Propfan experimental data analysis

A data reduction method, which is consistent with the performance prediction methods used for analysis of new aircraft designs, is defined and compared to the method currently used by NASA using data obtained from an Ames Res. Center 11 foot transonic wind tunnel test. Pressure and flow visualization data from the Ames test for both the powered straight underwing nacelle, and an unpowered contoured overwing nacelle installation is used to determine the flow phenomena present for a wind mounted turboprop installation. The test data is compared to analytic methods, showing the analytic methods to be suitable for design and analysis of new configurations. The data analysis indicated that designs with zero interference drag levels are achieveable with proper wind and nacelle tailoring. A new overwing contoured nacelle design and a modification to the wing leading edge extension for the current wind tunnel model design are evaluated. Hardware constraints of the current model parts prevent obtaining any significant performance improvement due to a modified nacelle contouring. A new aspect ratio wing design for an up outboard rotation turboprop installation is defined, and an advanced contoured nacelle is provided

Estimating Learning Models with Experimental Data

We study the statistical properties of three estimation methods for a model of learning that is often tted to experimental data: quadratic deviation measures without unobserved heterogeneity, and maximum likelihood with and without unobserved heterogeneity. After discussing identi cation issues, we show that the estimators are consistent and provide their asymptotic distribution. Using Monte Carlo simulations, we show that ignoring unobserved heterogeneity can lead to seriously biased estimations in samples which have the typical length of actual experiments. Better small sample properties are obtained if unobserved heterogeneity is introduced. That is, rather than estimating the parameters for each individual, the individual parameters are considered random variables, and the distribution of those random variables is estimated

Model updating using uncertain experimental modal data

The propagation of parameter uncertainty in structural dynamics has become a feasible method to determine the probabilistic description of the vibration response of industrial scale �nite element models. Though methods for uncertainty propagation have been developed extensively, the quanti�cation of parameter uncertainty has been neglected in the past. But a correct assumption for the parameter variability is essential for the estimation of the uncertain vibration response. This paper shows how to identify model parameter means and covariance matrix from uncertain experimental modal test data. The common gradient based approach from deterministic computational model updating was extended by an equation that accounts for the stochastic part. In detail an inverse approach for the identi�cation of statistical parametric properties will be presented which will be applied on a numerical model of a replica of the GARTEUR SM-AG19 benchmark structure. The uncertain eigenfrequencies and mode shapes have been determined in an extensive experimental modal test campaign where the aircraft structure was tested repeatedly while it was 130 times dis- and reassembled in between each experimental modal analysis

Molecular simulations minimally restrained by experimental data

One popular approach to incorporating experimental data into molecular simulations is to restrain the ensemble average of observables to their experimental values. Here I derive equations for the equilibrium distributions generated by restrained ensemble simulations and the corresponding expected values of observables. My results suggest a method to restrain simulations so that they generate distributions that are minimally perturbed from the unbiased distributions while reproducing the experimental values of the observables within their measurement uncertainties

Quantum Experimental Data in Psychology and Economics

We prove a theorem which shows that a collection of experimental data of probabilistic weights related to decisions with respect to situations and their disjunction cannot be modeled within a classical probabilistic weight structure in case the experimental data contain the effect referred to as the 'disjunction effect' in psychology. We identify different experimental situations in psychology, more specifically in concept theory and in decision theory, and in economics (namely situations where Savage's Sure-Thing Principle is violated) where the disjunction effect appears and we point out the common nature of the effect. We analyze how our theorem constitutes a no-go theorem for classical probabilistic weight structures for common experimental data when the disjunction effect is affecting the values of these data. We put forward a simple geometric criterion that reveals the non classicality of the considered probabilistic weights and we illustrate our geometrical criterion by means of experimentally measured membership weights of items with respect to pairs of concepts and their disjunctions. The violation of the classical probabilistic weight structure is very analogous to the violation of the well-known Bell inequalities studied in quantum mechanics. The no-go theorem we prove in the present article with respect to the collection of experimental data we consider has a status analogous to the well known no-go theorems for hidden variable theories in quantum mechanics with respect to experimental data obtained in quantum laboratories. For this reason our analysis puts forward a strong argument in favor of the validity of using a quantum formalism for modeling the considered psychological experimental data as considered in this paper.Comment: 15 pages, 4 figure

Recovery of a quarkonium system from experimental data

For confining potentials of the form q(r)=r+p(r), where p(r) decays rapidly and is smooth for r>0, it is proved that q(r) can be uniquely recovered from the data {E_j,s_j}, where E_j are the bound states energies and s_j are the values of u'_j(0), and u_j(r) are the normalized eigenfunctions of the problem -u_j" +q(r)u_j=E_ju_j, r>0, u_j(0)=0, ||u_j||=1, where the norm is L^2(0, \infty) norm. An algorithm is given for recovery of p(r) from few experimental data

Survey of experimental data

>A review of meson emission in heavy ion collisions at incident energies from SIS up to collider energies is presented. A statistical model assuming chemical equilibrium and local strangeness conservation (i.e. strangeness conservation per collision) explains most of the observed features. Emphasis is put onto the study of $K^+$ and $K^-$ emission at low incident energies. In the framework of this statistical model it is shown that the experimentally observed equality of $K^+$ and $K^-$ rates at ``threshold-corrected'' energies $\sqrt{s} - \sqrt{s_{th}}$ is due to a crossing of two excitation functions. Furthermore, the independence of the $K^+/K^-$ ratio on the number of participating nucleons observed between SIS and RHIC is consistent with this model. It is demonstrated that the $K^-$ production at SIS energies occurs predominantly via strangeness exchange and that this channel is approaching chemical equilibrium. The observed maximum in the $K^+/\pi^+$ excitation function is also seen in the ratio of strange to non-strange particle production. The appearance of this maximum around 30 $A\cdot$GeV is due to the energy dependence of the chemical freeze-out parameters $T$ and $\mu_B$.Comment: 13 pages, 14 figures, SQM2001 in Frankfurt, Sept. 2001, submitted to IO