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Search Results: 1 - 10 of 134684 matches for " Wong V "
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Nanomechanics of Nonideal Single- and Double-Walled Carbon Nanotubes
C. H. Wong,V. Vijayaraghavan
Journal of Nanomaterials , 2012, DOI: 10.1155/2012/490872
Abstract: The buckling characteristics of nonideal single- and double-walled carbon nanotubes were studied in this work via molecular dynamics simulation method. An imperfectly straight nonideal single-walled carbon nanotube (SWCNT) with a bent along the tube axis was used to form an array which is subjected to compression. The change in orientation of bends will result in a variation of nonbonded interactions in an SWCNT array system. We find that these variations in the nonbonded interactions strongly affect the buckling resistance of the SWCNT array. Similarly, a nonideal double-walled carbon nanotube (DWCNT) is constructed by varying the interlayer distance by introducing a center offset on the inner core SWCNT. The inclusion of offset along the tube axis in such nonideal DWCNT can enhance or deteriorate the mechanical qualities of the DWCNT under compression. Our numerical studies on nonideal CNT systems suggest a possibility of designing high-performing CNTs for applications involving fiber reinforcements.
A Review of Additive Manufacturing
Kaufui V. Wong,Aldo Hernandez
ISRN Mechanical Engineering , 2012, DOI: 10.5402/2012/208760
A Review of Additive Manufacturing
Kaufui V. Wong,Aldo Hernandez
ISRN Mechanical Engineering , 2012, DOI: 10.5402/2012/208760
Abstract: Additive manufacturing processes take the information from a computer-aided design (CAD) file that is later converted to a stereolithography (STL) file. In this process, the drawing made in the CAD software is approximated by triangles and sliced containing the information of each layer that is going to be printed. There is a discussion of the relevant additive manufacturing processes and their applications. The aerospace industry employs them because of the possibility of manufacturing lighter structures to reduce weight. Additive manufacturing is transforming the practice of medicine and making work easier for architects. In 2004, the Society of Manufacturing Engineers did a classification of the various technologies and there are at least four additional significant technologies in 2012. Studies are reviewed which were about the strength of products made in additive manufacturing processes. However, there is still a lot of work and research to be accomplished before additive manufacturing technologies become standard in the manufacturing industry because not every commonly used manufacturing material can be handled. The accuracy needs improvement to eliminate the necessity of a finishing process. The continuous and increasing growth experienced since the early days and the successful results up to the present time allow for optimism that additive manufacturing has a significant place in the future of manufacturing. 1. Rapid Prototyping The first form of creating layer by layer a three-dimensional object using computer-aided design (CAD) was rapid prototyping, developed in the 1980’s for creating models and prototype parts. This technology was created to help the realization of what engineers have in mind. Rapid prototyping is one of the earlier additive manufacturing (AM) processes. It allows for the creation of printed parts, not just models. Among the major advances that this process presented to product development are the time and cost reduction, human interaction, and consequently the product development cycle [1], also the possibility to create almost any shape that could be very difficult to machine. However, at the present time it is not yet adopted in the manufacturing sector, but scientists, medical doctors, students and professors, market researchers, and artists use it [2–4]. With rapid prototyping, scientists and students can rapidly build and analyze models for theoretical comprehension and studies. Doctors can build a model of a damaged body to analyze it and plan better the procedure, market researchers can see what people think of a
Heat Transfer Mechanisms and Clustering in Nanofluids
Kaufui V. Wong,Michael J. Castillo
Advances in Mechanical Engineering , 2010, DOI: 10.1155/2010/795478
Abstract: This paper surveys heat transfer in nanofluids. It summarizes and analyzes the theories regarding heat transfer mechanisms in nanofluids, and it discusses the effects of clustering on thermal conductivity. The heat transfer associated with conduction is presented through various experiments followed by a discussion of the theories developed. Relationships between thermal conductivity and various factors such as temperature, concentration, and particle size are also displayed along with a discussion on clustering. There is a brief discussion on convection where the number of studies is limited. There is research currently being performed on the manipulation of the properties governing the thermal conductivity of nanofluids—the particle size, shape, and surface area. Other factors that affect heat transfer are the material of the nanoparticle, particle volume concentration, and the fluid used. Although the interest in this relatively new class of fluids has generated many experimental studies, there is still disagreement over several aspects of heat transfer in nanofluids, primarily concerning the mechanisms behind the increased thermal conductivity. Although nanoparticles have greatly decreased the risks, there is still evidence of unwanted agglomeration which causes erosion and affect the overall conductivity. Research is currently being conducted to determine how to minimize this unwanted clustering. 1. Introduction The growth of technology found in high-tech industries, such as microelectronics, transportation, and manufacturing, has created a cornucopia of ideas that would have wide ranging effects on many obstacles facing today’s scientific world including energy efficiency, pollution, and reusability. However, there are many factors hindering further development in these industries, one being the ability to rapidly cool the products being used. Cooling is necessary for maintaining the operational performance and reliability of new products, and as a result of increased heat loads and heat fluxes caused by the increase in power and decrease in feature sizes present in new products, the demand for a more efficient cooling process has increased dramatically in the last decade. Consequently, more companies are beginning to invest more capital into the research of more efficient heat transfer processes. The conventional method for enhancing heat transfer in a thermal system consists of increasing the heat transfer surface area as well as the flow velocity of the working fluid [1]. The dispersion of solid nanoparticles in heat transfer fluids is a
Applications of Nanofluids: Current and Future
Kaufui V. Wong,Omar De Leon
Advances in Mechanical Engineering , 2010, DOI: 10.1155/2010/519659
Abstract: Nanofluids are suspensions of nanoparticles in fluids that show significant enhancement of their properties at modest nanoparticle concentrations. Many of the publications on nanofluids are about understanding their behavior so that they can be utilized where straight heat transfer enhancement is paramount as in many industrial applications, nuclear reactors, transportation, electronics as well as biomedicine and food. Nanofluid as a smart fluid, where heat transfer can be reduced or enhanced at will, has also been reported. This paper focuses on presenting the broad range of current and future applications that involve nanofluids, emphasizing their improved heat transfer properties that are controllable and the specific characteristics that these nanofluids possess that make them suitable for such applications. 1. Introduction Nanofluids are dilute liquid suspensions of nanoparticles with at least one of their principal dimensions smaller than 100?nm. From previous investigations, nanofluids have been found to possess enhanced thermophysical properties such as thermal conductivity, thermal diffusivity, viscosity and convective heat transfer coefficients compared to those of base fluids like oil or water [1–6] From the current review, it can be seen that nanofluids clearly exhibit enhanced thermal conductivity, which goes up with increasing volumetric fraction of nanoparticles. The current review does concentrate on this relatively new class of fluids and not on colloids which are nanofluids because the latter have been used for a long time. Review of experimental studies clearly showed a lack of consistency in the reported results of different research groups regarding thermal properties [7, 8]. The effects of several important factors such as particle size and shapes, clustering of particles, temperature of the fluid, and dissociation of surfactant on the effective thermal conductivity of nanofluids have not been studied adequately. It is important to do more research so as to ascertain the effects of these factors on the thermal conductivity of wide range of nanofluids. Classical models cannot be used to explain adequately the observed enhanced thermal conductivity of nanofluids. Recently most developed models only include one or two postulated mechanisms of nanofluids heat transfer. For instance, there has not been much fundamental work reported on the determination of the effective thermal diffusivity of nanofluids nor heat transfer coefficients for nanofluids in natural convection [9]. There is a growth is the use of colloids which are nanofluids
Tension and Robustness in Multitasking Cellular Networks
Jeffrey V. Wong,Bochong Li,Lingchong You
PLOS Computational Biology , 2012, DOI: 10.1371/journal.pcbi.1002491
Abstract: Cellular networks multitask by exhibiting distinct, context-dependent dynamics. However, network states (parameters) that generate a particular dynamic are often sub-optimal for others, defining a source of “tension” between them. Though multitasking is pervasive, it is not clear where tension arises, what consequences it has, and how it is resolved. We developed a generic computational framework to examine the source and consequences of tension between pairs of dynamics exhibited by the well-studied RB-E2F switch regulating cell cycle entry. We found that tension arose from task-dependent shifts in parameters associated with network modules. Although parameter sets common to distinct dynamics did exist, tension reduced both their accessibility and resilience to perturbation, indicating a trade-off between “one-size-fits-all” solutions and robustness. With high tension, robustness can be preserved by dynamic shifting of modules, enabling the network to toggle between tasks, and by increasing network complexity, in this case by gene duplication. We propose that tension is a general constraint on the architecture and operation of multitasking biological networks. To this end, our work provides a framework to quantify the extent of tension between any network dynamics and how it affects network robustness. Such analysis would suggest new ways to interfere with network elements to elucidate the design principles of cellular networks.
Work functions, ionization potentials, and in-between: Scaling relations based on the image charge model
Kin Wong,Sascha Vongehr,Vitaly V. Kresin
Physics , 2002, DOI: 10.1103/PhysRevB.67.035406
Abstract: We revisit a model in which the ionization energy of a metal particle is associated with the work done by the image charge force in moving the electron from infinity to a small cut-off distance just outside the surface. We show that this model can be compactly, and productively, employed to study the size dependence of electron removal energies over the range encompassing bulk surfaces, finite clusters, and individual atoms. It accounts in a straightforward manner for the empirically known correlation between the atomic ionization potential (IP) and the metal work function (WF), IP/WF$\sim$2. We formulate simple expressions for the model parameters, requiring only a single property (the atomic polarizability or the nearest neighbor distance) as input. Without any additional adjustable parameters, the model yields both the IP and the WF within $\sim$10% for all metallic elements, as well as matches the size evolution of the ionization potentials of finite metal clusters for a large fraction of the experimental data. The parametrization takes advantage of a remarkably constant numerical correlation between the nearest-neighbor distance in a crystal, the cube root of the atomic polarizability, and the image force cutoff length. The paper also includes an analytical derivation of the relation of the outer radius of a cluster of close-packed spheres to its geometric structure.
The Compactification of QCD$_4$ to QCD$_2$ in a Flux Tube
Andrew V. Koshelkin,Cheuk-Yin Wong
Physics , 2012, DOI: 10.1103/PhysRevD.86.125026
Abstract: We show from the action integral that in the special environment of a flux tube, QCD$_4$ in (3+1) dimensional space-time can be approximately compactified into QCD$_2$ in (1+1) dimensional space-time. In such a process, we find out how the coupling constant $g_{2D}$ in QCD$_2$ is related to the coupling constant $g_{4D}$ in QCD$_4$. We show how the quark and the gluon in QCD$_2$ acquire contributions to their masses arising from their confinement within the tube, and how all these quantities depend on the excitation of the partons in the transverse degrees of freedom. The compactification facilitates the investigation of some dynamical problems in QCD$_4$ in the simpler dynamics of QCD$_2$ where the variation of the gluon fields leads to a bound state.
2D Gauge Field Theory
Andrey V. Koshelkin,Cheuk-Yin Wong
Physics , 2012,
Abstract: We show from the action integral that under the assumption of longitudinal dominance and transverse confinement, QCD4 in (3+1) dimensional space-time can be approximately compactified into QCD2 in (1+1) dimensional space-time. In such a process, we find the relation between the coupling constant g(2D) in QCD2 and the coupling constant $g(4D)$ in QCD4. We also show that quarks and gluons in QCD2 acquire masses as a result of the compactification.
A Novel Operational Partition between Neural Network Classifiers on Vulnerability to Data Mining Bias  [PDF]
Charles Wong
Journal of Software Engineering and Applications (JSEA) , 2014, DOI: 10.4236/jsea.2014.74027

It is difficult if not impossible to appropriately and effectively select from among the vast pool of existing neural network machine learning predictive models for industrial incorporation or academic research exploration and enhancement. When all models outperform all the others under disparate circumstances, none of the models do. Selecting the ideal model becomes a matter of ill-supported opinion ungrounded on the extant real world environment. This paper proposes a novel grouping of the model pool grounded along a non-stationary real world data line into two groups: Permanent Data Learning and Reversible Data Learning. This paper further proposes a novel approach towards qualitatively and quantitatively demonstrating their significant differences based on how they alternatively approach dynamic and raw real world data vs static and prescient data mining biased laboratory data. The results across 2040 separate simulation runs using 15,600 data points in realistically operationally controlled data environments show that the two-group division is effective and significant with clear qualitative, quantitative and theoretical support. Results across the empirical and theoretical spectrum are internally and externally consistent yet demonstrative of why and how this result is non-obvious.

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