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Report presents preliminary theoretical analysis of several alternative schemes for allocation of satellite resource among terrestrial subscribers of landmobile/satellite communication system. Demand-access and random-access approaches under code-division and frequency-division concepts compared.
This study was undertaken to determine the velocity of fragments generated by the range safety destruction (RSD) or random failure of a Space Transportation System (STS) Solid Rocket Motor (SRM). The specific requirement was to provide a fragment model for use in those Galileo and Ulysses RTG safety analyses concerned with possible fragment impact on the spacecraft radioisotope thermoelectric generators (RTGS). Good agreement was obtained between predictions and observations for fragment velocity, velocity distributions, azimuths, and rotation rates. Based on this agreement with the entire data base, the model was used to predict the probable fragment environments which would occur in the event of an STS-SRM RSD or randon failure at 10, 74, 84 and 110 seconds. The results of these predictions are the basis of the fragment environments presented in the Shuttle Data Book (NSTS-08116). The information presented here is in viewgraph form.
A new and comprehensive dielectric tensor characterization instrument is presented for characterization of magneto-optical recording media and non-magnetic thin films. Random and systematic errors of the system are studied. A series of TbFe, TbFeCo, and Co/Pt samples with different composition and thicknesses are characterized for their optical and magneto-optical properties. The optical properties of several non-magnetic films are also measured.
The applicability of some results from the theory of generalized harmonic analysis (or power-spectral analysis) to the analysis of gust loads on airplanes in continuous rough air is examined. The general relations for linear systems between power spectrums of a random input disturbance and an output response are used to relate the spectrum of airplane load in rough air to the spectrum of atmospheric gust velocity. The power spectrum of loads is shown to provide a measure of the load intensity in terms of the standard deviation (root mean square) of the load distribution for an airplane in flight through continuous rough air. For the case of a load output having a normal distribution, which appears from experimental evidence to apply to homogeneous rough air, the standard deviation is shown to describe the probability distribution of loads or the proportion of total time that the load has given values. Thus, for airplane in flight through homogeneous rough air, the probability distribution of loads may be determined from a power-spectral analysis. In order to illustrate the application of power-spectral analysis to gust-load analysis and to obtain an insight into the relations between loads and airplane gust-response characteristics, two selected series of calculations are presented. The results indicate that both methods of analysis yield results that are consistent to a first approximation.
This research includes theoretical studies of the formation and evolution of five types of interacting binary systems. Our main focus has been on developing a number of comprehensive population synthesis codes to study the following types of binary systems: (i) cataclysmic variables (#3, #8, #12, #15), (ii) low- and intermediate-mass X-ray binaries (#13, #20, #21), (iii) high-mass X-ray binaries (#14, #17, #22), (iv) recycled binary millisecond pulsars in globular clusters (#5, #10, #ll), and (v) planetary nebulae which form in interacting binaries (#6, #9). The numbers in parentheses refer to papers published or in preparation that are listed in this paper. These codes take a new unified approach to population synthesis studies. The first step involves a Monte Carlo selection of the primordial binaries, including the constituent masses, and orbital separations and eccentricities. Next, a variety of analytic methods are used to evolve the primary star to the point where either a dynamical episode of mass transfer to the secondary occurs (the common envelope phase), or the system evolves down an alternate path. If the residual core of the primary is greater than 2.5 solar mass, it will evolve to Fe core collapse and the production of a neutron star and a supernova explosion. In the case of systems involving neutron stars, a kick velocity is chosen randomly from an appropriate distribution and added to the orbital dynamics which determine the state of the binary system after the supernova explosion. In the third step, all binaries which commence stable mass transfer from the donor star (the original secondary in the binary system) to the compact object, are followed with a detailed binary evolution code. Finally, we include all the relevant dynamics of the binary system. For example, in the case of LMXBs, the binary system, with its recoil velocity from the supernova explosion, is followed in time through its path in the Galactic potential. For our globular cluster study, detailed three-body calculations are used to follow the interactions of the binary with passing field stars.
Simulated Annealing (SA) is a stochastic optimization algorithm used to search for global minima in complex design surfaces where exhaustive searches are not computationally feasible. The algorithm is derived by simulating the annealing process, whereby a solid is heated to a liquid state and then cooled slowly to reach thermodynamic equilibrium at each temperature. The idea is that atoms in the solid continually bond and re-bond at various quantum energy levels, and with sufficient cooling time they will rearrange at the minimum energy state to form a perfect crystal. The distribution of energy levels is given by the Boltzmann distribution: as temperature drops, the probability of the presence of high-energy bonds decreases. In searching for an optimal design, local minima and discontinuities are often present in a design surface. SA presents a distinct advantage over other optimization algorithms in its ability to escape from these local minima. Just as high-energy atomic configurations are visited in the actual annealing process in order to eventually reach the minimum energy state, in SA highly non-optimal configurations are visited in order to find otherwise inaccessible global minima. The SA algorithm produces a Markov chain of points in the design space at each temperature, with a monotonically decreasing temperature. A random point is started upon, and the objective function is evaluated at that point. A stochastic perturbation is then made to the parameters of the point to arrive at a proposed new point in the design space, at which the objection function is evaluated as well. If the change in objective function values (Delta)E is negative, the proposed new point is accepted. If (Delta)E is positive, the proposed new point is accepted according to the Metropolis criterion: rho((Delta)f) = exp((-Delta)E/T), where T is the temperature for the current Markov chain. The process then repeats for the remainder of the Markov chain, after which the temperature is decremented and the process repeats. Eventually (and hopefully), a near-globally optimal solution is attained as T approaches zero. Several exciting variants of SA have recently emerged, including Discrete-State Simulated Annealing (DSSA) and Simulated Tempering (ST). The DSSA algorithm takes the thermodynamic analogy one step further by categorizing objective function evaluations into discrete states. In doing so, many of the case-specific problems associated with fine-tuning the SA algorithm can be avoided; for example, theoretical approximations for the initial and final temperature can be derived independently of the case. In this manner, DSSA provides a scheme that is more robust with respect to widely differing design surfaces. ST differs from SA in that the temperature T becomes an additional random variable in the optimization. The system is also kept in equilibrium as the temperature changes, as opposed to the system being driven out of equilibrium as temperature changes in SA. ST is designed to overcome obstacles in design surfaces where numerous local minima are separated by high barriers. These algorithms are incorporated into the optimal design of the traveling-wave tube amplifier (TWTA). The area under scrutiny is the collector, in which it would be ideal to use negative potential to decelerate the spent electron beam to zero kinetic energy just as it reaches the collector surface. In reality this is not plausible due to a number of physical limitations, including repulsion and differing levels of kinetic energy among individual electrons. Instead, the collector is designed with multiple stages depressed below ground potential. The design of this multiple-stage collector is the optimization problem of interest. One remaining problem in SA and DSSA is the difficulty in determining when equilibrium has been reached so that the current Markov chain can be terminated. It has been suggested in recent literature that simulating the thermodynamic properties opecific heat, entropy, and internal energy from the Boltzmann distribution can provide good indicators of having reached equilibrium at a certain temperature. These properties are tested for their efficacy and implemented in SA and DSSA code with respect to TWTA collector optimization.
The fundamental nature of processes that led to the emergence of life has been a subject of long-standing debate. One view holds that the origin of life is an event governed by chance, and the result of so many random events is unpredictable. This view was eloquently expressed by Jacques Monod in his book Chance or Necessity. In an alternative view, the origin of life is considered a deterministic event. Its details need not be deterministic in every respect, but the overall behavior is predictable. A corollary to the deterministic view is that the emergence of life must have been determined primarily by universal chemistry and biochemistry rather than by subtle details of environmental conditions. In my lecture I will explore two different paradigms for the emergence of life and discuss their implications for predictability and universality of life-forming processes. The dominant approach is that the origin of life was guided by information stored in nucleic acids (the RNA World hypothesis). In this view, selection of improved combinations of nucleic acids obtained through random mutations drove evolution of biological systems from their conception. An alternative hypothesis states that the formation of protocellular metabolism was driven by non-genomic processes. Even though these processes were highly stochastic the outcome was largely deterministic, strongly constrained by laws of chemistry. I will argue that self-replication of macromolecules was not required at the early stages of evolution; the reproduction of cellular functions alone was sufficient for self-maintenance of protocells. In fact, the precise transfer of information between successive generations of the earliest protocells was unnecessary and could have impeded the discovery of cellular metabolism. I will also show that such concepts as speciation and fitness to the environment, developed in the context of genomic evolution also hold in the absence of a genome.
Traffic congestion problems provide a unique environment to study how multi-agent systems promote desired system level behavior. What is particularly interesting in this class of problems is that no individual action is intrinsically "bad" for the system but that combinations of actions among agents lead to undesirable outcomes, As a consequence, agents need to learn how to coordinate their actions with those of other agents, rather than learn a particular set of "good" actions. This problem is ubiquitous in various traffic problems, including selecting departure times for commuters, routes for airlines, and paths for data routers. In this paper we present a multi-agent approach to two traffic problems, where far each driver, an agent selects the most suitable action using reinforcement learning. The agent rewards are based on concepts from collectives and aim to provide the agents with rewards that are both easy to learn and that if learned, lead to good system level behavior. In the first problem, we study how agents learn the best departure times of drivers in a daily commuting environment and how following those departure times alleviates congestion. In the second problem, we study how agents learn to select desirable routes to improve traffic flow and minimize delays for. all drivers.. In both sets of experiments,. agents using collective-based rewards produced near optimal performance (93-96% of optimal) whereas agents using system rewards (63-68%) barely outperformed random action selection (62-64%) and agents using local rewards (48-72%) performed worse than random in some instances.
This study investigated the Shuttle Program s belief that Space Transportation System (STS) wiring damage occurrences are random, that is, a constant occurrence rate. Using Problem Reporting and Corrective Action (PRACA)-derived data for STS Space Shuttle OV-103, wiring damage was observed to increase over the vehicle s life. Causal factors could include wiring physical deterioration, maintenance and inspection induced damage, and inspection process changes resulting in more damage events being reported. Induced damage effects cannot be resolved with existent data. Growth analysis (using Crow-AMSAA, or CA) resolved maintenance/inspection effects (e.g., heightened awareness) on all wire damages and indicated an overall increase since Challenger Return-to-Flight (RTF). An increasing failure or occurrence rate per flight cycle was seen for each wire damage mode; these (individual) rates were not affected by inspection process effects, within statistical error.
Package features comprehensive selection of probabilistic distributions. Monte Carlo simulations resorted to whenever systems studied not amenable to deterministic analyses or when direct experimentation not feasible. Random numbers having certain specified distribution characteristic integral part of simulations. Package consists of collector of "pseudorandom" number generators for use in Monte Carlo simulations.
In the present water vapor lidar system, the detected signal is sent over long cables to a waveform digitizer in a CAMAC crate. This has the disadvantage of transmitting analog signals for a relatively long distance, which is subjected to pickup noise, leading to a decrease in the signal to noise ratio. Generally, errors in the measurement of water vapor with the DIAL method arise from both random and systematic sources. Systematic errors in DIAL measurements are caused by both atmospheric and instrumentation effects. The selection of the on-line alexandrite laser with a narrow linewidth, suitable intensity and high spectral purity, and its operation at the center of the water vapor lines, ensures minimum influence in the DIAL measurement that are caused by the laser spectral distribution and avoid system overloads. Random errors are caused by noise in the detected signal. Variability of the photon statistics in the lidar return signal, noise resulting from detector dark current, and noise in the background signal are the main sources of random error. This type of error can be minimized by maximizing the signal to noise ratio. The increase in the signal to noise ratio can be achieved by several ways. One way is to increase the laser pulse energy, by increasing its amplitude or the pulse repetition rate. Another way, is to use a detector system with higher quantum efficiency and lower noise, on the other hand, the selection of a narrow band optical filter that rejects most of the day background light and retains high optical efficiency is an important issue. Following acquisition of the lidar data, we minimize random errors in the DIAL measurement by averaging the data, but this will result in the reduction of the vertical and horizontal resolutions. Thus, a trade off is necessary to achieve a balance between the spatial resolution and the measurement precision. Therefore, the main goal of this research effort is to increase the signal to noise ratio by a factor of 10 over the current system, using a newly evaluated, very low noise avalanche photo diode detector and constructing a 10 MHz waveform digitizer which will replace the current CAMAC system.
The results of the initial testing of the Generalized Gate Level Logic Simulator (GGLOSS) are discussed. The simulator is a special purpose fault simulator designed to assist in the analysis of the effects of random hardware failures on fault tolerant digital computer systems. The testing of the simulator covers two main areas. First, the simulation results are compared with data obtained by monitoring the behavior of hardware. The circuit used for these comparisons is an incomplete microprocessor design based upon the MIL-STD-1750A Instruction Set Architecture. In the second area of testing, current simulation results are compared with experimental data obtained using precursors of the current tool. In each case, a portion of the earlier experiment is confirmed. The new results are then viewed from a different perspective in order to evaluate the usefulness of this simulation strategy.
Results are presented of a study conducted with a digital simulation model being used in the design of the Automatically Reconfigurable Modular Multiprocessor System (ARMMS), a candidate computer system for future manned and unmanned space missions. The model simulates the activity involved as instructions are fetched from random access memory for execution in one of the system central processing units. A series of model runs measured instruction execution time under various assumptions pertaining to the CPU's and the interface between the CPU's and RAM. Design tradeoffs are presented in the following areas: Bus widths, CPU microprogram read only memory cycle time, multiple instruction fetch, and instruction mix.
This work focuses on the properties of sheared granular materials near the jamming transition. The project currently involves two aspects. The first of these is an experiment that is a prototype for a planned ISS (International Space Station) flight. The second is discrete element simulations (DES) that can give insight into the behavior one might expect in a reduced-g environment. The experimental arrangement consists of an annular channel that contains the granular material. One surface, say the upper surface, rotates so as to shear the material contained in the annulus. The lower surface controls the mean density/mean stress on the sample through an actuator or other control system. A novel feature under development is the ability to 'thermalize' the layer, i.e. create a larger amount of random motion in the material, by using the actuating system to provide vibrations as well control the mean volume of the annulus. The stress states of the system are determined by transducers on the non-rotating wall. These measure both shear and normal components of the stress on different size scales. Here, the idea is to characterize the system as the density varies through values spanning dense almost solid to relatively mobile granular states. This transition regime encompasses the regime usually thought of as the glass transition, and/or the jamming transition. Motivation for this experiment springs from ideas of a granular glass transition, a related jamming transition, and from recent experiments. In particular, we note recent experiments carried out by our group to characterize this type of transition and also to demonstrate/ characterize fluctuations in slowly sheared systems. These experiments give key insights into what one might expect in near-zero g. In particular, they show that the compressibility of granular systems diverges at a transition or critical point. It is this divergence, coupled to gravity, that makes it extremely difficult if not impossible to characterize the transition region in an earth-bound experiment. In the DE modeling, we analyze dynamics of a sheared granular system in Couette geometry in two (2D) and three (3D) space dimensions. Here, the idea is to both better understand what we might encounter in a reduced-g environment, and at a deeper level to deduce the physics of sheared systems in a density regime that has not been addressed by past experiments or simulations. One aspect of the simulations addresses sheared 2D system in zero-g environment. For low volume fractions, the expected dynamics of this type of system is relatively well understood. However, as the volume fraction is increased, the system undergoes a phase transition, as explained above. The DES concentrate on the evolution of the system as the solid volume fraction is slowly increased, and in particular on the behavior of very dense systems. For these configurations, the simulations show that polydispersity of the sheared particles is a crucial factor that determines the system response. Figures 1 and 2 below, that present the total force on each grain, show that even relatively small (10 %) nonuniformity of the size of the grains (expected in typical experiments) may lead to significant modifications of the system properties, such as velocity profiles, temperature, force propagation, and formation shear bands. The simulations are extended in a few other directions, in order to provide additional insight to the experimental system analyzed above. In one direction, both gravity, and driving due to vibrations are included. These simulations allow for predictions on the driving regime that is required in the experiments in order to analyze the jamming transition. Furthermore, direct comparison of experiments and DES will allow for verification of the modeling assumptions. We have also extended our modeling efforts to 3D. The (preliminary) results of these simulations of an annular system in zero-g environment will conclude the presentation.
A model for a prebiotic environment in which concentration, condensation, and chemical evolution of biomolecules could have occurred is proposed. The principal reactions expected of proteins, nucleic acids, lipids, and some of their precursors in this environment are discussed. The model is based on the concept of a fluctuating system in which hydration and dehydration processes occur in a cyclic manner, with allowance for high concentrations of soluble salts, such as chlorides and sulfates. It is concluded that the proposed prebiotic environment with its dynamical characteristics is a plausible model for the chemical evolution from biomonomers through random oligomers toward self-replicating chemical systems.
Much of the previous work on load balancing and scheduling in distributed environments was concerned with homogeneous systems and homogeneous loads. Several of the results indicated that random policies are as effective as other more complex load allocation policies. The effects of heterogeneity on scheduling algorithms for hard real time systems is examined. A distributed scheduler specifically to handle heterogeneities in both nodes and node traffic is proposed. The performance of the algorithm is measured in terms of the percentage of jobs discarded. While a random task allocation is very sensitive to heterogeneities, the algorithm is shown to be robust to such non-uniformities in system components and load.
A review of literature associated with Pebble Bed and Particle Bed reactor core research has revealed a systemic problem inherent to reactor core concepts which utilize randomized rather than structured coolant channel flow paths. For both the Pebble Bed and Particle Bed Reactor designs; case studies reveal that for indeterminate reasons, regions within the core would suffer from excessive heating leading to thermal runaway and localized fuel melting. A thermal Computational Fluid Dynamics model was utilized to verify that In both the Pebble Bed and Particle Bed Reactor concepts randomized coolant channel pathways combined with localized high temperature regions would work together to resist the flow of coolant diverting it away from where it is needed the most to cooler less resistive pathways where it is needed the least. In other words given the choice via randomized coolant pathways the reactor coolant will take the path of least resistance, and hot zones offer the highest resistance. Having identified the relationship between randomized coolant channel pathways and localized fuel melting it is now safe to assume that other reactor concepts that utilize randomized coolant pathways such as the foam core reactor are also susceptible to this phenomenon.
The problem of phasing of the Space Solar Power System's transmission array has been addressed by developing a GPS-like radio navigation system. The goal of this system is to provide power transmission phasing control for each node of the array that causes the power signals to add constructively at the ground reception station. The phasing control system operates in a distributed manner, which makes it practical to implement. A leader node and two radio navigation beacons are used to control the power transmission phasing of multiple follower nodes. The necessary one-way communications to the follower nodes are implemented using the RF beacon signals. The phasing control system uses differential carrier phase relative navigation/timing techniques. A special feature of the system is an integer ambiguity resolution procedure that periodically resolves carrier phase cycle count ambiguities via encoding of pseudo-random number codes on the power transmission signals. The system is capable of achieving phasing accuracies on the order of 3 mm down to 0.4 mm depending on whether the radio navigation beacons operate in the L or C bands.