Monte Carlo Simulation Method Research Articles

Investigating the closure stress and crack initiation stress in fractured rocks using the student t distribution and Monte Carlo simulation method.

Traditional method of determining closure and initiation stress of fractured rocks by analyzing the stress-strain curve has problems such as strong subjectivity and large errors. This study utilized the rock closure stress values and onset stress values determined by three traditional methods, namely, axial strain method, fracture volume method and empirical value taking method, as the base database. The Student t distribution theory was used to obtain a confidence interval based on its overall distribution of values and to achieve a combination of the advantages of multiple methods. Within confidence interval, the Monte Carlo stochastic simulation was used to determine the convergence interval of the second stage to further improve the accuracy. Finally, mean value of the randomly sampled values after reaching the convergence stage was taken as the probability value of rock closure and crack initiation stress. The results showed that the 3 traditional methods for calculating rock closure and initiation stresses are significantly different. In contrast, the proposed method biases more towards multi-numerical distribution intervals and also considers the preference effects of different calculation methods. In addition, this method does not show any extreme values that deviate from the confidence intervals, and it has strong accuracy and stability compared to other methods.

Donnan equilibrium in charged slit-pores from a hybrid nonequilibrium molecular dynamics/Monte Carlo method with ions and solvent exchange.

Ion partitioning between different compartments (e.g., a porous material and a bulk solution reservoir), known as Donnan equilibrium, plays a fundamental role in various contexts such as energy, environment, or water treatment. The linearized Poisson-Boltzmann (PB) equation, capturing the thermal motion of the ions with mean-field electrostatic interactions, is practically useful to understand and predict ion partitioning, despite its limited applicability to conditions of low salt concentrations and surface charge densities. Here, we investigate the Donnan equilibrium of coarse-grained dilute electrolytes confined in charged slit-pores in equilibrium with a reservoir of ions and solvent. We introduce and use an extension to confined systems of a recently developed hybrid nonequilibrium molecular dynamics/grand canonical Monte Carlo simulation method ("H4D"), which enhances the efficiency of solvent and ion-pair exchange via a fourth spatial dimension. We show that the validity range of linearized PB theory to predict the Donnan equilibrium of dilute electrolytes can be extended to highly charged pores by simply considering renormalized surface charge densities. We compare with simulations of implicit solvent models of electrolytes and show that in the low salt concentrations and thin electric double layer limit considered here, an explicit solvent has a limited effect on the Donnan equilibrium and that the main limitations of the analytical predictions are not due to the breakdown of the mean-field description but rather to the charge renormalization approximation, because it only focuses on the behavior far from the surfaces.

Revealing the dual impact of VOCs on recycled rubber workers: Health risk and odor perception

Volatile organic compounds (VOCs) pose potential hazards to human health and contribute significantly to odor pollution. This study examined VOC emissions from a representative recycled rubber industry, evaluating the occupational health risks for frontline workers in various workshops. Variables such as gender and workshop-specific concentration variations were considered using Monte Carlo simulation methods. Employees in the five production workshops and office areas face noncarcinogenic health risks with hazard indices (HIs) greater than 1, with the rubber compounding phase presenting the highest risk. Acetaldehyde is identified as the primary noncarcinogenic health risk substance, with hazard quotient (HQ) values exceeding 1 in all workshops. Carcinogenic health risks vary by area, with the highest risks found in compounding and refining workshops. Formaldehyde poses the greatest risk in rubber grinding workshops and offices, with cumulative weights exceeding unacceptable levels of M80.58 % and W77.56 % in grinding and M94.98 % and W92.24 % in the office. Male workers face 4–7 % greater noncarcinogenic VOC health risks than females and 5–14 % greater carcinogenic risks from individual VOCs, increasing their susceptibility to health risks caused by VOCs. Additionally, our analysis of odor identification and intensity classification revealed that 53 VOCs are capable of causing odor pollution, with several substances reaching odor levels of 2 or higher. The predominant perceived odors, as reflected in the odor wheel, include categories such as "solvent/aromatic" and "sweet/fruit," with aldehydes being the primary odor-causing substances. In summary, emissions of VOCs from rubber industrial processes not only pose substantial health risks to workers but also contribute significantly to odor pollution. Consequently, enterprises must prioritize optimizing workplace conditions to ensure the occupational health and well-being of their employees.

Probabilistic load flow based voltage stability assessment of solar power integration into power grids

The interconnection of renewable energies increases the complexity of modern power systems. Hence, stability assessments should be made to ensure the system’s stability after penetration. Solar power is a type of renewable energy that has become a widespread energy source among renewable energy sources. About 1 MWp of the solar power plant has been prepared to be interconnected to the IEEE 8 bus of Manokwari grid, and this paper investigates the voltage profile, power losses, and stability of the solar power plant penetration using an adaptive kernel density estimator (AKDE) and compares it to a Monte Carlo simulation (MCS)-based probabilistic load flow (PLF). About 5000 samples have been used to test the grid after the connection. Results of simulations show that the solar penetration can reduce power losses from 0.4084 MW to 0.3080 MW and 0.3045 MW by the proposed method and MCS method, respectively, and further increase the bus voltage profile. The power network has the stability to be connected to solar power, as indicated by the small stability index values of each bus. The proposed method using the AKDE method has a more accurate result in stability indices indicated by small fast voltage stability index (FVSI), line stability index (Lmn), and line stability factor (LQP) indices.

A Stochastic Iterative Peer-to-Peer Energy Market Clearing in Smart Energy Communities Considering Participation Priorities of Prosumers

The penetration of distributed energy resources (DERs) has changed the role of a consumer to a prosumer, i.e., producer and consumer. This new role provides the opportunity for peer-to-peer (P2P) energy trading. In this paper, a three-stage iterative framework is proposed to clear the price and quantity of trading in P2P markets while addressing price and DER uncertainties by the Monte Carlo simulation (MCS) method. Initially, bids and offers of customers are determined by implementing an advanced satisfaction-based home energy management system (HEMS) at each home. Subsequently, the market operator prioritizes bids and offers according to the amount of customers’ participation in the market. Finally, the P2P market is cleared by application of the alternating direction method of multipliers (ADMM), and the market clearing prices (MCPs) are determined. MCPs are used as a parameter to repeat the three stages, and the procedure is redone until the stopping rule is met. The proposed method's effectiveness has been investigated in communities with 8, 50, and 100 prosumers. Results indicate a 69.51% cost reduction in a smart energy community with 50 homes through P2P energy market participation. The proposed market clearing method is compared with the common mid-market rate (MMR) and Stackelberg game methods and demonstrates over 25% reduction in community costs.

Optimization and Demonstration of PV Power Plant Investment Evaluation and Decision Model under the Background of Marketization

Based on the real option analysis method, this paper makes an empirical analysis of the evaluation and optimization of the model, and analyzes the main factors of the investment value of photovoltaic power plants in combination with the investment decision-making characteristics of photovoltaic power plant projects under the background of marketization. The electricity price and generation income, transaction price and income are all processed by geometric Brownian motion, and the random process is obtained by Monte Carlo simulation method. Based on the sensitivity analysis of the uncertain factors of the model, the investment risk of photovoltaic power station is evaluated and the suggestions are put forward. The results show that when the on-grid electricity price increases by 10%, the net present value is about 6 million yuan in the 26th year. Therefore, how to evaluate the uncertainty factors of photovoltaic power plant investment and make scientific and reasonable investment decisions is of great significance for the development of photovoltaic power generation.

Quantitative evaluation of uncertainty and interpretability in machine learning-based landslide susceptibility mapping through feature selection and explainable AI

Landslide susceptibility mapping (LSM) is essential for determining risk regions and guiding mitigation strategies. Machine learning (ML) techniques have been broadly utilized, but the uncertainty and interpretability of these models have not been well-studied. This study conducted a comparative analysis and uncertainty assessment of five ML algorithms—Random Forest (RF), Light Gradient-Boosting Machine (LGB), Extreme Gradient Boosting (XGB), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM)—for LSM in Inje area, South Korea. We optimized these models using Bayesian optimization, a method that refines model performance through probabilistic model-based tuning of hyperparameters. The performance of these algorithms was evaluated using accuracy, Kappa score, and F1 score, with accuracy in detecting landslide-prone locations ranging from 0.916 to 0.947. Among them, the tree-based models (RF, LGB, XGB) showed competitive performance and outperformed the other models. Prediction uncertainty was quantified using bootstrapping and Monte Carlo simulation methods, with the latter providing a more consistent estimate across models. Further, the interpretability of ML predictions was analyzed through sensitivity analysis and SHAP values. We also expanded our investigation to include both the inclusion and exclusion of predictors, providing insights into each significant variable through a comprehensive sensitivity analysis. This paper provides insights into the predictive uncertainty and interpretability of ML algorithms for LSM, contributing to future research in South Korea and beyond.

On the Use of Dimension-Reduction Methods in Fatigue Analysis of Flexible Risers Subjected to Bimodal Seas

Abstract Fatigue analyses of flexible pipes through the application of long-term response statistics, when considering bimodal seas, are particularly challenging due to the excessive computational costs involved, since they require, generally, to solve numerically a four-dimensional integral associated with time-domain finite element-based analyses of the flexible pipe. This paper investigates different dimension-reduction methods (DRMs) as effective approaches to compute fatigue damage of these structures. The performance of the DRMs was compared with that of the Monte Carlo simulation method (MCSM). Two case studies are presented: a 4 in., and a 7 in., flexible riser connected to a semi-submersible, and a spread-moored floating production storage and offloading unit platform, respectively. The studies demonstrated that some DRMs presented accurate results, exhibiting high efficacy with errors below 3%. Furthermore, the bivariate dimension-reduction method required only 18% of the computational cost associated with the standard Gauss–Hermite quadrature in space R4 (without any reduction), or much less when compared to the MCSM.

Alkaloid extract of seed Citrullus colocynthis as novel green inhibitor for mild steel corrosion in one molar HCl acid solution: DFT and MC/MD approaches

The study was designed to explore the corrosion prevention capabilities of Citrullus colocynthis seeds alkaloid-rich extract (CSEA) on MS in a 1 M HCl environment by use of electrochemical and theoretical methods. Notably, Electrochemical Impedance Spectroscopy (EIS) and potentiodynamic polarization were used to probe the impact of immersion time and temperature. Energy-dispersive X-ray spectroscopy (EDX) and spanning electron microscopy (SEM) were used to confirm the presence of a protective layer on the substrate surface. Density functional theory (DFT) method was used to optimize the investigated species by use of B3LYP/6–31 + G(d, p) level of theory. The global and local quantum chemical reactivity descriptors were calculated to explore the inhibition corrosion efficiency and to identify the most favorable sites for electrophilic and nucleophilic attacks. Monte Carlo (MC) and molecular dynamics simulation (MDS) methods were used to study the interactions between corrosion inhibitor and metal surface. Noteworthy, results showed that CSEA exhibited an impressive inhibition efficiency, which reached 94.3% with a concentration of 2 g/L at 298 K. Potentiodynamic polarization revealed that the extract acted as a mixed-type inhibitor. Nyquist graphs showed that charge-transfer resistance and dæouble-layer capacitance both rised with increasing CSEA concentration, suggesting better inhibition efficiency. Notably, the Langmuir adsorption isotherm is well-aligned with the adsorption of inhibitor compounds. Importantly, all aforementioned theoretical methods were in agreement with the experimental findings. The outcome of the present work supported using Citrullus colocynthis seeds alkaloid-rich extract as ecofriendly agents to prevent corrosion.

Effects of random meso‐defects on fatigue behavior of welded joints: Damage evolution and lifetime prediction

AbstractRandom meso‐scale defects within welded joints can affect the structural damage process and cause dispersion in the fatigue lifetime of welded joints. In this paper, a meso‐scale stochastic damage approach is used to investigate the effect of random, meso‐scale defects on the fatigue damage lifetime of welded joints under different loads. First, we established an elastic–plastic fatigue damage model considering meso‐defects in the weld metal. Secondly, the fatigue lifetimes for gas tungsten arc welding and gas metal arc welding welded joints were predicted, with most of the predictions within the twofold dispersion factor. Thirdly, the Monte Carlo simulation method was used to investigate the influence of meso‐defect size on the fatigue lifetime dispersion degree, and the fatigue lifetime distribution under different loads was statistically analyzed.

Computational prediction of MOFs with the potential to improve the efficiency of industrial CO2 capture

Capturing and storing carbon dioxide produced during industrial production operations has become a global focus in terms of energy conservation and emission reduction, especially for high-emission sources such as coal-fired power plants. In this study, the high-throughput Grand Canonical Monte Carlo (HT-GCMC) simulation method was used to assess the performance of 4,188 MOFs that were carefully selected from a comprehensive pool of 10,143 CoRE-MOFs using specific criteria. Exploratory data analysis for the population demonstratedthe effect of structural properties and isosteric enthalpy on MOFs’ adsorption performance. 90 MOFs were identified as superior adsorbents that show a strong adsorption performance when screening based on four filter criteria considered important for improving industrial economic efficiency. As the desorption pressure was increased, numerous MOFs maintained their potential and met the four screening conditions. This implies that there is room to increase the desorption pressure, which suggests a potential energy saving in practical applications. HT-GCMC was applied to calculate the adsorption properties of the 90 potential MOFs using various industrial CO2 concentration levels and showed the versatility of these MOFs for use in various industrial CO2 separation schemes. In addition, the adsorption mechanism of potential MOFs was analyzed, revealing specific numerical ranges for Qst and structural characteristics that correlate with their performance. Character analysis of 90 potential MOFs revealed that the elements O, N, Zn, and Cu have the highest content; C-O, Cu-O, C-N, and Cu-N are the bonded atom pairs most commonly seen in these 90 potential MOFs.

An active learning approach for reliability assessment of passive systems combining polynomial chaos expansion and adaptive sampling

Passive safety system is extensively employed in newly designed reactors to increase their inherent safety. However, the driving force is smaller, and the uncertainty of the thermal–hydraulic (T-H) process may potentially result in the inability of system to perform its intended function, which is often known as functional failure. Therefore, the passive systems reliability has received increasing attention. Unfortunately, the assessment of system failure probability utilizing Monte Carlo simulation (MCS) methods necessitates a significant quantity of repeated calculations employing the thermal–hydraulic code, which may be impractical in terms of computational cost. To enhance the assessment calculation efficiency, an adaptive surrogate model is proposed. The bootstrap method was employed to introduce an error estimate into the polynomial chaos expansion (PCE) model, which solves the obstacle of the PCE model in reliability evaluation applications due to the lack of error estimation. The method constructs an active learning approach through error estimation, strategically identifies and selects the best candidate samples, and iteratively updates the initial experimental design. The effectiveness of the method was verified through three benchmark cases, and it was employed for assessing the passive residual heat removal system (PRHRS) reliability within integral-type pressurized water reactor (IPWR200). The results demonstrated that the proposed method significantly diminishes the frequency of invocations to the T-H code and improves computational efficiency to a large extent. Furthermore, it can serve as a valuable guide for the design, operation, and reliability evaluation of the passive system.

Reliability assessment of discrete-time [formula omitted](G) retrial system based on different failure types and the [formula omitted]-shock model

The k/n(G) system is a typical reliability system in engineering practice. However, research on this system under discrete-time and retrial conditions is still limited. This paper establishes a k/n(G) retrial system model under a discrete-time condition based on different failure types and the δ-shock model, whose objective is to investigate its various reliability measures and the relationship between measures and parameters. Two methodologies are utilized in this paper to evaluate the system’s various measures: the analytical method for the discrete-time Markov process and the Monte Carlo simulation method. The two methodologies are compared, and optimization is performed on certain measures concerning system parameters. A discrete PH distribution representation method for component lifetime under the δ-shock is presented, the relationship between various reliability measures of the system and various parameters is determined, and a novel approach for system reliability research based on the discrete PH distribution and the δ-shock is provided in this paper.

An Improved Non-Probabilistic Monte Carlo Simulation Method for Structural Reliability Analysis Based on Convex Models

An Improved Non-Probabilistic Monte Carlo Simulation Method for Structural Reliability Analysis Based on Convex Models

Technical note: A GPU-based shared Monte Carlo method for fast photon transport in multi-energy x-ray exposures.

The Monte Carlo (MC) method is an accurate technique for particle transport calculation due to the precise modeling of physical interactions. Nevertheless, the MC method still suffers from the problem of expensive computational cost, even with graphics processing unit (GPU) acceleration. Our previous works have investigated the acceleration strategies of photon transport simulation for single-energy CT. But for multi-energy CT, conventional individual simulation leads to unnecessary redundant calculation, consuming more time. This work proposes a novel GPU-based shared MC scheme (gSMC) to reduce unnecessary repeated simulations of similar photons between different spectra, thereby enhancing the efficiency of scatter estimation in multi-energy x-ray exposures. The shared MC method selects shared photons between different spectra using two strategies. Specifically, we introduce spectral region classification strategy to select photons with the same initial energy from different spectra, thus generating energy-shared photon groups. Subsequently, the multi-directional sampling strategy is utilized to select energy-and-direction-shared photons, which have the same initial direction, from energy-shared photon groups. Energy-and-direction-shared photons perform shared simulations, while others are simulated individually. Finally, all results are integrated to obtain scatter distribution estimations for different spectral cases. The efficiency and accuracy of the proposed gSMC are evaluated on the digital phantom and clinical case. The experimental results demonstrate that gSMC can speed up the simulation in the digital case by ∼37.8% and the one in the clinical case by ∼20.6%, while keeping the differences in total scatter results within 0.09%, compared to the conventional MC package, which performs an individual simulation. The proposed GPU-based shared MC simulation method can achieve fast photon transport calculation for multi-energy x-ray exposures.

An artificial gorilla troops optimizer for stochastic unit commitment problem solution incorporating solar, wind, and load uncertainties.

The unit commitment (UC) optimization issue is a vital issue in the operation and management of power systems. In recent years, the significant inroads of renewable energy (RE) resources, especially wind power and solar energy generation systems, into power systems have led to a huge increment in levels of uncertainty in power systems. Consequently, solution the UC is being more complicated. In this work, the UC problem solution is addressed using the Artificial Gorilla Troops Optimizer (GTO) for three cases including solving the UC at deterministic state, solving the UC under uncertainties of system and sources with and without RE sources. The uncertainty modelling of the load and RE sources (wind power and solar energy) are made through representing each uncertain variable with a suitable probability density function (PDF) and then the Monte Carlo Simulation (MCS) method is employed to generate a large number of scenarios then a scenario reduction technique known as backward reduction algorithm (BRA) is applied to establish a meaningful overall interpretation of the results. The results show that the overall cost per day is reduced from 0.2181% to 3.7528% at the deterministic state. In addition to that the overall cost reduction per day is 19.23% with integration of the RE resources. According to the results analysis, the main findings from this work are that the GTO is a powerful optimizer in addressing the deterministic UC problem with better cost and faster convergence curve and that RE resources help greatly in running cost saving. Also uncertainty consideration makes the system more reliable and realistic.

Adsorption and Diffusion Properties of Gas in Nanopores of Kerogen: Insights from Grand Canonical Monte Carlo and Molecular Dynamics Simulations

Investigating the adsorption and diffusion processes of shale gas within the nanopores of kerogen is essential for comprehending the presence of shale gas in organic matter of shale. In this study, an organic nanoporous structure was constructed based on the unit structure of Longmaxi shale kerogen. Grand canonical Monte Carlo and molecular dynamics simulation methods were employed to explore the adsorption and diffusion mechanisms of pure CH4, CO2, and N2, as well as their binary mixtures with varying mole fractions. The results revealed that the physical adsorption characteristics of CH4, CO2, and N2 gases on kerogen adhered to the Langmuir adsorption law. The quantity of adsorbed gas molecules increased with rising pressure but decreased with increasing temperature. The variation in the heat of adsorption was also analyzed. Under identical temperature and pressure conditions, the adsorption of CH4 increased with higher mole fractions of CH4, whereas it decreased with greater mole fractions of CO2 and N2. Notably, CO2 molecules exhibited a robust interaction with kerogen molecules compared to the adsorption properties of CH4 and N2. Furthermore, the self-diffusion coefficient of gas within kerogen nanopores gradually decreased with increasing pressure or decreasing temperature. The diffusion capacity of gas molecules followed the descending order N2 > CH4 > CO2 under the same pressure and temperature conditions.

Research on stamping formability and process simulation of AZ31 magnesium alloy based on deep learning

To study the formability of AZ31 magnesium alloy under different temperature conditions and improve the forming quality of its parts, this paper aims to solve the minimization problem of pursuing the “maximum thinning rate”. This study obtained the mechanical properties of AZ31 magnesium alloy at 25 °C, 150 °C, 250 °C, and 350 °C through uniaxial tensile tests and explored the stamping formability of AZ31 magnesium alloy mobile phone cases using the finite element inverse calculation method (MSTEP). Based on the hot stamping of AZ31 magnesium alloy at 250 °C, the Monte Carlo Simulation (MCS) method was used to randomly sample the blank holder force, stamping speed, friction coefficient, and resistance coefficient, and then simulate to obtain the corresponding “maximum thinning rate”. In addition, a hybrid prediction model optimized by Genetic Algorithm (GA) and Dung Beetle Optimization (DBO) based on the Back Propagation Neural Network (BPNN) - Random Forest (RF) was used to explore the nonlinear relationship between blank holder force, stamping speed, friction coefficient, resistance coefficient, and the “maximum thinning rate”. The results show that the plasticity of AZ31 magnesium alloy enhances with the temperature increase, especially exhibiting the best stamping formability at 250 °C. Feature importance indicates that the resistance coefficient and stamping speed have a greater impact on the maximum thinning rate, while the influence of blank holder force and friction coefficient is relatively smaller. Mean Square Error (MSE) shows that the predictive capability of the hybrid model is significantly superior to that of the single BP neural network and Random Forest model, displaying optimal efficiency. The predicted maximum thinning rate by the model is 15.39 %, with an error rate of only 0.32 % compared to the DYNAFORM finite element software simulation results, confirming the correctness, accuracy, and effectiveness of the prediction model. Overall, this study provides practical optimization strategies for metal stamping forming processes, with significant practical application value.

Identification of the Effect of Concrete Carbonation on the Strength of Cooling Tower Structures Using the Monte Carlo Simulation Method

Identification of the Effect of Concrete Carbonation on the Strength of Cooling Tower Structures Using the Monte Carlo Simulation Method

Anomaly detection in groundwater monitoring data using LSTM-Autoencoder neural networks.

Groundwater monitoring data can be prone to errors and biases due to various factors like borehole and equipment malfunctions, or human mistakes. These inaccuracies can jeopardize the groundwater system, leading to reduced efficiency and potentially causing partial or complete failures in the monitoring system. Traditional anomaly detection methods, which rely on statistical and time-variant techniques, struggle to handle the complex and dynamic nature of anomalies. With advancements in artificial intelligence and the growing need for effective anomaly detection and prevention across different sectors, artificial neural network methods are emerging as capable of identifying more intricate anomalies by considering both temporal and contextual aspects. Nonetheless, there is still a shortage of comprehensive studies on groundwater anomaly detection. The intricate patterns of sequential data from groundwater present numerous challenges, necessitating sophisticated modeling techniques that combine mathematics, statistics, and machine learning for viable solutions. This paper introduces a model designed for high accuracy and efficient computation in detecting anomalies in groundwater monitoring data through a probabilistic approach. We employed the Monte Carlo method and SEAWAT numerical simulation to ascertain the uncertainty in groundwater salinity. Subsequently, a Long Short-Term Memory (LSTM)-Autoencoder model was trained and evaluated, forming the basis of an anomaly detection framework. Each piece of training data was assessed by the LSTM-Autoencoder using the Negative Log Likelihood (NLL) score and a predefined threshold to determine the data's abnormality percentage. The accuracy evaluation of the proposed LSTM-Autoencoder algorithm revealed that this approach achieved commendable performance, with an accuracy of 98.47% in anomaly detection.