The Detection, Genesis, and Modeling of Turbulence Intermittency in the Stable Atmospheric Surface Layer

Intermittent transitions between turbulent and non-turbulent states are ubiquitous in the stable atmospheric surface layer (ASL). Data from two field experiments in Utqiagvik, Alaska, and from direct numerical simulations are used to probe these state transitions so as to (i) identify statistical metrics for the detection of intermittency, (ii) probe the physical origin of turbulent bursts, and (iii) quantify intermittency effects on overall fluxes and their representation in closure models. The analyses reveal three turbulence regimes, two of which correspond to weakly turbulent periods accompanied by intermittent behavior (regime 1: intermittent, regime 2: transitional), while the third is associated with a fully turbulent flow. Based on time series of the turbulence kinetic energy (TKE), two non-dimensional parameters are proposed to diagnostically categorize the ASL state into these regimes; the first characterizes the weakest turbulence state, while the second describes the range of turbulence variability. The origins of intermittent turbulence activity are then investigated based on the TKE budget over the identified bursts. While the quantitative results depend on the height, the analyses indicate that these bursts are predominantly advected by the mean flow, produced locally by mechanical shear, or lofted from lower levels by turbulent ejections. Finally, a new flux model is proposed using the vertical velocity variance in combination with different mixing length scales. The model provides improved representation (correlation coefficients with observations of 0.61 for momentum and 0.94 for sensible heat) compared to Monin–Obukhov similarity (correlation coefficients of 0.0047 for momentum and 0.49 for sensible heat), thus opening new pathways for improved parametrizations in coarse atmospheric models.

Evaluation of Blow-Off Dynamics in Aero-Engine Combustors Using Recurrence Quantification Analysis

The lean blow-out performance of an engine and the ability to re-ignite the flame, especially at high-altitude conditions, are important aspects for the safe operability of airplanes. The operability margins of the engine could be extended if it was possible to predict the occurrence of flame blowout from in-flight measurements and take actions to dynamically control the flame behaviour before complete extinction. In this work, the use of Re-currence Quantification Analysis (RQA), an established tool for the analysis of non-linear dynamical systems, is explored to reconstruct and study the blow-off dynamics starting from pressure measurements taken from blow-off experiments of an engine rig. It is shown that the dynamics of the combustor exhibit chaotic characteristics far away from blow-off and that the dynamics become more coherent as the blow-off condition is approached. The degree of determinism and recurrence rate are studied during the entire combustor’s dynamics, from stable flame to flame extinction. It is shown that the flame extinction is anticipated by an increase of the degree of determinism and recurrence rate at all investigated conditions, which indicates intermittent behavior of the combustor before the blow-off condition is reached. Therefore, in the configuration investigated here, the determinism and the recurrence rate of the system could be good predictors of blow-off occurrence and could potentially enable control actions to avoid flame extinction. This study opens up new possibilities for engine control and operability. The development of real-time RQA should be addressed in future research.

Application of Semi-Empirical Models Based on Satellite Images for Estimating Solar Irradiance in Korea

The application of solar energy as a renewable energy source has significantly escalated owing to its abundance and availability worldwide. However, the intermittent behavior of solar irradiance is a serious disadvantage for electricity grids using photovoltaic (PV) systems. Thus, reliable solar irradiance data are vital to achieve consistent energy production. Geostationary satellite images have become a solution to this issue, as they represent a database for solar irradiance on a massive spatiotemporal scale. The estimation of global horizontal irradiance (GHI) using satellite images has been developed based on physical and semi-empirical models, but only a few studies have been dedicated to modeling GHI using semi-empirical models in Korea. Therefore, this study conducted a comparative analysis to determine the most suitable semi-empirical model of GHI in Korea. Considering their applicability, the Beyer, Rigollier, Hammer, and Perez, models were selected to estimate the GHI over Seoul, Korea. After a comparative evaluation, the Hammer model was determined to be the best model. This study also introduced a hybrid model and applied a long short-term memory (LSTM) model in order to improve prediction accuracy. The hybrid model exhibited a smaller root-mean-square error (RMSE), 97.08 W/m2, than that of the Hammer model, 103.92 W/m2, while producing a comparable mean-bias error. Meanwhile, the LSTM model showed the potential to further reduce the RMSE by 11.2%, compared to the hybrid model.

Observing seismic signatures of slow slip events with unsupervised learning

<p>Slow slip events are observed in geodetic data, and are occasionally associated with seismic signatures such as slow earthquakes (low-frequency earthquakes, tectonic tremors). In particular, it was shown that swarms of slow earthquake can correlate with slow slip events occurrence, and allowed to reveal the intermittent behavior of several slow slip events. This observation was possible thanks to detailed analysis of slow earthquakes catalogs and continuous geodetic data, but in every case, was limited to particular classes of seismic signatures. In the present study, we propose to infer the classes of seismic signals that best correlate with the observed geodetic data, including the slow slip event. We use a scattering network (a neural network with wavelet filters) in order to find meaningful signal features, and apply a hierarchical clustering algorithm in order to infer classes of seismic signal. We then apply a regression algorithm in order to predict the geodetic data, including slow slip events, from the occurrence of inferred seismic classes. This allow to (1) identify seismic signatures associated with the slow slip events as well as (2) infer the the contribution of each classes to the overall displacement observed in the geodetic data. We illustrate our strategy by revisiting the slow-slip event of 2006 that occurred beneath Guerrero, Mexico.</p>

A plausible mechanism for Drosophila larva intermittent behavior

The behavior of many living organisms is not continuous. Rather, activity emerges in bouts that are separated by epochs of rest, a phenomenon known as intermittent behavior. Although intermittency is ubiquitous across phyla, empirical studies are scarce and the underlying neural mechanisms remain unknown. Here we present the first empirical evidence of intermittency during Drosophila larva free exploration. We report power-law distributed rest-bout and log-normal distributed activity-bout durations. We show that a stochastic network model can transition between power-law and non-power-law distributed states and we suggest a plausible neural mechanism for the alternating rest and activity in the larva. Finally, we discuss possible implementations in behavioral simulations extending spatial Levy-walk or coupled-oscillator models with temporal intermittency.

Generalized Description of Intermittency in Turbulence via Stochastic Methods

We present a generalized picture of intermittency in turbulence that is based on the theory of stochastic processes. To this end, we rely on the experimentally and numerically verified finding by R. Friedrich and J. Peinke [Phys. Rev. Lett. 78, 863 (1997)] that allows for an interpretation of the turbulent energy cascade as a Markov process of velocity increments in scale. It is explicitly shown that phenomenological models of turbulence, which are characterized by scaling exponents ζn of velocity increment structure functions, can be reproduced by the Kramers–Moyal expansion of the velocity increment probability density function that is associated with a Markov process. We compare the different sets of Kramers–Moyal coefficients of each phenomenology and deduce that an accurate description of intermittency should take into account an infinite number of coefficients. This is demonstrated in more detail for the case of Burgers turbulence that exhibits pronounced intermittency effects. Moreover, the influence of nonlocality on Kramers–Moyal coefficients is investigated by direct numerical simulations of a generalized Burgers equation. Depending on the balance between nonlinearity and nonlocality, we encounter different intermittency behavior that ranges from self-similarity (purely nonlocal case) to intermittent behavior (intermediate case that agrees with Yakhot’s mean field theory [Phys. Rev. E 63 026307 (2001)]) to shock-like behavior (purely nonlinear Burgers case).

Late Miocene contourite channel system reveals intermittent overflow behavior

Paleoceanographic information from submarine overflows in the vicinity of oceanic gateways is of major importance for resolving the role of ocean circulation in modulating Earth’s climate. Earth system models are currently the favored way to study the impact of gateways on global-scale processes, but studies on overflow-related deposits are more suitable to understand the detailed changes. Such deposits, however, had not yet been documented in outcrop. Here, we present a unique late Miocene contourite channel system from the Rifian Corridor (Morocco) related to the initiation of Mediterranean Outflow Water (MOW). Two channel branches were identified consisting of three vertically stacked channelized sandstone units encased in muddy deposits. Both branches have different channel-fill characteristics. Our findings provide strong evidence for intermittent behavior of overflow controlled by tectonic processes and regional climatic change. These fluctuations in paleo-MOW intermittently influenced global ocean circulation.

Noise metrics analysis in schools near airports: A Brazilian case study

This study compares metrics for environmental noise diagnosis in schools at airport vicinity. The goal is to analyze and identify the most suitable criteria for scaling aircraft noise impact over schools, during landing and take-off operations. A Brazilian case study is conducted, based on the noise mapping and sound level verification. The day-night average noise level (DNL) and the time above limit (TA) are investigated using acoustic simulation and noise mapping and in order to identify the critical receivers. Results of DNL and TA for two schools at airport surroundings show that the criteria adopted by the municipal and airport authorities to describe the airport noise are unsatisfactory and do not reflect the intermittent behavior of this type of noise. It was verified that individual receiver analysis, based on noise interruptions thought TA parameter is more suitable for evaluation of noise impact over schools at airport vicinity.

Single Residential Load Forecasting Using Deep Learning and Image Encoding Techniques

The integration of more renewable energy resources into distribution networks makes the operation of these systems more challenging compared to the traditional passive networks. This is mainly due to the intermittent behavior of most renewable resources such as solar and wind generation. There are many different solutions being developed to make systems flexible such as energy storage or demand response. In the context of demand response, a key factor is to estimate the amount of load over time properly to better manage the demand side. There are many different forecasting methods, but the most accurate solutions are mainly found for the prediction of aggregated loads at the substation or building levels. However, more effective demand response from the residential side requires prediction of energy consumption at every single household level. The accuracy of forecasting loads at this level is often lower with the existing methods as the volatility of single residential loads is very high. In this paper, we present a hybrid method based on time series image encoding techniques and a convolutional neural network. The results of the forecasting of a real residential customer using different encoding techniques are compared with some other existing forecasting methods including SVM, ANN, and CNN. Without CNN, the lowest mean absolute percentage of error (MAPE) for a 15 min forecast is above 20%, while with existing CNN, directly applied to time series, an MAPE of around 18% could be achieved. We find the best image encoding technique for time series, which could result in higher accuracy of forecasting using CNN, an MAPE of around 12%.