scholarly journals A Visual Sensitivity Analysis for Parameter-Augmented Ensembles of Curves

2019 ◽  
Vol 4 (4) ◽  
Author(s):  
Alejandro Ribés ◽  
Joachim Pouderoux ◽  
Bertrand Iooss
Keyword(s):  
Sensitivity Analysis ◽  
Initial Conditions ◽  
Principal Component ◽  
Visual Sensitivity ◽  
Descriptive Statistics ◽  
Depth Analysis ◽  
Input Parameters ◽  
Marine Current ◽  
Brushing And Linking ◽  
Spatio Temporal

Abstract Engineers and computational scientists often study the behavior of their simulations by repeated solutions with variations in their parameters, which can be, for instance, boundary values or initial conditions. Through such simulation ensembles, uncertainty in a solution is studied as a function of various input parameters. Solutions of numerical simulations are often temporal functions, spatial maps, or spatio-temporal outputs. The usual way to deal with such complex outputs is to limit the analysis to several probes in the temporal/spatial domain. This leads to smaller and more tractable ensembles of functional outputs (curves) with their associated input parameters: augmented ensembles of curves. This article describes a system for the interactive exploration and analysis of such augmented ensembles. Descriptive statistics on the functional outputs are performed by principal component analysis (PCA) projection, kernel density estimation, and the computation of high density regions. This makes possible the calculation of functional quantiles and outliers. Brushing and linking the elements of the system allows in-depth analysis of the ensemble. The system allows for functional descriptive statistics, cluster detection, and finally, for the realization of a visual sensitivity analysis via cobweb plots. We present two synthetic examples and then validate our approach in an industrial use-case concerning a marine current study using a hydraulic solver.

scholarly journals Structure of Coordination Motor Abilities in Male Basketball Players at Different Levels of Competition

2015 ◽  
Vol 21 (4) ◽  
pp. 234-239 ◽  
Author(s):  
Sadowski Jerzy ◽  
WoŁosz Paweł ◽  
Zieliński Janusz ◽  
Niźnikowski Tomasz ◽  
Buszta Mariusz
Keyword(s):  
Reaction Time ◽  
Principal Component ◽  
Temporal Orientation ◽  
Basketball Players ◽  
Sports Clubs ◽  
Motor Abilities ◽  
Depth Analysis ◽  
Motor Tests ◽  
Spatio Temporal ◽  
Different Levels

Abstract Introduction. The purpose of this investigation was to examine the structure of coordination motor abilities (CMA) in male basketball players at different levels of competition. Material and methods. The study included 183 male basketball players from 10 Polish sports clubs. The examined groups consisted of seniors (n=42) aged 24.5 (± 3.3), juniors (n=37) aged 16.8 (± 0.6), cadets (n=54) aged 14.5 (± 0.1) and children (n=50) aged 13.4 (± 0.2). A battery of motor tests was administered to assess the following CMA: kinesthetic differentiation of movements, spatio-temporal orientation, reaction time, movement coupling, sense of balance, sense of rhythm and adjustment of movements. The structure of CMA under investigation was determined based on the results of Hotelling's principal component analysis in Tucker's modification, completed with Kaiser's Varimax rotation [1, 2]. Results. The CMA structure of basketball players was composed of three or four factors. Most often these included rhythm, movement differentiation, movement coupling and adjustment of movements. Less frequently the structure consisted of spatio-temporal orientation, balance and reaction time. An in-depth analysis of the CMA structure revealed that factors ranged from heterogeneous (children and cadets) to homogeneous ones (juniors and seniors). The distribution of identified factors in the common variance was the smallest in children and cadets (58.9% and 62.9%, respectively) and the biggest in juniors and seniors (69.3% and 68.48%, respectively).

scholarly journals Coupling kinetic models and advection-diffusion equations. 2. Sensitivity analysis of an advection-diffusion-reaction model

2021 ◽  
Author(s):  
Lafras Uys ◽  
Jan-Hendrik S Hofmeyr ◽  
Johann M Rohwer
Keyword(s):  
Sensitivity Analysis ◽  
Initial Conditions ◽  
Phloem Loading ◽  
Reaction Model ◽  
Diffusion Reaction ◽  
Phloem Unloading ◽  
Advection Diffusion ◽  
Steady State Model ◽  
Sensitivity Analysis Method ◽  
Spatio Temporal

Abstract The accompanying paper (Uys et al., in silico Plants, XXXX) presented a core model of sucrose accumulation within the advection-diffusion-reaction framework, which is able to capture the spatio-temporal evolution of the system from a set of initial conditions. This paper presents a sensitivity analysis of this model. Because this is a non-steady-state model based on partial differential equations, we performed the sensitivity analysis using two approaches from engineering. The Morris method is based on a one-at-a-time design, perturbing parameters individually and calculating the influence on model output in terms of elementary effects. FAST is a global sensitivity analysis method, where all parameters are perturbed simultaneously, oscillating at different frequencies, enabling the calculation of the contribution of each parameter through Fourier analysis. Overall, both methods gave similar results. Perturbations in reactions tended to have a large influence on their own rate, as well as on directly connected metabolites. Sensitivities varied both with the time of the simulation and the position along the sugarcane stalk. Our results suggest that vacuolar sucrose concentrations are most sensitive to vacuolar invertase in the centre of the stalk, but that phloem unloading and vacuolar sucrose uptake also contribute, especially towards the stalk edges. Sucrose in the phloem was most sensitive to phloem loading at the nodes, but most sensitive to phloem unloading in the middle of the internodes. Sink concentrations of sucrose in the symplast were most sensitive to phloem unloading in the middle of the internodes, but at the nodes cytosolic invertase had the greatest effect.

Sobol′ Sensitivity Analysis Using a Neural Network Model of a LB-LOCA in the ZION Nuclear Power Plant With CATHARE-2 V2.5 Code

2013 ◽  
Author(s):  
Fabrice Fouet ◽  
Pierre Probst
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Nuclear Power ◽  
Uncertainty Propagation ◽  
Correlation Coefficients ◽  
Computer Code ◽  
Numerical Application ◽  
Loss Of Coolant Accident ◽  
Input Parameters ◽  
Artificial Neural Network Ann

In nuclear safety, the Best-Estimate (BE) codes may be used in safety demonstration and licensing, provided that uncertainties are added to the relevant output parameters before comparing them with the acceptance criteria. The uncertainty of output parameters, which comes mainly from the lack of knowledge of the input parameters, is evaluated by estimating the 95% percentile with a high degree of confidence. IRSN, technical support of the French Safety Authority, developed a method of uncertainty propagation. This method has been tested with the BE code used is CATHARE-2 V2.5 in order to evaluate the Peak Cladding Temperature (PCT) of the fuel during a Large Break Loss Of Coolant Accident (LB-LOCA) event, starting from a large number of input parameters. A sensitivity analysis is needed in order to limit the number of input parameters and to quantify the influence of each one on the response variability of the numerical model. Generally, the Global Sensitivity Analysis (GSA) is done with linear correlation coefficients. This paper presents a new approach to perform a more accurate GSA to determine and to classify the main uncertain parameters: the Sobol′ methodology. The GSA requires simulating many sets of parameters to propagate uncertainties correctly, which makes of it a time-consuming approach. Therefore, it is natural to replace the complex computer code by an approximate mathematical model, called response surface or surrogate model. We have tested Artificial Neural Network (ANN) methodology for its construction and the Sobol′ methodology for the GSA. The paper presents a numerical application of the previously described methodology on the ZION reactor, a Westinghouse 4-loop PWR, which has been retained for the BEMUSE international problem [8]. The output is the first maximum PCT of the fuel which depends on 54 input parameters. This application outlined that the methodology could be applied to high-dimensional complex problems.

scholarly journals Genetic diversity and phylogenetic behavior of some minor legumes

2016 ◽  
Vol 7 (1) ◽  
Author(s):  
James Oludare Agbolade ◽  
Ronke Justina Komolafe
Keyword(s):  
Genetic Diversity ◽  
Principal Component ◽  
Calyx Lobe ◽  
Descriptive Statistics ◽  
International Institute ◽  
Mean Values ◽  
Phylogenetic Relatedness ◽  
Petal Length ◽  
Morphological Differences ◽  
Range Test

Twenty-four accessions of twelve species minor legumes collected from the germplasm unit of the International Institute of Tropical Agriculture Ibadan, Nigeria were evaluated for their genetic diversities and phylogenetic relatedness. The accessions were planted into plots of 5 ridges of 5 meters long, spaced 1 meter apart and replicated three times at the Federal University Oye-Ekiti Teaching and Research Farm. The diversity and the relative phylogeny of the accessions were assessed through their floral morphological differences and the mean values between two accessions were evaluated by descriptive statistics. Principal component analysis was employed to identify the most discriminatory floral morphological traits and the similarities among the 24 accessions were assessed by cluster analysis (CA). Descriptive statistics through Duncan multiple range test adopted revealed genetic diversity and phylogenetic relatedness among the accessions. The first two principal component axes explained 64.66% of the total floral morphological variation. Standard petal length, calyx lobe length and stipule length contributed most of the variations in the legume accession. CA grouped the 24 accessions into six clusters. The study revealed intra-specific similarities and inter-specific floral morphological differences among the studied accessions.

scholarly journals Application of Multivariate Sensitivity Analysis Techniques to AGCM-Simulated Tropical Cyclones

2018 ◽  
Vol 146 (7) ◽  
pp. 2065-2088 ◽  
Author(s):  
Fei He ◽  
Derek J. Posselt ◽  
Naveen N. Narisetty ◽  
Colin M. Zarzycki ◽  
Vijayan N. Nair
Keyword(s):  
Sensitivity Analysis ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Tropical Cyclones ◽  
Radiative Forcing ◽  
Initial Conditions ◽  
Sea Surface ◽  
Precipitation Rate ◽  
Analysis Framework ◽  
Control Factors

Abstract This work demonstrates the use of Sobol’s sensitivity analysis framework to examine multivariate input–output relationships in dynamical systems. The methodology allows simultaneous exploration of the effect of changes in multiple inputs, and accommodates nonlinear interaction effects among parameters in a computationally affordable way. The concept is illustrated via computation of the sensitivities of atmospheric general circulation model (AGCM)-simulated tropical cyclones to changes in model initial conditions. Specifically, Sobol’s variance-based sensitivity analysis is used to examine the response of cyclone intensity, cloud radiative forcing, cloud content, and precipitation rate to changes in initial conditions in an idealized AGCM-simulated tropical cyclone (TC). Control factors of interest include the following: initial vortex size and intensity, environmental sea surface temperature, vertical lapse rate, and midlevel relative humidity. The sensitivity analysis demonstrates systematic increases in TC intensity with increasing sea surface temperature and atmospheric temperature lapse rates, consistent with many previous studies. However, there are nonlinear interactions among control factors that affect the response of the precipitation rate, cloud content, and radiative forcing. In addition, sensitivities to control factors differ significantly when the model is run at different resolution, and coarse-resolution simulations are unable to produce a realistic TC. The results demonstrate the effectiveness of a quantitative sensitivity analysis framework for the exploration of dynamic system responses to perturbations, and have implications for the generation of ensembles.

scholarly journals Prediction Model of Iron Ore Pellet Ambient Strength and Sensitivity Analysis on the Influence Factors

Metals ◽  
2018 ◽  
Vol 8 (8) ◽  
pp. 593 ◽  
Author(s):  
Qiangjian Gao ◽  
Yingyi Zhang ◽  
Xin Jiang ◽  
Haiyan Zheng ◽  
Fengman Shen
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Influence Factors ◽  
Principal Component ◽  
Bp Network ◽  
Input Variables ◽  
Artificial Neural Network Ann

The Ambient Compressive Strength (CS) of pellets, influenced by several factors, is regarded as a criterion to assess pellets during metallurgical processes. A prediction model based on Artificial Neural Network (ANN) was proposed in order to provide a reliable and economic control strategy for CS in pellet production and to forecast and control pellet CS. The dimensionality of 19 influence factors of CS was considered and reduced by Principal Component Analysis (PCA). The PCA variables were then used as the input variables for the Back Propagation (BP) neural network, which was upgraded by Genetic Algorithm (GA), with CS as the output variable. After training and testing with production data, the PCA-GA-BP neural network was established. Additionally, the sensitivity analysis of input variables was calculated to obtain a detailed influence on pellet CS. It has been found that prediction accuracy of the PCA-GA-BP network mentioned here is 96.4%, indicating that the ANN network is effective to predict CS in the pelletizing process.

Seismic hazard sensitivity analysis: A multi-parameter approach

1991 ◽  
Vol 81 (3) ◽  
pp. 796-817
Author(s):  
Nitzan Rabinowitz ◽  
David M. Steinberg
Keyword(s):  
Sensitivity Analysis ◽  
Seismic Hazard ◽  
Subjective Probability ◽  
Probability Distributions ◽  
Input Parameter ◽  
The Other ◽  
Probabilistic Assessment ◽  
Input Parameters ◽  
Parameter Approach

Abstract We propose a novel multi-parameter approach for conducting seismic hazard sensitivity analysis. This approach allows one to assess the importance of each input parameter at a variety of settings of the other input parameters and thus provides a much richer picture than standard analyses, which assess each input parameter only at the default settings of the other parameters. We illustrate our method with a sensitivity analysis of seismic hazard for Jerusalem. In this example, we find several input parameters whose importance depends critically on the settings of other input parameters. This phenomenon, which cannot be detected by a standard sensitivity analysis, is easily diagnosed by our method. The multi-parameter approach can also be used in the context of a probabilistic assessment of seismic hazard that incorporates subjective probability distributions for the input parameters.

Validation and Parametric Investigations of an Internal Permanent Magnet Motor Using a Lumped Parameter Thermal Model

2021 ◽  
Author(s):  
Sebastien Sequeira ◽  
Kevin Bennion ◽  
J. Emily Cousineau ◽  
Sreekant Narumanchi ◽  
Gilberto Moreno ◽  
...  
Keyword(s):  
Experimental Data ◽  
Sensitivity Analysis ◽  
Permanent Magnet ◽  
Power Density ◽  
Heat Removal ◽  
Maximum Difference ◽  
Modeling Framework ◽  
Element Analysis ◽  
Lumped Parameter ◽  
Input Parameters

Abstract One of the key challenges for the electric vehicle industry is to develop high-power-density electric motors. Achieving higher power density requires efficient heat removal from inside the motor. In order to improve thermal management, a multi-physics modeling framework that is able to accurately predict the behavior of the motor, while being computationally efficient, is essential. This paper first presents a detailed validation of a Lumped Parameter Thermal Network (LPTN) model of an Internal Permanent Magnet synchronous motor within the commercially available Motor-CAD® modeling environment. The validation is based on temperature comparison with experimental data and with more detailed Finite Element Analysis (FEA). All critical input parameters of the LPTN are considered in detail for each layer of the stator, especially the contact resistances between the impregnation, liner, laminations and housing. Finally, a sensitivity analysis for each of the critical input parameters is provided. A maximum difference of 4% - for the highest temperature in the slot-winding and the end-winding - was found between the LPTN and the experimental data. Comparing the results from the LPTN and the FEA model, the maximum difference was 2% for the highest temperature in the slot-winding and end-winding. As for the LTPN sensitivity analysis, the thermal parameter with the highest sensitivity was found to be the liner-to-lamination contact resistance.

scholarly journals Multi-temporal yield pattern analysis method for deriving yield zones in crop production systems

2020 ◽  
Vol 21 (6) ◽  
pp. 1263-1290
Author(s):  
Gerald Blasch ◽  
Zhenhai Li ◽  
James A. Taylor
Keyword(s):  
Crop Production ◽  
Pattern Analysis ◽  
Production Systems ◽  
Principal Component ◽  
Temporal Variations ◽  
Data Sets ◽  
Yield Data ◽  
Multi Temporal ◽  
Spatio Temporal ◽  
Cereal Fields

Abstract Easy-to-use tools using modern data analysis techniques are needed to handle spatio-temporal agri-data. This research proposes a novel pattern recognition-based method, Multi-temporal Yield Pattern Analysis (MYPA), to reveal long-term (> 10 years) spatio-temporal variations in multi-temporal yield data. The specific objectives are: i) synthesis of information within multiple yield maps into a single understandable and interpretable layer that is indicative of the variability and stability in yield over a 10 + years period, and ii) evaluation of the hypothesis that the MYPA enhances multi-temporal yield interpretation compared to commonly-used statistical approaches. The MYPA method automatically identifies potential erroneous yield maps; detects yield patterns using principal component analysis; evaluates temporal yield pattern stability using a per-pixel analysis; and generates productivity-stability units based on k-means clustering and zonal statistics. The MYPA method was applied to two commercial cereal fields in Australian dryland systems and two commercial fields in a UK cool-climate system. To evaluate the MYPA, its output was compared to results from a classic, statistical yield analysis on the same data sets. The MYPA explained more of the variance in the yield data and generated larger and more coherent yield zones that are more amenable to site-specific management. Detected yield patterns were associated with varying production conditions, such as soil properties, precipitation patterns and management decisions. The MYPA was demonstrated as a robust approach that can be encoded into an easy-to-use tool to produce information layers from a time-series of yield data to support management.

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