from the two approaches used in inflow field generation are compared. On the basis of simulation studies carried out for three different wind speeds in the turbine's operating range, it is shown that inflow turbulence described using 10-meter spatial resolution and 1 Hz temporal resolution is adequate for assessing turbine loads. Such studies on the investigation of adequate filtering or resolution of inflow wind fields help to establish efficient strategies for LES and other physical or stochastic simulation needed in turbine loads studies.

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“…Olsson, 1998;Basu et al, 2004;Marani and Zanetti, 2007) or assume multifractal behaviour (e.g. Olsson, 1998;Basu et al, 2004). Perica and Foufoula-Georgiou (1996) find that adding noise independent of the smallest resolved scale can lead to deviations in the spatial correlations of their rain fields.…”

Section: Introductionmentioning

confidence: 99%

“…Many other downscaling algorithms have been developed; most perform, however, their downscaling on 1D time series (e.g. Olsson, 1998;Basu et al, 2004;Marani and Zanetti, 2007) or assume multifractal behaviour (e.g. Olsson, 1998;Basu et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

A new algorithm for the downscaling of cloud fields

Venema

Garc

Simmer

2010

Quart J Royal Meteoro Soc

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We present a novel algorithm for the downscaling of three-dimensional cloud fields. The goal of the algorithm is to add realistic subscale variability to a coarse field taking the resolved variability into account. The method is tested by coarse graining high-resolution sparse cumulus and broken stratocumulus clouds in the horizontal plane, downscaling these coarse fields back to the high resolution and comparing the radiative and microphysical properties of these downscaled fields with the original high-resolution fields. The resolutions of the cumulus and stratocumulus clouds used for this purpose are increased by a factor of four and ten, respectively. The downscaling decreases the errors in the flux transmittance and reflectance of the cumulus and stratocumulus cloud fields by at least a factor of ten and three, respectively, compared to utilising the coarse cloud fields. A novel aspect of our algorithm is the fact that it constrains the high-resolution fields of cloud liquid water content as well as the subscale cloud fraction. An alternative version that does not include cloud fraction information is less accurate, but still significantly better than using the coarse fields. The latter downscaling algorithm can also be utilised for the disaggregation of geophysical fields for which fractional coverages are not defined. Furthermore, the downscaling algorithm can be combined with our other algorithms to generate surrogate fields with other constraints, for example, surrogate clouds with a prescribed liquid water content height distribution.

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Synthetic turbulence, fractal interpolation, and large-eddy simulation

Cited by 53 publications

References 49 publications

The suprachiasmatic nucleus functions beyond circadian rhythm generation

The suprachiasmatic nucleus functions beyond circadian rhythm generation

On Space-Time Resolution of Inflow Representations for Wind Turbine Loads Analysis

A new algorithm for the downscaling of cloud fields

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