Hill Instability Analysis Of Tlp Tether Subjected To Combined Platform Surge And Heave Motions

This paper presents the Hill instability analysis of Tension Leg Platform (TLP) tether it, deep sea. The 2-D nonlinear beam model which is Undergoing Coupled axial and transverse vibrations, is applied. The governing equations are reduced to nonlinear Hill equation by use of the Galerkin's method and the modes superposition principle. The Hill instability charted Lip to large parameters is obtained. An important parameter M is defined and can he expressed as the functions of tether length, the platform surge and heave motion amplitudes. Some example studies are performed for various environmental conditions. The results demonstrate that the nonlinear coupling between the axial and transverse vibrations has a significant effect on the response of structure.. It needs to be considered for the accurate dynamic analysis of long TLP tether subjected to the combined platform surge and heave motions.

The motion of a small bubble in waves

The motion of a single spherical small bubble due to buoyancy in the ideal fluid with waves is investigated theoretically and experimentally in this article. Assuming that the bubble has no effect on the wave field, equations of a bubble motion are obtained and solved. It is found that the nonlinear effect increases with the increase of the bubble radius and the rising time. The rising time and the motion orbit are given by calculations and experiments. When the radius of a bubble is smaller than 0.5mm and the distance from the free surface is greater than the wave height, the results of the present theory are in close agreement with measurements.

Vortex motion in the early stages of unsteady flow around a circular cylinder

In this paper, processes in the early stages of vortex motion and the development of flow structure behind an impulsively-started circular cylinder at high Reynolds number are investigated by combining the discrete vortex model with boundary layer theory, considering the separation of incoming flow boundary layer and rear shear layer in the recirculating flow region. The development of flow structure and vortex motion, particularly the formation and development of secondary vortex and a pair of secondary vortices and their effect on the flow field are calculated. The results clearly show that the flow structure and vortices motion went through a series of complicated processes before the symmetric main vortices change into asymmetric: development of main vortices induces secondary vortices; growth of the secondary vortices causes the main vortex sheets to break off and causes the symmetric main vortices to become “free” vortices, while a pair of secondary vortices is formed; then the vortex sheets, after breaking off, gradually extend downstream and the structure of a pair of secondary vortices becomes relaxed. These features of vortex motion look very much like the observed features in some available flow field visualizations. The action of the secondary vortices causes the main vortex sheets to break off and converts the main vortices into free vortices. This should be the immediate cause leading to the instability of the motion of the symmetric main vortices. The flow field structure such as the separation position of boundary layer and rear shear layer, the unsteady pressure distributions and the drag coefficient are calculated. Comparison with other results or experiments is also made.

Synthetic molecular machines for active self-assembly : prototype algorithms, designs, and experimental study

Computer science and electrical engineering have been the great success story of the twentieth century. The neat modularity and mapping of a language onto circuits has led to robots on Mars, desktop computers and smartphones. But these devices are not yet able to do some of the things that life takes for granted: repair a scratch, reproduce, regenerate, or grow exponentially fast–all while remaining functional.

This thesis explores and develops algorithms, molecular implementations, and theoretical proofs in the context of “active self-assembly” of molecular systems. The long-term vision of active self-assembly is the theoretical and physical implementation of materials that are composed of reconfigurable units with the programmability and adaptability of biology’s numerous molecular machines. En route to this goal, we must first find a way to overcome the memory limitations of molecular systems, and to discover the limits of complexity that can be achieved with individual molecules.

One of the main thrusts in molecular programming is to use computer science as a tool for figuring out what can be achieved. While molecular systems that are Turing-complete have been demonstrated [Winfree, 1996], these systems still cannot achieve some of the feats biology has achieved.

One might think that because a system is Turing-complete, capable of computing “anything,” that it can do any arbitrary task. But while it can simulate any digital computational problem, there are many behaviors that are not “computations” in a classical sense, and cannot be directly implemented. Examples include exponential growth and molecular motion relative to a surface.

Passive self-assembly systems cannot implement these behaviors because (a) molecular motion relative to a surface requires a source of fuel that is external to the system, and (b) passive systems are too slow to assemble exponentially-fast-growing structures. We call these behaviors “energetically incomplete” programmable behaviors. This class of behaviors includes any behavior where a passive physical system simply does not have enough physical energy to perform the specified tasks in the requisite amount of time.

As we will demonstrate and prove, a sufficiently expressive implementation of an “active” molecular self-assembly approach can achieve these behaviors. Using an external source of fuel solves part of the the problem, so the system is not “energetically incomplete.” But the programmable system also needs to have sufficient expressive power to achieve the specified behaviors. Perhaps surprisingly, some of these systems do not even require Turing completeness to be sufficiently expressive.

Building on a large variety of work by other scientists in the fields of DNA nanotechnology, chemistry and reconfigurable robotics, this thesis introduces several research contributions in the context of active self-assembly.

We show that simple primitives such as insertion and deletion are able to generate complex and interesting results such as the growth of a linear polymer in logarithmic time and the ability of a linear polymer to treadmill. To this end we developed a formal model for active-self assembly that is directly implementable with DNA molecules. We show that this model is computationally equivalent to a machine capable of producing strings that are stronger than regular languages and, at most, as strong as context-free grammars. This is a great advance in the theory of active self- assembly as prior models were either entirely theoretical or only implementable in the context of macro-scale robotics.

We developed a chain reaction method for the autonomous exponential growth of a linear DNA polymer. Our method is based on the insertion of molecules into the assembly, which generates two new insertion sites for every initial one employed. The building of a line in logarithmic time is a first step toward building a shape in logarithmic time. We demonstrate the first construction of a synthetic linear polymer that grows exponentially fast via insertion. We show that monomer molecules are converted into the polymer in logarithmic time via spectrofluorimetry and gel electrophoresis experiments. We also demonstrate the division of these polymers via the addition of a single DNA complex that competes with the insertion mechanism. This shows the growth of a population of polymers in logarithmic time. We characterize the DNA insertion mechanism that we utilize in Chapter 4. We experimentally demonstrate that we can control the kinetics of this re- action over at least seven orders of magnitude, by programming the sequences of DNA that initiate the reaction.

In addition, we review co-authored work on programming molecular robots using prescriptive landscapes of DNA origami; this was the first microscopic demonstration of programming a molec- ular robot to walk on a 2-dimensional surface. We developed a snapshot method for imaging these random walking molecular robots and a CAPTCHA-like analysis method for difficult-to-interpret imaging data.

Acceleration Sensing, Feedback Cooling, and Nonlinear Dynamics with Nanoscale Cavity-Optomechanical Devices

Light has long been used for the precise measurement of moving bodies, but the burgeoning field of optomechanics is concerned with the interaction of light and matter in a regime where the typically weak radiation pressure force of light is able to push back on the moving object. This field began with the realization in the late 1960's that the momentum imparted by a recoiling photon on a mirror would place fundamental limits on the smallest measurable displacement of that mirror. This coupling between the frequency of light and the motion of a mechanical object does much more than simply add noise, however. It has been used to cool objects to their quantum ground state, demonstrate electromagnetically-induced-transparency, and modify the damping and spring constant of the resonator. Amazingly, these radiation pressure effects have now been demonstrated in systems ranging 18 orders of magnitude in mass (kg to fg).

In this work we will focus on three diverse experiments in three different optomechanical devices which span the fields of inertial sensors, closed-loop feedback, and nonlinear dynamics. The mechanical elements presented cover 6 orders of magnitude in mass (ng to fg), but they all employ nano-scale photonic crystals to trap light and resonantly enhance the light-matter interaction. In the first experiment we take advantage of the sub-femtometer displacement resolution of our photonic crystals to demonstrate a sensitive chip-scale optical accelerometer with a kHz-frequency mechanical resonator. This sensor has a noise density of approximately 10 micro-g/rt-Hz over a useable bandwidth of approximately 20 kHz and we demonstrate at least 50 dB of linear dynamic sensor range. We also discuss methods to further improve performance of this device by a factor of 10.

In the second experiment, we used a closed-loop measurement and feedback system to damp and cool a room-temperature MHz-frequency mechanical oscillator from a phonon occupation of 6.5 million down to just 66. At the time of the experiment, this represented a world-record result for the laser cooling of a macroscopic mechanical element without the aid of cryogenic pre-cooling. Furthermore, this closed-loop damping yields a high-resolution force sensor with a practical bandwidth of 200 kHZ and the method has applications to other optomechanical sensors.

The final experiment contains results from a GHz-frequency mechanical resonator in a regime where the nonlinearity of the radiation-pressure interaction dominates the system dynamics. In this device we show self-oscillations of the mechanical element that are driven by multi-photon-phonon scattering. Control of the system allows us to initialize the mechanical oscillator into a stable high-amplitude attractor which would otherwise be inaccessible. To provide context, we begin this work by first presenting an intuitive overview of optomechanical systems and then providing an extended discussion of the principles underlying the design and fabrication of our optomechanical devices.

Photoelectrochemistry of GaAs and Si liquid junctions

A quantitative study has been performed on the stability of GaAs surfaces in a 0.10 M K₂Se-0.01 M K₂Se₂ aqueous solution. In this electrolyte, n-type GaAs electrodes displayed significant photocorrosion in competition with faradaic charge transfer to Se^2-. Chemisorption of group VIIIB metal ions onto the GaAs surfaces yielded improved current-voltage behavior of the GaAs photoanodes, and also resulted in a significant reduction in photocorrosion. This behavior implies that the chemisorbed metal ions act to increase the rate of hole transfer to the Se^2- species. Related experiments on n-GaAs, pGaAs, and Sn-doped In₂O₃ electrodes in Te^2-/- aqueous solutions have also been performed.

The majority carrier (electrons) transfer rate constant at a highly doped n⁺-Si/Co(Cp)₂Cl-methanol junction has been measured directly using the chronoamperometry electrochemical technique. The reduction reaction rate of Co(Cp)₂+ was 0.03 cm-s^-1 at the Si electrode, and was more than 100 times slower than at a hanging mercury electrode. The slower rate was attributed to the smaller optical and static dielectric constants, and the lower density of electrons of the semiconductor. The experimental results were compared to the Marcus theory of charge transfer.

The unique properties of high purity Si/liquid junctions have been investigated under illumination conditions in which the photogenerated carrier concentration exceeds the dopant concentration. Under these high injection conditions, negligible electric fields exist at the semiconductor/liquid interface, and carrier motion is driven by diffusion. Studies of the current-voltage properties of the Si in methanol solutions containing various redox couples suggested that high efficiency photoelectrochemical cells could be established through selective collection of carriers at the semiconductor/liquid junction. The quasi-Fermi levels of electrons and holes were measured directly against the solution potential. Steady-state and transient photovoltage measurements, and theoretical modeliug of the carrier transport, generation, and recombination dynamics indicated that the quasi-Fermi levels were flat across the semiconductor sample. The recombination velocities at the Si/liquid junctions have also been measured, and were shown to vary with the solution potential following the Shockley-Read-Hall theory on recombination.

Modelling and simulation of the second-order Doppler error of a laser dual-frequency interferometer

Only the first- order Doppler frequency shift is considered in current laser dual- frequency interferometers; however; the second- order Doppler frequency shift should be considered when the measurement corner cube ( MCC) moves at high velocity or variable velocity because it can cause considerable error. The influence of the second- order Doppler frequency shift on interferometer error is studied in this paper, and a model of the second- order Doppler error is put forward. Moreover, the model has been simulated with both high velocity and variable velocity motion. The simulated results show that the second- order Doppler error is proportional to the velocity of the MCC when it moves with uniform motion and the measured displacement is certain. When the MCC moves with variable motion, the second- order Doppler error concerns not only velocity but also acceleration. When muzzle velocity is zero the second- order Doppler error caused by an acceleration of 0.6g can be up to 2.5 nm in 0.4 s, which is not negligible in nanometric measurement. Moreover, when the muzzle velocity is nonzero, the accelerated motion may result in a greater error and decelerated motion may result in a smaller error.

Perspectives on the origin of microfilaments, microtubules, the relevant chaperonin system and cytoskeletal motors - a commentary on the spirochaete origin of flagella

The origin of cytoskeleton and the origin of relevant intracellular transportation system are big problems for understanding the emergence of eukaryotic cells. The present article summarized relevant information of evidences and molecular traces on the origin of actin, tubulin, the chaperonin system for folding them, myosins, kinesins, axonemal dyneins and cytoplasmic dyneins. On this basis the authors proposed a series of works, which should be done in the future, and indicated the ways for reaching the targets. These targets are mainly: 1) the reconstruction of evolutionary path from MreB protein of archaeal ancestor of eukaryotic cells to typical actin; 2) the finding of the MreB or MreB-related proteins in crenarchaea and using them to examine J. A. Lake's hypothesis on the origin of eukaryote from "eocytes" (crenarchaea); 3) the examinations of the existence and distribution of cytoskeleton made of MreB-related protein within coccoid archaea, especially in amoeboid archaeon Thermoplasm acidophilum; 4) using Thermoplasma as a model of archaeal ancestor of eukaryotic cells; 5) the searching for the homolog of ancestral dynein in present-day living archaea. During the writing of this article, Margulis' famous spirochaete hypothesis on the origin of flagella and cilia was unexpectedly involved and analyzed from aspects of tubulins, dyneins and spirochaetes. Actually, spirochaete cannot be reasonably assumed as the ectosymbiotic ancestor of eukaryotic flagella and cilia, since their swing depends upon large amount of bacterial flagella beneath the flexible outer wall, but not depends upon their intracellular tubules and the assumed dyneins. In this case, if they had "evolved" into cilia and lost their bacterial flagella, they would immediately become immobile! In fact, tubulin and dynein-like proteins have not been found in any spirochaete.

Three-dimensional effects on sliding and waving wings

Like large insects, micro air vehicles operate at low Reynolds numbers O(1; 000 - 10; 000) in a regime characterized by separated flow and strong vortices. The leading-edge vortex has been identified as a significant source of high lift on insect wings, but the conditions required for the formation of a stably attached leading-edge vortex are not yet known. The waving wing is designed to model the translational phase of an insect wing stroke by preserving the unsteady starting and stopping motion as well as three-dimensionality in both wing geometry (via a finite-span wing) and kinematics (via wing rotation). The current study examines the effect of the spanwise velocity gradient on the development of the leading-edge vortex along the wing as well as the effects of increasing threedimensionalityby decreasing wing aspect ratio from four to two. Dye flow visualization and particle image velocimetry reveal that the leading-edge vortices that form on a sliding or waving wing have a very high aspect ratio. The structure of the flow is largely two-dimensional on both sliding and waving wings and there is minimal interaction between the leading-edge vortices and the tip vortex. Significant spanwise flow was observed on the waving wing but not on the sliding wing. Despite the increased three-dimensionality on the aspect ratio 2 waving wing, there is no evidence of an attached leading-edge vortex and the structure of the flow is very similar to that on the higher-aspect-ratio wing and sliding wing. © Copyright 2010.

Achieving robust face recognition from video by combining a weak photometric model and a learnt generic face invariant

In spite of over two decades of intense research, illumination and pose invariance remain prohibitively challenging aspects of face recognition for most practical applications. The objective of this work is to recognize faces using video sequences both for training and recognition input, in a realistic, unconstrained setup in which lighting, pose and user motion pattern have a wide variability and face images are of low resolution. The central contribution is an illumination invariant, which we show to be suitable for recognition from video of loosely constrained head motion. In particular there are three contributions: (i) we show how a photometric model of image formation can be combined with a statistical model of generic face appearance variation to exploit the proposed invariant and generalize in the presence of extreme illumination changes; (ii) we introduce a video sequence re-illumination algorithm to achieve fine alignment of two video sequences; and (iii) we use the smoothness of geodesically local appearance manifold structure and a robust same-identity likelihood to achieve robustness to unseen head poses. We describe a fully automatic recognition system based on the proposed method and an extensive evaluation on 323 individuals and 1474 video sequences with extreme illumination, pose and head motion variation. Our system consistently achieved a nearly perfect recognition rate (over 99.7% on all four databases). © 2012 Elsevier Ltd All rights reserved.

Simulations to design an online motion compensation system for scanned particle beams

Respiration-induced target motion is a major problem in intensity-modulated radiation therapy. Beam segments are delivered serially to form the total dose distribution. In the presence of motion, the spatial relation between dose deposition from different segments will be lost. Usually, this results in over-and underdosage. Besides such interplay effects between target motion and dynamic beam delivery as known from photon therapy, changes in internal density have an impact on delivered dose for intensity-modulated charged particle therapy. In this study, we have analysed interplay effects between raster scanned carbon ion beams and target motion. Furthermore, the potential of an online motion strategy was assessed in several simulations. An extended version of the clinical treatment planning software was used to calculate dose distributions to moving targets with and without motion compensation. For motion compensation, each individual ion pencil beam tracked the planned target position in the lateral aswell as longitudinal direction. Target translations and rotations, including changes in internal density, were simulated. Target motion simulating breathing resulted in severe degradation of delivered dose distributions. For example, for motion amplitudes of +/- 15 mm, only 47% of the target volume received 80% of the planned dose. Unpredictability of resulting dose distributions was demonstrated by varying motion parameters. On the other hand, motion compensation allowed for dose distributions for moving targets comparable to those for static targets. Even limited compensation precision (standard deviation similar to 2 mm), introduced to simulate possible limitations of real-time target tracking, resulted in less than 3% loss in dose homogeneity.

A HIGH-RESOLUTION SOLID-STATE NMR AND DSC STUDY OF MISCIBILITY AND CRYSTALLIZATION BEHAVIOR OF POLY(VINYL ALCOHOL)/POLY(N-VINYL-2-PYRROLIDONE) BLENDS

Blends of crystallizable poly(vinyl alcohol) (PVA) with poly(N-vinyl-2-pyrrolidone) (PVPy) were studied by C-13 cross-polarization/magic angle spinning (CP/MAS) n.m.r. and d.s.c. The C-13 CP/MAS spectra show that the blends were miscible on a molecular level over the whole composition range studied, and that the intramolecular hydrogen bonds of PVA were broken and intermolecular hydrogen bonds between PVA and PVPy formed when the two polymers were mixed. The results of a spin-lattice relaxation study indicate that blending of the two polymers reduced the average intermolecular distance and molecular motion of each component, even in the miscible amorphous phase, and that addition of PVPy into PVA has a definite effect on the crystallinity of PVA in the blends over the whole composition range, yet there is still detectable crystallinity even when the PVPy content is as high as 80 wt%. These results are consistent with those obtained from d.s.c. studies.

Visual Navigation: Constructing and Utilizing Simple Maps of an Indoor Environment

The goal of this work is to navigate through an office environmentsusing only visual information gathered from four cameras placed onboard a mobile robot. The method is insensitive to physical changes within the room it is inspecting, such as moving objects. Forward and rotational motion vision are used to find doors and rooms, and these can be used to build topological maps. The map is built without the use of odometry or trajectory integration. The long term goal of the project described here is for the robot to build simple maps of its environment and to localize itself within this framework.

Simultaneous Learning of Nonlinear Manifold and Dynamical Models for High-dimensional Time Series

The goal of this work is to learn a parsimonious and informative representation for high-dimensional time series. Conceptually, this comprises two distinct yet tightly coupled tasks: learning a low-dimensional manifold and modeling the dynamical process. These two tasks have a complementary relationship as the temporal constraints provide valuable neighborhood information for dimensionality reduction and conversely, the low-dimensional space allows dynamics to be learnt efficiently. Solving these two tasks simultaneously allows important information to be exchanged mutually. If nonlinear models are required to capture the rich complexity of time series, then the learning problem becomes harder as the nonlinearities in both tasks are coupled. The proposed solution approximates the nonlinear manifold and dynamics using piecewise linear models. The interactions among the linear models are captured in a graphical model. By exploiting the model structure, efficient inference and learning algorithms are obtained without oversimplifying the model of the underlying dynamical process. Evaluation of the proposed framework with competing approaches is conducted in three sets of experiments: dimensionality reduction and reconstruction using synthetic time series, video synthesis using a dynamic texture database, and human motion synthesis, classification and tracking on a benchmark data set. In all experiments, the proposed approach provides superior performance.

Simultaneous Learning of Non-Linear Manifold and Dynamical Models for High-Dimensional Time Series

The goal of this work is to learn a parsimonious and informative representation for high-dimensional time series. Conceptually, this comprises two distinct yet tightly coupled tasks: learning a low-dimensional manifold and modeling the dynamical process. These two tasks have a complementary relationship as the temporal constraints provide valuable neighborhood information for dimensionality reduction and conversely, the low-dimensional space allows dynamics to be learnt efficiently. Solving these two tasks simultaneously allows important information to be exchanged mutually. If nonlinear models are required to capture the rich complexity of time series, then the learning problem becomes harder as the nonlinearities in both tasks are coupled. The proposed solution approximates the nonlinear manifold and dynamics using piecewise linear models. The interactions among the linear models are captured in a graphical model. The model structure setup and parameter learning are done using a variational Bayesian approach, which enables automatic Bayesian model structure selection, hence solving the problem of over-fitting. By exploiting the model structure, efficient inference and learning algorithms are obtained without oversimplifying the model of the underlying dynamical process. Evaluation of the proposed framework with competing approaches is conducted in three sets of experiments: dimensionality reduction and reconstruction using synthetic time series, video synthesis using a dynamic texture database, and human motion synthesis, classification and tracking on a benchmark data set. In all experiments, the proposed approach provides superior performance.