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The Squirrel Search Algorithm (SSA) is an innovative optimization method that takes inspiration from the foraging and gliding behavior of squirrels. Despite its simple structure and stable performance, it is prone to the same issues as other algorithms, such as falling into local optima and experiencing premature convergence. To address this problem, this paper proposes an improved squirrel search algorithm embedded with the Sine Cosine Algorithm (SCSSA). Firstly, the Sine Cosine Algorithm is introduced into the SSA to enhance its local exploitation ability. Secondly, the Sobol sequence is utilized to generate the initial population, resulting in higher quality initial solutions. Thirdly, dimensional learning is applied to squirrels on both hickory and oak trees, promoting population diversity and preventing local optima. Finally, the glide constant Gc in SSA is adjusted to decay nonlinearly with iteration count, starting with a large value that gradually decreases in the early stage to facilitate global exploration, and then rapidly decreasing in the later stage to promote local exploitation. Extensive experiments are conducted on 23 classic benchmark functions, the CEC2017 test set, and three engineering problems. The experimental results show that SCSSA can effectively maintain population diversity and can achieve a balance between exploration and exploitation. It consistently outperforms the comparison algorithms in terms of numerical optimization and convergence rate. [ABSTRACT FROM AUTHOR]

ABSTRACT Simulation of water flow in the soil in semiarid regions is fundamental for managing scarce water resources. Aiming for more accurate results, the sensitivity analysis (SA) of the hydrodynamic parameters involved in the simulations is very relevant. This study aims to evaluate the influence of hydrodynamic parameters on the hydrological response of an alluvial deposit in the dry bed of the Capibaribe River using the Hydrus-1D. A global sensitivity analysis method proposed by Sobol was chosen to evaluate the sensitivity of the soil water infiltration curve. A convergence analysis was carried out to investigate the evolution of the indices with increasing sample size. Values of the sensitivity indices showed gross variations at the beginning of the simulations for the different parameters, but they converged within the set of 10,000 samples. For parameters whose sensitivity indices are closer to 0, it is noted that 1,000 samples (14,000 simulations) were sufficient for the index to converge to their final value. The total and first-order sensitivity index calculated with 10,000 samples present the parameters n and Ks as the most important, being expressly more influential than the others.

This paper introduces an innovative Particle Swarm Optimization (PSO) Algorithm incorporating Sobol and Halton random number samplings. It evaluates the enhanced PSO's performance against conventional PSO employing Monte Carlo random number samplings. The comparison involves assessing the algorithms across nine benchmark problems and the renowned Travelling Salesman Problem (TSP). The results reveal consistent enhancements achieved by the enhanced PSO utilizing Sobol/Halton samplings across the benchmark problems. Particularly noteworthy are the Sobol-based PSO improvements in iterations and the computational times for the benchmark problems. These findings underscore the efficacy of employing Sobol and Halton random number generation methods to enhance algorithm efficiency. [ABSTRACT FROM AUTHOR]

Global sensitivity analysis combined with quantitative high-throughput screening (GSA-qHTS) uses random starting points of the trajectories in mixture design, which may lead to potential contingency and a lack of representativeness. Moreover, a scenario in which all factor levels were at stimulatory effects was not considered, thereby hindering a comprehensive understanding of GSA-qHTS. Accordingly, this study innovatively introduced an optimised experimental design, uniform design (UD), to generate non-random and representative sample points with smaller uniformity deviation as starting points of multiple trajectories. By combining UD with the previously optimised one-factor-at-a-time (OAT) method, a novel mixture design method was developed (UD-OAT). The single toxicity tests showed that three pyridinium and five imidazolium ionic liquids (ILs) exerted stimulatory effects on Vibrio qinghaiensis sp.-Q67; thus, four stimulatory effective concentrations of each IL were selected as factor levels. The UD-OAT generated 108 mixture samples with equal frequency and without repetition. High-throughput microplate toxicity analysis revealed that all 108 mixtures exhibited inhibitory effects. Among these, type B mixtures exhibited increasing toxicities that subsequently decreased, unlike type C mixtures, which consistently increased over time. GSA successfully identified three of the eight ILs as important factors influencing the toxicities of the mixtures. When individual ILs produced stimulatory effects, mixtures containing two to three ILs exhibited either stimulatory effects or none. In contrast, mixtures containing five to eight ILs exhibited inhibitory effects, while those containing four ILs showed a transition from stimulatory to inhibitory effects. This study provides a novel mixture design method for studying mixture toxicity and fills the application gap of GSA-qHTS. The phenomenon of individuals being beneficial while mixtures can be harmful challenges traditional mixture risk assessments.

To address the issue of low accuracy and inefficiency in the traditional parameter calibration methods for the SWMM model, this paper constructs an automatic parameter calibration model based on multi-objective optimisation algorithms. Firstly, the Sobol method and GLUE method are utilised to determine sensitive parameters and their ranges, aiming to narrow down the solution space and expedite the model-solving speed. Secondly, the NSGA-3 multi-objective optimisation algorithm based on the Pareto theory is applied for the optimisation and calibration of sensitive parameter sets. The model is validated in the rainwater drainage system with independent runoff in a residential area in a northwestern city in China. The results show that parameters such as N-Imperv and KSlope are highly sensitive to the model output under the land-use conditions of the study area. The simulation accuracy of the multi-objective continuous optimisation algorithm is significantly better than that of the single-objective genetic algorithm. The simulation results of the SWMM model under multi-objective optimisation demonstrate a certain level of reliability and stability. The research findings can provide technical support for the automatic calibration of SWMM model parameters, accurate model simulation, and application. HIGHLIGHTS The study provides a quantitative interpretation of the results of sensitivity parameter screening and ranking for the SWMM model.; During the construction of the objective function, the SWMM model system's multi-objective continuous optimisation criteria were adopted.; Combining the concepts of uncertainty and optimisation.;

Pandora neoaphidis is a common entomopathogenic fungus on Sitobion avenae, which is an important aphid pest on cereals in Europe. Pandora neoaphidis is known to cause epizootics (i.e. an unusually high prevalence of infected hosts) and the rapid collapse of aphid populations. We developed a weather-driven mechanistic model of the winter wheat-S. avenae-P. neoaphidis system to simulate the dynamics from spring to harvest. Aphid immigration was fixed at a rate that would lead to a pest outbreak, if not controlled by the fungus. We estimated the biocontrol efficacy by running pair-wise simulations, one with and one without the fungus. Uncertainty in model parameters and variation in weather was included, resulting in a range of simulation outcomes, and a global sensitivity analysis was performed. We identified two key understudied parameters that require more extensive experimental data collection to better assess the fungus biocontrol, namely the fungus transmission efficiency and the decay of cadaver, which defines the time window for possible disease transmission. The parameters with the largest influence on the improvement in yield were the weather, the lethal time of exposed aphids, the fungus transmission efficiency, and the humidity threshold for fungus development, while the fungus inoculum in the chosen range (between 10 and 70% of immigrant aphids carrying the fungus) was less influential. The model suggests that epizootics occurring early, around Zadoks growth stage (GS) 61, would lead to successful biocontrol, while later epizootics (GS 73) were a necessary but insufficient condition for success. These model predictions were based on the prevalence of cadavers only, not of exposed (i.e. infected but yet non-symptomatic) aphids, which in practice would be costly to monitor. The model suggests that practical Integrated Pest Management could thus benefit from including the cadavers prevalence in a monitoring program. We argue for further research to experimentally estimate these cadaver thresholds. [ABSTRACT FROM AUTHOR]

【Background and Objective】 The APEX model is a comprehensive watershed-scale model for simulating the effects of management practices on agricultural systems and their impacts on water quality, soil erosion, and nutrient cycling. This paper analyzes the sensitivity of its parameters to water status in soil. 【Method】 The analysis is based on data measured from 2016 to 2019 from an irrigation experiment conducted in Jiaodong in Shandong province. Winter wheat was used as the model plant; the Sobol, Morris, and FAST methods were used to analyze the sensitivities of the APEX model parameters associated with crop growth and water stress. We considered the influences of groundwater depth, rainfall and irrigation. 【Result】 When groundwater depth was 1.25 m, the maximum root depth (RDMX) was the most sensitive parameter affecting evapotranspiration, biomass, and yield, while the maximum potential leaf area index (DMLA) was the most sensitive parameter impacting leaf area index (LAI). When the groundwater depth was increased to 5 m, the sensitive parameters influencing crop evapotranspiration and yield differed, with PARM38 (weight coefficient of water stress calculation) and RWPC1 (proportion of root biomass during germination) becoming the most sensitive parameters. Results calculated from all three methods indicated that as irrigation water increased, the sensitivity of RDMX decreased, while the sensitivities of DMLA, DLAI (peak point in growth season), and WA (potential light energy utilization) increased. The sensitivity of RDMX was significantly higher in dry years than in humid years, as opposed to the sensitivity of DMLA. Uncertainty analysis demonstrated that wheat biomass, yield, and evapotranspiration fell within the 5% to 95% confidence interval of the simulated data. 【Conclusion】 The most sensitive parameters identified by the Sobol, Morris, and FAST methods were consistent, although their sensitivity indexes varied with irrigation treatments, rainfall patterns, and groundwater depth. Considering computational efficiency and accuracy, the Morris method is more suitable for parameter sensitivity analysis of the APEX model. These findings provide valuable insights into the application of the APEX to analyze the impact of environmental conditions on crops.

The properties of metal -based composites, such as their high strength -to -weight ratio and good resistance to wear and fatigue, have caused a significant growth in their use in the aerospace, automotive, and aircraft industries. Magnesium -based composites have particularly attracted the attention of researchers in various fields, especially aerospace scientists, due to their lower density than other metal -based composite alloys such as titanium and aluminum. However, due to the presence of very abrasive reinforcing material in these materials, machining them is difficult and presents numerous challenges. Therefore, it to study the machining process of these is necessary composites and to examine the effect of the main turning parameters such as cutting speed, feed rate, and depth of cut on machining forces and surface roughness. Sobol's sensitivity analysis method was used for this purpose. Using this method, it was determined that the feed rate, cutting depth, and cutting speed have the greatest effect on the machining forces, respectively. Additionally, the feed rate has a greater effect on the surface roughness than the cutting depth and cutting speed. As the feed rate increases, the surface roughness and cutting forces increase. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative Particle Swarm Optimization (PSO) Algorithm incorporating Sobol and Halton random number samplings. It evaluates the enhanced PSO’s performance against conventional PSO employing Monte Carlo random number samplings. The comparison involves assessing the algorithms across nine benchmark problems and the renowned Travelling Salesman Problem (TSP). The results reveal consistent enhancements achieved by the enhanced PSO utilizing Sobol/Halton samplings across the benchmark problems. Particularly noteworthy are the Sobol-based PSO improvements in iterations and the computational times for the benchmark problems. These findings underscore the efficacy of employing Sobol and Halton random number generation methods to enhance algorithm efficiency.

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Flexible DC transmission systems experience negative damping characteristics in the high-frequency range due to the delay in the system control link. This can lead to interactions between the impedance of modular multilevel converters (MMCs) and AC transmission lines, resulting in high-frequency oscillation (HFO) issues. To resolve this problem, a simplified MMC control model was developed, taking into account the current inner loop control link. The Sobol method was employed to analyze the factors influencing the impedance characteristics of MMC. Additionally, a combined approach involving a virtual passive filtering method and an equivalent current sampling method was proposed to provide additional damping. The study compared and analyzed the impact of additional damping control on the impedance characteristics and system stability of MMCs. Lastly, a flexible direct current grid connection simulation model was constructed using PSCAD to validate the effectiveness of the proposed additional damping control strategy. [ABSTRACT FROM AUTHOR]

Accurate load forecasting is critical to guarantee the security, steadiness and economic operation of the power system. Therefore, in order to improve the load forecasting accuracy, improved coyote optimization (ICOA) was proposed in this paper to optimizing the Extreme learning machine for load forecasting, which is a novel load forecasting model. The ICOA algorithm effectively improves the accuracy and speed of convergence by introducing the sobol sequence and improving the pack culture trend, which is approved beneath a set of benchmark capacities from the Organized of Electrical and Hardware Engineers (IEEE) Congress on Developmental Computation (CEC) 2017. The result comparisons have moreover shown the exceptional execution of ICOA, since it could rank to begin with within the optimization of 16 benchmark. In addition, the ELM was optimized using to address the problem that the randomly formed weights and thresholds of the ELM lead to an unstable model, as well as experiment with different models using real electricity load data. The results show that the ICOA-ELM model possesses excellent accuracy and stability in short-term load forecasting. Its R2 and RMSE are 0.9970 and 0.3840 MW respectively, which are much better than ELM and LSTM. Thus, the proposed ICOA-ELM is a viable strategy for short-term load forecasting.

Musculoskeletal models (MSKMs) are used to estimate the muscle and joint forces involved in human locomotion, often associated with the onset of degenerative musculoskeletal pathologies (e.g., osteoarthritis). Subject-specific MSKMs offer more accurate predictions than their scaled-generic counterparts. This accuracy is achieved through time-consuming personalisation of models and manual tuning procedures that suffer from potential repeatability errors, hence limiting the wider application of this modelling approach. In this work we have developed a methodology relying on Sobol's sensitivity analysis (SSA) for ranking muscles based on their importance to the determination of the joint contact forces (JCFs) in a cohort of older women. The thousands of data points required for SSA are generated using Gaussian Process emulators, a Bayesian technique to infer the input–output relationship between nonlinear models from a limited number of observations. Results show that there is a pool of muscles whose personalisation has little effects on the predictions of JCFs, allowing for a reduced but still accurate representation of the musculoskeletal system within shorter timeframes. Furthermore, joint forces in subject-specific and generic models are influenced by different sets of muscles, suggesting the existence of a model-specific component to the sensitivity analysis. [ABSTRACT FROM AUTHOR]

Accurate load forecasting is critical to guarantee the security, steadiness and economic operation of the power system. Therefore, in order to improve the load forecasting accuracy, improved coyote optimization (ICOA) was proposed in this paper to optimizing the Extreme learning machine for load forecasting, which is a novel load forecasting model. The ICOA algorithm effectively improves the accuracy and speed of convergence by introducing the sobol sequence and improving the pack culture trend, which is approved beneath a set of benchmark capacities from the Organized of Electrical and Hardware Engineers (IEEE) Congress on Developmental Computation (CEC) 2017. The result comparisons have moreover shown the exceptional execution of ICOA, since it could rank to begin with within the optimization of 16 benchmark. In addition, the ELM was optimized using to address the problem that the randomly formed weights and thresholds of the ELM lead to an unstable model, as well as experiment with different models using real electricity load data. The results show that the ICOA-ELM model possesses excellent accuracy and stability in short-term load forecasting. Its R2 and RMSE are 0.9970 and 0.3840 MW respectively, which are much better than ELM and LSTM. Thus, the proposed ICOA-ELM is a viable strategy for short-term load forecasting. [ABSTRACT FROM AUTHOR]

Parameter sensitivity analysis is a significant part of quantifying model uncertainty, effectively identifying key parameters, and improving the efficiency of parameter optimization. The Soil and Water Assessment Tool (SWAT) model was applied to the upper Heihe River basin (UHRB) in China to simulate the monthly runoff for 11 years (1990–2000). Four typical sensitivity analyses, namely, the Morris screening, Sobol analysis, Fourier amplitude sensitivity test (FAST), and extended Fourier amplitude sensitivity test (EFAST), were used to determine the critical parameters affecting hydrological processes. The results show that the sensitivity parameters defined by the four methods were significantly different, resulting in a specific difference in the simulation effect of the SWAT model. The reason may be the different sampling process, sensitivity index, and calculation principle of each method. The snow-melt base temperature (SMTMP) and snowfall temperature (SFTMP) related to the snow-melt process, the available water capacity of the soil layer (SOL_AWC), saturated hydraulic conductivity (SOL_K), depth from the soil surface to the bottom of the layer (SOL_Z), moist bulk density (SOL_BD), deep aquifer percolation fraction (RCHRG_DP), and threshold depth of water in the shallow aquifer required for return flow to occur (GWQMN) related to the soil water and groundwater movement, baseflow alpha factor for bank storage (ALPHA_BNK) related to the base flow regression, and average slope steepness (HRU_SLP) are all very sensitive parameters. The 10 key parameters were optimized 100 times with the sequential uncertainty fitting procedure version 2 (SUFI-2). The Nash–Sutcliffe efficiency coefficient (NSE), Kling–Gupta efficiency coefficient (KGE), mean square error (MSE), and percentage bias (PBIAS) were 0.89, 200, 8.60, and 0.90, respectively. The simulation results are better than optimizing the sensitive parameters defined by the single method and all the selected parameters. The differences illustrate the rationality and importance of parameter sensitivity analysis for hydrological models and the synthesis of multiple approaches to define sensitive parameters. These conclusions have reference significance in the parameter optimization of the SWAT model when studying alpine rivers by constructing the SWAT model. [ABSTRACT FROM AUTHOR]

[Display omitted] • Uniform design was introduced to develop an innovative mixture design method. • Four levels of eight ionic liquids (ILs) were used to assess stimulatory effects. • Individual ILs were stimulatory, while their 108 mixtures were inhibitory. • 108 mixtures showed two different temporal toxicity characteristics. • GSA identified three of eight ILs as inducing mixture toxicities. Global sensitivity analysis combined with quantitative high-throughput screening (GSA-qHTS) uses random starting points of the trajectories in mixture design, which may lead to potential contingency and a lack of representativeness. Moreover, a scenario in which all factor levels were at stimulatory effects was not considered, thereby hindering a comprehensive understanding of GSA-qHTS. Accordingly, this study innovatively introduced an optimised experimental design, uniform design (UD), to generate non-random and representative sample points with smaller uniformity deviation as starting points of multiple trajectories. By combining UD with the previously optimised one-factor-at-a-time (OAT) method, a novel mixture design method was developed (UD-OAT). The single toxicity tests showed that three pyridinium and five imidazolium ionic liquids (ILs) exerted stimulatory effects on Vibrio qinghaiensis sp.-Q67; thus, four stimulatory effective concentrations of each IL were selected as factor levels. The UD-OAT generated 108 mixture samples with equal frequency and without repetition. High-throughput microplate toxicity analysis revealed that all 108 mixtures exhibited inhibitory effects. Among these, type B mixtures exhibited increasing toxicities that subsequently decreased, unlike type C mixtures, which consistently increased over time. GSA successfully identified three of the eight ILs as important factors influencing the toxicities of the mixtures. When individual ILs produced stimulatory effects, mixtures containing two to three ILs exhibited either stimulatory effects or none. In contrast, mixtures containing five to eight ILs exhibited inhibitory effects, while those containing four ILs showed a transition from stimulatory to inhibitory effects. This study provides a novel mixture design method for studying mixture toxicity and fills the application gap of GSA-qHTS. The phenomenon of individuals being beneficial while mixtures can be harmful challenges traditional mixture risk assessments. [ABSTRACT FROM AUTHOR]

This study utilized the ECO Lab model calculation samples of Tai Lake, in combination with robust analysis and the GCV test, to promote a faster intelligent application of machine learning and evaluate the MARS machine learning method. The results revealed that this technique can be better trained with small-scale samples, as indicated by the R2 values of the water quality test results, which were all >0.995. In combination with the Sobol sensitivity analysis method, the contribution degree of the parameterized external conditions as well as the relationship with the water quality were examined, which indicated that TP and TN are primarily related to the external input water quality and flow, while Chl-a is related to inflow (36.42%), TP (26.65%), wind speed (25.89%), temperature (8.38%), thus demonstrating that the governance of Chl-a is more difficult. In general, the accuracy and interpretability of MARS machine learning are more in line with the actual situation, and the use of the Sobol method can save computer calculation time. The results of this research can provide a certain scientific basis for future intelligent management of lake environments. HIGHLIGHTS Introduce a MARS – machine learning method coupled with a Sobol sensitive analysis approach.; Coupled methods can solve the same problems with less time.; The declared goal of this research is to provide a certain scientific basis for future intelligent management of lake environments.;

Sensitivity analysis (SA) is widely acknowledged as advantageous and worthwhile in recognizing parameters for model calibration and optimization, especially in complex hydrological models. Although Sobol global SA is an efficient way to evaluate the sensitivity indices, the computational cost is a constraint. This study analyzes the potential of Morris global SA to achieve results tantamount to Sobol SA, at a much cheaper computational expense, using a new approach of increasing the number of replications for the Morris algorithm. SA for two catchments is performed on a coupled hydrological model using Morris and Sobol algorithms. Two target functions are used for each of the algorithms. Sobol SA required 660000 model simulations accounting for about 400 computing hours, whereas increasing the replications from 1000 to 3000, the Morris method called for 63000 runs and 06 computing hours to produce significantly similar results. The Morris parameter ranking improved 50% with respect to Sobol SA by a three-fold increase in replications with a small 5-h increase in the computational expense. The results also suggest that target functions and catchments influence parameter sensitivity. The new approach to employ the Morris method of SA shows promising results for highly parameterized hydrological models without compromising the quality of SA, specifically if there are time constraints. The study encourages the use of SA, which is mainly skipped because of higher computational demands. [ABSTRACT FROM AUTHOR]

The hybrid simulation (HS) is a cost‐effective and promising method for investigating the seismic performance of structures. This paper proposes a model updating method for HS to improve incomplete boundary conditions and model error, and develops criteria to select the updated parameters. First, an actual HS model updating is conducted to verify the effectiveness of uniform design as a parameter estimation method. This experiment shows that parameter sensitivity has a significant relationship with the update effect. Then, the Sobol global sensitivity analysis (GSA) method is applied to the HS model updating to obtain the sensitivity indices of parameters that change with time, which provides a sound basis for parameter selection and the determination of the search range. Following this, a whole‐structure module for dynamic time history analysis is proposed to improve the boundary conditions, simplify the implementation, and refine the numerical models. The errors in six boundary conditions of the traditional HS are then analyzed. The proposed method of model updating based on GSA is then obtained by integrating the above ideas. In the case of multiple inputs of ground motion, four strategies are developed to verify the effectiveness of the method. The results of a total of 216 numerical cases under different working conditions show that the proposed method can improve the accuracy of HSs and the identified parameters. [ABSTRACT FROM AUTHOR]

Musculoskeletal models (MSKMs) are used to estimate the muscle and joint forces involved in human locomotion, often associated with the onset of degenerative musculoskeletal pathologies (e.g., osteoarthritis). Subject-specific MSKMs offer more accurate predictions than their scaled-generic counterparts. This accuracy is achieved through time-consuming personalisation of models and manual tuning procedures that suffer from potential repeatability errors, hence limiting the wider application of this modelling approach. In this work we have developed a methodology relying on Sobol’s sensitivity analysis (SSA) for ranking muscles based on their importance to the determination of the joint contact forces (JCFs) in a cohort of older women. The thousands of data points required for SSA are generated using Gaussian Process emulators, a Bayesian technique to infer the input–output relationship between nonlinear models from a limited number of observations. Results show that there is a pool of muscles whose personalisation has little effects on the predictions of JCFs, allowing for a reduced but still accurate representation of the musculoskeletal system within shorter timeframes. Furthermore, joint forces in subject-specific and generic models are influenced by different sets of muscles, suggesting the existence of a model-specific component to the sensitivity analysis.

Parameter sensitivity analysis is a significant part of quantifying model uncertainty, effectively identifying key parameters, and improving the efficiency of parameter optimization. The Soil and Water Assessment Tool (SWAT) model was applied to the upper Heihe River basin (UHRB) in China to simulate the monthly runoff for 11 years (1990–2000). Four typical sensitivity analyses, namely, the Morris screening, Sobol analysis, Fourier amplitude sensitivity test (FAST), and extended Fourier amplitude sensitivity test (EFAST), were used to determine the critical parameters affecting hydrological processes. The results show that the sensitivity parameters defined by the four methods were significantly different, resulting in a specific difference in the simulation effect of the SWAT model. The reason may be the different sampling process, sensitivity index, and calculation principle of each method. The snow-melt base temperature (SMTMP) and snowfall temperature (SFTMP) related to the snow-melt process, the available water capacity of the soil layer (SOL_AWC), saturated hydraulic conductivity (SOL_K), depth from the soil surface to the bottom of the layer (SOL_Z), moist bulk density (SOL_BD), deep aquifer percolation fraction (RCHRG_DP), and threshold depth of water in the shallow aquifer required for return flow to occur (GWQMN) related to the soil water and groundwater movement, baseflow alpha factor for bank storage (ALPHA_BNK) related to the base flow regression, and average slope steepness (HRU_SLP) are all very sensitive parameters. The 10 key parameters were optimized 100 times with the sequential uncertainty fitting procedure version 2 (SUFI-2). The Nash–Sutcliffe efficiency coefficient (NSE), Kling–Gupta efficiency coefficient (KGE), mean square error (MSE), and percentage bias (PBIAS) were 0.89, 200, 8.60, and 0.90, respectively. The simulation results are better than optimizing the sensitive parameters defined by the single method and all the selected parameters. The differences illustrate the rationality and importance of parameter sensitivity analysis for hydrological models and the synthesis of multiple approaches to define sensitive parameters. These conclusions have reference significance in the parameter optimization of the SWAT model when studying alpine rivers by constructing the SWAT model.

Given the ever increasing spatial resolution of climate models and the significant role of lakes on the regional climate, it becomes important to represent water bodies in climate models. Such developments have started in the IPSL (Institut Pierre Simon Laplace) climate model and its land surface component, ORganizing Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE), with the Freshwater Lake model, FLake. To answer the questions raised by these new developments, such as the lake differentiation and related model parameters, we analyze spatial distributions of lake characteristics in the whole world to perform a global sensitivity analysis of the FLake parameters. As a result, three different climates and four lake depth configurations were selected as test cases. The Sobol method as sensitivity analysis based on variance decomposition was chosen to rank parameters impact on the model output, that is, lake surface water temperature, latent and sensible heat fluxes. We focus on the 11 parameters of the FLake model, which are the lake depth, the albedo and light extinction coefficient of water, snow, and ice respectively, the fetch, and the relaxation coefficient of the thermocline shape factor. The results show different sensitivity features according to the lake type and climate. The dominant role and time varying contribution of the lake depth, radiative parameters (albedo, light extinction coefficient) and thermocline relaxation coefficient linked to the atmospheric conditions, were clearly highlighted. These findings will lead us to distinguish between different lake categories in each grid cell of ORCHIDEE in the future implementation. Plain Language Summary: Lakes are often neglected in climate modeling because their spatial extent does not exceed 4% of the Earth land area. But given the ever increasing spatial resolution of climate models and the significant role of lakes on the regional climate, it becomes important to represent water bodies in climate models. Such developments have started in the French IPSL climate model with the implementation of a one‐dimensional freshwater lake model (FLake). To answer the questions raised by these new developments, such as the lake differentiation and related model parameters, we analyze spatial distributions of lake characteristics in the whole world to perform a global sensitivity analysis of the model parameters. Our results show the respective roles of the depth, fetch, thermocline, sediment, and radiative (albedo and light extinction coefficient) parameters in different climate conditions and at different time scales. Some avenues for further implementation and model parameters calibration are finally given. Key Points: FLake model parameters sensitivities on lake surface temperature and sensible and latent heat fluxes are analyzedParameter sensitivities to surface variables vary with depth, climate, and seasonRespective roles of depth, fetch, surface albedo, light extinction, and thermocline shape coefficients are quantified [ABSTRACT FROM AUTHOR]

Fungi are of great functional significance in terrestrial ecosystems as the main decomposers. To better understand their decomposing process and population coexistence, we first describe and quantify the decomposition rate, focusing on three traits of interest selected by machine learning algorithm: moisture tolerance, hyper extension rate, and hyphal density and obtain, and use a Ternary Linear Regression Decomposition Model (TLRDM) to quantify the decomposition rate. Then, to incorporate the interactions, we build an Interactive Decomposition Model (IDM) and creatively employ a Three-player Logistic-based Competition Population Model (TPLCM). Based on logistic growth, we formulate a differential equation group, fit the curves of this unsolvable equation group to obtain a function of population density versus time and compare the decomposition rates of three populations under interactive and non-interactive conditions, followed by analyzing the impact of the communications on decomposing ability. We obtain the population combinations that can coexist in certain climates. Furthermore, we include environmental factors, conducting a sensitivity analysis to describe how short-term and long-term climate changes affect our models.

Purpose: Bottom-up–based life cycle assessment (LCA) approaches are used to assess the greenhouse gas emissions of various products such as transportation fuels. Bottom-up spreadsheet–based models include numerous calculations and assumed values that are uncertain. Currently, most LCAs provide point estimates with a simple one-at-a-time sensitivity analysis, which provides limited insight into how the model assumptions affect the results. Additionally, the LCA models are generally presented with a limited number of scenarios to avoid overwhelming the reader; however, this limits the usefulness of the work, as each reader will be interested in different scenarios. The goal of this work is to use a global sensitivity and regression to provide as much information to the reader as possible in an easily digestible form. Methods: The Morris and Sobol global sensitivity methods are examined to determine if they can accurately identify the key inputs that have the largest effect on overall output variance. A multiparameter linear regression is then used to simplify the model into a single equation. Rstudio and Excel VBA are used to create an easy-to-use template called the Regression, Uncertainty, and Sensitivity Tool (RUST) that can be inserted into any Excel-based LCA model. This method is applied to the previously published FUNdamental ENgineering PrinciplEs-based ModeL for Estimation of GreenHouse Gases in Conventional Crude Oils and Oil Sands (FUNNEL-GHG-CCO/OS) as an example case. Results and discussion: Both the Morris and Sobol methods can identify the key parameters, but the Morris method requires less than 1/100th as many model evaluations. Of the model's 65 parameters, 14 key parameters were identified. The corresponding regression model was found to have an accuracy of ± 0.5 g CO2 eq/MJ 90% of the time and a maximum error of + 3 and − 1 g CO2 eq/MJ. Conclusions: This work found that the Morris method can be used to screen key parameters and that a stepwise multiparameter linear regression approach can be used to develop a simplified version of the model. The developed RUST Excel workbook can be used to perform the sensitivity and regression analysis of any Excel-based LCA models. The regression model can then be easily published, it does not require a large effort to make a user friendly version of the model, and it conceals confidential data if necessary. The simplified model makes it easy for policy markets to investigate how changes in critical parameters affect the LCA results without having to learn how to use the full complex model. [ABSTRACT FROM AUTHOR]

In a light water nuclear reactor environment, zirconium-alloy cladding undergoes corrosion that introduces hydrogen into the cladding. Hydrogen being slightly soluble in zirconium means that the terminal solid solubility of hydrogen is quickly reached. This leads to hydrogen precipitation into crystals of zirconium hydride. These hydrides decrease the ductility of the cladding, which increases the chance of cladding failure, especially during dry storage or transportation events, depending on the temperature and stress conditions. In this work, we tested the existing hydrogen migration and redistribution model implemented in the Bison fuel performance code by comparing Bison predictions of hydrogen distribution in various temperature profiles against historical published experimental data. Using 1D simulations of hydrogen evolution in zirconium-alloy, we demonstrated the accuracy of the existing model in the case of a sharp temperature gradient profile. We also performed a sensitivity analysis to investigate the impact of the model parameters on Bison predictions of hydrogen migration and redistribution to understand the discrepancies in the case of a low temperature gradient profile. In both cases, the Bison prediction of hydrogen distribution is accurate in the regions where the temperatures are higher. In these areas, the relative error on average does not exceed 1%. On the other hand, the error grows increasingly higher in the cold regions where hydrides are more likely to form and a steep gradient in the hydrogen concentration profile develops. The potential for the uncertainties in the model parameters to explain this behavior is investigated through the sensitivity analysis, including the impact of a clamping factor that artificially reduces the maximum allowable volume fraction of hydrides. This study also provides a literature review on hydrogen migration and redistribution experiments and a systematic comparison of the relative experimental data. [ABSTRACT FROM AUTHOR]

We demonstrate a global sensitivity and uncertainty analysis approach to quantify the impact of uncertainty in the hydrogen migration and redistribution models implemented in the U.S. Department of Energy Office of Nuclear Energy fuel performance code Bison. In this study, we provide a brief description of the physical phenomena studied and the sensitivity analysis methods used. To identify the key parameters related to the hydrogen migration and redistribution model in Bison, we study the impact of the variance of the model parameters on the amount of hydrides formed near the outer surface of the nuclear fuel cladding, where hydrides are more likely to form, under the normal operation conditions of a light water reactor. To quantify the impact of the input variance of the parameters on the output variations, we compute the variance-based indices (Sobol indices) and the Pearson correlation coefficients. The results of this work show that the activation energy for the terminal solid solubility of hydride precipitation, the hydrogen heat of transport and the activation energy for hydrogen diffusivity are the key parameters. An optimized set of these parameters was then determined as an attempt to increase the accuracy of Bison predictions by decreasing the root mean square error of the predictions versus experimental results, using a basin-hopping optimization framework. [ABSTRACT FROM AUTHOR]

Improvement of management strategies of epidemics is often hampered by constraints on experiments at large spatiotemporal scales. A promising approach consists of modeling the biological epidemic process and human interventions, which both impact disease spread. However, few methods enable the simultaneous optimization of the numerous parameters of sophisticated control strategies. To do so, we propose a heuristic approach (i.e., a practical improvement method approximating an optimal solution) based on sequential sensitivity analyses. In addition, we use an economic improvement criterion based on the net present value, accounting for both the cost of the different control measures and the benefit generated by disease suppression. This work is motivated by sharka (caused by Plum pox virus), a vector-borne disease of prunus trees (especially apricot, peach, and plum), the management of which in orchards is mainly based on surveillance and tree removal. We identified the key parameters of a spatiotemporal model simulating sharka spread and control and approximated optimal values for these parameters. The results indicate that the current French management of sharka efficiently controls the disease, but it can be economically improved using alternative strategies that are identified and discussed. The general approach should help policy makers to design sustainable and costeffective strategies for disease management. [ABSTRACT FROM AUTHOR]

Highlights • Application of Game theory principles in variance-based sensitivity analysis. • Shapley effect is compared to Sobol indices under correlated parameters. • Application is demonstrated using a lattice depletion problem. • Analysis of operating conditions effects on variance contribution. Abstract Analyzing the variance of complex computer models is an essential practice to assess and improve that model by identifying the influential parameters that cause the output variance. Variance-based sensitivity analysis is the process of decomposing the output variance into components associated with each input parameter. In this study, we applied a new concept of variance-based sensitivity analysis inspired by the game theory proposed by Shapley. The technique is called the Shapley effect, and it investigates the contribution of each input parameter as well as its interactions with every other parameter in the system by exploring all possible permutations between them. The Shapley effect is compared to the common Sobol indices technique (first order and total effects) to investigate their performance under correlated and uncorrelated parameters. The Shapley effect demonstrated superior performance when compared to the Sobol indices for correlated input parameters. Shapley effect captured the correlation between the input parameters, expressing the variance contribution in a single index instead of two indices, and normalization of the fractional indices is preserved without over or underestimation. On the other hand, the two algorithms we selected to calculate Sobol indices under correlated inputs experienced different issues including: over/underestimating the output variance, first order effect could be larger than the total effect, possibility of negative indices, unnormalized fractional indices, and difficulty to interpret the results. However, Sobol showed satisfactory performance when the inputs are uncorrelated as the numerical values and input ranking were in good agreement with Shapley effect. The main disadvantage of Shapley effect is its large computational cost especially for high dimensional problems where the number of possible input subsets becomes very large. The results of our tests showed that the thermal fission cross-section carried most of the uncertainty at BOL, and its contribution declines after fuel burnup, which is replaced by the uncertainty contribution of the fast cross-section parameters. [ABSTRACT FROM AUTHOR]

Maximum simulated likelihood is the preferred estimator of most researchers who deal with discrete choice. It allows estimation of models such as mixed multinomial logit (MXL), generalized multinomial logit, or hybrid choice models, which have now become the state-of-practice in the microeconometric analysis of discrete choice data. All these models require simulation-based solving of multidimensional integrals, which can lead to several numerical problems. In this study, we focus on one of these problems – utilizing from 100 to 1,000,000 draws, we investigate the extent of the simulation bias resulting from using several different types of draws: (1) pseudo random numbers, (2) modified Latin hypercube sampling, (3) randomized scrambled Halton sequence, and (4) randomized scrambled Sobol sequence. Each estimation is repeated up to 1 000 times. The simulations use several artificial datasets based on an MXL data generating process with different numbers of individuals (400, 800, 1 200), different numbers of choice tasks per respondent (4, 8, 12), different number of attributes (5, 10), and different experimental designs (D-optimal, D-efficient for the MNL and D-efficient for the MXL model). Our large-scale simulation study allows for comparisons and drawing conclusions with respect to (1) how efficient different types of quasi Monte Carlo simulation methods are and (2) how many draws one should use to make sure the results are of "satisfying" quality – under different experimental conditions. Our study is the first to date to offer such a comprehensive comparison. Overall, we find that the number of the best-performing Sobol draws required for the desired precision exceeds 2 000 in some of the 5-attribute settings, and 20,000 in the case of some 10-attribute settings considered. • Simulation error for the maximum simulated likelihood estimation of discrete choice models is investigated. • We use from 100 to 1,000,000 draws for 400, 800, and 1,200 individuals, 4, 8 and 12 choice tasks, and 5 or 10 attributes. • Pseudo random numbers, modified Latin hypercube sampling, scrambled Halton sequence, and scrambled Sobol sequence are compared. • We find that Sobol draws perform the best in these simulations. • We find that in the case of 5-attribute designs over 2,000 Sobol draws were needed to attain desired levels of precision. • In the case of 10 attributes over 20,000 draws were needed. [ABSTRACT FROM AUTHOR]

• To address long term food vulnerability issues, indicators like current consumption or dietary diversity are not reliable. • Vulnerability to food security index is a better indicator for long term targeting of intervention. • VFII captures vulnerability component of food security. • VFII is highly sensitive to shocks and stable to changes. This paper develops a Vulnerability to Food Insecurity Index (VFII). Currently, there is no standard indicator of vulnerability analysis in food security research, and this paper responds to this challenge. The primary objective in this paper is to demonstrate how to develop a potential indicator and establish its validity through comparison with other traditional food security indicators, such as per capita calorie consumption (PCC), food consumption score (FCS) and the coping strategy index (CPI). Structurally, Vulnerability to Food Insecurity Index is a multidimensional index of the probability of covariate shock occurring (exposure), the accumulative experience of food insecurity (sensitivity) and coping ability of households (adaptive capacity). The paper applies the index to households in southern Nigeria, using the World Bank's generalised household panel dataset. The results show 61% of households in the study to be highly vulnerable to food insecurity, 12% mildly vulnerable and 27% not vulnerable. Traditional and single indicators, such as FCS and PCC are not good indicators of vulnerability to food insecurity whereas CPI is a better indicator of vulnerability to food insecurity compared to FCS and PCC. The VFII developed in this paper includes components of FCS, PCC, and CPI and regarding ranking, the VFII was found to be reliable. Most importantly, the analysis using the VFII reveals how dietary diversity or calorie consumption indicators can exclude some households who are vulnerable to food insecurity. The paper concluded that accurately target long-term support to vulnerable households, policymakers who seek to address the underlying causes of food insecurity cannot rely on single indicators, and for this type of goal, the VFII makes a useful contribution. [ABSTRACT FROM AUTHOR]

Highlights • Friction coefficient determines the main mode of response (sliding/rocking). • Contact stiffness determines the magnitude and duration of the impact pulses. • Modelling properties determine the energy exchanges. • Spread forces and smooth pulses push toward sliding motion. • Eccentric forces with strong and sharp pulses give advantage to the rocking motion. • Computational cost is highly sensitive to the stiffness and number of contacts to solve. Abstract Free-standing racks are metallic structures designed to store fuel assemblies in the spent fuel pool. Their seismic analysis deals with complex physical phenomena such as transient motion, inertial effects, dynamic contacts, fluid-structure interaction, water coupling, etc. The associated computational effort leads to cost-effective finite element models made up of simple elements: beams, masses and contacts. The modelling properties that describe the physical behaviour of such conceptual elements are uncertain and difficult to estimate. However, they have a certain impact on the computation of the rack response. This paper investigates the weight of the main modelling properties on computed outcomes focusing on sliding displacements and reactions on supports. The conceptual approach is supported with a statistical analysis of 100 simulations conducted in ANSYS Mechanical 14.0. A multivariate sensitivity analysis with scatter plots and variance-based methods is conducted over modelling variables including friction coefficients, contact stiffnesses, assumed fuel gaps, inertias, etc. Furthermore, some advises and rule of thumbs are provided for future designs. [ABSTRACT FROM AUTHOR]

• An integrated methodology for ranking reactor physics uncertainties is presented. • An eigenvalue decomposition of the nuclear data covariance matrix is used. • Perturbed data is propagated through lattice and core calculations. • Comparison to a direct Sobol sample gives consistent results for cases examined. Nuclear data uncertainties lead to substantial uncertainties in the predictions of LWR core calculations. Such problems are typically solved using lattice calculations and nodal codes, such as the WIMS-PANTHER system. It is desirable to derive uncertainties of core simulation output parameters (e.g. enthalpy deposition in an REA) to nuclear data uncertainties for specific nuclide-reaction pairs. This allows the key uncertainty contributions to be identified and ranked and facilitates use of data assimilation techniques. Key challenges in ranking the contributions to uncertainty are (1) the large number of input parameters (2) burnup-dependence of nuclide populations and microscopic cross sections, which challenges the use of adjoint methods (3) propagation of uncertainties through a multi-step calculation sequence. In this paper, an integrated methodology for deriving such uncertainties is presented. This comprises the following steps: (1) sensitivity analysis to identify the key reactions in the problem and exclude insignificant reactions (2) orthogonal decomposition of the nuclear data covariance matrix to calculate and rank eigenvectors (3) direct and consistent perturbation of the microscopic cross sections for the eigenvectors with significant eigenvalues (4) separate lattice (WIMS) and core (PANTHER) calculations with nuclear data perturbed for each eigenvector. This method (which assumes linearity of perturbations) is compared to a direct Sobol sample of the nuclear data and for the cases examined gives consistent results for the importance ranking of reactions, while greatly reducing the number of calculations employed. Hence, it can complement sampling-based approaches in developing an overall understanding of the magnitude, distribution, and contributors to uncertainty in core calculations. Both the linear method and the Sobol sampling method are integrated within the Visual Workshop graphical user environment to automate the process with the WIMS and PANTHER codes. [ABSTRACT FROM AUTHOR]

KERNEL – A novel parameter-free surrogate building algorithm using Adaptive Neuro Fuzzy Inference System (ANFIS) is presented to provide an intelligent and robust technology to optimally estimate the configuration of ANFIS along with Sobol-based fast sample size determination (SSD) methodology. The proposed algorithm is capable of fine-tuning the existing knowledge base about the physics of the process in terms of human experience. It also enables knowledge discovery through a multi-objective optimization problem (MOOP) solved by non-dominated sorting genetic algorithm, NSGA-II, thus presenting machine-invented physics of the process. Experimentally validated polymerization reaction network model is considered and ANFIS surrogates are built using KERNEL. Surrogate-based optimization was found to be nine times faster than conventional optimization using the time expensive model, thus enabling its online implementation. Comparison of ANFIS with Kriging is also included. [ABSTRACT FROM AUTHOR]

Canine-rabies is endemic in parts of Indonesia and continues to spread eastwards through the Indonesian archipelago. Papua New Guinea (PNG) has a land border with Papua Province, Indonesia, as well as logging and fishing industry connections throughout Asia. PNG has a Human Development Index of 0.505; therefore, an incursion of canine-rabies could have devastating impacts on human (7.5 million) and animal populations. Given the known difficulties of rabies elimination in resource-scarce environments, an incursion of rabies into PNG would also likely compromise the campaign for global elimination of rabies. A previous qualitative study to determine routes for detailed risk assessment identified logging, fishing and three land-routes (unregulated crossers [“shopper-crossers”], traditional border crossers and illegal hunters) as potential high risk routes for entry of rabies-infected dogs into PNG. The objective of the current study was to quantify and compare the probability of entry of a rabies-infected dog via these routes into PNG and to identify the highest risk provinces and border districts to target rabies prevention and control activities. Online questionnaires were used to elicit expert-opinion about quantitative model parameter values. A quantitative, stochastic model was then used to assess risk, and parameters with the greatest influence on the estimated mean number of rabies-infected dogs introduced/year were identified via global sensitivity analysis (Sobol method). Eight questionnaires – including 7 online – were implemented and >220 empirical distributions were parameterised using >2900 expert-opinions. The highest risk provinces for combined sea routes were West Sepik, Madang and Western Province, driven by the number of vessels and the probability of bringing dogs. The highest risk border districts for combined land routes were Vanimo-Green River and South Fly, driven by the number of people crossing the border and the number of dogs (with hunters). Overall, the risk posed by land routes was much higher than the risk of rabies introduction by sea routes. This study provides a foundation to develop targeted border control measures, surveillance and response strategies for canine-rabies for the highest risk routes and regions in PNG. Sensitivity analysis using the Sobol method played a key role in this study and directed further data collection to refine risk estimates. The ease of expert-elicitation using online methods demonstrates the feasibility of using such methods for animal and human disease surveillance in PNG. [ABSTRACT FROM AUTHOR]

Basin and petroleum system modeling is an interdisciplinary endeavor, and utilizes integrated data to study sedimentary basins. Numerical models are constructed and simulated to quantitatively model the geodynamic processes in sedimentary basins. Often, the modeling process covers large spatial and temporal intervals with many uncertain input parameters. These can be continuous parameters characterized by certain statistical distribution, spatially distributed variables, or discrete parameters such as geological scenarios. Identifying sensitivities from these complex input model parameters and recognizing key uncertainties are crucial and challenging for basin modeling development and applications. The major contribution of this work is to introduce and implement efficient and accurate sensitivity analysis approach for basin and petroleum system modeling discipline. We investigated two types of sensitivity analysis methods and compared their performance for identifying the impact of uncertain parameters on both spatial and temporal model responses. The first approach utilizes the variance-based Sobol indices to quantify parameter sensitivities, while the second approach is a distance-based sensitivity analysis which utilizes the distance between model responses to determine sensitivities of different parameters. The sensitivity analysis approaches are illustrated through a basin modeling example involving the processes of sediment compaction, source rock maturation and hydrocarbon generation in the Piceance Basin, Colorado, US. Monte Carlo samples of the input uncertainties related to physical properties of the source rock, thermal boundary conditions, and geological setting scenarios are generated. Multiple basin models constructed using these uncertain input parameters are simulated across the geological time span, and time-varying model response (hydrocarbon generation from Lower Cretaceous to present-day) and spatial model response (pressure and porosity distribution at present-day) are obtained. Sensitive parameters that impact these spatio-temporal model responses are then analyzed. Results show that the distance-based sensitivity analysis approach could achieve similar results with fewer model runs compared to the variance-based Sobol method. Model responses in spatial and temporal domain are impacted by different uncertain input parameters. Subtle relationship between input parameters and model response could also be identified. In particular an unexpected link between chemical kinetics and porosity versus depth behavior was uncovered. The knowledge obtained from sensitivity studies enhance the understanding of the complex geological processes and can benefit the modeling development and forecast capability. Though the sensitivity results are case-specific, the approach and workflow are generally applicable to other basins and earth sciences modeling studies. [ABSTRACT FROM AUTHOR]

Climate change is increasing heatwaves occurrence, which consequently impact buildings indoor overheating during summers. While indoor overheating in free-running buildings during future typical summers has been quantified by many researchers, its intensification during future heatwaves remains relatively under-researched. Furthermore, analyzing the dynamic thermal response of free-running buildings to these varying local climate changes is current lacking. This paper objective and novelty is to understand the building dynamic thermal change during different indoor overheating periods induced by several hot outdoor sequences during present and future climates. Two sensitivity analysis methods are used for this analysis, applied to a free-running top floor apartment of a collective building: the Morris method, which allows to quickly select the most influential parameters and the Sobol method, which allows to conduct an in-depth temporal uncertainty and sensitivity analysis. Results show that in the semi-oceanic climate of the French capital Paris, while the indoor overheating is projected to drastically increase, the building dynamic thermal behavior is greatly modified amongst the three climate sequences under study: present and future typical summers, and future heatwave of the mid-century. During the future heatwave in Paris, the building response is projected to behave more similarly as it would in a French Mediterranean city. Static design parameters such as thermal mass, albedo and emissivity or glazing percentage, as well as dynamic design parameters such as the operation of the ventilative cooling and solar control strategies all play an important role in regulating the building indoor overheating, with a different ranking depending on the outdoor local climate conditions. These results are of foremost importance to provide guidance in the design of adapted buildings to climate change, which can withstand future extreme heat periods. [ABSTRACT FROM AUTHOR]

In this research the sensitivity analysis of the geometric parameters such as: length, thickness and width of a single link flexible manipulator on maximum deflection (MD) of the end effector and vibration energy (VE) of that point are conducted. The equation of motion of the system is developed based on Gibbs-Appel (G-A) formulation. Also for modeling the elastic property of the system the assumption of assumed modes method (AMM) is applied. In this study, two theories are used to obtain the end-point MD and VE of the end effector. Firstly, the assumption of Timoshenko beam theory (TBT) has been applied to consider the effects of shear and rotational inertia. After that, Euler-Bernoulli beam theory (EBBT) is used. Then Sobol’s sensitivity analysis method is applied to determine how VE and end-point MD is influenced by those geometric parameters. At the end of the research, results of two mentioned theories are compared.

Process modeling and optimization of polymerization processes with long chain branching is currently an area of extensive research owing to the advantages and growing popularity of branched polymers. The highly complex nature of these reaction networks results in a large set of stiff ordinary differential equations to model them mathematically with adequate precision and accuracy. In such a scenario, where execution time of the model is expensive, the idea of going for online optimization and control of such processes seems to be a near impossible task. Catering these problems in the ongoing research, optimization using surrogate model obtained from a novel algorithm is proposed in this work as a solution. A Sobol set assisted artificial neural network replaces the computationally expensive kinetic model of long chain branched poly vinyl acetate as the fast and efficient surrogate model. The proposed multi-objective methodology allows the computationally expensive first principle model to determine the configuration of the neural network, which can emulate it with maximum accuracy along with sample size required. The algorithm introduces a logical way of designing ANN architectures where the outperformance of multiple layer networks justifies the elimination of heuristics approach to consider only single layer. The results of the proposed algorithm are compared with the results obtained using Kriging interpolator based another surrogate approach, for testing, validation and scope of improvement. The use of fast and efficient Sobol assisted ANN surrogate model makes the optimization process ~10 times more efficient as compared to the case of optimization with computationally expensive kinetic model. The proposed ANN based surrogate is nearly 1.5 times as efficient as Kriging model in terms of number of expensive function evaluations. [ABSTRACT FROM AUTHOR]

Based on the theoretical framework for sensitivity analysis called 'Variogram Analysis of Response Surfaces' (VARS), developed in the companion paper, we develop and implement a practical 'star-based' sampling strategy (called STAR-VARS), for the application of VARS to real-world problems. We also develop a bootstrap approach to provide confidence level estimates for the VARS sensitivity metrics and to evaluate the reliability of inferred factor rankings. The effectiveness, efficiency, and robustness of STAR-VARS are demonstrated via two real-data hydrological case studies (a 5-parameter conceptual rainfall-runoff model and a 45-parameter land surface scheme hydrology model), and a comparison with the 'derivative-based' Morris and 'variance-based' Sobol approaches are provided. Our results show that STAR-VARS provides reliable and stable assessments of 'global' sensitivity across the full range of scales in the factor space, while being 1-2 orders of magnitude more efficient than the Morris or Sobol approaches. [ABSTRACT FROM AUTHOR]

Computer simulation models are continually growing in complexity with increasingly more factors to be identified. Sensitivity Analysis (SA) provides an essential means for understanding the role and importance of these factors in producing model responses. However, conventional approaches to SA suffer from (1) an ambiguous characterization of sensitivity, and (2) poor computational efficiency, particularly as the problem dimension grows. Here, we present a new and general sensitivity analysis framework (called VARS), based on an analogy to 'variogram analysis,' that provides an intuitive and comprehensive characterization of sensitivity across the full spectrum of scales in the factor space. We prove, theoretically, that Morris (derivative-based) and Sobol (variance-based) methods and their extensions are special cases of VARS, and that their SA indices can be computed as by-products of the VARS framework. Synthetic functions that resemble actual model response surfaces are used to illustrate the concepts, and show VARS to be as much as two orders of magnitude more computationally efficient than the state-of-the-art Sobol approach. In a companion paper, we propose a practical implementation strategy, and demonstrate the effectiveness, efficiency, and reliability (robustness) of the VARS framework on real-data case studies. [ABSTRACT FROM AUTHOR]