Integrating Multi-Criteria Decision Analysis and Uncertainty Quantification for Climate Adaptation

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.

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

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

Challenges and progress of uncertainty analysis for the pebble-bed high-temperature gas-cooled reactor

GIS multicriteria decision analysis (MCDA) methods are increasingly being used in landslide susceptibility mapping for the prediction of future hazards, decision making, as well as hazard mitigation plans. However, the uncertainties associated with MCDA techniques are inevitable and model outcomes are prone to multiple types of uncertainty. In this paper, the spatiality explicitly method is employed to assess the uncertainty associated with two methods of GIS-MCDA namely, Analytical Hierarchical Process (AHP) and Ordered Weighted Averaging (OWA). Our methodology consists of three stages. The first-stage analysis makes use of the AHP matrix weights for GIS-MCDA based landslide susceptibility mapping. This phase is based on a multicriteria evaluation which assesses the susceptibility areas and the landslide hazard potentiality by considering causal and diagnostic criteria. In the second stage the sensitivity and uncertainty analysis of the AHP weights is performed using Monte Carlo Simulation (MCS) and Global Sensitive Analysis (GSA). Finally, validation of results was performed using the existing landslide inventory. This paper carried out a GIS-MCDA uncertainty analysis and demonstrates a solution for the uncertainty modelling. Through the validation exercise with known existing landslides, the AHP clearly performed best. Results of this research demonstrate that further improvement of the accuracy of GIS-based MCDA can be achieved by employing the spatiality explicitly method and accordingly applying MCS and GSA for sensitivity analysis of the AHP weights.

In recent years, the demand to provide best-estimate predictions with confident bounds is increasing for the nuclear reactor performance and safety analysis. The Organisation for Economic Co-operation and Development Nuclear Energy Agency has been developing an international benchmark of the light water reactor (LWR) uncertainty analysis in modeling (UAM) for the examination of uncertainty quantification and propagation methodologies with various modeling and simulation code systems. The objective of the present work is to develop an uncertainty propagation mechanism based on the stochastic sampling method by taking into account the uncertainties of both basic nuclear data and fuel modeling parameters in the simulation of pressurized water reactors (PWRs) that can be incorporated in the conventional LWR simulation approach. More specifically, the Three Mile Island Unit 1–related exercises from the LWR-UAM benchmark have been modeled using the coupled TRACE/PARCS code system in the three-dimensional core representation. The input uncertainties of the neutronics simulation include few-group cross sections and kinetics parameters generated using the Sampler/Polaris sequence of SCALE 6.2.1. Several heat transfer–related variables for the fuel modeling were considered as sources of input uncertainty of the thermal-hydraulics simulations, including the thermal conductivity of fuel and cladding, fuel heat capacity, and the gap conductance. Dakota was used to sample input parameters of the coupled code system and to perform the uncertainty analysis. Two types of simulations were conducted: steady-state calculation at hot full-power condition and transient scenario initiated by the spatially asymmetric rod ejection accident. Quantities of interest for the steady-state calculation, including core multiplication factor and power peaking factors, were calculated with associated uncertainties. For transient calculations, best-estimate plus uncertainty results of the time evolution of core reactivity, core power, and peak fuel temperature were generated and analyzed. The Wilks’ formula was used to determine the necessary sample size to achieve a 95% confidence of the 95% limit of output quantities of interest. Although the uncertainty propagation and quantification method presented in this paper was developed for PWRs, it could be in general applicable to the multiphysics uncertainty quantification of other types of LWR cores.

The last decade has seen a surge in the need for uncertainty analysis (UA) for building energy assessment. The rigorous determination of uncertainty in model parameters is a vital but often overlooked part of UA. To undertake this, one has to turn one's attention to a thriving area in engineering statistics that focuses on uncertainty quantification (UQ) for short. This paper applies dedicated methods and theories that are emerging in this area of statistics to the field of building energy models, and specifically to the microclimate variables embedded in them. We argue that knowing the uncertainty in these variables is a vital prerequisite for ensuing UA of whole building behaviour. Indeed, significant discrepancies have been observed between the predicted and measured state variables of building microclimates. This paper uses a set of approaches from the growing UQ arsenal, mostly regression-based methods, to develop statistical models that quantify the uncertainties in the following most significant microclimate variables: local temperature, wind speed, wind pressure and solar irradiation. These are the microclimate variables used by building energy models to define boundary conditions that encapsulate the interaction of the building with the surrounding physical environment. Although our analysis is generically applicable to any of the current energy models, we will base our UQ examples on the energy model used in EnergyPlus.

Many sources of uncertainty exist when emissions are modelled for a gas turbine combustion system. They originate from uncertain inputs, boundary conditions, calibration, or lack of sufficient fidelity in the model. In this paper, a non-intrusive polynomial chaos expansion (NIPCE) method is coupled with a chemical reactor network (CRN) model using Python to rigorously quantify uncertainties of NOx emission in a premixed burner. The first objective of the uncertainty quantification (UQ) in this study is development of a global sensitivity analysis method based on NIPCE to capture aleatory uncertainty due to the variation of operating conditions and input parameters. The second objective is uncertainty analysis of Arrhenius parameters in the chemical kinetic mechanism to study the epistemic uncertainty in the modelling of NOx emission. A two-reactor CRN consisting of a perfectly stirred reactor (PSR) and a plug flow reactor (PFR) is constructed in this study using Cantera to model NOx for natural gas at the relevant operating conditions for a benchmark premixed burner. UQ is performed through the use of a number of packages in Python. The results of uncertainty and sensitivity analysis using NIPCE based on point collocation method (PCM) are then compared with the results of advanced Monte Carlo simulation (MCS). Surrogate models are also developed based on the NIPCE approach and compared with the forward model in Cantera to predict NOx emissions. The results show the capability of NIPCE approach for UQ using a limited number of evaluations to develop a UQ-enabled emission prediction tool for gas turbine combustion systems.

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

The credibility of Computational Fluid Dynamics (CFD) has been a topic of debate due to the significant uncertainties inherent in its modeling processes and numerical implementations. Uncertainty Quantification (UQ) offers a scientific framework for quantitatively assessing and mitigating uncertainties in CFD simulations. However, this procedure typically requires numerous CFD simulations and considerable manual effort for both simulation management and data analysis. To overcome these challenges, this work develops a platform called UQ4CFD, a browser–server software that provides automated and customized uncertainty quantification capabilities for CFD studies. The UQ4CFD platform integrates different kinds of methodologies to perform comprehensive uncertainty analysis, including uncertainty propagation, sensitivity analysis, surrogate modeling, numerical discretization uncertainty analysis, model validation, model calibration, etc. A tightly coupled CFD-UQ workflow is built to automate the complete analytical process, encompassing parameter sampling, simulation execution, and results analysis, which significantly improves computational efficiency while reducing risks associated with data processing errors. Comprehensive validation employing both analytical benchmark functions and practical CFD cases has been conducted to demonstrate the platform’s effectiveness and adaptability in diverse UQ scenarios.

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.

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

Sensitivity analysis for transit equilibrium assignment and applications to uncertainty analysis

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.

Simulation models are used to estimate, forecast, optimize and identify limiting factors and analyze changes in crop production. In order to obtain a functional and reliable mathematical model, it is necessary to know the source of uncertainty and identify the most influential parameters. This study aimed to carry out an uncertainty analysis (UA) and a global spatiotemporal sensitivity analysis (SA) for the parameters of the SIMPLE model, which uses 13 parameters, has two state variables and uses daily weather data to simulate crop growth and development. A Monte Carlo simulation was performed for the UA, and Sobol’s method was used for the SA. Four automatic weather stations representing the climatic conditions of the different bean-producing areas in Zacatecas, Mexico, and a four-year historical series of each station for irrigated and rainfed common bean crops were analyzed. From the UA the coefficients of variation (CV) for thermal time were 11.49% and 11.47%, for biomass the CV were 47.94% and 37.80% and for yield the CV were 49.52% and 39.70% for irrigated and rainfed beans, respectively. From the SA, the most influential parameters for irrigated beans were Tsum > Swater > Tbase > I50A > Topt and for rainfed beans, Tsum > Tbase > I50A > Topt > Swater, according to indices calculated on biomass and thermal time. In conclusion, UA was able to accurately quantify the uncertainty of the biomass, and SA allowed the identification of the most influential of the parameters of the SIMPLE model applied to a common bean crop.