Bayesian calibration and sensitivity analysis of heat transfer models for fire insulation panels

Wagner, Paul-Remo; Fahrni, Reto; Klippel, Michael; Frangi, Andréa; Sudret, Bruno

doi:10.1016/j.engstruct.2019.110063

Cited by 26 publications

(13 citation statements)

References 29 publications

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“…Since the normal force is relatively smooth in time, the solutions at different time instances are highly correlated, and dimensionality reduction techniques can be effectively applied. Commonly used techniques are based on principal component analysis (PCA) or the related singular value decomposition (SVD), see for example (Bottasso et al, 2014) and (Wagner et al, 2020). In this work, the normal forces are periodic in time and a suitable reduction technique is to decompose the output signal https://doi.org/10.5194/wes-2021-58 Preprint.…”

Section: New Mexico Case: Time-dependent Resultsmentioning

confidence: 99%

Efficient Bayesian calibration of aerodynamic wind turbine models using surrogate modeling

Sanderse

Dighe

Boorsma³

et al. 2021

Preprint

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Abstract. This paper presents an efficient strategy for the Bayesian calibration of parameters of aerodynamic wind turbine models. The strategy relies on constructing a surrogate model (based on adaptive polynomial chaos expansions), which is used to perform both parameter selection using global sensitivity analysis and parameter calibration with Bayesian inference. The effectiveness of this approach is shown in two test cases: calibration of airfoil polars based on the measurements from the DanAero MW experiments, and calibration of five yaw model parameters based on measurements on the New MEXICO turbine in yawed conditions. In both cases, the calibrated models yield results much closer to the measurement data, and in addition they are equipped with an estimate of the uncertainty in the predictions.

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Section: New Mexico Case: Time-dependent Resultsmentioning

confidence: 99%

Efficient Bayesian calibration of aerodynamic wind turbine models using surrogate modeling

Sanderse

Dighe

Boorsma³

et al. 2021

Preprint

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“…After simulating the model and performing the scenario analysis, a sensitivity analysis will be performed. The sensitivity analysis aims to identify which input parameters of a model (or combination thereof) explain, at best, the uncertainties in the model predictions [58].…”

Section: Sensitivity Analysismentioning

confidence: 99%

Development of a Multi-Methodological Approach to Support the Management of Water Supply Systems

Araujo

Oliveira-Esquerre

Sahin

2021

Water

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The benefits provided by a model of system dynamics are directly related to its correct construction. One of the main challenges in the process of building such models is that they must be able to effectively represent a specific problematic situation. Thus, the main objective of this study is to develop a multi-methodological approach, adapting the problem structuring method of strategic options development and analysis (SODA) in the initial stage of the system dynamics (SD) model. The role of each of them clearly represents the contribution of this study: the SODA in the structuring (representation) phase of the problem and proposition of alternatives and the SD in the evaluation phase of these alternatives. To illustrate its application, the multimethodological approach developed was used to simulate scenarios considering management strategies, and the various variables affecting a water supply system, including population growth, in order to evaluate more “assertive” water management strategy(s) that could have been adopted to address the water crisis (2012–2017) and analysis future scenarios. The results show that, based on the vision of specialists with enough experience for the case studied, it was possible to structure the problem, and therefore propose a set of strategies (alternatives), which were: water loss control, wastewater reuse, application of more efficient tariffs to reduce water waste, inter-basin water transfer, and awareness regarding the use of water resources. After the survey of alternatives, scenarios were simulated considering these water management strategies. Simulation results showed that actions taken on the demand side would only be effective for a short period of water scarcity, (for example, the impact of the scarcity-based tariff on water consumption reduction). For severe drought scenarios and with a water producing system heavily dependent on rainfall, such action would no longer be efficient. However, water supply management-oriented strategies, e.g., inter-basin water transfers (PISF) and wastewater reuse, are highly effective in securing water supply and preventing water supply collapse in the region. The development of this multi-methodological approach is expected to be useful to support managers in the decision-making and implementation of water management strategies.

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“…In a multidisciplinary context, the mainstream global sensitivity analysis (SA) focuses on model outputs [6]. In civil engineering, the global SA is used to study model outputs of surrogate models without a direct relationship to Pf, with the results reflecting model simplifications of approximation methods [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

New Importance Measures Based on Failure Probability in Global Sensitivity Analysis of Reliability

Kala

2021

Mathematics

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This article presents new sensitivity measures in reliability-oriented global sensitivity analysis. The obtained results show that the contrast and the newly proposed sensitivity measures (entropy and two others) effectively describe the influence of input random variables on the probability of failure Pf. The contrast sensitivity measure builds on Sobol, using the variance of the binary outcome as either a success (0) or a failure (1). In Bernoulli distribution, variance Pf(1-Pf) and discrete entropy—Pfln(Pf)-(1-Pf)ln(1-Pf) are similar to dome functions. By replacing the variance with discrete entropy, a new alternative sensitivity measure is obtained, and then two additional new alternative measures are derived. It is shown that the desired property of all the measures is a dome shape; the rise is not important. Although the decomposition of sensitivity indices with alternative measures is not proven, the case studies suggest a rationale structure of all the indices in the sensitivity analysis of small Pf. The sensitivity ranking of input variables based on the total indices is approximately the same, but the proportions of the first-order and the higher-order indices are very different. Discrete entropy gives significantly higher proportions of first-order sensitivity indices than the other sensitivity measures, presenting entropy as an interesting new sensitivity measure of engineering reliability.

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Bayesian calibration and sensitivity analysis of heat transfer models for fire insulation panels

Cited by 26 publications

References 29 publications

Efficient Bayesian calibration of aerodynamic wind turbine models using surrogate modeling

Efficient Bayesian calibration of aerodynamic wind turbine models using surrogate modeling

Development of a Multi-Methodological Approach to Support the Management of Water Supply Systems

New Importance Measures Based on Failure Probability in Global Sensitivity Analysis of Reliability

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