Uncertainty Analysis Procedure Research Articles

Uncertainty Analysis and Maneuver Simulation of Standard Ship Model

Maneuver simulation of a standard ship model gives indication of numerical accuracy. In the numerical calculation of ship maneuvering, uncertainty analysis is a necessary step to ensure the accuracy of the calculation. In this study, uncertainty pair analysis is carried out in the simulation of the turning circle motion of the standard ship model ONRT in waves. According to the uncertainty analysis procedure recommended by the International Towing Tank Conference (ITTC), the change of ship resistance caused by the number of grids is studied to determine the influence of grid density on the numerical prediction. The simulation of turning motion in waves is carried out based on the uncertainty analysis. It is found that the minimum number of overset grids for this simulation is 1.4 million. The numerical results are fairly accurate compared to experimental results, and this technique provides a method with low calculated cost for this simulation.

Complete characterization of ocean color radiometers

Verifying and validating waterleaving radiance measurements from space for an accurate derivation of Ocean/Water Colour biogeophysical products is based on concurrent high-quality fiducial reference measurements (FRM) carried out on the ground or water body. The FRM principles established by the Committee on Earth Observation Satellites (CEOS) recommend that in situ Ocean Colour radiometers (OCR) have a documented history of SI traceable calibrations including uncertainty budgets. Furthermore, there can be significant differences between calibration and use of the instruments in the field due to differences in operating temperature, angular variation of the light field (especially for irradiance sensors), the intensity of the measured radiation, and spectral variation of the target, among others. Each of these factors may interact with individual properties of the instrument when deployed in the field, and estimation of such uncertainties requires instrument characterization in addition to the absolute radiometric calibration if expanded uncertainties within ±10% (k = 2) are the aim. The FRM4SOC Phase 2 project - funded by the European Commission in the frame of the Copernicus Programme and implemented by EUMETSAT - contributes to these efforts, aiming at developing an operational and sustained network of radiometric measurements of FRM quality. Within FRM4SOC-2, scientists from the Tartu Observatory (TO) of the University of Tartu performed an unprecedented batch of calibrations and characterizations on a set of 37 hyperspectral field radiometers representative of the most used OCR classes within the OC community. The calibrations and characterizations performed include the determination of radiometric responsivity, long-term stability, the accuracy of the spectral scale, non-linearity and accuracy of integration times, spectral stray light, angular response of irradiance sensors in air, dark signal, thermal sensitivity, polarization sensitivity, and signal-to-noise ratio of individual OCRs. Consistent correction of biases and extended uncertainty analysis procedures of in situ data obtained from different instruments and measurement models need to be clearly defined, which is the objective of this paper.

Decision support algorithm to revamp homestead vegetable production for climate resilience and nutrition security

Inadequate micronutrient diversity in diets remains a serious cause of malnutrition which is prevalent in rural areas especially in developing countries. The current study proposed a decision support algorithm for nutrition-sensitive vegetable production in semi-subsistence farming systems to improve their access to micronutrient-diverse and dense foods without compromising on economics of cultivation. Multi-dimensional nutri-economic data was modelled to compute micronutrient profiles along with the relative abundance of vegetables in diets, giving an advantage over other nutrition indices that measured diversity. Besides, a novel micro-planning algorithm was developed for climate-resilient and micronutrient-sensitive vegetable production that generated multiple production plans as per farm properties, crop preferences and consumption patterns of farm households. The algorithm consisted of a cropcombination generator, uncertainty analysis and optimization procedure. The uncertainty analysis incorporated variations in climate, market rates, yields and cost of cultivation. The algorithm was demonstrated using 8 case studies based in India and investigated whether micronutrient diversity, micronutrient density, production diversity and net returns could be improved at the production stage. The results suggested that the generated plans performed better than existing plans for all four parameters in every case study. The improvement in micronutrient diversity, micronutrient density, production diversity and net returns ranged from 5.8 to 60 %, 7.2 to 79 %, 7.8 to 31 % and 5.8 to 60 % respectively. This study provides a simple framework to process large multi-dimensional nutri-economic data to measure essential well-being indices and produces sound food policies at field and regional levels.

Uncertainty analysis on support vector machine for measuring organizational factors in probabilistic risk assessment of nuclear power plants

This paper is a product of a line of research by the authors to explicitly incorporate organizational factors into the probabilistic risk assessment (PRA) of complex socio-technical systems. This explicit incorporation helps (i) assess the system risk due to organizational and managerial weaknesses, (ii) identify the critical organizational root causes of failure scenarios, aiding in effective corrective action, and (iii) avoid the potential of underestimating the system risk involving human error and organizational factors. To facilitate the measurement of organizational factors contributing to the PRA scenarios, the previous studies by the authors developed the Data-Theoretic methodology, where “data analytics” are guided by a “theory” of underlying causation to prevent misleading results from solely data-oriented approaches. The Data-Theoretic methodology consists of two submodules: (a) DT-BASE that develops a detailed causal model based on a theory-building process and is equipped with a baseline quantification utilizing analyst interpretation of information extracted from relevant references; and (b) DT-SITE that conducts data analytics (text mining using machine learning) to quantify the organizational causal elements based on industry event databases. This article investigates uncertainty analysis for the uncertainties associated with machine learning-based data mining in DT-SITE using a Support Vector Machine (SVM) classifier for industry event databases. This article conducts a literature review and proposes a categorization scheme of the SVM uncertainties to establish a theoretical foundation for identifying uncertainty sources associated with the SVM classifier. The implementation of uncertainty analysis for DT-SITE is then illustrated using a nuclear power plant case study. The potential uncertainty sources related to the SVM classifier used in DT-SITE are identified using the categorization scheme developed based on the literature review. The uncertainty analysis procedure for the SVM classifier in DT-SITE is proposed, and the linkage to the current uncertainty analysis process in PRA is discussed. The proposed uncertainty analysis procedure for the SVM classifier in DT-SITE is applied to the illustrative case study to assess the impact of one of the potential uncertainty sources (i.e., the selection of document sections included in the dataset) on the risk-informed decision-making.

Quantification of uncertainty in product stage embodied carbon calculations for buildings

Decarbonisation of the energy industry and enforcement of strict targets for operational energy consumption means that non-operational greenhouse gases (GHG) emissions, also known as embodied carbon (EC), will soon represent the majority of whole life carbon associated with buildings. EC assessments are often presented as deterministic, single-point values but contain a high degree of variability which is typically unacknowledged. Common sources of uncertainty are variability, data gaps, measurement error and epistemic uncertainty such as absence of detailed material specification (e.g. manufacturer, concrete mix, recycled content etc). Particularly during early design stages when such information is unconfirmed, average material data is used by necessity. While some material databases and LCA software can provide ranges of embodied carbon coefficients (ECC) between some materials and/or the uncertainty within individual manufacturers’ carbon data, the practice of reporting this is uncommon and has limited practicality for whole building assessments.This paper presents a simple procedure that selects the highest impact materials of the EC of an asset and implements a Monte-Carlo simulation to estimate the uncertainty behind the product stage EC assessment. Material coefficients of variation (CoV) are obtained from database values where available, and interpolated values are used in the absence of such data.A product stage EC assessment of a UK educational building, initially undertaken using single data points for each material, gave an EC prediction of 525 kgCO2e/m2 GIFA. Two scenarios were then assessed using our proposed procedure: 1) the full building scope and 2) substructure and superstructure only.It was demonstrated that, for scenario one, the EC can range from 50 to 140% of the original result when considering the extreme results from the Monte-Carlo simulation. Scenario one (considering the full building scope) resulted in an average EC value (mean ± CoV) of 526 kgCO2e/m2 GIFA ± 10.0%. The second scenario (sub- and super-structure only) resulted in an average EC value of 312 kgCO2e/m2 GIFA ± 11.9% with a full range of 45–155% of the original result.This paper shows that a straightforward uncertainty analysis procedure can support designers in understanding the possible range of asset product-stage EC and, therefore, inform construction product selections at an early stage where detailed information is not known. The variation also gives a degree of confidence/caution in the average EC prediction in lieu of a single-point result. The construction product CoV results can be used to set target ECCs on projects to help ensure reliable low-carbon products are specified. If these target ECCs were met, a minimum of 29% and 33% (excl. EPD uncertainty) in product stage EC reductions could be achieved. Future work should extend this method to include additional life cycle assessment (LCA) stages and other uncertainty factors. And, the method could be applied to comparative life cycle assessments and optioneering exercises, as well as including more specific construction product variability data.

Uncertainty analysis of rock tunnel based on fractional moment and dimensional reduction method

In this study, fractional moment was adopted to estimate the uncertainty in a tunnel excavated in rock. The multiplicative dimensional reduction method (M-DRM) was adopted to approximate the relationship between random variables and response of the tunnel and to predict its behavior. Integer and non-integer (fractional) moments were estimated by Gaussian quadrature based on M-DRM. The maximum entropy principle was also used to estimate the probability distribution of the tunnel response using the optimization technology of Excel Solver based on fractional moment. The example of a circular tunnel in a rock mass subjected to hydrostatic stress is given. This demonstrates the uncertainty analysis procedure applied to the displacements and plastic zone induced by the excavation, for different rock-support pressures and far-field stresses. Statistical moment, standard deviation and probability distribution of the tunnel response all agree well with Monte Carlo simulation (MCS). M-DRM closely approximates the tunnel response, and improves the efficiency of the uncertainty analysis. The proposed method estimates the probability distribution based on frictional moment without any prior knowledge. The results show that the proposed uncertainty analysis method is robust, efficient and precise, and is suitable for a complex practical engineering system as encountered in rock engineering.

A best estimate plus uncertainty safety analysis framework based on RELAP5-3D and RAVEN platform for research reactor transient analyses

This paper develops a best estimate plus uncertainty (BEPU) framework for research reactor transient safety analysis. The BEPU framework is developed based on the system level reactor safety analysis code RELAP5-3D and the data analysis platform RAVEN developed by Idaho National Laboratory. Within the framework, a sensitivity analysis procedure is first conducted to identify the contributions and ranks of individual input parameters to the user-defined figures-of-merit (FOMs) associated with specific transient phenomena. An uncertainty analysis procedure is then performed to quantify the uncertainties of the FOMs resulting from the uncertainties of the input parameters. Many useful outcomes can be realized through the BEPU analysis. Specifically, the sensitivity information obtained from the sensitivity analysis will provide insights about the influence of each different input parameter on FOMs. The uncertainty information obtained from the uncertainty analysis will imply the range of response deviations caused by the propagation of errors existing in various input components.As a case study for research reactors, the developed BEPU framework was employed to perform design-basis accident (DBA) analysis for one conceptual research reactor design proposed at the National Institute of Standards and Technology (NIST). Two hypothetical DBA scenarios, namely the reactivity insertion accident (RIA) and the loss of flow accident (LOFA), were modeled and analyzed through the BEPU framework. To demonstrate the value of the BEPU framework, the BEPU analysis results were compared to that obtained from the conventional transient safety analysis procedure, which was conducted by using commonly used transient safety analysis codes including RELAP5-3D and PARET. The comparison shows that the BEPU analysis is capable of providing additional sensitivity and uncertainty information that help confirm safety margins of the NIST conceptual research reactor during both RIA and LOFA situations, which justifies the advantages and benefits of the BEPU safety analysis framework developed in this work.

Force Measurements of the Flow Around Arrays of Three and Four Columns With Different Geometry Sections, Spacing Ratios, and Incidence Angles

Abstract An experimental campaign for the flow around a stationary array of three and four columns with low aspect ratio, H/L = 1.5, piercing the water free surface, was carried out in a towing tank. These numbers of columns correspond to typical multi-column offshore systems, such as semi-submersibles (SS), tension leg platforms (TLPs), and floating offshore wind turbines (FOWTs). Three parameters were investigated: the spacing ratio between column centers (from two up to four characteristic lengths), current incidence angles, and column section geometries (circular, square, and diamond). The Reynolds number of the experiments was 100,000. Forces were measured in each column using a three degrees-of-freedom load cell, and results of lift and drag forces were presented for each column separately and the whole system. The results of mean and standard deviation of forces were assessed using a statistical uncertainty analysis procedure for finite length measurements’ signals. This methodology not only assesses the quality of the experimental data but also facilitates validation of numerical tools. The objectives of the current work were therefore manifold: to better understand the influence of the relative position, shape, and incidence angle on multi-column offshore structures; to create a reliable database for computational fluid dynamics (CFD) validation; and to prepare the path to flow-induced motions (FIMs) experimental and numerical work of free-moving multi-column offshore systems.

Failure Probability of Structural Systems in the Presence of Imprecise Uncertainties

Structural reliability evaluation is considered to be the solution for modern complex engineering systems possessing uncertain parameters. Reliability estimation involves probabilistic theory when the uncertainties are defined as random variables, whereas with limited resources, it is strenuous to estimate precise parameters in the structural model. Therefore, for such cases, imprecise parameters should be treated appropriately in the design and analysis stage for the improvement of serviceability of the system. On the other side, analyses involving multi-dimensional, computationally expensive, and highly nonlinear structures are formidable in simulation-based methods in the presence of uncertainties. An efficient uncertainty analysis procedure is presented in this paper for analysing the systems with imprecise uncertainties defined as probability-box variables. The estimated bounds of failure probability for the numerical examples from structural mechanics are compared with the traditional approaches to demonstrate the efficiency of the methodology.

Proposed Consecutive Uncertainty Analysis Procedure of the Greenhouse Gas Emission Model Output for Products

The study objective was to develop a method for an uncertainty analysis of the greenhouse gas (GHG) emission model output based on consecutive use of an analytical and a stochastic approach. The contribution to variance (CTV) analysis followed by the data quality analysis are the main feature of the procedure. When a set of data points of a certain input variable has a high CTV, but its data quality indicator (DQI) is good, then there is no need to iterate data collection of this input variable. This is because the DQI of this data set indicates that there is no room for the reduction of its variance, and the high variance must be its inherent attribute. Through the CTV analysis and data quality analysis, the identified input variables were selected as the input variables for the data from the iteration of data collection. The statistical parameters of the GHG emissions of the model were calculated using the Monte Carlo simulation (MCS). In the case study of a cattle dairy farm, the relative reduction in the CV value was 47.6%. In this study, a procedure was developed for the selection of the input variables for iteration of data collection to reduce their variance and subsequently reduce the uncertainty in the model output. The dairy cow case study showed that the uncertainty in the model output was decreased by the iteration of data collection, indicating that CTV analysis can be used to identify the input variables, contributing considerably to the uncertainty in the model output.

The Macquarie Island (LoFlo2G) high-precision continuous atmospheric carbon dioxide record

Abstract. The Southern Ocean (south of 30∘ S) is a key global-scale sink of carbon dioxide (CO2). However, the isolated and inhospitable nature of this environment has restricted the number of oceanic and atmospheric CO2 measurements in this region. This has limited the scientific community's ability to investigate trends and seasonal variability of the sink. Compared to regions further north, the near-absence of terrestrial CO2 exchange and strong large-scale zonal mixing demands unusual inter-site measurement precision to help distinguish the presence of midlatitude to high latitude ocean exchange from large CO2 fluxes transported southwards in the atmosphere. Here we describe a continuous, in situ, ultra-high-precision Southern Ocean region CO2 record, which ran at Macquarie Island (54∘37′ S, 158∘52′ E) from 2005 to 2016 using a LoFlo2 instrument, along with its calibration strategy, uncertainty analysis and baseline filtering procedures. Uncertainty estimates calculated for minute and hourly frequency data range from 0.01 to 0.05 µmol mol−1 depending on the averaging period and application. Higher precisions are applicable when comparing Macquarie Island LoFlo measurements to those of similar instruments on the same internal laboratory calibration scale and more uncertain values are applicable when comparing to other networks. Baseline selection is designed to remove measurements that are influenced by local Macquarie Island CO2 sources, with effective removal achieved using a within-minute CO2 standard deviation metric. Additionally, measurements that are influenced by CO2 fluxes from Australia or other Southern Hemisphere land masses are effectively removed using model-simulated radon concentration. A comparison with flask records of atmospheric CO2 at Macquarie Island highlights the limitation of the flask record (due to corrections for storage time and limited temporal coverage) when compared to the new high-precision, continuous record: the new record shows much less noisy seasonal variations than the flask record. As such, this new record is ideal for improving our understanding of the spatial and temporal variability of the Southern Ocean CO2 flux, particularly when combined with data from similar instruments at other Southern Hemispheric locations.

Nitrogen flow assessment in rapidly urbanizing Hai Duong province, downstream of Cau River Basin, Vietnam

Hai Duong is a rapidly urbanizing province with a condensed network of rivers, located downstream of the Cau River Basin, one of the three most polluted river systems in Vietnam. Intensive agriculture and industrial development in the province has caused profound impact on the river water. This research aims to develop a material flow analysis (MFA) model, which can simulate and visualize nitrogen flows in Hai Duong province and its districts, to access the impact of agricultural activities and industrial parks on the surrounding environments. MFA was applied for Hai Duong province and its districts to estimate nitrogen flows in the whole province. The nitrogen loads to surface water, air, and soil/groundwater annually were 50,531 ± 10,801, 12,981 ± 1541, and 6055 ± 3121 tons, respectively. The quality of the developed model was assessed through a modified uncertainty analysis procedure. Also, MFA results of nitrogen loads to the water environment were consistent with the analytical results. Nitrogen was largely derived from agricultural activities and domestic and industrial wastewater. Environmental flows in the next 10 years and reasonable solutions for improving environmental quality in the province are proposed.

Making a meal out of uncertainty

The European Food Safety Authority (EFSA)’s prepared a guidance document on uncertainty, and the lead article by Lofstedt and Bouder considers the implications the document raises for how scientists communicate the limiting factors in their scientific discoveries and insights to regulatory bodies and the public. The aim of this commentary is to echo and perhaps even amplify the sentiments of the lead article, which in essence recommends a cautionary approach to two issues: rolling out uncertainty analysis procedures across other European agencies, and making scientific uncertainties transparent for public consumption.

Use of Emulator Methodology for Uncertainty-Reduction Quantification

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 169405, “Use of Emulator Methodology for Uncertainty-Reduction Quantification,” by C. Ferreira, Universidade Estadual de Campinas; I. Vernon, Durham University; D.J. Schiozer, SPE, Universidade Estadual de Campinas; and M. Goldstein, Durham University, prepared for the 2014 SPE Latin American and Caribbean Petroleum Engineering Conference, Maracaibo, Venezuela, 21–23 May. The paper has not been peer reviewed. Most simulation models go through a series of iterations before being judged as giving an adequate representation of the physical system. This can be difficult because the input space to be searched may be high dimensional, the collection of outputs to be matched may be very large, and each single evaluation may take a long time. Because the uncertainty analysis is complex and time consuming, in this paper, a stochastic representation of the computer model, called an emulator, was constructed to quantify the reduction in the parameter input space. Introduction Reservoir simulators are important and widely used in reservoir management. They are used in reservoir-performance prediction and for decision making. These simulators are computer implementations of high-dimensional mathematical models for reservoirs, where the model inputs are physical parameters and the outputs are observable characteristics such as well-pressure measurements and fluid production. Uncertainties are always present in the reservoir-characterization process; thus, input parameters are usually uncertain and so is the simulator output. The procedure to calibrate the reservoir-simulation model is called history matching. On the basis of observed data, a set of possible input choices for the reservoir model is identified. Two different procedures can be used to perform the history matching: deterministic and probabilistic approaches. The deterministic approach involves running the initial simulation model with different input values to obtain one simulation model between many probable matches to the field data. In a probabilistic approach, in which several reservoir-model scenarios are considered, the uncertainty analysis procedure is used. Identifying the input parameters for which the simulation outputs match the observed data can be a difficult task because the input space to be searched may be high dimensional, the collection of outputs to be matched may be very large, and each single evaluation may take a long time.

Uncertainty analysis procedure for the ship inclining experiment

The inclining experiment is typically performed for all new-build ships and after any major refit. The purpose of the inclining experiment is to establish the vertical distance of the centre-of-mass of the ship above its keel in the lightship condition. This value is then taken as the point of reference when loading the ship, for establishing the ‘in-service’ stability, throughout the life of the ship. Experimental uncertainty analysis is commonly utilised in hydrodynamic testing to establish the uncertainty in a result as a function of the input variables. This can in turn be utilised to establish an interval about the result that may be expected to encompass a large fraction of the distribution of values that could reasonably be attributed to the measurement. This paper provides a methodology for calculating a confidence interval for the location of the centre-of-mass of a ship from an inclining experiment; and ultimately, in any load condition.The uncertainty compared to an assumed metacentric height of 0.15m is provided for four classes of ship: buoy tender 0.15±0.15m (±100%); super yacht 0.150±0.033m (±22.0%); supply ship 0.150±0.047m (±31.3%), container ship 0.150±0.029m (±19.3%), ropax 0.150±0.077m (±100%).

Benchmark CFD validation data for surface combatant 5415 in PMM maneuvers – Part I: Force/moment/motion measurements

Part I of this two-part paper presents benchmark CFD validation force/moment/motion measurements for surface combatant 5415 in planar-motion-mechanism (PMM) maneuvers. The experiments are conducted in the IIHR towing tank as part of an international collaboration for development/application of uncertainty analysis procedures and assessment of scale effects and facility biases. Stationarity and normality tests and statistical convergence errors, single and multiple run methods for obtaining the maneuvering mathematical model hydrodynamic derivatives, and the effects of heave, pitch and roll motions and fixed/free mount conditions are assessed. Assessment of the methods for obtaining the hydrodynamic derivatives is based on reconstructions of the measured force and moment time histories. Part 2 presents phase-averaged stereo PIV flow field measurements. The data is used as a test case at the SIMMAN 2008 and 2014 workshops on verification and validation of ship maneuvering simulation methods.

Modification of uncertainty analysis in adapted material flow analysis: Case study of nitrogen flows in the Day-Nhue River Basin, Vietnam

Nitrogen flows impacted by human activities in the Day-Nhue River Basin in northern Vietnam have been modeled using adapted material flow analysis (MFA). This study introduces a modified uncertainty analysis procedure and its importance in MFA. We generated a probability distribution using a Monte Carlo simulation, calculated the nitrogen budget for each process and then evaluated the plausibility under three different criterion sets. The third criterion, with one standard deviation of the budget value as the confidence interval and 68% as the confidence level, could be applied to effectively identify hidden uncertainties in the MFA system. Sensitivity analysis was conducted for revising parameters, followed by the reassessment of the model structure by revising equations or flow regime, if necessary. The number of processes that passed the plausibility test increased from five to nine after reassessment of model uncertainty with a greater model quality. The application of the uncertainty analysis approach to this case study revealed that the reassessment of equations in the aquaculture process largely changed the results for nitrogen flows to environments. The significant differences were identified as increased nitrogen load to the atmosphere and to soil/groundwater (17% and 41%, respectively), and a 58% decrease in nitrogen load to surface water. Thus, modified uncertainty analysis was considered to be an important screening system for ensuring quality of MFA modeling.

Uncertainty analysis on process responses of conventional spinning using finite element method

Conventional spinning is a widely used metal forming process to manufacture rotationally axis-symmetric and asymmetric products. Considerable efforts have been made to investigate the forming quality of spun parts using the process in recent years. However, inherent uncertainty properties involved in the spinning process are rarely considered in previous studies. In this paper, an uncertainty analysis and process optimisation procedure have been developed and implemented on conventional spinning with 3D Finite Element Method (FEM). Three process variables are randomized by Gaussian distribution to study the probabilistic characteristics of two process responses. Linear and quadratic approximate representations are constructed by Monte Carlo based Response Surface Method (RSM) with Latin Hypercube Sampling (LHS). The Most Probable Point (MPP) method, which has been widely used to estimate the failure probability in other applications, is further developed in this paper to obtain the probability distribution of the system responses. Following an evaluation of the system responses conducted by the MPP method, a control variable method is used to reduce the variance of spun part wall thickness and total roller force to satisfy the 3? quality requirement. This uncertainty analysis and process optimisation procedure can be easily implemented in other metal spinning processes.

Uncertainty Quantification of Hydrologic Model

Generalized Likelihood Uncertainty Estimation (GLUE), a simplified Bayesian method, was adopted to determine the parametric uncertainty in hydrological modeling. A preliminary analysis of the summer flows of the Kootenay Watershed, Canada, was modeled to portray a typical uncertainty analysis procedure. SLURP, a robust hydrologic model was chosen for this procedure. The results demonstrated the viability of applying the GLUE method in conjunction with the SLURP hydrological model, following which the posterior probability distributions of the parameters was analyzed. The performance of this technique was verified by examining the flows’ prediction intervals for a period of 2 years, enabling valid future hydrological forecasting for the watershed.

Uncertainty and sensitivity analysis for the nominal scenario class in the 2008 performance assessment for the proposed high-level radioactive waste repository at Yucca Mountain, Nevada

Extensive work has been carried out by the U.S. Department of Energy (DOE) in the development of a proposed geologic repository at Yucca Mountain (YM), Nevada, for the disposal of high-level radioactive waste. In support of this development and an associated license application to the U.S. Nuclear Regulatory Commission (NRC), the DOE completed an extensive performance assessment (PA) for the proposed YM repository in 2008. This presentation describes uncertainty and sensitivity analysis results for the nominal scenario class (i.e., for undisturbed conditions) obtained in the 2008 YM PA. The following topics are addressed: (i) uncertainty and sensitivity analysis procedures, (ii) drip shield and waste package failure, (iii) engineered barrier system conditions, (iv) radionuclide release results for the engineered barrier system, unsaturated zone, and saturated zone, and (v) dose to the reasonably maximally exposed individual specified in the NRC regulations for the YM repository. The present article is part of a special issue of Reliability Engineering and System Safety devoted to the 2008 YM PA; additional articles in the issue describe other aspects of the 2008 YM PA.