A Computational Efficient Method to Assess the Sensitivity of Finite-Element Models: An Illustration With the Hemipelvis

2016 ◽  
Vol 138 (12) ◽  
Author(s):  
Dermot O'Rourke ◽  
Saulo Martelli ◽  
Murk Bottema ◽  
Mark Taylor
Keyword(s):  
Finite Element ◽  
Factorial Design ◽  
Surrogate Model ◽  
Computational Cost ◽  
Bone Geometry ◽  
Surrogate Models ◽  
Full Factorial Design ◽  
Fe Model ◽  
Kriging Surrogate Model ◽  
Full Factorial

Assessing the sensitivity of a finite-element (FE) model to uncertainties in geometric parameters and material properties is a fundamental step in understanding the reliability of model predictions. However, the computational cost of individual simulations and the large number of required models limits comprehensive quantification of model sensitivity. To quickly assess the sensitivity of an FE model, we built linear and Kriging surrogate models of an FE model of the intact hemipelvis. The percentage of the total sum of squares (%TSS) was used to determine the most influential input parameters and their possible interactions on the median, 95th percentile and maximum equivalent strains. We assessed the surrogate models by comparing their predictions to those of a full factorial design of FE simulations. The Kriging surrogate model accurately predicted all output metrics based on a training set of 30 analyses (R2 = 0.99). There was good agreement between the Kriging surrogate model and the full factorial design in determining the most influential input parameters and interactions. For the median, 95th percentile and maximum equivalent strain, the bone geometry (60%, 52%, and 76%, respectively) was the most influential input parameter. The interactions between bone geometry and cancellous bone modulus (13%) and bone geometry and cortical bone thickness (7%) were also influential terms on the output metrics. This study demonstrates a method with a low time and computational cost to quantify the sensitivity of an FE model. It can be applied to FE models in computational orthopaedic biomechanics in order to understand the reliability of predictions.

Using Kriging Surrogate Models to Predict the Vibration Responses of a Submerged Riser

2020 ◽  
Author(s):  
Marcelo Damasceno ◽  
Hélio Ribeiro Neto ◽  
Tatiane Costa ◽  
Aldemir Cavalini Júnior ◽  
Ludimar Aguiar ◽  
...  
Keyword(s):  
Surrogate Model ◽  
High Performance ◽  
Computational Cost ◽  
Offshore Structures ◽  
Surrogate Models ◽  
Computational Time ◽  
Kriging Surrogate Model ◽  
Efficient Manner ◽  
Modeling Tools ◽  
Fluid Structure

Abstract Fluid-structure interaction modeling tools based on computational fluid dynamics (CFD) produce interesting results that can be used in the design of submerged structures. However, the computational cost of simulations associated with the design of submerged offshore structures is high. There are no high-performance platforms devoted to the analysis and optimization of these structures using CFD techniques. In this context, this work aims to present a computational tool dedicated to the construction of Kriging surrogate models in order to represent the time domain force responses of submerged risers. The force responses obtained from high-cost computational simulations are used as outputs for training and validated the surrogate models. In this case, different excitations are applied in the riser aiming at evaluating the representativeness of the obtained Kriging surrogate model. A similar investigation is performed by changing the number of samples and the total time used for training purposes. The present methodology can be used to perform the dynamic analysis in different submerged structures with a low computational cost. Instead of solving the motion equation associated with the fluid-structure system, a Kriging surrogate model is used. A significant reduction in computational time is expected, which allows the realization of different analyses and optimization procedures in a fast and efficient manner for the design of this type of structure.

scholarly journals Model-Based Estimation of the Strength of Laser-Based Plastic-Metal Joints Using Finite Element Microstructure Models and Regression Models

Materials ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5004
Author(s):  
Julius Moritz Berges ◽  
Kira van der Straeten ◽  
Georg Jacobs ◽  
Jörg Berroth ◽  
Arnold Gillner
Keyword(s):  
Finite Element ◽  
Surrogate Model ◽  
Joint Strength ◽  
Mechanical Design ◽  
Surrogate Models ◽  
Fe Model ◽  
Model Based ◽  
Plastic Metal ◽  
Microstructure Parameters ◽  
Micro Level

Plastic-metal joints with a laser-structured metal surface have a high potential to reduce cost and weight compared to conventional joining technologies. However, their application is currently inhibited due to the absence of simulation methods and models for mechanical design. Thus, this paper presents a model-based approach for the strength estimation of laser-based plastic-metal joints. The approach aims to provide a methodology for the efficient creation of surrogate models, which can capture the influence of the microstructure parameters on the joint strength. A parametrization rule for the shape of the microstructure is developed using microsection analysis. Then, a parameterized finite element (FE) model of the joining zone on micro level is developed. Different statistical plans and model fits are tested, and the predicted strength of the FE model and the surrogate models are compared against experiments for different microstructure geometries. The joint strength is predicted by the FE model with a 3.7% error. Surrogate modelling using half-factorial experimental design and linear regression shows the best accuracy (6.2% error). This surrogate model can be efficiently created as only 16 samples are required. Furthermore, the surrogate model is provided as an equation, offering the designer a convenient tool to estimate parameter sensitivities.

Modeling and Optimizationof an Ultrasonic Setup Based on Combination of Finite Element Method and Mathematical Full Factorial Design

2011 ◽  
Vol 320 ◽  
pp. 553-558 ◽  
Author(s):  
Mohsen Ghahramani Nick ◽  
J. Akbari ◽  
Mohamad.R. Movahhedy ◽  
S.Mehdi Hoseini
Keyword(s):  
Mathematical Model ◽  
Finite Element ◽  
Objective Function ◽  
Factorial Design ◽  
Element Model ◽  
Optimization Method ◽  
Full Factorial Design ◽  
High Frequency Oscillation ◽  
Design Parameters ◽  
Full Factorial

Ultrasonic assisted machining (UAM) is an efficient nontraditional machining operation for brittle, hard-to-cut and poor-machinability materials. In UAM, high frequency oscillation in ultrasonic range at low amplitude is imposed on the workpiece or cutting tool. In most cases, the equipments that generates and transfers the vibration, have a complicated structure, and requires significant effort to achieve their optimum function. In this work, a mathematical model is developed and an optimization method is employed for design process. This makes it possible to achieve proper setup and reduce the amount of calculation. For this purpose, the combination of a two level full factorial design is performed with data that are obtained from finite element model(FEM)are used. Based on the mathematical model, an objective function is defined with the objective of maximizing the vibration amplitude at longitudinal resonance frequency of 22 kHz. Genetic algorithm is used to optimize the design parameters according to the defined objective function. The obtained results are shown just an error about 0.03 percent between FEM modal analysis and mathematical model answer. The advantages of the proposed optimization method in ultrasonic setup design in case of complicated geometries is discussed. Therefore optimization methods via FEM data could be regarded as an efficient approach for design of complicated structures.

Application of 32 Full Factorial Design and Desirability Function for Optimizing The Manufacturing Process for Directly Compressible Multi-Functional Co-Processed Excipient

2020 ◽  
Vol 17 (6) ◽  
pp. 523-539
Author(s):  
Jalpa Patel ◽  
Dhaval Mori
Keyword(s):  
Factorial Design ◽  
Spray Drying ◽  
Desirability Function ◽  
Full Factorial Design ◽  
Angle Of Repose ◽  
P Value ◽  
Independent Variables ◽  
Full Factorial ◽  
32 Full Factorial Design ◽  
Response Variables

Background: Developing a new excipient and obtaining its market approval is an expensive, time-consuming and complex process. Compared to that, the co-processing of already approved excipients has emerged as a more attractive option for bringing better characteristic excipients to the market. The application of the Design of Experiments (DoE) approach for developing co-processed excipient can make the entire process cost-effective and rapid. Objective: The aim of the present investigation was to demonstrate the applicability of the DoE approach, especially 32 full factorial design, to develop a multi-functional co-processed excipient for the direct compression of model drug - cefixime trihydrate using spray drying technique. Methods: The preliminary studies proved the significant effect of atomization pressure (X1) and polymer ratio (microcrystalline cellulose: mannitol - X2) on critical product characteristics, so they were selected as independent variables. The angle of repose, Carr’s index, Hausner’s ratio, tensile strength and Kuno’s constant were selected as response variables. Result: The statistical analysis proved a significant effect of both independent variables on all response variables with a significant p-value < 0.05. The desirability function available in Design Expert 11® software was used to prepare and select the optimized batch. The prepared co-processed excipient had better compressibility than individual excipients and their physical mixture and was able to accommodate more than 40 percent drug without compromising the flow property and compressibility. Conclusion: The present investigation successfully proved the applicability of 32 full factorial design as an effective tool for optimizing the spray drying process to prepare a multi-functional co-processed excipient.

Formulation of Extended-Release Beads of Lamotrigine Based on Alginate and Cassia fistula Seed Gum by QbD Approach

2020 ◽  
Vol 17 (5) ◽  
pp. 422-437
Author(s):  
Dixita Jain ◽  
Akshay Sodani ◽  
Swapnanil Ray ◽  
Pranab Ghosh ◽  
Gouranga Nandi
Keyword(s):  
Drug Release ◽  
Factorial Design ◽  
Extended Release ◽  
Polymer Concentration ◽  
Green Manufacturing ◽  
Full Factorial Design ◽  
Cassia Fistula ◽  
Negative Environmental Impact ◽  
Seed Gum ◽  
Full Factorial

Aim: This study was focused on the formulation of the multi-unit extended-release peroral delivery device of lamotrigine for better management of epilepsy. Background: The single-unit extended-release peroral preparations often suffer from all-or-none effect. A significant number of multi-unit delivery systems have been reported as a solution to this problem. But most of them are found to be composed of synthetic, semi-synthetic or their combination having physiological toxicity as well as negative environmental impact. Therefore, fabrication and formulation of multi-unit extended-release peroral preparations with natural, non-toxic, biodegradable polymers employing green manufacturing processes are being appreciated worldwide. Objective: Lamotrigine-loaded extended-release multi-unit beads have been fabricated with the incorporation of a natural polysaccharide Cassia fistula seed gum in calcium-cross-linked alginate matrix employing a simple green process and 23 full factorial design. Methods: The total polymer concentration, polymer ratio and [CaCl2] were considered as independent formulation variables with two different levels of each for the experiment-design. The extended-release beads were then prepared by the ionotropic gelation method using calcium chloride as the crosslinkerions provider. The beads were then evaluated for drug encapsulation efficiency and drug release. ANOVA of all the dependent variables such as DEE, cumulative % drug release at 2h, 5h, 12h, rate constant and dissolution similarity factor (f2) was done by 23 full factorial design using Design-Expert software along with numerical optimization of the independent variables in order to meet USP-reference release profile. Results: The optimized batch showed excellent outcomes with DEE of 84.7 ± 2.7 (%), CPR2h of 8.41± 2.96 (%), CPR5h of 36.8± 4.7 (%), CPR12h of 87.3 ± 3.64 (%) and f2 of 65.9. Conclusion: This approach of the development of multi-unit oral devices utilizing natural polysaccharides might be inspiring towards the world-wide effort for green manufacturing of sustained-release drug products by the QbD route.

scholarly journals Shear strength optimization for FSSW AA6060-T5 joints by Taguchi and full factorial design

2020 ◽  
Vol 9 (6) ◽  
pp. 16072-16079
Author(s):  
C.A.G. Aita ◽  
I.C. Goss ◽  
T.S. Rosendo ◽  
M.D. Tier ◽  
A. Wiedenhöft ◽  
...  
Keyword(s):  
Shear Strength ◽  
Factorial Design ◽  
Full Factorial Design ◽  
Full Factorial

scholarly journals Updated Kriging-Assisted Shape Optimization of a Gravity Dam

Water ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 87
Author(s):  
Yongqiang Wang ◽  
Ye Liu ◽  
Xiaoyi Ma
Keyword(s):  
Surrogate Model ◽  
Computational Cost ◽  
Region Of Interest ◽  
Optimization Procedure ◽  
Optimal Shape ◽  
Small Sample ◽  
Gravity Dam ◽  
Kriging Surrogate Model ◽  
Gravity Dams ◽  
Sample Set

The numerical simulation of the optimal design of gravity dams is computationally expensive. Therefore, a new optimization procedure is presented in this study to reduce the computational cost for determining the optimal shape of a gravity dam. Optimization was performed using a combination of the genetic algorithm (GA) and an updated Kriging surrogate model (UKSM). First, a Kriging surrogate model (KSM) was constructed with a small sample set. Second, the minimizing the predictor strategy was used to add samples in the region of interest to update the KSM in each updating cycle until the optimization process converged. Third, an existing gravity dam was used to demonstrate the effectiveness of the GA–UKSM. The solution obtained with the GA–UKSM was compared with that obtained using the GA–KSM. The results revealed that the GA–UKSM required only 7.53% of the total number of numerical simulations required by the GA–KSM to achieve similar optimization results. Thus, the GA–UKSM can significantly improve the computational efficiency. The method adopted in this study can be used as a reference for the optimization of the design of gravity dams.

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