Delamination Mechanics of Carbon Nanotube Micropillars

Brown, Josef; Hajilounezhad, Taher; Dee, Nicholas T.; Kim, Sanha; Hart, A. John; Maschmann, Matthew R.

doi:10.1021/acsami.9b09979

Cited by 30 publications

(39 citation statements)

References 44 publications

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“…The experimental investigations to measure τ i is challenging or impractical due to the limitations associated with nanoscale sizes. Therefore, theoretical approaches might be used to determine the interface properties [43]. In this study, the Pukanszky model [44] is used to correlate the composite strength with the interface properties.…”

Section: Modified Kelly-tyson Modelmentioning

confidence: 99%

Probabilistic model for flexural strength of carbon nanotube reinforced cement-based materials

Ramezani

Kim

Sun

2020

Composite Structures

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The bond between carbon nanotubes (CNTs) and cementitious materials is the key characteristic for predicting the flexural strength. However, CNT dispersion quality may change the bonding mechanism. A probabilistic approach can benefit the deterministic models to capture the uncertainties affecting these characteristics. This study proposes a probabilistic model using the Kelly-Tyson theory to predict the flexural strength of CNT-cement nanocomposites. The proposed model considers the effects of experimental variables on CNT dispersion quality, bonding mechanism and the flexural strength. To this end, a Bayesian methodology is employed to calibrate the unknown model parameters and various sources of uncertainty using extensive test data. The model is then used to identify the optimum ranges of variables to maximize the flexural strength through computing the failure probability which is defined as the probability of not meeting certain strength requirements (herein, 50% increase compared with the control). The model suggests that CNT aspect ratio ranges from 400 to 800 and concentration between 0.08 and 0.18 c-wt% yields the highest flexural strength. Finally, the effect of changes in experimental variables on the probability estimates is examined using sensitivity and importance measures. The analysis reveals that the proposed model can capture the experimentally observed trends with reasonable accuracy. For example, the importance of age increases as CNT concentration increases.

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Section: Modified Kelly-tyson Modelmentioning

confidence: 99%

Probabilistic model for flexural strength of carbon nanotube reinforced cement-based materials

Ramezani

Kim

Sun

2020

Composite Structures

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“…The quantity of hydrogen and oxygen necessary for the electrochemical reaction can be calculated at this identified current amount. Later, the fuel utilization as well as the air utilization can be calculated by equations (4,5), followed by determining the inlet molar flow rates of anode and cathode employing the mass balance equations for each gas component equations (6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20). Then, the cell voltage may be found by Nernst equation 21, also the polarization equations at given operating cell temperature and pressure can be calculated by solving the set of equations (21)(22)(23)(24)(25)(26)(27)(28)(29).…”

Section: Solid Oxide Fuel Cellmentioning

confidence: 99%

“…However, the combination of SOFC and GT yields better performance [2][3][4][5]. This hybrid energy conversion system will provide superior utilization of natural resources that considerably decreases harmful impacts on the environment as well as making higher profit [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of Solid Oxide Fuel Cell/GT Combined Heat and Power System: A comparison between Particle Swarm and Genetic Algorithms

Hajilounezhad¹,

Safari²,

Ehyaei³

2020

Preprint

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Many studies have attempted to optimize integrated Solid Oxide Fuel Cell-Gas Turbine (SOFC-GT), although different and somehow conflicting results are reported employing various algorithms. In this study, Multi-Objective Optimization (MOO) is employed to approach the optimal design of SOFC-GT considering all prevailing factors. The emphasis is placed on the evaluation of the Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) performance as two effective approaches for solving the multi-objective and non-linear optimization problems. Multi- objective optimization is carried out on two vital objectives; the electrical efficiency and the overall output power of the system. The considerable achievements are the set of optimal points that aim to identify the system optimal performance which provides a practical basis for the decision-makers to choose the appropriate target functions. For the studied conditions, the two algorithms nearly exhibit similar performance, while the PSO is faster and more efficient in terms of computational effort. The PSO appears to achieve its ultimate parameter values in fewer generations compared to the GA algorithm under the examined circumstances. It is found that the maximum power of 410 kW is accomplished employing the GA optimization method with an efficiency of 64%, while PSO method yields the maximum power of 419.19 kW at the efficiency of 58.9%. The results stress that PSO offers more satisfactory convergence and fidelity of the solution for the SOFC-GT MOO problems.

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“…In this study, a time-resolved mechanical simulation model is employed to nucleate and synthesize CNT forests. The finite element model is discussed in detail elsewhere [35] [36][37] [38]. Here, we have grown CNT forests with different user-defined synthesis inputs, namely CNT number density and CNT outer diameter to evaluate their effect on the forest structure.…”

Section: Introductionmentioning

confidence: 99%

Exploration of Carbon Nanotube Forest Synthesis-Structure Relationships Using Physics-Based Simulation and Machine Learning

Hajilounezhad¹,

Oraibi²,

Surya³

et al. 2020

Preprint

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The parameter space of CNT forest synthesis is vastand multidimensional, making experimental and/or numericalexploration of the synthesis prohibitive. We propose a morepractical approach to explore the synthesis-process relationshipsof CNT forests using machine learning (ML) algorithms toinfer the underlying complex physical processes. Currently, nosuch ML model linking CNT forest morphology to synthesisparameters has been demonstrated. In the current work, weuse a physics-based numerical model to generate CNT forestmorphology images with known synthesis parameters to trainsuch a ML algorithm. The CNT forest synthesis variablesof CNT diameter and CNT number densities are varied togenerate a total of 12 distinct CNT forest classes. Images of theresultant CNT forests at different time steps during the growthand self-assembly process are then used as the training dataset.Based on the CNT forest structural morphology, multiplesingle and combined histogram-based texture descriptors areused as features to build a random forest (RF) classifier topredict class labels based on correlation of CNT forest physicalattributes with the growth parameters. The machine learningmodel achieved an accuracy of up to 83.5% on predicting thesynthesis conditions of CNT number density and diameter.These results are the first step towards rapidly characterizingCNT forest attributes using machine learning. Identifying therelevant process-structure interactions for the CNT forests usingphysics-based simulations and machine learning could rapidlyadvance the design, development, and adoption of CNT forestapplications with varied morphologies and properties.

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Delamination Mechanics of Carbon Nanotube Micropillars

Cited by 30 publications

References 44 publications

Probabilistic model for flexural strength of carbon nanotube reinforced cement-based materials

Probabilistic model for flexural strength of carbon nanotube reinforced cement-based materials

Multi-Objective Optimization of Solid Oxide Fuel Cell/GT Combined Heat and Power System: A comparison between Particle Swarm and Genetic Algorithms

Exploration of Carbon Nanotube Forest Synthesis-Structure Relationships Using Physics-Based Simulation and Machine Learning

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