China
Africa
Explore chapters and articles related to this topic
Development of a Methodology to Guaranteed Energy Performance
Published in Bruno Peuportier, Fabien Leurent, Jean Roger-Estrade, Eco-Design of Buildings and Infrastructure, 2020
Simon Ligier, Patrick Schalbart, Bruno Peuportier
The Morris method (Morris, 1991) is an easily applicable screening sensitivity method that requires only a relatively small number of simulations. The uncertain inputs are described according to a variation range discretised into a finite (and even) number of points. Starting from an initial draw of values in the input space, simulations are performed by changing the value of only one parameter for each new simulation until each entry has been modified. This process is repeated hereafter. The elementary effects of the variations associated with a parameter are studied within each repetition. Their mean and standard deviation characterise the influence of the parameter on the output, identifying the non-linearity effects and interactions. The following Dp standard is used to characterise the influence of a parameter. The influencing parameters correspond to the high Dp values: Dp=σp2+μp*2
Critical evaluation of different mass transfer equations to model N2O emissions from water resource recovery facilities with diffuse aeration
Published in Environmental Technology, 2023
Vasileios Chrysochoidis, Mikkel H. Andersen, Enrico U. Remigi, Maria Faragó, Barth F. Smets, Carlos Domingo-Félez, Borja Valverde-Pérez
The GSA was performed using the Morris screening method [41], which follows these steps: (i) sampling the parameter space; (ii) simulating the BSM1 using the sampled parameter sets; and (iii) calculating the sensitivity measures of the model parameters. The Morris method performs local sensitivity calculations in a global context. For this purpose, the method estimates the distribution of the elementary effects (EE) of each input parameter on the model output (EEj,k) at randomly selected points in the sampled parameter space. Morris results are evaluated by comparing the mean of the absolute values of the EE distributors for each parameter (μ*). Simultaneously, the average of the values of the EE (μ) describes the effect of each parameter on the target variable [42]. If both μ* and μ have similar absolute values, the parameter effect on the target variable does not depend on other parameters. On the contrary, if the absolute value of μ is significantly lower than μ* the effect of the parameter will have different directions depending on additional or different factors (i.e. interactions with other parameters). Negative and positive μ values indicate decreasing and increasing effects on the target variables [43]. Only parameters with a μ* values exceeding 0.1 are considered significant [44]. Model parameters were classified under four different uncertainty classes based on literature [45,46] (Table S7).
Global sensitivity analysis of VISSIM parameters for project-level traffic emissions: a case study at a signalized intersection
Published in Environmental Technology, 2022
Yuhong Chen, Chao Wen, Chaozhe Jiang, Xi Jiang
The most common global SA methods include qualitative Morris, FAST, quantitative Extend FAST, and Sobol. Among them, the Morris method is suitable for the nonlinear model, and it can deal with high-dimensional problems efficiently, but it can only obtain rough indices; it cannot obtain full effect indices [26]. The Sobol method is suitable for nonlinear and non-monotonic models, and while it can provide a real quantitative ranking for input parameters, it is not easily applied when there are many input parameters because of the extensive calculations required [27,28]. It can be seen that Morris and Sobol have their own characteristics in terms of accuracy, robustness, and efficiency. Campolongo [29] proposed that two consecutive experiments could be built to consider both sensitive information and calculation costs; that is, first, used a qualitative method to initially screen out non-influential parameters and to reduce parameter dimensions to a reasonable range, and then used a quantitative approach to further select the model parameters. Qiao [30] used the methods of Morris and Sobol to analyze the impacts of parameters in Aimsun and VISSIM on vehicle travel time. The results show that the two approaches can identify to a certain degree the critical parameters that affect travel time.
Nonlinear modeling of industrial boiler NOx emissions
Published in Journal of the Air & Waste Management Association, 2022
Guillermo Ronquillo-Lomeli, Noé Amir Rodríguez-Olivares, Leonardo Barriga-Rodríguez, Antonio Ramírez-Martínez, Jorge Alberto Soto-Cajiga, Luciano Nava-Balanzar
According to (Evaluation of measurement data-Guide to the expression of uncertainty in measurement 2008), the partial derivatives of a model function are called sensitivity coefficients, describing how the output estimate varies with changes in the input values . In particular, the change in produced by a small change in the input estimate is given by . Instead of being calculated from the function f, sensitivity coefficients are sometimes determined experimentally when the function f is unknown. For experimental sensitivity analysis, the Morris method is commonly used, which has been used to test the impact of parameters. The Morris method evaluates the model output response for small changes in each input variable. The mean impact of a single variable from the complete dataset points of size M and N variables is presented as