Topology optimization based on deep representation learning (DRL) for compliance and stress-constrained design

Deng, Hao; To, Albert C.

doi:10.1007/s00466-020-01859-5

Cited by 42 publications

(19 citation statements)

References 77 publications

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“…Microgrid energy distribution means optimizing distributed generation characteristics, power quality requirements, and demand management. As part of a specific control strategy, it works economically as part of the microarray and determines the optimal processing distribution and configuration of each micro power supply to achieve the microgrid [17]. Figure 2 is a power distribution system diagram based on blockchain technology.…”

Section: Microgrid System Based On Blockchainmentioning

confidence: 99%

Deep Learning Optimization of Microgrid Economic Dispatch and Wireless Power Transmission Using Blockchain

Chen

Guo

Zhao

et al. 2022

Wireless Communications and Mobile Computing

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The purpose is to realize the decentralized microgrid economic dispatch, improve the information transparency and security of microgrid systems, and make the power grid move towards a clean, safe, efficient, and reliable development path. Deep learning optimization of microgrid economic dispatch and wireless power transmission based on blockchain technology are studied. First, the related theories and methods of microgrid systems, wireless power transmission, and deep learning optimization based on blockchain technology are introduced. Next, the microgrid economic dispatch is simulated and analyzed on a large scale. Finally, the comparison results between microgrid economic dispatch and common radio energy transmission technologies are analyzed. The results show that daily planning can better coordinate the state of distributed generation, energy storage system, and public connection. The operation results of the previous day correspond to the long-term operation economy of the microgrid. The total operation cost of the microgrid is 4668 yuan/day, and the remaining power is maintained between 500 and 600 kW, which helps to prevent excessive battery discharge, prolong battery life, and reduce operation cost. The simulation results show that the total power imbalance of the microgrid can reduce the output fluctuation of controllable load shedding of distributed generation. When the load characteristics are not important, the output fluctuation of controllable distributed generation can be reduced. The proposed economic dispatch model can optimize the data security, information storage, and information release of the microgrid and has a certain guiding role for the development of the national power grid and power industry.

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Section: Microgrid System Based On Blockchainmentioning

confidence: 99%

Deep Learning Optimization of Microgrid Economic Dispatch and Wireless Power Transmission Using Blockchain

Chen

Guo

Zhao

et al. 2022

Wireless Communications and Mobile Computing

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“…To distinguish the high-density elements and low-density elements with a smooth continuous approach, a Heaviside function is introduced to ensure a smooth and differentiable transition. The elemental strain energy of intermediate density region, named as transition elements, is described in Equation (27). For the TO method proposed in this paper, since the intermediate density region is only near the boundary of topology, the transition elements only locate at boundary area as shown in Figure 7.…”

Section: Fictitious Domain Approachmentioning

confidence: 99%

“…[16][17][18][19][20][21][22][23] Meanwhile, several other advanced TO methods are proposed in recent years. [24][25][26][27][28][29][30][31][32] In recent years, designing the flexible electronics, soft robots and wearable electronic devices draw great attention from academia and industry due to their extraordinary mechanical response. [33][34][35] Such devices and structures usually experience large deformations under external loading conditions, which is different from the traditional stiff structure design.…”

Section: Introductionmentioning

confidence: 99%

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A density‐based boundary evolving method for buckling‐induced design under large deformation

Deng

2020

Numerical Meth Engineering

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This paper proposes a density-based boundary evolving algorithm for continuum-based topology optimization. The boundary of voids in the design domain is described by RBF (radial basis function) function controlled by RBF knots in polar coordinate, where the voids are projected onto a fixed grid using Heaviside function. For merging overlapped multiple voids, the p-norm function is introduced to describe composite density field. The differentiability of the projection-based boundary description algorithm allows for the sensitivity analysis via the chain rule, and therefore, it enables an efficient gradient-based optimization method. The goal of this paper is to optimize the initial design to generate buckling-induced mechanism under large deformation, and without loss of generality, the hyperelastic material model is chosen to describe the material behavior. Notably, this method possesses the merit of level set method, where the intermediate density only exists at the boundary of topology shape. At the same time, the proposed method is still in the density-based optimization framework and standard sensitivity analysis of density-based methods can be directly derived based on the chain rule. Several numerical examples are presented and discussed in detail to demonstrate the effectiveness of the proposed density-based boundary evolving method.

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“…Topology optimization is a mathematical analysis of a design space, optimizing the material distribution to improve performance for a given metric (i.e., compliance, stress, etc.). Beginning with the work by Bendsoe and Kikuchi [ 1 ], the field has grown to include new approaches such as solid isotropic material with penalization (SIMP), level set (LS), and bi-directional evolutionary structural optimization (BESO) [ 2 , 3 ] and various design problems such as static, dynamic, thermo-elastic, and manufacturability [ 4 , 5 ]. These advances all improve the functional use of the optimized design.…”

Section: Introductionmentioning

confidence: 99%

Integrating Geometric Data into Topology Optimization via Neural Style Transfer

et al. 2021

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This research proposes a novel topology optimization method using neural style transfer to simultaneously optimize both structural performance for a given loading condition and geometric similarity for a reference design. For the neural style transfer, the convolutional layers of a pre-trained neural network extract and quantify characteristic features from the reference and input designs for optimization. The optimization analysis is evaluated as a single weighted objective function with the ability for the user to control the influence of the neural style transfer with the structural performance. As seen in architecture and consumer-facing products, the visual appeal of a design contributes to its overall value along with mechanical performance metrics. Using this method, a designer allows the tool to find the ideal compromise of these metrics. Three case studies are included to demonstrate the capabilities of this method with various loading conditions and reference designs. The structural performances of the novel designs are within 10% of the baseline without geometric reference, and the designs incorporate features in the given reference such as member size or meshed features. The performance of the proposed optimizer is compared against other optimizers without the geometric similarity constraint.

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Topology optimization based on deep representation learning (DRL) for compliance and stress-constrained design

Cited by 42 publications

References 77 publications

Deep Learning Optimization of Microgrid Economic Dispatch and Wireless Power Transmission Using Blockchain

Deep Learning Optimization of Microgrid Economic Dispatch and Wireless Power Transmission Using Blockchain

A density‐based boundary evolving method for buckling‐induced design under large deformation

Integrating Geometric Data into Topology Optimization via Neural Style Transfer

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