Subgrid-scale contributions to Lagrangian time correlations in isotropic turbulence

The application of large-eddy simulation （LES） to particle-laden turbulence raises such a fundamental question as whether the LES with a subgrid scale （SGS） model can correctly predict Lagrangian time correlations （LTCs）. Most of the currently existing SGS models are constructed based on the energy budget equations. Therefore, they are able to correctly predict energy spectra, but they may not ensure the correct prediction on the LTCs. Previous researches investigated the effect of the SGS modeling on the Eulerian time correlations. This paper is devoted to study the LTCs in LES. A direct numerical simulation （DNS） and the LES with a spectral eddy viscosity model are performed for isotropic turbulence and the LTCs are calculated using the passive vector method. Both a priori and a posteriori tests are carried out. It is observed that the subgrid;scale contributions to the LTCs cannot be simply ignored and the LES overpredicts the LTCs than the DNS. It is concluded from the straining hypothesis that an accurate prediction of enstrophy spectra is most critical to the prediction of the LTCs.

Analysis of shock wave unsteadiness using space and time correlations applied to shadowgraph flow visualization data

Unsteady flow characteristics of a normal shock wave,a lambda foot,and a separated turbulent boundary layer are investigated within a unique test section with supersonic inlet flow.The supersonic wind tunnel facility,containing this test section,provides a Mach number of approximately 1.54 at the test section entrance.Digitized shadowgraph flow visualization data are employed to visualize shock wave structure within the test section.These data are analyzed to determine shock wave unsteadiness characteristics,including grayscale spectral energy variations with frequency,as well as time and space correlations,which give coherence and time lag properties associated with perturbations associated with different flow regions.Results illustrate the complexity and unsteadiness of shock-wave-boundary-layerinteractions,including event frequencies from grayscale spectral energy distributions determined using a Lagrangian approach applied to shock wave location,and by grayscale spectral energy distributions determined using ensemble-averaging applied to multiple closely-located stationary pixel locations.Auto-correlation function results and two-point correlation functions(in the form of magnitude squared coherence)quantify the time-scales of periodic events,as well as the coherence of flow perturbations associated with different locations,over a range of frequencies.Associated time lag data provide information on the originating location of perturbation events,as well as the propagation direction and event sequence associated with different flow locations.Additional insight into spatial variations of time lag and flow coherence is provided by application of magnitude squared coherence analysis to multiple locations,relative to a single location associated with the normal shock wave.

Stratigraphical Time——Correlation and Mass Extinction Event Near Permian——Triassic Boundary in South China

Shaw's method used to correlate 40 sections across the Permo-Triassic boundary in South China is applied in the paper. Two steps are adopted to get an Integral Composite Section (ICS) by synthesizing these data : First , South China is divided into five areas and composite section developed for each area . Then the second step . the Changxing composite section is regarded as a composite standard (CSRS) while the ICS is produced by matching the CSRS with composite sections of the other areas. Three biozones in the Changxingian and two biozones in the Griesbachian can be discerned on the basis of computing Z values in the ICS. These biozones are marked by the Z values which quantitatively represent their time ranges ; therefore , they may increase accuracy of stratigraphic time correlation . The mass extinction at the end of the Permian is an abrupt event that is supported by the relative rate of extinction near the P/T boundary . About 90% of invertebrate species died out by the end of the Permian . The duration of the mass extinction is rather short ,approximately 0.018Ma .

Correlation between detrended fluctuation analysis and the Lempel-Ziv complexity in nonlinear time series analysis

We study the correlation between detrended fluctuation analysis（DFA） and the Lempel-Ziv complexity（LZC） in nonlinear time series analysis in this paper.Typical dynamic systems including a logistic map and a Duffing model are investigated.Moreover,the influence of Gaussian random noise on both the DFA and LZC are analyzed.The results show a high correlation between the DFA and LZC,which can quantify the non-stationarity and the nonlinearity of the time series,respectively.With the enhancement of the random component,the exponent α and the normalized complexity index C show increasing trends.In addition,C is found to be more sensitive to the fluctuation in the nonlinear time series than α.Finally,the correlation between the DFA and LZC is applied to the extraction of vibration signals for a reciprocating compressor gas valve,and an effective fault diagnosis result is obtained.

Reduced combustion time model for methane in gas turbine flow fields

Computational fluid dynamics （CFD） modeling of the complex processes that occur within the burner of a gas turbine engine has become a critical step in the design process. However, due to computer limitations, it is very difficult to completely couple the fluid mechanics solver with the full combustion chemistry. Therefore, simplified chemistry models are required, and the topic of this research was to provide reduced chemistry models for CH4/O2 gas turbine flow fields to be integrated into CFD codes for the simulation of flow fields of natural gas-fueled burners. The reduction procedure for the CH4/O2 model utilized a response modeling technique wherein the full mechanism was solved over a range of temperatures, pressures, and mixture ratios to establish the response of a particular variable, namely the chemical reaction time. The conditions covered were between 1000 and 2500 K for temperature, 0.1 and 2 for equivalence ratio in air, and 0.1 and 50 atm for pressure. The kinetic time models in the form of ignition time correlations are given in Arrhenius-type formulas as functions of equivalence ratio, temperature, and pressure; or fuel-to-air ratio, temperature, and pressure. A single ignition time model was obtained for the entire range of conditions, and separate models for the low-temperature and high-temperature regions as well as for fuel-lean and rich cases were also derived. Predictions using the reduced model were verified using results from the full mechanism and empirical correlations from experiments. The models are intended for （but not limited to） use in CFD codes for flow field simulations of gas turbine combustors in which initial conditions and degree of mixedness of the fuel and air are key factors in achieving stable and robust combustion processes and acceptable emission levels. The chemical time model was utilized successfully in CFD simulations of a generic gas turbine combustor with four different cases with various levels of fuel-air premixing.

Several Kinematic Data Processing Methods for Time-Correlated Observations

Time correlations always exist in modern geodetic data,and ignoring these time correlations will affect the precision and reliability of solutions.In this paper,several methods for processing kinematic time-correlated observations are studied.Firstly,the method for processing the time-correlated observations is expanded and unified.Based on the theory of maximum a posteriori estimation,the third idea is proposed after the decorrelation transformation and differential transformation.Two types of situations with and without common parameters are both investigated by using the decorrelation transformation,differential transformation and maximum a posteriori estimation solutions.Besides,the characteristics and equivalence of above three methods are studied.Secondly,in order to balance the computational efficiency in real applications and meantime effectively capture the time correlations,the corresponding reduced forms based on the autocorrelation function are deduced.Finally,with GPS real data,the correctness and practicability of derived formulae are evaluated.

Comparison of Semi-Parametric Shared Frailty Models for Bees’Survival

Survival analysis is a fundamental tool in medical science for time-to-event data. However, its application to colony organisms like bees poses challenges due to their social nature. Traditional survival models may not accurately capture the interdependence among individuals within a colony. Frailty models, accounting for shared risks within groups, offer a promising alternative. This study evaluates the performance of semi-parametric shared frailty models (gamma, inverse normal, and positive stable-in comparison to the traditional Cox model using bees’ survival data). We examined the effect of misspecification of the frailty distribution on regression and heterogeneity parameters using simulation and concluded that the heterogeneity parameter was more sensitive to misspecification of the frailty distribution and choice of initial parameters (cluster size and true heterogeneity parameter) compared to the regression parameter. From the data, parameter estimates for covariates were close for the four models but slightly higher for the Cox model. The shared gamma frailty model provided a better fit to the data in comparison with the other models. Therefore, when focusing on regression parameters, the gamma frailty model is recommended. This research underscores the importance of tailored survival methodologies for accurately analyzing time-to-event data in social organisms.

The statistical fluctuation of a single-mode laser system driven by coloured pump noise with signal modulation and the quantum noise with cross-correlation between its real and imaginary parts

Using the linear approximation method, this paper studies the statistical property of a single-mode laser driven by both coloured pump noise with signal modulation and the quantum noise with cross-correlation between its real and imaginary parts, and calculates the steady-state mean normalized intensity fluctuation and intensity correlation time. It analyses the influences of the modulation signal, the net gain coefficient, the noise and its correlation form on the statistical fluctuation of the laser system respectively. It is found that the coloured pump noise modulated by the signal has a great suppressing action on the statistical fluctuation of the laser system; the pump noise self-correlation time and the specific frequency of modulation signal have the result that the statistical fluctuation tends to zero. Furthermore, the ＇colour＇ correlation of pump noise has much influences on the statistical fluctuation of the laser system. Increasing the intensity of pump noise will augment the statistical fluctuation of the laser system, but the intensity of quantum noise and the coefficient of cross-correlation between its real and imaginary parts have less influence on the statistical fluctuation of the laser system. Therefore, from the conclusions of this paper the statistical property can be known and a theoretical basis for steady operation and output of the laser system can be provided.

Times Series Applied to Study Vitamin D Seasonality in Argentina

In this study, we analyze how vitamin D (VD) serum levels flow with latitude and throughout seasons of the year within a population sample over three years, taking into account that VD is mainly photosynthesized in the skin from sun exposure. Vitamin D levels have been measured in 80,763 patients during 2013, 2014, and 2015. To accomplish the objectives, we first perform some inference tests like two-way Analysis of Variance (ANOVA) followed by post-hoc tests. Secondly, we develop time series techniques including cross correlation calculations. Least than 10% of the sample had healthy VD levels, which should be a fact of public health major concern. The effect of the interaction between the two factors, zones and seasons, was proved by ANOVA. The mean values which are significantly different were determined by post hoc test. Furthermore, we find that mean serum VD levels, measured as 25-hydroxy-VD, follow a seasonal lag pattern of 9 weeks, a delay for minimum and maximum values after the respective equinoxes and daily sunlight duration. Reliable estimates of the population are provided in the present study, since one of the strengths is its huge sample size. We have quantitatively characterized the seasonality of serum vitamin D levels in the Argentine and the seasonal lag pattern has been determined for the study region.

Research on overlapping structures and evolution properties of co-citation network

Purpose: This paper intends to explore methodologies and indicators for the analysis of overlapping structures and evolution properties in a co-citation network, and provide reference for overlapping structure analysis of other scientific networks.Design/methodology/approach: The Q-value variance is defined to achieve overlapping structures of different levels in the scientific networks. At the same time, analyses for time correlation variance and subject correlation variance are used to present the formation of overlapping structures in scientific networks. As a test, a co-citation network of highly cited papers on Molecular Biology & Genetics from Essential Science Indicator(ESI) is taken as an example for an empirical analysis.Findings: Our research showed that the Q-value variance is effective for achieving the desired overlapping structures. Meanwhile, the time correlation variance and subject correlation variance are equally useful for uncovering the evolution progress of scientific research, and the properties of overlapping structures in the research of co-citation network as well.Research limitations: In this paper, the theoretical analysis and verification of time and subject correlation variances are still at its initial stage. Further studies in this regard need to take actual evolution of research areas into consideration.Practical implications: Evolution properties of overlapping structures pave the way for overlapping and evolution analysis of disciplines or areas, this study is of practical value for the planning of scientific and technical innovation.Originality/value: This paper proposes an analytical method of time correlation variance and subject correlation variance based on the evolution properties of overlapping structures, which would provide the foundation for the evolution analysis of disciplines and interdisciplinary research.

Noise Controlling Methods in Frequency Selection Detecting Radar

Based on time correlation characteristic, width correlation characteristic and frequency correlation characteristic of detecting pulses, several methods are introduced to control random or periodic noise whose width is narrower than 1 ms or wider than 3 ms in Frequency Selection Detecting Radar System. The software flow chart and the results of the experiment are also given.

Two Methods to Solve the Ionospheric Electron Concentration Horizontal Gradient at Chongqing

The electron concentration horizontal gradient vector of the ionosphere and its south-north and east-west components over Chongqing station are analyzed and calculated, using the first approximation, time correlation and space correlation and another approach introduced. And then, the validity of the two methods is analyzed and compared.

Effects of correlation time between noises on the noise enhanced stability phenomenon in an asymmetric bistable system

The effects of the correlation time τ between noises on the noise-enhanced stability （NES） phe- nomenon in an asymmetric bistable system driven by cross-correlated noise are investigated. The ex- pressions for the average escape time from the left metastable state TL and from the right metastable state TR are derived. The results indicate that： i） The NES effect is suppressed as the correlation time τ increases for two metastable states; ii） The increase in τ speeds up the escape process from the right state for positively correlated noise, whereas its role is reverses for negatively correlated; iii） In the escape process from the left state, the role of τ is opposite to that in escape from the right state.

Frequency correlation and group time delay of ambient noise and ship radiated-noise in the sea

The characteristics of frequency correlation and group time delay of ambient noise and ship radiated-noise in the sea are studied. The theoretical and experimental results show that the frequency correlation of ship radiated-noise is much greater than that of ambient noise,and the frequency correlation of ship radiated-noise at long distance has obvious group time delay

An EPR Study on Intramolecular Spin Exchange and Rotational Mobility of Nitroxides Linked by a Long-chain

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Influence of the colored noise on determining the period of a torsion pendulum

Based on statistical properties, two typical models are considered to calculate the uncertainties for some random noise sequences on the period extraction of a torsion pendulum, which is important and instructive in the measurement of gravitational constant G with the time-of-swing method. An expression of the uncertainty for the period measurement is obtained, which is dependent on the ratio ？t/（1/λ） where ？t is the interval of the sample time and 1/λ is the length of the correlation time. The result of processing experimental data shows that as the interval of the sample time ？t gradually shortens, the uncertainty of the period becomes smaller, and further when the ratio ？t/（1/λ） is less than 1, the uncertainty remains substantially unchanged.

Rolling Iterative Prediction for Correlated Multivariate Time Series

Correlated multivariate time series prediction is an effective tool for discovering the chang rules of temporal data,but it is challenging tofind these rules.Recently,deep learning methods have made it possible to predict high-dimensional and complex multivariate time series data.However,these methods cannot capture or predict potential mutation signals of time series,leading to a lag in data prediction trends and large errors.Moreover,it is difficult to capture dependencies of the data,especially when the data is sparse and the time intervals are large.In this paper,we proposed a prediction approach that leverages both propagation dynamics and deep learning,called Rolling Iterative Prediction(RIP).In RIP method,the Time-Delay Moving Average(TDMA)is used to carry out maximum likelihood reduction on the raw data,and the propagation dynamics model is applied to obtain the potential propagation parameters data,and dynamic properties of the correlated multivariate time series are clearly established.Long Short-Term Memory(LSTM)is applied to capture the time dependencies of data,and the medium and long-term Rolling Iterative Prediction method is established by alternately estimating parameters and predicting time series.Experiments are performed on the data of the Corona Virus Disease 2019(COVID-19)in China,France,and South Korea.Experimental results show that the real distribution of the epidemic data is well restored,the prediction accuracy is better than baseline methods.

INVESTIGATION OF LAGGED CORRELATION BETWEEN THE TEMPERATURE OF CHINA AND THE GLOBAL OUTGOING LONGWAVE RADIATION IN 1974-1985

1-12 month lagged correlations between winter temperatures at 22 stations in China and the global dis- tribution of the Outgoing Longwave Radiation(OLR)are investigated.The basic results are:(1)The monthly averaged temperatures of Dec.,Jan.and Feb.of China are correlated to the global distribution of OLR of Oct.,Nov.and Dec.,respectively.It is consistent with the response period(about 50 days)of 500 hPa geopotential height field in extratropical latitudes to the thermal forcing in tropical latitudes,and also consis- tent with the Walker cell of a 40-60 day oscillation.(2)There is a significant positive correlation between the winter temperature of the most part of China and the OLR in the Gulf of Mexico.It shows that when the thermal forcing of the Gulf of Mexico is stronger,the excited barotropic instability of westerlies in northern Atlantic could influence the East Asian circulation and cause the temperature of China to be below normal. (3)The temperature around the Tibetan(Xizang)Plateau is negatively correlated to the OLR in western equa- torial Pacific,showing that when the Walker cell is stronger,the local Hadley cell in Southeast Asia is stronger and it causes the temperature around the Tibetan Plateau to be higher than normal.

A digital time correlative accumulation(Ⅱ)——cross-correlative accumulation

The digital time cross-correlative accumulation ( DTCCA ) scheme is described in the paper. The basic principle and structure of the scheme are discussed first , the detection probability, false-alarm probability and input S/ N are then analyzed for some typical sonar systems . The superior performances have been proved true through theoretical estimations and field experiments . Like DTACA , the DTCCA , as a signal processing scheme , has a wide application in complicated shallow acoustical channel at low data rate.

An exploration of the relationship between wastewater viral signals and COVID-19 hospitalizations in Ottawa,Canada

Monitoring of viral signal in wastewater is considered a useful tool for monitoring the burden of COVID-19,especially during times of limited availability in testing.Studies have shown that COVID-19 hospitalizations are highly correlated with wastewater viral signals and the increases in wastewater viral signals can provide an early warning for increasing hospital admissions.The association is likely nonlinear and time-varying.This project employs a distributed lag nonlinear model(DLNM)(Gasparrini et al.,2010)to study the nonlinear exposure-response delayed association of the COVID-19 hospitalizations and SARS-CoV-2 wastewater viral signals using relevant data from Ottawa,Canada.We consider up to a 15-day time lag from the average of SARS-CoV N1 and N2 gene concen-trations to COVID-19 hospitalizations.The expected reduction in hospitalization is adjusted for vaccination efforts.A correlation analysis of the data verifies that COVID-19 hospital-izations are highly correlated with wastewater viral signals with a time-varying rela-tionship.Our DLNM based analysis yields a reasonable estimate of COVID-19 hospitalizations and enhances our understanding of the association of COVID-19 hospi-talizations with wastewater viral signals.