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spatio-temporal patterns and chaos control (boundary conditions). [microform]
spatio-temporal patterns and chaos control (boundary conditions). [microform]
상세정보
- 자료유형
- 마이크로피시
- 청구기호
- 저자명
- 서명/저자
- spatio-temporal patterns and chaos control (boundary conditions). - [microform]
- 발행사항
- 형태사항
- 123 p. : microfiches ; 11×15 cm.
- 총서명
- UMI Dissertation
- 주기사항
- Source: Dissertation Abstracts International, Volume: 57-10, Section: B, page: 6336.
- 학위논문주기
- thesis (ph.d.)-- - university of maryland college park, 1996.
- 초록/해제
- 요약We present a technique for, finding the numerical solutions, as well as simulation and analysis of a specific class of non-linear partial differential equations. The partial differential equations considered model much of the dynamics involved in spatio-temporal complexity in general and pattern formations in particular.
- 초록/해제
- 요약A pattern forming partial differential equation, the Kuramoto-Sivashinsky equation, is motivated (derived) from general principles. In this context, the equation governs the time evolution of the interfacial fluctuations separating two fluids. Using our numerical tools we explain the stark difference that exists in the literature among two sets of contradictory observations in regards to the Kuramoto-Sivashinsky equation.
- 초록/해제
- 요약We also introduce a more effective method, that takes advantage of the high (time and space) resolution data sets, for presenting the data generated by spatially extended dynamical systems. The numerical techniques we introduce, allow one to simulate the pattern forming partial differential equations and generate large and well resolved space and time series data in a manner that is much less time and memory intensive than the traditional explicit and implicit methods. In so far as the study of spatio-temporal complexity is concerned, the spatial and time series data analysis plays a central role in estimating many of the quantities (Lyapunov exponents, dimensions, etc.) used in characterizing the complex time evolution of spatially extended dynamical systems. Hence, the ability to generate large and accurate data sets in a timely fashion is an issue of central importance and the motivation behind the results presented here.
- 초록/해제
- 요약We also present a straightforward and easy to implement method for dealing with pattern forming equations with non-periodic boundary conditions. The method is semi-implicit and is based on an operator splitting technique, it is fast and numerically robust. We present new results indicating a radical deviation from the results obtained when periodic boundary conditions are imposed.
- 초록/해제
- 요약In the second part of this report, we introduce and implement a new method of small perturbations control based on optimization. An error function, which is a measure of deviation from the desired time evolution, periodic motion, is constructed and then minimized along the trajectory in order to stabilize an unstable periodic orbit. The process of optimization is not arbitrary but constrained to the dynamics and requires no specific knowledge of the desired state. We show, orbits of "high" period may similarly be controlled with no overhead processing to content with.
- 복제주기
- Microfiche : UMI . microfiches;11×15 cm.
- 일반주제명
- 일반주제명
- 키워드
- 기타저자
- 기본자료저록
- Dissertation Abstracts International. 57-10B.
MARC
008970923s1996 us eng■001MOKWON00234157
■001AAV9707604
■00519990922143947
■008970923s1996 us eng
■035 ▼a(UnM)AAV9707604
■040 ▼aUnM▼cUnM▼dMOKWON
■090 ▼a530▼bF767s
■1001 ▼afouladi semnani, ali.
■24510▼aspatio-temporal patterns and chaos control (boundary conditions).▼h[microform]
■260 ▼aU.S.▼buniversity of maryland college park▼c1996.
■300 ▼a123 p.▼bmicrofiches▼c11×15 cm.
■350 ▼a$50.6
■44000▼aUMI Dissertation
■500 ▼aSource: Dissertation Abstracts International, Volume: 57-10, Section: B, page: 6336.
■502 ▼athesis (ph.d.)--▼buniversity of maryland college park▼d1996.
■520 ▼aWe present a technique for, finding the numerical solutions, as well as simulation and analysis of a specific class of non-linear partial differential equations. The partial differential equations considered model much of the dynamics involved in spatio-temporal complexity in general and pattern formations in particular.
■520 ▼aA pattern forming partial differential equation, the Kuramoto-Sivashinsky equation, is motivated (derived) from general principles. In this context, the equation governs the time evolution of the interfacial fluctuations separating two fluids. Using our numerical tools we explain the stark difference that exists in the literature among two sets of contradictory observations in regards to the Kuramoto-Sivashinsky equation.
■520 ▼aWe also introduce a more effective method, that takes advantage of the high (time and space) resolution data sets, for presenting the data generated by spatially extended dynamical systems. The numerical techniques we introduce, allow one to simulate the pattern forming partial differential equations and generate large and well resolved space and time series data in a manner that is much less time and memory intensive than the traditional explicit and implicit methods. In so far as the study of spatio-temporal complexity is concerned, the spatial and time series data analysis plays a central role in estimating many of the quantities (Lyapunov exponents, dimensions, etc.) used in characterizing the complex time evolution of spatially extended dynamical systems. Hence, the ability to generate large and accurate data sets in a timely fashion is an issue of central importance and the motivation behind the results presented here.
■520 ▼aWe also present a straightforward and easy to implement method for dealing with pattern forming equations with non-periodic boundary conditions. The method is semi-implicit and is based on an operator splitting technique, it is fast and numerically robust. We present new results indicating a radical deviation from the results obtained when periodic boundary conditions are imposed.
■520 ▼aIn the second part of this report, we introduce and implement a new method of small perturbations control based on optimization. An error function, which is a measure of deviation from the desired time evolution, periodic motion, is constructed and then minimized along the trajectory in order to stabilize an unstable periodic orbit. The process of optimization is not arbitrary but constrained to the dynamics and requires no specific knowledge of the desired state. We show, orbits of "high" period may similarly be controlled with no overhead processing to content with.
■533 ▼aMicrofiche▼cUMI▼emicrofiches;11×15 cm.
■590 ▼aSchool code: 0117.
■650 4▼aPhysics, Fluid and Plasma
■650 4▼aMathematics
■653 ▼aspatio-temporal▼apatterns▼aand▼achaos▼acontrol▼a(boundary▼aconditions).
■690 ▼a0759
■690 ▼a0405
■71020▼auniversity of maryland college park.
■7730 ▼tDissertation Abstracts International▼g57-10B.
■790 ▼a0117
■791 ▼aPH.D.
■792 ▼a1996
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