scholarly journals Enhancement of Mixing Performance of Two-Layer Crossing Micromixer through Surrogate-Based Optimization

Micromachines ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 211
Author(s):  
Shakhawat Hossain ◽  
Nass Toufiq Tayeb ◽  
Farzana Islam ◽  
Mosab Kaseem ◽  
P.D.H. Bui ◽  
...  
Keyword(s):  
Surrogate Modeling ◽  
Latin Hypercube Sampling ◽  
Mixing Performance ◽  
Optimum Configuration ◽  
Design Variables ◽  
Optimization Study ◽  
Surrogate Based Optimization ◽  
Channel Thickness ◽  
Reference Design ◽  
Pitch Length

Optimum configuration of a micromixer with two-layer crossing microstructure was performed using mixing analysis, surrogate modeling, along with an optimization algorithm. Mixing performance was used to determine the optimum designs at Reynolds number 40. A surrogate modeling method based on a radial basis neural network (RBNN) was used to approximate the value of the objective function. The optimization study was carried out with three design variables; viz., the ratio of the main channel thickness to the pitch length (H/PI), the ratio of the thickness of the diagonal channel to the pitch length (W/PI), and the ratio of the depth of the channel to the pitch length (d/PI). Through a primary parametric study, the design space was constrained. The design points surrounded by the design constraints were chosen using a well-known technique called Latin hypercube sampling (LHS). The optimal design confirmed a 32.0% enhancement of the mixing index as compared to the reference design.

scholarly journals Hybrid Seven-bar Press Mechanism

2018 ◽  
Vol 12 (3) ◽  
pp. 181-187
Author(s):  
M. Erkan Kütük ◽  
L. Canan Dülger
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Design Optimization ◽  
Hybrid System ◽  
Degrees Of Freedom ◽  
Dimensional Synthesis ◽  
Design Variables ◽  
Optimization Study ◽  
Metal Stamping

An optimization study with kinetostatic analysis is performed on hybrid seven-bar press mechanism. This study is based on previous studies performed on planar hybrid seven-bar linkage. Dimensional synthesis is performed, and optimum link lengths for the mechanism are found. Optimization study is performed by using genetic algorithm (GA). Genetic Algorithm Toolbox is used with Optimization Toolbox in MATLAB®. The design variables and the constraints are used during design optimization. The objective function is determined and eight precision points are used. A seven-bar linkage system with two degrees of freedom is chosen as an example. Metal stamping operation with a dwell is taken as the case study. Having completed optimization, the kinetostatic analysis is performed. All forces on the links and the crank torques are calculated on the hybrid system with the optimized link lengths

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.

scholarly journals Investigation of improving the hydraulic turbine cascade performance using non-axisymmetric endwall contouring

2020 ◽  
Vol 12 (4) ◽  
pp. 168781402091541
Author(s):  
Lin Meng ◽  
XiaoDong Zhang ◽  
Gong Yan ◽  
Cancan Peng
Keyword(s):  
Gas Turbines ◽  
Experimental Studies ◽  
Latin Hypercube Sampling ◽  
Hydraulic Turbine ◽  
Dynamics Simulation ◽  
Radial Coordinate ◽  
Control Points ◽  
Nsga Ii ◽  
Design Variables ◽  
Endwall Contouring

Non-axisymmetric endwall contouring techniques have been widely applied in gas turbines; numerous papers and experimental studies have shown that it can significantly improve the overall performance of the turbine. This article first presents the non-axisymmetric endwall profile construction and optimization both for the rotor hub and shroud of a turbine drilling hydraulic cascade in the presence of an axisymmetric rotor, in order to improve the performance and investigate non-axisymmetric endwall contouring’s influence laws on a hydraulic cascade. Rotor cascade endwall design is studied by confining the axisymmetric variation near hub, shroud, and both endwalls. Endwall surface is parameterized with control points of Bezier curve, and control points are considered as design variables having a height constraint of 10%, 12%, or 15% span to move in radial coordinate. This methodology also combines Latin hypercube sampling with NSGA-II algorithm to obtain optimum solution. Computational fluid dynamics simulation results show that the non-axisymmetric endwall contouring technology applied to hub and shroud all realize an improvement in efficiency, while the NEW_S_15% has the best comprehensive performance and it causes an improvement of 1.52% in efficiency and 5.1% in torque. Off-design experiment shows that NEW_S_15% improves the output torque and efficiency as well even it deviates from design working condition, which proves valuableness of the non-axisymmetric endwall contouring technology.

Geometrical Optimization and Experimental Validation of a Tripod Film Cooling Hole With Asymmetric Side Holes

2014 ◽  
Author(s):  
Zhongran Chi ◽  
Chang Han ◽  
Xueying Li ◽  
Jing Ren ◽  
Hongde Jiang
Keyword(s):  
Flat Plate ◽  
Film Cooling ◽  
Experimental Results ◽  
Geometrical Parameters ◽  
Vortex System ◽  
Cooling Effectiveness ◽  
Film Cooling Effectiveness ◽  
Optimum Configuration ◽  
Optimization Study ◽  
Cooling Hole

A tripod cylindrical film hole with asymmetric side holes is studied numerically and experimentally on a flat plate for higher film cooling effectiveness. Firstly, the influences of geometrical parameters are studied and the optimum configurations of the asymmetric tripod hole are found in a DoE optimization study based on an improved numerical model for film cooling prediction, in which more than one hundred 3D CFD simulations are carried out. Then one optimum configuration of the asymmetric tripod hole is examined experimentally using pressure-sensitive paint (PSP) measurements, and compared against the experimental results of the simple cylindrical film hole and a well-designed shaped film hole. The flow and heat transfer characteristics of the asymmetric tripod holes were explored from the DoE results. The side holes can form a shear vortex system or an anti-kidney vortex system when proper spanwise distances of them are adopted, which laterally transports the coolant and form a favorable coolant coverage. According to the experimental results, the cooling performance of the optimized asymmetric tripod hole is significantly better than that of the simple cylindrical hole, especially at high blowing ratios. And the optimized asymmetric tripod hole can provide almost the same or even higher film cooling effectiveness on the flat plate compared with the shaped hole in the same flow conditions.

Optimization Study of a Hybrid Solar Air Conditioner

2013 ◽  
Author(s):  
Ali Al-Alili ◽  
Yunho Hwang ◽  
Reinhard Radermacher
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Global Optimum ◽  
Optimized Design ◽  
Air Conditioner ◽  
Air Conditioners ◽  
Multi Objective Genetic Algorithm ◽  
Design Variables ◽  
Optimization Study ◽  
Systems Simulation

In order for the solar air conditioners (A/Cs) to become a real alternative to the conventional systems, their performance and total cost has to be optimized. In this study, an innovative hybrid solar A/C was simulated using the transient systems simulation (TRNSYS) program, which was coupled with MATLAB in order to carry out the optimization study. Two optimization problems were formulated with the following design variables: collector area, collector mass flow rate, storage tank volume, and number of batteries. The Genetic Algorithm (GA) was selected to find the global optimum design for the lowest electrical consumption. To optimize the two objective functions simultaneously, a Multi-Objective Genetic Algorithm (MOGA) was used to find the Pareto front within the design variables’ bounds while satisfying the constraints. The optimized design was also compared to a standard vapor compression cycle. The results show that coupling TRNSYS and MATLAB expands TRNSYS optimization capability in solving more complicated optimization problems.

An Approach to Identify Six Sigma Robust Solutions of Multi/Many-Objective Engineering Design Optimization Problems

2015 ◽  
Vol 137 (5) ◽  
Author(s):  
Tapabrata Ray ◽  
Md Asafuddoula ◽  
Hemant Kumar Singh ◽  
Khairul Alam
Keyword(s):  
Design Optimization ◽  
Engineering Design ◽  
Optimization Problems ◽  
Product Family ◽  
Latin Hypercube Sampling ◽  
General Aviation ◽  
Design Variables ◽  
Engineering Design Optimization ◽  
The Face ◽  
And Performance

In order to be practical, solutions of engineering design optimization problems must be robust, i.e., competent and reliable in the face of uncertainties. While such uncertainties can emerge from a number of sources (imprecise variable values, errors in performance estimates, varying environmental conditions, etc.), this study focuses on problems where uncertainties emanate from the design variables. While approaches to identify robust optimal solutions of single and multi-objective optimization problems have been proposed in the past, we introduce a practical approach that is capable of solving robust optimization problems involving many objectives building on authors’ previous work. Two formulations of robustness have been considered in this paper, (a) feasibility robustness (FR), i.e., robustness against design failure and (b) feasibility and performance robustness (FPR), i.e., robustness against design failure and variation in performance. In order to solve such formulations, a decomposition based evolutionary algorithm (DBEA) relying on a generational model is proposed in this study. The algorithm is capable of identifying a set of uniformly distributed nondominated solutions with different sigma levels (feasibility and performance) simultaneously in a single run. Computational benefits offered by using polynomial chaos (PC) in conjunction with Latin hypercube sampling (LHS) for estimating expected mean and variance of the objective/constraint functions has also been studied in this paper. Last, the idea of redesign for robustness has been explored, wherein selective component(s) of an existing design are altered to improve its robustness. The performance of the strategies have been illustrated using two practical design optimization problems, namely, vehicle crash-worthiness optimization problem (VCOP) and a general aviation aircraft (GAA) product family design problem.

Studies on Design Optimization of Coronary Stents

2008 ◽  
Vol 2 (1) ◽  
Author(s):  
K. Srinivas ◽  
T. Nakayama ◽  
M. Ohta ◽  
S. Obayashi ◽  
T. Yamaguchi
Keyword(s):  
Design Optimization ◽  
Cross Sections ◽  
Latin Hypercube Sampling ◽  
Coronary Stents ◽  
Point Of View ◽  
Two Dimensional ◽  
Reattachment Length ◽  
Design Variables ◽  
Two Dimensional Flow ◽  
The Relationship

The stent design itself seems to be one of the factors responsible for restenosis. As a remedy, the present work attempts to perform a design optimization of coronary stents from a hemodynamic point of view. For the purpose, we have applied the principles of modern exploration of design space restricting ourselves to two-dimensional considerations. Width, thickness, and spacing of the struts of the stent formed the design variables. The objectives chosen for optimization were the vorticity generated, length of recirculation zone, and the reattachment distance in between the struts. Both semicircular and rectangular cross sections of stents were included. Starting with the range of design variables, sample stent cases were generated using Latin hypercube sampling. Objective functions were calculated for each of these by computing the two-dimensional flow using software FLUENT under the assumption of a steady, Newtonian flow considering a model stent with three struts. This was followed by Kriging to construct a response surface, which gives the relationship between the objectives and the design variables. The procedure gave nondominated fronts, which consist of optimized designs. Stents with minimum vorticity, with minimum recirculation distance, and the ones with maximum reattachment length in between struts were generated. The procedure is capable of producing the optimum set of design variables to achieve the prescribed objectives.

Solar Hybrid Air Conditioner: Model Validation and Optimization

2016 ◽  
Vol 138 (3) ◽  
Author(s):  
Ali Al-Alili ◽  
Yunho Hwang ◽  
Reinhard Radermacher
Keyword(s):  
Genetic Algorithm ◽  
Energy Storage ◽  
Solar Collector ◽  
Optimization Problems ◽  
Electrical Energy ◽  
Global Optimum ◽  
Air Conditioner ◽  
Solar Thermal Energy ◽  
Design Variables ◽  
Optimization Study

Solar air conditioners (A/Cs) have attracted much attention in research, but their performance and cost have to be optimized in order to become a real alternative to conventional A/C systems. In this study, a hybrid solar A/C is simulated using the transient systems simulation program(trnsys), which is coupled with matlab in order to carry out the optimization study. The trnsys model is experimentally validated prior to the optimization study. Two optimization problems are formulated with the following design variables: solar collector area, solar collector mass flow rate, solar thermal energy storage volume, and solar electrical energy storage size. The genetic algorithm (GA) is selected to solve the single-objective optimization problem and find the global optimum design for the lowest electrical consumption. To optimize the two objective functions simultaneously, energy consumption and total cost (TC), a multi-objective genetic algorithm (MOGA) is used to find the Pareto curve within the design variables' bounds while satisfying the constraints. The overall cost of the optimized solar A/C design is also compared to a standard vapor compression cycle (VCC). The results show that coupling trnsys and matlab expands trnsys optimization capability in solving more complex optimization problems. The results also show that the optimized solar hybrid A/C is not very competitive when the electricity prices are low and no governmental support is provided.

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