scholarly journals Supporting Expensive Physical Models With Geometric Moment Invariants to Accelerate Sensitivity Analysis for Shape Optimisation

2022 ◽  
Author(s):  
Shahroz Khan ◽  
Panagiotis Kaklis ◽  
Andrea Serani ◽  
Matteo Diez
Keyword(s):  
Sensitivity Analysis ◽  
Physical Models ◽  
Moment Invariants ◽  
Shape Optimisation ◽  
Geometric Moment

Vision-based force measurement using geometric moment invariants

2012 ◽  
Vol 226 (10) ◽  
pp. 2589-2601
Author(s):  
Wen Hu ◽  
Shigang Wang ◽  
Chun Hu ◽  
Hongtao Liu ◽  
Jinqiu Mo
Keyword(s):  
Measurement Method ◽  
Feature Vector ◽  
Force Measurement ◽  
Shape Descriptor ◽  
Support Vector ◽  
Moment Invariants ◽  
Geometric Moment ◽  
New Vision ◽  
Multi Class Classification ◽  
Standard Library

This article presents a new vision-based force measurement method to measure microassembly forces without directly computing the deformation. The shape descriptor of geometric moment invariants is used as a feature vector to describe the implicit relationship between an applied force and the deformation. Then, a standard library is established to map the corresponding relationship between the deformed cantilever under known forces and a set of feature vectors. Finally, a support vector machine compares the feature vector of deformed cantilever under an unknown force with those in the standard library, implements multi-class classification and predicts the unknown force. The vision-based force measurement method is validated for eight simulated microcantilevers of different sizes. Both regional and boundary moment invariants are used to constitute the feature vector. Simulated results show that the force measurement precision varies with length, width and height of cantilevers. If length increases and width and height decrease, the precision is higher. This trend can provide a reference for mechanism design of microcantilevers and microgrippers.

Sensitivity Analysis of the Response of PWR Containment During a Loss of Coolant Accident Using RELAP5-3D and MELCOR

2014 ◽  
Author(s):  
Rodolfo Vaghetto ◽  
Andrew Franklin ◽  
Alessandro Vanni ◽  
Yassin A. Hassan
Keyword(s):  
Sensitivity Analysis ◽  
Discharge Coefficient ◽  
Physical Models ◽  
Geometrical Parameters ◽  
Pressurized Water Reactor ◽  
Pressurized Water ◽  
Flow Models ◽  
Loss Of Coolant Accident ◽  
Loss Of Coolant ◽  
Selection Of

The prediction of specific parameters for the reactor containment, such as pressure and sump pool temperature, is of paramount importance when studying the thermal-hydraulic phenomena involved in the debris generation, transport, and accumulation during Loss of Coolant Accidents (LOCA). The response of the reactor containment during these events may significantly vary depending of several factors such as break size and location, and other plant-specific features. When modeling the reactor and containment response using systems codes, the predictions may also depend on the selection of physical models, correlations and their coefficients. A sensitivity analysis of the response of a typical Pressurized Water Reactor (PWR) 4-loop reactor system and associated containment during a large break LOCA was conducted using RELAP5-3D and MELCOR to investigate the influence of geometrical parameters (break location), physical models (chiked flow models), and related coefficients (discharge coefficient at the break), on the containment response. The simulation results showed how the containment response changed by varying the selected parameters and confirmed the importance of identifying and studying the factors triggering the containment engineered features (containment sprays) when simulation the containment response.

scholarly journals Two-scale shape optimisation based on numerical homogenisation techniques and variational sensitivity analysis

2021 ◽  
Author(s):  
Wojciech Kijanski ◽  
Franz-Joseph Barthold
Keyword(s):  
Sensitivity Analysis ◽  
Shape Optimisation ◽  
Shape Sensitivity ◽  
Shape Sensitivity Analysis ◽  
Composite Matrix ◽  
Computational Framework ◽  
Static Loads ◽  
Nonlinear Elastic Structures ◽  
Nonlinear Elastic ◽  
Intrinsic Frame

AbstractThis contribution presents a theoretical and computational framework for two-scale shape optimisation of nonlinear elastic structures. Particularly, minimum compliance optimisation problems with composite (matrix-inclusion) microstructures subjected to static loads and volume-type design constraints are focused. A homogenisation-based FE$$^2$$ 2 scheme is extended by an enhanced formulation of variational (shape) sensitivity analysis based on Noll’s intrinsic, frame-free formulation of continuum mechanics. The obtained overall two-scale sensitivity information couples shape variations across micro- and macroscopic scales. A numerical example demonstrates the capabilities of the proposed variational sensitivity analysis and the (shape) optimisation framework. The investigations involve a mesh morphing scheme for the design parametrisation at both macro- and microscopic scales.

Uncertainty Quantification in Support of Severe Accident Analysis Code User Confidence Using MELCOR-DAKOTA

2021 ◽  
Author(s):  
Emmanuel Boafo ◽  
Emmanuel Numapau Gyamfi
Keyword(s):  
Sensitivity Analysis ◽  
Uncertainty Quantification ◽  
Hydrogen Generation ◽  
Input Parameter ◽  
Distribution Functions ◽  
Physical Models ◽  
Severe Accident ◽  
Accident Analysis ◽  
Melt Flow Rate ◽  
Input Parameters

Abstract Uncertainty and Sensitivity analysis methods are often used in severe accident analysis for validating the complex physical models employed in the system codes that simulate such scenarios. This is necessitated by the large uncertainties associated with the physical models and boundary conditions employed to simulate severe accident scenarios. The input parameters are sampled within defined ranges based on assigned probability distribution functions (PDFs) for the required number of code runs/realizations using stochastic sampling techniques. Input parameter selection is based on their importance to the key FOM, which is determined by the parameter identification and ranking table (PIRT). Sensitivity analysis investigates the contribution of each uncertain input parameter to the uncertainty of the selected FOM. In this study, the integrated severe accident analysis code MELCOR was coupled with DAKOTA, an optimization and uncertainty quantification tool in order to investigate the effect of input parameter uncertainty on hydrogen generation. The methodology developed was applied to the Fukushima Daiichi unit 1 NPP accident scenario, which was modelled in another study. The results show that there is approximately 22.46% uncertainty in the amount of hydrogen generated as estimated by a single MELCOR run given uncertainty in selected input parameters. The sensitivity analysis results also reveal that MELCOR input parameters; COR_SC 1141(Melt flow rate per unit width at breakthrough candling) , COR_ZP (Porosity of fuel debris beds) and COR_EDR (Characteristic debris size in core region) contributed most significantly to the uncertainty in hydrogen generation.

scholarly journals Variable Selection in Regression Models Using Global Sensitivity Analysis

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
William Becker ◽  
Paolo Paruolo ◽  
Andrea Saltelli
Keyword(s):  
Sensitivity Analysis ◽  
Variable Selection ◽  
Regression Models ◽  
Global Sensitivity Analysis ◽  
Physical Models ◽  
Model Fit ◽  
Global Sensitivity ◽  
Testing Sequence ◽  
Input Variables

Abstract Global sensitivity analysis is primarily used to investigate the effects of uncertainties in the input variables of physical models on the model output. This work investigates the use of global sensitivity analysis tools in the context of variable selection in regression models. Specifically, a global sensitivity measure is applied to a criterion of model fit, hence defining a ranking of regressors by importance; a testing sequence based on the ‘Pantula-principle’ is then applied to the corresponding nested submodels, obtaining a novel model-selection method. The approach is demonstrated on a growth regression case study, and on a number of simulation experiments, and it is found competitive with existing approaches to variable selection.

Geometric moment invariants to spatial transform and N-fold symmetric blur

2021 ◽  
Vol 115 ◽  
pp. 107887
Author(s):  
Hanlin Mo ◽  
Hongxiang Hao ◽  
Hua Li
Keyword(s):  
Moment Invariants ◽  
Geometric Moment

Collision stage reconstruction in traffic accidents: Sensitivity analysis

2005 ◽  
Vol 219 (3) ◽  
pp. 285-294 ◽  
Author(s):  
D Vangi
Keyword(s):  
Sensitivity Analysis ◽  
Error Propagation ◽  
Input Data ◽  
Traffic Accidents ◽  
Permanent Deformation ◽  
Physical Models ◽  
Redundant Data

The reconstruction approach is typically a process in which the effects (positions of vehicles at rest, various traces, permanent deformation, etc.) are examined to determine the causes (positions, velocities, acceleration of vehicles and occupants, etc.), and is in general numerically ill-conditioned, with the consequence that minor disturbance in the input data can produce broad variations in the final results. Since the input data and the parameters necessary for utilizing the physical models are known or estimated only with a certain degree of uncertainty, it follows that, for a given accident, substantially different scenarios may be envisaged. In this article, sensitivity to uncertainty in the input data for reconstructing traffic accidents is analysed. The effect of redundant data on calculation is analysed and parameters useful for identifying the data having the greatest effect on error propagation are indicated, for the purpose of reducing dispersion in the results.

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