267 research outputs found

    Synchronization framework for modeling transition to thermoacoustic instability in laminar combustors

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    We, herein, present a new model based on the framework of synchronization to describe a thermoacoustic system and capture the multiple bifurcations that such a system undergoes. Instead of applying flame describing function to depict the unsteady heat release rate as the flame's response to acoustic perturbation, the new model considers the acoustic field and the unsteady heat release rate as a pair of nonlinearly coupled damped oscillators. By varying the coupling strength, multiple dynamical behaviors, including limit cycle oscillation, quasi-periodic oscillation, strange nonchaos, and chaos can be captured. Furthermore, the model was able to qualitatively replicate the different behaviors of a laminar thermoacoustic system observed in experiments by Kabiraj et al.~[Chaos 22, 023129 (2012)]. By analyzing the temporal variation of the phase difference between heat release rate oscillations and pressure oscillations under different dynamical states, we show that the characteristics of the dynamical states depend on the nature of synchronization between the two signals, which is consistent with previous experimental findings.Comment: 18 pages, 7 figure

    Extension of Lorenz Unpredictability

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    It is found that Lorenz systems can be unidirectionally coupled such that the chaos expands from the drive system. This is true if the response system is not chaotic, but admits a global attractor, an equilibrium or a cycle. The extension of sensitivity and period-doubling cascade are theoretically proved, and the appearance of cyclic chaos as well as intermittency in interconnected Lorenz systems are demonstrated. A possible connection of our results with the global weather unpredictability is provided.Comment: 32 pages, 13 figure

    Experiments with a Malkus-Lorenz water wheel: Chaos and Synchronization

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    We describe a simple experimental implementation of the Malkus-Lorenz water wheel. We demonstrate that both chaotic and periodic behavior is found as wheel parameters are changed in agreement with predictions from the Lorenz model. We furthermore show that when the measured angular velocity of our water wheel is used as an input signal to a computer model implementing the Lorenz equations, high quality chaos synchronization of the model and the water wheel is achieved. This indicates that the Lorenz equations provide a good description of the water wheel dynamics.Comment: 12 pages, 7 figures. The following article has been accepted by the American Journal of Physics. After it is published, it will be found at http://scitation.aip.org/ajp

    Complexity and Human Gait

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    Recently, the complexity of the human gait has become a topic of major interest within the field of human movement sciences. Indeed, while the complex fluctuations of the gait patterns were, for a long time, considered as resulting from random processes, the development of new techniques of analysis, so-called nonlinear techniques, has open new vistas for the understanding of such fluctuations. In particular, by connecting the notion of complexity to the one of chaos, new insights about gait adaptability, unhealthy states in gait and neural control of locomotion were provided. Through methods of evaluation of the complexity, experimental results obtained both with healthy and unhealthy subjects and theoretical models of gait complexity, this review discusses the tremendous progresses made about the understanding of the complexity in the human gait variability. Recientemente, la complejidad de la marcha humana se estรก convirtiendo en un tema de gran interรฉs en el campo de la ciencia del movimiento humano. De hecho, mientras las fluctuaciones complejas de los patrones de la marcha fueron, durante mucho tiempo, consideradas como resultado de procesos al azar, el desarrollo de nuevas tรฉcnicas de anรกlisis, las llamadas tรฉcnicas no lineales, ha abierto nuevas vรญas para el entendimiento de tales fluctuaciones. En particular, mediante la conexiรณn de la nociรณn de complejidad con la de caos, se estรกn obteniendo nuevos conocimientos sobre la adaptabilidad de la marcha, las condiciones patolรณgicas en la marcha y el control neural de la locomociรณn. Mediante mรฉtodos de evaluaciรณn de la complejidad, los resultados experimentales obtenidos tanto con individuos sanos como no sanos y con modelos teรณricos de la complejidad de la marcha, esta revisiรณn habla de los enormes progresos efectuados sobre el entendimiento de la complejidad en la variabilidad de la marcha humana

    The JEREMI-project on thermocapillary convection in liquid bridges. Part A : Overview of particle accumulation structures

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    The rapid accumulation of particles suspended in a thermocapillary liquid bridge is planned to be investigated during the JEREMI experiment on the International Space Station scheduled for 2016. An overview is given of the current status of experimental and numerical investigations of this phenomenon

    ๊ณ ์ฐจ์› ๋กœ๋ Œ์ธ  ์‹œ์Šคํ…œ, ๋Œ€๊ธฐ์˜ˆ์ธก์„ฑ ๋ฐ ์ž๋ฃŒ๋™ํ™”

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ์ง€๊ตฌํ™˜๊ฒฝ๊ณผํ•™๋ถ€, 2021.8. ๋ฌธ์Šน์ฃผ.๋กœ๋ Œ์ธ  ์‹œ์Šคํ…œ์€ ๋ ˆ์ผ๋ฆฌ ๋ฒ ๋‚˜๋ฅด ๋Œ€๋ฅ˜ ํ˜„์ƒ์˜ ๋‹จ์ˆœํ•œ ๋ชจํ˜•์œผ๋กœ ์ฒ˜์Œ ๊ณ ์•ˆ๋˜์—ˆ์œผ๋‚˜, ์ดํ›„ ์•ผ๋ฆ‡ํ•œ ๋Œ๊ฐœ์˜ ๋ฐœ๊ฒฌ ๋ฐ ํ˜ผ๋ˆ ์ด๋ก ์˜ ๊ธ‰์†ํ•œ ๋ฐœ์ „์— ๋Œ€ํ•œ ๊ธฐ์—ฌ ๋“ฑ์„ ํ†ตํ•ด ๊ทธ ์ค‘์š”์„ฑ์ด ๊พธ์ค€ํžˆ ๋ถ€๊ฐ๋˜์–ด ์™”๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋‘ ๊ฐ€์ง€ ์ ‘๊ทผ ๋ฐฉ์‹์„ ํ†ตํ•ด ๋กœ๋ Œ์ธ  ์‹œ์Šคํ…œ์„ ๊ณ ์ฐจ์›์œผ๋กœ ํ™•์žฅํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์ฒซ๋ฒˆ์งธ ์ ‘๊ทผ ๋ฐฉ์‹์€ ์œ ๋„ ๊ณผ์ •์—์„œ ๋น„๋กฏ๋˜๋Š” ํ‘ธ๋ฆฌ์— ๊ธ‰์ˆ˜์˜ ์ ˆ๋‹จ์— ์žˆ์–ด ์ถ”๊ฐ€ ๋ชจ๋“œ๋ฅผ ํ†ตํ•ด ์ฐจ์ˆ˜๋ฅผ ํ™•์žฅํ•˜๋Š” ๋ฐฉ๋ฒ•์œผ๋กœ, ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ด๋ฅผ ์ผ๋ฐ˜ํ™” ํ•˜์—ฌ ์ž„์˜์˜ ์ž์—ฐ์ˆ˜ NN์— ๋Œ€ํ•œ (3N)(3N) ๋ฐ (3N+2)(3N+2)์ฐจ ๋กœ๋ Œ์ธ  ์‹œ์Šคํ…œ์„ ์œ ๋„ํ•˜์˜€๋‹ค. ๋‘๋ฒˆ์งธ๋Š” ๋ฌผ๋ฆฌ์  ํ™•์žฅ์ด๋ผ ๋ถˆ๋ฆฌ๋Š” ๋ฐฉ์‹์œผ๋กœ, ๋ ˆ์ผ๋ฆฌ ๋ฒ ๋‚˜๋ฅด ๋Œ€๋ฅ˜ ํ˜„์ƒ์„ ๊ด€์žฅํ•˜๋Š” ์ง€๋ฐฐ๋ฐฉ์ •์‹์— ๋‚˜ํƒ€๋‚ด๊ณ ์ž ํ•˜๋Š” ๋ฌผ๋ฆฌ ์„ฑ๋ถ„์„ ์ถ”๊ฐ€ํ•˜์—ฌ ๋” ๋†’์€ ์ฐจ์ˆ˜์˜ ๋ฐฉ์ •์‹๊ณ„๋ฅผ ์–ป๋Š” ๊ณผ์ •์ด๋‹ค. ์ด์— ์ถ”๊ฐ€ ๋ฌผ๋ฆฌ ์„ฑ๋ถ„์œผ๋กœ ๋ชจํ˜• ํ”„๋ ˆ์ž„์˜ ํšŒ์ „๊ณผ ๋‚ด๋ถ€์— ๋ถ€์œ ํ•˜๋Š” ์˜ค์—ผ ๋ฌผ์งˆ ๋”ฐ์œ„์˜ ์Šค์นผ๋ผ๋ฅผ ๊ณ ๋ คํ•˜์—ฌ ์ƒˆ๋กœ์šด 6์ฐจ ๋กœ๋ Œ์ธ  ์‹œ์Šคํ…œ์„ ์œ ๋„ ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ด๋ ‡๊ฒŒ ์–ป์–ด์ง„ ๊ณ ์ฐจ์› ๋กœ๋ Œ์ธ  ์‹œ์Šคํ…œ์€ ๋น„์„ ํ˜•์„ฑ, ๋Œ€์นญ์„ฑ, ์†Œ์‚ฐ์„ฑ ๋“ฑ์˜ ๊ณตํ†ต๋œ ํŠน์ง•์„ ์ง€๋‹Œ๋‹ค. ์ƒˆ๋กญ๊ฒŒ ํ™•์žฅ๋œ ๋กœ๋ Œ์ธ  ์‹œ์Šคํ…œ์˜ ํ•ด์˜ ํŠน์„ฑ ๋ฐ ๊ทธ๋“ค์ด ๋‚˜ํƒ€๋‚ด๋Š” ๋‹ค์–‘ํ•œ ๋น„์„ ํ˜• ํ˜„์ƒ์˜ ๊ทœ๋ช…์€ ์ˆ˜์น˜ ์ ๋ถ„์„ ํ†ตํ•ด ์–ป์€ ํ•ด์˜ ๋ถ„์„์„ ๋ฐ”ํƒ•์œผ๋กœ ์ด๋ฃจ์–ด์กŒ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์นด์˜ค์Šค ์ด๋ก ์— ์ž…๊ฐํ•œ ์—ฌ๋Ÿฌ๊ฐ€์ง€ ๋ถ„์„ ๋ฐฉ๋ฒ•์ด ํ™œ์šฉ๋˜์—ˆ๋Š”๋ฐ, ์ด๋Ÿฌํ•œ ๋ถ„์„๋ฐฉ๋ฒ•์—๋Š” ํŒŒ๋ผ๋ฏธํ„ฐ ๊ณต๊ฐ„ ์ƒ์˜ ์ฃผ๊ธฐ์„ฑ๋„ํ‘œ, ๋ถ„๊ธฐ๋„ํ‘œ ๋ฐ ๋ฆฌ์•„ํ‘ธ๋…ธํ”„ ์ง€์ˆ˜ ๊ทธ๋ฆฌ๊ณ  ์œ„์ƒ ๊ณต๊ฐ„ ๋‚ด ํ•ด์˜ ๊ถค๋„ ๋ฐ ํ”„๋ ‰ํƒˆ ํก์ธ๊ฒฝ๊ณ„ ๋“ฑ์ด ์žˆ๋‹ค. ๋ฐํ˜€์ง„ ๋น„์„ ํ˜• ๋™์—ญํ•™์  ํ˜„์ƒ ์ค‘ ํŠนํžˆ ์ฃผ๋ชฉํ•  ๋งŒํ•œ ํ˜„์ƒ์—๋Š” ํŒŒ๋ผ๋ฏธํ„ฐ ๊ฐ’์— ๋”ฐ๋ฅธ ๋ถ„๊ธฐ ๊ตฌ์กฐ์˜ ๋ณ€๋™, ํ•˜๋‚˜์˜ ์œ„์ƒ ๊ณต๊ฐ„ ๋‚ด ์กด์žฌํ•˜๋Š” ์—ฌ๋Ÿฌ ํƒ€์ž…์˜ ํ•ด์˜ ๊ณต์กด, ์นด์˜ค์Šค์˜ ๋™๊ธฐํ™” ๋“ฑ์ด ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ํ˜„์ƒ์˜ ์ˆ˜ํ•™์ ~โ‹…\cdot~์ˆ˜์น˜์  ๋ถ„์„๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์ด๊ฒƒ์ด ๋Œ€๊ธฐ๊ณผํ•™ ํŠนํžˆ ์ž๋ฃŒ๋™ํ™”์™€ ๋Œ€๊ธฐ์˜ˆ์ธก์„ฑ ๋ถ„์•ผ์— ํ•จ์˜ํ•˜๋Š” ๋ฐ”๊ฐ€ ๋ฌด์—‡์ธ์ง€๋„ ํƒ๊ตฌํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ œ์•ˆ๋œ ์ผ๋ฐ˜ํ™” ๋ฐฉ์‹์— ๋”ฐ๋ผ ๋กœ๋ Œ์ธ  ์‹œ์Šคํ…œ์˜ ์ฐจ์ˆ˜๋ฅผ ์˜ฌ๋ฆฌ๋ฉด ๋ถ„๊ธฐ ๊ตฌ์กฐ์— ๋ณ€๋™์ด ์ผ์–ด๋‚˜ ์ž„๊ณ„ ๋ ˆ์ผ๋ฆฌ ํŒŒ๋ผ๋ฏธํ„ฐ์˜ ์ฆ๊ฐ€๊ฐ€ ๋น„๋กฏ๋œ๋‹ค. ์—ฌ๊ธฐ์„œ ์ž„๊ณ„ ๋ ˆ์ผ๋ฆฌ ํŒŒ๋ผ๋ฏธํ„ฐ๋Š” ์นด์˜ค์Šค๊ฐ€ ์ฒ˜์Œ ๋ฐœ์ƒํ•˜๋Š” ๊ฐ€์žฅ ๋‚ฎ์€ ๋ ˆ์ผ๋ฆฌ ํŒŒ๋ผ๋ฏธํ„ฐ ๊ฐ’์ด๋ฏ€๋กœ ์ด๊ฒƒ์ด ์ฐจ์ˆ˜์— ๋”ฐ๋ผ ์ฆ๊ฐ€ํ•œ๋‹ค๋Š” ๊ฒƒ์€ ์ฆ‰ ๊ณ ์ฐจ์› ๋กœ๋ Œ์ธ  ์‹œ์Šคํ…œ์—์„œ๋Š” ์นด์˜ค์Šค์˜ ๋ฐœ์ƒ์ด ์ €์ฐจ์›์—์„œ๋ณด๋‹ค ๋” ์–ด๋ ต๋‹ค๋Š” ๊ฒƒ์„ ์˜๋ฏธํ•œ๋‹ค. ์ฐจ์ˆ˜ ๋ฐ ํŒŒ๋ผ๋ฏธํ„ฐ ๊ณต๊ฐ„์— ๊ทธ๋ ค์ง„ ์ฃผ๊ธฐ์„ฑ ๋„ํ‘œ๋ฅผ ๋ณด๋ฉด ์นด์˜ค์Šค๊ฐ€ ์กด์žฌํ•˜๋Š” ์˜์—ญ์ด ์ฐจ์ˆ˜์— ๋”ฐ๋ผ ์ ์  ์ค„์–ด๋“ค๊ณ , ์–ด๋Š ์ฐจ์ˆ˜ ์ด์ƒ๋ถ€ํ„ฐ๋Š” ์‚ฌ๋ผ์ง€๋Š” ๊ฒƒ์„ ํ™•์ธ ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋งˆ์ฐฌ๊ฐ€์ง€๋กœ ๋ฌผ๋ฆฌ์ ์œผ๋กœ ํ™•์žฅ๋œ ๋กœ๋ Œ์ธ  ์‹œ์Šคํ…œ์—์„œ๋Š” ์ž„๊ณ„ ๋ ˆ์ผ๋ฆฌ ํŒŒ๋ผ๋ฏธํ„ฐ๊ฐ€ ์ถ”๊ฐ€๋œ ๋ฌผ๋ฆฌํ˜„์ƒ์„ ๋‚˜ํƒ€๋‚ด๋Š” ์ƒˆ๋กœ์šด ํŒŒ๋ผ๋ฏธํ„ฐ์˜ ๊ฐ’์„ ์ฆ๊ฐ€์‹œํ‚ด์— ๋”ฐ๋ผ ์ฆ๊ฐ€ํ•˜๋Š” ๊ฒƒ์„ ์•Œ ์ˆ˜ ์žˆ๋‹ค. ํ•œํŽธ ์œ ์ฒด ๋‚ด ์Šค์นผ๋ผ ํšจ๊ณผ์™€ ์—ฐ๊ด€๋œ ํŒŒ๋ผ๋ฏธํ„ฐ๋งŒ ์ ์ง„์ ์œผ๋กœ ์˜ฌ๋ฆด ๊ฒฝ์šฐ์—๋Š” ์‹œ์Šคํ…œ์˜ ๋ถˆ์•ˆ์ •์„ ์•ผ๊ธฐํ•˜๋Š” ๋ ˆ์ผ๋ฆฌ ํŒŒ๋ผ๋ฏธํ„ฐ์™€ ์•ˆ์ •์„ ์•ผ๊ธฐํ•˜๋Š” ์Šค์นผ๋ผ ๊ด€๋ จ ํŒŒ๋ผ๋ฏธํ„ฐ ๊ฐ„์˜ ๊ฒฝ์Ÿ์œผ๋กœ ์ธํ•ด ์‹œ์Šคํ…œ์ด ์™„์ „ํžˆ ์•ˆ์ •ํ™” ๋˜๊ธฐ ์ „ ์นด์˜ค์Šค ํ•ด๊ฐ€ ํ•œ๋ฒˆ ๋” ๋ฐœ์ƒํ•˜๋Š” ํ˜„์ƒ์ด ์ผ์–ด๋‚œ๋‹ค. ์ด ๋‘๋ฒˆ์งธ ์นด์˜ค์Šค์— ๋Œ€์‘๋˜๋Š” ๋Œ๊ฐœ๋Š” ๊ธฐ์กด์— ์•Œ๋ ค์ง„ ๋กœ๋ Œ์ธ  ๋Œ๊ฐœ์™€๋Š” ์‚ฌ๋ญ‡ ๋‹ค๋ฅธ ๋ชจ์–‘์ƒˆ๋ฅผ ๋ณด์ธ๋‹ค. ํ•ด์˜ ๊ณต์กด ํ˜„์ƒ์€ ๋กœ๋ Œ์ธ ์— ์˜ํ•ด ๋ฐํ˜€์ง„ ํ•ด์˜ ์ดˆ๊ธฐ์กฐ๊ฑด์— ๋Œ€ํ•œ ๋ฏผ๊ฐ๋„์™€๋Š” ๊ตฌ๋ถ„๋˜๋Š” ๊ฐœ๋…์œผ๋กœ, ์ดˆ๊ธฐ์กฐ๊ฑด์œผ๋กœ ์ธํ•œ ์นด์˜ค์Šค ํ•ด ๊ฐ„์˜ ์ฐจ์ด๊ฐ€ ์ฆํญ๋˜๋Š” ์ด๋ฅธ๋ฐ” ๋‚˜๋น„ํšจ๊ณผ์™€๋Š” ๋‹ฌ๋ฆฌ ์ดˆ๊ธฐ์กฐ๊ฑด์— ๋”ฐ๋ผ ์™„์ „ํžˆ ๋‹ค๋ฅธ ํƒ€์ž…์˜ ํ•ด๋ฅผ ๋‚˜ํƒ€๋‚ด๋Š” ๋Œ๊ฐœ๊ฐ€ ๊ฐ™์€ ์œ„์ƒ๊ณต๊ฐ„์— ๊ณต์กดํ•จ์„ ์˜๋ฏธํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ๋งŒ์•ฝ ์‹ค์ œ ๋‚ ์”จ๋ฅผ ๋‚˜ํƒ€๋‚ด๋Š” ์‹œ์Šคํ…œ์ด ์ƒ์กดํ•˜๋Š” ์œ„์ƒ๊ณต๊ฐ„์—์„œ ์ด๋Ÿฌํ•œ ํ•ด์˜ ๊ณต์กด์ด ์‹ค์ œํ•œ๋‹ค๋ฉด ์ด๊ฒƒ์€ ์นด์˜ค์Šค์˜ ์ดˆ๊ธฐ์กฐ๊ฑด์— ๋Œ€ํ•œ ๋ฏผ๊ฐ์„ฑ๊ณผ ๋”๋ถˆ์–ด ๋Œ€๊ธฐ์˜ˆ์ธก์„ฑ ํŠนํžˆ ์•™์ƒ๋ธ” ์˜ˆ๋ณด์— ์ด๋ก ์ ์œผ๋กœ ์‹œ์‚ฌํ•˜๋Š” ๋ฐ”๊ฐ€ ํด ๊ฒƒ์œผ๋กœ ์ƒ๊ฐ๋œ๋‹ค. ๋ฌผ๋ฆฌ์ ์œผ๋กœ ํ™•์žฅ๋œ ๋กœ๋ Œ์ธ  ์‹œ์Šคํ…œ์—์„œ๋Š” ๊ธฐ์กด ๋กœ๋ Œ์ธ  ์‹œ์Šคํ…œ๊ณผ ๊ฐ™์ด ๋ ˆ์ผ๋ฆฌ ํŒŒ๋ผ๋ฏธํ„ฐ์˜ ๋ณ€ํ™”์— ๋”ฐ๋ฅธ ๋ถ„๊ธฐ ๊ตฌ์กฐ์˜ ๋ถ€์ •ํ•ฉ์œผ๋กœ ์ธํ•ด ๋น„๋กฏ๋˜๋Š” ํ•ด์˜ ๊ณต์กด์ด ๋‚˜ํƒ€๋‚œ๋‹ค. ํ•ด์˜ ๊ณต์กด ๊ฐ€๋Šฅ์„ฑ์ด ๋†’์€ ํŒŒ๋ผ๋ฏธํ„ฐ ์กฐํ•ฉ์„ ์ฐพ์•„๋‚ด๊ธฐ ์œ„ํ•ด ๋ฌผ๋ฆฌ์ ์œผ๋กœ ํ™•์žฅ๋œ 6์ฐจ ๋กœ๋ Œ์ธ  ์‹œ์Šคํ…œ์˜ ๋ถ„๊ธฐ๊ตฌ์กฐ๋ฅผ ์ˆ˜์น˜์ ~โ‹…\cdot~ํ•ด์„์  ๋ฐฉ๋ฒ•์œผ๋กœ ๋„์ถœํ•˜์˜€๊ณ  ์ดˆ๊ธฐ์กฐ๊ฑด์— ๋”ฐ๋ผ ์„œ๋กœ ๋‹ค๋ฅธ ๋‘๊ฐ€์ง€ ์ข…๋ฅ˜์˜ ๋ถ„๊ธฐ ์ฆ‰ ํ˜ธํ”„ ๋ฐ ํ—คํ…Œ๋กœํด๋ฆฌ๋‹‰ ๋ถ„๊ธฐ๊ฐ€ ์—‡๊ฐˆ๋ฆฌ๋Š” ๊ตฌ๊ฐ„์„ ์ง‘์ค‘์ ์œผ๋กœ ๋ถ„์„ํ•˜์˜€๋‹ค. ๊ธฐ์กด 3์ฐจ ๋กœ๋ Œ์ธ  ์‹œ์Šคํ…œ์—์„œ ํ•˜๋‚˜์˜ ๋ณ€์ˆ˜์— ๋Œ€ํ•œ ์ •๋ณด ์ „๋‹ฌ ๋งŒ์œผ๋กœ๋„ ์ž๊ธฐ๋™๊ธฐํ™” ํ˜„์ƒ์ด ์ผ์–ด๋‚จ์€ ์ด๋ฏธ ์ž˜ ์•Œ๋ ค์ง„ ์‚ฌ์‹ค์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋ฌผ๋ฆฌ์ ์œผ๋กœ ํ™•์žฅ๋œ ๋กœ๋ Œ์ธ  ์‹œ์Šคํ…œ์—์„œ๋„ ๊ธฐ์กด ๋กœ๋ Œ์ธ  ์‹œ์Šคํ…œ๊ณผ ๊ฐ™์€ ์กฐ๊ฑด ํ•˜์—์„œ ์นด์˜ค์Šค์˜ ์ž๊ธฐ๋™๊ธฐํ™”๊ฐ€ ์ผ์–ด๋‚˜๋Š” ์ ์„ ์ ์ ˆํ•œ ๋ฆฌ์•„ํ‘ธ๋…ธํ”„ ํ•จ์ˆ˜์˜ ์ œ์‹œ๋ฅผ ํ†ตํ•ด ์ฆ๋ช…ํ•˜์˜€๋‹ค. ์ผ๋ฐ˜ํ™”๋œ ๋กœ๋ Œ์ธ  ์‹œ์Šคํ…œ์˜ ์ž๊ธฐ๋™๊ธฐํ™”์— ๋Œ€ํ•ด์„œ๋Š” ๋น„๋ก ์ˆ˜ํ•™์  ์ฆ๋ช…์ด ๋™๋ฐ˜๋˜์ง€๋Š” ์•Š์•˜์ง€๋งŒ ์ˆ˜์น˜์  ๋ฐฉ๋ฒ•์„ ํ†ตํ•ด ์—ญ์‹œ ๊ฐ™์€ ์กฐ๊ฑด ํ•˜์—์„œ ์ž๊ธฐ๋™๊ธฐํ™”๊ฐ€ ์ผ์–ด๋‚จ์„ ๋’ท๋ฐ›์นจ ํ•  ๊ทผ๊ฑฐ๋ฅผ ์ œ์‹œํ•˜์˜€๋‹ค. ๋˜ํ•œ ์ˆ˜์น˜ ์‹คํ—˜์„ ํ†ตํ•ด ์„œ๋กœ ๋‹ค๋ฅธ ์ฐจ์ˆ˜๋ฅผ ๊ฐ€์ง„ ์ผ๋ฐ˜ํ™”๋œ ๋กœ๋ Œ์ธ  ์‹œ์Šคํ…œ ๊ฐ„ ๋™๊ธฐํ™”๊ฐ€ ์ผ์–ด๋‚˜๋Š” ์ •๋„๊ฐ€ ์ƒํ˜ธ ์ฐจ์ˆ˜๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ๋‘ ์‹œ์Šคํ…œ ์‚ฌ์ด์˜ ๊ฑฐ๋ฆฌ์™€ ์Œ์˜ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๊ฐ€์ง„๋‹ค๋Š” ์ ๋„ ํ™•์ธํ•˜์˜€๋‹ค. ์ถ”๊ฐ€ ํ‘ธ๋ฆฌ์— ๋ชจ๋“œ๋ฅผ ํฌํ•จํ•˜์—ฌ ๋” ์ž‘์€ ์Šค์ผ€์ผ์˜ ์šด๋™์„ ๋ถ„ํ•ดํ•  ์ˆ˜ ์žˆ๋Š” ๊ณ ์ฐจ์› ๋กœ๋ Œ์ธ  ์‹œ์Šคํ…œ๊ณผ ๊ทธ๋ ‡๊ฒŒ ํ•˜์ง€ ๋ชปํ•˜๋Š” ์ €์ฐจ์› ๋กœ๋ Œ์ธ  ์‹œ์Šคํ…œ ๊ฐ„ ๋™๊ธฐํ™”์˜ ๊ฐ€๋Šฅ์„ฑ์€ ๋Œ€๊ธฐ๊ณผํ•™์—์„œ ํŠนํžˆ ๋Œ€๊ธฐ ๋ชจํ˜• ๋ฐ ์ž๋ฃŒ๋™ํ™”์— ์žˆ์–ด ์ค‘์š”ํ•œ ๊ฐœ๋…์ ์ธ ํ•จ์˜๋ฅผ ๊ฐ€์ง„๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํŠน๋ณ„ํžˆ ์•™์ƒ๋ธ” ์นผ๋งŒ ํ•„ํ„ฐ ์ž๋ฃŒ๋™ํ™” ๊ธฐ๋ฒ•์„ ์ผ๋ก€๋กœ ์ผ๋ฐ˜ํ™”๋œ ๋กœ๋ Œ์ธ  ์‹œ์Šคํ…œ์ด ์ž๋ฃŒ๋™ํ™” ๊ธฐ๋ฒ•์˜ ๋น„๊ต์  ๋‹จ์ˆœํ•œ ํ…Œ์ŠคํŠธ๋ฒ ๋“œ๋กœ์จ์˜ ์—ญํ• ์„ ํ•  ์ˆ˜ ์žˆ๋Š”์ง€ ํƒ๊ตฌํ•˜์˜€๋‹ค. ์นด์˜ค์Šค ๋™๊ธฐํ™” ํ˜„์ƒ์— ๊ธฐ๋ฐ˜์„ ๋‘” ๊ฐœ๋…์  ๋„์‹์œผ๋กœ ๋ฐœ์‹ ์ž๋ฅผ ์‹ค์ œ ๋Œ€๊ธฐ ํ˜„์ƒ, ์ˆ˜์‹ ์ž๋ฅผ ๋Œ€๊ธฐ ๋ชจํ˜•, ๊ทธ๋ฆฌ๊ณ  ๋ฐœ์‹ ์ž์—์„œ ์ˆ˜์‹ ์ž๋กœ ์ „๋‹ฌ๋˜๋Š” ์ •๋ณด๋ฅผ ๊ด€์ธก์— ๋Œ€์‘์‹œํ‚ด์œผ๋กœ์จ ์ˆ˜์‹ ์ž์™€ ๋ฐœ์‹ ์ž ๊ฐ„์˜ ์˜ค์ฐจ, ๋ฐœ์‹ ์ž์—์„œ ์ˆ˜์‹ ์ž๋กœ ์ „๋‹ฌํ•  ์ •๋ณด ์ถ”์ถœ ๊ณผ์ •์—์„œ ๋น„๋กฏ๋˜๋Š” ์˜ค์ฐจ ๋“ฑ์„ ํ†ตํ•ด ์‹ค์ œ ๋Œ€๊ธฐ ๋ชจํ˜•๊ณผ ๊ด€์ธก์˜ ๋ถˆ์™„์ „ํ•จ์„ ๊ฐœ๋…์ ์œผ๋กœ ๋ชจ์˜ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ผ๋ฐ˜ํ™”๋œ ๋กœ๋ Œ์ธ  ์‹œ์Šคํ…œ์—์„œ ์ดˆ๊ธฐ์กฐ๊ฑด์— ์•„์ฃผ ์ž‘์€ ์„ญ๋™์„ ์ค€ ํ•ด์™€ ๊ทธ๋ ‡์ง€ ์•Š์€ ํ•ด ๊ฐ„์˜ ๋น„๊ต๋ฅผ ํ†ตํ•ด ์ด๊ฒƒ์ด ๋Œ€๊ธฐ์˜ˆ์ธก์„ฑ์— ํ•จ์˜ํ•˜๋Š” ๋ฐ”๊ฐ€ ๋ฌด์—‡์ธ์ง€ ํƒ๊ตฌํ•˜์˜€๋‹ค. ์ด๋•Œ ์ด๋ ‡๊ฒŒ ๋‘ ํ•ด๊ฐ€ ๋ฒŒ์–ด์ง€๋Š” ์ •๋„๊ฐ€ ๊ธฐ์ค€๊ฐ’์„ ๋„˜๊ฒŒ ๋˜๋Š” ์‹œ๊ฐ„์„ ํŽธ์ฐจ์‹œ๊ฐ„์ด๋ผ ์นญํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํŽธ์ฐจ์‹œ๊ฐ„์ด ์ ์–ด๋„ ์ฃผ์–ด์ง„ ํŒŒ๋ผ๋ฏธํ„ฐ ๊ฐ’ ํ•˜์—์„œ๋Š” ๋กœ๋ Œ์ธ  ์‹œ์Šคํ…œ์˜ ์ฐจ์ˆ˜์— ๋Œ€ํ•œ ๊ฐ•ํ•œ ๋น„๋‹จ์กฐ์  ์˜์กด์„ฑ์„ ๋ณด์ž„์„ ๋ฐœ๊ฒฌํ•˜์˜€๋‹ค. ๋˜ํ•œ ์ด๋ ‡๊ฒŒ ์ •์˜๋œ ํŽธ์ฐจ์‹œ๊ฐ„์„ ํ™œ์šฉํ•˜์—ฌ ์‹ค์ œ ๋‚ ์”จ ์‚ฌ๋ก€์˜ ์ˆ˜์น˜ ์˜ˆ๋ณด ๋ชจ์˜์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ๋Œ€๊ธฐ์˜ˆ์ธก์„ฑ์„ ์ธก์ •ํ•˜์˜€์„๋•Œ, ๋งˆ์ฐฌ๊ฐ€์ง€๋กœ ๋Œ€๊ธฐ์˜ˆ์ธก์„ฑ์ด ์—ฐ์งํ•ด์ƒ๋„์— ๋Œ€ํ•œ ๋น„๋‹จ์กฐ์  ์˜์กด์„ฑ์„ ๋ณด์ด๋Š” ๊ฒƒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ด์— ์ด๋Ÿฌํ•œ ๋น„๋‹จ์กฐ์  ์˜์กด์„ฑ์˜ ๊ทผ๋ณธ์ ์ธ ์›์ธ์€ ๋ชจํ˜•์˜ ๋Œ€๊ธฐ ๋‚˜์•„๊ฐ€ ์‹ค์ œ ๋‚ ์”จ์— ๋‚ด์žฌ๋œ ์นด์˜ค์Šค์— ์žˆ์„ ์ˆ˜ ์žˆ์Œ์„ ์ œ์•ˆํ•˜์˜€๋‹ค.The Lorenz system is a simplified model of Rayleigh--B\'{e}nard convection whose importance lies not only in understanding the fluid convection problem but also in its formative role in the discovery of strange attractors and the subsequent development of the modern theory of chaos. In this dissertation, two different approaches to extending the Lorenz system to higher dimensions are considered. First, by including additional wavenumber modes at the series truncation stage of the derivation, the so-called high-order Lorenz systems are obtained up to dimension 11, which are then generalized into (3N)(3N) and (3N+2)(3N+2) dimensions for any positive integer NN. Second, by incorporating additional physical ingredients, namely, rotation and density-affecting scalar in the governing equations, a new 6-dimensional physically extended Lorenz system is derived. All of these high-dimensional extensions of the Lorenz system are shown to share some basic properties such as nonlinearity, symmetry, and volume contraction. The numerically obtained solutions of the extended Lorenz systems are studied through periodicity diagrams, bifurcation diagrams, and Lyapunov exponent spectra in parameter spaces and also through solution trajectories and basin boundaries in the phase space, illuminating various nonlinear dynamical phenomena such as shifts in the bifurcation structures, attractor coexistence, and chaos synchronization. Accompanying these results are discussions about their applicability and theoretical implications, particularly in the context of data assimilation and atmospheric predictability. The shifts in bifurcation structures induced by raising the dimension lead to higher critical Rayleigh parameter values, implying that it gets more difficult for chaos to emerge at higher dimensions. Periodicity diagrams reveal that the parameter ranges in which chaos resides tend to diminish with rising dimensions, eventually vanishing altogether. Likewise, simultaneously increasing the newly added parameters in the physically extended Lorenz system leads to higher critical Rayleigh parameter values; however, raising only the scalar-related parameter leads to an eventual return of chaos albeit with an attractor with qualitatively distinct features from the Lorenz attractor. The peculiar bifurcation structure shaped by the competition between the opposing effects of raising the Rayleigh and the scalar-related parameters helps explain this second onset of chaos. Attractor coexistence refers to the partition of the phase space by basin boundaries so that different types of attractors emerge depending on the initial condition. Similar to the original Lorenz system, the physically extended Lorenz system is found to exhibit attractor coexistence stemming from mismatches between the Hopf and heteroclinic bifurcations. If the atmosphere is found to exhibit such behavior, it can have grave implications for atmospheric predictability and ensemble forecasting beyond mere sensitive dependence on initial conditions, which only applies to chaotic solutions. Chaos synchronization is another curious phenomenon known to occur in the Lorenz system. By finding an appropriate Lyapunov function, the physically extended Lorenz system is shown to self-synchronize under the same condition that guarantees self-synchronization in the original Lorenz system. Regarding the generalized Lorenz systems, numerical evidence in support of self- as well as some degree of generalized synchronization, that is, synchronization between two Lorenz systems differing in their dimensions, is provided. Numerical results suggest that the smaller the dimensional difference between the two, the stronger they tend to synchronize. Some conceptual implications of such results are discussed in relation to atmospheric modeling and data assimilation. Especially, the feasibility of using the (3N)(3N)-dimensional Lorenz systems as a testbed for data assimilation methods is explored. For demonstration, the ensemble Kalman filter method is implemented to assimilate observations with ensembles of model outputs generated using the generalized Lorenz systems, whose imperfections are simulated through varying the severity of ensemble over- or underdispersion, dimensional differences, random forcing, and model or observation biases. Further investigation of the generalized Lorenz systems is carried out from the perspective of predictability, showing that predictability measured by deviation time, which is the time when the threshold-exceeding deviations among ensemble members occur, can respond non-monotonically to increases in the system's dimension. Accordingly, deviation time is put forward as a direct measure of predictability due to weather's sensitive dependence on initial conditions. Raising the dimension under the proposed generalizations is thought to be analogous to resolving smaller-scale motions in the vertical direction. The estimated deviation times in an ensemble of real-case simulations using a realistic numerical weather forecasting model reveal that the predictability of real-case simulations also depend non-monotonically on model vertical resolution. It is suggested that beneath this non-monotonicity fundamentally lies chaos inherent to the model atmospheres and, by extension, weather at large.1 Overview 1 1.1 Chaos and the Lorenz system 1 1.2 Extending the Lorenz system 6 1.3 Bifurcations and related phenomena 8 1.4 Chaos in the atmosphere 14 1.5 Organization of the dissertation 16 2 Chaos and Periodicity of the High-Order Lorenz Systems 18 2.1 Introduction 18 2.2 The high-order Lorenz systems 20 2.2.1 Derivation 22 2.2.2 Some properties of the Lorenz systems 24 2.3 Numerical methods 26 2.4 Results 32 2.4.1 Periodicity diagrams 32 2.4.2 Bifurcation diagrams and phase portraits 34 2.5 Discussion 40 3 A Physically Extended Lorenz System with Rotation and Density-Affecting Scalar 42 3.1 Introduction 42 3.2 Derivation 45 3.3 Effects of rotation and scalar 49 3.3.1 Fixed points and stability 49 3.3.2 Bifurcation structure in the rT-ฯƒ space 52 3.3.3 Bifurcations along rC and s 55 3.4 The case when ฮฒ < 0 65 3.4.1 Bifurcation and the onset of chaos 67 3.4.2 Chaotic attractors and associated flow patterns 73 3.5 Self-synchronization 81 3.6 Discussion 85 4 Coexisting Attractors in the Physically Extended Lorenz System 87 4.1 Introduction 87 4.2 Methodology 89 4.3 Results 92 4.3.1 Coexisting attractors in the LorenzStenflo system 92 4.3.2 Coexisting attractors under rotation and scalar 100 4.4 Discussion 110 5 The (3N)- and (3N + 2)-Dimensional Generalizations of the Lorenz System 113 5.1 Introduction 113 5.2 The generalized Lorenz systems 115 5.2.1 The Pk- and Qk-sets for nonlinear terms 115 5.2.2 The (3N)- and (3N + 2)-dimensional systems 116 5.2.3 Choosing the nonlinear pairs 117 5.3 Derivation 119 5.3.1 The (3N)-dimensional generalization 121 5.3.2 The (3N + 2)-dimensional generalization 126 5.4 Effects of dimension in parameter spaces 126 5.4.1 Linear stability analysis 126 5.4.2 Chaos in dimension-parameter spaces 130 5.5 Perspectives on predictability 136 5.5.1 Notions of predictability 136 5.5.2 Twin experiments and deviation time 138 5.6 Discussion 144 6 Chaos Synchronization in the Generalized Lorenz Systems 147 6.1 Introduction 147 6.2 Self-synchronization 149 6.2.1 Numerical evidence 149 6.2.2 Error subsystems 155 6.3 Application in image encryption 157 6.3.1 Demonstration: A simple approach 157 6.3.2 Demonstration: An alternative approach 168 6.4 Beyond self-synchronization 172 6.5 Discussion 180 7 The Generalized Lorenz Systems as a Testbed for Data Assimilation: The Ensemble Kalman Filter 182 7.1 Introduction 182 7.2 Methodology 187 7.2.1 Implementation of the ensemble Kalman filter 188 7.3 Results 191 7.3.1 Effects of ensemble size and model accuracy 191 7.3.2 Effects of observation frequency and accuracy 205 7.3.3 Effects of observation and model biases 214 7.4 Discussion 218 8 Can Chaos Theory Explain Non-Monotonic Dependence of Atmospheric Predictability on Model Vertical Resolution 220 8.1 Introduction 220 8.2 Background 222 8.2.1 Lorenz's ideas about atmospheric predictability 222 8.2.2 Model vertical resolution and predictability in numerical weather prediction 224 8.3 Results 229 8.3.1 Deviation time in the Lorenz systems revisited 229 8.3.2 WRF model control simulations 232 8.3.3 WRF model ensemble experiments and deviation time 241 8.3.4 Spatial distribution of deviation time 254 8.4 Discussion 261 9 Summary and Final Remarks 264 Bibliography 271 Abstract in Korean 295 Acknowledgments 299 Index 303๋ฐ•

    Recurrence analysis of forced synchronization in a self-excited thermoacoustic system

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    We use recurrence analysis to investigate the forced synchronization of a self-excited thermoacoustic system. The system consists of a swirl-stabilized turbulent premixed flame in an open-ended duct. We apply periodic acoustic forcing to this system at different amplitudes and frequencies around its natural self-excited frequency, and examine its response via unsteady pressure measurements. On increasing the forcing amplitude, we observe two bifurcations: from a periodic limit cycle (unforced) to quasiperiodicity (weak forcing) and then to lock-in (strong forcing). To analyse these bifurcations, we use cross-recurrence plots (CRPs) of the unsteady pressure and acoustic forcing. We find that the different time scales characterizing the quasiperiodicity and the transition to lock-in appear as distinct structures in the CRPs. We then examine those structures using cross recurrence quantification analysis (CRQA) and find that their recurrence quantities change even before the system transitions to lock-in. This shows that CRPs and CRQA can be used as alternative nonlinear tools to study forced synchronization in thermoacoustic systems, complementing classical linear tools such as spectral analysis.EPSR

    Entropy in Dynamic Systems

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    In order to measure and quantify the complex behavior of real-world systems, either novel mathematical approaches or modifications of classical ones are required to precisely predict, monitor, and control complicated chaotic and stochastic processes. Though the term of entropy comes from Greek and emphasizes its analogy to energy, today, it has wandered to different branches of pure and applied sciences and is understood in a rather rough way, with emphasis placed on the transition from regular to chaotic states, stochastic and deterministic disorder, and uniform and non-uniform distribution or decay of diversity. This collection of papers addresses the notion of entropy in a very broad sense. The presented manuscripts follow from different branches of mathematical/physical sciences, natural/social sciences, and engineering-oriented sciences with emphasis placed on the complexity of dynamical systems. Topics like timing chaos and spatiotemporal chaos, bifurcation, synchronization and anti-synchronization, stability, lumped mass and continuous mechanical systems modeling, novel nonlinear phenomena, and resonances are discussed

    Dynamic primitives of motor behavior

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    We present in outline a theory of sensorimotor control based on dynamic primitives, which we define as attractors. To account for the broad class of human interactive behaviorsโ€”especially tool useโ€”we propose three distinct primitives: submovements, oscillations, and mechanical impedances, the latter necessary for interaction with objects. Owing to the fundamental features of the neuromuscular systemโ€”most notably, its slow responseโ€”we argue that encoding in terms of parameterized primitives may be an essential simplification required for learning, performance, and retention of complex skills. Primitives may simultaneously and sequentially be combined to produce observable forces and motions. This may be achieved by defining a virtual trajectory composed of submovements and/or oscillations interacting with impedances. Identifying primitives requires care: in principle, overlapping submovements would be sufficient to compose all observed movements but biological evidence shows that oscillations are a distinct primitive. Conversely, we suggest that kinematic synergies, frequently discussed as primitives of complex actions, may be an emergent consequence of neuromuscular impedance. To illustrate how these dynamic primitives may account for complex actions, we brieflyreviewthree typesof interactivebehaviors: constrained motion, impact tasks, and manipulation of dynamic objects.United States. National Institutes of Health (T32GM008334)American Heart Association (11SDG7270001)National Science Foundation (U.S.) (NSF DMS-0928587
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