• Nuclear Engineering and Technology
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• 제54권8호
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• pp.2783-2791
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• 2022

Molecular dynamics simulations were performed to predict the behavior of graphite atoms under neutron irradiation using large-scale atomic/molecular massively parallel simulator (LAMMPS) package with adaptive intermolecular reactive empirical bond order (AIREBOM) potential. Defect structures of graphite were compared with results from previous studies by means of density functional theory (DFT) calculations. The quantitative relation between primary knock-on atom (PKA) energy and irradiation damage on graphite was calculated. and the effect of PKA direction on the amount of defects is estimated by counting displaced atoms. Defects are classified into four groups: structural defects, energy defects, vacancies, and near-defect structures, where a structural defect is further subdivided into six types by decision tree method which is one of the supervised machine learning techniques.

• 한국전기전자재료학회논문지
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• 제35권5호
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• pp.419-430
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• 2022

Defects in solids play a vital role on thermoelectric properties through the direct impacts of electronic band structure and electron/phonon transports, which can improve the electronic and thermal properties of a given thermoelectric semiconductor. Defects in semiconductors can be divided into four different types depending on their geometric dimensions, and thus understanding the effects on thermoelectric properties of each type is of a vital importance. This paper reviews the recent advances in the various thermoelectric semiconductors through defect engineering focusing on the charge carrier and phonon behaviors. First, we clarify and summarize each type of defects in thermoelectric semiconductors. Then, we review the recent achievements in thermoelectric properties by applying defect engineering when introducing defects into semiconductor lattices. This paper ends with a brief discussion on the challenges and future directions of defect engineering in the thermoelectric field.

• 전기전자학회논문지
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• 제24권1호
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• pp.1-8
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• 2020

본 논문에서는 전자기 주기구조인 주파수 선택 구조를 레이돔에 적용하는 과정에서 발생하는 결함요소를 해석하기 위해 반-무한배열구조 해석방법을 제안하였다. 3D 전자기 해석 소프트웨어를 이용하여 N×N 유한배열 구조를 해석 하게 되면 많은 해석시간과 메모리가 소요되는 반면 제안한 반-무한배열구조 해석을 이용하면 적은 계산시간으로 전자기성능을 비교적 정확하게 예측할 수 있다. 주기구조해석과 반-무한배열구조의 해석 결과를 비교하여 ±3% 이내의 오차를 확인하였다. 주기적으로 배열된 패턴의 중심부에 발생한 결함요소에 대해 해석과 측정결과를 비교하였으며, 일부 해석과 측정결과의 오차는 존재하지만, 성능변화 지점에서 동일한 경향성과 관심대역에서 유사한 성능저하를 확인하였으며, 반-무한배열구조 해석을 이용하여 전자기 주기구조 내 발생하는 결함요소의 해석 가능성을 확인하였다.

• Advances in nano research
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• 제7권3호
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• pp.209-221
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• 2019

Graphene, with impressive electronic properties, have high potential in the microelectronic field. However, graphene itself is a zero bandgap material which is not suitable for digital logic gates and its application. Thus, much focus is on graphene nanoribbons (GNRs) that are narrow strips of graphene. During GNRs fabrication process, the occurrence of defects that ultimately change electronic properties of graphene is difficult to avoid. The modelling of GNRs with defects is crucial to study the non-idealities effects. In this work, nearest-neighbor tight-binding (TB) model for GNRs is presented with three main simplifying assumptions. They are utilization of basis function, Hamiltonian operator discretization and plane wave approximation. Two major edges of GNRs, armchair-edged GNRs (AGNRs) and zigzag-edged GNRs (ZGNRs) are explored. With single vacancy (SV) defects, the components within the Hamiltonian operator are transformed due to the disappearance of tight-binding energies around the missing carbon atoms in GNRs. The size of the lattices namely width and length are varied and studied. Non-equilibrium Green's function (NEGF) formalism is employed to obtain the electronics structure namely band structure and density of states (DOS) and all simulation is implemented in MATLAB. The band structure and DOS plot are then compared between pristine and defected GNRs under varying length and width of GNRs. It is revealed that there are clear distinctions between band structure, numerical DOS and Green's function DOS of pristine and defective GNRs.

• Steel and Composite Structures
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• 제34권2호
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• pp.261-277
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• 2020

The main objective of this research paper is to consider vibration analysis of vacancy defected graphene sheet as a nonisotropic structure via molecular dynamic and continuum approaches. The influence of structural defects on the vibration of graphene sheets is considered by applying the mechanical properties of defected graphene sheets. Molecular dynamic simulations have been performed to estimate the mechanical properties of graphene as a nonisotropic structure with single- and double- vacancy defects using open source well-known software i.e., large-scale atomic/molecular massively parallel simulator (LAMMPS). The interactions between the carbon atoms are modelled using Adaptive Intermolecular Reactive Empirical Bond Order (AIREBO) potential. An isogeometric analysis (IGA) based upon non-uniform rational B-spline (NURBS) is employed for approximation of single-layered graphene sheets deflection field and the governing equations are derived using nonlocal elasticity theory. The dependence of small-scale effects, chirality and different defect types on vibrational characteristic of graphene sheets is investigated in this comprehensive research work. In addition, numerical results are validated and compared with those achieved using other analysis, where an excellent agreement is found. The interesting results indicate that increasing the number of missing atoms can lead to decrease the natural frequencies of graphene sheets. It is seen that the degree of the detrimental effects differ with defect type. The Young's and shear modulus of the graphene with SV defects are much smaller than graphene with DV defects. It is also observed that Single Vacancy (SV) clusters cause more reduction in the natural frequencies of SLGS than Double Vacancy (DV) clusters. The effectiveness and the accuracy of the present IGA approach have been demonstrated and it is shown that the IGA is efficient, robust and accurate in terms of nanoplate problems.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 2002년도 추계학술발표회요약집
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• pp.271-271
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• 2002

• 건축구조
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• 제10권4호
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• pp.30-33
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• 2003

• 한국정보디스플레이학회:학술대회논문집
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• 한국정보디스플레이학회 2000년도 제1회 학술대회 논문집
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• pp.13-14
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• 2000

21.6" a-Si:H TFT array for direct conversion X-ray detector with 2480 by 3072 pixel is successfully developed. To obtain good X-ray image quality, novel structure, storage on BCB structure, is proposed. The structure reduces the parasitic capacitance of data line, one of the main sources of signal noise. Also, the structure shows higher failure resistance against defects than that of the old design.

• Smart Structures and Systems
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• 제24권6호
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• pp.803-812
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• 2019

Triplex composite is an epoxy-bonded joint structure, which constitutes the secondary barrier in a liquefied natural gas (LNG) carrier. Defects in the triplex composite weaken its shear strength and may cause leakage of the LNG, thus compromising the structural integrity of the LNG carrier. This paper proposes an autonomous triplex composite inspection (ATCI) system for visualizing and classifying hidden defects in the triplex composite installed inside an LNG carrier. First, heat energy is generated on the surface of the triplex composite using halogen lamps, and the corresponding heat response is measured by an infrared (IR) camera. Next, the region of interest (ROI) is traced and noise components are removed to minimize false indications of defects. After a defect is identified, it is classified as internal void or uncured adhesive and its size and shape are quantified and visualized, respectively. The proposed ATCI system allows the fully automated and contactless detection, classification, and quantification of hidden defects inside the triplex composite. The effectiveness of the proposed ATCI system is validated using the data obtained from actual triplex composite installed in an LNG carrier membrane system.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.42.2-42
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• 2002

We discuss a neural network solver for the inverse optimization problem. The problem is that find functional relations between input and output data, which are include defects. Finding the relations, predictions of the defect parts are also required. The part of finding the defects in the input data is an inverse problem . We consider the meanings to solve the problem on the neural network system at first. Next, we consider the network structure of the system, the learning scheme of the network, and at last, examine the precision on the numerical calculations. In the paper, we proposed the high-precision learning method for plural three-layer neural network system that is series-connect...