Energy Variance Research Articles

Relaxation process of some Bismuthate glasses at low temperatures

The glass set, 70 Bi2O3-20 ZnO-(10-x) PbO-x Sm2O3 with x = 0, 1, 2, 3, 4, 5, 6, and 7 mol percent, was designed to test longitudinal ultrasonic attenuation between 120 and 300 K at 2, 4, 6, and 14 MHz frequencies. Well-defined wide absorption curve peaks have been seen at various temperatures based on the structure of the glass as well as the switching frequency. Maximum peaks were observed to be moved to temperatures that were higher, with the rise in overall frequency suggesting the existence of some form of relaxation mechanism. A quiet approach has been defined as a consequence of a thermally triggered relaxation mechanism. The variance of the mean energy of the activation of the mechanism counts primarily on the amount of Sm2O3 mol percent. Such dependency has been evaluated based on loss of normal linear solid form, attaining low dispersion, and large allocation of Arrhenius-type relaxation through temperature-autonomous relaxation power. The amount of loss centers (the number of oxygen atoms that now move at a double-well potential) has been quantifiably represented as the measured acoustical energy of activation worth.

Energy-variance extrapolation for high-spin states with fully optimized variation after projection wave functions

We have implemented an energy-variance extrapolation method with the variation after projection (VAP) wave functions which are fully optimized. Thanks to the adopted VAP wave functions which are simplified by taking only one projected state for each selected reference state, the present energy variance extrapolation calculation can be easily applied to high-spin states, even over the backbending. Moreover, the energy variance can be calculated very quickly because the required matrix elements are already available in our previous VAP calculations. The calculated results in $pf$ model space have clearly shown that the extrapolated energies for all calculated spin states are very close to the exacted shell model ones within 20 keV.

Sensing of ocean mixing via forward scattering acoustic scintillation

Ocean mixing process observations show complex spatio-temporal patterns as well as energy and dissipation levels that vary over six or more orders of magnitude, creating a challenging sampling problem. Acoustic imaging of ocean turbulent structures is informative, but extraction of quantities like spectra and dissipation rates of kinetic energy, ε, and temperature variance, Χ, requires geometric sampling constraints. Here, a remote sensing approach is proposed in the spirit of Di Iorio and Farmer (1994), and Duda et al. (1988), based on weak multiple-forward scattering physics. In the correct scattering regime, observations of the wavenumber spectrum of acoustic intensity obtained on a vertical array gives a direct measure of the range average turbulence index of the refraction spectrum in a plane transverse to the acoustic path. In this talk, the theory will be reviewed and some illustrative simulations will be presented; importantly, one-way acoustic transmission measures a combination of both ε and Χ. Experimental arrangements (frequency/range) where the theory is expected to apply will be outlined with an eye towards observing bottom boundary layer mixing. With a suitable vertical array, one could resolve scales from the smallest internal waves (∼several meters), through the transition regime, and down to scales of turbulence (∼tens of cm).

GDR: A Game Algorithm Based on Deep Reinforcement Learning for Ad Hoc Network Routing Optimization

Ad Hoc networks have been widely used in emergency communication tasks. For dynamic characteristics of Ad Hoc networks, problems of node energy limited and unbalanced energy consumption during deployment, we propose a strategy based on game theory and deep reinforcement learning (GDR) to improve the balance of network capabilities and enhance the autonomy of the network topology. The model uses game theory to generate an adaptive topology, adjusts its power according to the average life of the node, helps the node with the shortest life to decrease the power, and prolongs the survival time of the entire network. When the state of the node changes, reinforcement learning is used to automatically generate routing policies to improve the average end-to-end latency of the network. Experiments show that, under the condition of ensuring connectivity, GDR has smaller residual energy variance, longer network lifetime, and lower network delay. The delay of the GDR model is 10.5% higher than that of existing methods on average.

General flux model in the turbulence driven by multiscale forces

A general flux model describing the transport of kinetic energy and scalar variance in turbulence driven by a multiscale force has been established, with a universal conservative equation. In a complete cascade picture of turbulence there exist four different cases, with fluxes of kinetic energy and scalar variance being either constant or nonconstant.

Variational quantum eigensolvers by variance minimization

The original variational quantum eigensolver (VQE) typically minimizes energy with hybrid quantum-classical optimization that aims to find the ground state. Here, we propose a VQE based on minimizing energy variance and call it the variance-VQE, which treats the ground state and excited states on the same footing, since an arbitrary eigenstate for a Hamiltonian should have zero energy variance. We demonstrate the properties of the variance-VQE for solving a set of excited states in quantum chemistry problems. Remarkably, we show that optimization of a combination of energy and variance may be more efficient to find low-energy excited states than those of minimizing energy or variance alone. We further reveal that the optimization can be boosted with stochastic gradient descent by Hamiltonian sampling, which uses only a few terms of the Hamiltonian and thus significantly reduces the quantum resource for evaluating variance and its gradients.

Simulating Dropsondes to Assess Moist Static Energy Variability in Tropical Cyclones

AbstractInteractions between clouds, water vapor, radiation, and circulation influence tropical cyclone (TC) development. Many of these interactions can be quantified by understanding tendencies of the spatial variance of moist static energy (MSE). Dropsondes from aircraft reconnaissance sample profiles needed to compute MSE at fine vertical resolution, and may be useful in analyzing these feedbacks on TCs in situ. However, dropsondes are spatially sparse, and sample limited column depths depending on the type of reconnaissance mission. We use idealized convection‐permitting simulations to examine how MSE variability, and the feedbacks that influence it, are resolved using selected patterns of grid points meant to resemble dropsonde launch points in reconnaissance flight patterns. We first examine the column depth necessary to capture the MSE variability of the full atmosphere. We then study how these simulated flight patterns depict MSE variance and its relevant diabatic feedbacks in TCs of varying structure and intensity.

Numerical investigation on the clogging-collapsing events in granular discharge

Granular flow in hoppers is widely encountered in industries and daily life. Despite its ubiquity, understanding on its basic flow behavior is still limited. Typically, the flow may be interrupted by the formation of arches and the phase transition ensues, and in general these arches may not collapse without external energy input such as vibration. However, in this study, the rare events of granular flow arrested by the formation of unstable arches and then restarted by their spontaneous collapsing, or the clogging-collapsing events, were observed in both lab experiments and numerical simulations in the discrete element method (DEM). It was demonstrated that the clogging may result from the coordinated evolution of the entire force chain system and the kinetic energy dissipated incompletely in bulk is mainly responsible for the collapsing. The evolution of the kinetic energy displays different modes in the clogging-collapsing and permanent clogging events, suggesting that the collapsing can be predicted by monitoring the variance of the kinetic energy. • Experimental and numerical study on the clogging-collapsing events in hoppers. • Imperfect arches with concave links found in temporal clogging. • Temporal arches bearing light load and supported by tangential contact forces. • Kinetic energy fluctuating at fine amplitude during temporal clogging. • Kinetic energy level and its fluctuation may indicate instable packing.

On the Nile Fan's wave power potential and controlling factors integrating spectral and geostatistical techniques

Wave energy is foreseen to contribute largely to the renewable energy projected to supply 20% of Egypt's electricity budget to meet burgeoning energy demand. Therefore, this research analyses the Nile Fan wave energy forecasted from numerical modeling for 2020 of the Copernicus Marine Environment Monitoring Service (CMEMS) database, hourly spatially sampled at 0.042° as the finest scale remotely-sensed data available. Wave energy spatial distribution is analyzed using data from 259 points proposed as Wave Energy Converters (WECs). Spectral analyses techniques were appraised for disclosing the frequency and energy return periods of the significant wave height and peak periods and to understand the similarity among selected WECs of varied conditions. Factor analysis is conducted to investigate the magnitude of factors controlling the wave dynamic and energy potential. K-means clustering was used to distinguish energy classes with large inter-class variances. The obtained resources (average wave energy density, around 5.32 KWh/m; maximum recorded value of 112.9 KWh/m; annual wave energy density sum of about 46.96 MWh/m) are among the largest found in the Mediterranean Sea. Spectral analysis clarified a strong periodicity at 21 days, intermittent periods of 2.66 days, and 7.5 days dominate over the year while a 22.6 hourly period dominates in summer. January attained the strongest peak frequency of 8.4 in the west, 6.6 between the NW and SE sites, 5.20 for the near shore sites. Directional analysis indicated a bimodal wave system (325° and 285°-295°) in the west and unimodal (285°295°) in the east, while the central area showed combined distribution; bimodal in the deep water and unimodal near the shore. The 285°-295° wave direction showed largest contribution to the wave energy density. The northwest is the most energetic and most productive area. Monthly and seasonal wave energy clarified maximum of 9.98 MWh/m in January and 22.6 MWh/m in winter that contributes to 21% and 53% of the yearly mean, respectively. The wave energy resource is more than 3.5 times greater in winter than in summer. Exploitable energy density level exceeding 2 KWh/m are recorded for 250 sites out of the 259 site. Factor and detrended correspondence analyses confirmed that more than 98% of the Nile Fan's wave energy variance is controlled, in decreasing order of influence, by the depth, distance to shore, significant wave height, wave peak period, and wave principal direction. Four classes of varied statistics and hence different wave behavior were distinguished affected by the depth, morphology of the shoreline, and the dominating wave direction. Deepest water and near shore classes attained the largest wave energy. Further details on the effect of the environmental factors on the wave shape and corresponding energy are concluded.

Applications of the Three-cornered Hat Method to the Error Variance Estimations of FY-4A Atmospheric Temperature Profiles

ABSTRACT This study applied the three-cornered hat method to the error variance estimations of temperature profiles retrieved from the Geosynchronous Interferometric Infrared Sounder (GIIRS), the first hyperspectral infrared sounder onboard Fengyun-4A (FY-4A) geostationary satellite. An accurate observational error is a crucial input parameter for FY-4A temperature profile assimilation. Using multiple data sets from 2020, this study examined the error variances of FY-4A temperature profiles in clear-sky and cloudy-sky respectively. Because of the effects of clouds on GIIRS, the error variances in cloudy-sky are much larger than in clear-sky. Provided that error variances less than 4K2 are significant for data assimilation, the FY-4A temperature profiles in clear-sky from 900 to 150 hPa are meaningful to be assimilated. Results reveal that the latitudinal dependency of error variances is strong at 100 hPa. Encouraged by the vertical distributions of the temperature profile differences between FY-4A and other data sets, the error variance of total potential energy is also examined in this study. Although the detailed structures of temperature profiles are removed, the vertical integration obtains a more minor percentage error. Further studies on data assimilation are worth discussing if total potential energy is more meaningful than the FY-4A temperature profiles.

Spectral information from photon statistics in x-ray radiography and computed tomography

Measuring spectral information requires distinguishing photons of different energies, typically achieved using filters or advanced detectors sensitive to individual photons. We prove that when the arrival of photons follows a Poisson distribution (i.e., is shot-noise dominated), the variance and mean of the total photon energy collected over a fixed interval provide independent information about the photon energy spectrum. This immediately leads to several striking corollaries. First, the variance data can be used in the same way as the mean data in both x-ray radiography and tomography. Second, the ratio of the variance data and mean data provides an indication of the average energy of the photon spectrum. Third, the variance data, when compared to the mean data, reveal information analogous to using a higher-energy spectrum. These corollaries show that spectral information can be recovered even from common x-ray detectors which only measure aggregate photon energy. As a demonstration, we performed beam-hardening correction in computed tomography using variance data for both simulations and experiments. We also achieved quantitative tomographic reconstruction in simulation and recover accurate attenuation coefficient values in addition to material density and atomic number.

Collecting data in smart cities using energy harvesting technology

<p>This work investigates the problems of extending the sensors network lifetime in smart cities. The limited capacity of the sensors’ batteries, and the difficulty of replacing the sensors’ batteries in hard-to-reach areas are some of the main challenges that contribute in reducing the lifetimes of the networks. The direction of this study is to use renewable energy as an energy source for collecting data from various infrastructures that are distributed throughout these cities. We present a model for data collection based on combining energy harvesting (EH) with the cluster head rotation feature, which results in flexible and sustainable networks that can be used in smart cities. Simulation results depict the performance of the proposed model with and without EH technology. The metrics used to compare the performance of the proposed model with and without EH technology include the consumed energy by sensors, number of live and dead sensors, and energy variance. The results show that the network lifetime increases when EH technology is used.</p>

A Vertically Resolved MSE Framework Highlights the Role of the Boundary Layer in Convective Self-Aggregation

Abstract Convective self-aggregation refers to a phenomenon in which random convection can self-organize into large-scale clusters over an ocean surface with uniform temperature in cloud-resolving models. Previous literature studies convective aggregation primarily by analyzing vertically integrated (VI) moist static energy (MSE) variance. That is the global MSE variance, including both the local MSE variance at a given altitude and the covariance of MSE anomalies between different altitudes. Here we present a vertically resolved (VR) MSE framework that focuses on the local MSE variance to study convective self-aggregation. Using a cloud-resolving simulation, we show that the development of self-aggregation is associated with an increase of local MSE variance, and that the diabatic and adiabatic generation of the MSE variance is mainly dominated by the boundary layer (BL; the lowest 2 km). The results agree with recent numerical simulation results and the available potential energy analyses showing that the BL plays a key role in the development of self-aggregation. Additionally, we find that the lower free troposphere (2–4 km) also generates significant MSE variance in the first 15 days. We further present a detailed comparison between the global and local MSE variance frameworks in their mathematical formulation and diagnostic results, highlighting their differences.

Improvement of microstructure and texture homogeneity of tantalum by dynamic plastic deformation and subsequent annealing: Effect of pass number

In the paper, tantalum (Ta) samples were processed with one pass (1-pass), two passes (2-pass) and three passes (3-pass) dynamic plastic deformation (DPD). The deformed and annealed microstructures were studied by using multiple characterization techniques, such as electron backscatter diffraction (EBSD) and X-ray diffraction (XRD). Results showed that the yield strength increases from 611.81 MPa at the first DPD to 1055.19 MPa at the third DPD and all samples reached a 70% reduction finally. The deformed samples are mainly composed of {111}(<111>//LD) and {100}(<100>//LD) grains, and the grains are arranged alternately. Meanwhile, numerous micro shear bands (MSBs) appear in {111} grains of the 1-pass sample. In contrast, they are rarely observed in 2-pass and 3-pass samples, and the distribution of low-angle grain boundaries (LAGBs) and kernel average misorientation (KAM) are more uniform than 1-pass samples. Compared with the 1-pass sample, the 3-pass sample exhibits lower geometrically necessary dislocations (GND) density differences between {111} and {100} grains. The relative Schmidt factor (SR) results indicate that the 2-pass and 3-pass sample is more likely to activate multiple slips than the 1-pass sample. The stored energy variance between {111} and {100} grains in deformed samples is significantly weakened after increasing DPD passes, resulting in that microstructure and texture homogeneity after annealing are improved.

Three-Dimensional Structure of the Equatorial Kelvin Wave: Vertical Structure Functions, Equivalent Depths, and Frequency and Wavenumber Spectra

Abstract The wavenumber filtering of the Kelvin waves (KWs) is performed by a multivariate projection of the ERA5 data on the horizontal structures of the KWs from linear theory and vertical structure functions (VSFs) spanning the troposphere and stratosphere. The associated equivalent depths are consistent with solutions expected for the bounded atmosphere. The zonal wavenumber spectra of the KW energy and temporal variance are shown to be continuous and red with the rotational kinetic energy being dominant over the divergent kinetic energy at zonal wavenumber k = 1. Spatial and temporal variance in wavenumber space is analyzed in terms of periods from linear theory. The strongest variability is found in periods of 8–12 days at k = 1 and VSFs with a single zero-crossing in the troposphere and a structure resembling upward propagating waves across the tropopause and higher up. Secondary maxima are associated with VSFs that have multiple zero crossings in the troposphere and small equivalent depths. A comparison of the wavenumber and wavenumber–frequency methods shows a disagreement between frequencies from the Fourier time series and linear theory. A phase-locking experiment, in which the KW phases are replaced by linear theory estimates, demonstrates that power spectra from wavenumber–frequency filtering in the troposphere are largely defined by wave phases.

Non-Contact Abnormal Physiological Status Detection During Sport and Training

In sport and training, it is necessary to continue monitoring the physiological parameters of athletes to ensure that they can maintain a high level of competition. The previous monitoring physiological status methods mainly are contactable by sensors that are worn on the body. This paper adopts a non-contact physiological parameter monitoring method by using imaging photoplethysmography (iPPG). In order to eliminate the noises in iPPG signals, the correlation energy entropy threshold adaptive denoising and variance characterization series are introduced to resist the noises from external conditions. The noises are removed by a threshold which is estimated by noise energy entropy. The constructed signals after denoising are used to estimate physiological parameters, such as heart rate and respiratory rate. The experimental results demonstrate that it estimates the physiological parameters better by using iPPG-based physiological parameter monitoring method than previous methods.

A Framework to Evaluate Convective Aggregation: Examples With Different Microphysics Schemes

AbstractThis study introduces a modeling framework to evaluate convective aggregation (CA) under radiative‐convective equilibrium simulations using the vector vorticity equation cloud‐resolving model (VVM) coupled to a mixed‐layer slab ocean. The framework introduces the competing effects between the convection‐sea surface temperature (SST) feedback (CSF) and the moisture‐convection feedback (MCF) by modifying the initial SST gradient and the mixed layer depth. Examples of applying this framework are demonstrated by comparing simulations with different microphysics schemes. The conventional five‐category scheme (VVM‐Lin) and the predicted particle properties scheme (P3) are examined by the matrix formed by these two factors. A clear bifurcation of the aggregated/non‐aggregated states can be identified in both sets of experiments. The change of the bifurcation between two sets of experiments suggests that the P3 experiments tend to develop CA due to the stronger MCF. The budget analysis of spatial frozen moist static energy variance is applied to quantify the stronger MCF in the P3 simulations. Convective systems in P3 simulations develop more organized structures, which enhances MCF and leads to CA. The proposed modeling framework provides a reconciled view of the process‐based evaluation of CA among cloud‐resolving models that use different dynamics and physical parameterizations.

Unconventional quantum phase transitions in a one-dimensional Lieb-Schultz-Mattis system

We study quantum phases and phase transitions in a one-dimensional interacting fermion system with a Lieb-Schultz-Mattis (LSM) type anomaly. Specifically, the inversion symmetry enforces any symmetry-preserving gapped ground state of the system to be a Kitaev chain, following a Lieb-Schultz-Mattis type theorem that we prove. Alternatively, via the Jordan-Wigner transformation, this system describes a spin system whose gapped ground states must break either the inversion or the Ising symmetry associated with fermion parity. We obtain a phase diagram using analytical methods and variational matrix product state simulations, and study the critical behaviors of the quantum phase transitions therein using entanglement entropy, energy variance and finite size scaling of order parameters. In particular, we observe continuous phase transitions between different ordered phases that are beyond the Ginzburg-Landau-Wilson paradigm, in analogy to the deconfined quantum critical points in two spatial dimensions. We show this type of 1D deconfined quantum critical point is described by the Tomonaga-Luttinger liquid theory, and extract the Luttinger parameter and critical exponents. We also identify a gapless phase between two ordered phases, which cannot be described by a U(1) Luttinger liquid.

An Energy-Efficient Routing Algorithm Based on Greedy Strategy for Energy Harvesting Wireless Sensor Networks.

Energy harvesting wireless sensor network (EH-WSN) is considered to be one of the key enabling technologies for the internet of things (IoT) construction. Although the introduced EH technology can alleviate the energy limitation problem that occurs in the traditional wireless sensor network (WSN), most of the current studies on EH-WSN fail to adequately consider the relationship between energy state and data buffer constraint, and thereby they do not address well the issues of energy efficiency and long end-to-end delay. In view of the above problems, a brand new greedy strategy-based energy-efficient routing protocol is proposed in this paper. Firstly, in the system modeling process, we construct an energy evaluation model, which comprehensively considers the energy harvesting, energy consumption and energy classification factors, to identify the energy state of node. Then, we establish a channel feature-based communication range judgment model to determine the transmission area of nodes. Combining these two models, a reception state adjustment mechanism is designed. It takes the buffer occupancy and the MAC layer protocol into account to adjust the data reception state of nodes. On this basis, we propose a greedy strategy-based routing algorithm. In addition, we also analyze the correctness and computational complexity of the proposed algorithm. Finally, we conduct extensive simulation experiments to show that our algorithm achieves optimum performance in energy consumption, packet delivery ratio, average hop count and end-to-end delay and acceptable performance in energy variance.

DEM investigation of particle flow in a vertical rice mill: Influence of particle shape and rotation speed

The obscure milling mechanism, causing by diversity of rice grain shape, restricts appropriate selection of processing conditions for improving milling quality of different varieties. On this basis, the rice grain was represented as an axi-symmetric ellipsoid using discrete element method (DEM). Effects of particle shape and roller shaft rotation speed as well as the interplay of both on axial dispersion, radial exchange, spatial orientation and collision characteristics relating to the milling mechanism were analyzed. The results indicated that effects of aspect ratio on the milling uniformity depend on rotation speed, displaying the axial dispersion coefficient and mean effective collision rate increase with increasing aspect ratio in the lower rotation speed, whereas completely opposite in the higher rotation speed. Effects of aspect ratio on the milling intensity are independent of the rotation speed, showing radial ectopia number, variance of orientation distribution and mean collision energy all increase with increasing aspect ratio.