Several optimization techniques are proposed both to identify the aerodynamic coefficients and to reconstruct the trajectory of a fin-stabilized projectile from partial flight data. A reduced ballistic model is used instead of a more general six degree of freedom (6DOF) ballistic model to represent the flight of the projectile. Optimization techniques are proposed in order to identify the set of aerodynamic coefficients. These techniques are compared when identifying the aerodynamic coefficients from both exact and noisy simulated partial flight data.


Keywords: aerodynamic coefficients, identification, free flight data, regularization.

This paper presents the research results of milling process optimization in the electromagnetic mill to obtain the predetermined particle size distribution of brown coal. Because of an important role of brown coal in Polish energy industry (power plants produce 9433 MW of electrical power from brown coal, which corresponds to about 34% share in total fuel usage structure of energy industry in Poland-2nd quarter 2013 [1]), there is a great need to look for and develop highly efficient methods of its mining, valorisation and low-emission combustion alongside with CO₂ capture technology. This paper proposes, as one of the methods of adapting low-rank coal to being utilized in modernized and newly built plants, the process of simultaneous grinding and drying in an electromagnetic mill system. This method is energy efficient and what is more significant it reduces the space required for its adaptation, thanks to electromagnetic mill's compact installation design. It is essential to obtain the desired characteristics of the product through the adequate control of the processes. Major concern of this case study was focused on determination of optimal grinding parameters in the electromagnetic mill in order to obtain two products of a desired size distribution (1-6.3 mm for application in fluidized bed boilers and 0-315 µm for boiler burners). The authors presented some theoretical considerations of the mechanisms and physical phenomena occurring during a fragmentation of solid particles as well as the literature review of the subject. The process complexity level, taking place in the active area of electromagnetic mill, involves the influence of particle-milling rod and particle-particle interactions as well as the volume of milling rods or coal particle residence time on the size distribution of the product. All of the mentioned factors account for nonlinearity of the problem and make the conditions difficult to rescale. Hence, a heuristic approach to inverse problem was chosen to analyse the differences between the desired and obtained particle size distributions. The examinations concerned grinding parameters such as total amount of rods (volume-based) and rod sizes (single and multi-size combinations of milling elements) were conducted. Equivalent samples of Polish brown coal with a particle diameter size ranging from 0 to 10 mm were chosen as an investigated material. Influence of the total volume of rods was examined using three amounts: 100 ml, 150 ml and 200 ml. Two grinding aid sizes were chosen in the form of ferromagnetic rods: fine rods of the size of 10 x 1 mm and coarse rods of the size of 20 x 2 mm.


Keywords: milling, brown coal, particle size distribution, optimization, electromagnetic mill.

Microwave imaging is considered as a nonlinear inverse scattering problem and tackled in a Bayesian estimation framework. The object under test (a breast affected by a tumor) is assumed to be composed of compact regions made of a restricted number of different homogeneous materials. This a priori knowledge is defined by a Gauss-Markov-Potts distribution. First, we express the joint posterior of all the unknowns; then, we present in detail the variational Bayesian approximation used to compute the estimators and reconstruct both permittivity and conductivity maps. This approximation consists of the best separable probability law that approximates the true posterior distribution in the Kullback-Leibler sense. This leads to an implicit parametric optimization scheme which is solved iteratively. Some preliminary results, obtained by applying the proposed method to synthetic data, are presented and compared with those obtained by means of the classical contrast source inversion method.


Keywords: inverse scattering, microwave imaging, breast cancer detection, Gauss-Markov-Potts prior,variational Bayesian approximation.

This paper is devoted to a theoretical and numerical study of different ways of calculating the Fourier transform of a noisy signal where the boundary conditions at the lateral boundaries of the measurement interval are not precisely known. This happens in different characterization problems where infrared camera is used for temperature measurements. In order to overcome this difficulty, the interval where the Fourier transform (its support) is supposed to be larger than the measurement domain is defined. Thus, this virtual interval larger than the measurement interval is used. We show that regularization by truncated singular value decomposition is able to yield good estimates to this very ill-posed inverse problem.


Keywords: integral transforms, thermal quadrupoles, heat transfer in mini-channel, inverse heat conduction and convection.

This paper studies electrical impedance tomography (EIT) using Bayesian inference [1]. The resulting posterior distribution is sampled by Markov chain Monte Carlo (MCMC) [2]. This paper studies a toy model of EIT as the one presented in [3], and focuses on efficient MCMC sampling for this model. First, this paper analyses the computation of forward map of EIT which is the bottleneck of each MCMC update. The forward map is computed by the finite element method [4]. Here its exact computation was conducted up to five times more efficient, by updating the Cholesky factor of the stiffness matrix [5]. Since the forward map computation takes up nearly all the CPU time in each MCMC update, the overall efficiency of MCMC algorithms can be improved almost to the same amount. The forward map can also be computed approximately by local linearisation, and this approximate computation is much more efficient than the exact one. Without loss of efficiency, this approximate computation is more accurate here, after a log transformation is introduced into the local linearisation process. Later on, this improvement of accuracy will play an important role when the approximate computation of forward map will be employed for devising efficient MCMC algorithms. Second, the paper presents two novel MCMC algorithms for sampling the posterior distribution in the toy model of EIT. The two algorithms are made within the 'multiple prior update' [6] and 'the delayed-acceptance Metropolis-Hastings' [7] schemes respectively. Both of them have MCMC proposals that are made of localized updates, so that the forward map computation in each MCMC update can be made efficient by updating the Cholesky factor of the stiffness matrix. Both algorithms' performances are compared to that of the standard single-site Metropolis [8], which is considered hard to surpass [3]. The algorithm of 'multiple prior update' is found to be six times more efficient, while 'the delayed-acceptance Metropolis-Hastings' with single-site update is at least twice more efficient.


Keywords: electrical impedance tomography, Bayesian inference, Markov chain Monte Carlo.

An efficient, global meshless method has been developed for creating 3-D wind fields utilizing sparse meteorological tower data. Meshless methods do not require the need for a mesh in order to connect node points. In this study, node points are placed within the computational domain based on topological features. Wind speeds and directions are obtained from a set of instrumented meteorological towers. Inverse weighting is used to initially establish wind vectors at all nodal points. The Kansa technique, employing global basis functions, is then used to create a mass-consistent, 3-D wind field. The meshless method yields close approximations to results obtained with a high-order finite element technique. The method was implemented using MATLAB.


Keywords: mesh-free method, 3-D wind field, mass-consistent.

Recently, Bevilacqua, Galeão and co-workers have developed a new analytical formulation for the simulation of diffusion with retention phenomena. This new formulation aims at the reduction of all diffusion processes with retention to a unifying model that can adequately simulate the retention effect. This model may have relevant applications in a number of different areas such as population spreading with partial hold up of the population to guarantee territorial domain chemical reactions inducing adsorption processes and multiphase flow through porous media. In this new formulation a discrete approach is firstly formulated taking into account a control parameter which represents the fraction of particles that are able to diffuse. The resulting governing equation for the modelling of diffusion with retention in a continuum medium requires a fourth-order differential term. Specific experimental techniques, together with an appropriate inverse analysis, need to be determined to characterize complementary parameters. The present work investigates an inverse problem which does not allow for simultaneous estimation of all model parameter. In addition a two-step characterization procedure is proposed: in the first step the diffusion coefficient is estimated and in the second one the complementary parameters are estimated. In this paper, it is assumed that the first step is already completed and the diffusion coefficient is known with a certain degree of reliability. Therefore, this work is aimed at investigating the confidence intervals of the complementary parameters estimates considering both the uncertainties due to measurement errors in the experimental data and due to the uncertainty propagation of the estimated value of the diffusion coefficient. The inverse problem solution is carried out through the maximum likelihood approach, with the minimization problem solved with the Levenberg-Marquardt method, and the estimation of the confidence intervals is carried out through the Monte Carlo analysis.


Keywords: diffusion, inverse problems, uncertainty propagation, Monte Carlo method.

Aircraft have become increasingly costly and complex. Military and civil pilots and engineers have used flight simulators in order to increase safety of flight through the training of crew. It is necessary to calibrate the simulation for simulators to have good adherence to reality, that is, to identify the parameters that make the simulation as close as possible to the actual dynamics. After determining these parameters, the simulator will be ready to be used in human resources training or assessing the aircraft. Parameter identification characterizes the aerodynamic performance of the aircraft and can be formulated as a problem optimization. The calibration of a dynamic flight simulator is achieved by a new meta-heuristic called multiple particle collision algorithm (MPCA). Preliminary results show a good performance of the employed approach.


Keywords: flight dynamic, parameter identification, multiple particle collision algorithm (MPCA).