Strategies for adding features to CAD models in order to optimize performance

Robinson, Trevor; Armstrong, Cecil; Chua, Hung Soon

doi:10.1007/s00158-012-0770-z

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…Since small changes in component geometry will influence the load path and these the stress distribution within the component, the Saint Venant principle is also applied to small changes in geometry. This question has been explored by a number of authors [7][8][9] in the context of the justification of geometric simplification in initial stress analyses and subsequent redesign for enhanced performance. On closer consideration, it should be remembered that the Saint Venant principle applies to the gross stress distribution through the volume of the component: it clearly cannot apply to the regions of the component in close proximity to any geometric variation.…”

Section: Introductionmentioning

confidence: 99%

A computational study of the influence of surface roughness on material strength

et al. 2018

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In machine component stress analysis, it usually assumed that the geometry specified in CAD provides a fair representation of the geometry of the real component. While in particular circumstances, tolerance information, such as minimum thickness of a highly stressed region, might be taken into consideration, there is no standard practice for the representation of surface quality. It is known that surface roughness significantly influences fatigue life, but for this to be useful in the context of life prediction, there is a need to examine the nature of surface roughness and determine how best to characterise it. Non-smooth geometry can be represented in mathematics by fractals or other methods, but for a representation to have a practical value for a manufactured component, it is necessary to accept that there is a lower limit to surface profile measurement resolution. Resolution and mesh refinement also play a part in any computational analysis undertaken to assess surface profile effects: in the analyses presented, a nominal axi-symmetric geometry has been taken, with a finite non-smooth region on the boundary. Various surface roughness representations are modelled, and the significance of the characterized surface roughness type is investigated. It is shown that the applied load gives rise to a nominally uni-axial stress state of 90% of the yield, although surface roughness features have the effect of modifying the load path, and give rise to localized regions of plasticity near to the surface. The material of the test model is assumed to be elasto-plastic, and the development and evolution of plastic zones formed within the geometry are shown for multiple load cycles.

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Section: Introductionmentioning

confidence: 99%

A computational study of the influence of surface roughness on material strength

et al. 2018

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“…In this work, this is taken as an indication that it is better to reparametrize or add new features to the model before optimization. In [28], work was done to define the basic strategies for calculating the change in performance which could be achieved by the addition of small structural features including stiffener, boss, hole and pocket etc. But the authors did not detail how this could be used to automate the process as part of a general design strategy.…”

Section: Introductionmentioning

confidence: 99%

Enhancing CAD-based shape optimisation by automatically updating the CAD model’s parameterisation

Agarwal

Robinson

Armstrong

et al. 2018

Struct Multidisc Optim

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This paper presents an approach which increases the flexibility of a computer-aided design (CAD) model by automatically refining its parameterization and adding new CAD features to the model´s feature tree. It aims to overcome the limitations imposed by the choice of parameters used during the initial model creation, which constrains how the model shape can change during design optimization. Parametric Effectiveness compares the maximum performance improvement that can be achieved using a parameterization strategy, to the maximum performance improvement that can be obtained where the model is unconstrained in how it moves. As such, it provides a measure of how good a parameterization strategy is and allows different strategies to be compared. The change in parametric effectiveness due to inserting multiple different CAD features can be calculated using a single adjoint analysis, therefore the computational cost is essentially independent of the number of parameterisation strategies being analysed. The described approach can be used to automatically add new features to the model, and subsequently allows the use of the newly-added parameters, along with the existing parameters to be used for optimization, providing the opportunity for a better performing product. The developed approach is applied on CAD models created in CATIA V5 for 2D and 3D finite element and computational fluid dynamics problems.

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“…Other works, still with a predominantly mathematical approach, apply the models to non-aeronautical subjects. Among these, we can name [143] and [144].…”

Section: Multidisciplinary Design Optimizationmentioning

confidence: 99%

RPAS Design : an MDO Approach

Aliaga-Aguilar¹

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Unmanned aircraft and, particularly, RPAS (Remotely Piloted Aircraft Systems) are nowadays experiencing great growth both in the military and civil industries. This is due to the fact that removing the need to be manned has enabled the improvement of their endurance, range, and overall performance while, at the same time, reducing the risk to which human lives were exposed. In addition, RPAS can be made much smaller. This decrease of mass and size increases the variety of missions they can perform. However, the design process to manufacture such aircraft is often long and costly, which prevents small companies from undertaking it. Multidisciplinary Design Optimization (MDO) is an engineering field whose focus is to solve highly complex problems by the means of optimization techniques. It has been used in the design of commercial aircraft for a long time, but integrating the various engineering disciplines that take part in designing an RPAS within an MDO to simplify the design process is a challenge. In addition, there is an ample variety of architectures for MDO projects and, the reasons to choose a particular one, have to be discussed on a case by case basis. During the last years, distributed architectures have become widespread, given that they can take advantage of parallel computing (even with graphical platforms) and reduce computing time. Adapting the formulation of a problem to a particular vii Dissertation Overview Chapter 1: This is the introduction to the thesis. It presents the evolution of RPAS and the current state of the art both in MDO and in RPAS design methodologies. Chapter 2 introduces the Generic Parameter Penalty Architecture (GPPA): a new, flexible MDO architecture, oriented towards the solution of engineering problems that present different levels of complexity. Chapter 3 introduces the RPAS Advanced MDO Platform (RAMP). A new MDO environment aimed at the design of small RPAS. Chapters 4-7 present RAMP's main analysis models: aerodynamics, structure, economy and pricing, propulsion, and performance. Chapter 8 presents an application case of RAMP to a real-world mission. Chapter 8 does so by limiting RAMP's configuration availability to just classical. This provides the opportunity to compare its results to most commercially available RPAS, while Chapter 9 unleashes RAMP's full capabilities to also consider Blended Wing Body (BWB) and Canard configurations. Chapter 9 presents results for the various tests and hypothesis that are presented in the thesis, while Chapter 10 serves to give shape to the conclusions of the thesis and the future lines of research.

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Strategies for adding features to CAD models in order to optimize performance

Cited by 5 publications

References 17 publications

A computational study of the influence of surface roughness on material strength

A computational study of the influence of surface roughness on material strength

Enhancing CAD-based shape optimisation by automatically updating the CAD model’s parameterisation

RPAS Design : an MDO Approach

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