Design optimization of aircraft landing gear assembly under dynamic loading

The recent drive for producing lightweight and high performance designs on reduced timelines has promoted the need for computational design generation tools such as Multi-Material Topology Optimization (MMTO). However, MMTO has drawn some industry skepticism as it assumes different material elements to be perfectly fused together. To address this concern, in this article, a novel pseudo-cost domain (PCD) method is proposed which mathematically determines individual material interfaces in MMTO solutions. The proposed methodology employs a user defined joint cost model to weigh the distinct material interfaces relative to each other. An innovative approach to tailor the MMTO design considering the relative cost of each material interface is presented. The proposed methodology can consider any number of materials and their respective interfaces, and it is defined in such a way that increasing the number of materials has minimal effect on computational time. The methodology is formulated in a smooth and differentiable manner and the sensitivity expressions required by gradient-based optimization solvers are presented. A series of example problems are provided to demonstrate the efficacy of the proposed methodology.

Section: Introductionmentioning

confidence: 99%

Material interface control in multi‐material topology optimization using pseudo‐cost domain method

Shah

Pamwar

Sangha

et al. 2020

“…TO can be applied to a wide range of problems to generate novel solutions to practical design challenges. Wong et al have applied TO using the equivalent static loading method in order to improve the design of an aircraft landing gear system. Li and Kim used TO in conjunction with shape optimization to reduce the mass of an automotive cross beam.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous single‐loop multimaterial and multijoint topology optimization

Florea

Pamwar

Sangha

et al. 2019

Summary As the aerospace and automotive industries continue to strive for efficient lightweight structures, topology optimization (TO) has become an important tool in this design process. However, one ever‐present criticism of TO, and especially of multimaterial (MM) optimization, is that neither method can produce structures that are practical to manufacture. Optimal joint design is one of the main requirements for manufacturability. This article proposes a new density‐based methodology for performing simultaneous MMTO and multijoint TO. This algorithm can simultaneously determine the optimum selection and placement of structural materials, as well as the optimum selection and placement of joints at material interfaces. In order to achieve this, a new solid isotropic material with penalization‐based interpolation scheme is proposed. A process for identifying dissimilar material interfaces based on spatial gradients is also discussed. The capabilities of the algorithm are demonstrated using four case studies. Through these case studies, the coupling between the optimal structural material design and the optimal joint design is investigated. Total joint cost is considered as both an objective and a constraint in the optimization problem statement. Using the biobjective problem statement, the tradeoff between total joint cost and structural compliance is explored. Finally, a method for enforcing tooling accessibility constraints in joint design is presented.

“…9 Wong et al used topology optimization and the equivalent static loading method to reduce both mass and cost of an aircraft landing gear assembly under dynamic loading. 10 Kim et al developed the reducible design variable (DV) method to reduce computational expense in topology optimization problems by not considering DVs that quickly converge as DVs in future iterations. 11 Most types of topology optimization in continuum structures can be divided into four categories, ie, (i) density-based methods, including the solid isotropic material with penalization (SIMP) interpolation scheme; (ii) boundary variation methods, including the level set and phase field methods; (iii) hard-kill methods, including evolutionary structural optimization; and (iv) nongradient methods, including ant colony and genetic algorithms (GAs).…”

Section: Introductionmentioning

confidence: 99%

“…Cristello and Kim used multiobjective optimization to create an optimized automotive universal joint assembly, and also used multidisciplinary design optimization to study the trade‐offs between mass, deceleration during collision, and manufacturability of a hydroformed part . Wong et al used topology optimization and the equivalent static loading method to reduce both mass and cost of an aircraft landing gear assembly under dynamic loading . Kim et al developed the reducible design variable (DV) method to reduce computational expense in topology optimization problems by not considering DVs that quickly converge as DVs in future iterations …”

Section: Introductionmentioning

confidence: 99%

Multimaterial multijoint topology optimization

Woischwill

Kim

2018

Summary In this paper, a methodology that solves multimaterial topology optimization problems while also optimizing the quantity and type of joints between dissimilar materials is proposed. Multimaterial topology optimization has become a popular design optimization technique since the enhanced design freedom typically leads to superior solutions; however, the conventional assumption that all elements are perfectly fused together as a single piece limits the usefulness of the approach since the mutual dependency between optimal multimaterial geometry and optimal joint design is not properly accounted for. The proposed methodology uses an effective decomposition approach to both determine the optimal topology of a structure using multiple materials and the optimal joint design using multiple joint types. By decomposing the problem into two smaller subproblems, gradient‐based optimization techniques can be used and large models that cannot be solved with nongradient approaches can be solved. Moreover, since the joining interfaces are interpreted directly from multimaterial topology optimization results, the shape of the joining interfaces and the quantity of joints connecting dissimilar materials do not need to be defined a priori. Three numerical examples, which demonstrate how the methodology optimizes the geometry of a multimaterial structure for both compliance and cost of joining, are presented.