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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.;

【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.

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.;

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.

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.

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]

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]

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]

In this paper we show that it is possible to extend the use of quasi-Monte Carlo for applications of high effective dimension. This is achieved through a combination of a careful construction of the Sobol sequence and an appropriately chosen decomposition of a covariance matrix. The effectiveness of this procedure is demonstrated as we price average options with nominal dimensions ranging up to 550 (effective dimension around 300). We believe the method we present is easy to implement and should be of great interest to practitioners.Neste artigo mostramos que é possível usar métodos de simulação quase-Monte Carlo em problemas de alta dimensão efetiva. Isto é feito por meio de uma combinação de uma cuidadosa construção das seqüências de Sobol e de uma decomposição apropriada da matriz de covariância dos fatores de risco. A eficácia do método é ilustrada por meio da precificação de opções que envolve a solução de problemas com dimensão nominal da ordem de 550 (e dimensão efetiva da ordem de 300). Acreditamos que o método apresentado seja de fácil implementação e de grande interesse para os participantes do mercado financeiro.

Recent advances in computational tools and techniques have enabled engineers to tackle single-step and iterative complex computer-aided engineering (CAE) problems. They have also helped develop CAE optimization techniques capable of driving design parameters towards regions where selected system characteristics can be further improved. The present work was conducted in two steps; In the first step, the effects of adding hydrogen to diesel fuel in a turbocharged direct injection (TDI) compression ignition (CI) Audi engine was analyzed through 1D simulation in AVL BOOST. With no significant modifications to the engine, the analysis was carried out at 1900 rpm and 2500 rpm under 0.8 MPa nominal loading conditions. Hydrogen was injected with air at 30 L/min through the intake manifold before the turbocharger. The present work considered all the engine components that are thermodynamically involved in the combustion process. The second analysis step coupled the simulated model with modeFRONTIER to study the optimum parameter values for engine performance and emission and determined the effects of various input variables on these parameters. Optimization was carried out by adopting hybrid algorithms to design experiments and the non-dominated sorting genetic algorithm II (NSGA_II). The response level predictive capability of the Taguchi and SOBOL methods increases the convergence speed of the NSGA_II. The results from the first analysis step indicated that the hydrogen diesel blend reduced brake specific fuel consumption (BSFC), engine power and torque, CO 2 and NO x emissions, and increased entropy. The results from the second step suggested that in multivariable optimization, the peak pressure and NOx emissions were affected mainly, by combustion initiation time and the hydrogen added to the diesel fuel, respectively. Finally, it was found that hydrogen injection at 20 mg/min resulted in the best engine performance and emission. • Dual fuel hydrogen engine assessment with NSGA II operated by TOA and SOBOL. • 0D/1D turbocharged powertrain optimization with DoE method and data processing. • Optimal points 2-level Taguchi creates poor results owing to high number of input variables. • With the same number of runs, the SOBOL offered better output results than the Taguchi. • Hydrogen injection at 20 mg/min resulted in the best engine performance and emission. [ABSTRACT FROM AUTHOR]

Habitat suitability index (HSI) models are commonly used to predict habitat quality and species distributions and are used to develop biological surveys, assess reserve and management priorities, and anticipate possible change under different management or climate change scenarios. Important management decisions may be based on model results, often without a clear understanding of the level of uncertainty associated with model outputs. We present an integrated methodology to assess the propagation of uncertainty from both inputs and structure of the HSI models on model outputs (uncertainty analysis: UA) and relative importance of uncertain model inputs and their interactions on the model output uncertainty (global sensitivity analysis: GSA). We illustrate the GSA/UA framework using simulated hydrology input data from a hydrodynamic model representing sea level changes and HSI models for two species of submerged aquatic vegetation (SAV) in southwest Everglades National Park: Vallisneria americana (tape grass) and Halodule wrightii (shoal grass). We found considerable spatial variation in uncertainty for both species, but distributions of HSI scores still allowed discrimination of sites with good versus poor conditions. Ranking of input parameter sensitivities also varied spatially for both species, with high habitat quality sites showing higher sensitivity to different parameters than low-quality sites. HSI models may be especially useful when species distribution data are unavailable, providing means of exploiting widely available environmental datasets to model past, current, and future habitat conditions. The GSA/UA approach provides a general method for better understanding HSI model dynamics, the spatial and temporal variation in uncertainties, and the parameters that contribute most to model uncertainty. Including an uncertainty and sensitivity analysis in modeling efforts as part of the decision-making framework will result in better-informed, more robust decisions. [ABSTRACT FROM AUTHOR]

Sensitivity analyses are often carried out for the main model output and using a predefined evaluation period. It is possible, however, to get much more knowledge about how the system works when estimating the sensitivity for the output of individual modules for each time step (time-varying sensitivity analysis). This is shown here using a variance-based sensitivity analysis for a conceptual rainfall-runoff model applied to a mountainous catchment. The first-order and total sensitivities were computed using Sobol's method. Since the parameter ranges used in the sensitivity analysis were obtained through a Markov chain Monte Carlo (MCMC) sampling, the sensitivity indices reflect the parameter uncertainty and make a good use of the previous available information. As a first step, the variance of each flow component was calculated. The flow component with the highest variance at each time step can be regarded as the 'dominant physical control', which has been defined as the parameter to which the model output reacts in a highly sensitive way when the parameter varies within realistic ranges. This information about the dominant processes can be used for facilitating model calibration by identifying the periods on which to focus when calibrating different parameters. It also can be useful for estimating the amount of data available for calibrating each process. The second part presents the total sensitivity indices and interactions for individual flow components considering a 2-year period. The results show large differences in the time-varying sensitivity patterns of the flow components. It is concluded that such a high-resolution sensitivity analysis for each flow component is a good complement to a sensitivity analysis of the total discharge, increasing our understanding about the internal functioning of individual modules which can be helpful when comparing different model formulations. [ABSTRACT FROM AUTHOR]