Best estimate calculation and uncertainty quantification of sodium-cooled fast reactor using MARS-LMR code

A probabilistic model (SHEDS-Wood) was developed to examine children's exposure and dose to chromated copper arsenate (CCA)-treated wood, as described in Part 1 of this two-part article. This Part 2 article discusses sensitivity and uncertainty analyses conducted to assess the key model inputs and areas of needed research for children's exposure to CCA-treated playsets and decks. The following types of analyses were conducted: (1) sensitivity analyses using a percentile scaling approach and multiple stepwise regression; and (2) uncertainty analyses using the bootstrap and two-stage Monte Carlo techniques. The five most important variables, based on both sensitivity and uncertainty analyses, were: wood surface residue-to-skin transfer efficiency; wood surface residue levels; fraction of hand surface area mouthed per mouthing event; average fraction of nonresidential outdoor time a child plays on/around CCA-treated public playsets; and frequency of hand washing. In general, there was a factor of 8 for the 5th and 95th percentiles and a factor of 4 for the 50th percentile in the uncertainty of predicted population dose estimates due to parameter uncertainty. Data were available for most of the key model inputs identified with sensitivity and uncertainty analyses; however, there were few or no data for some key inputs. To evaluate and improve the accuracy of model results, future measurement studies should obtain longitudinal time-activity diary information on children, spatial and temporal measurements of residue and soil concentrations on or near CCA-treated playsets and decks, and key exposure factors. Future studies should also address other sources of uncertainty in addition to parameter uncertainty, such as scenario and model uncertainty.

Climate risks are increasing globally due to climate change and socio-economic development. Societies must implement adaptation measures today despite deep uncertainty regarding future climate trajectories, socio-economic pathways, and intervention effectiveness. Because no single strategy performs equally well across all impacts, for instance, protecting infrastructure versus saving lives, decisions depend on which outcomes are prioritized.Most assessments focus on a single criterion, most often the cost to benefit ratio of measures, overlooking other trade-offs and risking maladaptation. Multi-criteria decision analysis (MCDA) addresses this by explicitly evaluating and weighting multiple objectives. When coupled with probabilistic risk modeling and uncertainty quantification, MCDA can identify strategies that are robust across various futures and stakeholder priorities.In this project, we develop and test an integrated framework by coupling the new MCDA module of the open-source platform CLIMADA with its uncertainty and sensitivity quantification engine. Using a stylized case study from the Economics of Climate Adaptation (ECA), we explore how methodological and normative choices shape adaptation outcomes through three primary research questions:How do different impact units influence the prioritization of adaptation measures? We systematically compare rankings derived from multiple types of impact (e.g., population affected, economic losses, infrastructure exposure) to identify measures that perform consistently well across criteria versus those that are context-specific.How does the choice of risk metric affect the evaluation of adaptation measures? We quantify how rankings vary when using expected annual impact versus tail-risk metrics (high-impact, low-likelihood events), clarifying the normative implications of how "risk" is formulated.How sensitive and robust are MCDA-derived rankings to the weighting of decision criteria? We explore how results shift when assigning equal weights versus emphasizing specific priorities, making explicit how the assignment of preferences affects evaluations.Across these questions, we perform an uncertainty and sensitivity analysis that propagates uncertainty through all model components. This allows for a quantitative assessment of decision robustness and identifies the assumptions to which results are most sensitive.The key contributions of this work include the integration of MCDA with uncertainty analysis in a global modeling platform (CLIMADA); a systematic exploration of how normative modeling choices affect adaptation prioritizations; and a transparent, reproducible workflow for more integrated and value-aware climate-adaptation assessments.

Sensitivity analysis for transit equilibrium assignment and applications to uncertainty analysis

Sensitivity and uncertainty analysis within a methodology for evaluating environmental restoration technologies

This paper reviews five related types of analysis, namely (i) sensitivity or what-if analysis, (ii) uncertainty or risk analysis, (iii) screening, (iv) validation, and (v) optimization. The main questions are: when should which type of analysis be applied; which statistical techniques may then be used? This paper claims that the proper sequence to follow in the evaluation of simulation models is as follows. 1) Validation, in which the availability of data on the real system determines which type of statistical technique to use for validation. 2) Screening: in the simulation‘s pilot phase the really important inputs can be identified through a novel technique, called sequential bifurcation, which uses aggregation and sequential experimentation. 3) Sensitivity analysis: the really important inputs should be subjected to a more detailed analysis, which includes interactions between these inputs; relevant statistical techniques are design of experiments (DOE) and regression analysis. 4) Uncertainty analysis: the important environmental inputs may have values that are not precisely known, so the uncertainties of the model outputs that result from the uncertainties in these model inputs should be quantified; relevant techniques are the Monte Carlo method and Latin hypercube sampling. 5) Optimization: the policy variables should be controlled; a relevant technique is Response Surface Methodology (RSM), which combines DOE, regression analysis, and steepest-ascent hill-climbing. The recommended sequence implies that sensitivity analysis procede uncertainty analysis. Several case studies for each phase are briefly discussed in this paper.

Quantification and propagation of neutronics uncertainties of the Kozloduy-6 VVER-1000 fuel assembly using SCALE 6.2.1 within the NEA/OECD benchmark for uncertainty analysis in modelling of LWRs

Uncertainty and sensitivity analyses as a validation tool for BWR bundle thermal–hydraulic predictions

Sensitivity analysis in the context of uncertainty analysis for computationally intensive models

An interactive graphical interface tool for parameter calibration, sensitivity analysis, uncertainty analysis, and visualization for the Soil and Water Assessment Tool

Uncertainty quantification and sensitivity analysis are essential for improving the modeling and estimation of greenhouse gas emissions in livestock farming to evaluate and reduce the impact of uncertainty in input parameters to model output. The present study is a comprehensive review of the sources of uncertainty and techniques used in uncertainty analysis, quantification, and sensitivity analysis. The search process involved rigorous selection criteria and articles retrieved from the Science Direct, Google Scholar, and Scopus databases and exported to RAYYAN for further screening. This review found that identifying the sources of uncertainty, implementing quantifying uncertainty, and analyzing sensitivity are of utmost importance in accurately estimating greenhouse gas emissions. This study proposes the development of an EcoPrecision framework for enhanced precision livestock farming, and estimation of emissions, to address the uncertainties in greenhouse gas emissions and climate change mitigation.

Recent literature on building energy performance simulation leans toward implementing uncertainty analysis (UA), instead of deterministic solutions, to handle ever-existing and pivotal uncertainties in building design decision-making process. Variations in weather temperature, degradation of building envelope material properties over time, and random behavior of occupants, among all, are the key sources of uncertainty in building energy consumption predictions. The UA couples to the sensitivity analysis (SA) to identify the most influential inputs on the uncertainties of the building energy consumption. This paper describes a newly-developed UA and SA predictive tool for building energy performance simulations. Energy performance simulations are based on a resistance-capacitance thermal model for the building. For a hypothetical residential building in College Station, Texas, USA, the present work describes and compares predicted probability distribution and sensitivity indexes produced by the UA-SA tool using a transient (dynamic) response analysis (TRA) and static response analysis (SRA). For brevity, the analysis considers uncertainty only for the exterior walls’ parameters including thickness, thermal conductivity, heat transfer coefficient, density, and heat capacity; i.e., a five-dimensional problem is solved. Compared to the TRA, predictions from the SRA underestimate the annual energy consumption up to 30%; however, SRA is significantly faster. Nonetheless, sensitivity indexes from the SRA and TRA closely match.

Global environmental losses of plastics across their value chains

Dynamic and mechanistic models of an Electrical Submersible Pump (ESP) lifted oil field are frequently used for understanding the process dynamics and the interactions occurring between the oil wells under varying operating conditions. They are also used to calculate the total fluid produced from the oil field. In this article, uncertainty and sensitivity analysis of such a model is studied. If the model is used for control and optimization of an oil field, it is important to see if the output calculated using the mathematical model lies within a confidence interval under the presence of uncertainties. It is also important to understand which parameters have strong/weak influence on the model output. The uncertainty analysis is performed by using the Monte Carlo simulation method. Morris method of elementary effect is used for input factor screening. To quantify the effect of the input factors on the model output, a variance based method of sensitivity analysis is used.

Introducing uncertainty analysis of nucleation and crystal growth models in Process Analytical Technology (PAT) system design of crystallization processes

Uncertainty and sensitivity analysis of cooling and heating loads for building energy planning