Evaluating the forces generated during carbon nanotube forest growth and self-assembly

Hajilounezhad, Taher; Ajiboye, Damola M.; Maschmann, Matthew R.

doi:10.1016/j.mtla.2019.100371

Cited by 28 publications

(36 citation statements)

References 38 publications

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“…A time-resolved 2D simulation model is employed to simulate CNT forests. The simulation model is based on the finite-element analysis of concurrently growing CNTs whose mechanical equilibrium is evaluated at discrete time steps [35][36] [37]. Each CNT is modelled as numerous finite frame elements connected end-to-end.…”

Section: Methodology a Cnt Forest Simulationmentioning

confidence: 99%

“…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%

See 1 more Smart Citation

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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Section: Methodology a Cnt Forest Simulationmentioning

confidence: 99%

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

Self Cite

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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%

“…In such circumstances, multi-objective optimization (MOO) turns out a promising approach for finding various Pareto optimal solutions, to comprehend the quantitative tradeoffs between the objectives and also to acquire the optimal values of decision variables [6,9,10]. However, various arrangements of SOFC-GT combined systems have been studied with respect to several design parameters [11][12][13][14][15]. The SOFC-GT hybrid systems are usually fueled by natural gas, converted by means of an internal reformer.…”

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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“…Micro-scale fluid flow structure in microchannels or over cylinders, the physical interactions between them have become subject of many investigations recently [4,5]. An important subject in these studies was to investigate the behavior of flow and also the exerted force between that and the cylinder [6][7][8][9][10][11][12][13][14][15]. In recent years, the study of fluid flow in nanoscale has become a subject of interest, especially the direct interactions of flow and carbon nanotubes, which behave like cylindrical fibers in many aspects, with respect to a high number of applications for that and also the unusual properties of them [8,[16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Dynamics of liquid argon flow around carbon nanotube

Arabghahestani¹,

Sheikhshahrokhdehkordi²,

Akafuah³

2020

Preprint

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In this work non-equilibrium molecular dynamics (MD) simulations were performed to investigate the liquid argon flow passing a stationary carbon nanotube and to estimate kinetic energy of the flow and flow forces exerted on the nanotube. Effects of different factors were also investigated. CFD simulations of flow around a circular cylinder were also performed to compare the results with those of MD around the carbon nanotube. In molecular dynamics simulation, the fluid forces on the body are calculated directly from the summation of the molecular forces exerted on solid atoms by fluid atoms. In this work, this method is used to investigate the effects of different flow parameters such as flow velocity, flow temperature on the flow behavior over the carbon nanotube, atom positioning around the nanotube and forces exerted on it. The simulation is 3D, and the computational domain consists of a maximum of 33,700 liquid argon atoms as the fluid and 240 atoms of carbon which represent the solid nanotube. A single-walled carbon nanotube is simulated as a rigid body of fixed carbon atoms, and both argon-argon and argon-carbon interactions are modeled by the standard Lennard-Jonnes potential function. Flow is driven by rescaling fluid particles velocities at the inlet region with a length of 3% of the domain length in that direction every 50-time steps. Based on the information given, a parallel code was developed, and investigations have been conducted using this code. Results show that among all the parameters which were investigated in this paper, the flow velocity has the most significant effects on the exerted forces and kinetic energy on the carbon nanotube and the drag force is increased with an increment of the flow inlet velocity in all cases. Lift fluctuates around zero in all cases of the stationary carbon nanotube. Inlet flow velocities and temperatures have also changed atoms positioning in the vicinity of the nanotube and so atomic density in the nearby bins which is used to further clarify the difference in the drag force.

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Evaluating the forces generated during carbon nanotube forest growth and self-assembly

Cited by 28 publications

References 38 publications

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

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

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

Dynamics of liquid argon flow around carbon nanotube

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