Monte Carlo Simulation Method Research Articles

Sustainable Design of Pavements: Predicting Pavement Service Life

Pavement service life is an important factor that affects both the whole life cost and carbon footprint of a pavement. The service life of a pavement is affected by several different parameters which can be broadly classified into climate conditions, binder and mixture properties, pavement design, workmanship, and maintenance strategies. The current practice for determining service life of pavements involves the use of pavement design tools, which are used while constructing a new pavement or performing a reconstruction/resurfacing or pavement maintenance. In addition, field measurements using ground penetration radar, falling weight deflectometer, traffic speed deflectometer, and other techniques are also used to assess the condition of an existing pavement. The information from these measurements is then combined with pavement design software to predict potential pavement service life. The accuracy of the predicted pavement service life is affected by the associated uncertainties in the parameters that affect pavement life. The following paper presents various approaches that could be potentially used to determine the associated uncertainties in the estimation of pavement service life. The various uncertainty quantification techniques have been applied to a specific design, and the outcomes are discussed in this paper. The Monte Carlo simulation method, a system-level uncertainty quantification technique, can estimate a probabilistic pavement service life. The other uncertainty quantification schemes are software specific and provide probabilistic life factors by assumed statistical distributions. Hence, the Monte Carlo simulation technique could be one potential method that can be used for estimating a generalized pavement service utilizing predictions from various design software.

Durability fragility curves for service life prediction of concrete with various supplementary cementitious materials subjected to natural carbonation

This study estimates the service life of concretes containing supplementary cementitious materials (SCM) subjected to carbonation from a probabilistic and reliability-based perspective. The aim is to enhance understanding of the risks associated with degradation and to propose an alternative approach for estimating the durability of reinforced concrete (RC) structures. The inclusion of supplementary cementitious materials (SCMs) is crucial for making concrete more sustainable, as these materials can reduce the carbon footprint of concrete production and improve its durability. Using available natural carbonation data from the literature on samples containing SCM, computational codes were developed for reliability analyses via the Monte Carlo Simulation method. The results indicated that all concrete samples exhibited a 100% probability of failure for coverages of 20 mm and 25 mm at an age of 100 years. For a coverage of 30 mm, all samples maintained a failure probability of over 98% within the same period. Notably, concretes incorporating metakaolin showed a substantial reduction in failure probability across all evaluated time periods, indicating superior durability compared to other samples. These findings significantly contribute to a deeper understanding of concrete degradation processes and provide valuable insights for the design and maintenance of RC structures, particularly in the context of using SCM. By adopting a probabilistic approach, the study underscores the importance of considering variability and uncertainties in predicting concrete durability, ultimately aiding in the development of more resilient and long-lasting infrastructure.

Physics-constrained adaptive kernel interpolation for region-to-region acoustic transfer function: a Bayesian approach

A kernel interpolation method for the acoustic transfer function (ATF) between regions constrained by the physics of sound while being adaptive to the data is proposed. Most ATF interpolation methods aim to model the ATF for fixed source by using techniques that fit the estimation to the measurements while not taking the physics of the problem into consideration. We aim to interpolate the ATF for a region-to-region estimation, meaning we account for variation of both source and receiver positions. By using a very general formulation for the reproducing kernel function, we have created a kernel function that considers both directed and residual fields as two separate kernel functions. The directed field kernel considers a sparse selection of reflective field components with large amplitudes and is formulated as a combination of directional kernels. The residual field is composed of the remaining densely distributed components with lower amplitudes. Its kernel weight is represented by a universal approximator, a neural network, in order to learn patterns from the data freely. These kernel parameters are learned using Bayesian inference both under the assumption of Gaussian priors and by using a Markov chain Monte Carlo simulation method to perform inference in a more directed manner. We compare all established kernel formulations with each other in numerical simulations, showing that the proposed kernel model is capable of properly representing the complexities of the ATF.

Is Reinforcement Learning Good at American Option Valuation?

This paper investigates algorithms for identifying the optimal policy for pricing American Options. The American Option pricing is reformulated as a Sequential Decision-Making problem with two binary actions (Exercise or Continue), transforming it into an optimal stopping time problem. Both the least square Monte Carlo simulation method (LSM) and Reinforcement Learning (RL)-based methods were utilized to find the optimal policy and, hence, the fair value of the American Put Option. Both Classical Geometric Brownian Motion (GBM) and calibrated Stochastic Volatility models served as the underlying uncertain assets. The novelty of this work lies in two aspects: (1) Applying LSM- and RL-based methods to determine option prices, with a specific focus on analyzing the dynamics of “Decisions” made by each method and comparing final decisions chosen by the LSM and RL methods. (2) Assess how the RL method updates “Decisions” at each batch, revealing the evolution of the decisions during the learning process to achieve optimal policy.

Charge regulation of nanoparticles in the presence of multivalent electrolytes.

We explore the charge regulation (CR) of spherical nanoparticles immersed in an asymmetric electrolyte of a specified pH. Using a recently developed reactive canonical Monte Carlo (MC) simulation method, titration isotherms are obtained for suspensions containing monovalent, divalent, and trivalent coions. A theory based on the modified Poisson-Boltzmann approximation, which incorporates the electrostatic ion solvation free energy and discrete surface charge effects, is used to compare with the simulation results. A remarkably good agreement is found without any fitting parameters, both for the ion distributions and titration curves, suggesting that ionic correlations between coions and hydronium ions at the nanoparticle surface play only a minor role in determining the association equilibrium between hydroniums and the functional sites on the nanoparticle surface. On the other hand, if suspension contains multivalent counterions, we observe a large deviation between theory and simulations, showing that the electrostatic correlations between counterions and hydronium ions at the nanoparticle surface are very significant and must be properly taken into account to correctly describe CR for such solutions.

Improving safety in complex systems: A review of integration of functional resonance analysis method with semi‐quantitative and quantitative approaches

AbstractFunctional resonance analysis method (FRAM) is extensively employed in analyzing and managing performance variabilities. Additionally, semi‐quantitative and quantitative methods have been increasingly integrated with the FRAM to analyze complex socio‐technical systems to improve safety levels. This review article presents a comprehensive and updated survey of current literature focused on semi‐quantitative and quantitative methods employed for quantifying performance variabilities and exploring aggregation/propagation rules. A total of 1659 studies published between 2012 and March 2024 from various scientific databases were systematically examined using preferred reporting items for systematic review and meta‐analysis, identifying 29 studies that met inclusion criteria. The identified studies were categorized into four groups based on the quantitative methods employed: Monte Carlo simulation, fuzzy logic, cognitive reliability and error analysis method, and miscellaneous approaches. While different methodologies had unique strengths, they commonly relied on expert judgment for data collection, whether for defining probability distributions in Monte Carlo simulations, membership functions, and fuzzy rule bases in fuzzy inference systems, or selecting common performance conditions, determining their interrelationships, and assigning scores. Addressing bias from expert judgment in assessing performance variabilities can be achieved by using suitable experts' opinions integration techniques, and leading safety indicators in the analysis.

UNCERTAINTY EVALUATION USING LAW OF PROPAGATION AND MONTE CARLO SIMULATION METHODS WITH THE AUTORFPOWER MEASUREMENT SOFTWARE

RF power measurement is essential in RF and microwave metrology. For reliable and accurate power measurement, automatic measurement is preferred. A software application in C#, named AutoRFPower, was developed for automatic RF power measurement and uncertainty calculations at this study. According to the GUM document, this application is enhanced for uncertainty calculations by utilizing the Law of Propagation method and the Monte Carlo Simulation method. Trial measurements were performed at different RF power levels and frequencies between 50 MHz and 18 GHz using the AutoRFPower software. Law of Propagation and Monte Carlo Simulation uncertainty calculations were carried out by AutoRFPower based on the trial measurements and by the Oracle Crystal Ball simulation application. All measurements and their uncertainty calculations were compared with each other, and this study validated the uncertainty calculation of AutoRFPower. In addition, it was observed that in the Monte Carlo Simulation, uncertainty calculation results were non-symmetrical normal distribution, contrary to the assumption of symmetrical normal distribution according to the Low of Propagation method. Moreover, it has been observed that the statistical distribution of uncertainty changes depending on the dominant component of the parameters in the model function used for the uncertainty calculation with the Monte Carlo Simulation method.

Effects of Solar Radiation on the Performance of Long-distance Atmosphere-ocean Laser Communication Links

We investigated the effect of solar radiation noise on the performance of a long-range atmosphere-ocean laser communication system. To model a inhomogeneous channel in the atmosphere-ocean system, a Monte Carlo (MC) simulation method was utilized in combination with measured underwater chlorophyll profiles. We conducted field measurements to accurately assess the effects of background noise, specifically solar radiation at different underwater depths and times. The influence of different scenarios on the system's signal-to-noise ratio (SNR) was then scrutinized based on the detector type. Additionally, a comprehensive analytical model was proposed for diverse background light noises, enabling the exploration of the effects of solar noise and Photomultiplier Tube (PMT) on the communication link's performance in terms of Pulse Position Modulation (PPM) demodulation symbol error rate (SER). The results indicate that expanding the receiver's field of view (FOV) from 1 degree to 15 degree can enhance the system's communication depth by 11 m, while increasing the receiver's radius from 1 cm to 20 cm can elevate the communication depth by 37 m. Moreover, the study investigates the impact of transmit power and the multi-aperture receiving technique on communication depth, providing valuable insights for designing atmosphere-ocean laser communication links and offering robust support for the practical implementation of the Free-Space Optical (FSO) - Underwater Optical Communication (UWOC) system.

Optimal segmentation of non-linear and non-stationary time series based on fractal dimension and Poincare section and its application in solving EEG-signal

Non-linearity and non-stationarity characteristics in physiological and biological time series limit the reliability of conventional analysis. The main challenge in pattern recognition problems is determining the Optimal-Window-Duration (OWD) for segmenting these time series. Our study presents an innovative algorithm, based on Individual-Optimal-Segmentation (IOS), which integrates chaos theory using fractal dimension, and Poincare section into the estimation of the OWD for segmenting time series with non-linear and non-stationary characteristics. In the logistic map case study, the IOS algorithm proved to be effective in detecting cycles in a range of numerical examples, from period-2 to chaotic behavior. So that its performance was superior to previous approaches, especially when it comes to detecting chaotic behaviors. Furthermore, the combination of the IOS algorithm with the Welch signal processing method shows promise in identifying abnormal Power-Spectral-Density (PSD) curves related to Beta and Gamma rhythms in depression diagnosis scenarios. The generated depression diagnosis system attained an accuracy (%) of 99.07±0.35 (with 2-channel) using the Monte Carlo simulation method. These findings suggest a potentially valuable tool for addressing segmentation challenges in time series analysis, particularly in contexts involving complex, dynamic data. While, it is suggested that future research investigate the computational efficiency and robustness of the IOS algorithm when handling large-scale or real-time data and increase its application in various fields in data science and signal processing. Furthermore, expanding the application range of the IOS algorithm for other pattern recognition problems with physiological or biological data can strengthen the generalizability and practical application of the algorithm.

Dynamic response of a pipe conveying pulsating fluid under random vibration

This paper studies the dynamic response of a pipe conveying pulsating fluid under random vibration. Its nonlinear equation of motion is first developed using the method of Newton. The method of Galerkin is then used to discretize the equation of motion into a system of ordinary differential equations. Afterwards, the statistical moments and mean peak values of the displacement and velocity responses of the pipe are obtained using an efficient Monte Carlo simulation (MCS) method based on the explicit time-domain method (ETDM). Furthermore, the displacement-sensitive and velocity-sensitive locations of the pipe are obtained for different boundary conditions. The impacts of the fluid, random vibration, and structural parameters on the mean peak value of the dynamic response of the pipe are finally analyzed. The obtained results provide a reference for better understanding the dynamic characteristics of pulsating fluid transport pipes under random vibration and for accurately designing pipe structures.

An Orderly Charging and Discharging Strategy of Electric Vehicles Based on Space–Time Distributed Load Forecasting

Given the widespread adoption of electric vehicles, their charging load is influenced not only by vehicle numbers but also by driving and parking behaviors. This paper proposes a method for forecasting electric vehicle charging load based on these behaviors, considering both spatial and temporal distribution. Initially, the parking generation rate model predicts parking demand, establishing the spatial and temporal distribution model for electric vehicle parking needs across various vehicle types and destinations. Subsequently, analyzing daily mileage and parking demand distributions of electric vehicles informs charging demand assessment. Using the Monte Carlo simulation method, large-scale electric vehicle behaviors in different spatial and temporal contexts—parking, driving, and charging—are simulated to predict charging load distributions. Optimization of electric vehicle charging and discharging enhances grid stability, cost management, charging efficiency, and user experience, supporting smart grid development. Furthermore, charging load forecasting examples under diverse scenarios validate the model’s feasibility and effectiveness.

Reliability assessment of generation capacity in modern power systems via analytical methodologies

With the recent transition to a low-carbon electrical power system (EPS), the large-scale utilization of renewable energy resources in electrical power generation introduces a substantial amount of uncertainty on the generation side of the EPS. This uncertainty, along with the inherent uncertainty of electricity demand, makes assessing generation reliability a very computationally intensive process. To enhance the computation efficiency of EPS generation reliability assessment, it is crucial to have an efficient probabilistic model of available generation capacities that strikes a balance between improved computational performance and model accuracy. In this paper, various probabilistic models are proposed to characterize the variability and uncertainty of conventional and renewable power generations (photovoltaic and wind). On the basis of these models, an analytical formulation of probabilistic reliability indices (RIs) is implemented. The computation time and accurate RIs values found using the Monte Carlo simulation method serve as the basis for reporting solving time improvements with corresponding losses in the accuracy of the RIs for different analytical methodologies. The results of multiple case studies of an EPS are presented, considering various combinations of conventional and renewable generation capacity, levels of renewable power penetration, and system reliability levels. The results indicate the practical implementation of analytical assessment methodologies compared to the simulation method in terms of accuracy and computational effort. This study is of immediate relevance and potential importance to operational reliability and generation expansion planning studies in EPSs.

A local volatility correction to mean-reverting stochastic volatility model for pricing derivatives

Generally, in the real market, empirical findings suggest that either local volatility (LV) or stochastic volatility (SV) models have a limit to capture the full dynamics and geometry of the implied volatilities of the given equity options. In this study, to overcome the disadvantage of such LV and SV models, we propose a special type of hybrid stochastic-local volatility (SLV∗) model in which the volatility is given by the squared logarithmic function of the underlying asset price added to a function of a fast mean-reverting process. By making use of asymptotic analysis and Mellin transform, we derive analytic pricing formulas for European derivatives with both smooth and non-smooth payoffs under the SLV∗ model. We run numerical experiments to verify the accuracy of the pricing formulas using a Monte-Carlo simulation method and to display that the proposed new model fits the geometry of the market implied volatility more closely than other models such as the Heston model, the stochastic elasticity of variance (SEV) model, the hybrid stochastic and CEV type local volatility (SVCEV) model and the multiscale stochastic volatility (MSV) model, especially for short time-to-maturity options.

Time-Varying Reliability Analysis of Mechanisms with Strength Degradation Considering Clearances and Local Stiffness Effects

Mechanism systems are important components of dynamic mechanical systems. The time-varying reliability assessment of mechanism systems is of great significance for the safety and fault diagnosis of mechanical products. In this paper, a time-varying reliability evaluation method for mechanisms under the fatigue failure mode is proposed, which takes into account the joint effects of clearance, local structural stiffness, random load and random strength degradation. The proposed mechanism reliability models are established by nonlinear dynamic analysis considering failure factors and establishing random stress history characterization matrices. Additionally, the models are validated by the Monte Carlo simulation method. The results show that the mechanism clearance, local stiffness, and initial strength dispersion have a significant effect on the time-varying reliability of the mechanisms. Furthermore, both local stiffness and initial strength dispersion have a greater influence on the effect of clearance on the time-varying reliability of the mechanisms. The proposed reliability models can quantitatively analyze the dynamic effects of failure factors on the reliability of the mechanisms, which can be used for the stochastic remaining life evaluation and fault diagnosis of the mechanism systems.

Optimizing Cr(VI) reduction to Cr(III) using Pd-CNTs nanocatalyst: kinetic Monte Carlo simulation and experimental design insights

In this investigation, we explored the kinetics of Cr(VI) reduction to Cr(III) on carbon nanotube decorated with palladium (Pd-CNTs) nanocatalyst, using formic acid as the reducing agent. This study has been bone utilizing kinetic Monte Carlo simulation and experimental design methods. The mechanism and kinetic parameters of this reaction are provided. The effect various factors such as reaction time, pH level, dichromate (Cr2O72−) concentration, and formic acid concentration on Cr(VI) reduction was studied. Concentrations of HCOOH and Cr2O72− were identified as the crucial variables, while the HCOOH concentration has the most significant impact. Positive influences on Cr(VI) reduction were observed with increasing pH level and HCOOH concentration. Reaction time positively affects on Cr(VI) reduction efficiency. However, the concentration of Cr2O72− showed an increasing effect up to a threshold, negatively impacting the efficiency. The optimal conditions (Reaction time = 60 min, pH = 4.5, [Cr2O72−] = 5.05 × 10−3 M, and [HCOOH] = 0.95 M) for Cr(VI) reduction. At optimal conditions, the Cr(VI) reduction efficiency was obtained to be 100%.

Photon and thermal neutron shielding behaviors of aluminum calcium fluoroborate glass modified with barium oxide: FLUKA Monte Carlo, XCOM and experimental investigations

The borate glass system was prepared using the melt-quenching method. The XRD was used to confirm the amorphous structure of present samples, the FTIR was utilized to observe vibration modes, and the UV–Vis-NIR spectrophotometer was employed to measure transmittance spectra. The radiation shielding properties were examined by FLUKA Monte Carlo simulation, XCOM programs, and experimental methods. The density increased, while the molar volume decreased as the BaO content increased. For the X-ray and gamma-ray radiation, indicate that the addition of BaO instead of B2O3 improve the shielding properties, while the Σ is decreased for thermal neutron radiation. According to the results, adding BaO enhanced the X-ray and gamma-ray shielding properties. However, adding BaO led to the reduces its ability of shielding the thermal neutrons of the glass samples. These data are expected to be accommodate to considering for any applications which requires high efficiency of photon or thermal neutrons shielding.

A new protective glass material against gamma ray: Thorough analysis to determine the impact of adding gadolinium (III) oxide

This work presents a study of the effect of replacing lead dioxide and gadolinium (III) oxide with boron trioxide on the physical, mechanical, and radiation shielding properties for the B2O3-Na2O-ZnO-PbO2-Gd2O3 glass systems. The Archimedes method confirms that the increase in the PbO2+Gd2O3 concentration within the fabricated glass system in the range from 16 to 22 mol.% increases the fabricated glass samples' density from 4.052 to 4.408 g/cm3. Additionally, the Makishima-Mackenzie model was utilized to investigate the influence of PbO2+Gd2O3 on the mechanical properties of the investigated glass samples. The increase in the substituting of PbO2+Gd2O3 decreases the fabricated glass samples' mechanical properties and micro-hardness. Furthermore, the Monte Carlo simulation method was applied for the estimation of the impact of PbO2+Gd2O3 concentration on the fabricated samples' radiation shielding parameters. The increase in the concentration of PbO2+Gd2O3 with range of (16, 18, 20 and 22) leads to increase the linear attenuation coefficient (LAC) to 8.014–11.517 cm−1 at 0.06 MeV, 0.381–0.423 cm−1 at 0.6 MeV, 0.133–0.149 cm−1 at 5 MeV, and 0.132–0.154 cm−1 at 15 MeV with the same order, respectively. Therefore, the introduction of PbO2+Gd2O3 concentration enhances the fabricated glass samples' radiation shielding properties to be suitable for γ-ray shielding applications.

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.