Post Flight Dynamic Analysis Simulation

Digital six-degrees-of-freedom, open loop Saturn 5 first stage flight evaluation simulation program obtains post flight simulation of the launch vehicle using actual flight data as input. Results are compared with measured data. For preflight analysis, the program uses predicted flight data as input

Dynamic simulation of an electrorheological fluid

A molecular-dynamics-like method is presented for the simulation of a suspension of dielectric particles in a nonconductive solvent forming an electrorheological fluid. The method accurately accounts for both hydrodynamic and electrostatic interparticle interactions from dilute volume fractions to closest packing for simultaneous shear and electric fields. The hydrodynamic interactions and rheology are determined with the Stokesian dynamics methodology, while the electrostatic interactions, in particular, the conservative electrostatic interparticle forces, are determined from the electrostatic energy of the suspension. The energy of the suspension is computed from the induced particle dipoles by a method previously developed [R. T. Bonnecaze and J. F. Brady, Proc. R. Soc. London, Ser. A 430, 285 (1990)]. Using the simulation, the dynamics can be directly correlated to the observed macroscopic rheology of the suspension for a range of the so-called Mason number, Ma, the ratio of viscous to electrostatic forces. The simulation is specifically applied to a monolayer of spherical particles of areal fraction 0.4 with a particle-to-fluid dielectric constant ratio of 4 for Ma=10^−4 to [infinity]. The effective viscosity of the suspension increases as Ma^−1 or with the square of the electric field for small Ma and has a plateau value at large Ma, as is observed experimentally. This rheological behavior can be interpreted as Bingham plastic-like with a dynamic yield stress. The first normal stress difference is negative, and its magnitude increases as Ma^−1 at small Ma with a large Ma plateau value of zero. In addition to the time averages of the rheology, the time traces of the viscosities are presented along with selected "snapshots" of the suspension microstructure. In particular, at small Ma, the suspension dynamics exhibit two distinct motions: a slow elastic-body-like deformation where electrostatic energy is stored, followed by a rapid microstructural rearrangement where energy is viscously dissipated. It is suggested that the observed dynamic yield stress is associated with these dynamics

Dynamic simulation of hydrodynamically interacting suspensions

A general method for computing the hydrodynamic interactions among an infinite suspension of particles, under the condition of vanishingly small particle Reynolds number, is presented. The method follows the procedure developed by O'Brien (1979) for constructing absolutely convergent expressions for particle interactions. For use in dynamic simulation, the convergence of these expressions is accelerated by application of the Ewald summation technique. The resulting hydrodynamic mobility and/or resistance matrices correctly include all far-field non-convergent interactions. Near-field lubrication interactions are incorporated into the resistance matrix using the technique developed by Durlofsky, Brady & Bossis (1987). The method is rigorous, accurate and computationally efficient, and forms the basis of the Stokesian-dynamics simulation method. The method is completely general and allows such diverse suspension problems as self-diffusion, sedimentation, rheology and flow in porous media to be treated within the same formulation for any microstructural arrangement of particles. The accuracy of the Stokesian-dynamics method is illustrated by comparing with the known exact results for spatially periodic suspensions

An AD100 implementation of a real-time STOVL aircraft propulsion system

A real-time dynamic model of the propulsion system for a Short Take-Off and Vertical Landing (STOVL) aircraft was developed for the AD100 simulation environment. The dynamic model was adapted from a FORTRAN based simulation using the dynamic programming capabilities of the AD100 ADSIM simulation language. The dynamic model includes an aerothermal representation of a turbofan jet engine, actuator and sensor models, and a multivariable control system. The AD100 model was tested for agreement with the FORTRAN model and real-time execution performance. The propulsion system model was also linked to an airframe dynamic model to provide an overall STOVL aircraft simulation for the purposes of integrated flight and propulsion control studies. An evaluation of the AD100 system for use as an aircraft simulation environment is included

Nonequilibrium Phase Transition in the Kinetic Ising model: Critical Slowing Down and Specific-heat Singularity

The nonequilibrium dynamic phase transition, in the kinetic Ising model in presence of an oscillating magnetic field, has been studied both by Monte Carlo simulation and by solving numerically the mean field dynamic equation of motion for the average magnetisation. In both the cases, the Debye 'relaxation' behaviour of the dynamic order parameter has been observed and the 'relaxation time' is found to diverge near the dynamic transition point. The Debye relaxation of the dynamic order parameter and the power law divergence of the relaxation time have been obtained from a very approximate solution of the mean field dynamic equation. The temperature variation of appropiately defined 'specific-heat' is studied by Monte Carlo simulation near the transition point. The specific-heat has been observed to diverge near the dynamic transition point.Comment: Revtex, Five encapsulated postscript files, submitted to Phys. Rev.

Using Support Vector Machine for Prediction Dynamic Voltage Collapse in an Actual Power System

Abstract—This paper presents dynamic voltage collapse prediction on an actual power system using support vector machines. Dynamic voltage collapse prediction is first determined based on the PTSI calculated from information in dynamic simulation output. Simulations were carried out on a practical 87 bus test system by considering load increase as the contingency. The data collected from the time domain simulation is then used as input to the SVM in which support vector regression is used as a predictor to determine the dynamic voltage collapse indices of the power system. To reduce training time and improve accuracy of the SVM, the Kernel function type and Kernel parameter are considered. To verify the effectiveness of the proposed SVM method, its performance is compared with the multi layer perceptron neural network (MLPNN). Studies show that the SVM gives faster and more accurate results for dynamic voltage collapse prediction compared with the MLPNN. Keywor ds —Dynamic voltage collapse, prediction, artificial neural network, support vector machines