A Reduction Method for Nonhierarchichal Optimization-Based Design

1992 ◽  
Author(s):  
Bi-Chu Wu ◽  
Shapour Azarm
Keyword(s):  
Optimum Design ◽  
Driving Force ◽  
Reduction Method ◽  
Optimization Problems ◽  
Size Reduction ◽  
Simple Method ◽  
Size Problem ◽  
Optimization Based Design

Abstract Nonhierarchical decomposition-based optimization, especially when compared to the hierarchical one, has the advantage that no restriction is imposed on how the decomposed subsystems should interact with one another. However, the difficulty inherent in nonhierarchically decomposed subsystems is how to coordinate their activities so that an overall optimum design can be achieved. In this paper, a new and simple method for optimization-based design of nonhierarchically decomposed systems is presented. The driving force behind the method is to formulate and iteratively solve a reduced-size problem so that the decomposed subsystems can be coordinated. Since the size reduction measures developed in the method does not include any problem-dependent assumptions, the procedure is applicable to a wide variety of optimization problems. To illustrate the application of the method, a simple numerical and a flywheel design example are presented.

Fabrication of Silicon Nanowires by Electron Beam Lithography and Thermal Oxidation Size Reduction Method

2013 ◽  
Vol 832 ◽  
pp. 415-418 ◽  
Author(s):  
Mohammad Nuzaihan Md Nor ◽  
Uda Hashim ◽  
Taib Nazwa ◽  
Tijjani Adam
Keyword(s):  
Electron Beam ◽  
Thermal Oxidation ◽  
Silicon Nanowires ◽  
Electron Beam Lithography ◽  
Reduction Method ◽  
Size Reduction ◽  
Simple Method ◽  
Force Microscopy ◽  
Initial Silicon ◽  
20 Nm

A simple method for the fabrication of silicon nanowires using Electron Beam Lithography (EBL) combined with thermal oxidation size reduction method is presented. EBL is used to define the initial silicon nanowires of dimensions approximately 100 nm. Size-reduction method is employed for reaching true nanoscale of dimensions approximately 20 nm. Dry oxidation of silicon is well investigated process for self-limited size-reduction of silicon nanowires. In this paper, successful size reduction of silicon nanowires is presented and surface topography characterizations using Atomic Force Microscopy (AFM) are reported.

Fabrication of Poly-Si Nanowire Using Conventional Photolithography Technique

2014 ◽  
Vol 925 ◽  
pp. 460-463 ◽  
Author(s):  
Mohammad Nuzaihan Md Nor ◽  
Uda Hashim ◽  
Taib Nazwa ◽  
A. Rahim Ruslinda
Keyword(s):  
Thermal Oxidation ◽  
Reduction Method ◽  
Chemical Vapour ◽  
Size Reduction ◽  
Si Nanowires ◽  
Simple Method ◽  
Chemical Vapour Deposition Technique ◽  
Pressure Chemical ◽  
Polysilicon Layer ◽  
Conventional Photolithography

A simple method for the fabrication of polycrystalline silicon (poly-si) nanowires using conventional photolithography combined with thermal oxidation-size reduction method is presented. In our process, a polysilicon layer is deposited by low pressure chemical vapour deposition technique on SiO2 layer. Conventional photolithograpy is used to define the initial poly-si of dimensions 1 um. In order to miniaturize microwire to nanowire, size reduction method is employed using several time of dry thermal oxidation process. The characterization that is applied to measure the profile of poly-si nanowires using optical microscopy.

scholarly journals Embarrassingly Parallel Search in Constraint Programming

2016 ◽  
Vol 57 ◽  
pp. 421-464 ◽  
Author(s):  
Arnaud Malapert ◽  
Jean-Charles Régin ◽  
Mohamed Rezgui
Keyword(s):  
Cloud Computing ◽  
Efficient Method ◽  
Data Centers ◽  
Optimization Problems ◽  
State Of The Art ◽  
Parallel Search ◽  
Simple Method ◽  
Constraint Solver ◽  
Average Performance ◽  
Depth Analysis

We introduce an Embarrassingly Parallel Search (EPS) method for solving constraint problems in parallel, and we show that this method matches or even outperforms state-of-the-art algorithms on a number of problems using various computing infrastructures. EPS is a simple method in which a master decomposes the problem into many disjoint subproblems which are then solved independently by workers. Our approach has three advantages: it is an efficient method; it involves almost no communication or synchronization between workers; and its implementation is made easy because the master and the workers rely on an underlying constraint solver, but does not require to modify it. This paper describes the method, and its applications to various constraint problems (satisfaction, enumeration, optimization). We show that our method can be adapted to different underlying solvers (Gecode, Choco2, OR-tools) on different computing infrastructures (multi-core, data centers, cloud computing). The experiments cover unsatisfiable, enumeration and optimization problems, but do not cover first solution search because it makes the results hard to analyze. The same variability can be observed for optimization problems, but at a lesser extent because the optimality proof is required. EPS offers good average performance, and matches or outperforms other available parallel implementations of Gecode as well as some solvers portfolios. Moreover, we perform an in-depth analysis of the various factors that make this approach efficient as well as the anomalies that can occur. Last, we show that the decomposition is a key component for efficiency and load balancing.

Hierarchical Arrangement of Characteristics in Product Design Optimization Problems for Deeper Insight Into Optimized Results

2004 ◽  
Author(s):  
Masataka Yoshimura ◽  
Masahiko Taniguchi ◽  
Kazuhiro Izui ◽  
Shinji Nishiwaki
Keyword(s):  
Design Optimization ◽  
Product Design ◽  
Optimum Design ◽  
Design Problem ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Optimization Method ◽  
Hierarchical Level ◽  
Hierarchical Arrangement ◽  
Insight Into

This paper proposes a design optimization method for machine products that is based on the decomposition of performance characteristics, or alternatively, extraction of simpler characteristics, to accommodate the specific features or difficulties of a particular design problem. The optimization problem is expressed using hierarchical constructions of the decomposed and extracted characteristics and the optimizations are sequentially repeated, starting with groups of characteristics having conflicting characteristics at the lowest hierarchical level and proceeding to higher levels. The proposed method not only effectively enables achieving optimum design solutions, but also facilitates deeper insight into the design optimization results, and aids obtaining ideas for breakthroughs in the optimum solutions. An applied example is given to demonstrate the effectiveness of the proposed method.

Estimation of Dynamic Mechanical Error for Evaluation of Machine Tool Structures

2012 ◽  
Vol 6 (2) ◽  
pp. 147-153 ◽  
Author(s):  
Daisuke Kono ◽  
◽  
Sascha Weikert ◽  
Atsushi Matsubara ◽  
Kazuo Yamazaki ◽  
...  
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Machine Tool ◽  
Driving Force ◽  
Inertial Force ◽  
Element Analysis ◽  
Simple Method ◽  
Dynamic Mechanical ◽  
Machine Tool Structures ◽  
Multi Body

Dynamic motion errors of machine tools consist of errors in the mechanical system and the servo system. In this study, a simple method to estimate the dynamic mechanical error is proposed to evaluate machine tool structures. The dynamic mechanical error in the low frequency range is estimated from the static deformation due to the driving force, the counter force, and the inertial force. The error in a high-precision machine tool is estimated for comparison with measurements. Two calculation tools, finite element analysis and rigid multi-body simulation, are used for the estimation. Measured dynamic mechanical errors can be correctly estimated by the proposed method using finite element analysis. The tilt of driven bodies is the major reason for dynamic mechanical errors. When the reduction factor representing the structural deformation is properly determined, the rigid multi-body simulation is also an effective tool. Use of the proposed method for modification planning is demonstrated. Stiffness enhancement of the saddle was an effective modification candidate to reduce the dynamic mechanical error. If the error should be reduced to sub-micrometer level, the location of components should be modified because the Abbe offset and the offset of the driving force from the inertial force must be shortened.

scholarly journals Optimum design and operation of a hydropower reservoir considering uncertainty of inflow

2020 ◽  
Vol 22 (6) ◽  
pp. 1452-1467
Author(s):  
Toktam Hoseinzadeh ◽  
Mojtaba Shourian ◽  
Jafar Yazdi
Keyword(s):  
Optimum Design ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Stochastic Approach ◽  
Search Space ◽  
Hydropower Plant ◽  
Design Decision ◽  
Plant Design ◽  
Operation Optimization ◽  
Hydropower Reservoir

Abstract Due to the large number of variables and nonlinear relations, hydropower plant design and operation optimization problems belong to the Non-polynomial hard class of problems. In this study, optimum design and operation of a hydropower reservoir is compared in two cases using deterministic and stochastic inflows by two meta-heuristic algorithms. Particle swarm optimization (PSO) and cuckoo optimization algorithm (COA) are applied under two conditions of using the historical inflow time series as a deterministic approach and the eigenvector-based synthetic generations as a stochastic approach for optimum design and operation of the Bakhtiari hydropower plant in Iran. The problem is solved in two states of finding the optimum values for the reservoir and power plant capacities (as the design decision variables) with known standard operation policy (SOP) and optimum values for the capacities and the reservoir releases variables (as the design and operating variables). Results obtained by the models indicate that the role of operation optimization is negligible as the SOP used in the design models led to near optimum solutions. Considering uncertainty in the reservoir inflows resulted in an increase of the installation capacity and consequently the energy production. In addition, PSO demonstrated more efficiency compared to COA in dealing with the proposed optimization problem that has a complex feasible search space.

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