Topology optimization design of 3D electrothermomechanical actuators by using GPU as a co-processor

Ramírez-Gil, Francisco Javier; Silva, Emilío Carlos Nelli; Rubio, Wilfredo Montealegre

doi:10.1016/j.cma.2015.12.021

Cited by 16 publications

(3 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Within the last decade, TO method has been significantly improved [3], and its application to solve complex problems has expanded significantly [25]. Various multi-physics problems such as enhancing heat transfer processes [26,27], reducing liquid and solid interactions [28], optimizing the thermoelastic behaviour of structures [29], and improving the geometry of electro-thermal mechanical drives [30,31] are addressed using TO approaches, where given geometries are numerically discretized in 2D or 3D domains and optimized using TO for better performance. Although, the application of TO on such problems with multiple interacting physical properties in 2D or 3D domains leads to a noticeable increase in computational complexity, and hence an increase in the computation time of TO analysis.…”

Section: Topology Optimization Is Usually Achievedmentioning

confidence: 99%

Reduction of Material Usage in 3D Printable Structures Using Topology Optimization Accelerated with U-Net Convolutional Neural Network

RASULZADE

Maksum

Nogaibayeva

et al. 2022

Eurasian Chem.-Technol. J.

View full text Add to dashboard Cite

Today’s 3D printers allow the creation of very advanced structures from various materials, starting from simple plastics up to metal alloys. Since the printing time and amount of material used to create structures are considered very important in terms of cost and energy consumption, it is better to optimize structures for that particular application taking into account all the conditions. In the current work, U-Net convolutional neural network-based topology optimization method (TO) that allows to reduce the material usage and eventually reduces the cost of 3D printing is introduced. The results showed that the accuracy of the method is highly reliable and can be used for designing various 3D printable structures and it applies to any type of materials since properties of materials can be included in TO.

show abstract

Section: Topology Optimization Is Usually Achievedmentioning

confidence: 99%

Reduction of Material Usage in 3D Printable Structures Using Topology Optimization Accelerated with U-Net Convolutional Neural Network

RASULZADE

Maksum

Nogaibayeva

et al. 2022

Eurasian Chem.-Technol. J.

View full text Add to dashboard Cite

show abstract

“…GPU computing using the sparse-matrix representation permits the efficient assembly and solving of the system of equations of elasticity [33]. However, a higher performance can be achieved exploiting the grid regularity and performing the operations "on-the-fly".…”

Section: Introductionmentioning

confidence: 99%

GPU acceleration for evolutionary topology optimization of continuum structures using isosurfaces

Martnez-Frutos

Herrero-Prez

2017

Computers & Structures

View full text Add to dashboard Cite

Evolutionary topology optimization of three-dimensional continuum structures is a computationally demanding task in terms of memory consumption and processing time. This work aims to alleviate these constraints proposing a well-suited strategy for Graphics Processing Unit (GPU) computing. Such a proposal adopts a fine-grained GPU instance of matrix-free iterative solver for structural analysis and an efficient GPU implementation for isosurface extraction and volume fraction calculation. The performance of the solving stage is evaluated using two preconditioning techniques, including the comparison with the sparse-matrix CPU implementation. The proposal is evaluated using topology optimization problems for real-world applications.

show abstract

“…GPU computing using the sparse-matrix representation permits to efficiently assembly and solve the system of equations of elasticity [36]. However, a higher performance can be achieved exploiting the grid regularity and performing the operations "on-the-fly".…”

Section: Introductionmentioning

confidence: 99%

Efficient topology optimization using GPU computing with multilevel granularity

Martnez-Frutos

Martnez-Castejn

Herrero-Prez

2017

Advances in Engineering Software

View full text Add to dashboard Cite

This paper proposes a well-suited strategy for High Performance Computing (HPC) of density-based topology optimization using Graphics Processing Units (GPUs). Such a strategy takes advantage of Massively Parallel Processing (MPP) architectures to overcome the computationally demanding procedures of density-based topology design, both in terms of memory consumption and processing time. This is done exploiting data locality and minimizing both memory consumption and data transfers. The proposed GPU instance makes use of different granularities for the topology optimization pipeline, which are selected to properly balance the workload between the threads exploiting the parallelization potential of massive parallel architectures. The performance of the fine-grained GPU instance of the solving stage is evaluated using two preconditioning techniques. The proposal is also compared with the classical CPU implementation for diverse topology optimization problems, including stiffness maximization, heat sink design and compliant mechanism design.

show abstract

Topology optimization design of 3D electrothermomechanical actuators by using GPU as a co-processor

Cited by 16 publications

References 31 publications

Reduction of Material Usage in 3D Printable Structures Using Topology Optimization Accelerated with U-Net Convolutional Neural Network

Reduction of Material Usage in 3D Printable Structures Using Topology Optimization Accelerated with U-Net Convolutional Neural Network

GPU acceleration for evolutionary topology optimization of continuum structures using isosurfaces

Efficient topology optimization using GPU computing with multilevel granularity

Contact Info

Product

Resources

About