Fixture Configuration Design Using a Computer Experiment

2000 ◽  
Author(s):  
L. Shelley Xie ◽  
S. Jack Hu ◽  
Wei Li ◽  
A. Sudjianto
Keyword(s):  
Computer Experiments ◽  
Computer Experiment ◽  
Latin Hypercube Sampling ◽  
Surrogate Models ◽  
Rigid Bodies ◽  
Multivariate Adaptive Regression Splines ◽  
Configuration Design ◽  
Element Analysis ◽  
Design Variables ◽  
Fixture Configuration

Abstract A new approach to fixture configuration design is developed based on the design and analysis of computer experiments (DACE). In the past, fixture configuration design undergoes three steps in an iterative manner: locator position design, clamp position design, and clamping force design. The fixture and workpiece were generally assumed to be rigid bodies. In this study, a sampled computer experiment is conducted with a finite element analysis (FEA). Surrogate models are developed based on the responses of this experiment. The purpose of building these models is to represent complex physical relations among the design variables in a simple functional form such that design analysis can be conducted efficiently. The surrogate response models can be used to provide a directional guidance in optimization, to identify feasible fixture configuration designs for flexible workpieces, and more importantly to enable a simultaneous design of locator/clamp positions and clamping forces. In the surrogate models workpiece deflections and locator reaction forces are used as variables. Distance-based Latin Hypercube Sampling Design (LHD) is used to increase the number of the sample points and to evenly spread them in the design space. Multivariate Adaptive Regression Splines (MARS) and Gaussian Stochastic Kriging (GSK) models are employed to capture nonlinear relationship among the design variables. The proposed approach is illustrated with examples.

Applied Process Simulation-Driven Oil and Gas Separation Plant Optimization using Surrogate Modeling and Evolutionary Algorithms

2019 ◽  
Author(s):  
Anders Andreasen
Keyword(s):  
Evolutionary Algorithms ◽  
Gas Separation ◽  
Oil And Gas ◽  
Optimal Solution ◽  
Computer Experiment ◽  
Latin Hypercube Sampling ◽  
Surrogate Models ◽  
Inlet Temperature ◽  
Separation Plant ◽  
Separation Stage

In this article the optimization of a realistic oil and gas separation plant has been studied. Two different fluids are investigated and compared in terms of the optimization potential. Using Design of Computer Experiment (DACE) via Latin Hypercube Sampling (LHS) and rigorous process simulations, surrogate models using Kriging have been established for selected model responses. The surrogate models are used in combination with a variety of different evolutionary algorithms for optimizing the operating profit, mainly by maximizing the recoverable oil production. A total of 10 variables representing pressure and temperature various key places in the separation plant are optimized to maximize the operational profit. The optimization is bounded in the variables and a constraint function is included to ensure that the optimal solution allows export of oil with an RVP < 12 psia. The main finding is that, while a high pressure is preferred in the first separation stage, apparently a single optimal setting for the pressure in downstream separators does not appear to exist. In the second stage separator apparently two different, yet equally optimal, settings are revealed. In the third and final separation stage a correlation between the separator pressure and the applied inlet temperature exists, where different combinations of pressure and temperature yields equally optimal results.<br>

On Adaptive Sampling for Single and Multi-Response Bayesian Surrogate Models

2006 ◽  
Author(s):  
David A. Romero ◽  
Cristina H. Amon ◽  
Susan Finger
Keyword(s):  
Experimental Design ◽  
Adaptive Sampling ◽  
Design Space Exploration ◽  
A Priori ◽  
Computer Experiments ◽  
Latin Hypercube Sampling ◽  
Surrogate Models ◽  
Sampling Techniques ◽  
Design And Optimization ◽  
Sampling Points

In order to reduce the time and resources devoted to design-space exploration during simulation-based design and optimization, the use of surrogate models, or metamodels, has been proposed in the literature. Key to the success of metamodeling efforts are the experimental design techniques used to generate the combinations of input variables at which the computer experiments are conducted. Several adaptive sampling techniques have been proposed to tailor the experimental designs to the specific application at hand, using the already-acquired data to guide further exploration of the input space, instead of using a fixed sampling scheme defined a priori. Though mixed results have been reported, it has been argued that adaptive sampling techniques can be more efficient, yielding better surrogate models with less sampling points. In this paper, we address the problem of adaptive sampling for single and multi-response metamodels, with a focus on Multi-stage Multi-response Bayesian Surrogate Models (MMBSM). We compare distance-optimal latin hypercube sampling, an entropy-based criterion and the maximum cross-validation variance criterion, originally proposed for one-dimensional output spaces and implemented in this paper for multi-dimensional output spaces. Our results indicate that, both for single and multi-response surrogate models, the entropy-based adaptive sampling approach leads to models that are more robust to the initial experimental design and at least as accurate (or better) when compared with other sampling techniques using the same number of sampling points.

Applied Process Simulation-Driven Oil and Gas Separation Plant Optimization using Surrogate Modeling and Evolutionary Algorithms

2019 ◽  
Author(s):  
Anders Andreasen
Keyword(s):  
Evolutionary Algorithms ◽  
Gas Separation ◽  
Oil And Gas ◽  
Optimal Solution ◽  
Computer Experiment ◽  
Latin Hypercube Sampling ◽  
Surrogate Models ◽  
Inlet Temperature ◽  
Separation Plant ◽  
Separation Stage

In this article the optimization of a realistic oil and gas separation plant has been studied. Two different fluids are investigated and compared in terms of the optimization potential. Using Design of Computer Experiment (DACE) via Latin Hypercube Sampling (LHS) and rigorous process simulations, surrogate models using Kriging have been established for selected model responses. The surrogate models are used in combination with a variety of different evolutionary algorithms for optimizing the operating profit, mainly by maximizing the recoverable oil production. A total of 10 variables representing pressure and temperature various key places in the separation plant are optimized to maximize the operational profit. The optimization is bounded in the variables and a constraint function is included to ensure that the optimal solution allows export of oil with an RVP < 12 psia. The main finding is that, while a high pressure is preferred in the first separation stage, apparently a single optimal setting for the pressure in downstream separators does not appear to exist. In the second stage separator apparently two different, yet equally optimal, settings are revealed. In the third and final separation stage a correlation between the separator pressure and the applied inlet temperature exists, where different combinations of pressure and temperature yields equally optimal results.<br>

Applied Process Simulation-Driven Oil and Gas Separation Plant Optimization using Surrogate Modeling and Evolutionary Algorithms

2019 ◽  
Author(s):  
Anders Andreasen
Keyword(s):  
Evolutionary Algorithms ◽  
Gas Separation ◽  
Oil And Gas ◽  
Optimal Solution ◽  
Computer Experiment ◽  
Latin Hypercube Sampling ◽  
Surrogate Models ◽  
Inlet Temperature ◽  
Separation Plant ◽  
Separation Stage

In this article the optimization of a realistic oil and gas separation plant has been studied. Two different fluids are investigated and compared in terms of the optimization potential. Using Design of Computer Experiment (DACE) via Latin Hypercube Sampling (LHS) and rigorous process simulations, surrogate models using Kriging have been established for selected model responses. The surrogate models are used in combination with a variety of different evolutionary algorithms for optimizing the operating profit, mainly by maximizing the recoverable oil production. A total of 10 variables representing pressure and temperature various key places in the separation plant are optimized to maximize the operational profit. The optimization is bounded in the variables and a constraint function is included to ensure that the optimal solution allows export of oil with an RVP < 12 psia. The main finding is that, while a high pressure is preferred in the first separation stage, apparently a single optimal setting for the pressure in downstream separators does not appear to exist. In the second stage separator apparently two different, yet equally optimal, settings are revealed. In the third and final separation stage a correlation between the separator pressure and the applied inlet temperature exists, where different combinations of pressure and temperature yields equally optimal results.<br>

Finite Element Modeling for Steel Cord Analysis in Radial Tires

2013 ◽  
Vol 41 (1) ◽  
pp. 60-79 ◽  
Author(s):  
Wei Yintao ◽  
Luo Yiwen ◽  
Miao Yiming ◽  
Chai Delong ◽  
Feng Xijin
Keyword(s):  
Finite Element ◽  
High Efficiency ◽  
Force Measurement ◽  
Three Dimensional ◽  
Local Stress ◽  
Tension Force ◽  
Center Line ◽  
Element Analysis ◽  
Design Variables ◽  
Steel Cord

ABSTRACT: This article focuses on steel cord deformation and force investigation within heavy-duty radial tires. Typical bending deformation and tension force distributions of steel reinforcement within a truck bus radial (TBR) tire have been obtained, and they provide useful input for the local scale modeling of the steel cord. The three-dimensional carpet plots of the cord force distribution within a TBR tire are presented. The carcass-bending curvature is derived from the deformation of the carcass center line. A high-efficiency modeling approach for layered multistrand cord structures has been developed that uses cord design variables such as lay angle, lay length, and radius of the strand center line as input. Several types of steel cord have been modeled using the developed method as an example. The pure tension for two cords and the combined tension bending under various loading conditions relevant to tire deformation have been simulated by a finite element analysis (FEA). Good agreement has been found between experimental and FEA-determined tension force-displacement curves, and the characteristic structural and plastic deformation phases have been revealed by the FE simulation. Furthermore, some interesting local stress and deformation patterns under combined tension and bending are found that have not been previously reported. In addition, an experimental cord force measurement approach is included in this article.

Probabilistic finite element analysis in heat transfer to a nuclear fuel rod bumper support

2004 ◽  
Vol 218 (12) ◽  
pp. 1499-1505
Author(s):  
Rama Subba Reddy Gorla
Keyword(s):  
Heat Transfer ◽  
Finite Element ◽  
Nuclear Fuel ◽  
Cost Effective ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Element Analysis ◽  
Transfer Rates ◽  
Fuel Rod ◽  
Design Variables

Heat transfer from a nuclear fuel rod bumper support was computationally simulated by a finite element method and probabilistically evaluated in view of the several uncertainties in the performance parameters. Cumulative distribution functions and sensitivity factors were computed for overall heat transfer rates due to the thermodynamic random variables. These results can be used to identify quickly the most critical design variables in order to optimize the design and to make it cost effective. The analysis leads to the selection of the appropriate measurements to be used in heat transfer and to the identification of both the most critical measurements and the parameters.

The Finite Element Analysis and the Multi-Objective Optimization Design of Spindle Systems of CNC Gantry Machine Tools

2016 ◽  
Vol 693 ◽  
pp. 243-250
Author(s):  
Zhi Zhong Guo ◽  
Yun Shun Zhang ◽  
Shi Hao Liu
Keyword(s):  
Machine Tools ◽  
Optimization Design ◽  
Multi Objective Optimization ◽  
Element Analysis ◽  
Multi Objective ◽  
Vibration Resistance ◽  
Design Variables ◽  
Force Coupling ◽  
The Finite Element Analysis ◽  
Spindle Structure

It is discovered that the vibration resistance of spindle systems needs to be improved based on the statics analysis, modal analysis and heating-force coupling analysis of spindle systems of CNC gantry machine tools. The design variables of optimization are set according to sensitivity analysis, multi-objective and dynamic optimization design is realized and its designing scheme is gained for spindle structure. The research results show that vibration resistance can be improved without change of the quality and static property of spindle systems of CNC gantry machine tools.

Aerodynamic Efficiency Optimization of the 1st Stage of Transonic High Pressure Turbine through Lean and Sweep Angles

2019 ◽  
Vol 36 (3) ◽  
pp. 245-256
Author(s):  
Yoonki Kim ◽  
Sanga Lee ◽  
Kwanjung Yee ◽  
Young-Seok Kang
Keyword(s):  
High Pressure ◽  
Surrogate Model ◽  
Design Space ◽  
Latin Hypercube Sampling ◽  
Expected Improvement ◽  
Total Efficiency ◽  
High Pressure Turbine ◽  
Sweep Angle ◽  
Design Variables ◽  
Optimal Latin Hypercube Sampling

Abstract The purpose of this study is to optimize the 1st stage of the transonic high pressure turbine (HPT) for enhancement of aerodynamic performance. Isentropic total-to-total efficiency is designated as the objective function. Since the isentropic efficiency can be improved through modifying the geometry of vane and rotor blade, lean angle and sweep angle are chosen as design variables, which can effectively alter the blade geometry. The sensitivities of each design variable are investigated by applying lean and sweep angles to the base nozzle and rotor, respectively. The design space is also determined based on the results of the parametric study. For the design of experiment (DoE), Optimal Latin Hypercube sampling is adopted, so that 25 evenly distributed samples are selected on the design space. Sequentially, based on the values from the CFD calculation, Kriging surrogate model is constructed and refined using Expected Improvement (EI). With the converged surrogate model, optimum solution is sought by using the Genetic Algorithm. As a result, the efficiency of optimum turbine 1st stage is increased by 1.07 % point compared to that of the base turbine 1st stage. Also, the blade loading, pressure distribution, static entropy, shock structure, and secondary flow are thoroughly discussed.

Enhanced Vibration Control of Ultrasonic Tooling Using Finite Element Analysis

1991 ◽  
Author(s):  
Kevin O’Shea
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Cross Sections ◽  
High Frequency ◽  
Excellent Correlation ◽  
Accurate Identification ◽  
Element Analysis ◽  
Optimum Configuration ◽  
Slot Length ◽  
Design Variables

Abstract The use of finite element analysis (FEA) in high frequency (20–40 kHz), high power ultrasonics to date has been limited. Of paramount importance to the performance of ultrasonic tooling (horns) is the accurate identification of pertinent modeshapes and frequencies. Ideally, the ultrasonic horn will vibrate in a purely axial mode with a uniform amplitude of vibration. However, spurious resonances can couple with this fundamental resonance and alter the axial vibration. This effect becomes more pronounced for ultrasonic tools with larger cross-sections. The current study examines a 4.5″ × 6″ cross-section titanium horn which is designed to resonate axially at 20 kHz. Modeshapes and frequencies from 17–23 kHz are examined experimentally and using finite element analysis. The effect of design variables — slot length, slot width, and number of slots — on modeshapes and frequency spacing is shown. An optimum configuration based on the finite element results is prescribed. The computed results are compared with actual prototype data. Excellent correlation between analytical and experimental data is found.

Optimization of a vacuum cleaner fan suction and shaft power using Kriging surrogate model and MIGA

2021 ◽  
pp. 095765092110496
Author(s):  
Soheil Almasi ◽  
Mohammad Mahdi Ghorani ◽  
Mohammad Hadi Sotoude Haghighi ◽  
Seyed Mohammad Mirghavami ◽  
Alireza Riasi
Keyword(s):  
Optimization Design ◽  
Optimization Technique ◽  
Internal Flow ◽  
Latin Hypercube Sampling ◽  
Sample Space ◽  
Geometrical Parameters ◽  
Vacuum Cleaner ◽  
Time Saving ◽  
Design Variables ◽  
Shaft Power

Optimization of vacuum cleaner fan components is a low-cost and time-saving solution to satisfy the increasing requirement for compact energy-efficient cleaners. In this study, surrogate-based optimization technique is used and for the first time it is focused on maximization of Airwatt parameter, which describes the fan suction power, as an objective function (Case II). Besides, the shaft power is minimized (Case I) as another optimization target in order to reduce the power consumption of the vacuum cleaner. 11 geometrical variables of 3 fan components including impeller, diffuser and return channel are selected as the optimization design variables. 80 training points are distributed in the sample space using Advanced Latin Hypercube Sampling (ALHS) technique and the outputs of sample points are calculated by means of CFD simulations. Kriging and RSA surrogate models have been fitted to the outputs of the sample space. Through coupling of constructed Kriging models and Multi-Island Genetic Algorithm (MIGA), the optimal design for each of the optimization cases is presented and evaluated using numerical simulations. A 20.22% reduction in shaft power in Case I and an improvement of 27.73% in Airwatt in Case II have been achieved as the overall results of this study. Despite achieving goals in both optimization cases, a slight decrease in Airwatt in Case I (−6.20%) and a slight increase in shaft power in Case II (+4.82%) are observed relative to primary fan. Furthermore, the Analysis of Variance (ANOVA) determines the importance level of design variables and their 2-way interactions on the objective functions. It was concluded that geometrical parameters related to all of the fan components must be considered simultaneously to conduct a comprehensive optimization. The reasons of enhancement in optimal cases compared with the reference design have been further investigated by analysis of the fan internal flow field. Post-processing of the CFD results demonstrates that the applied geometrical modifications cause a more uniform flow through the flow passages of the optimal fan components.

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