Monte Carlo Simulation Approach Research Articles

Energy Filtering in Doping Modulated Nanoengineered Thermoelectric Materials: A Monte Carlo Simulation Approach

Using Monte Carlo electronic transport simulations, coupled self-consistently with the Poisson equation for electrostatics, we explore the thermoelectric power factor of nanoengineered materials. These materials consist of alternating highly doped and intrinsic regions on the scale of several nanometers. This structure enables the creation of potential wells and barriers, implementing a mechanism for filtering carrier energy. Our study demonstrates that by carefully designing the nanostructure, we can significantly enhance its thermoelectric power factor compared to the original pristine material. Importantly, these enhancements stem not only from the energy filtering effect that boosts the Seebeck coefficient but also from the utilization of high-energy carriers within the wells and intrinsic barrier regions to maintain relatively high electronic conductivity. These findings can offer guidance for the design and optimization of new-generation thermoelectric materials through improvements in the power factor.

Decreasing Trend Bumped Probability Distribution: its Properties, Simulations and Application

In this paper, a probability model is proposed which engaged the mathematical combination of Lindley distribution and a trigonometric component known as haversine function〖 δ〗_s. The one parameter model prototype sustains the capacity to forecast multimodal decreasing trend sinusoidal outcomes. By nomenclature, the proposed probability model is called Modified Lindley Trigonometric Distribution (MLTD). Some statistical properties studied include the hazard function, mean residual life function, moments, conditional moments and moment generating function, Bonferroni and Lorenz curve, entropy, asymptotic distribution, order statistics, and parameter estimation; where the hazard function specially features a cyclic or periodic bathtub and inverted bathtub shape in chain format. The numerical behavior of the estimates of the average bias and mean square error were examined under Monte Carlo simulation approach; and an applicative simulation is experimented to underscore the parametric behavior of MLTD in data modeling. A real life flood data is used to illustrate the essence of the development.

A framework to analyse the probability of accidental hull girder failure considering advanced corrosion degradation for risk-based ship design

Ship’s hull girder failure could result from maritime accident that can cause human life loss, environmental disaster, and major economic impacts. In risk-based ship design paradigm, accounting for rare phenomena (e.g. ship-ship collision or grounding) is important to provide safe and durable structure. In-service corrosion-induced hull degradation should be considered at the design stage, as it can significantly affect structural strength. The current study presents a novel framework to estimate the probability of ship hull girder failure, accounting for novel corrosion modelling techniques and accidental damage. The associated uncertainties are considered using statistical sampling from evidence-based distributions. A state-of-the-art deterministic model for ultimate strength calculation is applied using Monte Carlo simulation approach, resulting in the probability of hull failure through a reliability assessment. Wave and still-water bending moments are considered random variables. Two case studies of tanker ships with varying sizes are executed to show the applicability of the proposed framework. The results indicate that proper consideration of corrosion is of high importance, as ageing can significantly increase the probability of failure if accidental damage happens. Therefore, whereas future research and model refinement are discussed, the presented framework can serve for risk-based ship design tool and assess existing structures’ safety.

Fuzzy control charts for individual observations to analyze variability in health monitoring processes

In healthcare monitoring, the quality of medical services and patient outcomes are significantly influenced by the unnatural variations emphasizing the importance of effective control and monitoring strategies. In such scenarios the quality is compromised when a shift in the medical process is not detected timely. In this study, we commenced a comprehensive analysis of the variability present in the patient's hematocrit levels. We introduced a novel approach using fuzzy control charts for individual measurements to detect shift within hematocrit levels effectively. For the initial forecasting of hematocrit level variability, we employed the exponential smoothing method by using R software. Following this, we proposed a set of fuzzy control charts intended for individual measurements, including the fuzzy moving average control chart, fuzzy weighted moving control chart, and fuzzy moving range control chart. These control charts are defined with fuzzy control rules, allowing them to analyze and interpret the small shifts in the process precisely. Also, to measure the process's capability for providing a deeper understanding of the processes, fuzzy process capability indices are introduced. A Monte Carlo simulation approach is employed to obtain the performance metrics. Finally, a case study sourced from Kaggle was conducted to evaluate the performance of the proposed fuzzy control charts for assessing hematocrit levels.

Probabilistic Analysis of Critical Speed Values of a Rotating Machine as a Function of the Change of Dynamic Parameters.

Real-world rotordynamic systems exhibit inherent uncertainties in manufacturing tolerances, material properties, and operating conditions. This study presents a Monte Carlo simulation approach using MSC Adams View and Adams Insight to investigate the impact of these uncertainties on the performance of a Laval/Jeffcott rotor model. Key uncertainties in bearing damping, bearing clearance, and mass imbalance were modeled with probabilistic distributions. The Monte Carlo analysis revealed the probabilistic nature of critical speeds, vibration amplitudes, and overall system stability. The findings highlight the importance of probabilistic methods in robust rotordynamic design and provide insights for establishing manufacturing tolerances and operational limits.

Reliability framework for CLT floors in out-of-plane bending using Monte Carlo simulation

Sustainably sourced engineered wood products (EWP) such as cross-laminated timber (CLT) floor panels are viable alternatives to conventional concrete and steel, however their inherent natural variability increases perceived risks and limits wide-spread adoption. The reliability index (β) is the metric used by design standards to communicate allowable structural risk. Although not included directly in any structural capacity equations, it governs all the partial resistance factors and load amplification factors that inform those design code calculations. CLT floors have significant partial resistance factors due to the natural variability of the material that effectively penalise the calculated design strength of those products. By exploring their reliability there is the potential to allow these products to be designed more efficiently, leading to increased competitiveness among traditional materials and less waste. A novel statistical capacity prediction model that goes beyond a simple averaged-value strength assumption to include consideration of the variability between individual boards within a panel was validated against experimental data. A CLT database of 237 out-of-plane bending test results was collected for the first time to inform an appropriate model error factor. Monte Carlo simulation (MCS) approaches were adopted to predict the reliability levels of CLT panels by defining the material, geometrical and loading parameters as random variables and assigning each a mean, coefficient of variation and appropriate distribution. The target and calculated reliability levels were compared across both Eurocode 5 and the North American codes ANSI/APA PRG 320 and CSA O86 as the preeminent CLT design codes internationally. This study found that the calculated reliability levels met or exceeded both codes in most scenarios. The impacts of width, load ratio, material strength distribution, and CLT layups on the expected failure probabilities were explored with sensitivity analyses. The MCS framework was also applied to the calibration of an appropriate resistance factor for CLT design to inform the potential creation of an Australian CLT design code. A resistance factor of 0.85 was calibrated which falls between the equivalent European and North American code resistance factors.

Analyzing variability in urban energy poverty: A stochastic modeling and Monte Carlo simulation approach

Urban energy poverty remains a critical challenge affecting millions worldwide, with significant implications for socio-economic development and sustainability. This study employs Monte Carlo simulations to analyze the variability in energy poverty, as measured by the Multidimensional Energy Poverty Index (MEPI). Through this approach, we explored how different socio-economic predictors affect MEPI scores across five scenarios: economic growth and diversification, energy transition and technological innovation, climate change impacts and vulnerability, policy intervention and social safety nets, and stagnation and rising inequality. The sensitivity analysis revealed that household income, size, and food security are paramount in influencing urban energy poverty levels. Scenarios involving economic growth and technological innovation demonstrated positive impacts on reducing energy poverty, while scenarios of climate change and economic stagnation highlighted the vulnerabilities and widening disparities within populations. The results suggest that to effectively alleviate urban energy poverty, policy interventions must be comprehensive, targeting the identified influential factors. The study emphasizes the need for targeted policy interventions on social safety nets to alleviate energy poverty.

Unmanned Aerial Vehicle-Assisted Terahertz-Visible Light Communication Systems: An In-Depth Performance Analysis.

This paper investigates the performance of dual-hop unmanned aerial vehicle (UAV)-assisted communication channels, employing a decode-and-forward (DF) relay architecture. The system leverages terahertz (THz) communication for the primary hop and visible light communication (VLC) for the secondary hop. We conduct an in-depth analysis by deriving closed-form expressions for the end-to-end (E2E) bit error rate (BER). Additionally, we use a Monte Carlo simulation approach to generate best-fitting curves, validating our analytical expressions. A performance evaluation through BER and outage probability metrics demonstrates the effectiveness of the proposed system. Specifically, our results indicate that the proposed system outperforms Free-Space Optics (FSO)-VLC and Radio-Frequency (RF)-VLC at a higher signal-to-noise ratio (SNR). The results of this study provide valuable insights into the feasibility and limitations of UAV-assisted THz-VLC communication systems.

Identification and prioritization of risks for new entrants in automobile sector using Monte Carlo based approach

The automotive industry serves as a crucial support system for the economies of industrialized nations in their pursuit of international market competitiveness. Despite this industry's importance, most developing countries face the challenge of acquiring a reasonable economic position at the global level in the automotive sector for various reasons. The most salient reasons include inconsistent government policies, multiple taxes, investor insecurity, political instability, and currency devaluation. Identifying risks is crucial for a new entrant in the already-established automotive industry. The researchers have used multiple (qualitative and quantitative) techniques to identify and prioritize risks in setting up manufacturing plants. The efforts to tackle these identified risks are undertaken at the domestic and government levels to smoothen the establishment of industry. The risks are first identified, in the current study, by reviewing the previous literature and conducting interviews of the various stakeholders (automotive dealers, managers, and customers). Then this study uses Monte Carlo simulation (MCS) approach and develops a risk exposure (high, medium, or low) matrix for the automotive industry of Pakistan. The findings reveal that the depreciation of local currency against the foreign exchange, oligopoly nature of competition, and low market acceptability of new entrants due to their products' image are the most critical risks the automobile industry faces. These findings will help automotive research institutes in developing national policies that specifically aim to support new players in the automotive industry, particularly in addressing high-priority hazards. The results may also provide valuable insights for new participants seeking to identify and address the key challenges in the Pakistani automotive industry before entering it.

Estimating statistical power for structural equation models in developmental cognitive science: A tutorial in R : Power simulation for SEMs.

Determining the compositional structure and dimensionality of psychological constructs lies at the heart of many research questions in developmental science. Structural equation modeling (SEM) provides a versatile framework for formalizing and estimating the relationships among multiple latent constructs. While the flexibility of SEM can accommodate many complex assumptions on the underlying structure of psychological constructs, it makes a priori estimation of statistical power and required sample size challenging. This difficulty is magnified when comparing non-nested SEMs, which prevents the use of traditional likelihood-ratio tests. Sample size estimates for SEM model fit comparisons typically rely on generic rules of thumb. Such heuristics can be misleading because statistical power in SEM depends on a variety of model properties. Here, we demonstrate a Monte Carlo simulation approach for estimating a priori statistical power for model selection when comparing non-nested models in an SEM framework. We provide a step-by-step guide to this approach based on an example from our memory development research in children.

DNA-damage RBE assessment for combined boron and gadolinium neutron capture therapy.

Neutron capture therapy (NCT) by 10B and 157Gd agents is a unique irradiation-based method which can be used to treat brain tumors. Current study aims to quantitatively evaluate the relative biological effectiveness (RBE) and dose distributions during the combined BNCT and GdNCT modalities through a hybrid Monte Carlo (MC) simulation approach. Snyder head phantom as well as a cubic hypothetical tumor was at first modeled by Geant4 MC Code. Then, the energy spectra and dose distribution relevant to the released secondary particles during the combined Gd/BNCT were scored for different concentrations of 157Gd and 10B inside tumor volume. Finally, the scored energy spectra were imported to the MCDS code to estimate both RBESSB and RBEDSB values for different 157Gd concentrations. The results showed that combined Gd/BNCT increases the fluence-averaged RBESSB values by about 1.7 times when 157Gd concentration increments from 0 to 2000µg/g for both considered cell oxygen levels (pO2=10% and 100%). Besides, a reduction of about 26% was found for fluence-averaged RBEDSB values with an increment of 157Gd concentration in tumor volume. From the results, it can be concluded that combined Gd/BNCT technique can improve tumor coverage with higher dose levels but in the expense of RBEDSB reduction which can affect the clinical efficacy of the NCT technique.

Improving instrumental neutron activation analysis detection limit using Monte Carlo simulation for lanthanides determination in Algerian phosphate

ABSTRACT Instrumental Neutron Activation Analysis (INAA) is one of the best methods to analyse Rare Earth Elements (REE) with a low detection limit in phosphate samples. Since most lanthanides emit gamma rays at the energy below 1000 keV, this energy range is affected by the Bremsstrahlung noise of beta emission produced by the 31P(n,γ)32P reaction, when the irradiation of the P2O5 composing the phosphate matrix (approximately 30%). This beta (β−) spectrum with a maximum energy of 1.71 MeV (average energy of 695 keV) affects the gamma spectrum and will reduce the detection limit significantly, other factors decrease the detection limit of lanthanides like neutron self-shielding and interference. In the present work, an analysis methodology based on optimising the measurement parameters of the INAA technique was implemented to improve the detection limits for the precise evaluation of lanthanide concentrations in Algerian phosphate deposits. The complete procedure was developed using a combination of a Monte Carlo simulation approach and experimental measurements. Two models were developed based on Monte Carlo simulation for the calibration of HPGe detector efficiency and the optimisation of type and filter thickness to reduce the beta spectrum (Bremsstrahlung) emitted by the 32P produced by neutron capture reaction 31P(n, γ) 32P. The developed model allowed a considerable improvement in the detection limit of the INAA technique and reduced the uncertainty in the lanthanide concentration. Finally, the methodology was applied to determine lanthanide elements in phosphate samples with good precision.

An optimization-based design methodology to manage the sustainable biomass-to-biodiesel supply chain under disruptions: A case study

The detrimental effects of excessive fossil fuel consumption have recently become a major global concern. Moreover, fuel supply chains are vulnerable to disruptive and operational risks. This study proposes a robust-stochastic approach to design a resilient sustainable biodiesel supply chain from Jatropha and waste cooking oil, while facilities and transportation links are vulnerable to multiple site-dependent disruptions. The main contributions are implementing preventive resiliency strategies to manage risks, integrating sustainability and resiliency, and selecting biodiesel conversion technology based on byproducts’ demands. Herein, candidate locations are identified to cultivate Jatropha using the best-worst method and geographical information system. Moreover, the supply chain network is designed to optimize economic performance, network non-resiliency, and social impacts. The Monte Carlo simulation approach, k-means clustering, augmented epsilon constraint, and Mulvey robust optimization are adopted to generate and reduce probable scenarios, manage multiple objectives, and handle uncertainty, respectively. The results showed that the non-resilient supply chain experiences a 20.11 % additional cost in disruption scenarios; however, even with a higher initial cost, adopting resiliency strategies reduces disruption cost effects by 9.46 %. Managerial insights have demonstrated that resilience and sustainability are positively correlated in biofuel supply chain management, and rising biodiesel demand enhances social welfare and economic performance.

Optimized energy management of a solar battery microgrid: An economic approach towards voltage stability

As the adoption of renewable energy sources (RESs) continues to surge, and the concept of microgrids (MGs) gains widespread recognition, the need for efficient battery energy storage system (BESS) management within MGs has become increasingly prominent. This paper introduces a novel Energy Management System (EMS) designed for grid-connected MGs, with the primary objectives of enhancing the energy economics of the MG, minimizing BESS degradation, and ensuring that the point of common coupling (PCC) voltage remains within prescribed limits. The proposed EMS utilizes a finite-horizon Model Predictive Control (MPC) framework to seamlessly coordinate BESS operations, encompassing charging, discharging, and reactive power provision. This coordination is done in conjunction with solar power generation, load demand, electricity pricing, and feed-in tariffs. Furthermore, the EMS accounts for uncertainties related to load and solar demand using a Monte Carlo simulation approach. The optimization goal of the proposed EMS is to minimize the expected total operational cost of the MG over a 24-hour prediction horizon. This cost encompasses both economic costs and battery degradation costs. The optimization process adheres to a set of constraints that consider BESS limitations, such as state of charge (SoC) constraints and charging/discharging limits, as well as inverter capacity limits and PCC voltage requirements. Conducting rigorous case studies on the MATLAB Simscape Electrical platform and validating them in real-time using the OPAL-RT simulator demonstrate the effectiveness of the proposed EMS across diverse operational scenarios and pricing models. The findings of these case studies provide valuable insights into the effectiveness of the EMS in real-world MG applications.

Estimating Stage-Frequency Curves for Engineering Design in Small Ungauged Arctic Watersheds

The design of hydraulic structures in the Arctic is complicated by shallow relief, which cause unique runoff processes that promote snow-damming and refreeze of runoff. We discuss the challenges encountered in modeling snowmelt runoff into two coastal freshwater lagoons in Utqiaġvik, Alaska. Stage-frequency curves with quantified uncertainty were required to design two new discharge gates that would allow snowmelt runoff flows through a proposed coastal revetment. To estimate runoff hydrographs arriving at the lagoons, we modeled snowpack accumulation and ablation using SnowModel which in turn was used to force a physically-based hydraulic runoff model (HEC-RAS). Our results demonstrate the successful development of stage-frequency curves by incorporating a Monte Carlo simulation approach that quantifies the variability in runoff timing and volume. Our process highlights the complexities of Arctic hydrology by incorporating significant delays in runoff onset due to localized snow accumulation and melting processes. This methodology not only addresses the uncertainty in snow-damming and refreeze processes which affect the arrival time of snowmelt inflow peaks, but is also adaptable for application in other challenging environments where secondary runoff processes are predominant.

Heavy metal pollution and health risk assessment using deterministic and Monte Carlo simulation approaches in the Himalayan spring and surface water systems of Kulgam District, Kashmir valley, India

Heavy metal pollution and health risk assessment using deterministic and Monte Carlo simulation approaches in the Himalayan spring and surface water systems of Kulgam District, Kashmir valley, India

Quantitative microbial risk assessment of gastrointestinal illness due to recreational exposure to E. coli and enterococci on the southern coasts of the Caspian Sea

BackgroundGastrointestinal illness refers to a broad range of diseases that affect the digestive system, including infections caused by bacteria, viruses, and parasites. Quantitative Microbial Risk Assessment (QMRA) is a powerful tool used to evaluate the risks associated with microbial pathogens in various environments. The main objective of this study was to conduct a quantitative assessment of gastrointestinal illnesses that occur as a result of exposure to E. coli and enterococci during recreational activities on the southern coasts of the Caspian Sea. MethodsSamples were collected from the recreational beaches along the border line of the Caspian Sea. The samples were analyzed for the presence and enumeration of E. coli and enterococci using the microplate method and membrane filtration techniques. Then, the annual and daily infection risks were computed using the Monte Carlo simulation approach. ResultsThe results revealed that the risk of daily and annual infections on the coasts of Babolsar was higher than that on the coasts of Sari. Also, in the recreational waters of these beaches, the risk of infection by enterococci was higher than that posed by E. coli. In Babolsar, the average annual infection risk caused by E. coli and enterococci was 0.365 and 1 for children and 0.181 and 0.986 for adults. Also, in Sari, the average annual infection risk caused by E. coli and enterococci was 0.060 and 0.908 for children and 0.027 and 0.815 for adults. In addition, children were more likely than adults to become infected. ConclusionIn light of the study's findings, due to the entry of untreated urban wastewater into the southern part of the Caspian Sea (northern Iran) and the high risk of infectious diseases for children, more control and health measures are necessary for children's swimming.

A simulation of the photoconversion efficiency of P3HT:PCBM bulk heterojunction: An insight on the charge collection efficiency

In organic bulk heterojunction solar cells, the internal quantum efficiency can be considered as the product of the absorption efficiency, the exciton diffusion efficiency, the charge transfer efficiency, and the charge collection efficiency. In previous studies, it has been reported that charge transfer and charge collection is very high in these solar cells, approaching 100 %. With a Monte Carlo simulation approach that considers the hopping of charges within the two materials of the heterojunctions, it is highlighted that an estimate of the charge collection efficiency can be evaluated considering that a certain number of charges do not reach the electrodes because of the random arrangement of the two materials in the heterojunction. The case of P3HT:PCBM (1:1 ratio) has been analyzed in this study.

Risk–Reward Share Allocation under Different Integrated Project Delivery Relational Structures: A Monte-Carlo Simulation and Cooperative Game Theoretic Solutions Approach

Risk–Reward Share Allocation under Different Integrated Project Delivery Relational Structures: A Monte-Carlo Simulation and Cooperative Game Theoretic Solutions Approach

Reliability of Monte Carlo simulation approach for estimating uniaxial compressive strength of intact rock

The strength of rock has significant influence on its performance, and is, therefore, a key input during modelling and analysis of mining and geotechnical engineering structures. The uniaxial compressive strength (UCS), which is a popular parameter to quantifying rock strength can be determined in the laboratory using suggested method by International Society of Rock Mechanics (ISRM). However, the laboratory determination of UCS consumes time, it is costly, and sometimes may not be feasible to perform because of different conditions of rock. Hence, this study attempts to employ Monte Carlo simulation (MCS) approach to estimate UCS, and to overcome various uncertainties associated with UCS estimation. To use MCS approach for UCS estimation, block punch index (BPI), Brazilian tensile strength (BTS), point load index (IS(50)), and P-wave velocity (Vp) were selected as the model inputs. A multiple linear regression (MLR) equation was developed and used to predict UCS by the MCS approach. The methodology was applied to estimate UCS using real BPI, BTS, Is(50), and Vp data as inputs. The proposed approach simulated UCS values that are consistent with UCS values measured in the laboratory. The mean of the UCS values simulated through the MCS approach is 119.10 MPa, while the mean of the UCS values measured in the laboratory is 118.42 MPa. In addition, hypothesis testing revealed that the Brazilian tensile strength (BTS) is the parameter with the most influence on UCS of rock for the site investigated.