Good morning, everyone! It is my pleasure to present our recent work on nonlinear energy harvesting. The work was done by Dr Ze-Qi Lu, a Postdoc fellow, and me. We come from Shanghai University. First of all, I’ll show you the outline of the talk. The talk is divided into 5 parts. It begins with an introduction to explain the background and the motivation of the investigation. Then the physical model and the mathematical model are presented and the frequency response equation is derived from the method of harmonic balance. It follows some numerical results to demonstrate the forming and the disappearance of bubble shaped frequency response curves. Then some numerical simulations are done to examine the harvesting performance under Gaussian random excitation. Finally, some concluding remarks end the talk. Now turn to the Introduction. As we well know, energy harvesting is a significant issue. Especially, vibratory energy harvesting is to transform the kinetic energy of waste vibration into electricity. Linear models have been widely used to design, to analyze, and to simulate energy harvesters. However, there is an essential limitation of linear energy harvesting. As a resonator, a linear energy harvester works only in a narrow frequency range near the resonance. Actually, its reason can be found in any textbooks on vibration. To overcome the limitation, several approaches have been proposed. For example, multi-modal energy harvesters have been designed so that the working frequency range contains several resonant frequencies. Anyway, our talk is not on the topic. Another promising approach is to introduce nonlinearity intentionally. Our work belongs to this aspect. Among many features of nonlinear oscillation, the jumping is a striking one. As we all know, an amplitude-frequency response curve of a linear oscillator is with a symmetric peak. The introduction of nonlinearity may destroy the symmetry. Hardening nonlinearity bends the peak to the increasing frequency direction, while softening nonlinearity the decreasing frequency direction. As you may observe, the bending of the frequency response curve makes the amplitude sufficiently large in a wider frequency range. Therefore, the jumping provides with a possibility of developing broadband vibration-based energy harvesters. The detailed discussions can be found in a recent review paper published in Applied Mechanics Reviews. If jumping can enhance energy harvesting, it is a natural idea that double-jumping, jumping in both sides, does the job better. Internal resonance leads to double-jumping. Our group constructed conceptually an electromagnetic energy harvester with internal resonance. The method of multiple scales was developed to reveal the double-jumping in the power frequency response curves. The analytical outcome was supported by the numerical integrations. Numerical results also showed that the internal resonance designproduces more power than other designs under the Gaussian white noise. The results were published in ASME Journal of Applied Mechanics. By the way, our analytical and numerical prediction on enlarged band width has been experimentally supported. For example, an experiment was reported in recent Applied PhysicsLetters. Our group’s experimental work would probably published in ASME Journalof Vibration and Acoustics. In double-jumping, bending is back-to-back. That is, the right branch of the response curve bends to the right, while the leftone to the left. Now, the problem is what happens if the bending is face-to-face. That is the right branch of the response curve bends to the left, and the lift right. In this case, there may be a bubble shaped response curve. It is the motivation of the work. In other words, the objective of the work is to explore the possibility of using bubble shaped response curves to enhance vibration-based energy harvesting. To do so, a system should be conceptually designed with the bubble shaped response curve. Fortunately, previous works on vibration isolations shed some lights on the issue. An electromagnetic energy harvester is proposed as a linear-nonlinear coupled system. In addition, numerical simulations are done to examine the harvester performance under Gaussian white noise.
When talking about Finnish archival management, the word Web Harvesting is appeared by accident. It made me confused by another concepts: Legal Deposit and Web Archive so that I searched online and storage some paragraphs for memorize it. It is an interested thing that I had been touched it several year before when Academy of Sciences first built digital library on 2001. The application was called net radar or similiar name. Web harvesting has the same content as radar or spider. Even in Mass Media field, the researchers use it as the analysis tool, such as focus track. Three modes are used for harvesting: content, structure, and usage harvesting. http://blog.sina.com.cn/s/blog_6cd614af0100lpgf.html Web Harvesting As the amount of information on the Web grows, that information becomes ever harder to keep track of and use. Search engines are a big help, but they can do only part of the work, and they are hard-pressed to keep up with daily changes. Consider that even when you use a search engine to locate data, you still have to do the following tasks to capture the information you need: scan the content until you find the information，mark the information (usually by highlighting with a mouse)，switch to another application (such as a spreadsheet, database or word processor),paste the information into that application. A better solution, especially for companies that are aiming to exploit a broad swath of data about markets or competitors, lies with Web harvesting tools. Web harvesting software automatically extracts information from the Web and picks up where search engines leave off, doing the work the search engine can't. Extraction tools automate the reading, copying and pasting necessary to collect information for analysis, and they have proved useful for pulling together information on competitors, prices and financial data of all types. There are three ways we can extract more useful information from the Web. The first technique, Web content harvesting, is concerned directly with the specific content of documents or their descriptions, such as HTML files, images or e-mail messages. Since most text documents are relatively unstructured (at least as far as machine interpretation is concerned), one common approach is to exploit what's already known about the general structure of documents and map this to some data model. Another approach to Web content harvesting involves trying to improve on the content searches that tools like search engines perform. This type of content harvesting goes beyond keyword extraction and the production of simple statistics relating to words and phrases in documents. Another technique, Web structure harvesting, takes advantage of the fact that Web pages can reveal more information than just their obvious content. Links from other sources that point to a particular Web page indicate the popularity of that page, while links within a Web page that point to other resources may indicate the richness or variety of topics covered in that page. This is like analyzing bibliographical citations—a paper that's often cited in bibliographies and other papers is usually considered to be important. The third technique, Web usage harvesting, uses data recorded by Web servers about user interactions to help understand user behavior and evaluate the effectiveness of the Web structure. General access-pattern tracking analyzes Web logs to understand access patterns and trends in order to identify structural issues and resource groupings. Customized usage tracking analyzes individual trends so that Web sites can be personalized to specific users. Over time, based on access patterns, a site can be dynamically customized for a user in terms of the information displayed, the depth of the site structure and the format of the resources presented.
标签: harvesting
