Monte Carlo Simulation Methodology Research Articles

Probabilistic analysis of an offshore monopile foundation taking into account the soil spatial variability

Numerical 3D deterministic models of offshore monopile foundations are computationally-expensive and thus they present a great obstacle to the use of the conventional Monte Carlo Simulation (MCS) methodology for the probabilistic analysis. In this paper, a reliable and efficient Kriging-based probabilistic model called Global Sensitivity Analysis enhanced Surrogate (GSAS) modeling is used. The objective is to perform a probabilistic analysis of a monopile foundation subjected to a combined loading. An undrained normally consolidated clayey soil with spatially varying soil properties was considered. Some probabilistic numerical results are presented and discussed.

Separation in D‐optimal experimental designs for the logistic regression model

AbstractThe D‐optimality criterion is often used in computer‐generated experimental designs when the response of interest is binary, such as when the attribute of interest can be categorized as pass or fail. The majority of methods in the generation of D‐optimal designs focus on logistic regression as the base model for relating a set of experimental factors with the binary response. Despite the advances in computational algorithms for calculating D‐optimal designs for the logistic regression model, very few have acknowledged the problem of separation, a phenomenon where the responses are perfectly separable by a hyperplane in the design space. Separation causes one or more parameters of the logistic regression model to be inestimable via maximum likelihood estimation. The objective of this paper is to investigate the tendency of computer‐generated, nonsequential D‐optimal designs to yield separation in small‐sample experimental data. Sets of local D‐optimal and Bayesian D‐optimal designs with different run (sample) sizes are generated for several “ground truth” logistic regression models. A Monte Carlo simulation methodology is then used to estimate the probability of separation for each design. Results of the simulation study confirm that separation occurs frequently in small‐sample data and that separation is more likely to occur when the ground truth model has interaction and quadratic terms. Finally, the paper illustrates that different designs with identical run sizes created from the same model can have significantly different chances of encountering separation.

Kriging-Based Reliability Analysis of Strip Footings Resting on Spatially Varying Soils

AbstractThe probabilistic analysis of geotechnical structures presenting spatial variability in the soil properties is generally performed using Monte Carlo simulation (MCS) methodology. Despite be...

Sequential Screening to Improve Behavioral Health Needs Detection in Primary Care

This study evaluates sequential screening to improve behavioral health needs detection, reduce unnecessary referrals, and discern adverse impacts (false negatives) for pediatric primary care populations. Monte Carlo simulation methodology was used to generate performance data for six sequential screening programs based on known technical properties of three broadband behavioral health measures and general psychopathology base rate estimates in pediatric primary care. Descriptive statistics, least-squares power regression, and data visualization were used to compare performance across programs. Ratio of reduced referrals to net false negatives was differentially affected by measure choice, administration order, and technical properties. Certain screening programs showed greater differences at lower base rates of psychopathology (approximately 12.8:1 ratio at 3% prevalence for SDQ-PSC [SDQ= Strengths and Difficulties Questionnaire, PSC= Pediatric Symptom Checklist] program), despite observed net sensitivity/specificity (0.47/0.97) that was comparable to other programs. Sequential screening is a viable alternative to traditional single-measure behavioral health screening practices in primary care. However, stakeholders must be deliberate with instrument selection and implementation to maximize anticipated benefits and minimize costs. Closer examination of practical issues is necessary to further discern advantages of a screening workflow in pediatric primary care.

Stringent sampling inspection plans using upper envelopes for cell counts

A new type of stringent sampling inspection plan is proposed for cell counts generally in line with a Poisson or a mixed-Poisson type of distribution. The proposed plan is based on setting an upper envelope estimated from the sample counts which should remain below the given microbiological limit m. We used Monte-Carlo simulation methodology to evaluate the proposed plans and showed that the lot acceptance criterion using the upper envelope improves consumer protection in general. Both two and three class attribute plans are investigated for establishing the upper envelope. Our study reveals that the proposed plans can offer better consumer protection and economy when compared to both two and three class attribute plans. It is also demonstrated that a slightly stringent plan can be found in place of lognormal type variables plans. The limitations of the proposed new plans, and a discussion on alternative stringent rules for batch acceptance are also provided. The new plan was also validated using the APC cell counts observed for 2000 milk powder batches. Our conclusion is that the proposed stringent plans can improve protection to the consumer, particularly for exported food products.

Integrated production, quality control and condition-based maintenance for imperfect production systems

This paper considers an integrated problem of production lot sizing, quality control and condition-based maintenance for an imperfect production system subject to both reliability and quality degradations. The system produces a single type of product to meet constant demand. To provide protection to the stock against uncertainties, a make-to-stock production policy is employed. The condition-based maintenance policy consists in carrying out inspections at the end of production runs to evaluate system condition and performing imperfect preventive maintenance if detected degradation level exceeds the threshold. The quality control is performed by using 100% inspection policy to obtain the proportion of defectives. Based on the quality information feedback, an overhaul action is conducted once the proportion reaches a given threshold during production runs. The aim of this paper is to jointly optimize the lot size, the inventory threshold, the preventive maintenance and overhaul thresholds such that the total cost per unit time is minimized. A stochastic mathematical model is formulated and solved by a simulation-based optimization approach coupling Monte Carlo Simulation and Response Surface Methodology. Finally, an illustrative example and sensitivity analyses are provided to demonstrate the proposed model.

Recent progress in the molecular simulation of thermodynamic properties of aqueous electrolyte solutions

We review progress in the development and application of molecular simulation methodology to predict the thermodynamic properties of aqueous electrolytes, focussing on work published since our previous review along similar lines [I. Nezbeda, et al., Mol. Phys. 114 (2016) 1665]. We consider such developments in the context of the use of Monte Carlo (MC) or Molecular Dynamics (MD) simulation methodologies using classical force fields. Special attention is paid to the incorporation of charge scaling approaches in the force fields, as well as to the simulation methodology used to compute solubility and osmotic pressure, and the use of the latter quantity to calculate the water activity and osmotic coefficient, and the electrolyte activity coefficient. We emphasize the importance of the statistical analysis of thermodynamic properties obtained from simulation data, and illustrate it with an example analyzing simulation osmotic pressure and electrolyte chemical potential data.

Monte Carlo Simulations Demonstrate Algorithmic Interventions Over Time Reduce Hospitalisation in Patients With Schizophrenia and Bipolar Disorder.

Non-adherence with pharmacologic treatment is associated with increased rates of relapse and rehospitalisation among patients with schizophrenia and bipolar disorder. To improve treatment response, remission, and recovery, research efforts are still needed to elucidate how to effectively map patient’s response to medication treatment including both therapeutic and adverse effects, compliance, and satisfaction in the prodromal phase of illness (ie, the time period in between direct clinical consultation and relapse). The Actionable Intime Insights (AI2) application draws information from Australian Medicare administrative claims records in real time when compliance with treatment does not meet best practice guidelines for managing chronic severe mental illness. Subsequently, the AI2 application alerts clinicians and patients when patients do not adhere to guidelines for treatment. The aim of this study was to evaluate the impact of the AI2 application on the risk of hospitalisation among simulated patients with schizophrenia and bipolar disorder. Monte Carlo simulation methodology was used to estimate the impact of the AI2 intervention on the probability of hospitalisation over a 2-year period. Results indicated that when the AI2 algorithmic intervention had an efficacy level of (>0.6), over 80% of actioned alerts were contributing to reduced hospitalisation risk among the simulated patients. Such findings indicate the potential utility of the AI2 application should replication studies validate its methodologic and ecological rigour in real-world settings.

Analysis of FPSO dropped objects combining Monte Carlo simulation and neural network-genetic approach

Dropped objects is one of the most hazardous accidents of floating production storage offloading (FPSO) due to the potentially severe consequences, therefore, the quantitative calculation of dropped objects impact risk, which is featured with uncertainties, is essential. Monte Carlo Simulation (MCS) is a method for this intention with flexibility and efficiency. However, wide calculating resources are needed for MCS-based accident analysis, especially when multiple random variables are concerned. Moreover, dropped objects collision damage models are perplexed and usually pronounced by the Finite Element Method (FEM), which is of a computational complexity. To address this issue, this paper considered a combined methodology: the Artificial Neural Network (ANN) adjusted by genetic algorithm (GA) is united with MCS to analyze dropped objects collision failure probability quantitatively. A time-dependent progressive simulation is employed to forecast the structural response caused by dropped objects collision. Then the ANN-GA is trained based upon the collision data and used as an alternative for the computational FEM performs with the MCS methodology. The risk level for the dropped objects collision of FPSO is assessed by comparing the proposed method and the DNV rules, and a qualitative uncertainty analysis is also conducted in this quantitative failure analysis.

The Effect of FEM Mesh Density on the Failure Probability Analysis of Structures

In the analysis of finite elements, mesh density can highly influence the accuracy of the results. Hence, researchers consider the determination and refinement of mesh an attractive issue. However, the subject has not been suitably investigated for the reliability evaluation of structures. This paper investigates the effects of mesh density on the reliability evaluation and the reliability-based sensitivity of structures. For this purpose, two common engineering problems modeled by Finite Element Method (FEM), with different mesh densities and their reliability results determined by Monte Carlo simulation and polynomial Response Surface Methodology (RSM). The analytical solutions to the proposed problems were present in the literature. Hence, the effects of the FEM mesh densities on the reliability results could be compared with the theoretical results. The outcomes based on the FEM results showed that RSM can very accurately evaluate the performance of these structures. However, the main achievement of the study was the finding that though a determined mesh density can be considered acceptable from the deterministic analysis viewpoint, its employment in reliability analysis could produce 100% error in estimating the failure probability of a structure.

Performance prediction for silicon photonics integrated circuits with layout-dependent correlated manufacturing variability.

This work develops an enhanced Monte Carlo (MC) simulation methodology to predict the impacts of layout-dependent correlated manufacturing variations on the performance of photonics integrated circuits (PICs). First, to enable such performance prediction, we demonstrate a simple method with sub-nanometer accuracy to characterize photonics manufacturing variations, where the width and height for a fabricated waveguide can be extracted from the spectral response of a racetrack resonator. By measuring the spectral responses for a large number of identical resonators spread over a wafer, statistical results for the variations of waveguide width and height can be obtained. Second, we develop models for the layout-dependent enhanced MC simulation. Our models use netlist extraction to transfer physical layouts into circuit simulators. Spatially correlated physical variations across the PICs are simulated on a discrete grid and are mapped to each circuit component, so that the performance for each component can be updated according to its obtained variations, and therefore, circuit simulations take the correlated variations between components into account. The simulation flow and theoretical models for our layout-dependent enhanced MC simulation are detailed in this paper. As examples, several ring-resonator filter circuits are studied using the developed enhanced MC simulation, and statistical results from the simulations can predict both common-mode and differential-mode variations of the circuit performance.

No-confounding designs with 24 runs for 7-12 factors

When experimenting with more than six independent variables, researchers under significant resource or time constraints often require alternatives to large Resolution V 2k-p fractional factorial designs. Many researchers also desire to avoid the expense of a foldover experiment required to de-alias completely confounded two-factor interactions (2FIs) when using resolution IV 2k-p designs. No-confounding designs are an excellent solution to this problem, as they have orthogonal main effects (ME) and no 2FI is completely confounded with another ME or 2FI. This paper introduces 24-run no-confounding designs for 7-12 factors. It presents a Monte Carlo simulation methodology used to evaluate algorithmically constructed designs and those in the existing literature. The results report the best-performing designs and metrics related to their types I and II error rate from the variable-selection process during repeated simulations of regression analyses.

No-confounding designs with 24 runs for 7-12 factors

When experimenting with more than six independent variables, researchers under significant resource or time constraints often require alternatives to large Resolution V 2k-p fractional factorial designs. Many researchers also desire to avoid the expense of a foldover experiment required to de-alias completely confounded two-factor interactions (2FIs) when using resolution IV 2k-p designs. No-confounding designs are an excellent solution to this problem, as they have orthogonal main effects (ME) and no 2FI is completely confounded with another ME or 2FI. This paper introduces 24-run no-confounding designs for 7-12 factors. It presents a Monte Carlo simulation methodology used to evaluate algorithmically constructed designs and those in the existing literature. The results report the best-performing designs and metrics related to their types I and II error rate from the variable-selection process during repeated simulations of regression analyses.

Multicomponent adsorption in mesoporous flexible materials with flat-histogram Monte Carlo methods.

We demonstrate an extensible flat-histogram Monte Carlo simulation methodology for studying the adsorption of multicomponent fluids in flexible porous solids. This methodology allows us to easily obtain the complete free energy landscape for the confined fluid-solid system in equilibrium with a bulk fluid of any arbitrary composition. We use this approach to study the adsorption of a prototypical coarse-grained binary fluid in "Hookean" solids, where the free energy of the solid may be described as a simple spring. However, our approach is fully extensible to solids with arbitrarily complex free energy profiles. We demonstrate that by tuning the fluid-solid interaction ranges, the inhomogeneous fluid structure inside the pore can give rise to enhanced selective capture of a larger species through cooperative adsorption with a smaller one. The maximum enhancement in selectivity is observed at low to intermediate pressures and is especially pronounced when the larger species is very dilute in the bulk. This suggest a mechanism by which the selective capture of a minor component from a bulk fluid may be enhanced.

Impact of operators’ performance in the reliability of cyber‐physical power distribution systems

Cyber-Physical Systems are the result of integrating information and communication technologies into physical systems. One particular case are Cyber-Physical Power Systems (CPPS), which use communication technologies to perform real-time monitoring and operation. These kinds of systems have become more complex, impacting on the systems' characteristics, such as their reliability. In addition, it is already known that in terms of the reliability of Cyber-Physical Power Distribution Systems (CPPDS), the failures of the communication network are just as relevant as the electrical network failures. However, some of the operators' performances, such as response time and decision quality, during CPPDS contingencies have not been investigated yet. In this paper, we introduce a model to the operator response time, present a Sequential Monte Carlo Simulation methodology that incorporates the response time in CPPDS reliability indices estimation, and evaluate the impact of the operator response time in reliability indices. Our method is tested on a CPPDS using different values for the average response time of operators. The results show that the response time of the operators affects the reliability indices that are related to the durations of the failure, indicating that a fast decision directly contributes to the system performance. We conclude that the improvement of CPPDS reliability is not only dependent on the electric and communication components, but also dependent on operators' performance.

Stochastic volatility models for the Brent oil futures market: forecasting and extracting conditional moments

AbstractThis paper builds and implements a multifactor stochastic volatility model for the latent (and observable) volatility from the front month future contracts at the Intercontinental Commodity Exchange (ICE), London, applying Bayesian Markov chain Monte Carlo simulation methodologies for estimation, inference and model adequacy assessment. Stochastic volatility is the main way time‐varying volatility is modelled in financial markets. An appropriate scientific model description, specifying volatility as having its own stochastic process, broadens the applications into derivative pricing purposes, risk assessment and asset allocation and portfolio management. From an estimated optimal and appropriate stochastic volatility model, the paper reports risk and portfolio measures, extracts conditional one‐step‐ahead moments (smoothing), forecasts one‐step‐ahead conditional volatility (filtering), evaluates shocks from conditional variance functions, analyses multistep‐ahead dynamics and calculates conditional persistence measures. (Exotic) option prices can be calculated using the re‐projected conditional volatility. Observed market prices and implied volatilities establish market risk premiums. The analysis adds insight and enables forecasts to be made, building up the methodology for developing valid scientific commodity market models.

Estimating the Accuracy of the Return on Investment (ROI) Performance Evaluations

Return on Investment (ROI) is one of the most popular performance measurement and evaluation metrics. ROI analysis (when applied correctly) is a powerful tool in comparing solutions and making informed decisions on the acquisitions of information systems. The purpose of this study is to provide a systematic research of the accuracy of the ROI evaluations in the context of information systems implementations. Measurements theory and error analysis, specifically propagation of uncertainties methods, were used to derive analytical expressions for ROI errors. Monte Carlo simulation methodology was used to design and deliver a quantitative experiment to model costs and returns estimating errors and calculate ROI accuracies. Spreadsheet simulation (Microsoft Excel spreadsheets enhanced with Visual Basic for Applications) was used to implement Monte Carlo simulations. The main contribution of the study is that this is the first systematic effort to evaluate ROI accuracy. Analytical expressions have been derived for estimating errors of the ROI evaluations. Results of the Monte Carlo simulation will help practitioners in making informed decisions based on explicitly stated factors influencing the ROI uncertainties.

Nonprofit Collaboration And The Resurrection Of Market Failure

Collaboration is among the core values of many in the nonprofit sector (Oster 1995, Huxam and Vangen 2005. Though research has examined the prevalence of collaboration and network governance in the nonprofit sector, little empirical work has examined the potential economic outcomes of a resource sharing environment. This paper uses Monte Carlo simulation methodology to demonstrate that while collaboration and resource sharing can maximize achievement of social objectives in resource-rich environments, it may be detrimental to the achievement of social objectives in a limited resource environment. These findings suggest that some social objectives that are not met in market or government settings may also not be met in nonprofit settings when collaboration is encouraged. This result has key implications for the matching of social institutions and social objectives.

Stochastic Volatility Models for the European Electricity Markets: Forecasting and Extracting Conditional Moments for Option Pricing and Implied Market Risk Premiums

This paper builds and implements a multifactor stochastic volatility model for the latent (and observable) volatility from the quarter and year forward contracts at the NASDAQ OMX Commodity Exchanges, applying Bayesian Markov chain Monte Carlo simulation methodologies for estimation, inference, and model adequacy assessment. Stochastic volatility is the main way time-varying volatility is modelled in financial markets. An appropriate scientific model description, specifying volatility as having its own stochastic process, broadens the applications into derivative pricing purposes, risk assessment and asset allocation and portfolio management. From an estimated optimal and appropriate stochastic volatility model, the paper reports risk and portfolio measures, extracts conditional one-step-ahead moments (smoothing), forecast one-step-ahead conditional volatility (filtering), evaluates shocks from conditional variance functions, analyses multi-step-ahead dynamics, and calculates conditional persistence measures. (Exotic) option prices can be calculated using the re-projected conditional volatility. Observed market prices and implied volatilities establish market risk premiums. The analysis adds insight and enables forecasts to be made, building up the methodology for developing valid scientific commodity market models.

Analytical uncertainty quantification for modal frequencies with structural parameter uncertainty using a Gaussian process metamodel

Quantifying the uncertainty in the dynamic properties of large-scale complex engineering structures presents significant computational challenges. Monte Carlo simulation (MCS) method is extensively employed to perform uncertainty quantification (UQ) because of its generality, stability, and easy implementation. However, a brute-force MCS approach may be unaffordable and impractical when the target model contains a large number of uncertain parameters. In this circumstance, MCS requires a potentially burdensome (if not computationally intractable) number of model evaluations to obtain a credible estimate of the global statistics. In this study, a general framework for analytical UQ of model outputs using a Gaussian process (GP) metamodel is presented, where case inputs are characterized as normal and/or uniform random variables. A detailed derivation of important low-order statistical moments (mean and variance) is given analytically. This analytical method is adopted to characterize the uncertainty of modal frequencies of two bridges with assumed normally- and uniformly-distributed parameters. Meanwhile, the brute-force MCS approach is used for comparison of GP metamodel-derived statistics. Results show that the GP method outperforms the MCS methodology in terms of computational cost, with consistency in the ”true” values obtained by MCS. It demonstrates that this GP method is feasible and reliable for modal frequency UQ of complex structures.