Showing papers on "Monte Carlo method published in 2022"
TL;DR: In this article, a hybrid enhanced Monte Carlo simulation (HEMCS) method was proposed to estimate the failure probability with low computation cost and high computational burden. But, the authors focused on developing a novel enhanced MCS approach with an advanced machine learning method for achieving accurate approximation of failure probability using high-efficiency computations.
57 citations
TL;DR: In this paper , a hybrid enhanced Monte Carlo simulation (HEMCS) approach with an advanced machine learning method was proposed to achieve accurate approximation of failure probability with high-efficiency computations.
57 citations
TL;DR: Bryant et al. as mentioned in this paper showed that one can predict the structure of large protein complexes starting from predictions of subcomponents using Monte Carlo tree search, with a median TM-score of 0.51.
Abstract: AlphaFold can predict the structure of single- and multiple-chain proteins with very high accuracy. However, the accuracy decreases with the number of chains, and the available GPU memory limits the size of protein complexes which can be predicted. Here we show that one can predict the structure of large complexes starting from predictions of subcomponents. We assemble 91 out of 175 complexes with 10-30 chains from predicted subcomponents using Monte Carlo tree search, with a median TM-score of 0.51. There are 30 highly accurate complexes (TM-score ≥0.8, 33% of complete assemblies). We create a scoring function, mpDockQ, that can distinguish if assemblies are complete and predict their accuracy. We find that complexes containing symmetry are accurately assembled, while asymmetrical complexes remain challenging. The method is freely available and accesible as a Colab notebook https://colab.research.google.com/github/patrickbryant1/MoLPC/blob/master/MoLPC.ipynb .
44 citations
TL;DR: In this paper, an energy theory method was proposed to solve the problem of modal overdecomposition, which can not only improve the safety of the system, but also optimize dispatch and reduce economic losses.
43 citations
TL;DR: In this paper , a probabilistic energy flow calculation approach for regional integrated energy system (RIES), taking into account those cross-system failures (CSF), as well as fluctuations of renewable generation and multi-energy loads, is proposed.
40 citations
TL;DR: In this article , a dual-energy gamma source and two sodium iodide detectors were used with the help of artificial intelligence to determine the flow pattern and volume percentage in a two-phase flow by considering the thickness of the scale in the tested pipeline.
Abstract: One of the factors that significantly affects the efficiency of oil and gas industry equipment is the scales formed in the pipelines. In this innovative, non-invasive system, the inclusion of a dual-energy gamma source and two sodium iodide detectors was investigated with the help of artificial intelligence to determine the flow pattern and volume percentage in a two-phase flow by considering the thickness of the scale in the tested pipeline. In the proposed structure, a dual-energy gamma source consisting of barium-133 and cesium-137 isotopes emit photons, one detector recorded transmitted photons and a second detector recorded the scattered photons. After simulating the mentioned structure using Monte Carlo N-Particle (MCNP) code, time characteristics named 4th order moment, kurtosis and skewness were extracted from the recorded data of both the transmission detector (TD) and scattering detector (SD). These characteristics were considered as inputs of the multilayer perceptron (MLP) neural network. Two neural networks that were able to determine volume percentages with high accuracy, as well as classify all flow regimes correctly, were trained.
39 citations
TL;DR: In this article , a battery charging and swapping optimization model is established for electric vehicles (EVs) and battery charging/swapping stations (BCSS), where the charging behaviors of PrEVs are modeled based on the Monte Carlo (MC) method, and the battery swapping strategies of ETs are optimized by bi-level dynamic game.
Abstract: In this paper, a battery charging and swapping optimization model is established for electric vehicles (EVs) and battery charging/swapping stations (BCSS). The EVs are categorized into private electric vehicles (PrEVs) and electric taxis (ETs), where the charging behaviors of PrEVs are modeled based on the Monte Carlo (MC) method, and the battery swapping (BS) strategies of ETs are optimized by bi-level dynamic game. Moreover, the voltage deviation of the power grid is considered in the load regulation process of ETs. A “path-location” model is established combining with the Floyd algorithm in the simulation and the IEEE 14-Bus system is used to derive the node voltage. Numerical results show that the proposed strategy can simultaneously increase the revenue of BCSS and ETs and reduce the voltage deviation.
38 citations
TL;DR: In this article , an advanced Hybrid-Physics Guided-Variational Bayesian Spatial-Temporal neural network was proposed for real-time concentration spatio-temporal evolution forecasting of natural gas release.
35 citations
TL;DR: In this paper , an easy-to-use and non-redundant machine learning model was proposed for very high-cycle fatigue (VHCF) analysis and Monte Carlo simulation (MCs) was run to enlarge dataset size and a ML method was proposed to investigate the synergic influence of defect size, depth, location and build orientation on Ti-6Al-4V.
34 citations
TL;DR: In this article , a Monte Carlo simulation procedure is developed to select the optimum location of wind farms by using major decision criteria and applying subjective judgments from decision-makers, which is applied to offshore wind farms located in Spain, and the most appropriate locations for turbine positioning are ranked.
33 citations
TL;DR: Wang et al. as mentioned in this paper proposed an energy theory method to solve the problem of modal over-decomposition, and combined effective modal recognition, uses different prediction methods according to modal characteristics and proposes a set of new optimization algorithms to improve nonlinear prediction capabilities.
TL;DR: In this paper, a simple and effective adaptive surrogate model to structural reliability analysis using deep neural network (DNN) is introduced, in which initial design of experiments (DoEs) are randomly selected from a given Monte Carlo Simulation (MCS) population to build the global approximate model of performance function (PF).
Abstract: This article introduces a simple and effective adaptive surrogate model to structural reliability analysis using deep neural network (DNN). In this paradigm, initial design of experiments (DoEs) are randomly selected from a given Monte Carlo Simulation (MCS) population to build the global approximate model of performance function (PF). More important points on the boundary of limit state function (LSF) and their vicinities are subsequently added relied on the surrogate model to enhance its accuracy without any complex techniques. A threshold is proposed to switch from a globally predicting model to a locally one for the approximation of LSF by eradicating previously used unimportant and noise points. Accordingly, the surrogate model becomes more precise for the MCS-based failure probability assessment with only a small number of experiments. Six numerical examples with highly nonlinear properties, various distributions of random variables and multiple failure modes, namely three benchmark ones regarding explicit mathematical PFs and the others relating to finite element method (FEM)-programmed truss structures under free vibration, are examined to validate the present approach.
TL;DR: In this paper , a Bayesian neural network (BNN) is integrated with a strong non-linear fitting capability and uncertainty, which has not previously been used in geotechnical engineering, to propose a modelling strategy in developing prediction models for soil properties.
Abstract: This study adopts the Bayesian neural network (BNN) integrated with a strong non-linear fitting capability and uncertainty, which has not previously been used in geotechnical engineering, to propose a modelling strategy in developing prediction models for soil properties. The compression index C c and undrained shear strength s u of clays are selected as examples. Variational inference (VI) and Monte Carlo dropout (MCD), two theoretical frameworks for solving and approximating BNN, respectively, are employed and compared. The results indicate that the BNN focused on identifying patterns in datasets, and the predicted C c and s u show excellent agreement with the actual values. The reliability of the predicted results using BNN is high in the area of dense datasets. In contrast, the BNN demonstrates low reliability in the predicted result in the area of sparse datasets. Additionally, a novel parametric analysis method in combination with the cumulative distribution function is proposed. The analysis results indicate that the BNN-based models are capable of capturing the relationships of input parameters to the C c and s u . BNN, with its strong prediction capability and reliable evaluation, therefore, shows great potential to be applied in geotechnical design.
TL;DR: In this article , the authors focused on the radiation shielding, structural and specifically mechanical features of the Hydroxyapatite (HAP) bio-composites which can be used instead of the bone and tooth tissues in the human body via FLUKA Monte Carlo Code (FMCC), Phy-X: PSD software, and an analytical method.
Abstract: This paper focuses on the radiation shielding, structural and specifically mechanical features of the Hydroxyapatite (HAP) bio-composites which can be used instead of the bone and tooth tissues in the human body via FLUKA Monte Carlo Code (FMCC), Phy-X: PSD software, and an analytical method. Since HAP bio-composites are so brittle, their use is limited instead of bone in the human body. This challenging issue persuaded the scientists and researchers to solve the problem by inserting various oxide or dioxide materials into HAP bio-composites. Thus, in this work, TiO2 and CeO2 with different ratios of x = 0, 3, 7.5, 10 wt.% and y = 0, 2, 6, 7.5 wt.% are inserted into HAP bio-composites and thus eight types of S (S1, S2, S3, and S4) and B (B1, B2, B3, and B4) samples are produced. Using Artificial Neural Network (ANN), this study predicts and demonstrates the system's behavior. Outcomes reveal that increasing the TiO2 and CeO2 concentrations in the (100-x) HAP + xTiO2 and (100-y) HAP + yCeO2 bio-composites will improve the gamma photon shielding performance of the S and B samples. Furthermore, the photon and electron spatial maps for simulated geometries related to the S4 sample are extracted by the FLUKA Monte Carlo Code and are represented graphically. The produced electrons with the highest energy are monitored in lead volumes due to various interactions of gamma photons with lead shields. In addition, sharp peaks are reported for Zeff curves related to the B samples which may be due to the K-edge absorption of the Ce in HAP samples. The FLUKA results are in full agreement with the predicted targets via the ANN algorithm. Moreover, increasing the CeO2/TiO2 concentrations in HAP bio-composites will enhance the rigidity of the chosen S and B samples. The rising percentage of the mechanical moduli related to the S and B series vary between 30% and 90% which may be due to the relationship between the density of the selected HAP bio-composites and mechanical moduli.
01 Mar 2022
TL;DR: In this article , a simple and effective adaptive surrogate model to structural reliability analysis using deep neural network (DNN) is introduced, in which initial design of experiments (DoEs) are randomly selected from a given Monte Carlo Simulation (MCS) population to build the global approximate model of performance function (PF).
Abstract: This article introduces a simple and effective adaptive surrogate model to structural reliability analysis using deep neural network (DNN). In this paradigm, initial design of experiments (DoEs) are randomly selected from a given Monte Carlo Simulation (MCS) population to build the global approximate model of performance function (PF). More important points on the boundary of limit state function (LSF) and their vicinities are subsequently added relied on the surrogate model to enhance its accuracy without any complex techniques. A threshold is proposed to switch from a globally predicting model to a locally one for the approximation of LSF by eradicating previously used unimportant and noise points. Accordingly, the surrogate model becomes more precise for the MCS-based failure probability assessment with only a small number of experiments. Six numerical examples with highly nonlinear properties, various distributions of random variables and multiple failure modes, namely three benchmark ones regarding explicit mathematical PFs and the others relating to finite element method (FEM)-programmed truss structures under free vibration, are examined to validate the present approach.
TL;DR: In this article , the critical and compensation behaviors of a graphyne bilayer are explored by applying Monte Carlo simulation, and the effects of the crystal field and exchange coupling on the magnetic behaviors and the phase diagrams are presented in detail.
TL;DR: In this paper , the authors presented a techno-economic analysis of three offshore wind power plant arrangements, including distributed hydrogen production, centralized hydrogen production and on-shore hydrogen production by using the proton exchange membrane electrolysis system.
TL;DR: A convolutional neural network (CNN)-based regression approach is proposed to determine the minimum amount of load curtailments of sampled states without solving optimal power flow (OPF) except in the training stage, which is computationally efficient (fast and accurate) in calculating the most common composite system reliability indices.
TL;DR: In this paper, a hybrid planning of distributed generation and distribution automation in distribution networks aiming to improve the reliability and operation indices is presented, where the objective function minimizes the sum of the expected daily investment, operation, energy loss and reliability costs.
TL;DR: In this article , the authors explored the efficacy of micro lead (Pb) loaded polymer composites for radio protective applications such as a fabrication of protective enclosures and showed that mass attenuation coefficients of the composites at different photon energies are proportional to the filler loading.
Abstract: Researches on advanced composites to protect environment health towards radioactive pollution have drawn attention with the rising use of radioactive elements. From this point, polymer micro composites are quite encouraging in terms of multifunctional properties in mechanical, electrical, thermal, as well as nuclear shielding. The present study has explored the efficacy of micro lead (Pb) loaded polymer composites for radio protective applications such as a fabrication of protective enclosures. High energetic photon shielding experiments have been applied through gamma spectrometer equipped with HPGe detector and various radioactive point sources namely 137Cs, 22Na, 152Eu, 133Ba, 241Am and 57,60Co which are widely used in several medical and industrial applications. The results demonstrated that mass attenuation coefficients of the composites at different photon energies are proportional to the filler loading. The validation of FLUKA and GEANT4 Monte Carlo software has been performed in the simulation of transmission experiments as well as WinXCOM software. The tests of the Pb (20%) micro composite for the nuclear radiation shielding reveal that it has high attenuation coefficients for photon radiation.
TL;DR: In this article, a full simulation of random parameters is undertaken for out-of-sample injury-severity predictions, and the prediction accuracy of the estimated models was assessed, not surprisingly, that the random parameters logit model with heterogeneity in the means and variances outperformed other models in predictive performance.
TL;DR: In this paper , a full simulation of random parameters is undertaken for out-of-sample injury-severity predictions, and the prediction accuracy of the estimated models was assessed, not surprisingly, that the random parameters logit model with heterogeneity in the means and variances outperformed other models in predictive performance.
TL;DR: In this paper , the authors reviewed probabilistic and deterministic research methods, such as the Wells-Riley equation, the dose-response model, the Monte-Carlo model, computational fluid dynamics (CFD) with the Eulerian method, CFD with the Lagrangian method and the experimental approach, that have been used for studying the airborne transmission mechanism.
Abstract: Since the outbreak of COVID-19 in December 2019, the severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) has spread worldwide. This study summarized the transmission mechanisms of COVID-19 and their main influencing factors, such as airflow patterns, air temperature, relative humidity, and social distancing. The transmission characteristics in existing cases are providing more and more evidence that SARS CoV-2 can be transmitted through the air. This investigation reviewed probabilistic and deterministic research methods, such as the Wells–Riley equation, the dose-response model, the Monte-Carlo model, computational fluid dynamics (CFD) with the Eulerian method, CFD with the Lagrangian method, and the experimental approach, that have been used for studying the airborne transmission mechanism. The Wells–Riley equation and dose-response model are typically used for the assessment of the average infection risk. Only in combination with the Eulerian method or the Lagrangian method can these two methods obtain the spatial distribution of airborne particles' concentration and infection risk. In contrast with the Eulerian and Lagrangian methods, the Monte-Carlo model is suitable for studying the infection risk when the behavior of individuals is highly random. Although researchers tend to use numerical methods to study the airborne transmission mechanism of COVID-19, an experimental approach could often provide stronger evidence to prove the possibility of airborne transmission than a simple numerical model. All in all, the reviewed methods are helpful in the study of the airborne transmission mechanism of COVID-19 and epidemic prevention and control.
TL;DR: In this paper , a hybrid planning of distributed generation and distribution automation in distribution networks aiming to improve the reliability and operation indices is presented, where the objective function minimizes the sum of the expected daily investment, operation, energy loss and reliability costs.
TL;DR: In this article , path integral Monte Carlo (PIMC) is used to model the effect of the complex many-body medium on the interaction and force between two electrons in the presence of the uniform electron gas.
Abstract: The rigorous description of correlated quantum many-body systems constitutes one of the most challenging tasks in contemporary physics and related disciplines. In this context, a particularly useful tool is the concept of effective pair potentials that take into account the effects of the complex many-body medium consistently. In this work, we present extensive, highly accurate ab initio path integral Monte Carlo (PIMC) results for the effective interaction and the effective force between two electrons in the presence of the uniform electron gas. This gives us a direct insight into finite-size effects, thereby, opening up the possibility for novel domain decompositions and methodological advances. In addition, we present unassailable numerical proof for an effective attraction between two electrons under moderate coupling conditions, without the mediation of an underlying ionic structure. Finally, we compare our exact PIMC results to effective potentials from linear-response theory, and we demonstrate their usefulness for the description of the dynamic structure factor. All PIMC results are made freely available online and can be used as a thorough benchmark for new developments and approximations.
TL;DR: Wang et al. as mentioned in this paper put forward an optimized structure-adaptive grey model by theoretically providing the generalized time response function and accurately modifying the background value based on Simpson's rule to predict nuclear energy consumption in China and America.
TL;DR: In this article , a 3D equivalent discrete fracture network (DFN) model was proposed and thoroughly validated for fractured rock masses, which can analyze the scale effect and anisotropy of fracture properties effectively.
TL;DR: In this article , a fourteen-equation fluid-structure coupling model of an L-shaped liquid-filled pipe with elastic support is established by the transfer matrix method (TMM).