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Rotor dynamics plays a crucial role in the performance and safety of rotating machinery, with disk position and unbalance significantly impacting system behavior. This study investigates the dynamic characteristics of two rotor configurations: a centrally mounted unbalanced disk (Rotor05un) and an off-center unbalanced disk (Rotor025un). Using numerical simulations and Monte Carlo analysis, we examined critical speeds and orbital patterns for both configurations. Probability distributions of shaft orbital positions revealed distinct patterns for each configuration. Quantile analysis revealed approximate linear trends for Rotor025un, suggesting higher system stiffness and more predictable behavior near critical speeds. Cross-sectional analyses of the orbits provided insights into the complex interactions between disk position, gyroscopic effects, and system natural frequencies. These findings provide valuable insights for rotor system design, particularly for applications with non-ideal mass distributions. The study goes beyond traditional critical speed analysis to examine orbital patterns and point on orbit occurrence from a probabilistic perspective. Based on the simulation of the orbits, an orbital is determined that allows the probability of the shaft occurring at the analyzed distance from the origin to be determined. The paper also offers insights into the complex interaction behavior of chosen rotor configurations and highlights the importance of considering disk position in predicting and optimizing rotor dynamic behavior, contributing to the development of more robust and efficient rotating machinery. [ABSTRACT FROM AUTHOR]

The seabed of the Gunneklev Fjord in south-eastern Norway is covered with exceptionally soft contaminated sediment containing mercury and dioxins. The sediments have an undrained shear strength less than 1 kPa and a variable thickness of up to 2.5 m. To reduce the potential for leaching of contaminants from these sediments, Hydro Energy AS developed a remediation plan involving capping of the sediment. Significant uncertainties are associated with the sediment's undrained shear strength and the cap thickness. An unfortunate combination of low shear strength and thick cap could cause slope failure (e.g. translational sliding of the sediment and/or bearing capacity failure (e.g. punching through). Failure, particularly slope failure, can cause spreading of the contaminants in the fjord causing serious consequences. This paper presents an assessment of the probability of slope failure associated with the contaminated sediment before and after the cap placement. Probability of bearing capacity failure is considered to have more local effect and is also discussed briefly. The study used the Monte Carlo method with random undrained shear strength and sediment thickness. The simulations show high slope failure probability when the seabed inclination was ≥ 1:50 combined with an average undrained shear strength ≤ 0.4 kPa. Based on the probabilistic analyses, a pilot field testing campaign was carried out (after this study) with 20-cm thickness cap in the "gentle" sloping area and 5-cm thickness in the "steep" areas to avoid causing a failure. The analyses were used to support evidence-based decision-making on the cap design and implementation for further field testing. [ABSTRACT FROM AUTHOR]

Rotor dynamics plays a crucial role in the performance and safety of rotating machinery, with disk position and unbalance significantly impacting system behavior. This study investigates the dynamic characteristics of two rotor configurations: a centrally mounted unbalanced disk (Rotor05un) and an off-center unbalanced disk (Rotor025un). Using numerical simulations and Monte Carlo analysis, we examined critical speeds and orbital patterns for both configurations. Probability distributions of shaft orbital positions revealed distinct patterns for each configuration. Quantile analysis revealed approximate linear trends for Rotor025un, suggesting higher system stiffness and more predictable behavior near critical speeds. Cross-sectional analyses of the orbits provided insights into the complex interactions between disk position, gyroscopic effects, and system natural frequencies. These findings provide valuable insights for rotor system design, particularly for applications with non-ideal mass distributions. The study goes beyond traditional critical speed analysis to examine orbital patterns and point on orbit occurrence from a probabilistic perspective. Based on the simulation of the orbits, an orbital is determined that allows the probability of the shaft occurring at the analyzed distance from the origin to be determined. The paper also offers insights into the complex interaction behavior of chosen rotor configurations and highlights the importance of considering disk position in predicting and optimizing rotor dynamic behavior, contributing to the development of more robust and efficient rotating machinery.

This case illustrates capital budgeting in a service industry context. Three features should make this case attractive to instructors. First, the firm's rationing of capital means that students must select one investment among competing investment alternatives. Second, the project involves renovation of an existing hotel. Most cases analyze a business expansion by estimating the net present value of a single series of cash flows (i.e., either future cash flows occur or do not occur). In this case, students model cash flows if the project is accepted, comparing those cash flows to a model of cash flows if the hotel continues without renovation. Third, we introduce Monte Carlo analysis, which is an advanced technique for assessing uncertainty. The extensive data students use in this case are from an actual hotel chain's project database. The case has been used in undergraduate and graduate managerial accounting classes. Data Availability: Available as downloadable supplemental material files. [ABSTRACT FROM AUTHOR]

Socio‐economic development pathways and their implications for the environment are highly uncertain, and energy transitions will involve complex interactions among sectors. Here, traditional Monte Carlo analysis is paired with scenario discovery techniques to provide a richer portrait of these complexities. Modeled uncertain input variables include costs of advanced energy technologies, energy efficiency trends, fossil fuel resource availability, elasticities of substitution for labor, capital, and energy across economic sectors, population growth, and labor and capital productivity. The sampled values are simulated through a multi‐sector, multi‐region, recursively dynamic model of the world economy to explore a range of possible future outcomes. We find that many patterns of energy and technology development are possible for various long‐term environmental pathways and that sectoral output for most sectors is little affected through 2050 by the long‐term temperature target, but with tight constraints on emissions, emission intensities must fall much more rapidly. Scenario discovery techniques are applied to the large uncertainty ensembles to explore if there are prevailing storylines behind outcomes of interest. An illustrative investigation focused on different levels of economic growth shows many combinations of pathways and no single storyline emerging for a given economic outcome. This method can be extended to other outcomes of interest, exploring the nature of scenarios with both tail and median outcomes. Sampling from a Monte Carlo generated ensemble provides a rich set of scenarios to investigate, and potentially aids in avoiding heuristic biases in less structured scenario approaches. Plain Language Summary: Simulation models are often used to explore future development pathways and their impacts on energy, emissions, economies and the environment. This requires making assumptions about various socio‐economic conditions, such as how fast populations and economies will grow, the cost of technology options, or the amount of fossil fuels available. Different assumptions have significant impacts on model results, yet analyses typically only test a few alternatives. Here, we develop and use probability distributions to capture this uncertainty. We draw samples from these distributions, run an energy‐economic model hundreds of times, and quantify the resulting uncertainty in model outcomes, providing insight into their likelihood. We focus on results related to emissions and output from different economic sectors, as well as energy and electricity technologies. We also apply approaches to find scenarios of interest from within the database of scenarios. We find that many patterns of energy and technology development are possible under a given long‐term environmental pathway (such as a 2C scenario) or a given economic outcome (such as high or low GDP). This approach can help identify biases in perceptions of what "needs" to happen to achieve certain outcomes, and shows that there are many pathways to a successful energy transition. Key Points: Traditional uncertainty quantification approaches can be combined with scenario discovery techniques to provide new insightsMany patterns of energy and technology development are possible under a given long‐term environmental pathwayLong‐term temperature targets have a much larger impact on sectoral emissions intensities than on sectoral output through 2050 [ABSTRACT FROM AUTHOR]

Abstract Socio‐economic development pathways and their implications for the environment are highly uncertain, and energy transitions will involve complex interactions among sectors. Here, traditional Monte Carlo analysis is paired with scenario discovery techniques to provide a richer portrait of these complexities. Modeled uncertain input variables include costs of advanced energy technologies, energy efficiency trends, fossil fuel resource availability, elasticities of substitution for labor, capital, and energy across economic sectors, population growth, and labor and capital productivity. The sampled values are simulated through a multi‐sector, multi‐region, recursively dynamic model of the world economy to explore a range of possible future outcomes. We find that many patterns of energy and technology development are possible for various long‐term environmental pathways and that sectoral output for most sectors is little affected through 2050 by the long‐term temperature target, but with tight constraints on emissions, emission intensities must fall much more rapidly. Scenario discovery techniques are applied to the large uncertainty ensembles to explore if there are prevailing storylines behind outcomes of interest. An illustrative investigation focused on different levels of economic growth shows many combinations of pathways and no single storyline emerging for a given economic outcome. This method can be extended to other outcomes of interest, exploring the nature of scenarios with both tail and median outcomes. Sampling from a Monte Carlo generated ensemble provides a rich set of scenarios to investigate, and potentially aids in avoiding heuristic biases in less structured scenario approaches.

Purpose: It has been recognised by life cycle assessment (LCA) practitioners that uncertainty analysis needs to be incorporated into LCA studies to improve the reliability of the results; however, case studies still report results without uncertainty. Reasons for ignoring uncertainty include resource constraints or a lack of knowledge or expertise. This paper presents a structured uncertainty management method that aims to improve uncertainty reporting in LCA. Methods: The most common uncertainty classification for LCA is parameter, model and scenario; however, multiple classifications exist in literature. The latest classification published by Igos et al. (2019) divides uncertainty into three dimensions: location, level and nature, based on previous research (Walker et al. 2003; Warmink et al. 2010). In this paper, the three-dimensional uncertainty classification is further developed for practical implementation in LCA. The classification is incorporated into an uncertainty management methodology that is divided into five steps: identification, classification, quantification or qualification, reduction and reporting, and is integrated into the iterative steps of an LCA in accordance with ISO 14044 (2006). The method is demonstrated in a tiered-hybrid case study of an Irish apartment development from cradle-to-gate that focuses on climate change. The data sources include the bill of quantities, Ecoinvent datasets, Irish input-output tables and Irish environmental accounts data. Results and discussion: The initial uncertainty assessment of the case study found that the deterministic value likely underestimates the total tonnes of carbon dioxide equivalents (t CO2-eq.) for the apartment development. The probability that the impact is greater than the deterministic value is approximately 93%, prior to uncertainty reduction. The main contributors to the total uncertainty were identified as the choice of Ecoinvent dataset, the sectoral emission intensities and the Intergovernmental Panel for Climate Change Global Warming Potentials. Therefore, work to reduce the total uncertainty should focus on identifying the most suitable dataset for the building material to reduce the input distribution for that material and on acquiring more product-specific data. Conclusions and recommendations: The developed uncertainty management method improves the way uncertainty is managed in practice in LCA case studies by providing a detailed and structured way for uncertainty to be identified, classified, measured and reported. It further identifies where resources can be focused to iteratively reduce the overall uncertainty of the results and thus improve their reliability. It is recommended that the developed method is tested across other case studies, life cycle stages and impact categories in further work. [ABSTRACT FROM AUTHOR]

Parameterization of transformation and transport processes of dissolved organic carbon (DOC) in soils is challenging especially under variable hydrological conditions. In this study, DOC concentrations in stormflow were analysed with a physically based modelling approach. A one‐dimensional dual‐continuum vertical flow and transport model was applied to simulate subsurface processes in a macroporous forest hillslope soil over a period of 4.5 years. Microbially mediated transformations of DOC were assumed to depend primarily on soil moisture and soil temperature. Two conceptually different descriptions of the sorption of DOC to soil were examined with equilibrium and kinetic approaches. In order to quantify the uncertainties associated with the model parameterization, Monte Carlo analyses in conjunction with Latin hypercube sampling was performed. Despite the complexity of microbial transformations, the simulated temporal patterns of DOC concentrations in stormflow showed similar behaviour to those reflected in the observed DOC fluxes. Due to preferential flow, the hillslope DOC export (5.0 ± 0.5 g C · m−2 · year−1) was higher than the amounts usually reported in the literature. Overall DOC transport in hillslope scenarios could be described appropriately using the equilibrium sorption assumption. The performed analyses showed that the inclusion of the kinetic description of DOC sorption only slightly improved the predictions of the DOC hillslope export. Moreover, influences of seasonal hydro‐climatological conditions on hillslope export of DOC could be observed. Reduced DOC transport during an extreme warm and dry summer was described with lower accuracy, thus indicating the difficulties in the representation of DOC transformations under dry conditions. [ABSTRACT FROM AUTHOR]

There is always uncertainty in industrial manufacturing. These uncertainties have an undesirable impact on the products if deterministic optimization approaches are employed. In order to have products as desired, uncertainties must be quantified in the design process. This paper presents a robust design optimization of an outer rotor surface mounted permanent magnet motor with particular application in the hybrid vehicle using the design for six-sigma methodology. Due to very long computational time of robust optimization, a ten high-dimensional surrogate model of the system using the Box–Behnken response surface methodology (RSM) is integrated with the particle swarm optimization (PSO). This causes a significant improvement in the effectiveness and efficiency of optimization. The results acquired from RSM are verified by their simulation using the finite-element method, and the accuracy of RSM is proved. Finally, the deterministic and robust optimized motors are simulated in mass production using Monte Carlo analysis, and six-sigma quality achievements are demonstrated. [ABSTRACT FROM AUTHOR]

Highlights • Probabilistic assessment of the technical feasibility of achieving the 1.5 °C target. • Analysis of alternative pathways to achieve the Paris 1.5 and 2 degrees targets. • Limiting warming to 1.5 °C is feasible but it needs early and rapid global action. • Key pillars are biomass, development of CO 2 removal and energy efficiency. Abstract In this paper we develop a probabilistic assessment of the energy transition and economic consequences of limiting global warming by the end of the century to 1.5 °C. The assessment is made by applying a Monte Carlo analysis in MERGE-ETL, a technology rich integrated assessment model with endogenous learning. We assume a deterministic 1.5 °C target and uncertainty in other factors such as economic growth, resources, and technology costs. The distributions of these variables are obtained by the PROMETHEUS stochastic world energy model. The study assumes early actions and quantifies the market penetration of low carbon technologies, the emission pathways and the economic costs for an efficient reduction of greenhouse gas emissions such that the temperature limit is not exceeded. We find that achieving the 1.5 °C Paris target is technically feasible but it requires immediate and global action. Key pillars in the decarbonization of the energy system are large deployment of renewable energy, early retirement of fossil based power plants, energy efficiency and negative emission technologies. Furthermore, that the availability of biomass resources, the rapid decrease of the costs of renewables and improvements in energy efficiency are the factors with the largest effect on the cost of carbon. [ABSTRACT FROM AUTHOR]

Energy system optimization models (ESOMs) have been used extensively in providing insights to decision makers on issues related to climate and energy policy. However, there is a concern that the uncertainties inherent in the model structures and input parameters are at best underplayed and at worst ignored. Compared to other types of energy models, ESOMs tend to use scenarios to handle uncertainties or treat them as a marginal issue. Without adequately addressing uncertainties, the model insights may be limited, lack robustness, and may mislead decision makers. This paper provides an in-depth review of systematic techniques that address uncertainties for ESOMs. We have identified four prevailing uncertainty approaches that have been applied to ESOM type models: Monte Carlo analysis, stochastic programming, robust optimization, and modelling to generate alternatives. For each method, we review the principles, techniques, and how they are utilized to improve the robustness of the model results to provide extra policy insights. In the end, we provide a critical appraisal on the use of these methods. [ABSTRACT FROM AUTHOR]

International climate agreements are negotiated in the face of uncertainties concerning the costs and benefits of abatement and in the presence of incentives for free-riding. Numerical climate coalition models provide estimates of the challenges affecting cooperation, but often resort to assuming certainty with respect to the values of model parameters. We study the impact of uncertainty on the stability of coalitions in the Model of International Climate Agreements using the technique of Monte Carlo analysis. We extend the existing literature by (1) calibrating parametric uncertainty about damages and abatement costs to estimates from meta-studies and by (2) explicitly considering uncertainty in the curvature of the damage function. We find that stability is more sensitive to uncertainty in damages than in abatement costs and most sensitive to uncertainty about the regional distribution of damages. Our calculations suggest that heterogeneity can increase stability of coalitions; however, this depends on the availability of transfers. [ABSTRACT FROM AUTHOR]

This paper presents a method for determining and mapping suitable locations for development using Multi Criteria Analysis and the Analytical Hierarchy Process and considering uncertainties in the process. The method is applied to the case study of Howth (Dublin), where development suitability is assessed against specific protection and conservation areas as well as ground water vulnerability. Uncertainty is incorporated using a Monte Carlo simulation into the Analytical Hierarchy Process calculations to determine criteria weightings. A map is derived, which includes, for all locations, both site suitability for development and the level of uncertainty attached to this suitability. The map combines a double categorization of suitability and uncertainty. The method allows for increased transparency in decision making regarding site suitability for development, as well as increased confidence in decision making to allow for reduced risk in terms of the potential impact of development. [ABSTRACT FROM AUTHOR]

Summary This study develops a new probabilistic multi-objective fast harmony search algorithm (PMOFHS) for optimal design of groundwater remediation systems under uncertainty associated with the hydraulic conductivity ( K ) of aquifers. The PMOFHS integrates the previously developed deterministic multi-objective optimization method, namely multi-objective fast harmony search algorithm (MOFHS) with a probabilistic sorting technique to search for Pareto-optimal solutions to multi-objective optimization problems in a noisy hydrogeological environment arising from insufficient K data. The PMOFHS is then coupled with the commonly used flow and transport codes, MODFLOW and MT3DMS, to identify the optimal design of groundwater remediation systems for a two-dimensional hypothetical test problem and a three-dimensional Indiana field application involving two objectives: (i) minimization of the total remediation cost through the engineering planning horizon, and (ii) minimization of the mass remaining in the aquifer at the end of the operational period, whereby the pump-and-treat (PAT) technology is used to clean up contaminated groundwater. Also, Monte Carlo (MC) analysis is employed to evaluate the effectiveness of the proposed methodology. Comprehensive analysis indicates that the proposed PMOFHS can find Pareto-optimal solutions with low variability and high reliability and is a potentially effective tool for optimizing multi-objective groundwater remediation problems under uncertainty. [ABSTRACT FROM AUTHOR]

The use of numerical modeling in the field of industrial fire accidentology has become common nowadays and this tendency is expected to increase with the development of performance simulation tools. Despite the constant development of fire modeling tools, the current state of the art is not yet able to accurately predict fire phenomena. This gap between the reality and simulations is probably due to the presence of some level of uncertainty, which may occur from the meteorological inputs, diffusion assumptions, plume dynamics, or emission production. To cope with the presence of uncertainties in the input data, we propose an uncertainty analysis enabling to avoid as much as possible bad decisions that may have a large impact in domains such as safety. In this study, we are interested in the uncertainty propagation related to NO atmospheric dispersion resulting from a crude oil tank fire. Uncertainties were defined a priori for each of the following input parameters: wind speed, NO emission rate, and viscosity and diffusivity coefficients. For that purpose, a Monte Carlo approach has been used. In order to evaluate the importance of the considered parameters on the NO dispersion, new sensitivity indicator has been developed. The obtained results showed that the viscosity coefficient and the wind speed are the most significant input parameters with respect to NO concentration near to the source of fire, while the wind speed and the initial concentration are the important parameters for distant areas. [ABSTRACT FROM AUTHOR]

A methodology is presented for the fast electromagnetic analysis of the impact of statistical disorder on the transmission properties of periodic waveguides. The proposed methodology makes use of ideas from the Anderson localization theory to derive closed-form expressions for the calculation of an effective exponential decay ratio that quantifies the impact of periodicity disorder on the transmission properties of the waveguide. With regard to the quantification of the statistics of periodicity disorder from data obtained from a limited number of manufactured devices, a nonparametric probability density estimation process is examined and found to be satisfactory for our purposes. The computational efficiency of the proposed method over brute-force Monte-Carlo-based alternatives is demonstrated through specific examples involving a periodically loaded parallel-plate waveguide. Furthermore, this numerical study is used to examine the accuracy of calculating the overall change in the propagation constant of the structure due to several sources of disorder as the sum of the changes calculated with each one of the sources of disorder considered individually. [ABSTRACT FROM AUTHOR]

Abstract: The geologically related uncertainty in groundwater modeling originates from two main sources: geological structures and hydraulic parameter values within these structures. Within a geological structural element the parameter values will always exhibit local scale heterogeneity, which can be accounted for, but is often neglected, in assessments of prediction uncertainties. Strategies for assessing prediction uncertainty due to geologically related uncertainty may be divided into three main categories, accounting for uncertainty due to: (a) the geological structure; (b) effective model parameters; and (c) model parameters including local scale heterogeneity. The most common methodologies for uncertainty assessments within each of these categories, such as multiple modeling, Monte Carlo analysis, regression analysis and moment equation approach, are briefly described with emphasis on their key characteristics. Based on reviews of previous studies, assessments are made on the relative importance of the three uncertainty categories for different types of model predictions. Furthermore, the strengths, limitations and interactions of these methodologies are discussed and conclusions are made with respect to identifying key subjects for which further research is needed. When all sources of uncertainty are analyzed by exploring model parameter and local scale heterogeneity uncertainty for several plausible geological model structures the joint uncertainties can be assessed by use of model averaging techniques, such as Bayesian Model Averaging (BMA). General challenge in model averaging with respect to choosing mutually exclusive and collectively exhaustive choice models, as well as to assign weights when models are used beyond their calibration base, are discussed. [Copyright &y& Elsevier]

Abstract: Although embodied CO2-eq analysis has seen recent developments as evident in the establishment of the ISO 14040 and 14044 LCA standards, it is recognized that due to weaknesses in gathering data on product-related emissions, embodied CO2-eq values are probabilistic. This paper presents a stochastic analysis of hybrid embodied CO2-eq in buildings to account for this weakness in traditional methods and, by way of example, applies it to an Irish construction-sector case study. Using seven apartment buildings, 70,000 results are simulated with Monte Carlo analysis and used to derive probabilistic and cumulative embodied CO2-eq intensity distributions for apartment buildings in Ireland. A Wakeby distribution with known statistical parameters and uncertainty was derived for the average embodied CO2-eq intensity of apartment building in Ireland. The mean hybrid embodied CO2-eq (ECO2-eq) intensity was estimated to be 1636gCO2-eq/€ with an uncertainty of 73gCO2-eq/€. The stochastic analysis helps to account for variability in input variables into embodied CO2-eq analysis. The application of the stochastic embodied CO2-eq analysis as demonstrated in this study can be extended to other building sectors and countries and can form the basis for the development of evidence-based policy formulation since it provides greater information on embodied CO2-eq intensities of buildings than deterministic approaches. [Copyright &y& Elsevier]

Many ambient water quality criteria established to protect aquatic life from acute toxicity are calculated using a procedure described in the US Environmental Protection Agency's '1985 Guidelines' (USEPA ). The procedure yields a final acute value (FAV) from acceptable median lethal or effective concentrations (LC50 or EC50, respectively) that is a single-point, deterministic estimate of the concentration of a chemical substance that will protect 95% of aquatic species from >50% mortality or other acute toxic effects. However, because of variation and uncertainty associated with toxicity test results, uncertainty in the estimated FAV exists that is not accounted for by the 1985 Guidelines procedure. Here, Monte Carlo analysis is used to characterize this uncertainty. The analysis uses Cu EC50 values adjusted for differences in test water chemistry obtained from USEPA's final freshwater Cu criteria guidance published in 2007. Additional Monte Carlo simulations illustrate Cu FAV distributions obtained using a subset of tested species and assuming fewer replicate tests. The deterministic procedure yields an FAV of 4.68 µg/L for the complete data set. By comparison, 3 replicate Monte Carlo simulations yielded mean FAVs of 4.66 µg/L. The 5th and 95th percentiles of the distribution of calculated FAVs were 4.14 µg/L and 5.20 µg/L, respectively. Reducing the number of tested genera from 27 to 8 (the minimum recommended by the 1985 Guidelines) and setting the number of tests per species equal to 3 yielded 5th and 95th percentiles of 1.22 µg/L and 6.18 µg/L, respectively, compared to a deterministic estimate of 2.80 µg/L. Results of this study indicate that Monte Carlo analysis can be used to improve the understanding and communication of uncertainty associated with water quality criteria derived from acute toxicity data using the 1985 Guidelines. This may benefit the development, revision, and application of these criteria in the future. Integr Environ Assess Manag 2011;7:269-279. © 2010 SETAC [ABSTRACT FROM AUTHOR]

This paper reviews and demonstrates methods available for estimating standard deviations for carbon multipliers in a multi-regional input-output (MRIO) framework. We attempt to capture all possible variations of underlying data and calculation procedures in a global MRIO model constructed with particular focus on the UK. We consider these variations to be random, and determine the stochastic variation of the whole MRIO system using Monte Carlo techniques. 5000 simulation runs were carried out to determine the standard deviations of multipliers. From these, the standard deviations of components of the UK's carbon footprint were estimated using error propagation. We estimate an 89% probability that the UK's carbon footprint has increased between 1994 and 2004. [ABSTRACT FROM AUTHOR]

Environmental Input-Output Analysis (EIOA) is a tool for environmental analysis of broad classes of sectoral activities, taking into account indirect effects in other sectors in the supply chain. The core of EIOA is an input-output table (IOT) and national accounting matrix including environmental accounts (NAMEA) for a fixed base-year. We evaluate the uncertainty in EIOA using a time series of current-price IOT and NAMEA for 13 years from 1990 to 2002. We find annual variations in the current-price IOT and NAMEA, which may represent either realistic changes in production or measurement error. We assume the changes are errors and apply a regression analysis to remove the trends from the underlying data and estimate the uncertainty in the raw IOT. We then calculate the emissions for various final users and sectors to estimate the uncertainties from typical EIOA investigations. Using Monte Carlo analysis, we then investigate how well the variations in the current-price IOT and NAMEA over time may represent uncertainties. The results of this work have several implications for both statistical offices and the analyst. Statistical offices can provide details on data sources, methodologies, and estimates of annual variations. Analysts can incorporate this uncertainty information to understand the implications of uncertainty on their calculations and ultimately the policy recommendations derived from their studies. [ABSTRACT FROM AUTHOR]

Abstract: The complexity of mathematical models used by practicing engineers is increasing due to the growing availability of sophisticated mathematical modeling tools and ever-improving computational power. For this reason, the need to define a well-structured process for validating these models against experimental results has become a pressing issue in the engineering community. This validation process is partially characterized by the uncertainties associated with the modeling effort as well as the experimental results. The net impact of the uncertainties on the validation effort is assessed through the “noise level of the validation procedure”, which can be defined as an estimate of the 95% confidence uncertainty bounds for the comparison error between actual experimental results and model-based predictions of the same quantities of interest. Although general descriptions associated with the construction of the noise level using multivariate statistics exists in the literature, a detailed procedure outlining how to account for the systematic and random uncertainties is not available. In this paper, the methodology used to derive the covariance matrix associated with the multivariate normal pdf based on random and systematic uncertainties is examined, and a procedure used to estimate this covariance matrix using Monte Carlo analysis is presented. The covariance matrices are then used to construct approximate 95% confidence constant probability contours associated with comparison error results for a practical example. In addition, the example is used to show the drawbacks of using a first-order sensitivity analysis when nonlinear local sensitivity coefficients exist. Finally, the example is used to show the connection between the noise level of the validation exercise calculated using multivariate and univariate statistics. [Copyright &y& Elsevier]

Abstract: A conceptual model of Escherichia coli behaviour in catchments, the EG model, coupled with a standard hydrological model has been previously developed and tested. This paper presents an analysis of the uncertainty of the modelled pathogen concentrations and loads due to uncertainties in the models data inputs. The data collected at three different large Australian catchments were used. Firstly, uncertainties in the models input data, i.e. hourly rainfall, monthly potential evapotranspiration, catchment size and daily surface pathogen deposition rates, were assessed. Random and systematic sources of errors were taken into account. It was found that systematic errors in rainfall and random errors in pathogen deposition rates have the biggest impact on uncertainty in the models output. [Copyright &y& Elsevier]

In flood modeling, many one-dimensional (1D) hydrodynamic and water-quality models are too restricted in capturing the spatial differentiation of processes within the floodplain and two-dimensional (2D) models are too demanding in data requirements and computational resources. The latter is an important consideration when uncertainty analyses using the Monte Carlo techniques are to complement the modeling exercises. This paper describes the development of a quasi-2D modeling approach that still calculates the dynamic wave in 1D but the discretization of the computational units is in 2D, allowing a better spatial representation of the flow and substance transport processes in the floodplain without a large additional expenditure on data preprocessing and simulation processing. The DYNHYD (1D hydrodynamics) and TOXI (sediment and micropollutant transport) models from the WASP5 modeling package were used as a basis for the simulations. The models were extended to incorporate the quasi-2D approach and a Monte Carlo analysis was used to investigate the contribution of uncertainty from parameters and boundary conditions to the resulting substance concentrations. A flood event on the Saale River, Germany, was simulated as a test case. The results show a plausible differentiation of suspended sediment and zinc concentrations between the floodplain and the main channel. Based on the results it appears that for flood simulations, uncertainties in boundary conditions are higher and need to be given more attention that uncertainties in model parameters. [ABSTRACT FROM AUTHOR]

The chemical mass balance (CMB) model was applied for source apportionment of PM2.5 in Atlanta in order to explore levels and causes of uncertainties in source contributions. Monte Carlo analysis with Latin hypercube sampling (MC-LHS) was performed to evaluate the source impact uncertainties and quantify how uncertainties in ambient measurement and source profile data affect results. In general, uncertainties in the source profile data contribute more to the final uncertainties in source apportionment results than do those in ambient measurement data. Uncertainty contribution estimates suggest that non-linear interactions among source profiles also affect the final uncertainties although their influence is typically less than uncertainties in source profile data. [Copyright &y& Elsevier]

Uncertainty and sensitivity of ozone and aerosol to variations in selected input parameters are investigated with a Monte Carlo methodology using a three-dimensional air quality model. The selection of input parameters is based on their potential to affect concentration levels of ozone and predicted by the model and to reflect changes in emissions due to the implementation of distributed generation (DG) in the South Coast Air Basin (SoCAB) of California. Numerical simulations are performed with the CIT air quality model. Response of the CIT predictions to the variation of selected input parameters is investigated to separate the potential air quality impacts of DG from model uncertainty. This study provides a measure of the model errors for selected species concentrations. A spatial sensitivity analysis is used to investigate the effect of placing DG in specific regions of the SoCAB. In general, results show that confidence in the model results is greatest in locations where ozone and concentrations are the highest. Changes no greater than 80% in the nominal values of selected input variables, cause changes of 18% in ozone mixing ratios and 25% for aerosol concentrations. Sensitivity analysis reveals that nitrogen oxides () emissions and side boundary conditions of volatile organic compounds (VOC) are the major contributors to uncertainty and sensitivity of ozone predictions. An increase in emissions leads to reductions in ozone mixing ratios at peak times and sites where the maximum values are located. aerosol is most sensitive to changes in and emissions. Increasing these emissions leads to higher aerosol concentrations. Sensitivity analyses show that the impacts of DG implementation are highly dependent on both space and time. In particular, ozone concentrations are reduced during the nighttime nearby locations where DGs are installed. However, during the daytime ozone concentrations increase downwind from the sources. A major finding of this study is that the emissions of DG installed in coastal areas produce a significant impact on the production of ozone and aerosol in the eastern regions of the SoCAB. [Copyright &y& Elsevier]

The US Office of Management and Budget introduced in 2003 a new requirement for the treatment of uncertainty in Regulatory Impact Analyses (RIAs) of proposed regulations, requiring agencies to carry out a formal quantitative uncertainty assessment regarding a regulation’s benefits and costs if either is expected to reach $1 billion annually. Despite previous use in other contexts, such formal assessments of uncertainty have rarely been employed in RIAs or other regulatory analyses. We describe how formal quantitative assessments of uncertainty – in particular, Monte Carlo analyses – can be conducted, we examine the challenges and limitations of such analyses in the context of RIAs, and we assess how the resulting information can affect the evaluation of regulations. For illustrative purposes, we compare Monte Carlo analysis with methods typically used in RIAs to evaluate uncertainty in the context of economic analyses carried out for the US Environmental Protection Agency’s Nonroad Diesel Rule, which became effective in 2004. [ABSTRACT FROM AUTHOR]

Abstract: This study evaluates and compares two methodologies, Monte Carlo simple genetic algorithm (MCSGA) and noisy genetic algorithm (NGA), for cost-effective sampling network design in the presence of uncertainties in the hydraulic conductivity (K) field. Both methodologies couple a genetic algorithm (GA) with a numerical flow and transport simulator and a global plume estimator to identify the optimal sampling network for contaminant plume monitoring. The MCSGA approach yields one optimal design each for a large number of realizations generated to represent the uncertain K-field. A composite design is developed on the basis of those potential monitoring wells that are most frequently selected by the individual designs for different K-field realizations. The NGA approach relies on a much smaller sample of K-field realizations and incorporates the average of objective functions associated with all K-field realizations directly into the GA operators, leading to a single optimal design. The efficacy of the MCSGA-based composite design and the NGA-based optimal design is assessed by applying them to 1000 realizations of the K-field and evaluating the relative errors of global mass and higher moments between the plume interpolated from a sampling network and that output by the transport model without any interpolation. For the synthetic application examined in this study, the optimal sampling network obtained using NGA achieves a potential cost savings of 45% while keeping the global mass and higher moment estimation errors comparable to those errors obtained using MCSGA. The results of this study indicate that NGA can be used as a useful surrogate of MCSGA for cost-effective sampling network design under uncertainty. Compared with MCSGA, NGA reduces the optimization runtime by a factor of 6.5. [Copyright &y& Elsevier]

Designing intelligent management strategies for shrimp aquaculture systems require recognising the uncertainty and risks associated with different processes of aquaculture production. To account for natural variability and other sources of uncertainty, estimates of appropriate aquaculture bio-economic indicators are needed to re-evaluate periodically the production system and establish new reference points and corresponding management strategies. This paper concentrates on this aspect of the management process and presents a simple classification of indicators and reference points for aquaculture production systems. The uncertainty causing variability in the estimated values of bio-economic parameters is incorporated through the use of Monte Carlo analysis to estimate the probability of exceeding limit reference points. To illustrate this process, the risks (i.e. probabilities of exceeding bioeconomic limit reference points) associated to alternative timing of harvesting decisions were estimated by randomly generating uncertain prices and natural mortalities with the appropriate probability density functions and corresponding variance. Alternative approaches to deal with risk and uncertainty in data limited management contexts are discussed. [ABSTRACT FROM AUTHOR]

Uncertainties are usually evaluated by Monte Carlo analysis. However, multiplicative models with lognormal uncertainties, which are ubiquitous in risk assessments, allow for a simple and quick analytical uncertainty analysis. The necessary formulae are given, which may be evaluated by a desk calculator. Two examples illustrate the method. [ABSTRACT FROM AUTHOR]

When viewed at a high level, performance assessments (PAs) for complex systems involve two types of uncertainty: stochastic uncertainty, which arises because the system under study can behave in many different ways, and subjective uncertainty, which arises from a lack of knowledge about quantities required within the computational implementation of the PA. Stochastic uncertainty is typically incorporated into a PA with an experimental design based on importance sampling and leads to the final results of the PA being expressed as a complementary cumulative distribution function (CCDF). Subjective uncertainty is usually treated with Monte Carlo techniques and leads to a distribution of CCDFs. This presentation discusses the use of the Kaplan/Garrick ordered triple representation for risk in maintaining a distinction between stochastic and subjective uncertainty in PAs for complex systems. The topics discussed include (1) the definition of scenarios and the calculation of scenario probabilities and consequences, (2) the separation of subjective and stochastic uncertainties, (3) the construction of CCDFs required in comparisons with regulatory standards (e.g., 40 CFR Part 191, Subpart B for the disposal of radioactive waste), and (4) the performance of uncertainty and sensitivity studies. Results obtained in a preliminary PA for the Waste Isolation Pilot Plant, an uncertainty and sensitivity analysis of the MACCS reactor accident consequence analysis model, and the NUREG-1150 probabilistic risk assessments are used for illustration. [ABSTRACT FROM AUTHOR]