First-order Sensitivity Indices Research Articles

Adaptive Design of Experiments to Fit Surrogate Gaussian Process Regression Models Allows Fast Sensitivity Analysis of the Input Waveform for Patient-Specific 3D CFD Models of Liver Radioembolization

Background and objectivePatient-specific 3D computational fluid dynamics (CFD) models are increasingly being used to understand and predict transarterial radioembolization procedures used for hepatocellular carcinoma treatment. While sensitivity analyses of these CFD models can help to determine the most impactful input parameters, such analyses are computationally costly. Therefore, we aim to use surrogate modelling to allow relatively cheap sensitivity analysis. As an example, we compute Sobol's sensitivity indices for three input waveform shape parameters. MethodsWe extracted three characteristic shape parameters from our input mass flow rate waveform (peak systolic mass flow rate, heart rate, systolic duration) and defined our 3D input parameter space by varying these parameters within 75%-125% of their nominal values. To fit our surrogate model with a minimal number of costly CFD simulations, we developed an adaptive design of experiments (ADOE) algorithm. The ADOE uses 100 Latin hypercube sampled points in 3D input space to define the initial design of experiments (DOE). Subsequently, we re-sample input space with 10,000 Latin Hypercube sampled points and cheaply estimate the outputs using the surrogate model. In each of 27 equivolume bins which divide our input space, we determine the most uncertain prediction of the 10,000 points, compute the true outputs using CFD, and add these points to the DOE. For each ADOE iteration, we calculate Sobol's sensitivity indices, and we continue to add batches of 27 samples to the DOE until the Sobol indices have stabilized. ResultsWe tested our ADOE algorithm on the Ishigami function and showed that we can reliably obtain Sobol's indices with an absolute error <0.1. Applying ADOE to our waveform sensitivity problem, we found that the first-order sensitivity indices were 0.0550, 0.0191 and 0.407 for the peak systolic mass flow rate, heart rate, and the systolic duration, respectively. ConclusionsAlthough the current study was an illustrative case, the ADOE allows reliable sensitivity analysis with a limited number of complex model evaluations, and performs well even when the optimal DOE size is a priori unknown. This enables us to identify the highest-impact input parameters of our model, and other novel, costly models in the future.

A Systematic Approach of Global Sensitivity Analysis and Its Application to a Model for the Quantification of Resilience of Interconnected Critical Infrastructures

We consider a model for the resilience analysis of interconnected critical infrastructures (ICIs) that describes the dependencies among the subsystems within the ICIs and their time-varying behavior. The model response is a function of uncertain inputs comprising ICIs design parameters and failure magnitudes of vulnerable elements in the system, etc. In this methodological paper, we present a systematic approach based on an innovative blend of methods to perform a sensitivity analysis for identifying the most relevant variables affecting the system resilience at different stages, during a disruptive event. The methods considered include the following: the use of the graphical representation of Cusunoro curves for a visualization of the impact of an input on the resilience metric and an understanding of whether the associated dependence is monotonic, increasing, or decreasing; the introduction of an ensemble of indicators related to different properties of the resilience metric to allow the prioritization of variable importance and avoid false negatives, meaning to regard a variable as non-influential when, instead, it plays a relevant role in the determination of the model response; the calculation of first-order variance-based sensitivity indices to have an appreciation on the relevance of interactions when inputs are independent; and a data approach to visually identify relevant second-order interactions. All the sensitivity methods considered are performed on a provided sample, and do not require additional model evaluations. They allow the analyst to post-process the data to extract, simultaneously, several desirable insights. The systematic approach proposed to apply these methods allows us to identify the model input variables and parameters that are not very relevant, while it enables the identification of the relevant ones which allows prioritizing interventions on the vulnerable elements of the system for its resilience at different stages during a disruptive event. Given the methodological nature of the work, a simplified infrastructure model describing an interconnected gas network and electric power grid is taken as case study: this allows us to show that the approach is straightforward to understand and implement, and the results obtained show the usefulness of the approach in providing meaningful insights that can be used by stakeholders and decision makers to inform strategies for the improvement of system resilience. By the application of the simplified ICIs model to the case study, it is shown that the approach can be straightforwardly implemented to identify the most relevant variables on system resilience and obtain the most important subsystems. The key factors which affect system resilience in multiple initial failures scenarios are found; this allows us to identify the key resilience improvement measurements, and their priorities.

Global Sensitivity Analysis of Factors Influencing the Surface Temperature of Mold during Autoclave Processing.

During the process of forming carbon fiber reinforced plastics (CFRP) in an autoclave, deeply understanding the global sensitivity of factors influencing mold surface temperature is of paramount importance for optimizing large frame-type mold thermally and enhancing curing quality. In this study, the convective heat transfer coefficient (CHTC), the thickness of composite laminates (TCL), the thickness of mold facesheet (TMF), the mold material type (MMT), and the thickness of the auxiliary materials layer (TAL) have been quantitatively assessed for the effects on the mold surface temperature. This assessment was conducted by building the thermal-chemical curing model of composite laminates and utilizing the Sobol global sensitivity analysis (GSA) method. Additionally, the interactions among these factors were investigated to gain a comprehensive understanding of their combined effects. The results show that the sensitivity order of these factors is as follows: CHTC > MMT > TMF > TCL > TAL. Moreover, CHTC, MMT, and TMF are the main factors influencing mold surface temperature, as the sum of their first-order sensitivity indices accounts for over 97.3%. The influence of a single factor is more significant than that of the interaction between factors since the sum of the first-order sensitivity indices of the factors is more than 78.1%. This study will support the development of science-based guidelines for the thermal design of molds and associated heating equipment design.

Analysis of Factors Affecting Vacuum Formation and Drainage in the Siphon-Vacuum Drainage Method for Marine Reclamation

Traditional drainage methods for marine reclamation typically consume large amounts of energy and have a negative environmental impact. The siphon-vacuum drainage method (SVD) automatically forms a vacuum and drains using less energy. It has significant potential for research and application. In this study, a theoretical model is used to calculate the vacuum formation process and drainage rate. Qualitative analysis and global sensitivity analysis were conducted to investigate the effect of various factors in the SVD on vacuum formation and drainage. The qualitative analysis suggests that modifying the length and diameter of the siphon pipe and the thickness of the sealing soil column to increase the siphon rate can improve the vacuum degree and drainage efficiency. Sobol global sensitivity analysis reveals that the sealing soil column thickness is the main factor affecting the vacuum, with a first-order sensitivity index accounting for up to 79.48%. The impact of cylinder diameter and the local resistance coefficient (0.43%) can be almost neglected. A fitting equation for estimating the maximum achievable vacuum is provided. Calculations show that the vacuum formed by the SVD can reach over 80 kPa. This work can help optimize SVD design and advance environmentally friendly marine reclamation projects.

Variance-based global sensitivity analysis and uncertainty quantification of the AquaCrop model parameters for basil (Ocimum basilicum L.) under different nitrogen fertilizer rates

Dynamic crop models are powerful tools for evaluating crop responses to alternative management options. These models consist of various input parameters that need calibration before being implemented in a new environment. Sensitivity analysis (SA) has proven to be an efficient method for identifying dominant model parameters. This study aimed to determine influential parameters of the AquaCrop model for basil and also quantify output uncertainty under different nitrogen fertilizer rates. The Sobol’ SA method was employed to compute both first-order and total-order sensitivity indices. Results indicated that normalized water productivity (WP*) and crop coefficient (KcTr,x) were the most influential parameters across all treatments. Furthermore, as fertility stress increased, the interaction effects among parameters decreased. The temporal analysis of model output (biomass) revealed that parameter sensitivities were highly time-dependent. In addition, the uncertainty of predicted biomass was determined using combined violin and box plots. Results demonstrated that the optimal nitrogen fertilizer application rate extended the distribution of model output. In summary, these findings offer valuable insights for model simplification and effective decision-making.

Study on flow and heat transfer characteristics in rectangular channels with lantern-shaped pin fin array: Part II—Global sensitivity analysis and optimization

Increasing gas turbine inlet temperatures are placing more stringent requirements on the efficiency of internal cooling. Pin fin arrays are a common tool for cooling electronic components or trailing edge cooling channels of gas turbine blades. A crucial concern for researchers is how to achieve a higher Nusselt number with constrained pressure loss to guarantee that the efficiency of the gas turbine will not be impacted by the power dissipation generated by the driving flow passing through the internal cooling loops. In Part I of this study, a lantern-shaped pin is proposed. To seek a more efficient design solution for the pin fin shape, the second part combines the surrogate model, Sobol’s method, and NSGA-II optimization method to perform the overall and spatial sensitivity analysis and optimization for the shape variables on the generatrix of different pin rows. The first-order and total-order sensitivity indices of different variables to Nu T and f are calculated using the surrogate model-based Sobol’s method. The variables that significantly affect the channel performance are identified. The overall and spatial sensitivity analysis results indicate that the variable X 3 i -1 contributes the most to Nu T and f, with total-order sensitivity indices of 70.59–64.26 and 58.16–59.09%, respectively. The overall optimization increases the Nu T and f by 6.95 and 9.91%, respectively, while the spatial optimization leads to an increase in the Nu T by 2.29% and a decrease in the f by 4.56%. Correspondingly, the two optimizations resulted in an increase in the TPI by 3.61 and 3.89%, respectively. The Pareto front solution obtained by the spatial optimization has a more concentrated distribution. Although the TPI obtained by the spatial optimization is only 0.21% higher than that of the overall optimization, it provides designers with a strategy considering the Nusselt number and pressure loss.

Research on the Diffusion Law of Fire Extinguishment Slurry to Prevent Coal Spontaneous Combustion in Regenerated Roof

ABSTRACT Coal is a major part to stable energy structure. Borehole Grouting is a primary method used for the prevention and control of coal spontaneous combustion in the regenerated roof. However, the construction design of high-position grouting for the regenerated roof lacks a sufficient theoretical foundation due to the limited advancement in theoretical research on the high-position grouting of fracture. To fill the theory research gap of high-position grouting in regenerated roof, a fracture network high-position grouting numerical model was established. Based on the numerical model and assumptions of the equivalent path, a mathematical model of fracture network high-position grouting was established and a global sensitivity analysis was conducted. The results showed that the morphology of slurry diffusion is influenced by gravity and fracture orientation. The diffusion front undergoes three successive phases of transformation: hemispherical diffusion, curved diffusion, and plane diffusion. Based on the mathematical model, the fracture aperture has the largest first-order sensitivity and total-sensitivity index to grouting pressure during the whole phase of grouting. The second-order sensitivity index between fracture aperture and slurry viscosity, grouting pipe radius, grouting rate, dip, and azimuth is greater than other parameters. By focusing on key parameters and their interactions, this research contributes to enhancing the effectiveness of grouting construction while simultaneously reducing design complexity.

Parameter sensitivity and uncertainty of a one-dimensional morphodynamic model in the Lower Yellow River

One-dimensional morphodynamic models are effective tools to conduct flood risk assessment and sediment management of alluvial rivers. In order to gain better understanding of morphodynamic models and improve the simulation efficiency and accuracy, a comprehensive investigation into parameter sensitivity and uncertainty has been conducted for a one-dimensional morphodynamic model. This model was calibrated using the measured flow and sediment data in the Lower Yellow River (LYR). The variance-based Sobol' and GLUE (Generalized Likelihood Uncertainty Estimation) tools were applied to quantify the parameter sensitivity and uncertainty to the simulation of different hydrological indicators in the meandering reach, with various model response surfaces selected. Results show that the importance of input parameters varied with different model outputs. The simulated water level and discharge hydrographs were more sensitive to the roughness coefficient at middle and high flows, with high total- and first-order sensitivity indices to the NSEs and RMSEs, while the simulated sediment concentration was more sensitive to the input parameters related to sediment transport capacity (K and M) and recovery coefficient (bd). Notably, the pairwise interactions between K and different roughness coefficients were significant in the simulated sediment concentration hydrographs. Spatio-temporal variability in the effect of input parameters is also revealed. The most contributing factor was screened as K for the maximum sediment concentration in the uppermost and lowermost reaches of the meandering reach, which turned to be bd in the middle reach. The most sensitive parameter to the simulated sediment concentration was screened as K in the initial period and then turned to be the exponent of recovery coefficient in case of erosion (be), which was bd in the last period over the simulation period. An analysis of parameter uncertainty indicates that the morphodynamic model can obtain satisfactory accuracy with most observed data concentrating in the 95% confidence interval, and the uncertainty of simulation outputs increased downstream. Improved understanding of parameter sensitivity and uncertainty in morphodynamic modelling can provide the scientific guidance for factor prioritization in the future model calibration.

Importance ranking of correlated variables in one analysis

This paper addresses the problem of ranking correlated random variables according to relative importance. The importance of a variable derives from its influence on the variability of the response from a model. Applications include any input–output model for which response derivatives are available from each response analysis. Structural analysis models, i.e., finite element models, represent the specific motivation for this paper. The response derivatives are collected in a vector and transformed into a standardized parameter space. Points along that vector are transformed back to the original parameter space and utilized for the purpose of model insights and parameter ranking. Comparisons are made with the first-order Sobol sensitivity index, which requires sampling instead of the proposed single-analysis approach. Results suggest that the proposed importance measure matches the first-order Sobol index in many situations. However, for pure multiplicative “interaction” models, the first-order Sobol index tends to be anchored at the zero-correlation case. In contrast, the proposed measures are sensitive to correlation and the effect of correlation can be significant.

Variance-based global sensitivity analysis of a multi-population, single-chamber microbial fuel cell operating in continuous flow mode at steady state

Microbial fuel cells (MFCs) are environmentally friendly devices which are used to convert chemical energy in organic wastes to electrical energy. MFCs have a strong non-linearity that requires a very sophisticated controlling system. Consequently, this makes optimization and performance study of MFCs a difficult process. For better estimation of the constants used for optimization of MFCs, global sensitivity analysis is performed. The global sensitivity method based on Sobol’s indices coupled with Monte Carlo simulations was applied on multi-population, single-chamber MFC operating in a continuous flow at steady state for the first time. In this paper, first-order and total-order sensitivity indices were used to visualize the impacts associated with six main parameters resulted from the maximization of power density using Matlab. Such parameters are maximum anodophilic-specific growth rate, half-rate constant of anodophilics, curve steepness factor, mediator half-rate constant, number of electrons transferred per mole mediator and decay rate constant of anodophilic bacteria. The results showed that the curve steepness factor has almost no impact on the power density of MFC. While all other studied, factors are sensitive parameters that impact the power density of MFC. It is worth mentioning that maximum anodophilic growth rate and the number of electrons transferred per mole of mediator are the most sensitive parameters that affecting the power density production having total indices of 0.74 and 0.624, respectively. While the half-rate constant of anodophilics, mediator half-rate constant and decay rate constant of anodophilics have almost similar impact by having total-order indices of 0.127, 0.144 and 0.192, respectively. The findings herein are critical in understanding and further model improvement of microbial fuel cells as the most impacting parameters on MFC power density can be optimized further to reduce uncertainty associated with the experimental parameters in the model.Graphical abstract

Global sensitivity analysis of hydrodynamic lubrication performance for textured surfaces

The computational model of textured surfaces is highly nonlinear, and the texturing parameters interact with each other. The Fourier Amplitude Sensitivity Test(FAST) method was introduced to calculate the first-order sensitivity indices of texturing parameters to the tribological characteristics. Results show that both the inlet suction and the cumulative effect influence the tribological performance, but which mechanism plays the leading role is related to the convergence ratio. Besides, the texture density along the velocity direction is the most crucial geometric parameter; the effects of the cell size and the bottom profile are negligible. The sensitivity indices provide a novel perspective to quantify the influence of input on the output and the FAST method proved ideally suited to the texturing model.

Spatial heterogeneity effects on land surface modeling of water and energy partitioning

Abstract. Understanding the influence of land surface heterogeneity on surface water and energy fluxes is crucial for modeling earth system variability and change. This study investigates the effects of four dominant heterogeneity sources on land surface modeling, including atmospheric forcing (ATM), soil properties (SOIL), land use and land cover (LULC), and topography (TOPO). Our analysis focused on their impacts on the partitioning of precipitation (P) into evapotranspiration (ET) and runoff (R), partitioning of net radiation into sensible heat and latent heat, and corresponding water and energy fluxes. An initial set of 16 experiments were performed over the continental US (CONUS) using the E3SM land model (ELMv1) with different combinations of heterogeneous and homogeneous datasets. The Sobol' total and first-order sensitivity indices were utilized to quantify the relative importance of the four heterogeneity sources. Sobol' total sensitivity index measures the total heterogeneity effects induced by a given heterogeneity source, consisting of the contribution from its own heterogeneity (i.e., the first-order index) and its interactions with other heterogeneity sources. ATM and LULC are the most dominant heterogeneity sources in determining spatial variability of water and energy partitioning, mainly contributed by their own heterogeneity and slightly contributed by their interactions with other heterogeneity sources. Their heterogeneity effects are complementary, both spatially and temporally. The overall impacts of SOIL and TOPO are negligible, except TOPO dominates the spatial variability of R/P across the transitional climate zone between the arid western and humid eastern CONUS. Accounting for more heterogeneity sources improves the simulated spatial variability of water and energy fluxes when compared with ERA5-Land reanalysis dataset. An additional set of 13 experiments identified the most critical components within each heterogeneity source, which are precipitation, temperature, and longwave radiation for ATM, soil texture, and soil color for SOIL and maximum fractional saturated area parameter for TOPO.

Understanding Dynamics of Pandemic Models to Support Predictions of COVID-19 Transmission: Parameter Sensitivity Analysis of SIR-Type Models.

Despite efforts made to model and predict COVID-19 transmission, large predictive uncertainty remains. Failure to understand the dynamics of the nonlinear pandemic prediction model is an important reason. To this end, local and multiple global sensitivity analysis approaches are synthetically applied to analyze the sensitivities of parameters and initial state variables and community size (N) in susceptible-infected-recovered (SIR) and its variant susceptible-exposed-infected-recovered (SEIR) models and basic reproduction number (R0), aiming to provide prior information for parameter estimation and suggestions for COVID-19 prevention and control measures. We found that N influences both the maximum number of actively infected cases and the date on which the maximum number of actively infected cases is reached. The high effect of N on maximum actively infected cases and peak date suggests the necessity of isolating the infected cases in a small community. The protection rate and average quarantined time are most sensitive to the infected populations, with a summation of their first-order sensitivity indices greater than 0.585, and their interactions are also substantial, being 0.389 and 0.334, respectively. The high sensitivities and interaction between the protection rate and average quarantined time suggest that protection and isolation measures should always be implemented in conjunction and started as early as possible. These findings provide insights into the predictability of the pandemic models by estimating influential parameters and suggest how to effectively prevent and control epidemic transmission.

New Importance Measures Based on Failure Probability in Global Sensitivity Analysis of Reliability

This article presents new sensitivity measures in reliability-oriented global sensitivity analysis. The obtained results show that the contrast and the newly proposed sensitivity measures (entropy and two others) effectively describe the influence of input random variables on the probability of failure Pf. The contrast sensitivity measure builds on Sobol, using the variance of the binary outcome as either a success (0) or a failure (1). In Bernoulli distribution, variance Pf(1 − Pf) and discrete entropy—Pfln(Pf) − (1 − Pf)ln(1 − Pf) are similar to dome functions. By replacing the variance with discrete entropy, a new alternative sensitivity measure is obtained, and then two additional new alternative measures are derived. It is shown that the desired property of all the measures is a dome shape; the rise is not important. Although the decomposition of sensitivity indices with alternative measures is not proven, the case studies suggest a rationale structure of all the indices in the sensitivity analysis of small Pf. The sensitivity ranking of input variables based on the total indices is approximately the same, but the proportions of the first-order and the higher-order indices are very different. Discrete entropy gives significantly higher proportions of first-order sensitivity indices than the other sensitivity measures, presenting entropy as an interesting new sensitivity measure of engineering reliability.

Socio-cognitive constraints and opportunities for sustainable intensification in South Asia: insights from fuzzy cognitive mapping in coastal Bangladesh

Appreciating and dealing with the plurality of farmers’ perceptions and their contextual knowledge and perspectives of the functioning and performance of their agroecosystems—in other words, their ‘mental models’—is central for appropriate and sustainable agricultural development. In this respect, the sustainable development goals (SDGs) aim to eradicate poverty and food insecurity by 2030 by envisioning social inclusivity that incorporates the preferences and knowledge of key stakeholders, including farmers. Agricultural development interventions and policies directed at sustainable intensification (SI), however, do not sufficiently account for farmers’ perceptions, beliefs, priorities, or interests. Considering two contrasting agroecological systems in coastal Bangladesh, we used a fuzzy cognitive mapping (FCM)-based simulation and sensitivity analysis of mental models of respondents of different farm types from 240 farm households. The employed FCM mental models were able to (1) capture farmers’ perception of farming system concepts and relationships for each farm type and (2) assess the impact of external interventions (drivers) on cropping intensification and food security. We decomposed the FCM models’ variance into the first-order sensitivity index (SVI) and total sensitivity index (TSI) using a winding stairs algorithm. Both within and outside polder areas, the highest TSIs (35–68%) were observed for effects of agricultural extension on changes in other concepts in the map, particularly food security and income (SI indicators), indicating the importance of extension programs for SI. Outside polders, drainage and micro-credit were also influential; within polders, the availability of micro-credit appears to affect farmer perceptions of SI indicators more than drainage. This study demonstrated the importance of reflection on the differing perspectives of farmers both within and outside polders to identify entry points for development interventions. In addition, the study underscores the need for micro-farming systems-level research to assess the context-based feasibility of introduced interventions as perceived by farmers of different farm types.

Variance-based sensitivity analysis: The quest for better estimators and designs between explorativity and economy

Variance-based sensitivity indices have established themselves as a reference amongst practitioners of sensitivity analysis of model outputs. A variance-based sensitivity analysis typically produces the first-order sensitivity indices Sj and the so-called total-effect sensitivity indices Tj for the uncertain factors of the mathematical model under analysis.Computational cost is critical in sensitivity analysis. This cost depends upon the number of model evaluations needed to obtain stable and accurate values of the estimates. While efficient estimation procedures are available for Sj (Tarantola et al., 2006), this availability is less the case for Tj (Iooss and Lemaître, 2015). When estimating these indices, one can either use a sample-based approach whose computational cost depends on the number of factors or use approaches based on meta-modelling/emulators (e.g., Gaussian processes).The present work focuses on sample-based estimation procedures for Tj for independent inputs and tests different avenues to achieve an algorithmic improvement over the existing best practices. To improve the exploration of the space of the input factors (design) and the formula to compute the indices (estimator), we propose strategies based on the concepts of economy and explorativity. We then discuss how several existing estimators perform along these characteristics.Numerical results are presented for a set of seven test functions corresponding to different settings (few important factors with low cross-factor interactions, all factors equally important with low cross-factor interactions, and all factors equally important with high cross-factor interactions). We conclude the following from these experiments: a) sample-based approaches based on the use of multiple matrices to enhance the economy are outperformed by designs using fewer matrices but with better explorativity; b) amongst the latter, asymmetric designs perform the best and outperform symmetric designs having corrective terms for spurious correlations; c) improving on the existing best practices is fraught with difficulties; and d) ameliorating the results comes at the cost of introducing extra design parameters.

CO2-prepad injection EOR simulation and sensitivity analysis considering miscibility and geomechanics in tight oil reservoirs

Injecting CO2 pre-pad into tight reservoirs before hydraulic fracturing is a promising way to improve the productivity of single well. In this method, CO2 should be injected into the formation at a high rate. Also the miscibility of CO2 and oil with the changes of intricate geomechanics during this process are complex. Meanwhile, the geological and operating parameters have a dramatic influence to oil production and is lack of systematic research. In this study, a fluid model of crude oil was established to evaluate the impact of CO2 on oil volume and viscosity. Then, a compositional numerical model coupled miscibility and geomechanics were set up to analyze enhanced oil recovery (EOR) potential under different miscible degree with in-situ stress field. Ultimately, in accordance with multiple nonlinear regression production forecast models, Sobol, FAST (Fourier Amplitude Sensitivity Test), and DGSM (derivation-based Global Sensitivity Measure) methods were utilized to study the main factors affecting cumulative production. The results shown that CO2 almost has the same effect on the volume of these tight oil and has the most significant effect on the viscosity of crude oil we studied by comparing. The production of miscible flooding is 38.2% and 16.4% higher than that of immiscible and near-miscible flooding, respectively. The formation were compacted during production especially in the early stage, hence the production of the model with geomechanics was 23.3% less than the model of non-geomechanics. The first-order sensitivity indexes of bottom-hole flow pressure, reservoir thickness and fracture conductivity are about 0.808, 0.176 and 0.004 respectively obtained by Sobol method, consistent with two other methods. This result shows that bottom-hole flow pressure, reservoir thickness and fracture conductivity are the most important factors affecting production, and bottom-hole flow pressure has the most significant impact on production. This work investigates how miscibility and geomechanics effect on production as well as clarified the factors affecting production for CO2 pre-pad injection, providing vital insights into operating in tight reservoirs for oil field.

Comparison of evapotranspiration methods in the DSSAT Cropping System Model: I. Global sensitivity analysis

Global sensitivity analysis (GSA) is useful for evaluating the responsiveness of agroecosystem models to input parameter adjustments. Evaluations of model sensitivity for diverse water status conditions and evapotranspiration (ET) algorithms will facilitate better use of models to provide water management recommendations. The objective of this study was to conduct a GSA to identify influential parameters in the Decision Support System for Agrotechnology Transfer (DSSAT) Cropping System Model (CSM), specifically using the CROPGRO-Cotton module with data from cotton field studies conducted in 2000, 2001, and 2008 at Bushland, Texas. The field studies tested fully-irrigated, deficit-irrigated, and dryland cotton production in a semi-arid environment. Using high performance computing resources, a GSA was conducted to evaluate the sensitivity of 24 model outputs with respect to 37 model input parameters. The GSA was conducted for six different ET methodologies available in the model. With first-order sensitivity indices <0.05, nearly half of the tested input parameters did not influence any model output for any of the tested simulation scenarios. Among the parameters with first-order sensitivity indices >0.05, eleven were cultivar parameters that controlled crop development and growth, and five were soil parameters that specified initial soil water conditions, soil water limits, drainage rate, and root growth characteristics. The influences of another soil parameter and one ET parameter were relevant only for the ET methods that required them. Large differences in sensitivity indices were found based on the choice between two soil water evaporation methods. In addition to providing insights for other applications of this model, the results specifically informed further efforts to evaluate the model using measured data from the Bushland cotton studies to compare performance among the six ET methods, as reported in a companion paper.

Sensitivity Analysis in Probabilistic Structural Design: A Comparison of Selected Techniques

Although more and more reliability-oriented sensitivity analysis (ROSA) techniques are now available, review and comparison articles of ROSA are absent. In civil engineering, many of the latest indices have never been used to analyse structural reliability for very small failure probability. This article aims to analyse and compare different sensitivity analysis (SA) techniques and discusses their strengths and weaknesses. For this purpose, eight selected sensitivity indices are first described and then applied in two different test cases. Four ROSA type indices are directly oriented on the failure probability or reliability index beta, and four other indices (of a different type) are oriented on the output of the limit state function. The case study and results correspond to cases under common engineering assumptions, where only two independent input variables with Gaussian distribution of the load action and the resistance are applied in the ultimate limit state. The last section of the article is dedicated to the analysis of the different results. Large differences between first-order sensitivity indices and very strong interaction effects obtained from ROSA are observed for very low values of failure probability. The obtained numerical results show that ROSA methods lack a common platform that clearly interprets the relationship of indices to their information value. This paper can help orientate in the selection of which sensitivity measure to use.

Global Sensitivity Analysis of Parameters in the ENSO model Based on Sobol' Method

Cao, X.; Su, S.; Leng, H., and Liu, B., 2020. Global sensitivity analysis of parameters in the ENSO model based on Sobol' method. In: Zheng, C.W.; Wang, Q.; Zhan, C., and Yang, S.B. (eds.), Air-Sea Interaction and Coastal Environments of the Maritime and Polar Silk Roads. Journal of Coastal Research, Special Issue No. 99, pp. 340–345. Coconut Creek (Florida), ISSN 0749-0208.In this work, a global sensitivity analysis (GSA) is performed on physical parameters and initial values of a nonlinear El Nino Southern Oscillation (ENSO) model, to determine the influential parameters at different prediction times. The anomaly of sea surface temperature (SST) at each prediction time is viewed as the response quantity of interest in the sensitivity analysis by Sobol' method. And the Latin Hypercube Sampling technique is applied to all input parameters. Then, the first-order sensitivity indexes (FSI) and total sensitivity indexes (TSI) of physical parameters and initial values are calculated, respectively. The results of numerical simulations show that the proposed method is very effective and feasible for sensitivity analysis of nonlinear ENSO model. It is concluded that the sensitive parameters and relative ranking vary considerably at different prediction times. In the beginning period, initial values of the model have a great influence on the SST anomaly forecast, while for the long-term prediction, physical coefficients become main sensitive parameters, and the initial values have little effect on the output variable. In addition, the interaction between input parameters in the model increases with forecast time, which is indicated by that the difference between the TSI and FSI of each parameter becomes larger.