An augmented method for free boundary problems with moving contact lines

An augmented immersed interface method (IIM) is proposed for simulating one-phase moving contact line problems in which a liquid drop spreads or recoils on a solid substrate. While the present two-dimensional mathematical model is a free boundary problem, in our new numerical method, the fluid domain enclosed by the free boundary is embedded into a rectangular one so that the problem can be solved by a regular Cartesian grid method. We introduce an augmented variable along the free boundary so that the stress balancing boundary condition is satisfied. A hybrid time discretization is used in the projection method for better stability. The resultant Helmholtz/Poisson equations with interfaces then are solved by the IIM in an efficient way. Several numerical tests including an accuracy check, and the spreading and recoiling processes of a liquid drop are presented in detail. (C) 2010 Elsevier Ltd. All rights reserved.

Heat Transfer Problems Induced By Multi-shocks Interaction

Numerical simulations of the multi-shock interactions observable around hypersonic vehicles were carried out by solving Navier-Stokes equations with the AUSMPW scheme and the new type of the IV interaction created by two incident shock waves was investigated in detail. Numerical results show that the intersection point of the second incident shock with the bow shock plays important role on the flow pattern, peak pressures and heat fluxes. In the case of two incident shocks interacting with the bow shock at the same position, the much higher peak pressure and more severe heat transfer rate are induced than the classical IV interaction. The phenomenon is referred to as the multi-shock interaction and higher requirements will be imposed on thermal protection systems.

环境CO2浓度和群落密度对红桦(Betula albosinensis Burk.)幼苗碳氮吸收和分配的影响

当前大气CO2浓度升高是全球变化的主要趋势之一，CO2浓度升高还会引起全球变暖等其它环境问题，因而CO2浓度浓度升高对植物影响的研究已经成为全球变化领域的焦点。红桦是川西亚高山地区暗针叶林演替初期的先锋树种和演替后期的建群种，在群落演替过程中它对环境因子的响应决定红桦群落的演替进程。本文通过控制CO2浓度的气候室试验，研究了CO2浓度倍增环境下，不同密度水平红桦碳氮固定、分配可能发生的改变，并探讨了升高大气CO2浓度对群体内部竞争的影响。以期通过本研究明确川西亚高山地区代表性物种红桦对未来气候变化的响应，为今后采取措施应对气候变化、妥善进行森林管理提供理论依据和科学指导。主要研究结果如下： 1.升高CO2浓度对红桦幼苗生长的影响以及树皮、树干响应的不同 (1) CO2浓度升高显著促进红桦幼苗的生物量、株高、基茎的生长，同时也改变生物量在体内的分配格局，主要是增加根和主茎、减少叶在总生物量中的比重。(2)树皮和树干对升高CO2浓度的影响有差异，它们对CO2浓度升高的反应程度不同，但反应方向一致。 2.密度的副效应 (1) 增加种植密度对单株生物量、株高和基径的生长具有副效应，也降低升高CO2浓度对红桦生长的正效应。(2) 增加种植密度，显著增加红桦幼苗的群体生物量，从而使红桦群体固定更多的大气CO2气体。可见密度在决定红桦生物量及固碳能力方面具有重要意义。探索适合未来大气CO2浓度升高条件下植物生长的密度，对未来的森林经济生产、生态恢复具有重要意义。 3. 升高CO2浓度对红桦幼苗苗冠结构及冠层内部竞争的影响 (1) 冠幅、冠高、苗冠表面积和苗冠体积等树冠特征均受CO2浓度升高的影响而增加，但是受密度增加的影响而降低。(2) 单位苗冠投影面积叶片数(LDcpa)和单位苗冠体积叶片数(LDcv)均低于相应的现行CO2浓度处理，这主要是由于冠幅和冠高的快速生长所造成的。(3) LDcpa和LDcv的降低表明，红桦在升高CO2浓度的条件下，会作出积极的响应，从而缓解由于群体和个体生长的增加所引起的竞争压力的增加。 4. 升高CO2浓度对红桦幼苗养分元素吸收与分配的影响 (1) CO2浓度升高，植株各器官N、P含量降低，但单株N、P总吸收量均增加。红桦幼苗体内N、P浓度的下降是由于生物量迅速增加引起的稀释效应造成的。(2) CO2浓度升高，N、P向主茎和根的分配增加，向叶片的分配减少，主要是由于前者在总生物量中的比重增加，而后者减少了。(3) CO2浓度升高，氮磷利用效率(NUE和PUE)提高，氮磷累积速率(NAcR和PAcR)显著增加。而NUE和PUE的提高可以有效缓解CO2浓度升高后，亚高山和高山地区森林土壤中养分元素不足对森林生产力的限制。 5. 升高CO2浓度对红桦幼苗群体碳平衡的影响 (1) 升高CO2浓度对植物的光合作用、呼吸速率和生长均具有促进作用。(2) 土壤有机碳含量在实验前期迅速增加，后期积累速率下降。(3) 升高CO2浓度以后，土壤呼吸显著增强；土壤呼吸还具有明显的季节变化。(4) 红桦群体日固碳量受到升高CO2浓度的促进作用。结果(1)-(4)说明所研究群落的碳动态对现行的气候波动是敏感的；所研究群落在作为大气CO2气体的源-汇关系方面至少存在季节间的源汇飘移。(5)种植密度的升高显著增加了群体固碳量。 6. 升高CO2浓度对红桦幼苗生长后期叶片衰老的影响 升高CO2浓度有利于减缓红桦幼苗叶片生长季节末期的衰老。生长季节末期，随着CO2浓度的升高光合速率和可溶性蛋白含量均呈上升趋势，同时MDA（丙二醛）含量下降，保护酶SOD（超氧化物岐化酶）、CAT（过氧化氢酶）活性升高。由此说明，升高CO2浓度有利于减缓生长季节后期叶片的衰老，使叶片维持较高的光合速率，也从生理学的角度支持了本文及前人有关CO2浓度升高促进植物光合和生长的假说及结果。 The increased CO2 concentration is one of the most important problems among global changes. The increase of CO2 will also cause other environmental problems, such as global warming, etc. So the effects of elevated CO2 on plant have drawn sights of many scientists in the research field of global change. Red birch (Betula albosinensis) usually emerges as the pioneer species in initial stage and as constructive species in later stages of forest community succession of the dark coniferous forests in Western Sichuan, China. It’s response to elevated CO2 may determine the succession process of the community where it lives in. By controlling CO2 at the ambient and twice as the ambient level (ambient + 350 umol mol-1) using enclosed-top chambers (ETC), possible effects of elevated CO2 on carbon fixation and allocation under two plantation densities are investigated. The effects of elevated CO2 on competition within canopy of red birch seedlings are also observed in the present paper. We hope to make sure of the effects of elevated CO2 on the representative species, red birch. And so that, our results could provide a strong theoretical evidence and scientific direction for forest management and afforestation under a future, CO2 elevated world. The results are as fowllows: 1. The effects of elevated CO2 on growth and the different responses of wood and bark of red birch seedlings (1) Elevated CO2 increases the growth of seedling biomass, seedling height and basal diameter of red birch. It also changed the biomass allocation in red birch seedlings. The ratio of root and main stem to all biomass is increased and the ratio of leaf is decreased. (2) Tree bark and wood show different response degree but similar response direction to elevated CO2. 2. Negative effects of planting density (1) The increase of planting density showes negative effects on the individual growth of seedling biomass, seedling height and basal diameter of red birch. It also eliminates the positive effects of elevated CO2 on growth of red birch seedlings. (2) Community biomass is increased by the elevated planting density, which means that the high density red birch community could fix more CO2 than the low density one. These results show that planting density plays an important role in determining biomass and carbon fixation ability of red birch community. Thus, exploring proper planting density becomes economically important for the future, CO2 elevated word. 3. The effects of elevated CO2 on crown architecture and competition within canopy of red birch seedlings (1) Crown width, crown depth, crown surface area and crown volume are all increased under the influence of elevated CO2. (2) Leaf number per unit area of projected crown area (LDcpa) and per unit volume of crown volume (LDcv) are lower under elevated CO2. This is resulted from the stimulated growth of tree crown features. (3) The decrease of LDcpa and LDcv indicate that plants will respond forwardly to reduce the possible increase of competition resulted from stimulated growth of individual plant and collectives in conditions of elevated CO2. 4. The effects of elevated CO2 on nutrition accumulation and allocation of red birch seedlings (1) Contents of N and P decrease due to the prompt increase of biomass of plant organs caused by elevated CO2. However, their accumulations increase under elevated CO2. (2) Elevated CO2 increases the allocation of N, P to main stem but reduced its allocation to leaf for that dry weight of the former increased but the dry weight of the later decreased. (3) Using efficiencies of N, P (NUE and PUE) and their accumulation rates (NAcR and PAcR) are found to increase under elevated CO2. Soil nutrition contents are always the limiting factors for plant growth at subalpine and alpine region. The increased NUE and PUE are helpful to eliminate the nutrition limitation in this area in the future world, when CO2 concentration doubles the ambient. 5. The effects of elevated CO2 on carbon balance of red birch communities (1) Net photosynthetic rates (Pn), dark respiration rates (Rd) and growth are all stimulated by elevated CO2. (2) Content soil organic carbon increases sharply at the primary stage of experiments and then the increasing rates decrease to a low level at later stages. (3) Soil respiration rates increase significantly with the elevation of CO2 concentration. (4) The daily carbon fixations of whole community are heightened by elevated CO2. The results (1)-(4) suggest that, the community being studied are sensitive to current climate change; the studied community, as a sink of atmospheric CO2, is pool-sink alternative between seasons. (5) The carbon fixations are increased along the increase of planting densities. 6. The effects of elevated CO2 on physiological features of leaf senescences of red birch seedlings at the later stage of growing season Elevated CO2 helps to postpone the leaf senescences of red birch at the end of the growth season. CO2 enrichment increases the photosynthetic rates, contents of soluble proteins and photosynthetic pigments. And meanwhile contents of malondialdehyde (MDA) decreases and activities of superoxide dismutase (SOD) and catalase (CAT) are both increased. These results suggest that the senescences of red birch leaves are delayed by elevated CO2, which keep the photosynthetic rates at relatively high levels. Our results lend supports to hypothesis and results on stimulated photosynthetic rates and growth from both other researchers and the present paper.

solving global unconstrained optimization problems by symmetry-breaking

IEEE Computer Society; International Association for; Computer and Information Science, ACIS

a computational proof of complexity of some restricted counting problems

South Central University

A HYBRID METHOD OF FRACTIONAL STEPS WITH PREDICTOR-CORRECTOR DIFFERENCE-PSEUDOSPECTRUM FOR NUMERICAL-SOLUTION OF THE CONVECTION-DOMINATED FLOW PROBLEMS

A fractional-step method of predictor-corrector difference-pseudospectrum with unconditional L(2)-stability and exponential convergence is presented. The stability and convergence of this method is strictly proved mathematically for a nonlinear convection-dominated flow. The error estimation is given and the superiority of this method is verified by numerical test.

多波多分量地震资料走时近似方法的研究与应用

Compared with the conventional P wave, multi-component seismic data can markedly provide more information, thus improve the quality of reservoir evaluation like formation evaluation etc. With PS wave, better imaging result can be obtained especially in areas involved with gas chimney and high velocity formation. However, the signal-to-noise of multi-component seismic data is normally lower than that of the conventional P wave seismic data, while the frequency range of converted wave is always close to that of the surface wave which adds to the difficulty of removing surface wave. To realize common reflection point data stacking from extracted common conversion point data is a hard nut to crack. The s wave static correction of common receiver point PS wave data is not easy neither. In a word, the processing of multi-component seismic data is more complicated than P wave data. This paper shows some work that has been done, addressing those problems mentioned above. (1) Based on the AVO feature of converted wave, this paper has realized the velocity spectrum of converted waves by using Sarkar’s generalized semblance method taking into account of AVO factor in velocity analysis. (2)We achieve a method of smoothly offset division normal method.Firstly we scan the stacking velocities in different offset divisions for a t0, secondly obtain some hyperbolas using these stacking velocities, then get the travel time for every trace using these hyperbolas; in the end we interpolate the normal move out between two t0 for every trace. (3) Here realize a method of stepwise offset division normal moveout.It is similar to the method of smoothly offset division normal moveout.The main difference is using quadratic curve, sixth order curve or fraction curve to fit these hyperbolas. (4)In this paper, 4 types of travel time versus distance functions in inhomogeneous media whose velocity or slowness varies with depth and vertical travel time have been discussed and used to approximate reflection travel time. The errors of ray path and travel time based on those functions in four layered models were analyzed, and it has shown that effective results of NMO in synthetic or real data can be obtained. (5) The velocity model of converted PS-wave can be considered as that of P -wave based on the ghost source theory, thus the converted wave travel time can be approximated by calculation from 4 equivalent velocity functions: velocity or slowness vary linearly with depth or vertical travel time. Then combining with P wave velocity analysis, the converted wave data can be corrected directly to the P-wave vertical travel time. The improvements were shown in Normal Move out of converted waves with numerical examples and real data. (6) This paper introduces the methods to compute conversion point location in vertical inhomogeneous media based on linear functions of velocity or slowness versus depth or vertical travel time, and introduce three ways to choose appropriate equivalent velocity methods, which are velocity fitting, travel time approximation and semblance coefficient methods.

非言语学习不良青少年空间关系推理及其影响因素研究

It has reported that individuals with nonverbal learning disabilities (NLD) have deficits in visual-spatial organization and strengths in rote language abilities. At present, there are few studies on higher order cognitive abilities of adolescents with NLD, such as the reasoning about spatial relations. The study sampled three groups: a normal group (a control group, C), a nonverbal learning disabilities group (NLD), and a verbal learning disabilities group (VLD). The aim of this study was to examine spatial and nonspatial relation reasoning abilities in adolescents with NLD under figure and word conditions, and assessed the relative involvement of different working memory components in four types of reasoning tasks: reasoning about figure-spatial, figure-nonspatial, verbal-spatial, and verbal-nonspatial relations. Using the double-tasks methodology, visual, spatial, central-executive, and phonological loads were realized. We tried to find how working memory components impact on adolescents with NLD spatial and nonspatial reasoning. The main results of present research are as follows. (1) The NLD group didn’t differ from normal group on reasoning about figure-nonspatial relations. The NLD group scored lower than the C group in spatial problems. So, adolescents with NLD showed a dissociation between spatial and non-spatial relation reasoning. They scored higher in non-spatial problems than in spatial ones. Adolescents with VLD developed well in reasoning about figure-nonspatial relations, but showed deficits in other three tasks. (2) For each reasoning task, the difficult of four types of reasoning problem had different changing trend. For figure and verbal spatial problems, mental model approach can interpret performance of the four problems well. For verbal nonspatial problems, a logical rule approach can interpret performance of the four problems well. (3) Adolescents with NLD did not differ from adolescents with VLD and normal adolescents in phonological, central-executive, and visual dual tasks. But the NLD group had lower performance than the other two groups in spatial dual task. The results showed a dissociation between visual and spatial working memory in NLD group. The VLD group only experienced deficits in central-executive subsystem. (4) The studies found that spatial reasoning mainly loaded spatial working memory, whist the involvement of spatial resources in nonspatial reasoning was little. Visual working memory mainly involved in reasoning about spatial and figure-nonspatial relations, especially in figure-nonspatial problems, and had few impacts on verbal-nonspatial reasoning. Central executive system was involved in all reasoning tasks. The role of phonological loop in the reasoning tasks required further explored. (5) According to the findings, we concluded that the deficits in spatial working memory resulted in poor spatial reasoning abilities for teenagers with NLD, whist because of the limited central executive capability, teenagers with VLD showed poor reasoning abilities. (6) The three groups can used multiple strategies during the reasoning process. They didn’t differ from each other in reasoning strategies. They all used mental model strategy to solve figure and verbal spatial problems, and used logic rule strategy to solve verbal nonspatial problems.

Basis Reduction Algorithms and Subset Sum Problems

This thesis investigates a new approach to lattice basis reduction suggested by M. Seysen. Seysen's algorithm attempts to globally reduce a lattice basis, whereas the Lenstra, Lenstra, Lovasz (LLL) family of reduction algorithms concentrates on local reductions. We show that Seysen's algorithm is well suited for reducing certain classes of lattice bases, and often requires much less time in practice than the LLL algorithm. We also demonstrate how Seysen's algorithm for basis reduction may be applied to subset sum problems. Seysen's technique, used in combination with the LLL algorithm, and other heuristics, enables us to solve a much larger class of subset sum problems than was previously possible.

Model Selection for Solving Kinematics Problems

There has been much interest in the area of model-based reasoning within the Artificial Intelligence community, particularly in its application to diagnosis and troubleshooting. The core issue in this thesis, simply put, is, model-based reasoning is fine, but whence the model? Where do the models come from? How do we know we have the right models? What does the right model mean anyway? Our work has three major components. The first component deals with how we determine whether a piece of information is relevant to solving a problem. We have three ways of determining relevance: derivational, situational and an order-of-magnitude reasoning process. The second component deals with the defining and building of models for solving problems. We identify these models, determine what we need to know about them, and importantly, determine when they are appropriate. Currently, the system has a collection of four basic models and two hybrid models. This collection of models has been successfully tested on a set of fifteen simple kinematics problems. The third major component of our work deals with how the models are selected.

Combining Associational and Causal Reasoning to Solve Interpretation and Planning Problems

This report describes a paradigm for combining associational and causal reasoning to achieve efficient and robust problem-solving behavior. The Generate, Test and Debug (GTD) paradigm generates initial hypotheses using associational (heuristic) rules. The tester verifies hypotheses, supplying the debugger with causal explanations for bugs found if the test fails. The debugger uses domain-independent causal reasoning techniques to repair hypotheses, analyzing domain models and the causal explanations produced by the tester to determine how to replace faulty assumptions made by the generator. We analyze the strengths and weaknesses of associational and causal reasoning techniques, and present a theory of debugging plans and interpretations. The GTD paradigm has been implemented and tested in the domains of geologic interpretation, the blocks world, and Tower of Hanoi problems.

Probabilistic Solution of Inverse Problems

In this thesis we study the general problem of reconstructing a function, defined on a finite lattice from a set of incomplete, noisy and/or ambiguous observations. The goal of this work is to demonstrate the generality and practical value of a probabilistic (in particular, Bayesian) approach to this problem, particularly in the context of Computer Vision. In this approach, the prior knowledge about the solution is expressed in the form of a Gibbsian probability distribution on the space of all possible functions, so that the reconstruction task is formulated as an estimation problem. Our main contributions are the following: (1) We introduce the use of specific error criteria for the design of the optimal Bayesian estimators for several classes of problems, and propose a general (Monte Carlo) procedure for approximating them. This new approach leads to a substantial improvement over the existing schemes, both regarding the quality of the results (particularly for low signal to noise ratios) and the computational efficiency. (2) We apply the Bayesian appraoch to the solution of several problems, some of which are formulated and solved in these terms for the first time. Specifically, these applications are: teh reconstruction of piecewise constant surfaces from sparse and noisy observationsl; the reconstruction of depth from stereoscopic pairs of images and the formation of perceptual clusters. (3) For each one of these applications, we develop fast, deterministic algorithms that approximate the optimal estimators, and illustrate their performance on both synthetic and real data. (4) We propose a new method, based on the analysis of the residual process, for estimating the parameters of the probabilistic models directly from the noisy observations. This scheme leads to an algorithm, which has no free parameters, for the restoration of piecewise uniform images. (5) We analyze the implementation of the algorithms that we develop in non-conventional hardware, such as massively parallel digital machines, and analog and hybrid networks.