788 research outputs found

    Grid sensitivity for aerodynamic optimization and flow analysis

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    After reviewing relevant literature, it is apparent that one aspect of aerodynamic sensitivity analysis, namely grid sensitivity, has not been investigated extensively. The grid sensitivity algorithms in most of these studies are based on structural design models. Such models, although sufficient for preliminary or conceptional design, are not acceptable for detailed design analysis. Careless grid sensitivity evaluations, would introduce gradient errors within the sensitivity module, therefore, infecting the overall optimization process. Development of an efficient and reliable grid sensitivity module with special emphasis on aerodynamic applications appear essential. The organization of this study is as follows. The physical and geometric representations of a typical model are derived in chapter 2. The grid generation algorithm and boundary grid distribution are developed in chapter 3. Chapter 4 discusses the theoretical formulation and aerodynamic sensitivity equation. The method of solution is provided in chapter 5. The results are presented and discussed in chapter 6. Finally, some concluding remarks are provided in chapter 7

    Geometric Modeling of Cellular Materials for Additive Manufacturing in Biomedical Field: A Review

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    Advances in additive manufacturing technologies facilitate the fabrication of cellular materials that have tailored functional characteristics. The application of solid freeform fabrication techniques is especially exploited in designing scaffolds for tissue engineering. In this review, firstly, a classification of cellular materials from a geometric point of view is proposed; then, the main approaches on geometric modeling of cellular materials are discussed. Finally, an investigation on porous scaffolds fabricated by additive manufacturing technologies is pointed out. Perspectives in geometric modeling of scaffolds for tissue engineering are also proposed

    XVoxel-Based Parametric Design Optimization of Feature Models

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    Parametric optimization is an important product design technique, especially in the context of the modern parametric feature-based CAD paradigm. Realizing its full potential, however, requires a closed loop between CAD and CAE (i.e., CAD/CAE integration) with automatic design modifications and simulation updates. Conventionally the approach of model conversion is often employed to form the loop, but this way of working is hard to automate and requires manual inputs. As a result, the overall optimization process is too laborious to be acceptable. To address this issue, a new method for parametric optimization is introduced in this paper, based on a unified model representation scheme called eXtended Voxels (XVoxels). This scheme hybridizes feature models and voxel models into a new concept of semantic voxels, where the voxel part is responsible for FEM solving, and the semantic part is responsible for high-level information to capture both design and simulation intents. As such, it can establish a direct mapping between design models and analysis models, which in turn enables automatic updates on simulation results for design modifications, and vice versa -- effectively a closed loop between CAD and CAE. In addition, robust and efficient geometric algorithms for manipulating XVoxel models and efficient numerical methods (based on the recent finite cell method) for simulating XVoxel models are provided. The presented method has been validated by a series of case studies of increasing complexity to demonstrate its effectiveness. In particular, a computational efficiency improvement of up to 55.8 times the existing FCM method has been seen.Comment: 22 page

    Evolutionary structural pptimisation based on boundary element representation of b-spline geometry

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    Evolutionary Structural Optimisation (ESO) has become a well-established technique for determining the optimum shape and topology of a structure given a set of loads and constraints. The basic ESO concept that the optimum topology design evolves by slow removal and addition of material has matured over the last ten years. Nevertheless, the development of the method has almost exclusively considered finite elements (FE) as the approach for providing stress solutions. This thesis presents an ESO approach based on the boundary element method. Non-uniform rational B-splines (NURBS) are used to define the geometry of the component and, since the shape of these splines is governed by a set of control points, use can be made of the locations of these control points as design variables. The developed algorithm creates internal cavities to accomplish topology changes. Cavities are also described by NURBS and so they have similar behaviour to the outside boundary. Therefore, both outside and inside are optimised at the same time. The optimum topologies evolve allowing cavities to merge between each other and to their closest outer boundary. Two-dimensional structural optimisation is investigated in detail exploring multi-load case and multi-criteria optimisation. The algorithm is also extended to three-dimensional optimisation, in which promising preliminary results are obtained. It is shown that this approach overcomes some of the drawbacks inherent in traditional FE-based approaches, and naturally provides accurate stress solutions on smooth boundary representations at each iteration

    A Survey of Shape Parameterization Techniques

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    This paper provides a survey of shape parameterization techniques for multidisciplinary optimization and highlights some emerging ideas. The survey focuses on the suitability of available techniques for complex configurations, with suitability criteria based on the efficiency, effectiveness, ease of implementation, and availability of analytical sensitivities for geometry and grids. The paper also contains a section on field grid regeneration, grid deformation, and sensitivity analysis techniques

    ๊ธฐํ•˜ํ•™์ ์œผ๋กœ ์ •๋ฐ€ํ•œ ๋น„์„ ํ˜• ๊ตฌ์กฐ๋ฌผ์˜ ์•„์ด์†Œ-์ง€์˜ค๋ฉ”ํŠธ๋ฆญ ํ˜•์ƒ ์„ค๊ณ„ ๋ฏผ๊ฐ๋„ ํ•ด์„

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์กฐ์„ ํ•ด์–‘๊ณตํ•™๊ณผ, 2019. 2. ์กฐ์„ ํ˜ธ.In this thesis, a continuum-based analytical adjoint configuration design sensitivity analysis (DSA) method is developed for gradient-based optimal design of curved built-up structures undergoing finite deformations. First, we investigate basic invariance property of linearized strain measures of a planar Timoshenko beam model which is combined with the selective reduced integration and B-bar projection method to alleviate shear and membrane locking. For a nonlinear structural analysis, geometrically exact beam and shell structural models are basically employed. A planar Kirchhoff beam problem is solved using the rotation-free discretization capability of isogeometric analysis (IGA) due to higher order continuity of NURBS basis function whose superior per-DOF(degree-of-freedom) accuracy over the conventional finite element analysis using Hermite basis function is verified. Various inter-patch continuity conditions including rotation continuity are enforced using Lagrage multiplier and penalty methods. This formulation is combined with a phenomenological constitutive model of shape memory polymer (SMP), and shape programming and recovery processes of SMP structures are simulated. Furthermore, for shear-deformable structures, a multiplicative update of finite rotations by an exponential map of a skew-symmetric matrix is employed. A procedure of explicit parameterization of local orthonormal frames in a spatial curve is presented using the smallest rotation method within the IGA framework. In the configuration DSA, the material derivative is applied to a variational equation, and an orientation design variation of curved structure is identified as a change of embedded local orthonormal frames. In a shell model, we use a regularized variational equation with a drilling rotational DOF. The material derivative of the orthogonal transformation matrix can be evaluated at final equilibrium configuration, which enables to compute design sensitivity using the tangent stiffness at the equilibrium without further iterations. A design optimization method for a constrained structure in a curved domain is also developed, which focuses on a lattice structure design on a specified surface. We define a lattice structure and its design variables on a rectangular plane, and utilize a concept of free-form deformation and a global curve interpolation to obtain an analytical expression for the control net of the structure on curved surface. The material derivative of the analytical expression eventually leads to precise design velocity field. Using this method, the number of design variables is reduced and design parameterization becomes more straightforward. In demonstrative examples, we verify the developed analytical adjoint DSA method in beam and shell structural problems undergoing finite deformations with various kinematic and force boundary conditions. The method is also applied to practical optimal design problems of curved built-up structures. For example, we extremize auxeticity of lattice structures, and experimentally verify nearly constant negative Poisson's ratio during large tensile and compressive deformations by using the 3-D printing and optical deformation measurement technologies. Also, we architect phononic band gap structures having significantly large band gap for mitigating noise in low audible frequency ranges.๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋Œ€๋ณ€ํ˜•์„ ๊ณ ๋ คํ•œ ํœ˜์–ด์ง„ ์กฐ๋ฆฝ ๊ตฌ์กฐ๋ฌผ์˜ ์—ฐ์†์ฒด ๊ธฐ๋ฐ˜ ํ•ด์„์  ์• ์กฐ์ธ ํ˜•์ƒ ์„ค๊ณ„ ๋ฏผ๊ฐ๋„ ํ•ด์„ ๊ธฐ๋ฒ•์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ํ‰๋ฉด Timoshenko ๋น”์˜ ์„ ํ˜•ํ™”๋œ ๋ณ€ํ˜•๋ฅ ์˜ invariance ํŠน์„ฑ์„ ๊ณ ์ฐฐํ•˜์˜€๊ณ  invariant ์ •์‹ํ™”๋ฅผ ์„ ํƒ์  ์ถ•์†Œ์ ๋ถ„(selective reduced integration) ๊ธฐ๋ฒ• ๋ฐ B-bar projection ๊ธฐ๋ฒ•๊ณผ ๊ฒฐํ•ฉํ•˜์—ฌ shear ๋ฐ membrane ์ž ๊น€ ํ˜„์ƒ์„ ํ•ด์†Œํ•˜์˜€๋‹ค. ๋น„์„ ํ˜• ๊ตฌ์กฐ ๋ชจ๋ธ๋กœ์„œ ๊ธฐํ•˜ํ•™์ ์œผ๋กœ ์ •๋ฐ€ํ•œ ๋น” ๋ฐ ์‰˜ ๋ชจ๋ธ์„ ํ™œ์šฉํ•˜์˜€๋‹ค. ํ‰๋ฉด Kirchhoff ๋น” ๋ชจ๋ธ์„ NURBS ๊ธฐ์ €ํ•จ์ˆ˜์˜ ๊ณ ์ฐจ ์—ฐ์†์„ฑ์— ๋”ฐ๋ฅธ ์•„์ด์†Œ-์ง€์˜ค๋ฉ”ํŠธ๋ฆญ ํ•ด์„ ๊ธฐ๋ฐ˜ rotation-free ์ด์‚ฐํ™”๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๋‹ค๋ฃจ์—ˆ์œผ๋ฉฐ, ๊ธฐ์กด์˜ Hermite ๊ธฐ์ €ํ•จ์ˆ˜ ๊ธฐ๋ฐ˜์˜ ์œ ํ•œ์š”์†Œ๋ฒ•์— ๋น„ํ•ด ์ž์œ ๋„๋‹น ํ•ด์˜ ์ •ํ™•๋„๊ฐ€ ๋†’์Œ์„ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ๋ผ๊ทธ๋ž‘์ง€ ์Šน์ˆ˜๋ฒ• ๋ฐ ๋ฒŒ์น™ ๊ธฐ๋ฒ•์„ ๋„์ž…ํ•˜์—ฌ ํšŒ์ „์˜ ์—ฐ์†์„ฑ์„ ํฌํ•จํ•œ ๋‹ค์–‘ํ•œ ๋‹ค์ค‘ํŒจ์น˜๊ฐ„ ์—ฐ์† ์กฐ๊ฑด์„ ๊ณ ๋ คํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๊ธฐ๋ฒ•์„ ํ˜„์ƒํ•™์  (phenomenological) ํ˜•์ƒ๊ธฐ์–ตํด๋ฆฌ๋จธ (SMP) ์žฌ๋ฃŒ ๊ตฌ์„ฑ๋ฐฉ์ •์‹๊ณผ ๊ฒฐํ•ฉํ•˜์—ฌ ํ˜•์ƒ์˜ ํ”„๋กœ๊ทธ๋ž˜๋ฐ๊ณผ ํšŒ๋ณต ๊ณผ์ •์„ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ํ•˜์˜€๋‹ค. ์ „๋‹จ๋ณ€ํ˜•์„ ๊ฒช๋Š” (shear-deformable) ๊ตฌ์กฐ ๋ชจ๋ธ์— ๋Œ€ํ•˜์—ฌ ๋Œ€ํšŒ์ „์˜ ๊ฐฑ์‹ ์„ ๊ต๋Œ€ ํ–‰๋ ฌ์˜ exponential map์— ์˜ํ•œ ๊ณฑ์˜ ํ˜•ํƒœ๋กœ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ๊ณต๊ฐ„์ƒ์˜ ๊ณก์„  ๋ชจ๋ธ์—์„œ ์ตœ์†ŒํšŒ์ „ (smallest rotation) ๊ธฐ๋ฒ•์„ ํ†ตํ•ด ๊ตญ์†Œ ์ •๊ทœ์ง๊ต์ขŒํ‘œ๊ณ„์˜ ๋ช…์‹œ์  ๋งค๊ฐœํ™”๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ํ˜•์ƒ ์„ค๊ณ„ ๋ฏผ๊ฐ๋„ ํ•ด์„์„ ์œ„ํ•˜์—ฌ ์ „๋ฏธ๋ถ„์„ ๋ณ€๋ถ„ ๋ฐฉ์ •์‹์— ์ ์šฉํ•˜์˜€์œผ๋ฉฐ ํœ˜์–ด์ง„ ๊ตฌ์กฐ๋ฌผ์˜ ๋ฐฐํ–ฅ ์„ค๊ณ„ ๋ณ€ํ™”๋Š” ๊ตญ์†Œ ์ •๊ทœ์ง๊ต์ขŒํ‘œ๊ณ„์˜ ํšŒ์ „์— ์˜ํ•˜์—ฌ ๊ธฐ์ˆ ๋œ๋‹ค. ์ตœ์ข… ๋ณ€ํ˜• ํ˜•์ƒ์—์„œ ์ง๊ต ๋ณ€ํ™˜ ํ–‰๋ ฌ์˜ ์ „๋ฏธ๋ถ„์„ ๊ณ„์‚ฐํ•จ์œผ๋กœ์จ ๋Œ€ํšŒ์ „ ๋ฌธ์ œ์—์„œ ์ถ”๊ฐ€์ ์ธ ๋ฐ˜๋ณต ๊ณ„์‚ฐ์—†์ด ๋ณ€ํ˜• ํ•ด์„์—์„œ์˜ ์ ‘์„ ๊ฐ•์„ฑํ–‰๋ ฌ์— ์˜ํ•ด ํ•ด์„์  ์„ค๊ณ„ ๋ฏผ๊ฐ๋„๋ฅผ ๊ณ„์‚ฐํ•  ์ˆ˜ ์žˆ๋‹ค. ์‰˜ ๊ตฌ์กฐ๋ฌผ์˜ ๊ฒฝ์šฐ ๋ฉด๋‚ด ํšŒ์ „ ์ž์œ ๋„ ๋ฐ ์•ˆ์ •ํ™”๋œ ๋ณ€๋ถ„ ๋ฐฉ์ •์‹์„ ํ™œ์šฉํ•˜์—ฌ ๋ณด๊ฐ•์žฌ(stiffener)์˜ ๋ชจ๋ธ๋ง์„ ์šฉ์ดํ•˜๊ฒŒ ํ•˜์˜€๋‹ค. ๋˜ํ•œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํœ˜์–ด์ง„ ์˜์—ญ์— ๊ตฌ์†๋˜์–ด์žˆ๋Š” ๊ตฌ์กฐ๋ฌผ์— ๋Œ€ํ•œ ์„ค๊ณ„ ์†๋„์žฅ ๊ณ„์‚ฐ ๋ฐ ์ตœ์  ์„ค๊ณ„๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•˜๋ฉฐ ํŠนํžˆ ๊ณก๋ฉด์— ๊ตฌ์†๋œ ๋น” ๊ตฌ์กฐ๋ฌผ์˜ ์„ค๊ณ„๋ฅผ ์ง‘์ค‘์ ์œผ๋กœ ๋‹ค๋ฃฌ๋‹ค. ์ž์œ ํ˜•์ƒ๋ณ€ํ˜•(Free-form deformation)๊ธฐ๋ฒ•๊ณผ ์ „์—ญ ๊ณก์„  ๋ณด๊ฐ„๊ธฐ๋ฒ•์„ ํ™œ์šฉํ•˜์—ฌ ์ง์‚ฌ๊ฐ ํ‰๋ฉด์—์„œ ํ˜•์ƒ ๋ฐ ์„ค๊ณ„ ๋ณ€์ˆ˜๋ฅผ ์ •์˜ํ•˜๊ณ  ๊ณก๋ฉด์ƒ์˜ ๊ณก์„  ํ˜•์ƒ์„ ๋‚˜ํƒ€๋‚ด๋Š” ์กฐ์ •์  ์œ„์น˜๋ฅผ ํ•ด์„์ ์œผ๋กœ ํ‘œํ˜„ํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ ์ด์˜ ์ „๋ฏธ๋ถ„์„ ํ†ตํ•ด ์ •ํ™•ํ•œ ์„ค๊ณ„์†๋„์žฅ์„ ๊ณ„์‚ฐํ•œ๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ์„ค๊ณ„ ๋ณ€์ˆ˜์˜ ๊ฐœ์ˆ˜๋ฅผ ์ค„์ผ ์ˆ˜ ์žˆ๊ณ  ์„ค๊ณ„์˜ ๋งค๊ฐœํ™”๊ฐ€ ๊ฐ„ํŽธํ•ด์ง„๋‹ค. ๊ฐœ๋ฐœ๋œ ๋ฐฉ๋ฒ•๋ก ์€ ๋‹ค์–‘ํ•œ ํ•˜์ค‘ ๋ฐ ์šด๋™ํ•™์  ๊ฒฝ๊ณ„์กฐ๊ฑด์„ ๊ฐ–๋Š” ๋น”๊ณผ ์‰˜์˜ ๋Œ€๋ณ€ํ˜• ๋ฌธ์ œ๋ฅผ ํ†ตํ•ด ๊ฒ€์ฆ๋˜๋ฉฐ ์—ฌ๋Ÿฌ๊ฐ€์ง€ ํœ˜์–ด์ง„ ์กฐ๋ฆฝ ๊ตฌ์กฐ๋ฌผ์˜ ์ตœ์  ์„ค๊ณ„์— ์ ์šฉ๋œ๋‹ค. ๋Œ€ํ‘œ์ ์œผ๋กœ, ์ „๋‹จ ๊ฐ•์„ฑ ๋ฐ ์ถฉ๊ฒฉ ํก์ˆ˜ ํŠน์„ฑ๊ณผ ๊ฐ™์€ ๊ธฐ๊ณ„์  ๋ฌผ์„ฑ์น˜์˜ ๊ฐœ์„ ์„ ์œ„ํ•ด ํ™œ์šฉ๋˜๋Š” ์˜ค๊ทธ์ œํ‹ฑ (auxetic) ํŠน์„ฑ์ด ๊ทน๋Œ€ํ™”๋œ ๊ฒฉ์ž ๊ตฌ์กฐ๋ฅผ ์„ค๊ณ„ํ•˜๋ฉฐ ์ธ์žฅ ๋ฐ ์••์ถ• ๋Œ€๋ณ€ํ˜• ๋ชจ๋‘์—์„œ ์ผ์ •ํ•œ ์Œ์˜ ํฌ์•„์†ก๋น„๋ฅผ ๋‚˜ํƒ€๋ƒ„์„ 3์ฐจ์› ํ”„๋ฆฐํŒ…๊ณผ ๊ด‘ํ•™์  ๋ณ€ํ˜• ์ธก์ • ๊ธฐ์ˆ ์„ ์ด์šฉํ•˜์—ฌ ์‹คํ—˜์ ์œผ๋กœ ๊ฒ€์ฆํ•œ๋‹ค. ๋˜ํ•œ ์šฐ๋ฆฌ๋Š” ์†Œ์Œ์˜ ์ €๊ฐ์„ ์œ„ํ•ด ํ™œ์šฉ๋˜๋Š” ๊ฐ€์ฒญ ์ €์ฃผํŒŒ์ˆ˜ ์˜์—ญ๋Œ€์—์„œ์˜ ๋ฐด๋“œ๊ฐญ์ด ๊ทน๋Œ€ํ™”๋œ ๊ฒฉ์ž ๊ตฌ์กฐ๋ฅผ ์ œ์‹œํ•œ๋‹ค.Abstract 1. Introduction 2. Isogeometric analysis of geometrically exact nonlinear structures 3. Isogeometric confinguration DSA of geometrically exact nonlinear structures 4. Numerical examples 5. Conclusions and future works A. Supplements to the geometrically exact Kirchhoff beam model B. Supplements to the geometrically exact shear-deformable beam model C. Supplements to the geometrically exact shear-deformable shell model D. Supplements to the invariant formulations E. Supplements to the geometric constraints in design optimization F. Supplements to the design of auxetic structures ์ดˆ๋กDocto
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