61 
Machine learning in engineering : techniques to speed up numerical optimizationCerbone, G. (Giuseppe) 13 April 1992 (has links)
Many important application problems in engineering can be formalized as nonlinear
optimization tasks. However, numerical methods for solving such problems
are brittle and do not scale well. For example, these methods depend critically
on choosing a good starting point from which to perform the optimization search.
In highdimensional spaces, numerical methods have difficulty finding solutions
that are even locally optimal. The objective of this thesis is to demonstrate how
machine learning techniques can improve the performance of numerical optimizers
and facilitate optimization in engineering design.
The machine learning methods have been tested in the domain of 2dimensional
structural design, where the goal is to find a truss of minimum weight that bears a
set of fixed loads. Trusses are constructed from pure tension and pure compression
members. The difference in the loadbearing properties of tension and compression
members causes the gradient of the objective function to be discontinuous, and this
prevents the application of powerful gradientbased optimization algorithms in this
domain.
In this thesis, the approach to numerical optimization is to find ways of transforming
the initial problem into a selected set of subproblems where efficient,
gradientbased algorithms can be applied. This is achieved by a threestep "compilation"
process.
The first step is to apply speedup learning techniques to partition the overall
optimization task into subproblems for which the gradient is continuous. Then,
the second step is to further simplify each subproblem by using inductive learning
techniques to identify regularities and exploit them to reduce the number of
independent variables.
Unfortunately, these first two steps have the potential to produce an exponential
number of subproblems. Hence, in the third step, selection rules are derived
to identify those subproblems that are most likely to contain the global optimum.
The numerical optimization procedures are only applied to these selected
subproblems.
To identify good subproblems, a novel ID3like inductive learning algorithm
called UTILITYID3 is applied to a collection of training examples to discover
selection rules. These rules analyze the problem statement and identify a small
number of subproblems (typically 3) that are likely to contain the global optimum.
In the domain of 2dimensional structural design, the combination of these
three steps yields a 6fold speedup in the time required to find an optimal solution.
Furthermore, it turns out that this method is less reliant on a good starting point
for optimization.
The methods developed in this problem show promise of being applied to a
wide range of numerical optimization problems in engineering design. / Graduation date: 1992

62 
A Genetic Algorithm For Structural OptimizationTaskinoglu, Evren Eyup 01 December 2006 (has links) (PDF)
In this study, a design procedure incorporating a genetic algorithm (GA) is developed for optimization of structures. The objective function considered is the total weight of the structure. The objective function is minimized subjected to
displacement and strength requirements. In order to evaluate the design constraints, finite element analysis are performed either by using conventional finite element solvers (i.e. MSC/NASTRAN® / ) or by using inhouse codes. The application of the algorithm is shown by a number of design examples. Several strategies for reproduction, mutation and crossover are tested. Several conclusions drawn from the research results are presented.

63 
A Genetic Algorithm For 2d Shape OptimizationChen, Weihang 01 August 2008 (has links) (PDF)
In this study, an optimization code has been developed based on genetic algorithms associated with the finite element modeling for the shape optimization of plane stress problems.
In genetic algorithms, constraints are mostly handled by using the concept of penalty functions, which penalize infeasible solutions by reducing their fitness values in proportion to the degrees of constraint violation. In this study, An Improved GA
Penalty Scheme is used. The proposed method gives information about unfeasible individual fitness as near as possible to the feasible region in the evaluation function.
The objective function in this study is the area of the structure. The area is minimized considering the VonMisses stress criteria. In order to minimize the objective function, onepoint crossover with roulettewheel selection approach is used.
Optimum dimensions of four problems available in the literature have been solved by the code developed .
The algorithm is tested using several strategies such as / different initial population number, different probability of mutation and crossover. The results are compared with the ones in literature and conclusions are driven accordingly.

64 
A Study of the Parallel Hybrid Multilevel Genetic Algorithms for Geometrically Nonlinear Structural OptimizationLiang, JunWei 21 June 2000 (has links)
The purpose of this study is to discuss the fitness of using PHMGA (Parallel Multilevel Hybrid Genetic Algorithm), which is a fast and efficient method, in the geometrically nonlinear structural optimization. Parallel genetic algorithms can solve the problem of traditional sequential genetic algorithms, such as premature convergence, large number of function evaluations, and a difficulty in setting parameters. By using several concurrent subpopulation, parallel genetic algorithms can avoid premature convergence resulting from the single genetic searching environment of sequential genetic algorithms. It is useful to speed up the operation rate of joining timely multilevel optimization with parallel genetic algorithms. Because multilevel optimization can resolve one problem into several smaller subproblems, each subproblem is independent and not interference with one another. Then the subsystem of each level can be connected by sensitivity analysis. So we can solve the entire problem. Because each subproblem contains less variables and constrains, it can achieve the faster converge rate of the entire optimization. PHMGA integrates advantages of two methods including the parallel genetic algorithms and the multilevel optimization.
In this study, PHMGA is adopted to solve several design optimization problems for nonlinear geometrically trusses on the parallel computer IBM SP2. The use of PHMGA helps reduce execution time because of integrating a multilevel optimization and a parallel technique. PHMGA helps speed up the searching efficiency in solving structural optimization problems of nonlinear truss. It is hoped that this study will demonstrate PHMGA is an efficient and powerful tool in solving large geometrically nonlinear structural optimization problems.

65 
Shape and medial axis approximation from samplesZhao, Wulue, January 2003 (has links)
Thesis (Ph. D.)Ohio State University, 2003. / Title from first page of PDF file. Document formatted into pages; contains xvi, 131 p.; also includes graphics (some col.). Includes abstract and vita. Advisor: Tamal K. Dey, Dept. of Computer and Information Science. Includes bibliographical references (p. 126131).

66 
Determining the Optimal Orientation of Orthotropic Material for Maximizing Frequency BandgapsHaystead, Dane 20 November 2012 (has links)
As the use of carbon fiber reinforced polymers (CFRP) increases in aerospace struc
tures it is important to use this material in an efficient manner such that both the weight
and cost of the structure are minimized while maintaining its performance. To com
bat undesirable vibrational characteristics of a structure an optimization program was
developed which takes advantage of the orthotropic nature of composite materials to
maximize eigenfrequency bandgaps. The results from the optimization process were then
fabricated and subjected to modal testing. The experiments show that local fiber angle
optimization is a valid method for modifying the natural frequencies of a structure with
the theoretical results generally predicting the performance of the optimized composite
plates.

67 
Determining the Optimal Orientation of Orthotropic Material for Maximizing Frequency BandgapsHaystead, Dane 20 November 2012 (has links)
As the use of carbon fiber reinforced polymers (CFRP) increases in aerospace struc
tures it is important to use this material in an efficient manner such that both the weight
and cost of the structure are minimized while maintaining its performance. To com
bat undesirable vibrational characteristics of a structure an optimization program was
developed which takes advantage of the orthotropic nature of composite materials to
maximize eigenfrequency bandgaps. The results from the optimization process were then
fabricated and subjected to modal testing. The experiments show that local fiber angle
optimization is a valid method for modifying the natural frequencies of a structure with
the theoretical results generally predicting the performance of the optimized composite
plates.

68 
Structural acoustic design optimization of cylinders using FEM/BEMCrane, Scott P. 08 1900 (has links)
No description available.

69 
Displacement and interstory drift constraint design in GT STRUDLMaham, Andrew S. 08 1900 (has links)
No description available.

70 
A Configurable Bspline Parameterization Method for Structural Optimization of Wing BoxesYu, Alan Tao 28 September 2009 (has links)
This dissertation presents a synthesis of methods for structural optimization of aircraft wing boxes. The optimization problem
considered herein is the minimization of structural weight with respect to component sizes, subject to stress constraints. Different aspects of structural optimization methods representing the current stateoftheart are discussed, including sequential quadratic programming, sensitivity analysis, parameterization of design variables, constraint handling, and multiple load
treatment. Shortcomings of the current techniques are identified and a Bspline parameterization representing the structural sizes is proposed to address them. A new configurable Bspline parameterization
method for structural optimization of wing boxes is developed that makes it possible to flexibly explore design spaces. An automatic
scheme using different levels of Bspline parameterization configurations is also
proposed, along with a constraint aggregation method in order to reduce the computational effort. Numerical results are compared to evaluate the effectiveness of the Bspline approach and the constraint
aggregation method. To evaluate the new formulations and explore design spaces, the wing box of an airliner is optimized for the minimum weight subject to stress constraints under multiple load conditions. The new approaches are shown to significantly reduce the computational time required to perform structural optimization and to yield designs
that are more realistic than existing methods.

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