Local Sensitivity Analysis Method Research Articles

Chemical kinetic model reduction based on species‐targeted local sensitivity analysis

AbstractReduction of large combustion mechanisms is usually conducted based on the detection and elimination of redundant species and reactions. Reaction elimination methods are mostly based on sensitivity analysis, which can provide insight into the kinetic system, while species elimination methods are more efficient. In this work, the species‐targeted local sensitivity analysis (STLSA) method is proposed to evaluate the importance of species and eliminate non‐crucial species and their related reactions to simplify kinetic models. This paper comprehensively evaluates the effectiveness of STLSA across various combustion scenarios, including high and low‐temperature ignition and laminar flame speed, using diverse mechanisms like USC Mech II, JetSurf 1.0, POLIMI_TOT_1412, NUIGMech1.1 and so on. Comparisons with graph‐based methods, such as DRG and DRGEP, highlight STLSA's superior efficiency and accuracy. Moreover, STLSA is compared to species‐targeted global sensitivity analysis (STGSA), demonstrating significant computation cost savings and comparable model reduction capabilities. The study concludes that STLSA is a robust and versatile tool for mechanism reduction, offering substantial improvements in computational efficiency while maintaining high accuracy in predicting key combustion properties.

Accurate solution of the SAIL model by leaf inclination angle calculation based on laser point clouds

ABSTRACT The canopy radiative transfer model enables quantitative inversion of vegetation biophysical parameters by rapidly simulating spectral information. However, structural input parameters, such as the leaf inclination angle distribution (LAD), are often set to empirical values in classic models, leading to simulation errors. Laser point cloud technology has developed rapidly in recent years, and the 3D structure of vegetation can be detailed using laser sensors and processing software, providing the necessary conditions for accurate solutions of radiative transfer models of the canopy. However, the mechanisms for improving existing models of canopy radiative transfer by introducing laser point clouds have not yet been theoretically analyzed in detail. In this study, a precise solution was proposed by incorporating the accurate LAD obtained from laser point clouds into the intermediate function of the traditional Scattering by Arbitrarily Inclined Leaves (SAIL) model, which is a widely used multiangle radiative transfer model. The calculation of the leaf inclination angle and the derivation of the improved model were detailed in this paper. Experiments from different observation angles and bands compared the similarity between the actual measured spectra and the simulated spectra for both the traditional SAIL model and the proposed model with accurate structural parameters. The overall similarity of the proposed model with respect to the conventional SAIL model was improved, indicating that introducing laser point clouds improved the simulation accuracy. The proposed model achieved more significant improvements in the blue 450 nm and red 670 nm bands. Moreover, the similarity improvements differed between different angles. Finally, we analyzed the sensitivity of the structural parameters of the SAIL model and the proposed model using local and global sensitivity analysis methods.

Stochastic simulation and uncertainty analysis of karst conduit network using pyKasso

Owing to the intrinsic heterogeneity and anisotropy of karst systems, traditional hydrological exploration methods face significant challenges when investigating karst conduit networks. This study employs pyKasso for the stochastic simulation of karst conduit networks in Panzhou City, focusing on uncertainty analysis through local sensitivity analysis and Monte Carlo methods. The simulation process incorporates geological, topographic, and fracture data to create a realistic representation of the karst network. We found that the spatial configuration and characteristics of the karst network are significantly influenced by various input parameters such as fracture parameters, inlets, outlets, and cost ratios. We highlight the minimal influence of fracture densities and the substantial impact of the count of inlets and outlets on crucial network metrics. The results demonstrate the critical role of parameter sensitivity and variability in modeling the intricate karst systems, providing valuable insights for hydrogeological studies and the management of karst water.

Comparative Study of Global Sensitivity Analysis and Local Sensitivity Analysis in Power System Parameter Identification

In the process of parameter identification, sensitivity analysis is mainly used to determine key parameters with high sensitivity in the model. Sensitivity analysis methods include local sensitivity analysis (LSA) and global sensitivity analysis (GSA). The LSA method has been widely used for power system parameter identification for a long time, while the GSA has started to be used in recent years. However, there is no clear conclusion on the impact of different sensitivity analysis methods on parameter identification results. Therefore, this paper compares and studies the roles that LSA and GSA can play in different parameter identification methods, providing clear guidance for the selection of sensitivity analysis methods and parameter identification methods. The conclusion is as follows. If the identification strategy that only identifies key parameters with high sensitivity is adopted, we recommend still using the existing LSA method. If using a groupwise alternating identification strategy (GAIS) for high- and low-sensitivity parameters, either LSA or GSA can be used. To improve the identification accuracy, it is more important to improve the identification strategy than to change the sensitivity analysis method. When the accuracy of the non-key parameters with low sensitivity cannot be confirmed, using the GAIS is an effective method for ensuring identification accuracy. In addition, it should be noted that the high sensitivity of a parameter does not necessarily mean that the parameter is identifiable, which is revealed by the examples used in this paper.

Comparison of local and global sensitivity analysis methods and application to thermal hydraulic phenomena

The best estimate plus uncertainty analysis have been widely applied for the safety assessment of nuclear systems, and sensitivity analysis is one of the essential steps to identify and rank the important phenomena and parameters. The conventionally used local sensitivity methods are based on linear hypothesis and have limitations in the application process of nonlinear systems, while the more complicated global sensitivity methods may have problem in time costs. To compare and propose the proper method for evaluation of thermal-hydraulic calculation in a reactor system, local methods such as Pearson correlation coefficient, Spearman rank correlation coefficient, and Kendall rank correlation coefficient, global methods such as the variance-based method, moment-independent method, and elementary effects method are reviewed and applied for numerical examples and specific reactor thermal-hydraulic problem in this paper. Results show that the rank of parameter importance measured by various local analysis methods is the same, but away from the global methods. Different types of global sensitivity analysis methods have similar results for parameters with higher importance, and among these methods, the elementary effects method has the lowest computational cost with the same number of samples.

Nonlinear creep-damage constitutive model of surrounding rock in salt cavern reservoir

Salt cavern energy storage is a key technology to promote the optimization of China's energy reserve structure, but the surrounding rock of the reservoir cavity is prone to creep and damage during the long-term injection production process, which seriously endangers the safety of the reservoir operation. Therefore, it is urgent to carry out salt rock creep test research to reveal the creep mechanical properties and damage evolution failure mechanism of the surrounding rock of the reservoir cavity, so as to provide a theoretical basis for the safe construction and operation of the salt cavern reservoir. In order to deeply study the creep mechanical properties of salt rock, a long-term graded loading creep test was carried out on salt rock specimens. The loading time of each stage was about 30 days, and the total creep time exceeded 3 months. The long-term strength of salt rock is determined to be about 8 MPa by the improved isochronous stress-strain method and steady-state creep rate method. A nonlinear creep-damage constitutive model of salt rock based on elastic element and dashpot element is established and compared with the fitting results of several models for the sake of describing the creep behavior of salt rock more accurately. The local and global sensitivity analysis methods are applied to the model to verify its robustness and the rationality of parameter setting. The results showed that the fitting results of the model were in good agreement with the test data, and could well describe the whole creep process of salt rock, especially the nonlinear accelerated creep stage. In addition, the constitutive relation of the established model is simple, and the meaning of the model parameters is clear, which provides a theoretical basis for the study of the creep law of salt rock and the safe operation of the salt cavern storage.

Sensitivity Analysis for Pedestrian-Induced Vibration in Footbridges

This paper aims to provide a novel insight into the influence of uncertainties in system- and pedestrian-induced load parameters on the vibration response of footbridges. The study begins with a sensitivity analysis for the vertical vibration response of a representative footbridge to two loading cases: a single pedestrian and a crowd. Two methods are utilized: the Sobol’-based global sensitivity analysis method and the local sensitivity analysis method. Uncertainties in all model parameters (which include bridge and human body dynamics in a walking posture, as well as dynamic force generated by humans) are considered in stochastic response estimation. Parametric analysis is then performed to investigate the influence of the variation of the mean values of the bridge modal mass, damping ratio, and natural frequency on the results of global and local sensitivity analysis. Systematic comparison of the results of global and local sensitivity analysis is performed to identify their similarities and differences. It has been found that the sensitive parameters and their importance ranking strongly depend on bridge modal properties and loading scenarios (i.e., a single pedestrian or a crowd crossing). The damping ratio and natural frequency of the human body are found to be the only two insensitive parameters. Therefore, they could be treated as deterministic parameters in the stochastic estimation of human-induced vibration. Global sensitivity analysis is recommended as a choice for the sensitivity analysis of pedestrian-induced vibration of footbridges as it leads to more reliable results, owing to the advantage of characterizing model sensitivity over the entire input spaces.

Sensitivity analysis of the DehumReq model to evaluate the impact of predominant factors on dehumidification requirement of greenhouses in cold regions

In this study, the sensitivity of a novel dehumidification requirement model (DehumReq) is analyzed to evaluate the effect of the predominant factors on the dehumidification needs of the greenhouses. The hourly dehumidification demand and sensitivity coefficient (SC) are estimated for three different seasons: warm (July), mild (May), and cold (November), by using the local sensitivity analysis method. Based on SC values, the solar radiation, air exchange, leaf area index (LAI), and indoor setpoints (temperature, relative humidity (RH), and water vapor partial pressure (WVPP)) have significant impact on the dehumidification needs, and the impact varies from season to season. Most parameters have a higher SC in summer, whereas solar radiation and LAI have a higher SC in mild season. The dehumidification load increases 4 times of its base value with increasing solar radiation by 200 W/m2, and the highest LAI (10) caused 5 times increment of the load. The changing of WVPP from its base value (1.5 kPa) to maximum (2.9 kPa) reduces the load 70% in summer. Air exchange was found to be the most crucial parameter because it is the main dehumidification approach that has a large range and is easily adjustable for any greenhouses. Sufficient air exchange by ventilation or infiltration will reduce the dehumidification load to zero in May and November and minimizes it to only nighttime load in July. For the other parameters, higher ambient air RH and indoor air speed will result in higher the dehumidification load; whereas higher inner surface condensation will result in lower dehumidification load. The result of this study will assist in the selection of the most efficient moisture control strategies and techniques for greenhouse humidity control.

Application of machine learning in predicting the adsorption capacity of organic compounds onto biochar and resin

Detailed prediction of the adsorption amounts of organic pollutants in water is essential to the clean development and management of water resources. In this study, Kriging and polyparameter linear free energy relationship model are coupled to predict adsorption capacity of organic pollutants by biochar and resin. It's based on 1750 adsorption experimental data sets which contains 73 organic compounds on 50 biochars and 30 polymer resins. The Kriging-LFER model shows better accuracy and predictive performance for adsorption (R2 are 0.940 and 0.976) than the published NN-LFER model (R2 are 0.870 and 0.880). Local sensitivity analysis method is adopted to evaluate the influence of each variable on the adsorption coefficient of resin and find out that top sensitive parameters are V and log Ce, to guide parameter optimization. Data's uncertainty analysis is presented by Monte Carlo method. It predicts that the adsorption coefficient will range from 0.062 to 0.189 under the 95% confidence interval. The Kriging-LFER model provides great significance for understanding the importance of various parameters, reducing the number of experiments, adjusting the direction of experimental improvement, and evaluating the fate of organic pollutants in the environment.

Sampling Design of Hydraulic and Quality Model Calibration Based on a Global Sensitivity Analysis Method

AbstractMost sampling design (SD) methods in water distribution networks are focused on the calibration of a hydraulic model and use local sensitivity analysis and demand-driven simulation methods ...

Identification of models using analog sensitivity functions

The paper proposes an algorithm for retrospective and adaptive estimation of sensitivity functions in relation to the problem of parametric identification of a dynamic model of an object. The proposed approach is based on the combined use of local and global sensitivity analysis methods. The algorithm is based on the use of analogs of sensitivity functions. They are not sensitivity functions in strict sense in the case of large parameter deviations. Analogs of sensitivity functions become their estimates with reduction deviations. The procedure for compressing the region of variation is based on the approach used in the selective coordinate averaging algorithm. The results are used for evaluating the sensitivity functions of the Monod model, and for parametric identification of the simple distillation process model using the adaptive least squares method.

The Sensitivity Analysis of the Core Lower Head Molten Pool Model Based on Variance Decomposition

Abstract The lower head molten pool model is an important model for the in-vessel retention (IVR) effectiveness evaluation. It has the characteristics of complex relationship, many input parameters, and large uncertainty. The traditional local sensitivity analysis method has the disadvantages of large amount of calculation and low efficiency when performing sensitivity analysis of complex models. Based on variance decomposition of the global sensitivity analysis method, the sensitivity analysis library (SALib) and IVR analysis code developed by Nuclear Power Institute of China (NPIC) are adopted to analyze the sensitivity of the input parameters for four key parameters, such as the lower head wall heat flux ratio. In this paper, the sensitivity coefficient and influence trend of input parameters on key results are obtained, which provides a reference for the optimization of the lower head molten pool model and severe accident mitigation strategies.

Improvement of the uncertainty method with model treatment and optimized global sensitivity analysis and its application to pressurized water reactor

Best estimate plus uncertainty methodology is an advanced technology for licensing application of the nuclear power plants, but some aspects can still be improved in the conventional input-driven methodologies. For instance, uncertainties of the closure models are not properly handled; local sensitivity analysis methods not suitable for complex nuclear systems are still widely utilized; the sensitivity analysis process needs to be performed again every time new parameters are identified. Optimization researches were carried out in the study. A structural method was proposed to quantify the uncertainties of the closure models, and a low-cost moment-independent global sensitivity analysis method was developed to improve the sensitivity analysis framework. By integrating the improvements into the conventional input-driven framework, an uncertainty method with model treatment and improved sensitivity analysis was obtained. Finally, the method was validated through a large break loss of coolant accident in a three-loop pressurized water reactor.

Sensitivity and identifiability of rheological parameters in debris flow modeling

Abstract. Over the past decades, several numerical models have been developed to understand, simulate and predict debris flow events. Typically, these models simplify the complex interactions between water and solids using a single-phase approach and different rheological models to represent flow resistance. In this study, we perform a sensitivity analysis on the parameters of a debris flow numerical model (FLO-2D) for a suite of relevant variables (i.e., maximum flood area, maximum flow velocity, maximum height and deposit volume). Our aims are to (i) examine the degree of model overparameterization and (ii) assess the effectiveness of observational constraints to improve parameter identifiability. We use the Distributed Evaluation of Local Sensitivity Analysis (DELSA) method, which is a hybrid local–global technique. Specifically, we analyze two creeks in northern Chile (∼29∘ S, 70∘ W) that were affected by debris flows on 25 March 2015. Our results show that SD (surface detention) and β1 (a parameter related to viscosity) provide the largest sensitivities. Further, our results demonstrate that equifinality is present in FLO-2D and that the final deposited volume and maximum flood area contain considerable information to identify model parameters.

Optimization of sensitivity analysis in best estimate plus uncertainty and the application to large break LOCA of a three-loop pressurized water reactor

The best estimate plus uncertainty (BEPU) methodology is developed to carry out realistic safety analysis of the nuclear power plant. However, the sensitivity analysis in the conventional methodologies often leads to inaccurate results when lacking of the phenomenon identification and ranking table (PIRT) or adopting the local sensitivity analysis methods. As the initial inputs determined in short of the PIRT may neglect some important parameters or identify some unimportant parameters, and the local methods are not appropriate for complex and non-linear systems. In this study, an optimized moment-independent global sensitivity analysis method with very low computational cost was utilized to improve the sensitivity analysis framework of the BEPU methodology. The reliability as well as the applicability of the method were assessed through some tests and a large break loss of coolant accident (LBLOCA) of the LOFT facility. The improved sensitivity analysis framework was then applied to the BEPU analysis of a typical three-loop pressurized water reactor CPR1000, and the reliability of the optimized moment-independent method was further confirmed by comparing the sensitivity analysis result of the method to the results of the BEMUSE project and the Spearman method. Besides, two new parameters were identified to have great influence on the PCT, and the two parameters should be taken into consideration in the uncertainty analysis of the BEPU methodology.

Global sensitivity analysis of LOFT large break loss of coolant accident with optimized moment-independent method

Best estimate plus uncertainty (BEPU) analysis has been widely adopted for safety evaluation of the nuclear reactor, and the sensitivity analysis is an important part of the BEPU methodology. Local sensitivity analysis methods are still widely utilized in the methodology, which is not suitable for complex non-linear nuclear systems. The global sensitivity analysis is essential but the biggest problem is that the computational cost can hardly be accepted. In this study, the moment-independent global method was adopted and optimized, and a low-cost method was obtained and assessed. The sensitivity analysis of a large break loss of coolant accident (LBLOCA) of the LOFT facility was carried out by using the method. Results show that the optimized method can well evaluate the sensitivity indices with very low cost and relatively high accuracy, and the result obtained through the optimized method is much more reliable than that of the local sensitivity analysis method.

Maximum Loadability of Islanded Microgrids With Renewable Energy Generation

A microgrid (MG) is a small-scale power system which is fed by constrained distributed generation (DG) units and its continuous operation is affected by the variability of available generation resources. In this paper, a global sensitivity analysis (GSA) method is proposed to evaluate the impact of variable energy resources on the maximum loadability of islanded MGs (IMGs). First, a probabilistic optimization problem is formulated to calculate the IMG load margin considering the droop characteristics of DG units and uncertainties of renewable generation, loads and distribution feeder parameters. Next, the global sensitivity (GS) is introduced that can identify the impact of independent and correlated variables on the IMG loadability. Last, the sparse polynomial chaos expansion method is used to obtain the probabilistic models for IMG load margins, and an efficient GSA method is proposed to calculate the GS of IMG loadability to prevailing variables. The probabilistic models are considered for IMG input variables and the impact of variable correlations on IMG loadability is analyzed. The proposed method for calculating the maximum loadability is tested using a 33-bus IMG and the results are compared with those of other GSA and local sensitivity analysis methods.

Modeling and uncertainty analysis of seawater intrusion in coastal aquifers using a surrogate model: a case study in Longkou, China

Seawater intrusion is a complex problem involving groundwater with variable density. It has an important impact on the development and utilization of groundwater resources in coastal areas. In previous studies, numerical simulations of seawater intrusion were performed by assigning fixed values to model parameters, thereby not taking into account the influence of stochastic variability of hydrogeological parameters on model predictions. In this study, we developed a three-dimensional mathematical model of seawater intrusion by modeling vertical solute transport due to density changes resulting from seawater intrusion. We then applied a Monte Carlo–based method to analyze uncertainty in model simulations of seawater intrusion. Because the Monte Carlo method requires the simulation model to be repeatedly run, we used the Kriging method to build a surrogate model that significantly reduced the computational load compared to the simulation model while ensuring the desired accuracy. Then we used a local sensitivity analysis method to select the two parameters with the greatest influence on model output. We treated the two selected parameters as random variables, and results show that (1) the three-dimensional, variable-density seawater intrusion model can effectively simulate and predict the distribution of seawater intrusion in the study area; (2) the local sensitivity analysis can accurately identify the hydrogeological parameters that most influence model output; (3) the uncertainty analysis based on the surrogate model reduces computing time substantially and provides a realistic assessment of the effect of hydrogeological parameter variability on seawater intrusion numerical simulation results.

Smoke spreading along corridor induced by outdoor wind: numerical simulation and parameter sensitivity analysis

During a fire in building of high or low height, 80% of deaths are caused by the inhalation of smoke. Considering the fact that outdoor wind is one of the important driving forces of the smoke movement of the fire, this work investigates its effects on the dynamics of smoke along corridor induced by compartment fire. This study is carried out using numerical simulation based on experimental data. The computational fluid dynamic code used is FDS. Through the analysis of mesh resolution, it is shown that the numerical results can agree well with the experimental data. Simulations are performed with different wind speeds. It can be seen that the outdoor wind has effects on the smoke stratification. Indeed, the more wind speed increase, the more the smoke stratification is disturbed. Under these conditions, smoke occupies entire corridor volume and thus presents a high risk of toxicity to people. It can be found that the temperature of the smoke increases suddenly and decreases to converge to a homogeneous value in the entire enclosure. Considering the importance of the input data used for the numerical simulation, a sensitivity analysis is made on parameters related to the material, fuel, combustion and extinction models. This analysis is carried out using a local sensitivity analysis method. It turns out that mass flux of fuel and activation energy as well as thermal conductivity of the walls have significant effects (more than 10%) on the smoke temperature near the celling.

A surrogate-based simulation–optimization approach application to parameters’ identification for the HydroGeoSphere model

A systematic approach is needed to use water more productively, because water shortages limit socio-economic development in many parts of the world. The aim of this paper is to establish a surrogate-based simulation–optimization approach to identify parameter values for a fully integrated surface water and groundwater flow coupling simulation. A surface water and groundwater flow coupling simulation model was implemented using HydroGeoSphere (HGS) model and the parameter sensitivities in the model were analyzed using local sensitivity analysis method. The parameters that exerted a large influence on the output results of the HGS model were then selected as stochastic variables, and the stochastic variable data sets were generated using the latin hypercube sampling (LHS) method, which, thereby, were used as inputs in HGS model to obtain the corresponding outputs. On the basis of input and output data sets, a kriging surrogate model of the HGS model was then established and verified, and parameter values of HGS model were identified using a surrogate-based simulation–optimization approach. The results of this study show that parameters that exert a large influence on the simulation output results include hydraulic conductivity, porosity, the van genuchten parameter ( $$\alpha$$ ), and channel manning coefficient. The established kriging surrogate model is an ideal alternative to the HGS model for simulating and predicting, while optimal parameter values can be identified effectively and accurately using the established approach. The results of this research reveal that huge computational loads can be mitigated while using the kriging surrogate as an alternative for a simulation model in the solution process of optimization model.