Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Fractal modelling”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

184 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Active Upper-atmosphere Chemistry and Dynamics from Polar Circulation Reversal on Titan

Saturn's moon Titan has a nitrogen atmosphere comparable to Earth's, with a surface pressure of 1.4 bar. Numerical models reproduce the tropospheric conditions very well but have trouble explaining the observed middle-atmosphere temperatures, composition and winds. The top of the middle-atmosphere circulation has been thought to lie at an altitude of 450 to 500 kilometres, where there is a layer of haze that appears to be separated from the main haze deck. This 'detached' haze was previously explained as being due to the colocation of peak haze production and the limit of dynamical transport by the circulation's upper branch. Herewe report a build-up of trace gases over the south pole approximately two years after observing the 2009 post-equinox circulation reversal, from which we conclude that middle-atmosphere circulation must extend to an altitude of at least 600 kilometres. The primary drivers of this circulation are summer-hemisphere heating of haze by absorption of solar radiation and winter-hemisphere cooling due to infrared emission by haze and trace gases; our results therefore imply that these effects are important well into the thermosphere (altitudes higher than 500 kilometres). This requires both active upper-atmosphere chemistry, consistent with the detection of high-complexity molecules and ions at altitudes greater than 950 kilometres, and an alternative explanation for the detached haze, such as a transition in haze particle growth from monomers to fractal structures.

Titan Atmosphere↗
NASA NTRS2003

Examination of the Atomic Pair Distribution Function (PDF) of SiC Nanocrystals by In-situ High Pressure Diffraction

Key properties of nanocrystals are determined by their real atomic structure, therefore a reasonable understanding and meaningful interpretation of their properties requires a realistic model of the structure. In this paper we present an evidence of a complex response of the lattice distances to external pressure indicating a presence of a complex structure of Sic nanopowders. The experiments were performed on nanocrystalline Sic subjected to hydrostatic or isostatic pressure using synchrotron and neutron powder diffraction. Elastic properties of the samples were examined based on X-ray diffraction data using a Diamond Anvil Cell (DAC) in HASYLAB at DESY. The dependence'of the lattice parameters and of the Bragg reflections width with pressure exhibits a ha1 nature of the properties (compressibilities) of the powders and indicates a complex structure of the grains. We interpreted tws behaviour as originating from different elastic properties of the grain interior and surface. Analysis of the dependence of individual interatomic distances on pressure was based on in-situ neutron diffraction measurements done with HbD diffractometer at LANSCE in Los Alamos National Laboratory with the Paris-Edinburgh cell under pressures up to 8 GPa (Qmax = 26/A). Interatomic distances were obtained by PDF analysis using the PDFgetN program. We have found that the interatomic distances undergo a complex, non-monotonic changes. Even under substantial pressures a considerable relaxation of the lattice may take place: some interatomic distances increase with an increase in pressure. We relate this phenomenon to: (1), changes of the microstructure of the densified material, in particular breaking of its fractal chain structure and, (2), its complex structure resembling that of a material composed of two phases, each with its distinct elastic properties.

Grzanka, E.↗
NASA NTRS2017

Time and Measurement Days

Questions in data analysis involving the concepts of time and measurement are often pushed into the background or reserved for a philosophical discussion. Some examples are: a) Is causality a consequence of the laws of physics, or can the arrow of time be reversed? b) Can we determine the arrow of time of an event? c) Do we need the continuum hypothesis for the underlying function in any measurement process? d) Can we say anything about the analyticity of the underlying process of an event? e) Would it be valid to model a non-analytical process as function of time? f) What are the implications of all these questions for classical Fourier techniques? However, in the age of big data gathered either from space missions supplying ultra-precise long time series, or e.g. LIGO data from the ground, the moment to bring these questions to the foreground seems arrived. The limitations of our understanding of some fundamental processes is emphasized by the lack of solution for problems open for more than 2 decades, such as the non-detection of solar g-modes, or the modal identification of main sequence stellar pulsators like delta Scuti stars. Flicker noise or 1/f noise, for example, attributed in solar-like stars to granulation, is analyzed mostly only to apply noise reduction techniques, neither considering the classical problem of 1/f noise that was introduced a 100 years ago, nor taking into account ergodic or non-ergodic solutions that make inapplicable spectral analysis techniques in practice. This topic was discussed by Nicholas W. Watkins during the ITISE meeting held in Granada in 2016. There he presented preliminary results of his research on Mandelbrot's related work. We reproduce here his quotation of Mandelbrot (1999) "There is a sharp contrast between a highly anomalous ("non-white") noise that proceeds in ordinary clock time and a noise whose principal anomaly is that it is restricted to fractal time", suggesting a connection with the above proposed topics that could be phrased as the following additional questions:a) Is self-organized criticality (SOC) frequent in astrophysical phenomena? b) Could all fractals in nature be considered stochastic? c) Could we establish mathematical/physical relationships between chaotic and fractal behaviors in time series? d) Could the differences between fractals and chaos in terms of analyticity be used to understand the residuals of the fitting of stellar light curves? In this meeting we would like to approximate these problems from a holistic and multidisciplinary perspective, taking into account not only technical issues but also the deeper implications. In particular the concept of connectivity (introduced in Pascual-Granado et al. A&A, 2015) could be used to implement, within the framework of ARMA processes, an "arrow of time" (see attached document), and so studying the possible implications in the concept of time as envisaged by Watkins.

data analysis↗
NASA NTRS1995

Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series

The healthy heartbeat is traditionally thought to be regulated according to the classical principle of homeostasis whereby physiologic systems operate to reduce variability and achieve an equilibrium-like state [Physiol. Rev. 9, 399-431 (1929)]. However, recent studies [Phys. Rev. Lett. 70, 1343-1346 (1993); Fractals in Biology and Medicine (Birkhauser-Verlag, Basel, 1994), pp. 55-65] reveal that under normal conditions, beat-to-beat fluctuations in heart rate display the kind of long-range correlations typically exhibited by dynamical systems far from equilibrium [Phys. Rev. Lett. 59, 381-384 (1987)]. In contrast, heart rate time series from patients with severe congestive heart failure show a breakdown of this long-range correlation behavior. We describe a new method--detrended fluctuation analysis (DFA)--for quantifying this correlation property in non-stationary physiological time series. Application of this technique shows evidence for a crossover phenomenon associated with a change in short and long-range scaling exponents. This method may be of use in distinguishing healthy from pathologic data sets based on differences in these scaling properties.

NASA Discipline Number 14-10↗