Iterative Monte Carlo Simulation Research Articles

Benefit maximization and optimal scheduling of renewable energy sources integrated system considering the impact of energy storage device and Plug-in Electric vehicle load demand

The intermittent nature of renewable-based generation may cause the dip or rise in generation and load imbalances. This paperwork obtains optimal generation scheduling, market benefit maximization, and daily energy loss minimization considering the impact of Plug-in Electric vehicles (PEV) and battery energy storage devices using nonlinear programming. The Plug-in EV load demand has been modelled using the Monte Carlo simulation (MCS), and the size of the energy storage device is obtained in the renewable energy source (RES) Microgrid distribution system. The proposed work's main contributions are (i) total cost-benefit maximization, (ii) daily energy loss minimization, (iii) optimal generation scheduling of RES based Microgrid and (iv) probabilistic modelling of PEV load demand and BES sizing. Furthermore, the results were compared to those available in the literature. The voltage variations in the Microgrid are also calculated with the effects of PEV and BES into account. The IEEE-33 bus test system was the subject of the investigation. The multi-objective problem has been solved using iterative Monte Carlo Simulation (MCS) and Nonlinear Programming (NLP). The proposed approach was implemented using MATLAB and GAMS interface. The daily energy loss has been decreased by 30.085 %, and the voltage deviation (VD) has been minimized by1.165 %.

Impact of Electric Vehicle Load Demand and Energy Storage Device in Integrated Renewable Energy Sources

ABSTRACT The intermittent nature of renewable-based generation may cause the dip or rise generation and load imbalances. Besides, the higher penetration of Electric Vehicles can affect the voltage profile and imbalances. The main contribution of the proposed work is to determine (i) the realistic load model of electric vehicle (EV) charging station (ii) the size of battery energy storage (BES) considering the EV load demand with the most realistic ZIP load. (iii) the cost-benefit analysis and the probabilistic modeling of EV load demand are considered for BES sizing. (iv) minimization of the daily energy loss (v), maximizing the total benefit of the system, (vi) the nature of the electric vehicle load demand has been modeled for size determination of energy storage devices. The fuel cost of the thermal generator has been minimized, and the operating and maintenance cost of the Solar Photovoltaic (PV), and Wind Turbine (WT) have been considered in the objective function. The daily energy loss has been reduced to 30.085%. The voltage deviation has been reduced to 1.165%. The analysis has been carried out for the IEEE-33 bus test system. The iterative Monte Carlo Simulation and Nonlinear Programming have been used to solve the multi-objective problem.

Development of the software tool Sample Size for Arbitrary Distributions and exemplarily applying it for calculating minimum numbers of moss samples used as accumulation indicators for atmospheric deposition

BackgroundDo we measure enough to calculate statistically valid characteristic values from random sample measurements, or do we measure too much—without any further increase in knowledge? This question is actually one of the key issues of every empirical measurement design, but is rarely investigated in environmental monitoring.ResultsIn this study, the methodology used for the design of the German Moss Survey 2015 network to determine statistically valid minimum sample numbers (MSN) for the calculation of the arithmetic mean value in compliance with certain accuracy requirements was further developed for data that are neither normally nor lognormally distributed. The core element of the procedure for estimating MSN without prerequisite to the distribution of data is an iterative Monte Carlo simulation. The methodological principle consists of using reference data (values measured in Moss Surveys preceding that in 2015) for a series of MSN candidate values to determine what accuracy would be achieved with these, and then calculating the MSN with which the specified accuracy requirement is met from a quadratic function between MSN candidates and their accuracy. The program Sample Size for Arbitrary Distributions (SSAD) was developed for the calculation of the MSN in the open programming language R.ConclusionsThe SSAD procedure closes a gap in the existing methodology for calculating statistically valid minimum sample numbers.

Mixing among lowest-lying scalar mesons and scalar glueball

Scalar glueball is implemented in single nonet linear sigma model (SNLSM) which basically includes lowest lying scalar and pseudoscalar mesons. Our new version of SNLSM involves mixing among scalar matter fields and glueball field which is enforced by scale symmetry considerations and the associated anomaly. Performing iterative Monte Carlo simulations, it is found that among the three candidates of scalar glueball, i.e., f0(1370), f0(1500) and f0(1710), only f0(1500) is predominately a glueball state with the mass prediction of 1.566±0.046 GeV and the other two are predominately quarkonium. The ππ, πK and πη scatterings are reinvestigated in the presence of scalar glueball and it is found that the overall behavior of the real part of the K-matrix unitarized ππ scattering amplitude is more compatible with observed data compared with the case of SNLSM without glueball. We have also presented the predictions of the model for the masses and decay widths of the scalars obtained from the poles of the K-matrix unitarized ππ, πK and πη scattering amplitudes. Moreover the ππ scattering phase shift predicted by our model is compared with the prediction of generalized linear sigma model (GLSM) which contains two nonets of scalar mesons and two nonets of pseudoscalar mesons (a quark–antiquark nonet and a four-quark nonet). Despite the fact that at this stage our model lacks the second meson nonet above 1 GeV, its prediction for the ππ scattering phase shift is close to the prediction of GLSM for s<1.1 GeV and in better agreement with experimental data for s>1.1 GeV in comparison with GLSM.

They are small worlds after all: revised properties ofKeplerM dwarf stars and their planets

We classified the reddest ($r-J>2.2$) stars observed by the NASA $Kepler$ mission into main sequence dwarf or evolved giant stars and determined the properties of 4216 M dwarfs based on a comparison of available photometry with that of nearby calibrator stars, as well as available proper motions and spectra. We revised the properties of candidate transiting planets using the stellar parameters, high-resolution imaging to identify companion stars, and, in the case of binaries, fitting light curves to identify the likely planet host. In 49 of 54 systems we validated the primary as the host star. We inferred the intrinsic distribution of M dwarf planets using the method of iterative Monte Carlo simulation. We compared several models of planet orbital geometry and clustering and found that one where planets are exponentially distributed and almost precisely coplanar best describes the distribution of multi-planet systems. We determined that $Kepler$ M dwarfs host an average of $2.2 \pm 0.3$ planets with radii of 1-4$R_{\oplus}$ and orbital periods of 1.5-180 d. The radius distribution peaks at $\sim 1.2R_{\oplus}$ and is essentially zero at $4R_{\oplus}$, although we identify three giant planet candidates other than the previously confirmed Kepler-45b. There is suggestive but not significant evidence that the radius distribution varies with orbital period. The distribution with logarithmic orbital period is flat except for a decline for orbits less than a few days. Twelve candidate planets, including two Jupiter-size objects, experience an irradiance below the threshold level for a runaway greenhouse on an Earth-like planet and are thus in a "habitable zone".

Iterative Monte Carlo simulation with the Compton kinematics-based GEB in a plastic scintillation detector

Plastic scintillators have been used for gamma ray detection in the fields of dosimetry and homeland security because of their desired characteristics such as a fast decay time, a low production cost, availability in a large-scale, and a tissue-equivalence. Gaussian energy broadening (GEB) in MCNP simulation is an effective treatment for tallies to calculate the broadened response function of a detector similarly to measured spectra. The full width at half maximum (FWHM) of a photopeak has been generally used to compute input parameters required for the GEB treatment. However, it is hard to find the photopeak in measured gamma spectra with plastic scintillators so that computation of the input parameters for the GEB has to be taken with another way. In this study, an iterative method for the GEB treated MCNP simulation to calculate the response function of a plastic scintillator is suggested. Instead of the photopeak, Compton maximum and Compton edge were used to estimate energy broadening in the measured spectra and to determine the GEB parameters. In a demonstration with a CsI(Tl) scintillator, the proposed iterative simulation showed the similar gamma spectra to the existing method using photopeaks. The proposed method was then applied to a polystyrene scintillator, and the simulation result were in agreement with the measured spectra with only a little iteration.

Development and Applications of a Laboratory Micro X-ray Fluorescence (μXRF) Spectrometer Using Monochromatic Excitation for Quantitative Elemental Analysis.

The analytical characterization and an application example of a novel laboratory X-ray fluorescence (μXRF) microprobe is presented, which combines monochromatic, focused X-ray beam excitation with a high-performance silicon drift detector (SDD) and two-dimensional/three-dimensional (2D/3D) scanning capability. Because of the monochromatic excitation, below the (multiple) Compton/Rayleigh scattering peak region, the XRF spectra obtained by this laboratory spectrometer has similarly high peak-to-background ratios as those which can be obtained at synchrotron sources. However, the flux density difference between the proposed laboratory instrument and current synchrotron end stations is on the order of several orders of magnitude. As a result, sub-ppm minimum detection limits (MDL) for transition metals are obtained for a variety of sample matrices. The monochromatic excitation also allows for the efficient use of an iterative Monte Carlo simulation algorithm to obtain quantitative information on the analyzed samples. The analytical characteristics of this instrument and quantitative results, in combination with an iterative reverse Monte Carlo simulation algorithm, will be demonstrated using measurements conducted on an iron-containing meteorite.

Probing the substructure off0(1370)

Within an effective nonlinear chiral Lagrangian framework the substructure of $f_0(1370)$ is studied. The investigation is conducted in the context of a global picture of scalar mesons in which the importance of the underlying connections among scalar mesons below and above 1 GeV is recognized and implemented. These connections are due to the mixings among various quark-antiquarks, four-quarks and glue components and play a central role in understanding the properties of scalar mesons. Iterative Monte Carlo simulations are first performed on the 14-dimensional parameter space of the model and sets of points in this parameter space (the global sets) that give an overall agreement with all experimental data on mass spectrum, various decay widths and decay ratios of all isosinglet scalar states below 2 GeV are determined. Then within each global set, subsets that give closest agreement for the properties of $f_0(1370)$ are studied. Unlike the properties of other isosinglet states that show a range of variation within each global set, it is found that there is a clear signal for $f_0(1370)$ to be predominantly a quark-antiquark state with a substantial $s {\bar s}$ component, together with small remnants of four-quark and glue components.

New Approach to Alpha Spectrum Analysis:Iterative Monte Carlo Simulations and Fitting

A novel alpha spectrum analysis program which combines the Monte Carlo simulations with spectrum fitting is introduced. The shapes used in the fitting are obtained from the simulations. The program is developed especially to analyze complex alpha particle energy spectra – such as those obtained from non-processed aerosol filters, swipe samples or isolated particles emitting alpha radiation. In addition to activities of the nuclides present in the sample, the program can provide source characterization. In particular, the program can be used to characterize samples of nuclear material, i.e. those containing fissionable isotopes such as U or Pu. In the present paper we illustrate the use of the program to identify and quantify alpha-particle emitting isotopes in a depleted U projectile found in Kosovo and to estimate the depth distribution of alpha-particle emitting aerosols in a glassfibre filter.

Monte Carlo simulation code for confocal 3D micro‐beam X‐ray fluorescence analysis of stratified materials

AbstractStratified materials are of great importance for many branches of modern industry, e.g. electronics or optics and for biomedical applications. Examination of chemical composition of individual layers and determination of their thickness helps to get information on their properties and function. A confocal 3D micro X‐ray fluorescence (3D µXRF) spectroscopy is an analytical method giving the possibility to investigate 3D distribution of chemical elements in a sample with spatial resolution in the micrometer regime in a non‐destructive way. Thin foils of Ti, Cu and Au, a bulk sample of Cu and a three‐layered sandwich sample, made of two thin Fe/Ni alloy foils, separated by polypropylene, were used as test samples. A Monte Carlo (MC) simulation code for the determination of elemental concentrations and thickness of individual layers in stratified materials with the use of confocal 3D µXRF spectroscopy was developed. The X‐ray intensity profiles versus the depth below surface, obtained from 3D µXRF experiments, MC simulation and an analytical approach were compared. Correlation coefficients between experimental versus simulated, and experimental versus analytical model X‐ray profiles were calculated. The correlation coefficients were comparable for both methods and exceeded 99%. The experimental X‐ray intensity profiles were deconvoluted with iterative MC simulation and by using analytical expression. The MC method produced slightly more accurate elemental concentrations and thickness of successive layers as compared to the results of the analytical approach. This MC code is a robust tool for simulation of scanning confocal 3D µXRF experiments on stratified materials and for quantitative interpretation of experimental results. Copyright © 2011 John Wiley &amp; Sons, Ltd.

Monte Carlo simulations towards semi-quantitative prompt gamma activation imaging

Numerous non-destructive techniques utilize neutron attenuation, scattering or capture to gain morphological, structural or elemental information about the material under study. However, few attempts have been made so far to use neutron-induced gamma radiation for 3D element mapping. The first ever facility using direct scanning for element imaging was set up at the Budapest Research Reactor. It was shown that the position-sensitive prompt-gamma detection (PGAI) enables us to determine the spatial distribution of major elements. Iterative Monte Carlo simulation technique has also been developed to provide not only qualitative but also semi-quantitative element distribution of a simple object.

A spectral stochastic element free Galerkin method for the problems with random material parameter

AbstractThis paper presents a spectral stochastic element free Galerkin method (SSEFGM) for the problems involving a random material property. The random material property and resulting system response quantity are represented by a probabilistic spectral expansion techniques (Karhunen–Loeve expansion and Polynomical Chaos series, respectively) and implemented into the element free Galerkin (EFG) analysis. Numerical solutions in 1D linear elastic problem with random elastic modulus are introduced, and compared with those of Monte Carlo simulation (MCS) so as to provide the validation of the proposed approach. The present SSEFGM approach can produce a probabilistic density distribution as well as a first‐ and second‐order statistical moments (mean and variance) of response quantity by a single calculation, which is distinguished from an iterative MCS. Moreover, the method is based on an element free analysis so that there is no need of nodal connectivities, which usually require more time and labourious task than main calculations. Thus the proposed SSEFGM approach can provide an alternative analysis tool for the problems contains a stochastic material property, and demands complex mesh structures. Copyright © 2004 John Wiley &amp; Sons, Ltd.

Evaluation of energy‐dispersive x‐ray spectra of low‐Z elements from electron‐probe microanalysis of individual particles

AbstractSeveral applications require the determination of low‐Z elements such as C, N and O in individual microparticles. Electron‐probe microanalysis (EPMA) using a thin‐window energy‐dispersive x‐ray detector offers the possibility to detect light elements. To achieve at least semi‐quantitative information on the chemical composition of each individual particle, accurate determination of x‐ray intensities is critical. The energy‐dispersive x‐ray spectra under 1 keV are complex. The L and M lines of heavier elements can strongly overlap with light‐element K lines. Also, the transmission of the detector window and the shape of the bremsstrahlung background should be taken into account. Because of the strong overlap of the characteristic lines, the ‘top‐hat’ filter used for on‐line evaluation of conventional computer‐controlled EPMA spectra cannot be applied for thin‐window EPMA spectra under 1 keV. Therefore, the spectra were processed using a non‐linear least‐squares fitting algorithm (AXIL). The applicability and the limitations of the method were tested by a series of measurements of particulate standards in the size range of 0.3–10 µm, made from analytical‐reagent grade chemicals. The composition and thickness of the thin window of the detector were modelled using the transmission data given by the manufacturer. The elemental composition of each individual particle was approximated using a new data evaluation method (EP‐PROC) based on an iterative Monte Carlo simulation with combination of successive approximation. The dependence of the K/L intensity ratios of the elements Ca–Zn on the size and composition of the particles was studied extensively by Monte Carlo simulations and measurements of standard particles. By taking into account the L‐line contributions of K, Ca, Ti or Cr in the spectral evaluation, the estimation of the light‐element concentrations was improved significantly, especially when K, Ca, Ti or Cr were present at high concentrations. Copyright © 2001 John Wiley &amp; Sons, Ltd.

COMPUTATIONAL ASPECTS OF THE LOCAL ITERATIVE MONTE CARLO TECHNIQUE

Lately, the Local Iterative Monte Carlo technique was introduced for an efficient simulation of effects connected to sparsely populated regions in semiconductor devices like hot electron effects in silicon MOSFETs. This paper focuses on computational aspects of this new Monte Carlo technique, namely the reduction of the computation time by parallel computation and the reuse of drift information. The Local Iterative Monte Carlo technique combines short Monte Carlo particle flight simulations with an iteration process to a complete device simulation. The separation between short Monte Carlo simulations and the iteration process makes a simple parallelization strategy possible. The necessary data transfer is small and can be performed via the Network File System. An almost linear speed up could be achieved. Besides by parallelization, the computational expenses can be significantly reduced, when the results of the short Monte Carlo simulations are memorized in a drift table and used several times. A comparison between a bulk, a one-dimensional and the two-dimensional Local Iterative Monte Carlo simulation reveals that by using the drift information more than once, becomes increasingly efficient with increasing dimension of the simulation.

Phase Equilibria of Mixtures Containing Chain Molecules Predicted through a Novel Simulation Scheme

A new iterative Monte Carlo simulation scheme is presented for the efficient prediction of fluid-phase equilibria of mixtures containing long chain molecules. Using realistic molecular potentials, the high-pressure vapor-liquid and liquid-liquid equilibria of binary mixtures of ethane or ethylene with ${\mathrm{C}}_{16}$ to ${\mathrm{C}}_{40}$ normal alkanes are calculated. Excellent agreement between experimental data and molecular simulation is obtained in all cases.