157,557 research outputs found
Stiffness and strength-based lightweight design of truss structures using multi-material topology optimization
Authors wish also to thank Professor Krister Svanberg (Royal Institute of Technology, Stockholm, Sweden) for the MMA optimization code.
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© 2021 9th International Conference on Computational Methods for Coupled Problems in Science and Engineering, COUPLED PROBLEMS 2021. All rights reserved.Stiffness and strength are important structural design criteria. However, most contributions to Topology Optimization (TO) deal with the compliance minimization problem. Controlling stresses in a structure is very important to avoid material failure, but that raises complications in TO, such as: nonlinearity, singularity and high computational cost. The total weight of a structure is also another important criterion in optimal design. The multi-material setting is considered in the present work as it opens the possibility to improve structural performance even further allowing extra weight reduction. Recursive SIMP is used as the material interpolation scheme and design solutions are sought using the ground structure approach. This means that truss-like (lattice) designs are obtained here. The problem is relaxed to the continuum by introducing an artificial density variable and it is solved by a gradientbased algorithm (MMA). A stress-constraint relaxation technique (qp-approach) is applied to overcome the stress singularity phenomenon. A continuation approach is used to guarantee discrete solutions, i.e., only the presence or absence of bars is identified. Therefore, design uniformity in terms of bars cross section areas is ensured. Hence, this work proposes a methodology to perform Multi-Material Topology Optimization (MMTO) of truss structures, with density-based design variables, and subject to stress constraints. To discuss the differences between stiffness and strength-oriented optimal designs, a compliance minimization problem subject to mass constraint is also considered. The example chosen demonstrates the viability of the proposed design methodology and it also reveals differences between the strongest and the stiffest designs.authorsversionpublishe
Complexity Reduction for Parameter-Dependent Linear Systems
We present a complexity reduction algorithm for a family of
parameter-dependent linear systems when the system parameters belong to a
compact semi-algebraic set. This algorithm potentially describes the underlying
dynamical system with fewer parameters or state variables. To do so, it
minimizes the distance (i.e., H-infinity-norm of the difference) between the
original system and its reduced version. We present a sub-optimal solution to
this problem using sum-of-squares optimization methods. We present the results
for both continuous-time and discrete-time systems. Lastly, we illustrate the
applicability of our proposed algorithm on numerical examples
SamACO: variable sampling ant colony optimization algorithm for continuous optimization
An ant colony optimization (ACO) algorithm offers
algorithmic techniques for optimization by simulating the foraging behavior of a group of ants to perform incremental solution
constructions and to realize a pheromone laying-and-following
mechanism. Although ACO is first designed for solving discrete
(combinatorial) optimization problems, the ACO procedure is
also applicable to continuous optimization. This paper presents
a new way of extending ACO to solving continuous optimization
problems by focusing on continuous variable sampling as a key
to transforming ACO from discrete optimization to continuous
optimization. The proposed SamACO algorithm consists of three
major steps, i.e., the generation of candidate variable values for
selection, the ants’ solution construction, and the pheromone
update process. The distinct characteristics of SamACO are the
cooperation of a novel sampling method for discretizing the
continuous search space and an efficient incremental solution
construction method based on the sampled values. The performance
of SamACO is tested using continuous numerical functions
with unimodal and multimodal features. Compared with some
state-of-the-art algorithms, including traditional ant-based algorithms
and representative computational intelligence algorithms
for continuous optimization, the performance of SamACO is seen
competitive and promising
State-of-the-art in aerodynamic shape optimisation methods
Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners
Algorithmic techniques for nanometer VLSI design and manufacturing closure
As Very Large Scale Integration (VLSI) technology moves to the nanoscale
regime, design and manufacturing closure becomes very difficult to achieve due to
increasing chip and power density. Imperfections due to process, voltage and temperature variations aggravate the problem. Uncertainty in electrical characteristic of
individual device and wire may cause significant performance deviations or even functional failures. These impose tremendous challenges to the continuation of Moore's
law as well as the growth of semiconductor industry.
Efforts are needed in both deterministic design stage and variation-aware design
stage. This research proposes various innovative algorithms to address both stages for
obtaining a design with high frequency, low power and high robustness. For deterministic optimizations, new buffer insertion and gate sizing techniques are proposed. For
variation-aware optimizations, new lithography-driven and post-silicon tuning-driven
design techniques are proposed.
For buffer insertion, a new slew buffering formulation is presented and is proved
to be NP-hard. Despite this, a highly efficient algorithm which runs > 90x faster
than the best alternatives is proposed. The algorithm is also extended to handle
continuous buffer locations and blockages.
For gate sizing, a new algorithm is proposed to handle discrete gate library in
contrast to unrealistic continuous gate library assumed by most existing algorithms. Our approach is a continuous solution guided dynamic programming approach, which
integrates the high solution quality of dynamic programming with the short runtime
of rounding continuous solution.
For lithography-driven optimization, the problem of cell placement considering
manufacturability is studied. Three algorithms are proposed to handle cell flipping
and relocation. They are based on dynamic programming and graph theoretic approaches, and can provide different tradeoff between variation reduction and wire-
length increase.
For post-silicon tuning-driven optimization, the problem of unified adaptivity
optimization on logical and clock signal tuning is studied, which enables us to significantly save resources. The new algorithm is based on a novel linear programming
formulation which is solved by an advanced robust linear programming technique.
The continuous solution is then discretized using binary search accelerated dynamic
programming, batch based optimization, and Latin Hypercube sampling based fast
simulation
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