The role of sensitivity analysis in the building performance analysis: A critical review

Pang, Zhihong; O’Neill, Zheng; Li, Yanfei; Niu, Fuxin

doi:10.1016/j.enbuild.2019.109659

Cited by 127 publications

(69 citation statements)

References 160 publications

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“…To account for the model uncertainties and nonlinearities in the sensitivity results, researchers across several disciplines suggest the use of global sensitivity analysis (GSA) [10]. GSA aims to assess the most relevant input-model parameters by analyzing the individual and combined effects that the uncertainty of each input parameter has on the total variance of the response.…”

Section: Introductionmentioning

confidence: 99%

“…To overcome this situation, the utilization of more than one GSA method has been proposed. For example, two efficient methods such as the Morris approach also called the elementary effects (EE) approach and rank regression was applied and showed good agreement in the results [10]. The application of the aforementioned methods, however, is limited due to their lack of robustness to identify the most important parameters, thereby reducing their utilization for model reduction, before using more sophisticated methods [11].…”

Section: Introductionmentioning

confidence: 99%

“…The application of the aforementioned methods, however, is limited due to their lack of robustness to identify the most important parameters, thereby reducing their utilization for model reduction, before using more sophisticated methods [11]. Analysis of variance approaches, i.e., Sobol indices, have been consistently applied in models with noncorrelated inputs, and are usually used as a benchmark for testing other sensitivity approaches [10]. That is explained by their capacity to accurately identify (1) noninfluential and influential parameters and (2) their interaction effects.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Uncertainty Quantification for Fatigue Life of Offshore Wind Turbine Structure

Nispel

Ekwaro-Osire

Dias

et al. 2021

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

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This study aims to address the question: can the structural reliability of an offshore wind turbine (OWT) under fatigue loading conditions be predicted more consistently? To respond to that question this study addresses the following specific aims: (1) to obtain a systematic approach that takes into consideration the amount of information available for the uncertainty modeling of the model input parameters, and (2) to determine the impact of the most sensitive input parameters on the structural reliability of the OWT through a surrogate model. First, a coupled model to determine the fatigue life of the support structure considering the soil-structure interaction under 15 different loading conditions was developed. Second, a sensitivity scheme using two global analyses was developed to consistently establish the most and least important input parameters of the model. Third, a systematic uncertainty quantification (UQ) scheme was employed to model the uncertainties of model input parameters based on their available-data-driven and physics-informed-information. Finally, the impact of the proposed UQ framework on the OWT structural reliability was evaluated through the estimation of the probability of failure of the structure based on the fatigue limit state design criterion. The results show high sensitivity for the wind speed and moderate sensitivity for parameters usually considered as deterministic values in design standards. Additionally, it is shown that applying systematic UQ not only produces a more efficient and better approximation of the fatigue life under uncertainty, but also a more accurate estimation of the structural reliability of offshore wind turbine's structure during conceptual design. Consequently, more reliable, and robust estimations of the structural designs for large offshore wind turbines with limited information may be achieved during the early stages of design.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Uncertainty Quantification for Fatigue Life of Offshore Wind Turbine Structure

Nispel

Ekwaro-Osire

Dias

et al. 2021

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

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“…This technique requires a large number of building simulations for all building parameters, which might be impractical. The main limitation is that this technique neglects the interaction between variables (Pang et al 2020;Bamdad Masouleh 2018). For example, the optimal size of windows may change with overhang size and building orientation.…”

Section: Introductionmentioning

confidence: 99%

“…For example, the optimal size of windows may change with overhang size and building orientation. Thus, parametric sensitivity analysis techniques may miss out on potential energy savings that can result from exploring these interactions (Pang et al 2020;Bamdad Masouleh 2018).…”

Section: Introductionmentioning

confidence: 99%

Building energy optimization using surrogate model and active sampling

Bamdad

Cholette

Bell

2020

Journal of Building Performance Simulation

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In order to improve the performance of a surrogate model-based optimization method for building optimization problems, a new active sampling strategy employing a committee of surrogate models is developed. This strategy selects new samples that are in the regions of the parameter space where the surrogate model predictions are highly uncertain and have low energy use. Results show that the new sampling strategy improves the performance of surrogate model-based optimization method. A comparison between the surrogate model-based optimization methods and two simulation-based optimization methods shows better performance of surrogate model-based optimization methods than a simulation-based optimization method using the PSO algorithm. However, the simulationbased optimization using Ant Colony Optimization found better results in terms of optimality in later stages of the optimization. However, the proposed method showed a better performance at the early optimization stages, yielding solutions within 1% of the best solution found in the fewest number of simulations.

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Input significance ranking of microalgae continuous culture models

Sangregorio‐Soto

Garzón‐Castro

Mazzanti

2023

J of Chemical Tech & Biotech

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Background Microalgal cultures are evolving into a promising ecofriendly technology for a host of applications. To be sustainable, culture conditions need to be optimized and then controlled. One way to develop robust controllers for a cultivation system is by using mathematical growth models to simulate microalga‐based production. In this scenario, engineering design tasks begin by selecting the critical variables of these models. Results A new methodology for determining the significance ranking of a model's input factors under steady‐state operation (parameters (e.g. biological, geometrical) and/or process variables) was designed. The sensitivity of biomass response to its inputs was investigated in four different photobioreactor growth models within a nominal operational region. The methodology ranks models’ input factors based on the one‐at‐a‐time Morris method of elementary effects and variance‐based Sobol's method. Such information provided by the presented procedure is valuable as it reveals which input parameters explain most of the variance in model predictions. Conclusion The methodology allowed the identification of controlled variables and biological parameters to be targeted for enhanced calibration. Furthermore, the presented methodology showed that in continuous reactors the dilution rate is a critical variable of the process. Therefore, it should be controlled. Additionally, most surprisingly, it is observed that controlling the light intensity within the optimum point of operation is not necessarily a crucial task. However, although its manipulation is still important, the accurate calibration of the parameters of the model may represent a greater influence on the biomass response. © 2023 Society of Chemical Industry (SCI).

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The role of sensitivity analysis in the building performance analysis: A critical review

Cited by 127 publications

References 160 publications

Uncertainty Quantification for Fatigue Life of Offshore Wind Turbine Structure

Uncertainty Quantification for Fatigue Life of Offshore Wind Turbine Structure

Building energy optimization using surrogate model and active sampling

Input significance ranking of microalgae continuous culture models

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