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At least 37 records · Page 2

Parameter estimation for terrain modeling from gradient data

A method is developed for modeling terrain surfaces for use on an unmanned Martian roving vehicle. The modeling procedure employs a two-step process which uses gradient as well as height data in order to improve the accuracy of the model's gradient. Least square approximation is used in order to stochastically determine the parameters which describe the modeled surface. A complete error analysis of the modeling procedure is included which determines the effect of instrumental measurement errors on the model's accuracy. Computer simulation is used as a means of testing the entire modeling process which includes the acquisition of data points, the two-step modeling process and the error analysis. Finally, to illustrate the procedure, a numerical example is included.

Dangelo, K. R.↗

Interaction of electromagnetic and acoustic waves in a stochastic atmosphere

This paper considers the interaction of electromagnetic and acoustic waves where a Radio Acoustic Sounding System (RASS) is operated in a stochastic environment characterized by turbulence, winds and mean-temperature gradients. It has been shown that for a RASS operating at acoustic frequencies below a few kilohertz propagating under typical atmospheric conditions, turbulence has little effect on the strength of the received radio signal scattered from the pulse at heights up to a few kilometers. This result implies that the received RF signal level (power) is primarily a function of sound intensity which decreases as x exp minus 2 where x is the altitude.

Bhatnagar, N.↗

Control of Complex Dynamic Systems by Neural Networks

This paper considers the use of neural networks (NN's) in controlling a nonlinear, stochastic system with unknown process equations. The NN is used to model the resulting unknown control law. The approach here is based on using the output error of the system to train the NN controller without the need to construct a separate model (NN or other type) for the unknown process dynamics. To implement such a direct adaptive control approach, it is required that connection weights in the NN be estimated while the system is being controlled. As a result of the feedback of the unknown process dynamics, however, it is not possible to determine the gradient of the loss function for use in standard (back-propagation-type) weight estimation algorithms. Therefore, this paper considers the use of a new stochastic approximation algorithm for this weight estimation, which is based on a 'simultaneous perturbation' gradient approximation that only requires the system output error. It is shown that this algorithm can greatly enhance the efficiency over more standard stochastic approximation algorithms based on finite-difference gradient approximations.

Spall, James C.↗

Comprehensive modeling of turbulent particulate flows using Eulerian and Lagrangian schemes

This paper addresses turbulent particle dispersion and modulation effects in dilute gas-particle turbulent flows using the Eulerian and Lagrangian modeling approaches. Gradient diffusion approximations are employed in the Eulerian formulation, while a stochastic procedure is utilized to simulate turbulent dispersion in the Lagrangian formulation. The k-epsilon turbulence model is used to characterize the time and length scales of the continuous phase turbulence. For the particle size and loading considered, the turbulence transport equations must be modified to account for the modulation effects. Models are proposed for both Eulerian and Lagrangian schemes. Comparisons and predictions are made in fully developed gas-solid pipe flow and confined coaxial jets laden with particles. For the monodispersed system investigated, Eulerian approach is less expensive and gives more consistent results than the Lagrangian approach. The Lagrangian technique should be further developed to eliminate current inherent inconsistencies especially with regard to the symmetry boundary condition.

Adeniji-Fashola, A.↗

Modeling of confined turbulent fluid-particle flows using Eulerian and Lagrangian schemes

Two important aspects of fluid-particulate interaction in dilute gas-particle turbulent flows (the turbulent particle dispersion and the turbulence modulation effects) are addressed, using the Eulerian and Lagrangian modeling approaches to describe the particulate phase. Gradient-diffusion approximations are employed in the Eulerian formulation, while a stochastic procedure is utilized to simulate turbulent dispersion in the Lagrangina formulation. The k-epsilon turbulence model is used to characterize the time and length scales of the continuous phase turbulence. Models proposed for both schemes are used to predict turbulent fully-developed gas-solid vertical pipe flow with reasonable accuracy.

Adeniji-Fashola, A.↗

Robust Trajectory Optimization Techniques Using a Sweeping Gradient Method and Linear Covariance Analysis

We present robust trajectory optimization techniques using a sweeping gradient method for ordinary differential equations with events (SGM) and linear covariance analysis (LinCov). SGM is a method for computing the gradient of trajectory analyses defined by performance indices over initial value problems with events with respect to static parameters. LinCov is an analytic technique for predicting stochastic behavior of dynamical systems. By combining SGM and LinCov, it is possible use efficient, off-the-shelf, gradient-based optimizers to solve robust optimal trajectory design problems. We describe the individual methods and some details on how they can be combined. Then we apply the combined techniques to a variety of orbital trajectory design problems to demonstrate its use, including minimum fuel transfer and mid-course correction burn scheduling.

Benjamin W L Margolis↗

Co-Optimization of Navigation System Requirements and Trajectory Design Using a Sweeping Gradient Method and Linear Covariance Analysis

We describe the application of a sweeping gradient method for ordinary differential equations with events (SGM) and linear covariance analysis (LinCov) to the co-optimization of navigation system requirement generation and robust trajectory design. SGM is a method for computing the gradient of trajectory analyses defined by performance indices over initial value problems with events with respect to static parameters. LinCov is an analytic technique for predicting stochastic behavior of dynamical systems. By combining SGM and LinCov, it is possible use efficient, off-the-shelf, gradient-based optimizers to solve a combined robust optimal trajectory and navigation system design problem. In this paper, we formulate the required models to apply the combined SGM and LinCov techniques to a Near-Rectilinear Halo Orbit rendezvous approach scenario and show results for several intermediate problems.

Benjamin W L Margolis↗

Dusty plasmas in the solar system

The processes that lead to charging of dust grains in a plasma are briefly reviewed. Whereas for single grains the results have been long known, the reduction of the average charge on a grain by 'Debye screening' has only recently been discovered. This reduction can be important in the Jovian ring and in the rings of Uranus. The emerging field of gravitoelectrodynamics which deals with the motion of charged grains in a planetary magnetosphere is then reviewed. Important mechanisms for distributing grains in radial distance are due to stochastic fluctuations of the grain charge and a systematic variation due to motion through plasma gradients. The electrostatic levitation model for the formation of spokes is discussed, and it is shown that the radial transport of dust contained in the spokes may be responsible for the rich radial structure in Saturn's rings. Finally, collective effects in dusty plasmas are discussed which affect various waves, such as density waves in planetary rings and low-frequency plasma waves. The possibility of charged grains forming a Coulomb lattice is briefly described.

Goertz, C. K.↗

Modeling of transition and surface roughness effects in boundary-layer flows

Experiments were carried out to examine the influence of three-dimensional, stochastic roughness on the growth of incompressible turbulent boundary layers, as well as the effect of streamwise pressure gradients and freestream turbulence intensity on smooth-wall boundary-layer transition. The modeling of these effects in a two-dimensional boundary-layer computation program was examined with the help of the experiments. A model for surface roughness was developed that relates directly measurable statistical parameters quantifying the roughness geometry to the aerodynamic effects. This model should be valid for a limited class of surfaces found on turbomachinery blading and in other engineering applications. Commonly used criteria for the transition onset performed poorly and presumably need to be modified to account for other factors influencing the process.

Feiereisen, W. J.↗

Optimal active control for Burgers equations

A method for active fluid flow control based on control theory is discussed. Dynamic programming and fixed point successive approximations are used to accommodate the nonlinear control problem. The long-term goal of this project is to establish an effective method applicable to complex flows such as turbulence and jets. However, in this report, the method is applied to stochastic Burgers equation as an intermediate step towards this goal. Numerical results are compared with those obtained by gradient search methods.

Ikeda, Yutaka↗

Hybrid Differential Dynamic Programming with Stochastic Search

Differential dynamic programming (DDP) has been demonstrated as a viable approach to low-thrust trajectory optimization, namely with the recent success of NASAs Dawn mission. The Dawn trajectory was designed with the DDP-based Static Dynamic Optimal Control algorithm used in the Mystic software. Another recently developed method, Hybrid Differential Dynamic Programming (HDDP) is a variant of the standard DDP formulation that leverages both first-order and second-order state transition matrices in addition to nonlinear programming (NLP) techniques. Areas of improvement over standard DDP include constraint handling, convergence properties, continuous dynamics, and multi-phase capability. DDP is a gradient based method and will converge to a solution nearby an initial guess. In this study, monotonic basin hopping (MBH) is employed as a stochastic search method to overcome this limitation, by augmenting the HDDP algorithm for a wider search of the solution space.

Aziz, Jonathan↗

Hybrid Differential Dynamic Programming with Stochastic Search

Differential dynamic programming (DDP) has been demonstrated as a viable approach to low-thrust trajectory optimization, namely with the recent success of NASA's Dawn mission. The Dawn trajectory was designed with the DDP-based Static/Dynamic Optimal Control algorithm used in the Mystic software.1 Another recently developed method, Hybrid Differential Dynamic Programming (HDDP),2, 3 is a variant of the standard DDP formulation that leverages both first-order and second-order state transition matrices in addition to nonlinear programming (NLP) techniques. Areas of improvement over standard DDP include constraint handling, convergence properties, continuous dynamics, and multi-phase capability. DDP is a gradient based method and will converge to a solution nearby an initial guess. In this study, monotonic basin hopping (MBH) is employed as a stochastic search method to overcome this limitation, by augmenting the HDDP algorithm for a wider search of the solution space.

Aziz, Jonathan↗

Optimal maneuvering and fine pointing control of large space telescope with a new magnetically suspended, single gimballed momentum storage device

This paper considers the application of an Annular Momentum Control Device (AMCD) to both fine pointing and large-angle maneuvering of a large space telescope (LST). The AMCD, which consists principally of a spinning rim suspended in noncontacting electromagnetic bearings, represents a new development in momentum storage devices. A nonlinear mathematical model of the AMCD/LST system is derived. An optimal stochastic fine-pointing controller is designed via LQG theory and the minimum-energy maneuvering problem is solved via a gradient technique. Number of state variable and control variable constraints, as well as all trigonometric nonlinearities, are considered in the latter part.

Nadkarni, A. A.↗

Gradient-Based Aeroservoelastic Optimization with Static Output Feedback

Static output feedback considers an optimal low-order feedback matrix which directly connects the sensors to the control inputs. This work demonstrates the numerical techniques needed to compute the analytical gradient of the optimal feedback matrix with respect to design variables, which may then be used for gradient-based optimization. The derivatives are demonstrated for aeroservoelastic optimization under a series of closed- loop gust load alleviation constraints, considering a continuous stochastic gust load applied to a transport vehicle configuration, among other design constraints such as utter and maneuver loads. The optimal trade-o s between passive load alleviation and active load alleviation for static output feedback are compared with those from full-state feedback, which may be considered an upper-bound for effective sensor-based control.

Stanford, Bret K.↗

Effect of Head-Down Bed Rest and Artificial Gravity Countermeasure on Cardiac Autonomic and Advanced Electrocardiographic Function

To study the effects of 21 days of head-down bed rest (HDBR), with versus without an artificial gravity (AG) countermeasure, on cardiac autonomic and advanced electrocardiographic function. Fourteen healthy men participated in the study: seven experienced 21 days of HDBR alone ("HDBR controls") and seven the same degree and duration of HDBR but with approximately 1hr daily short-arm centrifugation as an AG countermeasure ("AG-treated"). Five minute supine high-fidelity 12-lead ECGs were obtained in all subjects: 1) 4 days before HDBR; 2) on the last day of HDBR; and 3) 7 days after HDBR. Besides conventional 12-lead ECG intervals and voltages, all of the following advanced ECG parameters were studied: 1) both stochastic (time and frequency domain) and deterministic heart rate variability (HRV); 2) beat-to-beat QT interval variability (QTV); 3) T-wave morphology, including signal-averaged T-wave residua (TWR) and principal component analysis ratios; 4) other SAECG-related parameters including high frequency QRS ECG and late potentials; and 5) several advanced ECG estimates of left ventricular (LV) mass. The most important results by repeated measures ANOVA were that: 1) Heart rates, Bazett-corrected QTc intervals, TWR, LF/HF power and the alpha 1 of HRV were significantly increased in both groups (i.e., by HDBR), but with no relevant HDBR*group differences; 2) All purely "vagally-mediated" parameters of HRV (e.g., RMSSD, HF power, Poincare SD1, etc.), PR intervals, and also several parameters of LV mass (Cornell and Sokolow-Lyon voltages, spatial ventricular activation times, ventricular gradients) were all significantly decreased in both groups (i.e., by HDBR), but again with no relevant HDBR*group differences); 3) All "generalized" or "vagal plus sympathetic" parameters of stochastic HRV (i.e., SDNN, total power, LF power) were significantly more decreased in the AG-treated group than in the HDBR-only group (i.e., here there was a relevant HDBR*group difference); and 4) QTV index was also significantly more changed (increased) in the AG-treated group than in the HDBR-only group, although this was clearly due to a greater decrease in generalized HRV and not to a greater increase in QTV proper because there was no relevant HDBR*group effect for either the SDNN or the RMSSD of QTV. Brief daily AG treatment by short-arm centrifuge during each of 21 days of HDBR does not appear to protect against HDBR-related losses of cardiac autonomic function or of LV mass as estimated by ECG.

Schlegel, T. T.↗

Cross-tail current, field-aligned current, and B(y)

Orbits of individual charged particles were traced in a one-dimensional magnetic field model that included a uniform cross-tail component B(sub yo). The effects of B(sub yo) on the cross-tail current distribution j(sub y)(z), the average cross-tail drift velocity(nu(sub y)z), and the average pitch angle change(delta alpha) experienced during current sheet encounters were calculated. The addition of a B(sub yo) that exceeded several tenths of one nanotesla completely eliminated all resonance effects for odd-N orbits. An odd-N resonance involves ions that enter and exit the current sheet on the same side. Pitch angles of nearly all such ions changed substantially during a typical current sheet interaction, and there was no region of large cross-tail drift velocity in the presence of a modest B(sub yo). the addition of a very large B(sub yo) guide field in the direction that enhances the natural drift produces a large j(y) and small (Delta alpha) for ions with all energies. The addition of a modest B(sub yo) had less effect near even-N resonances. In this case, ions in a small energy range were found to undergo so little change in pitch angle that particles which originated in the ionosphere would pass through the current sheet and return to the conjugate ionosphere. Finally, the cross-tail drift of ions from regions dominated by stochastic orbits to regions dominated by either resonant or guiding center orbits was considered. The ion drift speed changed substantially during such transitions. The accompanying electrons obey the guiding center equations, so electron drift is more uniform. Any difference between gradients in the fluxes associated with electron and ion drifts requires the presence of a Birkeland current in order to maintain charge neutrality. This plasma sheet region therefore serves as a current generator. The analysis predicts that the resulting Birkeland current connects to the lowest altitude equatorial regions in which ions drift to or from a point at which stochastic orbits predominate. The proposed mechanism appears only in analyses that include non-guiding-center effects.

Kaufmann, Richard L.↗

Energy dissipation of Alfven wave packets deformed by irregular magnetic fields in solar-coronal arches

The importance of field line geometry for shear Alfven wave dissipation in coronal arches is demonstrated. An eikonal formulation makes it possible to account for the complicated magnetic geometry typical in coronal loops. An interpretation of Alfven wave resonance is given in terms of gradient steepening, and dissipation efficiencies are studied for two configurations: the well-known slab model with a straight magnetic field, and a new model with stochastic field lines. It is shown that a large fraction of the Alfven wave energy flux can be effectively dissipated in the corona.

Similon, Philippe L.↗

Stochastic inflation lattice simulations - Ultra-large scale structure of the universe

Non-Gaussian fluctuations for structure formation may arise in inflation from the nonlinear interaction of long wavelength gravitational and scalar fields. Long wavelength fields have spatial gradients, a (exp -1), small compared to the Hubble radius, and they are described in terms of classical random fields that are fed by short wavelength quantum noise. Lattice Langevin calculations are given for a toy model with a scalar field interacting with an exponential potential where one can obtain exact analytic solutions of the Fokker-Planck equation. For single scalar field models that are consistent with current microwave background fluctuations, the fluctuations are Gaussian. However, for scales much larger than our observable Universe, one expects large metric fluctuations that are non-Gaussian. This example illuminates non-Gaussian models involving multiple scalar fields which are consistent with current microwave background limits.

Salopek, D. S.↗