scholarly journals Computational Design and Optimization of Non‐Circular Gears

2020 ◽  
Vol 39 (2) ◽  
pp. 399-409
Author(s):  
Hao Xu ◽  
Tianwen Fu ◽  
Peng Song ◽  
Mingjun Zhou ◽  
Chi‐Wing Fu ◽  
...  
Keyword(s):  
Computational Design ◽  
Design And Optimization

scholarly journals Computational design and optimization of electro-physiological sensors

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Aditya Shekhar Nittala ◽  
Andreas Karrenbauer ◽  
Arshad Khan ◽  
Tobias Kraus ◽  
Jürgen Steimle
Keyword(s):  
Form Factors ◽  
Computational Design ◽  
Quantitative Agreement ◽  
Physiological Data ◽  
High Signal ◽  
Signal Acquisition ◽  
Design And Optimization ◽  
Graphical Tool ◽  
Small Device ◽  
Physiological Sensors

AbstractElectro-physiological sensing devices are becoming increasingly common in diverse applications. However, designing such sensors in compact form factors and for high-quality signal acquisition is a challenging task even for experts, is typically done using heuristics, and requires extensive training. Our work proposes a computational approach for designing multi-modal electro-physiological sensors. By employing an optimization-based approach alongside an integrated predictive model for multiple modalities, compact sensors can be created which offer an optimal trade-off between high signal quality and small device size. The task is assisted by a graphical tool that allows to easily specify design preferences and to visually analyze the generated designs in real-time, enabling designer-in-the-loop optimization. Experimental results show high quantitative agreement between the prediction of the optimizer and experimentally collected physiological data. They demonstrate that generated designs can achieve an optimal balance between the size of the sensor and its signal acquisition capability, outperforming expert generated solutions.

Computational Design and Optimization of Nerve Guidance Conduits for Improved Mechanical Properties and Permeability

2019 ◽  
Vol 141 (5) ◽  
Author(s):  
Shuo Zhang ◽  
Sanjairaj Vijayavenkataraman ◽  
Geng Liang Chong ◽  
Jerry Ying Hsi Fuh ◽  
Wen Feng Lu
Keyword(s):  
Mechanical Properties ◽  
Tissue Engineering ◽  
Mechanical Strength ◽  
Tensile Modulus ◽  
Computational Design ◽  
Structural Features ◽  
Nerve Tissue ◽  
High Porosity ◽  
Engineering Research ◽  
Design And Optimization

Nerve guidance conduits (NGCs) are tubular tissue engineering scaffolds used for nerve regeneration. The poor mechanical properties and porosity have always compromised their performances for guiding and supporting axonal growth. Therefore, in order to improve the properties of NGCs, the computational design approach was adopted to investigate the effects of different NGC structural features on their various properties, and finally, design an ideal NGC with mechanical properties matching human nerves and high porosity and permeability. Three common NGC designs, namely hollow luminal, multichannel, and microgrooved, were chosen in this study. Simulations were conducted to study the mechanical properties and permeability. The results show that pore size is the most influential structural feature for NGC tensile modulus. Multichannel NGCs have higher mechanical strength but lower permeability compared to other designs. Square pores lead to higher permeability but lower mechanical strength than circular pores. The study finally selected an optimized hollow luminal NGC with a porosity of 71% and a tensile modulus of 8 MPa to achieve multiple design requirements. The use of computational design and optimization was shown to be promising in future NGC design and nerve tissue engineering research.

Computational design and optimization of a biomimetic self-healing/cooling composite material

2007 ◽  
Author(s):  
Alejandro M. Aragón ◽  
Christopher J. Hansen ◽  
Willie Wu ◽  
Philippe H. Geubelle ◽  
Jennifer Lewis ◽  
...  
Keyword(s):  
Composite Material ◽  
Computational Design ◽  
Self Healing ◽  
Design And Optimization

Computational design and optimization of multilayered and functionally graded corrosion coatings

2013 ◽  
Vol 77 ◽  
pp. 297-307 ◽  
Author(s):  
Samuel R. Cross ◽  
Richard Woollam ◽  
Stephen Shademan ◽  
Christopher A. Schuh
Keyword(s):  
Computational Design ◽  
Functionally Graded ◽  
Design And Optimization

scholarly journals Systematic Computational Design and Optimization of Light Absorbing Dyes

2020 ◽  
Vol 124 (31) ◽  
pp. 6380-6388 ◽  
Author(s):  
Jelena Belić ◽  
Bas van Beek ◽  
Jan Paul Menzel ◽  
Francesco Buda ◽  
Lucas Visscher
Keyword(s):  
Computational Design ◽  
Design And Optimization

scholarly journals Advances in computational design and optimization with application to MEMS

2001 ◽  
Vol 52 (12) ◽  
pp. 23-62 ◽  
Author(s):  
Bing‐Chung Chen ◽  
Emílio C. N. Silva ◽  
Noboru Kikuchi
Keyword(s):  
Computational Design ◽  
Design And Optimization

Multi-scale Analysis of the Crystallization of Amorphous Germanium Telluride

2014 ◽  
Vol 1697 ◽  
Author(s):  
Jie Liu ◽  
Xu Xu ◽  
Lucien Brush ◽  
M. P. Anantram
Keyword(s):  
Nucleation Rate ◽  
Elastic Energy ◽  
Annealing Temperature ◽  
Scaling Limit ◽  
Computational Design ◽  
Growth Speed ◽  
Design And Optimization ◽  
Germanium Telluride ◽  
Multi Scale ◽  
Crystallization Properties

ABSTRACTThe crystallization properties of the phase change material (PCM) germanium telluride (GeTe) are investigated. It is shown that the critical nucleus radius of a crystalline cluster is smaller than 1.4nm when the annealing temperature is lower than 600K, indicating an extremely promising scaling scenario. It is revealed that the elastic energy, which is largely ignored in existing PCM crystallization studies, plays an important role in determining various crystallization properties and the ultimate scaling limit of the PCM. By omitting the influence of elastic energy, the critical formation energy (critical nuclei radius) will be underestimated by 41.7% (22.4%), and the nucleation rate will be overestimated by 74.2% when the annealing temperature is 600 K. The methodology proposed here is capable of quantitatively calculating the nucleation rate and growth speed of amorphous PCM from first principle calculations, which is relevant to computational design and optimization of PCM.

scholarly journals Computational design and optimization of noveld‐peptideTNFα inhibitors

FEBS Letters ◽  
2019 ◽  
Vol 593 (12) ◽  
pp. 1292-1302 ◽  
Author(s):  
Wei Yang ◽  
Qi Zhang ◽  
Changsheng Zhang ◽  
Annan Guo ◽  
Yanyan Wang ◽  
...  
Keyword(s):  
Computational Design ◽  
Design And Optimization
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