scholarly journals Sensitivity analysis of timber-concrete composite structures

2018 ◽  
Vol 16 (1) ◽  
pp. 125-134
Author(s):  
Nikola Velimirovic ◽  
Dragoslav Stojic
Keyword(s):  
Sensitivity Analysis ◽  
Composite Structures ◽  
Rank Order ◽  
Equivalent Stress ◽  
Latin Hypercube Sampling ◽  
Simulation Method ◽  
Design Parameters ◽  
Model Parameters ◽  
Design And Optimization ◽  
Stochastic Sensitivity Analysis

The sensitivity analysis could be defined as a study of how the variability of the output parameter of the considered model can be distributed to its sources, actually, on the variability of the various input model parameters. It helps to identify the most important design parameters of a particular structure and to focus on them during the design and optimization process. This paper is focused on the application of stochastic sensitivity analysis of maximum equivalent stress and maximum mid-span deflection of timber-concrete composite beam. All input parameters were considered to be random variables. Latin Hypercube Sampling numerical simulation method was employed. The estimation of the sensitivity was derived from Spearman rank-order correlation coefficient.

scholarly journals Analysis of Composite Structures in Curing Process for Shape Deformations and Shear Stress: Basis for Advanced Optimization

2021 ◽  
Vol 5 (2) ◽  
pp. 63
Author(s):  
Niraj Kumbhare ◽  
Reza Moheimani ◽  
Hamid Dalir
Keyword(s):  
Composite Structures ◽  
Latin Hypercube Sampling ◽  
Vital Role ◽  
Optimization Techniques ◽  
Design Parameters ◽  
Curing Process ◽  
Input Variables ◽  
Transient Thermal ◽  
Prototype Design ◽  
Shape Deformations

Identifying residual stresses and the distortions in composite structures during the curing process plays a vital role in coming up with necessary compensations in the dimensions of mold or prototypes and having precise and optimized parts for the manufacturing and assembly of composite structures. This paper presents an investigation into process-induced shape deformations in composite parts and structures, as well as a comparison of the analysis results to finalize design parameters with a minimum of deformation. A Latin hypercube sampling (LHS) method was used to generate the required random points of the input variables. These variables were then executed with the Ansys Composite Cure Simulation (ACCS) tool, which is an advanced tool used to find stress and distortion values using a three-step analysis, including Ansys Composite PrepPost, transient thermal analysis, and static structural analysis. The deformation results were further utilized to find an optimum design to manufacture a complex composite structure with the compensated dimensions. The simulation results of the ACCS tool are expected to be used by common optimization techniques to finalize a prototype design so that it can reduce common manufacturing errors like warpage, spring-in, and distortion.

Modeling bronchial circulation with application to soluble gas exchange: description and sensitivity analysis

1998 ◽  
Vol 84 (6) ◽  
pp. 2070-2088 ◽  
Author(s):  
Thien D. Bui ◽  
Donald Dabdub ◽  
Steven C. George
Keyword(s):  
Sensitivity Analysis ◽  
Gas Exchange ◽  
Pulmonary Circulation ◽  
Blood Flow Rate ◽  
Latin Hypercube Sampling ◽  
Inert Gases ◽  
Phase Iii ◽  
Model Parameters ◽  
Bronchial Circulation ◽  
Wide Range

The steady-state exchange of inert gases across an in situ canine trachea has recently been shown to be limited equally by diffusion and perfusion over a wide range (0.01–350) of blood solubilities (βblood; ml ⋅ ml−1 ⋅ atm−1). Hence, we hypothesize that the exchange of ethanol (βblood = 1,756 at 37°C) in the airways depends on the blood flow rate from the bronchial circulation. To test this hypothesis, the dynamics of the bronchial circulation were incorporated into an existing model that describes the simultaneous exchange of heat, water, and a soluble gas in the airways. A detailed sensitivity analysis of key model parameters was performed by using the method of Latin hypercube sampling. The model accurately predicted a previously reported experimental exhalation profile of ethanol ( R 2= 0.991) as well as the end-exhalation airstream temperature (34.6°C). The model predicts that 27, 29, and 44% of exhaled ethanol in a single exhalation are derived from the tissues of the mucosa and submucosa, the bronchial circulation, and the tissue exterior to the submucosa (which would include the pulmonary circulation), respectively. Although the concentration of ethanol in the bronchial capillary decreased during inspiration, the three key model outputs (end-exhaled ethanol concentration, the slope of phase III, and end-exhaled temperature) were all statistically insensitive ( P > 0.05) to the parameters describing the bronchial circulation. In contrast, the model outputs were all sensitive ( P < 0.05) to the thickness of tissue separating the core body conditions from the bronchial smooth muscle. We conclude that both the bronchial circulation and the pulmonary circulation impact soluble gas exchange when the entire conducting airway tree is considered.

A Sensitivity Analysis of Two Mesoscale Models: COAMPS and WRF

2020 ◽  
Vol 148 (7) ◽  
pp. 2997-3014
Author(s):  
Caren Marzban ◽  
Robert Tardif ◽  
Scott Sandgathe
Keyword(s):  
Sensitivity Analysis ◽  
Spatial Structure ◽  
Latin Hypercube Sampling ◽  
Forecast Model ◽  
The United States ◽  
Model Parameters ◽  
Analysis Methodology ◽  
Trigger Function ◽  
Multivariate Multiple Regression

Abstract A sensitivity analysis methodology recently developed by the authors is applied to COAMPS and WRF. The method involves varying model parameters according to Latin Hypercube Sampling, and developing multivariate multiple regression models that map the model parameters to forecasts over a spatial domain. The regression coefficients and p values testing whether the coefficients are zero serve as measures of sensitivity of forecasts with respect to model parameters. Nine model parameters are selected from COAMPS and WRF, and their impact is examined on nine forecast quantities (water vapor, convective and gridscale precipitation, and air temperature and wind speed at three altitudes). Although the conclusions depend on the model parameters and specific forecast quantities, it is shown that sensitivity to model parameters is often accompanied by nontrivial spatial structure, which itself depends on the underlying forecast model (i.e., COAMPS vs WRF). One specific difference between these models is in their sensitivity with respect to a parameter that controls temperature increments in the Kain–Fritsch trigger function; whereas this parameter has a distinct spatial structure in COAMPS, that structure is completely absent in WRF. The differences between COAMPS and WRF also extend to the quality of the statistical models used to assess sensitivity; specifically, the differences are largest over the waters off the southeastern coast of the United States. The implication of these findings is twofold: not only is the spatial structure of sensitivities different between COAMPS and WRF, the underlying relationship between the model parameters and the forecasts is also different between the two models.

Sensitivity Analysis of Stress State of Welded Tanks

2015 ◽  
Vol 769 ◽  
pp. 3-8
Author(s):  
Zdenek Kala ◽  
Jakub Gottvald ◽  
Jakub Stonis ◽  
Stanislav Vejvoda ◽  
Abayomi Omishore
Keyword(s):  
Sensitivity Analysis ◽  
Stress State ◽  
Hydrostatic Pressure ◽  
Crude Oil ◽  
Computational Model ◽  
Sampling Method ◽  
Latin Hypercube Sampling ◽  
Load Case ◽  
Cylindrical Tank ◽  
Stochastic Sensitivity Analysis

This article focuses on the reliability analysis of a welded cylindrical tank, which is used for the storage of crude oil. The dominant load case is loading of the inner shell by hydrostatic pressure of the crude oil. Stochastic sensitivity analysis was used to study the effect of the variability of the thickness of the ith course on the stress state of adjacent courses. The computational model was developed in the programme ANSYS. Meshing was performed using SHELL181 elements. The Latin Hypercube Sampling method was implemented during analysis. Sensitivity analysis based on the evaluation of the correlation between the random plate thicknesses and the stress state was performed.

scholarly journals Optimality-based non-Redfield plankton–ecosystem model (OPEM v1.1) in UVic-ESCM 2.9 – Part 2: Sensitivity analysis and model calibration

2020 ◽  
Vol 13 (10) ◽  
pp. 4691-4712
Author(s):  
Chia-Te Chien ◽  
Markus Pahlow ◽  
Markus Schartau ◽  
Andreas Oschlies
Keyword(s):  
Sensitivity Analysis ◽  
Climate Model ◽  
Driving Forces ◽  
Ingestion Rate ◽  
Latin Hypercube Sampling ◽  
Ecosystem Model ◽  
Model Parameters ◽  
Model Assessment ◽  
Model Solutions ◽  
Elemental Stoichiometry

Abstract. We analyse 400 perturbed-parameter simulations for two configurations of an optimality-based plankton–ecosystem model (OPEM), implemented in the University of Victoria Earth System Climate Model (UVic-ESCM), using a Latin hypercube sampling method for setting up the parameter ensemble. A likelihood-based metric is introduced for model assessment and selection of the model solutions closest to observed distributions of NO3-, PO43-, O2, and surface chlorophyll a concentrations. The simulations closest to the data with respect to our metric exhibit very low rates of global N2 fixation and denitrification, indicating that in order to achieve rates consistent with independent estimates, additional constraints have to be applied in the calibration process. For identifying the reference parameter sets, we therefore also consider the model's ability to represent current estimates of water-column denitrification. We employ our ensemble of model solutions in a sensitivity analysis to gain insights into the importance and role of individual model parameters as well as correlations between various biogeochemical processes and tracers, such as POC export and the NO3- inventory. Global O2 varies by a factor of 2 and NO3- by more than a factor of 6 among all simulations. Remineralisation rate is the most important parameter for O2, which is also affected by the subsistence N quota of ordinary phytoplankton (Q0,phyN) and zooplankton maximum specific ingestion rate. Q0,phyN is revealed as a major determinant of the oceanic NO3- pool. This indicates that unravelling the driving forces of variations in phytoplankton physiology and elemental stoichiometry, which are tightly linked via Q0,phyN, is a prerequisite for understanding the marine nitrogen inventory.

scholarly journals Model-Based Design and Optimization of Blood Oxygenators

2020 ◽  
Vol 14 (4) ◽  
Author(s):  
Ge He ◽  
Tao Zhang ◽  
Jiafeng Zhang ◽  
Bartley P. Griffith ◽  
Zhongjun J. Wu
Keyword(s):  
Experimental Data ◽  
Carbon Dioxide ◽  
Fiber Bundle ◽  
Cfd Modeling ◽  
Design Parameters ◽  
Model Parameters ◽  
Transfer Rates ◽  
Design And Optimization ◽  
Model Based ◽  
Model Predictions

Abstract Blood oxygenators, also known as artificial lungs, are widely used in cardiopulmonary bypass surgery to maintain physiologic oxygen (O2) and carbon dioxide (CO2) levels in blood, and also serve as respiratory assist devices to support patients with lung failure. The time- and cost-consuming method of trial and error is initially used to optimize the oxygenator design, and this method is followed by the introduction of the computational fluid dynamics (CFD) that is employed to reduce the number of prototypes that must be built as the design is optimized. The CFD modeling method, while having progress in recent years, still requires complex three-dimensional (3D) modeling and experimental data to identify the model parameters and validate the model. In this study, we sought to develop an easily implemented mathematical models to predict and optimize the performance (oxygen partial pressure/saturation, oxygen/carbon dioxide transfer rates, and pressure loss) of hollow fiber membrane-based oxygenators and this model can be then used in conjunction with CFD to reduce the number of 3D CFD iteration for further oxygenator design and optimization. The model parameters are first identified by fitting the model predictions to the experimental data obtained from a mock flow loop experimental test on a mini fiber bundle. The models are then validated through comparing the theoretical results with the experimental data of seven full-size oxygenators. The comparative analysis show that the model predictions and experimental results are in good agreement. Based on the verified models, the design curves showing the effects of parameters on the performance of oxygenators and the guidelines detailing the optimization process are established to determine the optimal design parameters (fiber bundle dimensions and its porosity) under specific system design requirements (blood pressure drop, oxygen pressure/saturation, oxygen/carbon dioxide transfer rates, and priming volume). The results show that the model-based optimization method is promising to derive the optimal parameters in an efficient way and to serve as an intermediate modeling approach prior to complex CFD modeling.

scholarly journals Groundwater pollution risk using a modified Latin hypercube sampling

2006 ◽  
Vol 8 (3) ◽  
pp. 223-234 ◽  
Author(s):  
Husam Baalousha
Keyword(s):  
Sensitivity Analysis ◽  
Groundwater Pollution ◽  
Probabilistic Models ◽  
Latin Hypercube Sampling ◽  
Model Parameters ◽  
Latin Hypercube ◽  
Deterministic Models ◽  
Estimation Errors ◽  
Mean Estimation ◽  
Longitudinal Dispersivity

Characterisation of groundwater modelling involves significant uncertainty because of estimation errors of these models and other different sources of uncertainty. Deterministic models do not account for uncertainties in model parameters, and thus lead to doubtful output. The main alternatives for deterministic models are the probabilistic models and perturbation methods such as Monte Carlo Simulation (MCS). Unfortunately, these methods have many drawbacks when applied in risk analysis of groundwater pollution. In this paper, a modified Latin Hypercube Sampling method is presented and used for risk, uncertainty, and sensitivity analysis of groundwater pollution. The obtained results were compared with other sampling methods. Results of the proposed method have shown that it can predict the groundwater contamination risk for all values of probability better than other methods, maintaining the accuracy of mean estimation. Sensitivity analysis results reveal that the contaminant concentration is more sensitive to longitudinal dispersivity than to velocity.

Design and optimization of an electromechanical actuator for the latch of a foldable vehicle seat

2020 ◽  
Vol 62 (7) ◽  
pp. 749-755
Author(s):  
Z. K. Kocabicak ◽  
U. Demir
Keyword(s):  
Neural Network ◽  
Design Parameters ◽  
Design Criteria ◽  
Model Parameters ◽  
The Finite Element Method ◽  
Electromechanical Actuator ◽  
Design And Optimization ◽  
Artificial Neural Network Ann ◽  
Critical Model ◽  
Vehicle Seat

Abstract This paper deals with the electromechanical actuator (EAct) design for a seat latch while maintaining required force and displacement according to the boundary conditions and design criteria for the finite element method (FEM) in an Ansys Maxwell environment. Before presenting the analysis studies, some EAct models are parameterized according to the Taguchi’s design of experiment (DoE) method. After that, analysis results are evaluated to define the critical model parameters of the EAct according to the DoE method. Furthermore, the DoE results and design parameters of the EAct are trained in some cases by an artificial neural network (ANN). The dynamic behavior of the models from the ANN and DoE results are analyzed and the results obtained are compared. Finally, the optimal EAct model is defined taking into account design criteria.

scholarly journals Optimality-Based Non-Redfield Plankton-Ecosystem Model (OPEMv1.0) in the UVic-ESCM 2.9. Part II: Sensitivity Analysis and Model Calibration

2020 ◽  
Author(s):  
Chia-Te Chien ◽  
Markus Pahlow ◽  
Markus Schartau ◽  
Andreas Oschlies
Keyword(s):  
Sensitivity Analysis ◽  
Climate Model ◽  
Driving Forces ◽  
Ingestion Rate ◽  
Latin Hypercube Sampling ◽  
Ecosystem Model ◽  
Model Parameters ◽  
Model Assessment ◽  
Model Solutions ◽  
Elemental Stoichiometry

Abstract. We analyse 400 perturbed-parameter simulations for two configurations of an optimality-based plankton-ecosystem model (OPEM), implemented in the University of Victoria Earth-System Climate Model (UVic-ESCM), using a Latin-Hypercube sampling method for setting up the parameter ensemble. A likelihood-based metric is introduced for model assessment and selection of the model solutions closest to observed distributions of NO3−, PO43−, O2, and surface chlorophyll a concentrations. According to our metric the optimal model solutions comprise low rates of global N2 fixation and denitrification. These two rate estimates turned out to be poorly constrained by the data. For identifying the “best” model solutions we therefore also consider the model’s ability to represent current estimates of water-column denitrification. We employ our ensemble of model solutions in a sensitivity analysis to gain insights into the importance and role of individual model parameters as well as correlations between various biogeochemical processes and tracers, such as POC export and the NO3− inventory. Global O2 varies by a factor of two and NO3− by more than a factor of six among all simulations. Remineralisation rate is the most important parameter for O2, which is also affected by the subsistence N quota of ordinary phytoplankton (QN0,phy) and zooplankton maximum specific ingestion rate. QN0,phy is revealed as a major determinant of the oceanic NO3− pool. This indicates that unraveling the driving forces of variations in phytoplankton physiology and elemental stoichiometry, which are tightly linked via QN0,phy, is a prerequisite for understanding the marine nitrogen inventory.

scholarly journals SENSITIVITY ANALYSIS OF THE STRESS STATE IN SHELL COURSES OF WELDED TANKS FOR OIL STORAGE

2014 ◽  
Vol 6 (1) ◽  
pp. 7-12 ◽  
Author(s):  
Zdeněk Kala ◽  
Jakub Gottvald ◽  
Jakub Stoniš ◽  
Abayomi Omishore
Keyword(s):  
Sensitivity Analysis ◽  
Sampling Method ◽  
Latin Hypercube Sampling ◽  
Bottom Plate ◽  
The Finite Element Method ◽  
The Czech Republic ◽  
Oil Storage ◽  
Nonlinear Contact ◽  
Oil Tank ◽  
Stochastic Sensitivity Analysis

The paper deals with the analysis of reliability and safety of a welded tank for the storage of oil, which is located in the Czech Republic. The oil tank has a capacity of 125 thousand cubic meters. It is one of the largest tanks of its kind in the world. Safety is ensured by a steel outer intercepting shell and a double bottom. The tank was modelled in the programme ANSYS. The computational model was developed using the finite element method – elements SHELL181. A nonlinear contact problem was analysed for the simulation of the interaction between the bottom plate and foundation. The normative approach in design and check of tanks according to standards API 650, ČSN EN 14015, EEMUA 159 and API 653 is mentioned. The dominant loading of the filled tank is from oil. The normative solution is based on the shell theory, which considers constant wall thickness. For real tanks sheet thicknesses of individual courses increase with increasing depth. Stochastic sensitivity analysis was used to study the effect of the variability of the thickness of the ith course on the stress of adjacent courses. The Latin Hypercube Sampling method was implemented during analysis.

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