Flávio Celso Trigo

Electrical impedance tomography using the extended Kalman filter

In this paper, we propose an algorithm that, using the extended Kalman filter, solves the inverse problem of estimating the conductivity/resistivity distribution in electrical impedance tomography (EIT). The algorithm estimates conductivity/resistivity in a wide range. The purpose of this investigation is to provide information for setting and controlling air volume and pressure delivered to patients under artificial ventilation. We show that, when the standard deviation of the measurement noise level raises up to 5% of the maximal measured voltage, the conductivity estimates converge to the expected vector within 7% accuracy of the maximal conductivity value, under numerical simulations, with spatial a priori information. A two-phase identification procedure is proposed. A cylindrical phantom with saline solution is used for experimental evaluation. An abrupt modification on the resistivity distribution of this solution is caused by the immersion of a glass object. Estimates of electrode contact impedances and images of the glass object are presented.

Identification of the state-space dynamics of oil flames through computer vision and modal techniques

This paper presents a new approach to estimate unstable flame dynamics.Computer vision processed images from CCD cameras provide a discriminant characteristic vector.A four-degree-of-freedom second order model is identified through Operational Modal Analysis.Unstable and stable flame models are cross-validated through spectral analysis.The method is capable of predicting flame extinction. In industrial oil furnaces, unstable flames can lead to potentially dangerous conditions. For this reason, elaborate control systems are used to monitor the various parameters of the process that could become the source of such problems. A current trend in research is the one that seeks to apply artificial intelligence techniques to efficiently identify a priory anomalous behavior of the flames, so as to help improving the time response of the automatic control. In system dynamics theory, it is common sense that an accurate modeling of the process under study directly affects the performance of the controlling apparatus. Unfortunately, due to the complexity of the process, physical models of flame propagation are still not as much faithful as they should to be used for control purposes. On the other hand, could the complex dynamics of flame propagation be described in terms of an identified assumed model, one would come up with a tool for the improvement of the control strategy. In this work, a new approach based on Operational Modal Analysis (OMA) tools is used to identify four degree-of-freedom second order state-space models of oil flame dynamics in a prototype furnace. Grabbed images of a CCD camera, after being processed through a computer vision method, provide sets of characteristic vectors which, then, serve as input data to an identification OMA algorithm based on the Ibrahim Time Domain Method. Models of unstable and stable flames are built and validated through spectral analysis of the reconstructed time-domain characteristic vectors. The truthfulness of the validation scheme was then confirmed by a quantitative modal assurance criterion modified to suit the current application. On the grounds of the results obtained, it is possible to assert that the proposed approach for the description of flame dynamics can likely predict the occurrence of unstable conditions, thus becoming another tool that might be used in an automated control system.

An inference model for combustion diagnostics in an experimental oil furnace

The continuous monitoring of the air/fuel ratio, oil/water/air temperatures, and gas/particulate emissions of combustion processes in oil-based furnaces allows experts to detect anomalies and act to prevent faults and critical conditions. These important but tedious tasks can be performed by an expert system designed to mimic the human abilities of recognizing relevant patterns and finding their most likely causes. In this article, we present the architecture of an expert system that uses flame images grabbed during the combustion process in an experimental oil furnace as input parameters. Computational processing of those images provides feature vectors for analysis by “artificial experts” that correlate changes in flame appearance with typical combustion states. The Dempster–Shafer method is used to build the knowledge base and the inference engine. The results of tests in which flame conditions are suddenly modified by altering the physical variables of the combustion process revealed that the method can properly combine measures from various flame image characteristics to issue diagnostics. Such diagnostics are similar to those given by human experts. This suggests that the proposed approach may fill the gap between models based on features extracted from images and real-world operating conditions. This is the intended contribution of this work.

Assessment of Uncertainties and Parameter Estimation in a Offshore Gas Pipeline

Natural gas has a great importance in actual economy, and its transport is done usually through pipeline networks. The operation of a gas pipeline uses numerical models for calculation of intermediate properties, prediction of future behavior and estimation of the integrated flow capacity. These models are based on physical assumptions, closure laws and field measurements of boundary conditions such as pressure, flow, temperature and composition of the natural gas. This paper presents a development proposed for state and parameter estimation based on the implementation of an extended Kalman filter, in order to determine appropriate values for the flow parameters and use of complementary measurements in the boundary conditions. These results are compared to the ones obtained by using the Equal Error Fraction Method. It was found reduced pressure and flow systematic errors when the Kalman filter was used to estimate parameters.

Modelling and Simulation of the Rolling Dynamics of a Tractor-Trailer Truck Vehicle

In this work, a 6 degree-of-freedom analytical model that describes the rolling dynamics of an articulated heavy-duty vehicle composed by a tractor and a trailer was developed and numerically simulated. The behaviour of a typical medium-duty truck-trailer set was evaluated in two common traffic manoeuvres, namely, performing a constant steering wheel angle curve and a change of direction, both at constant velocity (36 km/h). In the latter case, in order to simulate a sudden change, steering amplitude ranging from zero to 30° was imposed to the front wheels through a step function during 2 s. Results of the first trial revealed that the tractor roll angle presents a peak of 2.6° and achieves a steady value of 0.8° respectively at 4 and 20 s after the beginning of the manoeuvre. Roll angle amplitudes of the trailer, spanning from −0.015 to 0.023° (a negative value means the vehicle is leaning inwards at the curve), revealed an oscillatory characteristic, before reaching a stable value of 0.0025° in about 15 s. In the second trial, roll angles of both tractor and trailer reach maximum values higher than those of the previous test (respectively 11.5 and −0.5°); in addition, the oscillatory movement of the trailer was enhanced, since positive and negative roll angles alternated five times until both units return to a steady (zero roll angle) condition after about 20 s from the beginning of the manoeuvre. Those results are compatible with the expected behaviour of actual vehicles, thus suggesting that the proposed model can be tailored to include other truck-trailer configurations and manoeuvres.

Singularities in the Expression of the Kinetic Energy of a System of Particles Subject to Holonomous Constraints

In the textbooks on classical mechanics normally adopted for courses on mechanical engineering, there is no mention of the paradoxical fact that a material system subject to holonomous constraints can exhibit singular configurations for which the cross-products of their generalized velocities, not all identically nil, give rise to a zero value for the kinetic energy. Given the importance of this subject for the analysis of dynamic systems, and as a contribution to education in mechanics, we analyse in this article a material system composed of discrete masses subject to holonomous constraints and exhibiting the mentioned behaviour. After deriving an analytical expression for the kinetic energy of the system, numerical simulations permit the identification of some singular material configurations for which the initial statement holds.

On the Euler and Lagrange's Points of View in Rigid Body Mechanics

In this work we take Lagranges and Eulers important concepts from fluid mechanics and apply them to the movement of a rigid body. By means of two examples, namely motion around a fixed axis and around a fixed point, Lagrangian and Eulerian formulations of the problems are discussed. It is shown that Eulers approach suits better the description of rigid body kinematics, since the linearized equations of motion are simpler than the ones obtained by Lagranges formulation. This topic is rarely discussed in undergraduate courses on mechanics but it can provide students with a deeper comprehension of the movement of a rigid body and, at the same time, establish a connection with the scope of fluid mechanics.