Global Sensitivity Analysis Approach Research Articles

Unraveling the complex dynamics of temporal moments for multispecies contaminant transport in saturated porous media: A global sensitivity analysis approach

In this study, we focus on the application of global sensitivity analysis (GSA) to gain insight into the influence of model parameters on the outputs of multispecies contaminant transport model. In this context, employing GSA methods (Morris and FAST) allows us to comprehend the relative significance of each input parameter in the model. We specifically analyzed the effects of model parameters on the concentration breakthrough curves and associated temporal moments of concentration of contaminant species. The spatial variability of sensitivity indexes from Morris and FAST methods for parent and daughter contaminant species is also investigated. We represent uncertainties of longitudinal dispersivity, porosity, first-order degradation constants of parent and daughter contaminant species, and sorption distribution coefficient of parent species into the coupled multispecies contaminant transport model and GSA framework. Our results show a complex interplay between different model parameters and their varying influences on different output metrics. The ranking of parameters based on GSA shows that (i) the contaminant mass recovery of final daughter species of three species chain reaction is governed by the first-order degradation coefficient of parent and first daughter species; (ii) dispersivity and porosity are critical parameters that govern the mean residence time and variance of breakthrough curve of final daughter species; (iii) the coefficient of skewness is more sensitive to dispersivity, sorption distribution coefficient, and first-order degradation coefficient of parent species as compared to other model parameters. The variation in the sensitivity indices among contaminant species and with travel distance is observed. This study highlights the significance of global sensitivity analysis when simulating the multiple species contaminants in saturated porous media.

Improving maize yield estimation by assimilating UAV-based LAI into WOFOST model

ContextCrop growth models were widely applied for simulating the dynamic growth of crops at multi-scales. The data assimilation by integrating the remote sensing data retrieved crop parameters and crop models have showed great potentials for describing the crop growth and assessing the agricultural yields. ObjectiveThe purpose of this study was to integrates sequential observations of crop phenotyping traits from Unmanned Aerial Vehicles (UAV) remote sensing into World Food Studies (WOFOST) model to improve the simulation of crop growth processes. MethodsTwo years of Leaf Area Index (LAI) of summer maize retrieved from Unmanned Aerial Vehicles (UAV)-RGB images was assimilated into the WOFOST using the Ensemble Kalman Filter (EnKF). The sensitive crop parameters of WOFOST were firstly identified using the Extended Fourier Amplitude Sensitivity Test (EFAST) global sensitivity analysis approach, and then the parameters were adjusted and confirmed using the SUBPLEX optimization algorithm. The LAI data was assimilated into WOFOST model using EnKF by minimizing the differences between the UAV-retrieved LAI and crop-simulated LAI. Results indicated assimilating LAI into WOFOST model significantly improved the accuracy of maize yield prediction. ResultsCompared with non-assimilation, data assimilation reduced the Root Mean Square Error (RMSE) from 413 to 132 kg/ha for 2020, and from 392 to 215 kg/ha for 2021, respectively. Through the effects of different ensemble size and different time-point for data assimilation, it was obtained that the accuracy of yield prediction achieved the highest when ensemble size was 100 at reproductive growth stage. ConclusionsIntegrating UAV-based crop traits into WOFOST model using data assimilation (EnKF) could effectively improve the maize yield accuracy.

Trajectory-based global sensitivity analysis in multiscale models

This research introduces a novel global sensitivity analysis (GSA) framework for agent-based models (ABMs) that explicitly handles their distinctive features, such as multi-level structure and temporal dynamics. The framework uses Grassmannian diffusion maps to reduce output data dimensionality and sparse polynomial chaos expansion (PCE) to compute sensitivity indices for stochastic input parameters. To demonstrate the versatility of the proposed GSA method, we applied it to a non-linear system dynamics model and epidemiological and economic ABMs, depicting different dynamics. Unlike traditional GSA approaches, the proposed method enables a more general estimation of parametric sensitivities spanning from the micro level (individual agents) to the macro level (entire population). The new framework encourages the use of manifold-based techniques in uncertainty quantification, enhances understanding of complex spatio-temporal processes, and equips ABM practitioners with robust tools for detailed model analysis. This empowers them to make more informed decisions when developing, fine-tuning, and verifying models, thereby advancing the field and improving routine practice for GSA in ABMs.

Unified framework for geometric error compensation and shape-adaptive scanning in five-axis metrology systems

In response to the escalating demand for precise shape metrology of complex optical surfaces, this study unveils a unified geometric error compensation and trajectory planning framework tailored for high-accuracy five-axis scanning metrology systems, which remains a notably underexplored field compared to error compensation in machine tools. Founded on a unified geometric model, the proposed framework seamlessly integrates a versatile shape-adaptive trajectory planning strategy, a thorough global error sensitivity analysis approach, and an exhaustive geometric error compensation scheme. Leveraging inverse kinematics, an innovative shape-adaptive scanning trajectory generation strategy is mathematically formulated, thereby facilitating adaptable measurement trajectory generation for diverse surface geometries. Employing forward kinematics, an exhaustive geometric error model is established to extensively address the 53 distinct geometric errors in the metrology system. This proposed error model fundamentally augments conventional geometric error models in machine tool by managing not only the geometric errors from the motion system, but also those from the probe and workpiece. To streamline the error compensation procedure, a novel global error sensitivity analysis approach is introduced, identifying both system-oriented and process-oriented sensitive geometric errors for targeted compensation. Experimental validation using a standard ball, which achieved an exceptional 89.35% reduction in the root mean square of the measurement errors, further confirms the feasibility and effectiveness of the proposed framework. By offering an universal trajectory planning, sensitivity analysis and error compensation trinity for five-axis scanning metrology systems, this study sets the stage for precision advancements and design optimization across diverse configurations of metrology systems.

Data-Driven Global Sensitivity Analysis of Variable Groups for Understanding Complex Physical Interactions in Engineering Design

Abstract In engineering design, global sensitivity analysis (GSA) is used for analyzing the effects of inputs on the system response and is commonly studied with analytical or surrogate models. However, such models fail to capture nonlinear behaviors in complex systems and involve several modeling assumptions. Besides model-focused methods, a data-driven GSA approach, rooted in interpretable machine learning, would also identify the relationships between system components. Moreover, a special need in engineering design extends beyond performing GSA for input variables individually, but instead evaluating the contributions of variable groups on the system response. In this article, we introduce a flexible, interpretable artificial neural network model to uncover individual as well as grouped global sensitivity indices for understanding complex physical interactions in engineering design problems. The proposed model allows the investigation of the main effects and second-order effects in GSA according to functional analysis of variance (FANOVA) decomposition. To draw a higher-level understanding, we further use the subset decomposition method to analyze the significance of the groups of input variables. Using the design of a programmable material system (PMS) as an example, we demonstrate the use of our approach for examining the impact of material, architecture, and stimulus variables as well as their interactions. This information lays the foundation for managing design space complexity, summarizing the relationships between system components, and deriving design guidelines for PMS development.

A two-step Bayesian network-based process sensitivity analysis for complex nitrogen reactive transport modeling

Process-based nitrogen reactive transport models (NRTMs) are vital tools for simulating complex nitrogen reactive transport processes in soils and groundwater to reduce nutrient loss and contamination risk. To develop and improve these models, global sensitivity analysis (GSA) is used to identify key model parameters and processes. Because existing GSA approaches focus mainly on model parameters and not model processes, in this study, we developed a two-step GSA method to improve our understanding of model processes. First, we applied the Morris method to identify less influential parameters. Then, we developed a Bayesian network (BN)-based GSA method to identify the dominant processes for different model predictions. The BN-GSA method accounts for the hierarchical uncertainty in the structure of the model system and flexibly groups uncertain inputs into processes based on their characteristics. The two-step GSA method was applied to a real-world nitrogen reactive transport model with 68 uncertain parameters in three different climate scenarios. The results show that the dominant processes differed for various nitrogen and agricultural outputs; thus, different processes should be emphasized based on specific modeling purposes.

Enhancement of fatigue life modeling using a metamodel-based global sensitivity analysis framework

Global Sensitivity Analysis (GSA) is a well-established approach to support simulation-driven design decisions where the dependency between the simulation's output and the model's input is quantifed. However, classical GSA approaches, such as Sobol’ indices based on Monte Carlo Simulations (MCS), are not convenient when computationally expensive simulation models such as Representative Volume Elements (RVE) are used as the model to analyze. A simulation framework is developed with a metamodeling-based GSA to overcome the aforementioned cost of the MCS approaches. The developed framework has been applied in a Multi-Scale Modeling (MSM) framework replacing a micromechanical RVE simulation with three different metamodels for performing GSA. The micromechanical model predicts the stiffness of a Carbon Fiber Reinforced Polymer (CFRP) material and the GSA can quantify how the experimental material parameters affect the material properties. The obtained sensitivity analysis demonstrates that void size is the most influential parameter on the outputs of interest, and the metamodel-based GSA is a computationally convenient approach.

IPSAL: Implementation of the module to generate the Sobol sequence and indices

Sensitivity and uncertainty analysis hold significant importance across a range of applications, spanning from industrial problems to climate change, financial risk assessment, as well as mathematical and computational models. These analyses involve identifying influential input parameters in models to comprehend their impact on the output. Sensitivity analysis can be performed locally, examining parameter effects at a fixed value, or globally, evaluating the model across a range of parameter values. The Sobol method stands as a robust approach for global sensitivity analysis, employing a Sobol sequence to create samples more uniformly within the input parameter space, thus enabling efficient exploration of model inputs. This paper aims to introduce a computational implementation in Scilab to generate the Sobol sequence for utilization in sensitivity analysis through the Sobol method. A test case was applied to generate Sobol sequences and discuss the obtained results.

Global sensitivity analysis for seismic performance of shear wall with high-strength steel bars and recycled aggregate concrete

In terms of bearing lateral loads, a reinforced concrete shear wall (SW) is one of the most crucial structural elements in structures. Despite their significance, recent experimental research and post-earthquake surveys have exposed the inadequate safety margins of the SWs. Significant SW components cannot be accurately identified due to the absence of empirical and mechanistic models. This study introduces a novel paradigm for assessing the SW's crucial elements by using two consecutive modeling strategies. Initially, a finite element model is calibrated to mimic one of the laboratory-experimented SW structures. Then, deep residual neural networks (DRNNs) for non-parametric techniques are used to enhance the Abaqus software prediction skills and comprehensively understand the input parameters' influence on the selected SW structure's displacement value. The suggested DRNNs' design uses residual shortcuts (i.e., connections) that skip several levels in the deep network structure in order to address the issue of training with high accuracy. A thorough global sensitivity analysis (GSA) is done using the Latin Hypercube Simulation. Three distinct GSA techniques are used to emphasize the influence of each input variable on the amount of displacement in real-world applications while limiting the risk of result misinterpretation owing to interactions between input variables. In each GSA approach, an effort would be made to rank, filter, or map the input variables. The performance metrics of the DRNNs prediction models lend confidence to the GSA's results. The diameter of steel stirrups is reported to be the most significant component in the SW structure, while SW's displacement is more sensitive to higher and lower values of the lateral load domain.

Elementary effects for models with dimensional inputs of arbitrary type and range: Scaling and trajectory generation.

The Elementary Effects method is a global sensitivity analysis approach for identifying (un)important parameters in a model. However, it has almost exclusively been used where inputs are dimensionless and take values on [0, 1]. Here, we consider models with dimensional inputs, inputs taking values on arbitrary intervals or discrete inputs. In such cases scaling effects by a function of the input range is essential for correct ranking results. We propose two alternative dimensionless sensitivity indices by normalizing the scaled mean or median of absolute effects. Testing these indices with 9 trajectory generation methods on 4 test functions (including the Penman-Monteith equation for evapotranspiration) reveals that: i) scaled elementary effects are necessary to obtain correct parameter importance rankings; ii) small step-size methods typically produce more accurate rankings; iii) it is beneficial to compute and compare both sensitivity indices; and iv) spread and discrepancy of the simulation points are poor proxies for trajectory generation method performance.

Analysing Witczak 1-37A, Witczak 1-40D and Modified Hirsch Models for asphalt dynamic modulus prediction using global sensitivity analysis

ABSTRACT The dynamic modulus (∣E*∣) of hot-mix asphalt mixes is one of the most time-consuming and labour-intensive material metrics to evaluate in the laboratory. This study introduces a novel paradigm for assessing the ∣E*∣'s most influential elements by employing widely accepted literature models. Witczak 1-37A, Witczak 1-40D, and Modified Hirsch Models are selected for analysing the asphalt dynamic modulus. First, a thorough laboratory database of Arizona State University is used to account for all major input factors, such as mixture gradation, binder qualities, volumetric properties, and testing conditions parameters, during models' validation. Second, each model's performance is evaluated using standard measures to build confidence levels in the subsequent analysis stage. Finally, with the aid of Latin Hypercube Simulation, a comprehensive global sensitivity analysis (GSA) is performed. Three unique GSA approaches are used; namely, elementary effects, variance-based, and PAWN methods, to highlight the effect of each input variable on the magnitude of ∣E*∣. Different GSA tools are strongly recommended since there is no analytical tool for validating the findings with the complex formulations of the selected mathematical models. The GSA demonstrates that the voids ratio in total mix, binder shear modulus, viscosity, phase angle, and binder quantity are the most significant factors.

Efficient global sensitivity analysis of kinetic Monte Carlo simulations using Cramérs-von Mises distance.

Typically, the parameters entering a physical simulation model carry some kind of uncertainty, e.g., due to the intrinsic approximations in a higher fidelity theory from which they have been obtained. Global sensitivity analysis (GSA) targets quantifying which parameter uncertainties impact the accuracy of the simulation results, e.g., to identify which parameters need to be determined more accurately. We present a GSA approach based on the Cramérs-von Mises distance. Unlike prevalent approaches, it combines the following properties: (i) it is equally suited for deterministic as well as stochastic model outputs, (ii) it does not require gradients, and (iii) it can be estimated from numerical quadrature without further numerical approximations. Using quasi-Monte Carlo for numerical integration and a first-principles kinetic Monte Carlo model for the CO oxidation on RuO2(110), we examine the performance of the approach. We find that the results agree very well with what is known in the literature about the sensitivity of this model and that the approach converges in a modest number of quadrature points. Furthermore, it appears to be robust against even extreme relative noise. All these properties make the method particularly suited for expensive (kinetic) Monte Carlo models because we can reduce the number of simulations as well as the target variance of each of these.

Sensitivity and identifiability analysis of a conceptual-lumped model in the headwaters of the Benue River Basin, Cameroon: implications for uncertainty quantification and parameter optimization

Abstract Many hydrological applications employ conceptual-lumped models to support water resource management techniques. This study aims to evaluate the workability of applying a daily time-step conceptual-lumped model, HYdrological MODel (HYMOD), to the Headwaters Benue River Basin (HBRB) for future water resource management. This study combines both local and global sensitivity analysis (SA) approaches to focus on which model parameters most influence the model output. It also identifies how well the model parameters are defined in the model structure using six performance criteria to predict model uncertainty and improve model performance. The results showed that both SA approaches gave similar results in terms of sensitive parameters to the model output, which are also well-identified parameters in the model structure. The more precisely the model parameters are constrained in the small range, the smaller the model uncertainties, and therefore the better the model performance. The best simulation with regard to the measured streamflow lies within the narrow band of model uncertainty prediction for the behavioral parameter sets. This highlights that the simulated discharges agree with the observations satisfactorily, indicating the good performance of the hydrological model and the feasibility of using the HYMOD to estimate long time-series of river discharges in the study area.

Integrated multi-stage sensitivity analysis and multi-objective optimization approach for PCM integrated residential buildings in different climate zones

The early design stage provides the greatest opportunities to achieve energy-efficient buildings; however, designers require relevant performance data to manage interdisciplinary and conflicting objectives. Thus, for the first time an integrated multi-stage sensitivity analysis and multi-objective optimization approach was developed for four different zones of arid climate in PCM integrated residential buildings. A multi-stage sensitivity analysis is proposed to identify the relative importance of early design stage parameters, which covers envelope thermophysics, building layout and energy-efficiency measures, considering energy and economic indicators. Three global sensitivity analysis approaches (Standardized rank regression coefficient, Partial rank correlation coefficient and Morris method) were employed in the first stage, while the local sensitivity analysis was employed in the second stage. A multi-objective optimization using the genetic algorithm was performed to obtain the Pareto frontier, and two sets of solutions were proposed: the most energy-efficient and the most cost-effective. Finally, multi-criteria decision making was carried out. It was found that sensitivity analysis results showed different rankings of parameters. For each climatic zone, a different set of optimal solutions existed. Compared to reference building, the best solutions reduced total energy consumption by 11.1–34.6%, while the payback period reduced by 17.6–48.5 years.

Global Sensitivity Analysis for Impedance Spectrum Identification of Lithium-Ion Batteries Using Time-Domain Response

Accurate modeling is of great importance for describing battery dynamics under different working conditions. To achieve high accuracy and obtain insights into battery operations, it is critical to bridge the time-domain equivalent circuit model and frequency-domain electrochemical impedance spectroscopy. However, the practical battery running data are recorded at a very low sampling frequency, it is thus challenging to identify the frequency-domain impedance spectrum using time-domain response data. To solve this problem, this article proposes a systematic battery time-domain modeling approach considering impedance spectrum identification. First, a probabilistic approach for global sensitivity analysis is proposed to evaluate the impact of different parameters on model performance. The parameter sensitivity of both integer-order and fractional-order models are evaluated using the Sobol indices obtained by Monte Carlo simulations. Then, a Bayesian optimization algorithm is proposed to identify model parameters to reduce the evaluation times of the battery model. Finally, the experimental data conducted on LiFePO <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_{4}$</tex-math></inline-formula> cells are employed to verify the proposed method. The results indicate that the total Sobol indices show a miscellaneous sensitivity of parameters on impedance spectra, which consolidates the understanding of impedance characteristics, and the higher order integer-order models with well-initialized low sensitive parameters are more suitable for predicting frequency-domain characteristics.

Post-earthquake reliability assessment of segmental column structures based on nonlinear model updating

In this study, a Bayesian based nonlinear model updating approach is developed to enhance the post-earthquake reliability assessment of posttensioned segmental column structures. According to the proposed procedure, dynamic global sensitivity analysis (DGSA) approach is firstly performed to analyze the sensitivity of the model outputs to the model parameters, which aims to select the appropriate parameters for model updating, and then, the likelihood function of Bayesian estimation is constructed based on the residual errors between the measured and simulated dynamic responses of segmental column structures. Since the decomposed structural dynamic responses are conducted for model updating, the simulated error variance parameters are also considered as unknown. To simultaneously estimate the unknown model parameters and variance parameters, the maximum likelihood estimation approach is employed in this study. Then, the Cram-Rao lower bound (CRLB) analysis method is applied to estimate uncertainty of the identified model parameters that caused by measurement noise. Once nonlinear model updating is completed, the earthquake-induced failure probability of segmental column structure can be evaluated by performing reliability analysis. In numerical simulations, the feasibility of the proposed reliability assessment approach is validated by using a planar posttensioned segmental column model subjected to seismic loads. Furthermore, a scaled posttensioned segmental column structure subjected to bi-directional earthquake exactions is applied to verify the effectiveness of the proposed approach. Both numerical and experimental results have shown that the proposed strategy is accurate and effective for nonlinear model updating of segmental column structures, and the updated results are capable of using to evaluate the failure probability of such type of structures subjected to strong external excitations.

Non-Linear Partial-Least-Squares-based Polynomial Chaos Expansion (NLPLS-based PCE) approach for global sensitivity analysis of a High-Q Partially Air-Filled Pedestal Resonator Integrated in a Printed Circuit Board (PCB)

This paper presents a global sensitivity analysis of a high-Q partially air-filled pedestal resonator integrated in a printed circuit board. Nonlinear partial-least-squares-based polynomial chaos expansion (NLPLS-based PCE) approach is used for the global sensitivity analysis. Using NLPLS-based PCE a surrogate model is constructed with a reduced dimensionality, which enhances the performance of the algorithm. A standard PCE surrogate model, with all the system parameters, is created from the reduced NLPLS-based PCE surrogate model. The statistical information needed to perform the sensitivity analysis, i.e., variance, is extracted from the standard PCE surrogate model. A variance-based global sensitivity analysis is performed on the PCE model, each system parameter's sensitivity is quantified as the partial influence on the total variance of the performance variable S11. The chosen manufacturing technology involves a three-stage process: micromachining of the cavity, metallization, and thermos-diffusion stacking. During the three stages several problems may occur that can have an influence on the performance of the resonator, such as shape and size variation, and misalignment. The system parameters are set up according to these most common problems. The results show that, of the 8 system parameters chosen to evaluate, the height of the cavity and pedestal are the most sensitive parameters.

Using experimental design to assess rate-dependent numerical models

To enhance the accuracy of advanced constitutive models for soft natural clays, several parameters are necessary, resulting in complexity of numerical modelling. However, the detailed effects of these parameters are not rigorously quantified towards their constitutive relationships. Thus, the aim of the paper is to assess such advanced models in order to determine the most significant parameters over the time series data. Two methods for Global Sensitivity Analysis (GSA), i.e. Experimental Design and the Sobol method, were used and benchmarked to assess the model predictions on a discretised domain in time and space. A rate-dependent Finite Element model using Creep-SCLAY1S and a hydro-mechanically coupled formulation for consolidation was used to study a Constant Rate of Strain (CRS) test. The value of GSA approaches adopted herein was to investigate the model predictions both in the temporal and spatial domain. The temporal analyses indicate three sets of significant model parameters in different portions of the CRS compression curve. Furthermore, the non-stationary nature of sensitivity results is exposed, identifying the parameters that lead to unique solutions for the CRS loading path. The FE implementation enabled the quantification of the most sensitive model parameters in the spatial domain. The spatial results that are governed by the rate-dependent processes in the soil (i.e. consolidation and creep) illustrated that Experimental Design was capable of providing sensitivity maps with satisfactory accuracy similar to the Sobol method. Experimental Design was found to be the most efficient method, concerning execution time and storage costs, to assess rate-dependent problems in Geotechnics.

Sensitivity Analysis and Anaerobic Digestion Modeling: A Scoping Review

A growing awareness of global climate change has led to an increased interest in investigating renewable energy sources, such as the anaerobic digestion of biomass. This process utilizes a wide range of microbial communities to degrade biodegradable material in feedstock through a complex series of biochemical interactions. Anaerobic digestion exhibits nonlinear dynamics due to the complex and interacting biochemical processes involved. Due to its dynamic and nonlinear behavior, uncertain feedstock quality, and sensitivity to the process’s environmental conditions, anaerobic digestion is highly susceptible to instabilities. Therefore, in order to model and operate a biogas production unit effectively, it is necessary to understand which parameters are most influential on the model outputs. This also reduces the amount of estimation required. Through a scoping review, the present study analyzes the studies on the application of sensitivity analysis in anaerobic digestion modeling. Both local and global sensitivity analysis approaches were carried out using different mathematical models. The results indicate that anaerobic digestion model no.1 (ADM1) was the most commonly used model for analyzing sensitivity. Both local and global sensitivity analyses are widely employed to investigate the influence of key model parameters such as kinetic, stoichiometric, and mass transfer parameters on model outputs such as biogas production, methane concentration, pH, or economic viability of the plant.

Sensitivity-Analysis-Driven Surrogate Model for Molten Salt Reactors Control

The numerical analysis for the controllability assessment of a new design nuclear reactor is typically carried out by means of complex multiphysics codes, solving high fidelity partial differential equations governing the system neutronics as well as the fluid dynamics. Multiphysics codes deliver very accurate solutions at the expense of high computational times, which could be of several hours depending on the specific case study. In this work, to efficiently reduce runtimes, a sensitivity analysis (SA) is carried out to identify the most important input parameters affecting the solution of a multiphysics model developed for the controllability assessment of molten salt reactors (MSRs). The numerical modeling of these innovative systems is fundamental to allow for a safer and more sustainable power production (e.g., due to the lower radiotoxicity of the actinide inventory in MSRs and to the possibility of operation at atmospheric pressure). In this paper, four global sensitivity measures are calculated first, including the Pearson correlation coefficient, δ, Kolmogorov–Smirnov and Kuiper indices, whose results are aggregated by an ensemble strategy and confirmed by the CUmulative SUm of NOrmalized Reordered Output (CUSUNORO) plot. The results of the SA point out that the fuel density is the most important parameter yielding the largest variations in the system reactivity, fundamental for guaranteeing the MSR controllability. In light of this result, a simplified, surrogate model is then developed, which uses density as the only input parameter to determine reactivity, guaranteeing runtime reductions from several hours to a few seconds and, at the same time, a comparable level of accuracy of the multiphysics model. This result demonstrates the capability of global sensitivity analysis approaches to effectively identify the most relevant parameters in MSR systems, supporting the development of simplified, control-oriented models for these innovative reactors.