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At least 199 records · Page 11

What can solve the strong CP problem?

Three possible strategies have been advocated to solve the strong CP problem. The first is the axion, a dynamical mechanism that relaxes any initial value of the CP violating angle $\overline{θ}$ to zero. The second is the imposition of new symmetries that are believed to set $\overline{θ}$ to zero in the UV. The third is the acceptance of the fine tuning of parameters. We argue that the latter two solutions do not solve the strong CP problem. The θ term of QCD is not a parameter — it does not exist in the Hamiltonian. Rather, it is a property of the quantum state that our universe finds itself in, arising from the fact that there are CP violating states of a CP preserving Hamiltonian. It is not eliminated by imposing parity as a symmetry since the underlying theory is already parity symmetric and that does not preclude the existence of CP violating states. Moreover, since the value of θ realized in our universe is a consequence of measurement, it is inherently random and cannot be fine tuned by choice of parameters. Rather any fine tuning would require a tuning between parameters in the theory and the random outcome of measurement. Our results considerably strengthen the case for the existence of the axion and axion dark matter. The confusion around θ arises from the fact that unlike classical mechanics, the Hamiltonian and Lagrangian are not equivalent in quantum mechanics. The Hamiltonian defines the differential time evolution, whereas the Lagrangian is a solution to this evolution. Consequently, initial conditions could in principle appear in the Lagrangian but not in the Hamiltonian. This results in aspects of the initial condition such as θ misleadingly appearing in the Lagrangian as parameters. We comment on the similarity between the θ vacua and the violations of the constraint equations of classical gauge theories in quantum mechanics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Study of optimum discrete estimators in measurement analysis

Study of statistical techniques for obtaining estimates of true data parameters uses discrete measured quantities containing random error. These techniques develop estimation procedures as an iterative algorithm for digital computation in real time.

Hung, J. C.↗

Radio astronomy

The origins, generation, detection, and interpretation of radio signals are discussed for signals with an assumed random polarization. After defining the basic parameters, the discussion moves to such topics as synchrotron radiation, plasma effects, changes in the electron energy spectrum in the radiating regions, energy loss to ionization, bremsstrahlung, radio astronomical observations of high-energy particles, emission by energetic particles, observation of supernova remnants and pulsars, galactic background continuum radiation, and others.

Alexander, J.↗

Problems inherent in using aircraft for radio oceanography studies

Some of the disadvantages relating to altitude stability and proximity to the ocean are described for radio oceanography studies using aircraft. The random oscillatory motion introduced by the autopilot in maintaining aircraft altitude requires a more sophisticated range tracker for a radar altimeter than would be required in a satellite application. One-dimensional simulations of the sea surface (long-crested waves) are performed using both the JONSWAP spectrum and the Pierson-Moskowitz spectrum. The results of the simulation indicate that care must be taken in trying to experimentally verify instrument measurement accuracy. Because of the relatively few wavelengths examined from an aircraft due to proximity to the ocean and low velocity compared to a satellite, the random variation in the sea surface parameters being measured can far exceed an instrument's ability to measure them.

Walsh, E. J.↗

An efficient computational method for characterizing the effects of random surface errors on the average power pattern of reflectors

Based on the works of Ruze (1966) and Vu (1969), a novel mathematical model has been developed to determine efficiently the average power pattern degradations caused by random surface errors. In this model, both nonuniform root mean square (rms) surface errors and nonuniform illumination functions are employed. In addition, the model incorporates the dependence on F/D in the construction of the solution. The mathematical foundation of the model rests on the assumption that in each prescribed annular region of the antenna, the geometrical rms surface value is known. It is shown that closed-form expressions can then be derived, which result in a very efficient computational method for the average power pattern. Detailed parametric studies are performed with these expressions to determine the effects of different random errors and illumination tapers on parameters such as gain loss and sidelobe levels. The results clearly demonstrate that as sidelobe levels decrease, their dependence on the surface rms/wavelength becomes much stronger and, for a specified tolerance level, a considerably smaller rms/wavelength is required to maintain the low sidelobes within the required bounds.

Rahmat-Samii, Y.↗

Budgets of divergent and rotational kinetic energy during two periods of intense convection

The derivations of the energy budget equations for divergent and rotational components of kinetic energy are provided. The intense convection periods studied are: (1) synoptic scale data of 3 or 6 hour intervals and (2) mesoalphascale data every 3 hours. Composite energies and averaged budgets for the periods are presented; the effects of random data errors on derived energy parameters is investigated. The divergent kinetic energy and rotational kinetic energy budgets are compared; good correlation of the data is observed. The kinetic energies and budget terms increase with convective development; however, the conversion of the divergent and rotational energies are opposite.

Buechler, D. E.↗

Characterization of fault recovery through fault injection on FTMP

The development of fault-injection procedures and statistical analysis techniques to characterize the fault recovery of fault-tolerant systems is described. Pin-level fault-injection was conducted on a fault-tolerant microprocessor computer in order to generate data to assess the utility of current fault-injection sampling methods. The validity of common reliability-modeling assumptions concerning the statistical distribution of recovery times is investigated. A multiple comparison analysis for detecting behavior variations, and a distribution fitting for determining the best fit for the data were conducted. It is observed that the detection behavior is not homogeneous across all data sets, and that none of the factors under experimental control can account for the observed groupings of behavior. It is determined that no single distribution fits all the data sets, and that stratified random sampling and statistically robust parameter-estimation techniques are required to characterize fault detection time.

Finelli, George B.↗

Acceleration of the Fast Solar Wind by Solitary Waves in Coronal Holes

The purpose of this investigation is to develop a new model for the acceleration of the fast solar wind by nonlinear, time-dependent multidimensional MHD simulations of waves in solar coronal holes. Preliminary computational studies indicate that solitary-like waves are generated in coronal holes nonlinearly by torsional Alfven waves. These waves in addition to thermal conduction may contribute considerably to the accelerate the solar wind. Specific goals of this proposal are to investigate the generation of nonlinear solitary-like waves and their effect on solar wind acceleration by numerical 2.5D MHD simulation of coronal holes with a broad range of plasma and wave parameters; to study the effect of random disturbances at the base of a solar coronal hole on the fast solar wind acceleration with a more advanced 2.5D MHD model and to compare the results with the available observations; to extend the study to a full 3D MHD simulation of fast solar wind acceleration with a more realistic model of a coronal hole and solar boundary conditions. The ultimate goal of the three year study is to model the fast solar wind in a coronal hole, based on realistic boundary conditions in a coronal hole near the Sun, and the coronal hole structure (i.e., density, temperature, and magnetic field geometry) that will become available from the recently launched SOHO spacecraft.

Ofman, Leon↗

Acceleration of the Fast Solar Wind by Solitary Waves in Coronal Holes

The purpose of this investigation is to develop a new model for the acceleration of the fast solar wind by nonlinear. time-dependent multidimensional MHD simulations of waves in solar coronal holes. Preliminary computational studies indicate that nonlinear waves are generated in coronal holes by torsional Alfv\'{e}n waves. These waves in addition to thermal conduction may contribute considerably to the accelerate the solar wind. Specific goals of this proposal are to investigate the generation of nonlinear solitary-like waves and their effect on solar wind acceleration by numerical 2.5D MHD simulation of coronal holes with a broad range of plasma and wave parameters; to study the effect of random disturbances at the base of a solar coronal hole on the fast solar wind acceleration with a more advanced 2.5D MHD model and to compare the results with the available observations; to extend the study to a full 3D MHD simulation of fast solar wind acceleration with a more realistic model of a coronal hole and solar boundary conditions. The ultimate goal of the three year study is to model the, fast solar wind in a coronal hole, based on realistic boundary conditions in a coronal hole near the Sun, and the coronal hole structure (i.e., density, temperature. and magnetic field geometry,) that will become available from the recently launched SOHO spacecraft.

Ofman, Leon↗

Mapping an operator's perception of a parameter space

Operators monitored the output of two versions of the crossover model having a common random input. Their task was to make discrete, real-time adjustments of the parameters k and tau of one of the models to make its output time history converge to that of the other, fixed model. A plot was obtained of the direction of parameter change as a function of position in the (tau, k) parameter space relative to the nominal value. The plot has a great deal of structure and serves as one form of representation of the operator's perception of the parameter space.

Pew, R. W.↗

Probabilistic Design Analysis (PDA) Approach to Determine the Probability of Cross-System Failures for a Space Launch Vehicle

Quantifying the probability of significant launch vehicle failure scenarios for a given design, while still in the design process, is critical to mission success and to the safety of the astronauts. Probabilistic risk assessment (PRA) is chosen from many system safety and reliability tools to verify the loss of mission (LOM) and loss of crew (LOC) requirements set by the NASA Program Office. To support the integrated vehicle PRA, probabilistic design analysis (PDA) models are developed by using vehicle design and operation data to better quantify failure probabilities and to better understand the characteristics of a failure and its outcome. This PDA approach uses a physics-based model to describe the system behavior and response for a given failure scenario. Each driving parameter in the model is treated as a random variable with a distribution function. Monte Carlo simulation is used to perform probabilistic calculations to statistically obtain the failure probability. Sensitivity analyses are performed to show how input parameters affect the predicted failure probability, providing insight for potential design improvements to mitigate the risk. The paper discusses the application of the PDA approach in determining the probability of failure for two scenarios from the NASA Ares I project

Shih, Ann T.↗

Accelerating multicanonical sampling with irreversibility

Flat-histogram Monte Carlo simulations are well-established, robust methods to perform random walks in a physical observable or parameter space, making them suitable for finding ground states or studying phase transitions in complex systems in statistical physics. However, their efficiency can be limited by the time to attain the desired flat distribution, which is generally unknown prior to the simulations. In particular, they might suffer from slowing down towards the end of a simulation due to the diffusive nature of random walks. In this work we apply irreversibility to the multicanonical Monte Carlo method via the lifting approach to alleviate this behavior. We achieve a 2–4 times speedup in ground-state search for a two-dimensional (2D) Ising model, and up to an order of magnitude of speedup for finding the ground-state energy in an Edwards–Anderson spin glass, compared to traditional multicanonical sampling. In conclusion, the round-trip times between ground states show a narrower distribution and are significantly shorter compared to the reversible counterpart, suggesting that a lower convergence time with a smaller time variance is feasible.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Remote sensing of earth terrain

Two monographs and 85 journal and conference papers on remote sensing of earth terrain have been published, sponsored by NASA Contract NAG5-270. A multivariate K-distribution is proposed to model the statistics of fully polarimetric data from earth terrain with polarizations HH, HV, VH, and VV. In this approach, correlated polarizations of radar signals, as characterized by a covariance matrix, are treated as the sum of N n-dimensional random vectors; N obeys the negative binomial distribution with a parameter alpha and mean bar N. Subsequently, and n-dimensional K-distribution, with either zero or non-zero mean, is developed in the limit of infinite bar N or illuminated area. The probability density function (PDF) of the K-distributed vector normalized by its Euclidean norm is independent of the parameter alpha and is the same as that derived from a zero-mean Gaussian-distributed random vector. The above model is well supported by experimental data provided by MIT Lincoln Laboratory and the Jet Propulsion Laboratory in the form of polarimetric measurements.

Kong, J. A.↗

K-distribution and polarimetric terrain radar clutter

A multivariate K-distribution is proposed to model the statistics of fully polarimetric radar data from earth terrain with polarizations HH, HV, VH, and VV. In this approach, correlated polarizations of radar signals, as characterized by a covariance matrix, are treated as the sum of N n-dimensional random vectors; N obeys the negative binomial distribution with a parameter alpha and mean N-bar. Subsequently, an n-dimensional K-distribution, with either zero or nonzero mean, is developed in the limit of infinite N-bar or illuminated area. The probability density function (PDF) of the K-distributed vector normalized by its Euclidean norm is independent of the parameter alpha and is the same as that derived from a zero-mean Gaussian-distributed random vector.

Yueh, S. H.↗

Search strategy effects on PN acquisition performance

The present paper focusses on 'random' and 'expanding window' PN acquisition search strategies and analytically develops the PN acquisition time statistics as functions of salient system parameters such as prediction SNR, detection and false alarm probabilities and a priori information on epoch location. The significance of this analysis is its general applicability to arbitrary postdetection processing schemes. Computed performance results account for the above salient parameters, wherein sequential detection is employed in conjunction with random and selected expanding window search strategies.

Weinberg, A.↗

Structural Reliability Using Probability Density Estimation Methods Within NESSUS

A reliability analysis studies a mathematical model of a physical system taking into account uncertainties of design variables and common results are estimations of a response density, which also implies estimations of its parameters. Some common density parameters include the mean value, the standard deviation, and specific percentile(s) of the response, which are measures of central tendency, variation, and probability regions, respectively. Reliability analyses are important since the results can lead to different designs by calculating the probability of observing safe responses in each of the proposed designs. All of this is done at the expense of added computational time as compared to a single deterministic analysis which will result in one value of the response out of many that make up the density of the response. Sampling methods, such as monte carlo (MC) and latin hypercube sampling (LHS), can be used to perform reliability analyses and can compute nonlinear response density parameters even if the response is dependent on many random variables. Hence, both methods are very robust; however, they are computationally expensive to use in the estimation of the response density parameters. Both methods are 2 of 13 stochastic methods that are contained within the Numerical Evaluation of Stochastic Structures Under Stress (NESSUS) program. NESSUS is a probabilistic finite element analysis (FEA) program that was developed through funding from NASA Glenn Research Center (GRC). It has the additional capability of being linked to other analysis programs; therefore, probabilistic fluid dynamics, fracture mechanics, and heat transfer are only a few of what is possible with this software. The LHS method is the newest addition to the stochastic methods within NESSUS. Part of this work was to enhance NESSUS with the LHS method. The new LHS module is complete, has been successfully integrated with NESSUS, and been used to study four different test cases that have been proposed by the Society of Automotive Engineers (SAE). The test cases compare different probabilistic methods within NESSUS because it is important that a user can have confidence that estimates of stochastic parameters of a response will be within an acceptable error limit. For each response, the mean, standard deviation, and 0.99 percentile, are repeatedly estimated which allows confidence statements to be made for each parameter estimated, and for each method. Thus, the ability of several stochastic methods to efficiently and accurately estimate density parameters is compared using four valid test cases. While all of the reliability methods used performed quite well, for the new LHS module within NESSUS it was found that it had a lower estimation error than MC when they were used to estimate the mean, standard deviation, and 0.99 percentile of the four different stochastic responses. Also, LHS required a smaller amount of calculations to obtain low error answers with a high amount of confidence than MC. It can therefore be stated that NESSUS is an important reliability tool that has a variety of sound probabilistic methods a user can employ and the newest LHS module is a valuable new enhancement of the program.

Chamis, Chrisos C.↗

Effects of random path fluctuations on the accuracy of laser ranging systems

Effects of turbulence-induced pathlength fluctuations on the accuracy of single-color laser ranging systems are examined. Correlation and structure functions for the path deviations are derived using several proposed models for the variation of the turbulence structure parameter with altitude. For single-color systems, random pathlength fluctuations can limit the accuracy of a range measurement to a few centimeters when the turbulence is strong and the effective propagation path is long (greater than 10 km). Two-color systems can partially correct for the random path fluctuations so that in most cases their accuracy is limited to a few millimeters. However, at low elevation angles for satellite ranging (less than 20 deg) and over long horizontal paths, two-color systems can also have errors approaching a few centimeters.

Gardner, C. S.↗

Optimal airframe synthesis for gust loads

An optimization capability for sizing airframe structures that are subjected to a combination of deterministic and random flight loads was established. The random vibration environment introduces the need for selecting a statistical process that best describes the random loads and permits computation of the dynamic response parameters of interest. Furthermore, it requires a formulation of design constraints that would minimize the conservativeness in the design and retain computational viability. The random loads are treated as a stationary, homogeneous process with a Gaussian probability distribution. The formulation of the analysis problem, the structure of the optimization programming system and a representative numerical example are discussed.

Hajela, P.↗