Mesh‑free semi‑quantitative variance underestimation elimination method in Monte Caro algorithm

The inter-cycle correlation of fission source distributions(FSDs)in the Monte Carlo power iteration process results in variance underestimation of tallied physical quantities,especially in large local tallies.This study provides a mesh-free semiquantitative variance underestimation elimination method to obtain a credible confidence interval for the tallied results.This method comprises two procedures:Estimation and Elimination.The FSD inter-cycle correlation length is estimated in the Estimation procedure using the Sliced Wasserstein distance algorithm.The batch method was then used in the elimination procedure.The FSD inter-cycle correlation length was proved to be the optimum batch length to eliminate the variance underestimation problem.We exemplified this method using the OECD sphere array model and 3D PWR BEAVRS model.The results showed that the average variance underestimation ratios of local tallies declined from 37 to 87%to within±5%in these models.

Irreversible Markov chain Monte Carlo algorithm for self-avoiding walk

We formulate an irreversible Markov chain Monte Carlo algorithm for the self-avoiding walk （SAW）, which violates the detailed balance condition and satisfies tile balance condition. Its performance improves significantly compared to that of the Berretti-Sokal algorithm, which is a variant of the Metropolis Hastings method. The gained efficiency increases with spatial dimension （D）, from approximately 10 times in 2D to approximately 40 times in 5D. We simulate the SAW on a 5D hypercubic lattice with periodic boundary conditions, for a linear system with a size up to L = 128, and confirm that as for the 5D Ising model, the finite-size scaling of the SAW is governed by renormalized exponents, υ^＊ = 2/d and γ/υ^＊ = d/2. The critical point is determined, which is approximately 8 times more precise than the best available estimate.

Potential-Decomposition Strategy in Markov Chain Monte Carlo Sampling Algorithms

We introduce the potential-decomposition strategy （PDS）, which can be used in Markov chain Monte Carlo sampling algorithms. PDS can be designed to make particles move in a modified potential that favors diffusion in phase space, then, by rejecting some trial samples, the target distributions can be sampled in an unbiased manner. Furthermore, if the accepted trial samples are insumcient, they can be recycled as initial states to form more unbiased samples. This strategy can greatly improve efficiency when the original potential has multiple metastable states separated by large barriers. We apply PDS to the 2d Ising model and a double-well potential model with a large barrier, demonstrating in these two representative examples that convergence is accelerated by orders of magnitude.

A Monte Carlo Based COVID-19 Detection Framework for Smart Healthcare

COVID-19 is a novel coronavirus disease that has been declared as a global pandemic in 2019.It affects the whole world through personto-person communication.This virus spreads by the droplets of coughs and sneezing,which are quickly falling over the surface.Therefore,anyone can get easily affected by breathing in the vicinity of the COVID-19 patient.Currently,vaccine for the disease is under clinical investigation in different pharmaceutical companies.Until now,multiple medical companies have delivered health monitoring kits.However,a wireless body area network(WBAN)is a healthcare system that consists of nano sensors used to detect the real-time health condition of the patient.The proposed approach delineates is to fill a gap between recent technology trends and healthcare structure.If COVID-19 affected patient is monitored through WBAN sensors and network,a physician or a doctor can guide the patient at the right timewith the correct possible decision.This scenario helps the community to maintain social distancing and avoids an unpleasant environment for hospitalized patients Herein,a Monte Carlo algorithm guided protocol is developed to probe a secured cipher output.Security cipher helps to avoid wireless network issues like packet loss,network attacks,network interference,and routing problems.Monte Carlo based covid-19 detection technique gives 90%better results in terms of time complexity,performance,and efficiency.Results indicate that Monte Carlo based covid-19 detection technique with edge computing idea is robust in terms of time complexity,performance,and efficiency and thus,is advocated as a significant application for lessening hospital expenses.

Seismic fluid identification using a nonlinear elastic impedance inversion method based on a fast Markov chain Monte Carlo method

Elastic impedance inversion with high efficiency and high stability has become one of the main directions of seismic pre-stack inversion. The nonlinear elastic impedance inversion method based on a fast Markov chain Monte Carlo （MCMC） method is proposed in this paper, combining conventional MCMC method based on global optimization with a preconditioned conjugate gradient （PCG） algorithm based on local optimization, so this method does not depend strongly on the initial model. It converges to the global optimum quickly and efficiently on the condition that effi- ciency and stability of inversion are both taken into consid- eration at the same time. The test data verify the feasibility and robustness of the method, and based on this method, we extract the effective pore-fluid bulk modulus, which is applied to reservoir fluid identification and detection, and consequently, a better result has been achieved.

LS-SVM and Monte Carlo methods based reliability analysis for settlement of soft clayey foundation

A method which adopts the combination of least squares support vector machine（LS-SVM） and Monte Carlo（MC） simulation is used to calculate the foundation settlement reliability.When using LS-SVM,choosing the training dataset and the values for LS-SVM parameters is the key.In a representative sense,the orthogonal experimental design with four factors and five levels is used to choose the inputs of the training dataset,and the outputs are calculated by using fast Lagrangian analysis continua（FLAC）.The decimal ant colony algorithm（DACA） is also used to determine the parameters.Calculation results show that the values of the two parameters,and δ2 have great effect on the performance of LS-SVM.After the training of LS-SVM,the inputs are sampled according to the probabilistic distribution,and the outputs are predicted with the trained LS-SVM,thus the reliability analysis can be performed by the MC method.A program compiled by Matlab is employed to calculate its reliability.Results show that the method of combining LS-SVM and MC simulation is applicable to the reliability analysis of soft foundation settlement.

Numerical Evaluation on the Thermal Performance of the Solar External Cylinder Receiver using Monte Carlo Ray-Tracing Algorithm

The heat receiver is an essential part of the Concentrating Solar Power plant,directly affecting its operation and safety.In this paper,the Monte Carlo ray-tracing algorithm was introduced to evaluate a 50 MW(e)external cylindrical receiver’s thermal performance.The radiation heat flux concentrated from the heliostats field and the view factors between grids divided from the tubes were both calculated using Monte Carlo ray-tracing algorithm.Besides,an in-house code was developed and verified,including three modules of the view-factor calculation,thermal performance calculation,and thermal stress calculation.It was also employed to investigate the 50 MW(e)receiver,and the detailed 3D profiles of temperature and thermal stress in the receiver were analyzed.It was found that the molten salt was heated from 298℃to 565℃and the tube at the 50 MW(e)receiver’s outlet had a high temperature,while the high thermal stress came out at the receiver’s entrance.Finally,the over-temperature of the receiver was discussed,and an optimization algorithm was introduced.The tube wall temperature and film temperature at the overheated area matched the safety criteria,and the outlet molten salt temperature still reached 563℃after the optimization process,with only 2℃dropped.

Elastic Behavior of Polymer Chains

The elastic behavior of the polymer chain was investigated in a three-dimensional off-lattice model. We sample more than 109 conformations of each kind of polymer chain by using a Monte Carlo algorithm, then analyze them with the non-Gaussian theory of rubberlike elasticity, and end with a statistical study. Through observing the effect of the chain flexibility and the stretching ratio on the mean-square end-to-end distance, the average energy, the average Helmholtz free energy, the elastic force, the contribution of energy to the elastic force, and the entropy contribution to elastic force of the polymer chain, we find that a rigid polymer chain is much easier to stretch than a flexible polymer chain. Also, a rigid polymer chain will become difficult to stretch only at a quite high stretching ratio because of the effect of the entropy contribution. These results of our simulation calculation may explain some of the macroscopic phenomena of polymer and biomacromolecular elasticity.

Quantum Monte-Carlo simulation of FeAs-based superconductors

The generalized quantum Monte Carlo algorithm is developed and used to calculate the energy,occupation numbers,and correlations functions of finite FeAs clusters in the two-orbital model at finite temperatures.The coding of quantum states made it possible to take into account complex exchange terms between the orbitals.The results for the calculation of the thermodynamic characteristics of finite two-dimensional FeAs clusters simulating iron-based superconductors have been obtained.The calculated correlation functions indicate the possibility of the effective attraction of charge carriers.

Graphical representations and worm algorithms for the O(N) spin model

We present a family of graphical representations for the O(N)spin model,where N≥1 represents the spin dimension,and N=1,2,3 corresponds to the Ising,XY and Heisenberg models,respectively.With an integer parameter 0≤ℓ≤N/2,each configuration is the coupling of ℓ copies of subgraphs consisting of directed flows and N−2ℓ copies of subgraphs constructed by undirected loops,which we call the XY and Ising subgraphs,respectively.On each lattice site,the XY subgraphs satisfy the Kirchhoff flow-conservation law and the Ising subgraphs obey the Eulerian bond condition.Then,we formulate worm-type algorithms and simulate the O(N)model on the simple-cubic lattice for N from 2 to 6 at all possibleℓ.It is observed that the worm algorithm has much higher efficiency than the Metropolis method,and,for a given N,the efficiency is an increasing function ofℓ.Besides Monte Carlo simulations,we expect that these graphical representations would provide a convenient basis for the study of the O(N)spin model by other state-of-the-art methods like the tensor network renormalization.

Bayesian zero-failure reliability modeling and assessment method for multiple numerical control(NC) machine tools

A new problem that classical statistical methods are incapable of solving is reliability modeling and assessment when multiple numerical control machine tools(NCMTs) reveal zero failures after a reliability test. Thus, the zero-failure data form and corresponding Bayesian model are developed to solve the zero-failure problem of NCMTs, for which no previous suitable statistical model has been developed. An expert-judgment process that incorporates prior information is presented to solve the difficulty in obtaining reliable prior distributions of Weibull parameters. The equations for the posterior distribution of the parameter vector and the Markov chain Monte Carlo(MCMC) algorithm are derived to solve the difficulty of calculating high-dimensional integration and to obtain parameter estimators. The proposed method is applied to a real case; a corresponding programming code and trick are developed to implement an MCMC simulation in Win BUGS, and a mean time between failures(MTBF) of 1057.9 h is obtained. Given its ability to combine expert judgment, prior information, and data, the proposed reliability modeling and assessment method under the zero failure of NCMTs is validated.

Reconstruction and interpretation of photon Doppler velocimetry spectrum for ejecta particles from shock-loaded sample in vacuum

The photon Doppler velocimetry(PDV) spectrum is investigated in an attempt to reveal the particle parameters of ejecta from shock-loaded samples in a vacuum. A GPU-accelerated Monte–Carlo algorithm, which considers the multiplescattering effects of light, is applied to reconstruct the light field of the ejecta and simulate the corresponding PDV spectrum.The influence of the velocity profile, total area mass, and particle size of the ejecta on the simulated spectra is discussed qualitatively. To facilitate a quantitative discussion, a novel theoretical optical model is proposed in which the singlescattering assumption is applied. With this model, the relationships between the particle parameters of ejecta and the peak information of the PDV spectrum are derived, enabling direct extraction of the particle parameters from the PDV spectrum.The values of the ejecta parameters estimated from the experimental spectrum are in good agreement with those measured by a piezoelectric probe.

Effective Bandwidth Estimation in Data Networks: An Analysis for Two Traffic Characterizations

The Generalized Markov Fluid Model(GMFM)is assumed for modeling sources in the network because it is versatile to describe the traffic fluctuations.In order to estimate resources allocations or in other words the channel occupation of each source,the concept of effective bandwidth(EB)proposed by Kelly is used.In this paper we use an expression to determine the EB for this model which is of particular interest because it allows expressing said magnitude depending on the parameters of the model.This paper provides EB estimates for this model applying Kernel Estimation techniques in data networking.In particular we will study two differentiated cases:dispatches following a Gaussian and Exponential distribution.The performance of the proposed method is analyzed using simulated traffic traces generated by Monte Carlo Markov Chain algorithms.The estimation process worked much better in the Gaussian distribution case than in the Exponential one.

Influence of the Inverse-Time Protection Relays on the Voltage Dip Index

The probability-assessment analyses on the characteristic value of voltage dip by using Monte Carlo stochastic modeling method to stimulate the randomness of the short circuit fault are introduced. Using Matlab and Power Systems CAD(PSCAD), we design control interface which combines the electromagnetic transient simulation with the Monte Carlo method. Specifically, the designing of interface which is meant to employ the method of Matlab programming to control the electromagnetic transients including direct current(EMTDC)simulation is introduced. Furthermore, the influences of the protection devices on the voltage dip to ensure the authenticity and the referential reliability are simulated. A system with the inverse-time protection devices equipped on each line which can coordinate together is designed to cut off the short-circuit fault. The voltage dip of the designed system is assessed by the pre- and post-system average root mean square(RMS) variation frequency index, and the voltage dip index is compared with the Information Technology Industry Council(ITIC)curves. The simulation results demonstrate that the inverse-time relay protection equipments are well-coordinated,and the severity and the range of the voltage dip are influenced by the cooperation of the equipped inverse-time protection devices.

N-cluster correlations in four-and five-dimensional percolation

We study N-cluster correlation functions in four-and five-dimensional(4D)and 5D)bond percolation by extensive Monte Carlo simulation.We reformulate the transfer Monte Carlo algorithm for percolation[Phys.Reu.E 72,016126(2005)]using the disjoint-set data structure,and simulate a cylindrical geometry Ld^-1×∞,with the linear size up to L=512 for 4D and 128 for 5D.We determine with 1 high precision all possible N-cluster exponents,for N=2 and 3,and the universal amplitude for a logarithmic correlation function.From the symmetric correlator with N=2,we obtain the correlation-length critical exponent as 1/v=1.4610(12)for 4D and 1/v=1.737(2)for 5D,significantly improving over the existing results.Estimates for the other exponents and the universal logarithmic amplitude have not been reported before to our knowledge.Our work demonstrates the validity of logarithmic conformal field theory and adds to the growing knowledge for high-dimensional percolation.

A Solution to a Single Molecular Experiment Problem

Recently Hari Shroff and his collaborators[Nano Letters 5(2005)]developed a nanoscopic force sensor,but the force which they measured in their single molecular experiment was much lower than the theoretical critical value.In order to fix this problem,we investigate the micromechanics of dsDNA based on the worm-like chain model and flexible hinge model by using Monte Carlo algorithm.The simulation results not only address Hari Shroff’s experiment difficulty reasonably,but also provide strong support for flexible hinge mechanism put forward recently by Yan and Marko[Phys.Rev.Lett.93(2004)].

Low-frequency E-field Detection Array(LFEDA)-Construction and preliminary results

In recent years, locating total lightning at the VLF/LF band has become one of the most important directions in lightning detection. The Low-frequency E-field Detection Array(LFEDA) consisting of nine fast antennas was developed by the Chinese Academy of Meteorological Sciences in Guangzhou between 2014 and 2015. This paper documents the composition of the LFEDA and a lightning-locating algorithm that applies to the low-frequency electric field radiated by lightning pulse discharge events(LPDEs). Theoretical simulation and objective assessment of the accuracy and detection efficiency of LFEDA have been done using Monte Carlo simulation and artificial triggered lightning experiment, respectively. The former results show that having a station in the network with a comparatively long baseline improves both the horizontal location accuracy in the direction perpendicular to the baseline and the vertical location accuracy along the baseline. The latter results show that detection efficiencies for triggered lightning flashes and return strokes are 100% and 95%, respectively. The average planar location error for return strokes of triggered lightning flashes is 102 m. By locating LPDEs in thunderstorms, we find that LPDEs are consistent with convective regions as indicated by strong reflectivity columns, and present a reasonable distribution in the vertical direction.In addition, the LFEDA can reveal an image of lightning development through mapping the channels of lightning. Based on three-dimensional locations, the vertical propagation speed of the preliminary breakdown and the changing trend of the leader's speed in an intra-cloud and a cloud-to-ground flash are investigated. The research results show that the LFEDA has the capability for three-dimensional location of lightning, which provides a new technique for researching lightning development characteristics and thunderstorm electricity.

Chance-constrained Coordinated Optimization for Urban Electricity and Heat Networks

Urban electricity and heat networks(UEHN)consist of the coupling and interactions between electric power systems and district heating systems,in which the geographical and functional features of integrated energy systems are demonstrated.UEHN have been expected to provide an effective way to accommodate the intermittent and unpredictable renewable energy sources,in which the application of stochastic optimization approaches to UEHN analysis is highly desired.In this paper,we propose a chance-constrained coordinated optimization approach for UEHN considering the uncertainties in electricity loads,heat loads,and photovoltaic outputs,as well as the correlations between these uncertain sources.A solution strategy,which combines the Latin Hypercube Sampling Monte Carlo Simulation(LHSMCS)approach and a heuristic algorithm,is specifically designed to deal with the proposed chance-constrained coordinated optimization.Finally,test results on an UEHN comprised of a modified IEEE 33-bus system and a 32-node district heating system at Barry Island have verified the feasibility and effectiveness of the proposed framework.

Bayesian Estimation for the Extended t-process Regression Models with Independent Errors

The extended t-process regression model is developed to robustly model functional data with outlier functional curves.This paper applies Bayesian estimation to propose an estimation procedure for the model with independent errors.A Monte Carlo EM method is built to estimate parameters involved in the model.Simulation studies and real examples show the proposed method performs well against outliers.