•Model uncertainties quantification on the basis of CETs.•Benchmark of methods for model uncertainties quantification.•The constitutive relations of thermal-hydraulic system codes are addressed.•A ...strong user effect has been observed.
PREMIUM (Post BEMUSE Reflood Models Input Uncertainty Methods) was an activity launched with the aim of pushing forward the methods of quantification of physical model uncertainties in thermal-hydraulic codes. The benchmark PREMIUM was addressed to all who apply uncertainty evaluation methods based on input uncertainties quantification and propagation. The benchmark was based on a selected case of uncertainty analysis application to the simulation of quench front propagation in an experimental test facility. Applied to an experiment, enabled evaluation and confirmation of the quantified probability distribution functions on the basis of experimental data. The scope of the benchmark comprised a review of the existing methods, selection of potentially important uncertain input parameters, quantification of the ranges and distributions of the identified parameters using experimental results of tests performed on the FEBA test facility, verification of the performed quantification on the basis of tests performed at the FEBA test facility and validation on the basis of blind calculations of the Reflood 2-D PERICLES experiment. The benchmark has shown dependency of the results on the applied methodology and a strong user effect. The conclusion was that a systematic approach for the quantification of model uncertainties is necessary.
This paper presents an extended polynomial chaos formalism for epistemic uncertainties and a new framework for evaluating sensitivities and variations of output probability density functions (PDF) to ...uncertainty in probabilistic models of input variables. An ”extended” polynomial chaos expansion (PCE) approach is developed that accounts for both aleatory and epistemic uncertainties, modeled as random variables, thus allowing a unified treatment of both types of uncertainty. We explore in particular epistemic uncertainty associated with the choice of prior probabilistic models for input parameters. A PCE-based Kernel Density (KDE) construction provides a composite map from the PCE coefficients and germ to the PDF of quantities of interest (QoI). The sensitivities of these PDF with respect to the input parameters are then evaluated. Input parameters of the probabilistic models are considered. By sampling over the epistemic random variable, a family of PDFs is generated and the failure probability is itself estimated as a random variable with its own PCE. Integrating epistemic uncertainties within the PCE framework results in a computationally efficient paradigm for propagation and sensitivity evaluation. Two typical illustrative examples are used to demonstrate the proposed approach.
•The EPCE for quantification of multi-uncertainty is introduced.•EPCE-KDE coupling enables straightforward assessment of stochastic sensitivities.•The total variation in response PDF is estimated by stochastic sensitivities.•Failure probability is characterized as a random variable and represented by EPCE.•The framework enables scientific and efficient prediction from incomplete data.
•Framework for fatigue reliability analysis under multi-source uncertainties.•Manufacturing errors/tolerances are included for fatigue reliability analysis.•Sensitivity analysis of a turbine bladed ...disk is conducted for fatigue design.•Geometrical uncertainty shows critical influences on fatigue reliability.
Turbine bladed disks normally operate under complex loadings coupling with uncertainties originate from multiple sources, including material variability, load variation and geometrical uncertainty. The influence of these uncertainties on mechanical response of engineering components are critical for their fatigue assessment and reliability evaluation. In this work, a general framework for fatigue reliability analysis is developed by coupling the Latin hypercube sampling with FE analysis to describe the combined effects of multi-source uncertainties. Fatigue reliability analysis of a full-scale bladed disk under multi-source uncertainties was performed as well as sensitivity analysis for fatigue design. In order to describe the manufacturing errors or tolerances, random dimensions are inputted. Comparing the predicted fatigue lifetime distributions with/without geometrical uncertainty, it shows that geometrical uncertainty matters in structural fatigue reliability. Particularly, sensitivity analysis indicates that the geometrical uncertainty exerts more critical influences on the fatigue lifetime and reliability of the turbine bladed disk than others. The sensitivity factors of three typical dimensions emerges the influence of designed sizes and dimensional tolerances on the failure probability, which provides a reference for engineering design.
•“Predict and act” decision making is ineffective in the face of deep uncertainty.•Decision making under deep uncertainty (DMDU) methods support robust and adaptable decisions.•However, DMDU methods ...do not fully reflect the context in which decisions are made.•This influences the availability of feasible options and limits capacity for change.•Decision contexts signpost the selection of full or “hybrid” DMDU approaches.
Deep uncertainties like environmental and socio-economic changes create challenges to decision making. Decision Making under Deep Uncertainty (DMDU) methods are recognised approaches to navigate deep uncertainties and support robust and adaptable decisions. However, their ability to fully reflect the context in which these decisions are made has been criticised. This paper presents a synthesis across cases and methods to provide a holistic understanding of the application of DMDU methods to support long-term decision making. We carried out a structured literature review and analysed 37 infrastructure DMDU case studies. The analysis shows that DMDU methods are effective at developing plans to address a range of deep uncertainties and in some cases, reflecting the institutional context of the decision. However, they largely overlook the organisational and individual contexts in which decision making happens. We argue that the use of existing DMDU methods in practice should start with a better understanding of the institutional, organisational and individual contexts. We then suggest modifications to the applications of DMDU methods, i.e. internalising the context at different stages of the decision-making process and developing a decision typology to signpost decision makers to the best approach for a specific context.
A community integrated energy system (CIES) with an electric vehicle charging station (EVCS) provides a new way for tackling growing concerns of energy efficiency and environmental pollution, it is a ...critical task to coordinate flexible demand response and multiple renewable uncertainties. To this end, a novel bi-level optimal dispatching model for the CIES with an EVCS in multi-stakeholder scenarios is established in this paper. In this model, an integrated demand response program is designed to promote a balance between energy supply and demand while maintaining a user comprehensive satisfaction within an acceptable range. To further tap the potential of demand response through flexibly guiding users energy consumption and electric vehicles behaviors (charging, discharging and providing spinning reserves), a dynamic pricing mechanism combining time-of-use and real-time pricing is put forward. In the solution phase, by using sequence operation theory (SOT), the original chance-constrained programming (CCP) model is converted into a readily solvable mixed-integer linear programming (MILP) formulation and finally solved by CPLEX solver. The simulation results on a practical CIES located in North China demonstrate that the presented method manages to balance the interests between CIES and EVCS via the coordination of flexible demand response and uncertain renewables.
Abstract Multiple uncertainties exist in the optimal flood control decision‐making process, presenting risks involving flood control decisions. This paper defines the main steps in optimal flood ...control decision making that constitute the Forecast‐Optimization‐Decision Making (FODM) chain. We propose a framework for supporting optimal flood control decision making under multiple uncertainties and evaluate risk propagation along the FODM chain from a holistic perspective. To deal with uncertainties, we employ stochastic models at each link of the FODM chain. We generate synthetic ensemble flood forecasts via the martingale model of forecast evolution. We then establish a multiobjective stochastic programming with recourse model for optimal flood control operation. The Pareto front under uncertainty is derived via the constraint method coupled with a two‐step process. We propose a novel SMAA‐TOPSIS model for stochastic multicriteria decision making. Then we propose the risk assessment model, the risk of decision‐making errors and rank uncertainty degree to quantify the risk propagation process along the FODM chain. We conduct numerical experiments to investigate the effects of flood forecast uncertainty on optimal flood control decision making and risk propagation. We apply the proposed methodology to a flood control system in the Daduhe River basin in China. The results indicate that the proposed method can provide valuable risk information in each link of the FODM chain and enable risk‐informed decisions with higher reliability.
Key Points Proposes a framework for supporting optimal flood control decision making under uncertainty and evaluates the risk propagation process Develops a multiobjective stochastic programming with recourse model and a SMAA‐TOPSIS model for optimal flood control decision making Investigates the effects of flood forecast uncertainty on optimal flood control decision making and risk propagation
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