Monte Carlo-based Sensitivity Analysis Research Articles

Estimating theoretical stress regime for engineered geothermal energy systems in an arctic community (Kuujjuaq, Canada)

In remote northern regions, lacking deep geothermal exploratory boreholes, a theoretical approach to provide a first-order estimate of the stress regime seems a useful tool. Literature data was used in this context to evaluate the orientation of the stress components and empirical relationships were applied to calculate their magnitude in a community of Nunavik, northern Quebec. A Monte Carlo-based sensitivity analysis was carried out due to the uncertainty of the input parameters. Mohr–Coulomb friction and slip tendency analyses were additionally undertaken to assess the stress state and potential reactivation of existing fractures. The results highlight how the poor knowledge of the stress field has an important impact on the design and development of engineered geothermal energy systems in the Canadian off-grid community of Kuujjuaq.

Population-Based Prevalence of Cancer Family History in Southeastern Iran

Background: Cancer family history (CFH) could be an effective non-invasive preventive tool for cancer screening. There are limited data on the prevalence of CFH. Objectives: We aimed to estimate a robust population-based prevalence of CFH in southeastern Iran. Methods: This study is a population-based survey. Participants were recruited in a multistage proportional-to-size cluster sampling design. A validated interview form was used, including a pedigree table and a cancer detail table. A positive CFH was defined as a verbal self-report of cancer diagnosis in at least 1 relative. The CFH prevalence was estimated according to age, gender, residential area, relatives’ closeness, and cancer type. Estimated prevalence values were corrected for the sensitivity of self-reported CFH in a Monte Carlo-based sensitivity analysis. Results: A total of 2057 interviews with a male-female ratio of 1.14: 1 were analyzed. The overall prevalence (95% uncertainty level (UL)) of CFH in at least 1 relative (first or second-degree relatives (FDR/SDR)) was 37.06 (27.50, 47.05). The prevalence (95% UL) of CFH in at least 1 female and male FDR was 12.54 (8.62, 17.25) and 11.07 (7.22, 15.34), respectively. The prevalence (95% UL) of a positive history of lung, breast, and colorectal cancers in at least 1 FDR was 2.05 (1.36, 2.90), 1.36 (0.79, 2.08), and 1.23 (0.63, 2.02), respectively. Conclusions: The prevalence of positive CFH in FDRs is less than that of developed countries and exceeds 11.8% in the general population of southeastern Iran. CFH taking by general practitioners in routine visits is recommended as a screening tool in this population.

Nanofluids thermal conductivity prediction applying a novel hybrid data-driven model validated using Monte Carlo-based sensitivity analysis

Accurate estimation of the thermal conductivity of nanofluids plays a key role in industrial heat transfer applications. Currently available experimental and empirical relationships can be used to estimate thermal conductivity. However, since the environmental conditions and properties of the nanofluids constituents are not considered these models cannot provide the expected accuracy and reliability for researchers. In this research, a robust hybrid artificial intelligence model was developed to accurately predict wide variety of relative thermal conductivity of nanofluids. In the new approach, the improved simulated annealing (ISA) was used to optimize the parameters of the least-squares support vector machine (LSSVM-ISA). The predictive model was developed using a data bank, consist of 1800 experimental data points for nanofluids from 32 references. The volume fraction, average size and thermal conductivity of nanoparticles, temperature and thermal conductivity of base fluid were selected as influent parameters and relative thermal conductivity was chosen as the output variable. In addition, the obtained results from the LSSVM-ISA were compared with the results of the radial basis function neural network (RBF-NN), K-nearest neighbors (KNN), and various existing experimental correlations models. The statistical analysis shows that the performance of the proposed hybrid predictor model for testing stage (R = 0.993, RMSE = 0.0207) is more reliable and efficient than those of the RBF-NN (R = 0.970, RMSE = 0.0416 W/m K), KNN (R = 0.931, RMSE = 0.068 W/m K) and all of the existing empirical correlations for estimating thermal conductivity of wide variety types of nanofluids. Finally, robustness and convergence analysis were conducted to evaluate the model reliability. A comprehensive sensitivity analysis using Monte Carlo simulation was carried out to identify the most significant variables of the developed models affecting the thermal conductivity predictions of nanofluids.

Moving forward: Cost-Effectiveness of PrEP in HIV prevention for Men Who Have Sex with Men in Italy

Abstract Globally, in 2018 1.7 million people contracted Human Immunodeficiency Virus (HIV) and 770,000 people died from AIDS-related illnesses. Men Who Have Sex with Men (MSM) have one of the highest probabilities of HIV transmission. In Italy, in 2018 2,847 new HIV cases were diagnosed, of which 39% among MSM. The MSM population accounts for 16,690 individuals whereof 50% already underwent the Highly Active Antiretroviral Therapy (HAART). The aim of this study is to evaluate the cost-effectiveness of daily Pre-Exposure Prophylaxis (PrEP) in the Italian MSM susceptible population. A Markov transition model was calibrated to the HIV epidemic among MSM in Italy, comparing PrEP to naïve patients (NP). Model parameters were retrieved by querying scientific databases. Transition probabilities were adjusted for incidence of HIV while costs and benefits were discounted at an annual rate of 3%. The impact on results of critical parameters was explored through a Monte Carlo-based sensitivity analysis. The cost-effectiveness analysis results were reported as Incremental Cost-Effectiveness Ratio (ICER) express as € per Quality Adjusted Life Year (QALY) gained. The introduction of such a PrEP program would result in a total cost of €822,398,199 million and lead to a gain of 70,762 discounted QALYs over an 85-year time horizon. Assuming a 92% efficacy of PrEP therapy, the ICER for the PrEP program is €4,346.16/QALY gained. This value is definitively lower than acceptability NICE threshold (£20,000). PrEP can reduce the infection rate up to 60% in a five-year period. The Monte Carlo simulation confirmed the robustness of the model results. This analysis showed PrEP to be cost-effective when used in a susceptible population. Even if PrEP doesn't protect from other sex-related infectious diseases, it could prevent HIV transmission, thus breaking down HIV-infection incidence rate. Each country should reflect on the real possibility to implement a robust Public Health program pondering the adoption of PrEP. Key messages PrEP is very effective at reducing HIV infection when taken correctly. It is the backbone in the “combination prevention”, necessary to reach the SDG of ending the AIDS epidemic by 2030. This preliminary analysis suggests that the introduction of a daily PrEP program for MSM in Italy is cost-effective and possibly cost saving in the long term.

Sensitivity Analysis Based on Monte Carlo Simulations of a 1D Accelerated Mechanistic Model of a Proton Exchange Membrane Fuel Cell

Mathematical modelling of polymer electrolyte membrane fuel cells (PEMFCs) commenced with a one-dimensional (1D) model incorporating gas diffusion and chemical reaction mechanisms, movement at the membrane via electroosmotic drag and proton and water transport at the electrolyte membrane was developed. Subsequently more sophisticated two- and three-dimensional models incorporating the water and thermal management were established to improve the accuracy and thereby identify parameters that influence the performance of the PEMFCs. Recently, as the computational power improved, statistical analyses including sensitivity analyses of approximated models were carried out. However, employing a mechanistic model considering the conservation of charge, mass, momentum, species and energy, for sensitivity analysis is not trivial particularly because of the associated computational cost. In this study, a Monte Carlo based sensitivity analysis is carried out based on an accelerated, time-marched 1D mechanistic model of a PEMFC that captures the effects of dependent variables in all three dimensions. To achieve this, we investigate the effect of random, simultaneous, variation of over 30 normally distributed parameters (physical, geometric, operating electrochemical and fitting parameters) at different operating conditions of the PEMFC. Kolmogorov-Smirnov (KS) test is performed to ensure that the size of the drawn sample is reliable for stable results. An experimentally validated model is taken as the base case and all the parameters are varied with a standard deviation of 10%. The standard deviation in the current at different operating cell potentials is presented. Additionally, the contribution of each stochastic parameter to the current is ranked and quantified by multiple linear regression and sigma-normalized derivative method. Furthermore, a sample size of 10000 is chosen to generate enough variations to the model and identify optimal operating and physical parameters to improve the performance of PEMFC. Moreover, any artificial intelligence centered model predictor can estimate the best set of parameters by accessing the data generated from this study to fit an experimental curve at different operating cell potentials. This accelerated model prediction thereby enables improved accuracy of the predicted models, faster diagnosis and easier optimization of a PEMFC.

Design of Fuel Cell Systems for Aviation: Representative Mission Profiles and Sensitivity Analyses

The global transition to a clean and sustainable energy infrastructure does not stop at aviation. The European Commission defined a set of environmental goals for the “Flight Path 2050”: 75% CO2 reduction, 90% NOx reduction, and 65% perceived noise reduction. Hydrogen as an energy carrier fulfills these needs, while it would also offer a tenable and flexible solution for intermittent, large-scale energy storage for renewable energy networks. If hydrogen is used as an energy carrier, there is no better device than a fuel cell to convert its stored chemical energy. In order to design fuel cell systems for passenger aircraft, it is necessary to specify the requirements that the system has to fulfill. In this paper, a statistical approach to analyze these requirements is presented, which accounts for variations in the flight mission profile. Starting from a subset of flight data within the desired class (e.g., mid-range inter-European flights) a stochastic model of the random mission profile is inferred. This model allows for subsequent predictions under uncertainty as part of the aircraft design process. By using Monte Carlo-based sampling of flight mission profiles, the range of necessary component sizes, as well as optimal degrees of hybridization with a battery, is explored, and design options are evaluated. Furthermore, Monte Carlo-based sensitivity analysis of performance parameters explores the potential of future technological developments. Results suggest that the improvement of the specific power of the fuel cell is the deciding factor for lowering the energy system mass. The specific energy of the battery has a low influence but acts in conjunction with the specific power of the fuel cell.

Evaluation of potential confounding by smoking in the presence of misclassified smoking data in a cohort study of workers exposed to acrylonitrile.

To evaluate the extent to which lung cancer mortality risk estimates in relation to acrylonitrile (AN) exposure may have been confounded by smoking in the presence of misclassified smoking data. Subjects were 992 white men employed for three or more months between 1960 and 1996 at a chemical plant in Lima, Ohio. We used Monte Carlo-based sensitivity analysis to address possible confounding by smoking. In Monte Carlo simulations that accounted for the relationship between smoking and AN exposure, mean relative risks for lung cancer mortality in relation to AN exposure decreased and we observed somewhat less evidence of an exposure-response relationship. Our simulations suggest that the relationship between AN exposure and lung cancer mortality was positively confounded by smoking in the original Lima cohort study.

Variance-based sensitivity analysis of biological uncertainties in carbon ion therapy

PurposeBiological models to estimate the relative biological effectiveness (RBE) or the equivalent dose in 2 Gy fractions (EQD2) are needed for treatment planning and plan evaluation in carbon ion therapy. We present a model-independent, Monte Carlo based sensitivity analysis (SA) approach to quantify the impact of different uncertainties on the biological models. Methods and materialsThe Monte Carlo based SA is used for the evaluation of variations in biological parameters. The key property of this SA is the high number of simulation runs, each with randomized input parameters, allowing for a statistical variance-based ranking of the input variations. The potential of this SA is shown in a simplified one-dimensional treatment plan optimization. Physical properties of carbon ion beams (e.g. fragmentation) are simulated using the Monte Carlo code FLUKA. To estimate biological effects of ion beams compared to X-rays, we use the Local Effect Model (LEM) in the framework of the linear-quadratic (LQ) model. Currently, only uncertainties in the output of the biological models are taken into account. Results/conclusionsThe presented SA is suitable for evaluation of the impact of variations in biological parameters. Major advantages are the possibility to access and display the sensitivity of the evaluated quantity on several parameter variations at the same time. Main challenges for later use in three-dimensional treatment plan evaluation are computational time and memory usage. The presented SA can be performed with any analytical or numerical function and hence be applied to any biological model used in carbon ion therapy.

Convergence and uncertainty analyses in Monte-Carlo based sensitivity analysis

Sensitivity analysis plays an important role in model development, calibration, uncertainty analysis, scenario analysis, and, hence, decision making. With the availability of different sensitivity analysis techniques, selecting an appropriate technique, monitoring the convergence and estimating the uncertainty of the sensitivity indices are very crucial for environmental modelling, especially for distributed models due to their high non-linearity, non-monotonicity, highly correlated parameters, and intensive computational requirements. It would be useful to identify whether some techniques outperform others with respect to computational requirements, reliability, and other criteria. This paper proposes two methods to monitor the convergence and estimate the uncertainty of sensitivity analysis techniques. One is based on the central limit theorem and the other on the bootstrap technique. These two methods are implemented to assess five different sensitivity analysis techniques applied to an environmental model. These techniques are: the Sobol’ method, the Morris method, Linear Regression (LR), Regionalized Sensitivity Analysis (RSA), and non-parametric smoothing. The results show that: (i) the Sobol’ method is very robust in quantifying sensitivities and ranking parameters despite a large number of model evaluations; (ii) the Morris method is efficient to rank out unimportant parameters at a medium cost; (iii) the non-parametric smoothing is reliable and robust in quantifying the main effects and low-order interactions while requiring a small number of model evaluations; finally (iv) the other two techniques, that is, LR and RSA, should be used with care.

Monte Carlo based sensitivity analysis of two crop simulators and considerations on model balance

Sensitivity analysis is crucial for building, understanding and using complex mathematical models for agroenvironmental applications. In this study, a variance-based sensitivity analysis was carried out for the first time on CropSyst and WOFOST, two of the crop models most diffused worldwide and different in their degree of mechanism. The scenario assumed for the simulations refers to paddy rice grown in Northern Italy. After screening parameters using the Morris method, the Sobol’ approach was used for quantifying their influence on models output variability. Seven out of 34 parameters for WOFOST (mostly involved with CO 2 assimilation and photosynthetates conversion into plant organs) and 3 out of 15 for CropSyst (biomass-transpiration coefficient, base temperature and light extinction coefficient) were responsible for about 90% of the total output variability. Assuming the homogeneity among the sensitivity indices of the parameters as an indicator of model balance, the Grubbs test for outliers detection was used to check if the relevance of a parameter (or a few of them) was significantly higher than the others. In the explored conditions (non-limiting for water and nutrients availability), CropSyst resulted unbalanced, being mostly driven by the biomass-transpiration coefficient, whereas no significant differences were identified among the relevance of the WOFOST parameters.

Multiobjective Inverse Modeling for Soil Parameter Estimation and Model Verification

Due to the exponential increase in computational power and increasing awareness of problems associated with vadose zone parameter estimations based on laboratory and in situ measurements during the last decades, the process of automatic model calibration against laboratory or field data is being increasingly used. This is often referred to as inverse modeling and has as a major limitation the inability to identify a single optimal parameter set. A coupled HYDRUS1D‐SCE (shuffled complex evolution) simulation global optimization technique was developed and its suitability for multiobjective inverse modeling evaluated. In particular, the trade‐off between goodness of fit against leachate volume and soil moisture content in unirrigated and irrigated lysimeters was evaluated. After identification of the most sensitive model parameters using a Monte Carlo based sensitivity analysis, the technique was capable of finding effective Pareto optimal parameter sets that well simulated leachate volume and soil moisture content in both unirrigated and irrigated lysimeters. The parameter variation along the Pareto fronts was large and differences existed between soil hydraulic parameter distributions along the Pareto fronts of the irrigated and unirrigated treatments. The multiobjective optimization technique was then adopted for the verification of the suitability of the conceptual model of equal parameter sets for both treatments. The technique was able to objectively reject the hypothesis of equal parameter sets for both treatments. This is probably due to (i) physical parameter changes with time due to the effect that long‐term irrigation has on soil structure, and (ii) differences in acting transport mechanisms between the unirrigated and irrigated lysimeters.

Implementing a Glass Fracture Module in the BRANZFIRE Zone Model

A glass fracture routine has been successfully implemented in the BRANZFIRE zone model, based largely on the heat transfer model developed by Sincaglia and Barnett and the fracture criterion of Pagni and Joshi. A second implementation of the model has been successfully created as a spreadsheet. The zone model implementation has been compared against an existing glass fracture model, BREAK1 and against a series of experiments. This model has been found to predict fracture times consistent with BREAK1 over a range of simulations. Fracture time predictions have also compared well with experimental results. A Monte Carlo based sensitivity analysis found that fracture strength was the most important input variable, though this is largely attributable to the large standard deviation of the probability distribution used for this input.

Reconstruction of the Building Infrastructure: Two Performance Prediction Models

The total value of civil infrastructure (roads, buildings, and underground services) in North America is estimated at $20 trillion. Due to its large size and the need for minimal interruption to services, maintaining the infrastructure becomes a huge challenge. Motivated by the large cost overruns and delays in most infrastructure reconstruction work, this research sheds some light on the performance of such projects and their risky environment. The paper presents two predictive models of the overall performance of reconstruction projects using a simple measure, project performance factor to combine cost performance, schedule performance, and quality performance. Using 54 case studies of past reconstruction projects, a micromodel as well as a macromodel were developed through experimentation with statistical analysis and artificial neural networks. Using the developed models, a Monte Carlo-based sensitivity analysis was performed to assess the impact of uncertainty in project conditions on performance predictions. Guidelines for improving reconstruction of building infrastructure for owner organizations are then provided.

GIS-based extrapolation of land use-related nitrous oxide flux in the atlantic zone of Costa Rica

I estimated the areal nitrous oxide (N2O) flux from 281,347 ha of Costa Rican lowland covered with primary and secondary forest, pastures and banana plantations by linking the DeNitrification-DeComposition (DNDC) model with a Geographic Information System (GIS). Generalized soil, texture and land use maps were overlaid to yield unique combinations of N2O flux control factors. Overlay patches were associated with the nearest of seven available meteorological stations. Monte Carlo-based sensitivity analysis was used to identify DNDC’s key driving variables and required map attributes. Clay content, initial soil organic carbon (SOC), bulk density, and pH were selected as key driving variables. For 217 patch classes, DNDC simulations were carried out with climate data for seven different years. The estimated average areal flux was 6.8 kg N2O-N ha−1 yr−1. Possible applications of the GIS-DNDC interface presented include estimation of long-term areal flux dynamics from a changing land use mosaic, and prediction of areal fluxes resulting from alternative land use scenarios.

Simultaneous mapping of bulk and surface recombination in silicon

A method is presented which allows a fast, nondestructive, and laterally resolved separation of bulk and surface recombination of silicon samples. The method works without any assumptions on the values of diffusion length and surface recombination velocity. No special sample processing is necessary prior to the measurement. An extended Monte Carlo based sensitivity analysis shows that diffusion lengths below the sample thickness can be determined with an accuracy better than ±20%. The detection range for the surface recombination velocity is 102–105 cm/s with a maximum uncertainty of a factor of 2. Experimental data are presented that prove the applicability of the new scheme.

Yield sensitivity study of A1GaAs/GaAs high electron mobility transistor

A simple graphical statistical method is presented and is used to study the statistical sensitivity of the AlGaAs/GaAs High Electron Mobility Transistor (HEMT) small-signal performances to physical model parameters, Using an analytical model and applying numerical techniques, the small-signal performances, transconductance, gate-to-source capacitance, current gain cut-off frequency, and the optimum cut-off frequency are calculated for four different HEMTs. These are then used as the device specifications in the Monte Carlo-based sensitivity analysis. Based on the model, a device simulator is developed in which the gate length, the gate width, and the carrier mobility are statistically varied simultaneously about their nominal values. The yield factor histograms for each small-signal parameter and its sensitivity to the process parameter variations are determined. In this work, we report that the current gain cut-off frequency increases as the carrier mobility increases, but it is almost independent of the gate width. We observe that the gate-to-source capacitance is independent of the carrier mobility, but it is strongly dependent on the transistor dimension. All the performance yields analyzed here go down as the gate length increases.