A linear model of atmospheric circulation

Linear model of atmospheric circulation used for atmosphere of Venu

Computation of three-dimensional supersonic flows with shock waves

Computation of three dimensional supersonic flows with shock wave

A direct method for calculation of the flow about an axisymmetric blunt body at angle of attack

Direct calculation method for supersonic inviscid flow around axisymmetric blunt body with conically reentrant afterbody flying at large angle of attac

Supersonic flow over convex and concave shapes with radiation and ablation effects

Numerical method of unsteady adjustment for calculating inviscid flow fields about convex and concave shapes on atmospheric entry with ablatio

A method for determining catalytic efficiency of surfaces

Catalytic efficiency calculations for surface heat flux and heat transfer during atmospheric reentr

General circulation in the atmosphere of Venus driven by polar and diurnal variations of surface temperature

Mathematical model for Venus atmosphere circulation pattern determined by polar and diurnal temperature variation

A simulated annealing methodology for clusterwise linear regression

In many regression applications, users are often faced with difficulties due to nonlinear relationships, heterogeneous subjects, or time series which are best represented by splines. In such applications, two or more regression functions are often necessary to best summarize the underlying structure of the data. Unfortunately, in most cases, it is not known a priori which subset of observations should be approximated with which specific regression function. This paper presents a methodology which simultaneously clusters observations into a preset number of groups and estimates the corresponding regression functions' coefficients, all to optimize a common objective function. We describe the problem and discuss related procedures. A new simulated annealing-based methodology is described as well as program options to accommodate overlapping or nonoverlapping clustering, replications per subject, univariate or multivariate dependent variables, and constraints imposed on cluster membership. Extensive Monte Carlo analyses are reported which investigate the overall performance of the methodology. A consumer psychology application is provided concerning a conjoint analysis investigation of consumer satisfaction determinants. Finally, other applications and extensions of the methodology are discussed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45745/1/11336_2005_Article_BF02296405.pd

Stochastic Optimization And The Gambler’S Ruin Problem

An analogy between stochastic optimization and the gambler’s ruin problem is used to estimate the expected value of the number of function evaluations required to reach the extremum of a special objective function with a pafrticular random walk. The objective function is the sum of the squares of the independent variables. The optimization is accomplished when the random walk enters a suitably defined small neighborhood of the extremum. The results indicate that for this objective function the expected number of function evaluations increases as the number of dimensions to the five halves power. Results of extensive computations of optimizing random walks in spaces with dimensions anging from 2 to 30 agree with the analytically predicted behavior. © 1992 Taylor & Francis Group, LLC