scholarly journals Time-dependent global sensitivity analysis with active subspaces for a lithium ion battery model

2017 ◽  
Vol 10 (5) ◽  
pp. 243-262 ◽  
Author(s):  
Paul G. Constantine ◽  
Alireza Doostan
Keyword(s):  
Sensitivity Analysis ◽  
Lithium Ion Battery ◽  
Global Sensitivity Analysis ◽  
Lithium Ion ◽  
Time Dependent ◽  
Global Sensitivity ◽  
Battery Model ◽  
Active Subspaces

scholarly journals Global Sensitivity Analysis and Adaptive Stochastic Sampling of a Subsurface-Flow Model Using Active Subspaces

2019 ◽  
Author(s):  
Daniel Erdal ◽  
Olaf A. Cirpka
Keyword(s):  
Sensitivity Analysis ◽  
Flow Model ◽  
Global Sensitivity Analysis ◽  
Subsurface Flow ◽  
Full Model ◽  
Stochastic Sampling ◽  
Global Sensitivity ◽  
Parameter Combination ◽  
Active Subspaces ◽  
Active Subspace

Abstract. Integrated hydrological modelling of domains with complex subsurface features requires many highly uncertain parameters. Performing a global uncertainty analysis using an ensemble of model runs can help bring clarity which of these parameters really influence system behavior, and for which high parameter uncertainty does not result in similarly high uncertainty of model predictions. However, already creating a sufficiently large ensemble of model simulation for the global sensitivity analysis can be challenging, as many combinations of model parameters can lead to unrealistic model behavior. In this work we use the method of active subspaces to perform a global sensitivity analysis. While building-up the ensemble, we use the already existing ensemble members to construct low-order meta-models based on the first two active subspace dimensions. The meta-models are used to pre-determine whether a random parameter combination in the stochastic sampling is likely to result in unrealistic behavior, so that such a parameter combination is excluded without running the computationally expensive full model. An important reason for choosing the active subspace method is that both the activity score of the global sensitivity analysis and the meta-models can easily be understood and visualized. We test the approach on a subsurface flow model including uncertain hydraulic parameter, uncertain boundary conditions, and uncertain geological structure. We show that sufficiently detailed active subspaces exist for most observations of interest. The pre-selection by the meta-model significantly reduces the number of full model runs that must be rejected due to unrealistic behavior. An essential but difficult part in active subspace sampling using complex models is approximating the gradient of the simulated observation with respect to all parameters. We show that this can effectively and meaningful be done with second-order polynomials.

scholarly journals Assessment of the Supply Chain under Uncertainty: The Case of Lithium

Minerals ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 604
Author(s):  
Daniel Calisaya-Azpilcueta ◽  
Sebastián Herrera-Leon ◽  
Freddy A. Lucay ◽  
Luis A. Cisternas
Keyword(s):  
Sensitivity Analysis ◽  
Supply Chain ◽  
Lithium Ion Batteries ◽  
Stochastic Modeling ◽  
Global Sensitivity Analysis ◽  
Lithium Ion ◽  
Lithium Carbonate ◽  
Lithium Hydroxide ◽  
Deterministic Models ◽  
Global Sensitivity

Modeling the global markets is complicated due to the existence of uncertainty in the information available. In addition, the lithium supply chain presents a complex network due to interconnections that it presents and the interdependencies among its elements. This complex supply chain has one large market, electric vehicles (EVs). EV production is increasing the global demand for lithium; in terms of the lithium supply chain, an EV requires lithium-ion batteries, and lithium-ion batteries require lithium carbonate and lithium hydroxide. Realistically, the mass balance in the global lithium supply chain involves more elements and more markets, and together with the assortment of databases in the literature, make the modeling through deterministic models difficult. Modeling the global supply chain under uncertainty could facilitate an assessment of the lithium supply chain between production and demand, and therefore could help to determine the distribution of materials for identifying the variables with the highest importance in an undersupply scenario. In the literature, deterministic models are commonly used to model the lithium supply chain but do not simultaneously consider the variation of data among databases for the lithium supply chain. This study performs stochastic modeling of the lithium supply chain by combining a material flow analysis with an uncertainty analysis and global sensitivity analysis. The combination of these methods evaluates an undersupply scenario. The stochastic model simulations allow a comparison between the known demand and the supply calculated under uncertainty, in order to identify the most important variables affecting lithium distribution. The dynamic simulations show that the most probable scenario is one where supply does not cover the increasing demand, and the stochastic modeling classifies the variables by their importance and sensibility. In conclusion, the most important variables in a scenario of EV undersupply are the lithium hydroxide produced from lithium carbonate, the lithium hydroxide produced from solid rock, and the production of traditional batteries. The global sensitivity analysis indicates that the critical variables which affect the uncertainty in EV production change with time.

Global sensitivity analysis for nuclear reactor LBLOCA with time-dependent outputs

2022 ◽  
pp. 108337
Author(s):  
Qingwen Xiong ◽  
Peng Du ◽  
Jian Deng ◽  
Daishun Huang ◽  
Gongle Song ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Nuclear Reactor ◽  
Global Sensitivity Analysis ◽  
Time Dependent ◽  
Global Sensitivity

scholarly journals Time-Dependent Global Sensitivity Analysis for Long-Term Degeneracy Model Using Polynomial Chaos

2014 ◽  
Vol 6 ◽  
pp. 719825 ◽  
Author(s):  
Jianbin Guo ◽  
Shaohua Du ◽  
Yao Wang ◽  
Shengkui Zeng
Keyword(s):  
Sensitivity Analysis ◽  
Polynomial Chaos ◽  
Global Sensitivity Analysis ◽  
Time Dependent ◽  
Accurate Information ◽  
Test Case ◽  
Sobol Indices ◽  
Global Sensitivity ◽  
Mechanism Model

Global sensitivity is used to quantify the influence of uncertain model inputs on the output variability of static models in general. However, very few approaches can be applied for the sensitivity analysis of long-term degeneracy models, as far as time-dependent reliability is concerned. The reason is that the static sensitivity may not reflect the completed sensitivity during the entire life circle. This paper presents time-dependent global sensitivity analysis for long-term degeneracy models based on polynomial chaos expansion (PCE). Sobol’ indices are employed as the time-dependent global sensitivity since they provide accurate information on the selected uncertain inputs. In order to compute Sobol’ indices more efficiently, this paper proposes a moving least squares (MLS) method to obtain the time-dependent PCE coefficients with acceptable simulation effort. Then Sobol’ indices can be calculated analytically as a postprocessing of the time-dependent PCE coefficients with almost no additional cost. A test case is used to show how to conduct the proposed method, then this approach is applied to an engineering case, and the time-dependent global sensitivity is obtained for the long-term degeneracy mechanism model.

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