Screening Sensitivity Analysis Research Articles

Life cycle assessment of demountable building elements: Influential design and use parameters

Demountable building is put forward as a solution to reduce the environmental impact of the construction sector. However, uncertainty on the actual environmental benefits that can be achieved, hinders its implementation. Life cycle assessment (LCA) can be used to measure the potential of demountable building. Variable design and use parameters, such as the number of use cycles and material lifespan, should be incorporated in LCAs of demountable building elements to account for different possible use scenarios. Existing LCAs of demountable elements only vary one parameter at a time and/or consider simple reuse scenarios and building elements. The aim of this paper is to show how LCAs of demountable building elements can be organized by focusing on influential design and use parameters, while resolving the limitations from existing research. The influence of the parameters is determined through a two-step sensitivity analysis executed for two types of building elements: space dividing walls and façade systems. First, parameters with a low influence are omitted through a screening sensitivity analysis. Then, the Sobol’ indices of the remaining parameters are calculated. The results show the influential parameters are the same for both types of building elements. Finally, an LCA with variable influential parameters for a demountable and non-demountable space dividing wall is conducted. According to a non-variable LCA, the non-demountable wall has the lowest environmental impact, but when considering variable parameters, the use scenarios for which the demountable wall has the lowest impact become clear, for example, when the wall is relocated every five years.

Robust morris screening method (RMSM) for complex physiological models

Background and objectives: Morris screening sensitivity analysis (MSM) comes forth as the method needing the minimum number of model simulations to qualify the impact of input parameter variations on outputs of complex, nonlinear and overparametrized models. However, the reliability of MSM indices (mean and standard deviation) and the reproducibility of their results are rarely explored despite the input parameter tuning/identification needs. In fact, these models, such those used in medical applications as digital twins, often lie in this category and need efficient and robust tools to assess both sensitivity and reliability of the outputs to numerous input model parameters.Methods: In this study, a new Robust Morris Screening Method (RMSM) is proposed and based on new indices: the absolute median (χ*) and the median absolute deviation (ρ). The proposed RMSM approach is evaluated on a complex multi-scales neuromuscular electrophysiological model simulating HD-sEMG (high density surface electromyography) signals at the skin surface. The reliability and stability of new RMSM indicators are evaluated at different trajectories within the parameter space and compared to classical MSM results. For this purpose, We propose a new methodology for parameter screening based on the ratio ρ/χ* as a graphic indicator of (non)linearity and (non)monotonicity of parameter effects.Results: Firstly, the results demonstrated that the computed elementary effects (EE) of inputs are not normally distributed using MSM indices contrary to the proposed RMSM indices. Secondly, the ranking stability of RMSM indices was earlier obtained from 20 trajectories (T=20), while MSM ranking remained unstable until T = 100. Thirdly, The screening separation between influential and negligible input model parameters was more distinct and interpretable with RMSM than MSM.Conclusion: The proposed RMSM approach ensures a fast, reliable and stable ranking of parameters for complex and overparametrized models compared to classical MSM. this allows a more precise exploration of the model parameter influence space for future application in parameter tuning and identification.

A novel sensitivity analysis of commercial building hybrid energy-structure performance

The Architecture, Engineering, and Construction community is highly focused on designing buildings that can improve energy efficiency while maintaining adequate structural behavior (termed herein as energy-structure performance). Sensitivity analysis is a fundamental tool to identify the influential factors under this multidisciplinary setting which can be used for model calibration, model reduction, and design optimization. To enhance the study of energy-structure performance, a novel framework of sensitivity analysis, including initial studies and global sensitivity analysis, was developed based on a general and automated platform of multi-physics simulation. This platform meets the requirements of object-orientation and provides compatibility with parametrical representation, including geometric/non-geometric parameters and detailed modeling. A systematic methodology of sensitivity analysis considering sensitivity auditing and a Scaled Morris Method is developed within the context of hybrid building energy-structure performance. The refined global screening sensitivity analysis (Scaled Morris Method) is proposed to deal with different types of data inputs and multiple-output sensitivity index comparison problems. A building case study was part of the research, which included two types of façades totaling 15 input factors. More than 2000 simulations were conducted producing the yearly/peak energy performance indicators over four climate zones along with static/dynamic structural performance indicators. The results from the initial studies and global sensitivity analysis provided varying/sensitivity effects of building topology/layout, structural/nonstructural components, and inherited features, which were compared quantitatively. For the curtain wall building , the absolute mean (sensitivity indices) input factors were up to 2.3 for energy indicators and 3.0 for structural indicators. The precast concrete panel wall building exhibited the absolute mean of input factors up to 2.8 for energy indicators and 2.4 for structural indicators. Overall, the results indicate this new framework, methodology, and sensitivity analysis can be used for commercial hybrid building energy-structure performance analysis. • Introduced a novel sensitivity framework based on a multi-physics simulation. • Proposed a systematic sensitivity analysis for energy-structure performance. • Proposed a Scaled Morris Method for global sensitivity analysis. • Performed a case study to quantify the building energy-structure indicators.

Spatiotemporal Water Yield Variations and Influencing Factors in the Lhasa River Basin, Tibetan Plateau

Understanding the spatiotemporal characteristics of water yield and its influencing factors is important for water resources management. In this study, we used the seasonal water yield model (SWYM) to assess the spatiotemporal water yield changes of the Lhasa River Basin from 1990 to 2015, and analyzed its influencing factors by focusing on precipitation, land cover, and normalized difference vegetation index (NDVI) change. We first examined the model through Morris screening sensitivity analysis and validated it with the observed flow data. Spatiotemporal variation of three indices of water yield, baseflow, quick flow, and local recharge were then assessed. Results showed that from 1990 to 2015, the baseflow, local recharge, and quick flow decreased by 67.03%, 80.21%, and 37.03%, respectively. The spatial pattern of water yield remained mostly unchanged. According to the contribution analysis, precipitation and NDVI change were the main factors affecting water yield in the Lhasa River Basin, while land cover change began to exert greater influence after 2010. A combination of climate change and human activities therefore drive water yield change, especially through vegetation change. Water resources management strategies should thus take into account the combination of rapidly changing climate and human activities.

Sensitivity Analysis in Plasma Chemistry: Application to Oxygen Cold Plasmas and the LoKI Simulation Tool.

The number of numerical models and their complexity are increasing rapidly. This not only accompanies with a bigger impact in various fields but also with a growing opacity. In the case of a deterministic model with hundreds of inputs, it can become difficult, while crucial, to identify which mechanisms are the most influential on the results. Screening sensitivity analysis methods, such as the Morris method, were developed to establish the ranking of inputs' influence on a specified output. The advantages of these methods lie in their easy adaptation to different model formulations or input sets and in providing accurate rankings at relatively low computational costs. In this work, a sensitivity analysis based on the Morris method is applied to a cold oxygen plasma model. The robustness of the method and some of its enhancements are tested, and the rankings obtained are compared with other existing ones for similar conditions. Some significant results are nonintuitive, which highlights the interest of using well-designed sensitivity analysis to study, control, and optimize complex systems.

Effective sampling trajectory optimisation for sensitivity analysis of biological systems.

Sensitivity analysis has been widely applied to study the biological systems, including metabolic networks, signalling pathways, and genetic circuits. The Morris method is a kind of screening sensitivity analysis approach, which can fast identify a few key factors from numerous biological parameters and inputs. The parameter or input space is randomly sampled to produce a very limited number of trajectories for the calculation of elementary effects. It is clear that the sampled trajectories are not enough to cover the whole uncertain space, which eventually causes unstable sensitivity measures. This paper presents a novel trajectory optimisation algorithm for the Morris-based sensitivity calculation to ensure a good scan throughout the whole uncertain space. The paper demonstrates that this presented method gets more consistent sensitivity results through a benchmark example. The application to a previously published ordinary differential equation model of a cellular signalling network is presented. In detail, the parameter sensitivity analysis verifies the good agreement with data of the literatures.

Identification of Critical Parameters Affecting Voltage and Angular Stability Considering Load-Renewable Generation Correlations

The renewable energy source based generating technologies and flexible demand and storage devices exhibit significant temporal and spatial uncertainties in generating and loading profiles and introduce additional level of uncertainty in network operation. The dynamic behaviors of such a network can be affected and the stable operation may be compromised. This paper proposes a probabilistic analysis approach for the evaluation of the effect of uncertain parameters on power system voltage and angular stability. Load margin, the damping of critical eigenvalues and the transient stability index have been chosen as the relevant stability indices for voltage stability, small-disturbance stability, and transient stability analysis, respectively. The Morris screening sensitivity analysis method coupled with a multivariate Gaussian copula to account for parameter correlations is used for the priority ranking of uncertain parameters. The approach is illustrated on a number of case studies using modified IEEE 68-bus NETS-NYPS test system. The results obtained in this paper reveal that the critical parameters appear as groups if the input dataset is correlated, and, hence even a parameter (which may be uninfluential individually) can have a significant impact on system dynamic behavior due to its correlation with other influential parameters.

Sensitivity analysis of the STICS-MACRO model to identify cropping practices reducing pesticides losses.

STICS-MACRO is a process-based model simulating the fate of pesticides in the soil-plant system as a function of agricultural practices and pedoclimatic conditions. The objective of this work was to evaluate the influence of crop management practices on water and pesticide flows in contrasted environmental conditions. We used the Morris screening sensitivity analysis method to identify the most influential cropping practices. Crop residues management and tillage practices were shown to have strong effects on water percolation and pesticide leaching. In particular, the amount of organic residues added to soil was found to be the most influential input. The presence of a mulch could increase soil water content so water percolation and pesticide leaching. Conventional tillage was also found to decrease pesticide leaching, compared to no-till, which is consistent with many field observations. The effects of the soil, crop and climate conditions tested in this work were less important than those of cropping practices. STICS-MACRO allows an ex ante evaluation of cropping systems and agricultural practices, and of the related pesticides environmental impacts.

Model selection in spectroscopic ellipsometry data analysis: Combining an information criteria approach with screening sensitivity analysis

In the field of optical metrology, the selection of the best model to fit experimental data is absolutely nontrivial problem. In practice, this is a very subjective and formidable task which highly depends on metrology expert opinion. In this paper, we propose a systematic approach to model selection in ellipsometric data analysis. We apply two well-established statistical methods for model selection, namely, the Akaike (AIC) and Bayesian (BIC) Information Criteria, to compare different dispersion models with various complexities and objectively determine the “best” one from a set of candidate models. The information criteria suggest the most optimal way to quantify the balance between goodness of fit and model complexity. In combination with screening-type parametric sensitivity analysis based on so-called “elementary effects” (the Morris method) this approach allows to compare and rate various models, identify key model parameters and significantly enhance process of ellipsometric measurements evaluation.

Sensitivity analysis of offshore wind farm operation and maintenance cost and availability

Operation and Maintenance (O&M) costs are estimated to account for 14%–30% of total Offshore Wind Farm (OWF) project lifecycle expenditure according to a range of studies. In this respect, identifying factors affecting operational costs and availability are vital for wind farm operators to achieve the most profitable decisions. Many OWFs are built in stages and the important factors may not be consistent for the different phases. To address this issue, three OWF case studies are defined to represent two phases and a complete project. An initial qualitative screening sensitivity analysis was conducted to identify the most important factors of O&M affecting operating cost and availability. The study concluded that the important factors for total O&M cost were access and repair costs along with failure rates for both minor and major repairs. For time-based availability, the important factors identified were those related to the length of time conducting the maintenance tasks, i.e. the operation duration and the working day length. It was found that the two stages had similar results, but these were different compared to the complete project. In this case, the results provide valuable information to OWF operators during the project development and decision making process.

Sensitivity analysis of the CLARA Simplified Planning Tool using the Morris screening method.

The Morris screening sensitivity analysis (SA) has been used to assess how the uncertainty of input parameters influences the output of the CLARA Simplified Planning Tool (CLARA-SPT). To assess the sensitivity of the tool, four hypothetical waste collection and treatment alternatives, which planned to serve 10,000 people, have been proposed and analysed. These alternatives are (A1) dry sanitation with urine diversion dry toilets (UDDTs), (A2) water-aided sanitation with decentralised treatment units, (A3) water-aided sanitation with central technical treatment and (A4) water-aided sanitation with cesspits. The SA was used to identify the influence of two global and 29 technological input parameters on lifetime costs and residual values of sanitation alternatives. The top two important parameters identified for each alternative are: 'type of urine transport' and 'persons using one UDDT' for alternative A1, 'persons served per septic tank' and 'required surface area for vertical flow constructed wetland' for alternative A2, 'daily diesel generator working hours' and 'expected annual growth' for alternative A3 and 'cesspit volume' and 'expected annual growth' for alternative A4. Additionally, the Morris SA identified non-linearity and/or parameter interaction response. The SA of the specified alternatives shows that from the 29 technological parameters investigated, a subset of 14 important parameters need estimates that are more accurate, whereas a subset of 15 non-influential parameters can be fixed to a certain value. In particular, two parameters (i.e. cesspit volume and persons using one UDDT) that have been internally fixed in the SPT were found to be important and thus should be made available as input parameters to the user. Overall, the study provides guidance for further modification and simplification of the CLARA-SPT.

Significance of Conceptual Model Uncertainty in Simulating Carbon Sequestration in a Deep Inclined Saline Aquifer

AbstractIn modeling geologic carbon sequestration in a deep inclined aquifer in Wyoming, the impact of geologic, engineering, and environmental uncertainty factors on parameter importance and prediction uncertainty is evaluated. Given site characterization data, a suite of geologic model families were built to represent aquifer permeability heterogeneity at increasing complexity. With each family, the same CO2 experiment was simulated. Over a period of 50 years, 17 million tons of CO2 is injected into the aquifer at an approximate depth of 3,750 m. Postinjection simulation is then carried out for a total simulation time of 2,000 years. Based on the design of the experiment, a screening sensitivity analysis was first conducted for all families, systematically varying uncertain input parameters. Parameters with first-order impact on CO2 performance metrics (i.e., trapped gas, dissolved gas, brine leakage, storage ratio) are identified, which vary with time and modeling choice. When the model is of low compl...

Screening sensitivity analysis of a radionuclides atmospheric dispersion model applied to the Fukushima disaster

Numerical models used to forecast the atmospheric dispersion of radionuclides following nuclear accidents are subject to substantial uncertainties. Input data, such as meteorological forecasts or source term estimations, as well as poorly known model parameters contribute for a large part to this uncertainty.A sensitivity analysis with the method of Morris was carried out in the case of the Fukushima disaster as a first step towards the uncertainty analysis of the Polyphemus/Polair3D model. The main difficulties stemmed from the high dimension of the model's input and output. Simple perturbations whose magnitudes were devised from a thorough literature review were applied to 19 uncertain inputs. Several outputs related to atmospheric activity and ground deposition were aggregated, revealing different inputs rankings. Other inputs based on gamma dose rates measurements were used to question the possibility of calibrating the inputs uncertainties.Some inputs, such as the cloud layer thickness, were found to have little influence on most considered outputs and could therefore be safely discarded from further studies. On the contrary, wind perturbations and emission factors for iodine and caesium are predominant. The performance indicators derived from dose rates observations displayed strong sensitivities. This emphasises the share of the overall uncertainty due to input uncertainties and asserts the relevance of the simple perturbation scheme that was employed in this work.

Application of uncertainty and sensitivity analysis in river basin management

Considering uncertainty in the decision-making process in river basin management is important because uncertainty is regarded as one of the main obstacles to sound decision-making. In case of high uncertainty, the risks of making a wrong decision could be quite high, which may have severe consequences. This paper applies a screening sensitivity analysis method, the Morris method, to investigate the propagation of uncertainty from factors in a flood damage model into the model outputs and explores the importance of factors based on the sensitivity analysis. Uncertainty reduction in the most influential factors identified by the Morris method is proposed as a means to reduce the uncertainty in model outputs. In this way the risks of making a wrong decision could be reduced. The results in this paper show that the Morris method is an efficient approach to help reduce the uncertainty in model outputs.

Evaluation of the WAVE Model for Predicting Nitrate Leaching for Two Contrasted Soil and Climate Conditions

The integrated soil–crop–atmosphere model Water and Agrochemicals in the Vadose Environment (WAVE) (Vanclooster et al., 1995) was evaluated for two contrasted sets of data. One was from the tropical climate and ferrallitic soil conditions that exist in Maré Island, in New Caledonia. The other was from a glacial terrace under the continental climate of La Côte Saint-André (Isère) in France. Water and NO3 concentrations and fluxes were monitored during three consecutive years at instrumented sites with different surface covers (maize [Zea mays L.] or bare soil) or amount of applied fertilizer. The comprehensive set of measurements allowed us to evaluate the prediction capabilities of the WAVE model. Several parameters were determined independently, while others were adjusted on the basis of simulations for the wettest year at Maré and an average hydrological year at La Côte Saint-André. A stepwise approach was used to calibrate these parameters by sequentially integrating each individual model component. A screening sensitivity analysis was performed to address the most critical parameters. The predictive ability of the model was evaluated by comparing simulated and measured states variables and water and NO3 fluxes using two different years of data obtained at the same sites. For both sites, the model gave the best results for wet conditions, which actually posed the most critical problems in terms of groundwater pollution under our specific conditions. However, the model was used beyond its capacity as both soils had specificities for which the model was not designed. Overall, WAVE gave quite good predictions, but further studies are needed to fully evaluate WAVE with its crop growth model, SUCROS.

Evaluation of the WAVE Model for Predicting Nitrate Leaching for Two Contrasted Soil and Climate Conditions

The integrated soil–crop–atmosphere model Water and Agrochemicals in the Vadose Environment (WAVE) (Vanclooster et al., 1995) was evaluated for two contrasted sets of data. One was from the tropical climate and ferrallitic soil conditions that exist in Maré Island, in New Caledonia. The other was from a glacial terrace under the continental climate of La Côte Saint‐André (Isère) in France. Water and NO3 concentrations and fluxes were monitored during three consecutive years at instrumented sites with different surface covers (maize [Zea mays L.] or bare soil) or amount of applied fertilizer. The comprehensive set of measurements allowed us to evaluate the prediction capabilities of the WAVE model. Several parameters were determined independently, while others were adjusted on the basis of simulations for the wettest year at Maré and an average hydrological year at La Côte Saint‐André. A stepwise approach was used to calibrate these parameters by sequentially integrating each individual model component. A screening sensitivity analysis was performed to address the most critical parameters. The predictive ability of the model was evaluated by comparing simulated and measured states variables and water and NO3 fluxes using two different years of data obtained at the same sites. For both sites, the model gave the best results for wet conditions, which actually posed the most critical problems in terms of groundwater pollution under our specific conditions. However, the model was used beyond its capacity as both soils had specificities for which the model was not designed. Overall, WAVE gave quite good predictions, but further studies are needed to fully evaluate WAVE with its crop growth model, SUCROS.

A deterministic evaluation analysis applied to an integrated soil-crop model

The WAVE-model (Water and Agrochemicals in the soil and Vadose Environment), simulating one-dimensional transport of water, solute and heat in the unsaturated zone, was used to simulate the behaviour of water and nitrogen in soils cropped with winter wheat and sugar beet. Soil water transport was modelled using the Richards equation, while solute transport was described with a convection equation. Soil mineral nitrogen transformations were simulated using first-order kinetics, while for the organic matter turnover three organic matter pools were considered. A summary model was used to simulate crop growth. The performance of the integrated model was evaluated in a deterministic way using field data for a three-year period. The data of the first year were used for the model calibration. The remaining set of data was used to evaluate the capacity of the model to predict soil water content, the soil nitrate-N content, the soil ammonia-N content, the soil temperature, the soil water pressure head, the leaf area development and the dry matter accumulation in different plant organs. A screening sensitivity analysis indicated that the calculated nitrogen balance was sensitive to the soil hydraulic properties and the crop K c-factors for the given scenario. The effect of uncertainty of the sensitive hydraulic properties on the calculated nitrogen balance was investigated using Monte Carlo simulation. Taken into consideration the variability on the soil hydraulic parameters, predicted nitrate-N flux out of the soil profile at a depth of one meter ranged for the simulated period (three years) between −10 and +10 kg ha −1.