Relationship between statistical characteristics of transmitted and reflected waves in a medium with large-scale irregularities

1978 ◽  
Vol 21 (9) ◽  
pp. 895-897 ◽  
Author(s):  
A. I. Saichev
Keyword(s):  
Large Scale ◽  
Statistical Characteristics ◽  
Reflected Waves

A proposed wavy shield for suppression of supersonic jet noise utilizing reflections

2021 ◽  
Vol 20 (1-2) ◽  
pp. 4-34
Author(s):  
Reda R Mankbadi ◽  
Saman Salehian
Keyword(s):  
Large Scale ◽  
Flat Surface ◽  
Near Field ◽  
Supersonic Jet ◽  
Jet Noise ◽  
Dominant Frequency ◽  
Wavy Surface ◽  
Initial Region ◽  
Reflected Waves ◽  
Free Jet

In this work we propose replacing the conventional flat-surface airframe that shields the engine by a wavy surface. The basic principle is to design a wavy pattern to reflect the incoming near-field flow and acoustic perturbations into waves of a particular dominant frequency. The reflected waves will then excite the corresponding frequency of the large-scale structure in the initial region of the jet’s shear layer. By designing the frequency of the reflected waves to be the harmonic of the fundamental frequency that corresponds to the radiated peak noise, the two frequency-modes interact nonlinearly. With the appropriate phase difference, the harmonic dampens the fundamental as it extracts energy from it to amplify. The outcome is a reduction in the peak noise. To evaluate this concept, we conducted Detached Eddy Simulations for a rectangular supersonic jet with and without the wavy shield and verified our numerical results with experimental data for a free jet, as well as, for a jet with an adjacent flat surface. Results show that the proposed wavy surface reduces the jet noise as compared to that of the corresponding flat surface by as much as 4 dB.

scholarly journals Risk Assessment of Large-Scale Infrastructure Projects—Assumptions and Context

2020 ◽  
Vol 11 (1) ◽  
pp. 109
Author(s):  
Jana Korytárová ◽  
Vít Hromádka
Keyword(s):  
Risk Assessment ◽  
Economic Efficiency ◽  
Large Scale ◽  
Cash Flows ◽  
Statistical Characteristics ◽  
Individual Risk ◽  
Dependent Variables ◽  
Research Findings ◽  
Input Variables ◽  
Individual Risk Factors

This article deals with the partial outputs of large-scale infrastructure project risk assessment, specifically in the field of road and motorway construction. The Department of Transport spends a large amount of funds on project preparation and implementation, which however, must be allocated effectively, and with knowledge of the risks that may accompany them. Therefore, documentation for decision-making on project financing also includes their analysis. This article monitors the frequency of occurrence of individual risk factors within the qualitative risk analysis, with the support of the national risk register, and identifies dependent variables that represent part of the economic cash flows for determining project economic efficiency. At the same time, it compares these dependent variables identified by sensitivity analysis with critical variables, followed by testing the interaction of the critical variables’ effect on the project efficiency using the Monte Carlo method. A partial section of the research was focused on the analysis of the probability distribution of input variables, especially “the investment costs” and “time savings of infrastructure users” variables. The research findings conclude that it is necessary to pay attention to the setting of statistical characteristics of variables entering the economic efficiency indicator calculations, as the decision of whether or not to accept projects for funding is based on them.

Statistical Characteristics and Correlation Analysis for Control Index of Soil-Aggregate Mixture

2013 ◽  
Vol 718-720 ◽  
pp. 1872-1877 ◽  
Author(s):  
Xu Xi Chang ◽  
Xie Jian Ming ◽  
Jiang Ling Fa ◽  
Chen Shan Xiong
Keyword(s):  
Grain Size ◽  
Correlation Analysis ◽  
Normal Distribution ◽  
Large Scale ◽  
Soil Aggregate ◽  
Site Preparation ◽  
Statistical Characteristics ◽  
Coarse Grain ◽  
Systematic Research ◽  
Large Variability

Currently, the soil-aggregate mixture has been widely used in some large-scale site preparation projects, compaction characteristics has been pay more attention by many engineers and researchers. However, systematic research is insufficient on how to choose the filler. Moreover, some industry regulations are different on the requirements about filler. This paper relies on a certain big site preparation projects, discussing statistical characteristics and correlation on the maximal grain size, contents of the coarse grain, gradation and other parameters of soil-aggregate mixture. The results show that the maximal and the median grain size have small discreteness and normal distribution, indicating site filler is easy to reach the requirement; The coefficient of curvature, coefficient of nonuniformity and the coarse grain content have large discreteness, and dont obey normal distribution, indicating the filler has large variability. The median grain size is highly relevant to the coarse grain content; the maximal grain size isnt relevant to the coefficient of nonuniformity, the coefficient of curvature and the coarse grain content. According to the results of correlation analysis, we suggest that the importance order follow by coarse grain content, the maximum grain size and gradation for the control parameters of filler. This research may be significant to other similar projects.

scholarly journals Mountain waves produced by a stratified shear flow with a boundary layer. Part II: Form drag, wave drag, and transition from downstream sheltering to upstream blocking

2020 ◽  
Author(s):  
François Lott ◽  
Bruno Deremble ◽  
Clément Soufflet
Keyword(s):  
Reynolds Stress ◽  
Large Scale ◽  
Wave Drag ◽  
Wave Crest ◽  
Form Drag ◽  
Mountain Waves ◽  
Reflected Waves ◽  
Stable Case ◽  
Large Scale Flow ◽  
Neutral Case

AbstractThe non-hydrostatic version of the mountain flow theory presented in Part I is detailed. In the near neutral case, the surface pressure decreases when the flow crosses the mountain to balance an increase in surface friction along the ground. This produces a form drag which can be predicted qualitatively. When stratification increases, internal waves start to control the dynamics and the drag is due to upward propagating mountain waves as in part I. The reflected waves nevertheless add complexity to the transition. First, when stability increases, upward propagating waves and reflected waves interact destructively and low drag states occur. When stability increases further, the interaction becomes constructive and high drag state are reached. In very stable cases the reflected waves do not affect the drag much. Although the drag gives a reasonable estimate of the Reynolds stress, its sign and vertical profile are profoundly affected by stability. In the near neutral case the Reynolds stress in the flow is positive, with maximum around the top of the inner layer, decelerating the large-scale flow in the inner layer and accelerating it above. In the more stable cases, on the contrary, the large-scale flow above the inner layer is decelerated as expected for dissipated mountain waves. The structure of the flow around the mountain is also strongly affected by stability: it is characterized by non separated sheltering in the near neutral cases, by upstream blocking in the very stable case, and at intermediate stability by the presence of a strong but isolated wave crest immediately downstream of the ridge.

scholarly journals Statistical characteristics of amino acid covariance as possible descriptors of viral genomic complexity

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
C. K. Sruthi ◽  
Meher K. Prakash
Keyword(s):  
Amino Acid ◽  
Avian Influenza ◽  
Large Scale ◽  
Interaction Analysis ◽  
Amino Acid Level ◽  
Statistical Characteristics ◽  
Network Density ◽  
Node Degree ◽  
Host Immune System ◽  
Richter Scale

AbstractAt the sequence level it is hard to describe the complexity of viruses which allows them to challenge host immune system, some for a few weeks and others up to a complete compromise. Paradoxically, viral genomes are both complex and simple. Complex because amino acid mutation rates are very high, and yet viruses remain functional. Simple because they have barely around 10 types of proteins, so viral protein-protein interaction networks are not insightful. In this work we use fine-grained amino acid level information and their evolutionary characteristics obtained from large-scale genomic data to develop a statistical panel, towards the goal of developing quantitative descriptors for the biological complexity of viruses. Networks were constructed from pairwise covariation of amino acids and were statistically analyzed. Three differentiating factors arise: predominantly intra- vs inter-protein covariance relations, the nature of the node degree distribution and network density. Interestingly, the covariance relations were primarily intra-protein in avian influenza and inter-protein in HIV. The degree distributions showed two universality classes: a power-law with exponent −1 in HIV and avian-influenza, random behavior in human flu and dengue. The calculated covariance network density correlates well with the mortality strengths of viruses on the viral-Richter scale. These observations suggest the potential utility of the statistical metrics for describing the covariance patterns in viruses. Our host-virus interaction analysis point to the possibility that host proteins which can interact with multiple viral proteins may be responsible for shaping the inter-protein covariance relations. With the available data, it appears that network density might be a surrogate for the virus Richter scale, however the hypothesis needs a re-examination when large scale complete genome data for more viruses becomes available.

scholarly journals Detecting event-related changes of multivariate phase coupling in dynamic brain networks

2012 ◽  
Vol 107 (7) ◽  
pp. 2020-2031 ◽  
Author(s):  
Ryan T. Canolty ◽  
Charles F. Cadieu ◽  
Kilian Koepsell ◽  
Karunesh Ganguly ◽  
Robert T. Knight ◽  
...  
Keyword(s):  
Coupled Oscillators ◽  
Large Scale ◽  
Neuronal Excitability ◽  
Phase Locking ◽  
Brain Networks ◽  
Synthetic Data ◽  
Natural Generalization ◽  
Statistical Characteristics ◽  
Response Modulation ◽  
Phase Coupling

Oscillatory phase coupling within large-scale brain networks is a topic of increasing interest within systems, cognitive, and theoretical neuroscience. Evidence shows that brain rhythms play a role in controlling neuronal excitability and response modulation (Haider B, McCormick D. Neuron 62: 171–189, 2009) and regulate the efficacy of communication between cortical regions (Fries P. Trends Cogn Sci 9: 474–480, 2005) and distinct spatiotemporal scales (Canolty RT, Knight RT. Trends Cogn Sci 14: 506–515, 2010). In this view, anatomically connected brain areas form the scaffolding upon which neuronal oscillations rapidly create and dissolve transient functional networks (Lakatos P, Karmos G, Mehta A, Ulbert I, Schroeder C. Science 320: 110–113, 2008). Importantly, testing these hypotheses requires methods designed to accurately reflect dynamic changes in multivariate phase coupling within brain networks. Unfortunately, phase coupling between neurophysiological signals is commonly investigated using suboptimal techniques. Here we describe how a recently developed probabilistic model, phase coupling estimation (PCE; Cadieu C, Koepsell K Neural Comput 44: 3107–3126, 2010), can be used to investigate changes in multivariate phase coupling, and we detail the advantages of this model over the commonly employed phase-locking value (PLV; Lachaux JP, Rodriguez E, Martinerie J, Varela F. Human Brain Map 8: 194–208, 1999). We show that the N-dimensional PCE is a natural generalization of the inherently bivariate PLV. Using simulations, we show that PCE accurately captures both direct and indirect (network mediated) coupling between network elements in situations where PLV produces erroneous results. We present empirical results on recordings from humans and nonhuman primates and show that the PCE-estimated coupling values are different from those using the bivariate PLV. Critically on these empirical recordings, PCE output tends to be sparser than the PLVs, indicating fewer significant interactions and perhaps a more parsimonious description of the data. Finally, the physical interpretation of PCE parameters is straightforward: the PCE parameters correspond to interaction terms in a network of coupled oscillators. Forward modeling of a network of coupled oscillators with parameters estimated by PCE generates synthetic data with statistical characteristics identical to empirical signals. Given these advantages over the PLV, PCE is a useful tool for investigating multivariate phase coupling in distributed brain networks.

The Key Technical Research on V2G System

2013 ◽  
Vol 313-314 ◽  
pp. 494-497
Author(s):  
Zhen Po Wang ◽  
Shuo Wang
Keyword(s):  
Large Scale ◽  
Control Method ◽  
Statistical Characteristics ◽  
Peak Shaving ◽  
Ongoing Research ◽  
Decoupling Method ◽  
Driving System ◽  
Main Research ◽  
Power Battery ◽  
Key Technologies

Basing on the analysis, the functions of the V2G system, such as peak shaving, frequency adjusting, UPS and disturbance stabilizing, are discussed in this paper. And the key technologies of V2G are put forward according to the ongoing research and demonstration. The main research route and methods are established, which includes the nonlinear multivariable decoupling method of power battery pack, the integrated process control method of EV energy and driving system, the statistical characteristics of EV large-scale demonstration and the control theory and method.

scholarly journals A layer-averaged relative humidity profile retrieval for microwave observations: design and results for the Megha-Tropiques payload

2015 ◽  
Vol 8 (3) ◽  
pp. 1055-1071 ◽  
Author(s):  
R. G. Sivira ◽  
H. Brogniez ◽  
C. Mallet ◽  
Y. Oussar
Keyword(s):  
Neural Network ◽  
Relative Humidity ◽  
Large Scale ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Statistical Characteristics ◽  
Support Vector ◽  
Training Phase ◽  
Learning Machine

Abstract. A statistical method trained and optimized to retrieve seven-layer relative humidity (RH) profiles is presented and evaluated with measurements from radiosondes. The method makes use of the microwave payload of the Megha-Tropiques platform, namely the SAPHIR sounder and the MADRAS imager. The approach, based on a generalized additive model (GAM), embeds both the physical and statistical characteristics of the inverse problem in the training phase, and no explicit thermodynamical constraint – such as a temperature profile or an integrated water vapor content – is provided to the model at the stage of retrieval. The model is built for cloud-free conditions in order to avoid the cases of scattering of the microwave radiation in the 18.7–183.31 GHz range covered by the payload. Two instrumental configurations are tested: a SAPHIR-MADRAS scheme and a SAPHIR-only scheme to deal with the stop of data acquisition of MADRAS in January 2013 for technical reasons. A comparison to learning machine algorithms (artificial neural network and support-vector machine) shows equivalent performance over a large realistic set, promising low errors (biases < 2.2%RH) and scatters (correlations > 0.8) throughout the troposphere (150–900 hPa). A comparison to radiosonde measurements performed during the international field experiment CINDY/DYNAMO/AMIE (winter 2011–2012) confirms these results for the mid-tropospheric layers (correlations between 0.6 and 0.92), with an expected degradation of the quality of the estimates at the surface and top layers. Finally a rapid insight of the estimated large-scale RH field from Megha-Tropiques is presented and compared to ERA-Interim.

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