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

Power systems are increasingly affected by various sources of uncertainty at all levels. The investigation of their effects thus becomes a critical challenge for their design and operation. Sensitivity Analysis (SA) can be instrumental for understanding the origins of system uncertainty, hence allowing for a robust and informed decision-making process under uncertainty. The SA value as a support tool for model-based inference is acknowledged; however, its potential is not fully realized yet within the power system community. This is due to an improper use of long-established SA practices, which sometimes prevent an in-depth model sensitivity investigation, as well as to partial communication between the SA community and the final users, ultimately hindering non-specialists’ awareness of the existence of effective strategies to tackle their own research questions. This paper aims at bridging the gap between SA and power systems via a threefold contribution: (i) a bibliometric study of the state-of-the-art SA to identify common practices in the power system modeling community; (ii) a getting started overview of the most widespread SA methods to support the SA user in the selection of the fittest SA method for a given power system application; (iii) a user-oriented general workflow to illustrate the implementation of SA best practices via a simple technical example.

As the level of greenhouse gas emissions increases, so does the importance of the energy performance of buildings (EPB). One of the main factors to measure EPB is a structure’s heating load (HL) and cooling load (CL). HLs and CLs depend on several variables, such as relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution. This research uses deep neural networks (DNNs) to forecast HLs and CLs for a variety of structures. The DNNs explored in this research include multi-layer perceptron (MLP) networks, and each of the models in this research was developed through extensive testing with a myriad number of layers, process elements, and other data preprocessing techniques. As a result, a DNN is shown to be an improvement for modeling HLs and CLs compared to traditional artificial neural network (ANN) models. In order to extract knowledge from a trained model, a post-processing technique, called sensitivity analysis (SA), was applied to the model that performed the best with respect to the selected goodness-of-fit metric on an independent set of testing data. There are two forms of SA—local and global methods—but both have the same purpose in terms of determining the significance of independent variables within a model. Local SA assumes inputs are independent of each other, while global SA does not. To further the contribution of the research presented within this article, the results of a global SA, called state-based sensitivity analysis (SBSA), are compared to the results obtained from a traditional local technique, called sensitivity analysis about the mean (SAAM). The results of the research demonstrate an improvement over existing conclusions found in literature, which is of particular interest to decision-makers and designers of building structures.

Nitrogen loss in paddy fields has been widely recognized as a significant contributor to nonpoint source pollution. Predicting this process through modeling is crucial, yet model parameters always carry uncertainty. Clarifying the time-dependent importance of the model parameters can help to better know the process effect such as precipitation or chemical reaction on nitrogen loss. Therefore, to rank the parameter importance, a global sensitivity analysis (GSA) named Sobol method is applied to a nitrogen loss model in paddy fields based on the soil mixing layer theory via modifying HYDRUS-1D model. To reduce the computational cost, local sensitivity analysis (LSA) is applied to the prediction model firstly, and three important model parameters, including soil mixing layer depth ( d mix ), soil detachability coefficient ( α ) and precipitation intensity ( p ), are selected. Then, the Sobol method is applied to the prediction model to analyze the sensitivities of these three parameters. It is novel but reasonable that the Sobol sensitivity indices (including the first, second and total order indices, FOI, SOI and TOI) of d mix , α and p vary with time. The study results indicate that the importance of parameter varies with time during a rainfall. In surface runoff, α is most important at early times, while p becomes most important at later times for predicted urea and NO 3 − -N concentrations. α is always the most sensitive parameter for the predicted NH 4 + -N concentration in surface runoff. In soil, the GSA results are opposite for α and p . Generally, d mix is less important than α and p , and the interaction between each two parameters reflected by the SOIs has limited importance. d mix presented sensitivity in LSA but insensitivity in GSA. Sensitivity of α showed similar results in LSA (elasticity) and GSA (TOI and FOI), which decreases in surface runoff and increases in soil. All elasticities of p increase at first and decrease gradually later, while the GSA results of p vary oppositely in surface runoff and soil.

Sensitivity analysis of hydrological model parameters is a crucial step in the calibration process of hydrological simulation. In this paper, the improved Morris method with the double-Latin hypercube sampling is proposed for global sensitivity analysis of 10 parameters of the Xin'anjiang model. In addition, the local sensitivity is analyzed based on the rate validation of the model parameters. In general, the results show those parameters about evaporation coefficient in the deep layer (C), free water storage capacity (SM), impervious area as a percentage of total watershed area (IMP), free water storage capacity curve index (EX), groundwater outflow coefficient (KG) and subsurface runoff abatement factor (KKG) are all less than 0.01, insensitive parameters; the parameters about evaporation conversion factor (K) and square times of the storage capacity curve(B) are in the range of [0.01, 0.1], less sensitive parameters; the parameter for flow out coefficient in soil (KSS) is in the range of [0.1, 0.2], a low-sensitivity parameter; the parameter abatement coefficient of mid-soil flow (KKSS) is greater than 1, a high-sensitivity parameter; the improved Morris method better reflects the existence of interactions between parameters. This research result provides a new technical approach for the sensitivity analysis of hydrological model parameters.

The optimal selection of the composition of corrugated cardboard dedicated to specific packaging structures is not an easy task. The use of lighter boards saves material, but at the same time increases the risk of not meeting the guaranteed load capacity. Therefore, the answer to the question “in which layer the basis weight of the paper should be increased?” is not simple or obvious. The method proposed here makes it easy to understand which components and to what extent they affect the load-bearing capacity of packages of various dimensions. The use of numerical homogenization allows for a quick transformation of a cardboard sample, i.e., a representative volume element (RVE) into a flat plate structure with effective parameters describing the membrane and bending stiffness. On the other hand, the use of non-local sensitivity analysis makes it possible to find the relationship between the parameters of the paper and the load capacity of the packaging. The analytical procedures presented in our previous studies were used here to determine (1) the edge crush resistance, (2) critical load, and (3) the load capacity of corrugated cardboard packaging. The method proposed here allows for obtaining a comprehensive and hierarchical list of the parameters that play the most important role in the process of optimal packaging design.

Polyurea (PUA) elastomers are extensively used in a wide range of applications spanning from biomedical to defense fields due to their enabling mechanical properties. These materials can be further reinforced through the incorporation of nanoparticles to form nanocomposites. This study focuses on an IPDI‐based (isophorone diisocyanate) PUA matrix with exfoliated graphene nanoplatelet (xGnP) fillers. We propose a generalized‐order constitutive model by combining one Fractional Maxwell Model (FMM) and one Fractional Maxwell Gel (FMG) branch in a parallel configuration. This has been accomplished via introducing a new dimensionless number that bridges between these branches physically and mathematically. This model exhibits a maximum relative error of less than 2% when validated against the experimental master curves across more than ten decades of shifted frequency, demonstrating its robustness and accuracy. Through our systematic local and global (variance‐based) sensitivity analyses, we further investigate the behavior of the nanocomposites, leading to a priority list of model parameters in the order of their contribution to model uncertainty/sensitivity. The main contribution of the present study is to develop a robust and efficient framework to construct the most parsimonious constitutive models from data with a high degree of physical interpretability and generality of use in a range of applications. In this study, we proposed a new fractional‐order model for viscoelasticity in Polyurea‐Graphene nanocomposites in addition to the introduction of a new dimensionless number connecting fractional Maxwell branches to composite polymer morphology. Furthermore, we conducted a systematic local‐to‐global sensitivity analysis on the constitutive models to objectively identify a consistent set of most and least influential model parameters leading to the dimensionality reduction of models.

Sensitivity analysis (SA) intends to identify the key meteorological variables that affect the performance of reference crop evapotranspiration (ET0) models. It is of importance in assessing the variability of meteorological variables and ET0, especially in the face of increasing climate uncertainties. However, the surging of inconsistencies resulting from global changes in meteorological conditions due to climate change have impacted the ET0 model estimation in different regions, with detrimental effects on water resources and crop production. Therefore, efficient SA is necessary to evaluate the impact of changes in meteorological variables that influence ET0 model estimation. This mini review analyses the various SA methods applied in the field of ET0, based on a comprehensive and comparative analysis of existing SA methods from all around the world. The study discusses the advantages and disadvantages of each SA method, as well as the factors affecting the SA of ET0. The study also provides future prospects that may contribute to more solid and powerful analysis for ET0 model estimations and conclusions.

Physiologically-based pharmacokinetic (PBPK) modeling is important for studying drug delivery in the central nervous system, including determining antibody exposure, predicting chemical concentrations at target locations, and ensuring accurate dosages. The complexity of PBPK models, involving many variables and parameters, requires a consideration of parameter identifiability; i.e., which parameters can be uniquely determined from data for a specified set of concentrations. We introduce the use of a local sensitivity-based parameter subset selection algorithm in the context of a minimal PBPK (mPBPK) model of the brain for antibody therapeutics. This algorithm is augmented by verification techniques, based on response distributions and energy statistics, to provide a systematic and robust technique to determine identifiable parameter subsets in a PBPK model across a specified time domain of interest. The accuracy of our approach is evaluated for three key concentrations in the mPBPK model for plasma, brain interstitial fluid and brain cerebrospinal fluid. The determination of accurate identifiable parameter subsets is important for model reduction and uncertainty quantification for PBPK models.

Designing corrugated board packaging is a real challenge, especially when the packaging material comes from multiple recycling. Recycling itself is a pro-ecological and absolutely necessary process, but the mechanical properties of materials that are processed many times deteriorate with the number of cycles. Manufacturers are trying to use unprecedented design methods to preserve the load-bearing capacity of packaging, even when the material itself is of deteriorating quality. An additional obstacle in the process of designing the structure of paper packaging is the progressive systematic reduction of the grammage (the so-called lightweight process) of corrugated cardboard. Therefore, this research presents a critical look at the process of optimal selection of corrugated cardboard for packaging structures, depending on the paper used. The study utilizes analytical, simplified formulas to estimate the strength of cardboard itself as well as the strength of packaging, which are then analyzed to determine their sensitivity to changes in cardboard components, such as the types of paper of individual layers. In the performed sensitivity analysis, numerical homogenization was used, and the influence of initial imperfections on the packaging mechanics was determined. The paper presents a simple algorithm for the optimal selection of the composition of corrugated cardboard depending on the material used and the geometry of the packaging, which allows for a more conscious production of corrugated cardboard from materials derived, e.g., from multiple recycling.