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Significant gains have recently been made in our knowledge and understanding of the interplanetary manifestation of solar structure and solar dynamic phenomena in the vicinity of the Sun.
Acquiring quantitative metrics!based knowledge about the performance of various space physics modeling approaches is central for the space weather community. Quantification of the performance helps the users of the modeling products to better understand the capabilities of the models and to choose the approach that best suits their specific needs. Further, metrics!based analyses are important for addressing the differences between various modeling approaches and for measuring and guiding the progress in the field. In this paper, the metrics!based results of the ground magnetic field perturbation part of the Geospace Environment Modeling 2008 2009 Challenge are reported. Predictions made by 14 different models, including an ensemble model, are compared to geomagnetic observatory recordings from 12 different northern hemispheric locations. Five different metrics are used to quantify the model performances for four storm events. It is shown that the ranking of the models is strongly dependent on the type of metric used to evaluate the model performance. None of the models rank near or at the top systematically for all used metrics. Consequently, one cannot pick the absolute winner : the choice for the best model depends on the characteristics of the signal one is interested in. Model performances vary also from event to event. This is particularly clear for root!mean!square difference and utility metric!based analyses. Further, analyses indicate that for some of the models, increasing the global magnetohydrodynamic model spatial resolution and the inclusion of the ring current dynamics improve the models capability to generate more realistic ground magnetic field fluctuations.
The Observation and Analysis of Smectic Islands in Space (OASIS) project comprises a series of experiments that will probe the interfacial and hydrodynamic behavior of freely suspended liquid crystal films in space. These are the thinnest known stable condensed phase structures, making them ideal for studies of fluctuation and interface phenomena. The experiments seek to verify theories of coarsening dynamics, hydrodynamic flow, relaxation of hydrodynamic perturbations, and hydrodynamic interactions of a near two-dimensional structure. The effects of introducing islands or droplets on a very thin bubble will be studied, both as controllable inclusions that modify the flow and as markers of flow.
Gearbox-generated noise and vibration is objectionable in many vehicles, particularly helicopters. This noise excitation is caused by the load fluctuation as gear teeth enter and leave mesh. In high-quality gears, a common technique to reduce gear noise and vibration is to modify the tooth profile. Gear noise reduction is a NASA and U.S. Army goal, and a NASA/U.S. Army research project sponsored development of gear dynamics computer codes to help design quiet gears. As part of this project, a series of experiments was performed in the NASA Lewis Research Center's Gear Noise Rig to develop a data base of dynamic test data and to validate the predictions of the codes for several gear designs under a variety of test conditions.
A major factor in dust dynamics in a microgravity environment is the absence of settling; hence, Spacelab air will contain larger particles than a comparable laboratory on earth. In addition, the presence of low level acceleration fluctuations (thruster firings, crew motions, etc.) could inhibit dust removal by surface scavenging. Because of the presence of larger particles, aerosol dynamics at larger Reynolds numbers must be considered. An accurate drag coefficient for spherical particles was developed for the higher Reynolds number phenomena. A general graphic correlation for the drag coefficient as a function of Reynolds and Knudsen numbers was developed based on currently available drag data for spheres. The general equations that govern dust dynamics were reviewed.
The accuracy of velocity measurements made with a laser velocimeter is strongly dependent upon the response of the seeding particles to the dynamics of the flow field. The smaller the particle the better the response to flow fluctuations and gradients and therefore the more accurate velocity measurement. In direct conflict is the requirement of light scattering efficiency to obtain signals with the laser velocimeter which, in general, is better as the particle size is increased. In low speed flow fields these two requirements on particle size overlap and accurate measurements may be obtained. However in high speed flows, where the velocity gradients may be severe, very small particles are required to maintain sufficient dynamic response characteristics to follow the flow. Therefore if velocity measurements are to be made in these flows, the laser velocimeter must be designed with sufficient sensitivity to obtain signals from these small particles. An insitu determination of the size distribution of kaolin particles (Al2O3, .2 + or - SiO2 . 2H2O) in the 16-foot Transonic Tunnel and the sensitivity characteristics of the laser velocimeter system is described.
Preliminary results are based on analyses of data from 17 crewmembers. The initial analysis compares pre to post-flight changes in total cerebral blood flow (CBF) and craniospinal CSF flow volume. Total CBF is obtained by summation of the mean flow rates through the 4 blood vessels supplying the brain (right and left internal carotid and vertebral arteries). Volumetric flow rates were obtained using an automated lumen segmentation technique shown to have 3-4-fold improved reproducibility and accuracy over manual lumen segmentation (6). Two cohorts, 5 short-duration and 8 long-duration crewmembers, who were scanned within 3 to 8 days post landing were included (4 short-duration crewmembers with MRI scans occurring beyond 10 days post flight were excluded). The VIIP Clinical Practice Guideline (CPG) classification is being used initially as a measure for VIIP syndrome severity. Median CPG scores of the short and long-duration cohorts were similar, 2. Mean preflight total CBF for the short and long-duration cohorts were similar, 863+/-144 and 747+/-119 mL/min, respectively. Percentage CBF changes for all short duration crewmembers were 11% or lower, within the range of normal physiological fluctuations in healthy individuals. In contrast, in 4 of the 8 long-duration crewmembers, the change in CBF exceeded the range of normal physiological fluctuation. In 3 of the 4 subjects an increase in CBF was measured. Large pre to post-flight changes in the craniospinal CSF flow volume were found in 6 of the 8 long-duration crewmembers. Box-Whisker plots of the CPG and the percent CBF and CSF flow changes for the two cohorts are shown in Figure 4. Examples of CSF flow waveforms for a short and two long-duration (CPG 0 and 3) are shown in Figure 5. Changes in CBF and CSF flow dynamics larger than normal physiological fluctuations were observed in the long-duration crewmembers. Changes in CSF flow were more pronounced than changes in CBF. Decreased CSF flow dynamics were observed in a subject with VIIP signs. Study limitations include a slightly longer landing-to-MRI scan period for the short-duration cohort and limited sensitivity of the subjective discrete ordinal CPG scale. This limitation can be overcome by using imaging based parametric measures of VIIP severity such as globe deformation measures.
A method to analyze the static and dynamic loads in a planetary gear train was developed. A variable-variable mesh stiffness (VVMS) model was used to simulate the external and internal spur gear mesh behavior, and an equivalent conventional gear train concept was adapted for the dynamic studies. The analysis can be applied either involute or noninvolute spur gearing. By utilizing the equivalent gear train concept, the developed method may be extended for use for all types of epicyclic gearing. The method is incorporated into a computer program so that the static and dynamic behavior of individual components can be examined. Items considered in the analysis are: (1) static and dynamic load sharing among the planets; (2) floating or fixed Sun gear; (3) actual tooth geometry, including errors and modifications; (4) positioning errors of the planet gears; (5) torque variations due to noninvolute gear action. A mathematical model comprised of power source, load, and planetary transmission is used to determine the instantaneous loads to which the components are subjected. It considers fluctuating output torque, elastic behavior in the system, and loss of contact between gear teeth. The dynamic model has nine degrees of freedom resulting in a set of simultaneous second order differential equations with time varying coefficients, which are solved numerically. The computer program was used to determine the effect of manufacturing errors, damping and component stiffness, and transmitted load on dynamic behavior. It is indicated that this methodology offers the designer/analyst a comprehensive tool with which planetary drives may be quickly and effectively evaluated.
The acoustic pressure fluctuations due to large-scale finite amplitude disturbances in a free turbulent shear flow are calculated. The flow is decomposed into three component scales; the mean motion, the large-scale wave-like disturbance, and the small-scale random turbulence. The effect of the large-scale structure on the flow is isolated by applying both a spatial and phase average on the governing differential equations and by initially taking the small-scale turbulence to be in energetic equilibrium with the mean flow. The subsequent temporal evolution of the flow is computed from global energetic rate equations for the different component scales. Lighthill's theory is then applied to the region with the flowfield as the source and an observer located outside the flowfield in a region of uniform velocity. Since the time history of all flow variables is known, a minimum of simplifying assumptions for the Lighthill stress tensor is required, including no far-field approximations. A phase average is used to isolate the pressure fluctuations due to the large-scale structure, and also to isolate the dynamic process responsible. Variation of mean square pressure with distance from the source is computed to determine the acoustic far-field location and decay rate, and, in addition, spectra at various acoustic field locations are computed and analyzed. Also included are the effects of varying the growth and decay of the large-scale disturbance on the sound produced.
Results of an investigation to study the interaction between ocean and atmosphere on the annual to decadal time scale are reported. Separate studies of the ocean response to atmospheric forcing, and of atmospheric response to ocean forcing were also conducted. The main findings are the dynamics of sea surface temperature anomalies, the role of short time scale weather fluctuations in the seasonal cycle of the upper ocean variability, and the planetary wave response to sea surface temperature anomalies. A numerical model of the ocean atmosphere continent system and a two layer quasi-geostropic ocean model is discussed.
The structure of a transport theory presented by Zhou and Matthaeus (1989), in which coupling of 'inward' and 'outward'-type fluctuations appears in the leading order, is studied. Allowance is made for the dynamic behavior of the 'fast' scale variables, which are averaged over to obtain slow-scale transport equations. The relationship of the two-scale transport models to transport models derived from the WKB approximation (Jeffreys and Jeffreys, 1980: Weinberg 1962; Dewar, 1970) as it has been applied to the solar wind MHD wave problem (Parker, 1965; Hollweg, 1973, 1974) is discussed.
Supercomputer optimizations for a computational method of solving stochastic, multibody, dynamic programming problems are presented. The computational method is valid for a general class of optimal control problems that are nonlinear, multibody dynamical systems, perturbed by general Markov noise in continuous time, i.e., nonsmooth Gaussian as well as jump Poisson random white noise. Optimization techniques for vector multiprocessors or vectorizing supercomputers include advanced data structures, loop restructuring, loop collapsing, blocking, and compiler directives. These advanced computing techniques and superconducting hardware help alleviate Bellman's curse of dimensionality in dynamic programming computations, by permitting the solution of large multibody problems. Possible applications include lumped flight dynamics models for uncertain environments, such as large scale and background random aerospace fluctuations.
Geophysical data rarely show any smoothness at any scale, and this often makes comparison with theoretical model output difficult. However, highly fluctuating signals and fractal structures are typical of open dissipative systems with nonlinear dynamics, the focus of most geophysical research. High levels of variability are excited over a large range of scales by the combined actions of external forcing and internal instability. At very small scales we expect geophysical fields to be smooth, but these are rarely resolved with available instrumentation or simulation tools; nondifferentiable and even discontinuous models are therefore in order. We need methods of statistically analyzing geophysical data, whether measured in situ, remotely sensed or even generated by a computer model, that are adapted to these characteristics. An important preliminary task is to define statistically stationary features in generally nonstationary signals. We first discuss a simple criterion for stationarity in finite data streams that exhibit power law energy spectra and then, guided by developments in turbulence studies, we advocate the use of two ways of analyzing the scale dependence of statistical information: singular measures and qth order structure functions. In nonstationary situations, the approach based on singular measures seeks power law behavior in integrals over all possible scales of a nonnegative stationary field derived from the data, leading to a characterization of the intermittency in this (gradient-related) field. In contrast, the approach based on structure functions uses the signal itself, seeking power laws for the statistical moments of absolute increments over arbitrarily large scales, leading to a characterization of the prevailing nonstationarity in both quantitative and qualitative terms. We explain graphically, step by step, both multifractal statistics which are largely complementary to each other. The geometrical manifestations of nonstationarity and intermittency, 'roughness' and 'sparseness', respectively, are illustrated and the associated analytical (differentiability and continuity) properties are discussed. As an example, the two techniques are applied to a series of recent measurements of liquid water distributions inside marine stratocumulus decks; these are found to be multifractal over scales ranging from approximately 60 m to approximately 60 km. Finally, we define the 'mean multifractal plane' and show it to be a simple yet comprehensive tool with many applications including data intercomparison, (dynamical or stochastic) model and retrieval validations.
Geoid anomalies and dynamic topography are two important diagnostics of mantle convection. We present geoid and topography results for several time-dependent convection models in spherical axisymmetric geometry for Rayleigh numbers between 10(exp 6) and 10(exp 7) with depth-dependent viscosity and mixtures of bottom and internal heating. The models are strongly chaotic, with boundary layer instabilities erupting out of both thermal boundary layers. In some instances, instabilities from one boundary layer influence the development of instabilities in the other boundary layer. Such coupling between events at the top and bottom of the mantle has been suggested to play a role in a mid-Cretaceous episode of enhanced volcanism in the Pacific. These boundary layer instabilities produce large temporal variations in the geoid anomalies and dynamic nd to the topography associated with the convection. The amplitudes of these fluctuations depend on the detailed model parameter,.% it of this but fluctuations of 30-50% relative to the time-averaged geoid and topography are common. The convective planform is strongly sensitive to the specific initial conditions. Convection cells with larger aspect ratio tend to have larger fractional fluctuations in their geoid and topography amplitudes, because boundary layer instabilities have more time to develop in long cells. In some instances, we observe low-amplitude topographic highs adjacent to the topographic lows produced by cold downwellings. We discuss applications of these results to several situations, including the temporal variability of m basis. hotspots such as Hawaii, the topography of subduction zone outer rises, and the topography of coronae on Venus.
The Growth and Morphology of Supercritical Fluids (GMSF) is an international experiment facilitated by the NASA Glenn Research Center at Lewis Field and under the guidance of U.S. principal investigator Professor John Hegseth of the University of New Orleans and three French coinvestigators Daniel Beysens, Yves Garrabos, and Carole Chabot. In early 1999, GMSF experiments were operated for 20 days on the Russian Space Station Mir. Mir astronauts performed experiments One through Seven, which spanned the three science themes of near-critical phase separation rates, interface dynamics in near-critical boiling, and measurement of the spectrum of density fluctuation length scales very close to the critical point. The fluids used were pure CO2 or SF6. Three of the five thermostats used could adjust the sample volume with the scheduled crew time. Such a volume adjustment enabled variable sample densities around the critical density as well as pressure steps (as distinct from the usual temperature steps) to be applied to the sample.
The Growth and Morphology of Supercritical Fluids (GMSF) is an international experiment facilitated by the NASA Glenn Research Center and under the guidance of U.S. principal investor Professor Hegseth of the University of New Orleans and three French coinvestigators: Daniel Beysens, Yves Garrabos, and Carole Chabot. The GMSF experiments were concluded in early 1999 on the Russian space station Mir. The experiments spanned the three science themes of near-critical phase separation rates, interface dynamics in near-critical boiling, and measurement of the spectrum of density fluctuation length scales very close to the critical point. The fluids used were pure CO2 or SF6. Three of the five thermostats used could adjust the sample volume with the scheduled crew time. Such a volume adjustment enabled variable sample densities around the critical density as well as pressure steps (as distinct from the usual temperature steps) applied to the sample. The French-built ALICE II facility was used for these experiments. It allows tightly thermostated (left photograph) samples (right photograph) to be controlled and viewed/measured. Its diagnostics include interferometry, shadowgraph, high-speed pressure measurements, and microscopy. Data were logged on DAT tapes, and PCMCIA cards and were returned to Earth only after the mission was over. The ground-breaking near critical boiling experiment has yielded the most results with a paper published in Physical Review Letters (ref. 1). The boiling work also received press in Science Magazine (ref. 2). This work showed that, in very compressible near-critical two-phase pure fluids, a vapor bubble was induced to temporarily overheat during a rapid heating of the sample wall. The temperature rise in the vapor was 23-percent higher than the rise in the driving container wall. The effect is due to adiabatic compression of the vapor bubble by the rapid expansion of fluid near the boundary during heatup. Thermal diffusivity is low near the critical point, so getting heat out of the compressed bubble is observably slow. This gives the appearance of a backward heat flow, or heat flow from a cold surface to a warm fluid.
Data measurements made on the NASA MOD-OA 200-kw wind-turbine generator (WTG) installed on a utility grid form the basis for an overall performance analysis. Fuel displacement/-savings, dynamic interactions, and WTG excitation (reactive-power) control effects are studied. Continuous recording of a large number of electrical and mechanical variables on FM magnetic tape permit evaluation and correlation of phenomena over a bandwidth of at least 20 Hz. Because the wind-power penetration reached peaks of 60 percent, the impact of wind fluctuation and wind-turbine/diesel-utility interaction is evaluated in a worst-case scenario. The speed-governor dynamics of the diesel units exhibited an underdamped response, and the utility operation procedures were not altered to optimize overall WTG/utility performance. Primary findings over the data collection period are: a calculated 6.7-percent reduction in fuel consumption while generating 11 percent of the total electrical energy; acceptable system voltage and frequency fluctuations with WTG connected; and applicability of WTG excitation schemes using voltage, power, or VARS as the controlled variable.
Under healthy conditions, the normal cardiac (sinus) interbeat interval fluctuates in a complex manner. Quantitative analysis using techniques adapted from statistical physics reveals the presence of long-range power-law correlations extending over thousands of heartbeats. This scale-invariant (fractal) behavior suggests that the regulatory system generating these fluctuations is operating far from equilibrium. In contrast, it is found that for subjects at high risk of sudden death (e.g., congestive heart failure patients), these long-range correlations break down. Application of fractal scaling analysis and related techniques provides new approaches to assessing cardiac risk and forecasting sudden cardiac death, as well as motivating development of novel physiologic models of systems that appear to be heterodynamic rather than homeostatic.