Spectral Density Function Research Articles

Dynamic analysis of the wind-wave-current-bridge system considering pile-soil interaction

Coastal bridges are normally confronted with a complex marine environment including wind, wave and current. Present researches rarely take the current action and pile-soil interaction into consideration. It is thus essential to conduct the dynamic analysis of the cable-stayed bridge under wind-wave-current action considering pile-soil interaction. A coupled wind-wave-current-bridge (WWCB) system is proposed in this study to help understand the bridge dynamic performance. In the WWCB system, the bridge model is established through the finite element method, and the pile-soil interaction is considered through the pile-soil solid contact (PSSC) model. The wave-current action is obtained through a numerical wave tank, while the wind is simulated as the stochastic random process and generated with spectrums. The initial geostress, mass and density distribution are also considered in the WWCB system. To illustrate the bridge dynamic characteristics, dynamic responses under different load combinations are presented. Furthermore, the natural frequency and spectral density function comparison between the consolidation model, PSSC model and soil-spring model are provided to investigate the influence of the pile-soil interaction. The results show that different from previous conclusions, the wave-current action plays the dominant role instead of the wind action in some cases when considering the pile-soil interaction. Additionally, the consideration of the pile-soil interaction would decrease the structural stiffness, resulting in lower natural frequencies and wider frequency distribution. Consequently, the influence of the wave-current action and pile-soil interaction should be noted in the practical engineering.

Seismic Noise Analysis in InaTEWS Earthquake Station Network (Case Study: Flores Earthquake 7.4, 14 December 2021)

Meteorological, Climatology and Geophysics Agency (BMKG) is the agency responsible for providing information on earthquake parameters in Indonesia. BMKG has installed 411 seismometers network spread across Indonesia, this network is known as the Indonesia Tsunami Early Warning System (InaTEWS) network. The dense distribution of the seismometer is expected to be able to provide accurate earthquake parameter results. However, the accuracy of earthquake parameters does not only depend on many stations but also on the quality of the data recorded by the seismometer. In this study, an analysis of 15 data quality was carried out at the nearest station that recorded the Flores earthquake on December 14, 2021. Analysis of data quality using the power spectral density (PSD) and probability density functions (PDFs) methods, where good data quality can be seen in the spectrum of the seismograph recordings that are between the Peterson’s new high noise model (NHNM) and new low noise model (NLNM).

The influence of shock absorber characteristics’ nonlinearities on suspension Frequency Response Function estimation and possibilities of simplified characteristics modelling

The paper shortly presents shock absorber design evolution and resulting achieved characteristics. The way in which suspension performance is evaluated is described giving information about models used for suspension parameter tuning during simulation testing of suspension transmissibility (FRF - Frequency Response Function) for most important suspension assessment criteria. More detailed information about models of shock absorber (damper) nonlinearities of characteristics allows for description of methods of linear and nonlinear suspension models FRF estimation. Testing linear suspension model is possible with the use of analytical transfer function formulas which were used to verify methods for estimation FRF using estimated power spectral density functions of excitation and response signals. Designing appropriate input signal allowing to get useful response signals was necessary to for the success of this research. Proposed FRF estimation method was used for linear estimation of nonlinear suspension for a given range of working condition. It was demonstrated that there is no single value of a damping coefficient which would make the linear model responses similar to the responses of the nonlinear one. Then the bilinear model was proposed, giving good damper static nonlinear characteristic.

Fatigue life bounds for randomly excited structures with interval parameters via sensitivity analysis

The present study addresses fatigue life assessment of linear finite element modeled structures with uncertain parameters subjected to stationary multi-correlated Gaussian stochastic excitation. Uncertainties are modeled as interval variables so that all response quantities, including the expected fatigue life, turn out to have an interval nature. The problem formulation relies on the interval extension of an empirical spectral approach, known as α0.75− method, which expresses the expected fatigue life in terms of four spectral moments of the critical stress process. The mean-value effect is taken into account by applying a suitable transformation of the Power Spectral Density function of the critical stress process. The bounds of the interval expected fatigue life are efficiently evaluated by applying a sensitivity-based procedure in conjunction with the so-called Improved Interval Analysis via Extra Unitary Interval. The presented approach requires only two stochastic analyses of the randomly excited structure for assigned values of the uncertain parameters. Such values are identified by sensitivity analysis as the ones which yield the lower bound and upper bound of the interval expected fatigue life. A steel telecommunication antenna mast under wind excitation is selected as case study.

Model Parameter Estimation Method on Multichannel Structural Response from Turbulence Flutter Test

Flutter design is important for the design of new aircraft types, for which flutter tests are an important verification measure. Atmospheric turbulence excitation is a common form of excitation in flutter flight tests. The modal parameter estimation of the turbulence response is a key aspect to ensure accurate data processing of flutter test results. Atmospheric turbulence excitation acts on the structural system, and the turbulence response thus simultaneously contains both the randomness of the excitation signal and the determinism of the structure system. In view of the turbulence response characteristics, this paper addresses the incoherence of atmospheric turbulence excitation and the orthogonality of the frequency domain from a multichannel response. The turbulence response is used to perform modal parameter identification in the frequency domain. The power spectral density matrix can be calculated from the multichannel turbulence response using the periodogram method. Singular value decomposition is then performed on the power spectral density matrix at each spectral pin based on the orthogonality of the frequency domain. The maximum singular value of each spectral pin forms a curve over the entire frequency band, which is the autopower spectral density function of the system, the system is directly identified at the frequency domain using the polynomial fitting in the frequency domain, and the modal parameters (frequency, damping ratio) are calculated according to the fitted transfer function. This paper verifies the theoretical feasibility of the proposed method using simulation data. The engineering applicability is verified based on the turbulence response from the flutter flight test of a certain aircraft type.

Cross-correlated relaxation rates provide facile exchange signature in selectively labeled RNA

Gerhard Wagner has made numerous contributions to NMR spectroscopy, particularly his developments in the field of spin-relaxation stand out in directly mapping the spectral density functions of proteins. He and his group developed experimental techniques to reveal the importance of dynamics to protein biological function and drug discovery. On his 75th birthday, we take this opportunity to highlight how some of those seminal ideas developed for proteins are being extended to RNAs.The role of dynamics in the structure and function of RNA has been a major interest in drug design and therapeutics. Here we present the use of cross-correlated relaxation rates (ηxy) from anti-TROSY (R2α) and TROSY (R2β) to rapidly obtain qualitative information about the chemical exchange taking place within the bacterial and human A-site RNA system while reducing the sets of relaxation experiments required to map dynamics. We show that ηxy correlates with the order parameter which gives information on how flexible or rigid a residue is. We further show R2β/ηxy can rapidly be used to probe chemical exchange as seen from its agreement with Rex. In addition, we report the ability of R2β/ηxy to determine chemical exchange taking place within the bacterial A-site RNA during structural transitions at pH 6.2 and 6.5. Finally, comparison of the R2β/ηxy ratios indicates bacterial A-site has greater R2β/ηxy values for G19 (1.34 s−1), A20 (1.38 s−1), U23 (1.63 s−1) and C24 (1.51 s−1) than human A-site [A19 (0.76 s−1), A20 (1.01 s−1), U23 (0.74 s−1) and C24 (0.71 s−1)]. Taken together, we have shown that the chemical exchange can quickly be analyzed for RNA systems from cross-correlated relaxation rates.

Seismic Response Mitigation of Extra-Large LNG Storage Tanks

The paper investigates the effectiveness of a smart base-isolation system for seismic response mitigation of extra-large liquified natural gas (LNG) storage tanks. The mathematical model of the base-isolated LNG tank with smart dampers, the magneto-rheological (MR) dampers, is presented. The governing equations of motion of the smart system are derived and solved by the classical transition matrix method in the time domain. The linear quadratic regulator (LQR) control scheme is employed to command MR dampers. The LNG tank system is analyzed under six artificial accelerograms, compatible with operational basis earthquake (OBE) and safe shutdown earthquake (SSE) response spectrum, generated using a method of a random set of phase angles with amplitudes obtained from power spectral density function. A time-delay compensation procedure based on the Taylor series expansion is applied to reduce the deterioration of control performance due to time delay. To investigate the effectiveness of smart base-isolation, the responses are compared with the fixed-base tank, base-isolated tank, and tank with passive MR damper. The results show that the smart base-isolation is effective in reducing the seismic response of extra-large LNG tanks, especially the displacement at the isolation level without much altering other responses. The passive MR damper is also found effective and showed fail-safe behavior even under the failure of the control algorithm. Moreover, the application of time-delay compensation using the Taylor series increased the performance and overall efficiency of the LNG tank system.

Eyes-closed versus eyes-open differences in spontaneous neural dynamics during development

BackgroundAssessing brain activity during rest has become a widely used approach in developmental neuroscience. Extant literature has measured resting brain activity both during eyes-open and eyes-closed conditions, but the difference between these conditions has not yet been well characterized. Studies, limited to fMRI and EEG, have suggested that eyes-open versus -closed conditions may differentially impact neural activity, especially in visual cortices. MethodsSpontaneous cortical activity was recorded using MEG from 108 typically developing youth (9-15 years-old; 55 female) during separate sessions of eyes-open and eyes-closed rest. MEG source images were computed, and the strength of spontaneous neural activity was estimated in the canonical delta, theta, alpha, beta, and gamma bands, respectively. Power spectral density maps for eyes-open were subtracted from eyes-closed rest, and then submitted to vertex-wise regression models to identify spatially specific differences between conditions and as a function of age and sex. ResultsRelative alpha power was weaker in the eyes-open compared to -closed condition, but otherwise eyes-open was stronger in all frequency bands, with differences concentrated in the occipital cortex. Relative theta power became stronger in the eyes-open compared to the eyes-closed condition with increasing age in frontal cortex. No differences were observed between males and females. ConclusionsThe differences in relative power from eyes-closed to -open conditions are consistent with changes observed in task-based visual sensory responses. Age differences occurred in relatively late developing frontal regions, consistent with canonical attention regions, suggesting that these differences could be reflective of developmental changes in attention processes during puberty. Taken together, resting-state paradigms using eyes-open versus -closed produce distinct results and, in fact, can help pinpoint sensory related brain activity.

NONPARAMETRIC PREDICTION WITH SPATIAL DATA

We describe a (nonparametric) prediction algorithm for spatial data, based on a canonical factorization of the spectral density function. We provide theoretical results showing that the predictor has desirable asymptotic properties. Finite sample performance is assessed in a Monte Carlo study that also compares our algorithm to a rival nonparametric method based on the infinite$AR$representation of the dynamics of the data. Finally, we apply our methodology to predict house prices in Los Angeles.

Stochastic Buffeting Analysis of Uncertain Long-Span Bridge Deck with an Optimized Method

The buffeting analysis of an uncertain long-span bridge deck was carried out in this paper. Due to the effect of strong spatial correlation of wind excitation, it should be assumed as partially coherent multiple excitations. The following includes a theoretical formula for the buffeting analysis of a long-span bridge deck with uncertain parameters, which was achieved mainly by a combination of the stochastic pseudo excitation method (SPEM) and response surface method (RSM). The SPEM-RSM was firstly applied to deal with the complicated spectral density function matrix of wind excitation. The buffeting response of the bridge deck was then calculated and verified by the results from the Monte Carlo simulation (MCS). The efficiency and applicability of the hybrid method for strong spatial correlation was proved. After the comparison, the effect of uncertain structural parameters and wind speed on the buffeting performance of the bridge deck were computed. The results showed that the whole uncertainties essentially affected the buffeting response of the deck. The uncertain wind speed played the most significant role in the vertical and lateral motion of the deck. The joint influences between structural uncertainties and uncertain wind speed further affect the random characteristics of the responses. Finally, the effects of different wind speed and wind angle of attack on the aerodynamic performance of the bridge are examined. The variance of the responses increased with the development of wind speed. The effect of different attack angles on the buffeting responses was significant.

Seismic reliability analysis using a multi-fidelity surrogate model: Example of base-isolated buildings

An approach is presented for conducting simulation-based seismic reliability analysis using a multi-fidelity surrogate model. To illustrate the approach, a base-isolated building is considered comprising a reinforced concrete frame for primary building and lead-rubber bearings for isolation devices. The high-fidelity model is a nonlinear finite-element model used in time history analysis. Ground motions are generated from a parametric power spectral density function that is compatible with a target response spectrum at the site of interest. The low-fidelity model uses a linear-elastic stick model for the superstructure, while stochastic linearization is used to capture the nonlinearity of the base isolation. Cokriging is used as a multi-fidelity surrogate model for fusing a large number of low-fidelity model evaluations with few high-fidelity model evaluations, in order to attain high-fidelity model accuracy while mitigating the computational cost. After training the Cokriging model, Monte Carlo simulation is carried out by sampling the cheap multi-fidelity surrogate, and the probability of failure is subsequently evaluated for different response thresholds. The relative error in failure probability prediction of Cokriging is shown to be in the range of 6% with a maximum of 10%, for a critical range of sample response variable threshold values, in contrast to errors as high as 60% provided by the low-fidelity model.

A prediction perspective on the Wiener–Hopf equations for time series

The Wiener–Hopf equations are a Toeplitz system of linear equations that naturally arise in several applications in time series. These include the update and prediction step of the stationary Kalman filter equations and the prediction of bivariate time series. The celebrated Wiener–Hopf technique is usually used for solving these equations and is based on a comparison of coefficients in a Fourier series expansion. However, a statistical interpretation of both the method and solution is opaque. The purpose of this note is to revisit the (discrete) Wiener–Hopf equations and obtain an alternative solution that is more aligned with classical techniques in time series analysis. Specifically, we propose a solution to the Wiener–Hopf equations that combines linear prediction with deconvolution. The Wiener–Hopf solution requires the spectral factorization of the underlying spectral density function. For ease of evaluation it is often assumed that the spectral density is rational. This allows one to obtain a computationally tractable solution. However, this leads to an approximation error when the underlying spectral density is not a rational function. We use the proposed solution with Baxter's inequality to derive an error bound for the rational spectral density approximation.

Model predictive control for automatic carrier landing considering ship motion

This paper studies a control problem of UAV automatic landing on carrier considering carrier heave motion, which is given by an empirical model based on spectral density function. A linearized UAV dynamics model is applied as the predictive model, a MPC controller is then proposed for the flight dynamics control. Simulation is conducted with real parameters of the UAV “Silver Fox”, the results exhibit the great landing performance of MPC method.

Analysis of stochastic errors and Gaussianity of random vector process simulated by dimension-reduction representation method

The simulation of random vector process is of great importance, particularly for the nonlinear dynamic analysis and reliability evaluation of large-scale structures. Combining the probability density evolution method, which provides an efficient way to perform the structural dynamic reliability analysis, reducing the randomness degree of the simulation method becomes a new challenge task. In order to address this issue, dimension-reduction representation methods for simulating stationary vector processes were proposed recently. However, the features regarding the stochastic error of power spectral density functions and the Gaussianity of vector processes generated by the dimension-reduction representation methods remain unclear. In this paper, the analytical stochastic errors of power spectral density functions for dimension-reduction representation methods will be given, and be proved through the numerical example. Moreover, the Gaussianity of the generated vector processes will be discussed from the view of the first four statistical moments.

The effect of pulsating air injection on the development of two-phase flow instabilities in an airlift pump

This paper aims at characterizing the instabilities created by the dynamic redistribution of air liquid two-phases through the pump to allow for utilizing a pulsating air injection as a new mode to control and optimize the pump performance. Baseline performance experiments for the pump operating under continuous air injection and for three submergence ratios of 0.5, 0.7 and 0.9 are used in the present analysis. The most efficient operating points of the pump were recorded at a range of air flow rates between 1.1 and 2.3 kg/hr. The characteristics of non-developing two-phase flow were evaluated using both flow pattern visualization using high speed imaging system and instantons void fraction using capacitance sensors at the points with the highest pump efficiency. In these experiments, the dynamics void fraction signals were collected at seven different locations along the pump riser. Both the fast Fourier transform, and power spectral density functions of the void fraction signals were used to identify the effective frequency range at which to perform the pulsation air injection experiments. At lower submergence ratios, the void fraction signals along the pump riser found to take a longer time to reach quasi-fully developed values. Also, pulsating mode of injected air was found to improve the pump performance for all submergence ratios. This is mainly due to the significant changes in the two-phase flow pattern in the pump riser when air pulsating used. In this case, pulsating the air injection at the optimum frequency allowed for maintaining a slug flow pattern during the pump operation and the best pump performance was achieve. Pulsating air injection at different offset frequencies were also evaluated for a dual injection airlift pump. It was found that an overlapping offset frequency or an alternating frequency between the two air injection ports can achieve a better pump performance for high submergence ratios while synchronous pulsation mode was found to be the most effective at improving the pump performance at low submergence ratios.

Fragility analysis of floor micro vibrations induced by internal vehicles in high technology factories

This study explores the micro vibrations in long span floors induced by internal moving vehicles or automated guided vehicles (AGVs) in high technology fabrication factories (or fabs) by using a bivariate fragility assessment model that considers both the vehicle weight and moving speed. Dynamic time history analyses of an equivalent sub-structural multiple span continuous beam model with moving forces generated by a modified Kanai-Tajimi power spectral density function are performed. The maximum root-mean-square (RMS) floor velocity response of each pair of AGV weight and speed are obtained and in turn used to determine their best fitting median plane and standard deviation for constructing the graphical fragility surface. Probabilistic assessments of floor micro vibrations that exceed a specific vibration level for various vehicle weights and speeds can be directly determined from the fragility surfaces. Instead of using a deterministic approach, probability-based surfaces are a promising alternative for assessing floor micro vibrations that exceed a specific vibration level for various random AGV movements. Moreover, the fragility surfaces can be used to determine the appropriate weight and speed of the AGV or assist in the conceptual design of vibration-sensitive production floor systems based on a given exceedance probability of the desired vibration level.

Classification of diffraction patterns using a convolutional neural network in single-particle-imaging experiments performed at X-ray free-electron lasers.

Single particle imaging (SPI) at X-ray free-electron lasers is particularly well suited to determining the 3D structure of particles at room temperature. For a successful reconstruction, diffraction patterns originating from a single hit must be isolated from a large number of acquired patterns. It is proposed that this task could be formulated as an image-classification problem and solved using convolutional neural network (CNN) architectures. Two CNN configurations are developed: one that maximizes the F1 score and one that emphasizes high recall. The CNNs are also combined with expectation-maximization (EM) selection as well as size filtering. It is observed that the CNN selections have lower contrast in power spectral density functions relative to the EM selection used in previous work. However, the reconstruction of the CNN-based selections gives similar results. Introducing CNNs into SPI experiments allows the reconstruction pipeline to be streamlined, enables researchers to classify patterns on the fly, and, as a consequence, enables them to tightly control the duration of their experiments. Incorporating non-standard artificial-intelligence-based solutions into an existing SPI analysis workflow may be beneficial for the future development of SPI experiments.

Correlation effects obtained from optical spectra of Fe-pnictides using an extended Drude-Lorentz model analysis

We introduce an analysis model, an extended Drude–Lorentz model, and apply it to Fe-pnictide systems to extract their electron–boson spectral density functions (or correlation spectra). The extended Drude–Lorentz model consists of an extended Drude mode for describing correlated charge carriers and Lorentz modes for interband transitions. The extended Drude mode can be obtained by a reverse process starting from the electron–boson spectral density function and extending to the optical self-energy and, eventually, to the optical conductivity. Using the extended Drude–Lorentz model, we obtained the electron–boson spectral density functions of K-doped BaFe2As2 (Ba-122) at four different doping levels. We discuss the doping-dependent properties of the electron–boson spectral density function of K-doped Ba-122. We also can include pseudogap effects in the model using this approach. Therefore, this approach is very helpful for understanding and analyzing measured optical spectra of strongly correlated electron systems, including high-temperature superconductors (cuprates and Fe-pnictides).

Effective conductivity of random composites with shape-distributed near-spherical inclusions

While traditional and widely accepted mixing rules are based on the concept of a fixed inclusion shape, real particle composites are usually shape-distributed. For such composite materials, however, the analytical homogenization techniques are not well developed, while their numerical analysis becomes particularly complicated. We introduce and analyze two analytical models for the effective conductivity of a particular class of two-phase composites, namely those containing shape-distributed rigid inclusions. The models are derived with the use of the Bergman–Milton spectral density function approach. As we show, one of them, which involves the Gauss hypergeometric function, provides reasonable agreement with experimental data within the wide range of filling factors for ion-exchange resin beads in aqueous solutions. Another model, which is based on the uniform spectral density function, gives rise to a logarithmic singularity for the effective conductivity at the percolation threshold.

Application of the preference selection index method in multi-objective lightweight design of heavy commercial vehicle frames

An optimization strategy integrating contribution analysis, design of experiment, an approximate model and a preference selection index (PSI) method is proposed to establish an efficient multi-objective lightweight design approach for heavy commercial vehicle frames. First, finite element and rigid–flexible coupling virtual prototype models are established and their accuracy is verified using experiments. Secondly, the fatigue life of the frame is calculated using the power spectral density function, frequency response function and corrected stress–life curve. Finally, the thicknesses of frame components are regarded as design variables, and the mass and maximum root mean square stress are considered as design objectives in the process of lightweight design. Three optimization strategies are used to investigate the characteristics of the PSI method. The results showed that the optimization strategy combined with the PSI method demonstrates high computational efficiency and reliability.