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The DSpace@MIT digital repository system captures, stores, indexes, preserves, and distributes digital research material.Mon, 24 Apr 2017 14:42:11 GMT2017-04-24T14:42:11ZModel order reduction of fully parameterized systems by recursive least square optimization
http://hdl.handle.net/1721.1/108376
Model order reduction of fully parameterized systems by recursive least square optimization
Elfadel, Ibrahim M.; Zhang, Zheng; Daniel, Luca
This paper presents an approach for the model order reduction of fully parameterized linear dynamic systems. In a fully parameterized system, not only the state matrices, but also can the input/output matrices be parameterized. The algorithm presented in this paper is based on neither conventional moment-matching nor balanced-truncation ideas. Instead, it uses “optimal (block) vectors” to construct the projection matrix, such that the system errors in the whole parameter space are minimized. This minimization problem is formulated as a recursive least square (RLS) optimization and then solved at a low cost. Our algorithm is tested by a set of multi-port multi-parameter cases with both intermediate and large parameter variations. The numerical results show that high accuracy is guaranteed, and that very compact models can be obtained for multi-parameter models due to the fact that the ROM size is independent of the number of parameters in our approach.
Thu, 01 Dec 2011 00:00:00 GMThttp://hdl.handle.net/1721.1/1083762011-12-01T00:00:00ZThermal pulse energy harvesting
http://hdl.handle.net/1721.1/108375
Thermal pulse energy harvesting
McKay, Ian; Wang, Evelyn
This paper presents a new method to enhance thermal energy harvesting with pulsed heat transfer. By creating a phase shift between the hot and cold sides of an energy harvester, periodically pulsed heat flow can allow an available temperature gradient to be concentrated over a heat engine during each thermal pulse, rather than divided between the heat engine and a heat sink. This effect allows the energy harvester to work at maximum power and efficiency despite an otherwise unfavorable heat engine–heat sink thermal resistance ratio. In this paper, the analysis of a generalized energy harvester model and experiments with a mechanical thermal switch demonstrate how the pulse mode can improve the efficiency of a system with equal engine and heat sink thermal resistances by over 80%, although at reduced total power. At a 1:2 engine–sink resistance ratio, the improvement can simultaneously exceed 60% in power and 15% in efficiency. The thermal pulse strategy promises to enhance the efficiency and power density of a variety of systems that convert thermal energy, from waste heat harvesters to the radioisotope power systems on many spacecraft.
Sat, 01 Jun 2013 00:00:00 GMThttp://hdl.handle.net/1721.1/1083752013-06-01T00:00:00Z21W.732-1 Introduction to Technical Communication: Perspectives on Medicine and Public Health, Spring 2007
http://hdl.handle.net/1721.1/108374
21W.732-1 Introduction to Technical Communication: Perspectives on Medicine and Public Health, Spring 2007
Taft, Cynthia B.
Over the course of the semester we will explore the full range of writings by physicians and other health practitioners. Some of the writer/physicians that we encounter will be Atul Gawande, Danielle Ofri, Richard Selzer, and William Carlos Williams. Students need have no special training, only a general interest in medicine or in public health issues such as AIDS, asthma, malaria control, and obesity. The writing assignments, like the readings, will invite students to consider the distinctive needs of different audiences.
Fri, 01 Jun 2007 00:00:00 GMThttp://hdl.handle.net/1721.1/1083742007-06-01T00:00:00ZSeismic sparse-spike deconvolution via Toeplitz-sparse matrix factorization
http://hdl.handle.net/1721.1/108373
Seismic sparse-spike deconvolution via Toeplitz-sparse matrix factorization
Wang, Lingling; Zhao, Qian; Gao, Jinghuai; Xu, Zongben; Jiang, Xiudi; Fehler, Michael C
We have developed a new sparse-spike deconvolution (SSD) method based on Toeplitz-sparse matrix factorization (TSMF), a bilinear decomposition of a matrix into the product of a Toeplitz matrix and a sparse matrix, to address the problems of lateral continuity, effects of noise, and wavelet estimation error in SSD. Assuming the convolution model, a constant source wavelet, and the sparse reflectivity, a seismic profile can be considered as a matrix that is the product of a Toeplitz wavelet matrix and a sparse reflectivity matrix. Thus, we have developed an algorithm of TSMF to simultaneously deconvolve the seismic matrix into a wavelet matrix and a reflectivity matrix by alternatively solving two inversion subproblems related to the Toeplitz wavelet matrix and sparse reflectivity matrix, respectively. Because the seismic wavelet is usually compact and smooth, the fused Lasso was used to constrain the elements in the Toeplitz wavelet matrix. Moreover, due to the limitations of computer memory, large seismic data sets were divided into blocks, and the average of the source wavelets deconvolved from these blocks via TSMF-based SSD was used as the final estimation of the source wavelet for all blocks to deconvolve the reflectivity; thus, the lateral continuity of the seismic data can be maintained. The advantages of the proposed deconvolution method include using multiple traces to reduce the effect of random noise, tolerance to errors in the initial wavelet estimation, and the ability to preserve the complex structure of the seismic data without using any lateral constraints. Our tests on the synthetic seismic data from the Marmousi2 model and a section of field seismic data demonstrate that the proposed method can effectively derive the wavelet and reflectivity simultaneously from band-limited data with appropriate lateral coherence, even when the seismic data are contaminated by noise and the initial wavelet estimation is inaccurate.
Fri, 01 Apr 2016 00:00:00 GMThttp://hdl.handle.net/1721.1/1083732016-04-01T00:00:00Z