Nuestros investigadores

Sergio Ausejo Muñoz

Publicaciones científicas más recientes (desde 2010)

Autores: Cazón, Aitor; González, Jorge; García, E.; et al.
ISSN 1745-2759  Vol. 10  Nº 3  2015  págs. 149 - 162
Formula Student is an international competition governed by the Society of Automotive Engineers (SAE) which challenges university students to design and build a racing car that will subsequently be compared against other cars from universities around the world on homologated racing circuits by non-professional drivers. This study focuses on the design, analysis and manufacturing process of a new oil sump for a Formula Student car ¿ which involves combining a main ABS-plastic core created by an additive manufacturing (AM) printing process and a manual lay-up process with carbon fibre ¿ in order to reduce the sloshing effect due to the movement of the oil during racing. The new oil sump and the original sump were modelled with computer-aided design (CAD) software and five computational fluid dynamics (CFD) simulations were performed to compare the sloshing effect in both designs in three driving scenarios: acceleration, braking and changing direction. The simulations showed that acceleration is not a critical situation since the new internal design of the sump was capable of delaying the immersion time of the oil pick-up pipe from 0.75 seconds to 2 seconds during braking and from 0.4 seconds to 0.8 seconds during lateral acceleration. The new design was physically manufactured and subsequently integrated into an internal combustion engine for testing for 45 minutes. During this test, the engine was started and put at 9600 RPM, so the oil worked under realistic temperature conditions (80°C). It did not present any oil leak. After testing, it was disassembled and visually inspected. No failure in the inner surfaces of the oil sump was observed due to temperature. According to these results, the present research argues that the combination of AM technology (i.e., fused deposition modelling) and layers of carbon fibre is a real alternative to conventional manufacturing processes in order to create geometrically complex oil sumps that minimise the sloshing effect in competition automobiles.
Autores: Pasciuto, I.; Ausejo, Sergio; et al.
ISSN 1384-5640  Vol. 32  Nº 1  2014  págs. 27 - 53
In this paper, we present a novel method to predict human motion, seeking to combine the advantages of both data-based and knowledge-based motion prediction methods. Our method relies on a database of captured motions for reference and introduces knowledge in the prediction in the form of a motion control law, which is followed while resembling the actually performed reference motion. The prediction is carried out by solving an optimization problem in which the following conditions are imposed to the motion: must fulfill the goals of the task; resemble the reference motion selected from the database; follow a knowledge-based dynamic motion control law; and ensure the dynamic equilibrium of the human model, considering its interactions with the environment. In this work, we apply the proposed method to a database of clutch pedal depression motions, and we present the results for three predictions. The method is validated by comparing the results of the prediction to motions actually performed in similar conditions. The predicted motions closely resemble the motions in the validation database and no significant differences have been noted either in the motion's kinematics or in the motion's dynamics.
Autores: Pasciuto, I.; Ausejo, Sergio; et al.
ISSN 1615-147X  Vol. 49  Nº 1  2014  págs. 169 - 183
In this paper an optimization-based hybrid dynamic motion prediction method is presented. The method is hybrid as the prediction relies both on actually performed motions for reference (following a data-based approach) and on the definition of appropriate performance measures (following a knowledge-based approach). The prediction is carried out through the definition of a constrained non-linear optimization problem, in which the objective function is composed of a weighted combination of data-based and knowledge-based contributions. The weights of each contribution are varied in order to generate a battery of hybrid predictions, which range from purely data-based to purely knowledge-based. The results of the predictions are analyzed and compared against actually performed motions both qualitatively and quantitatively, using a measure of realism defined as the distance of the predicted motions from the mean of the actually performed motions. The method is applied to clutch pedal depression motions and the comparison between the different approaches favors the hybrid solution, which seems to combine the strengths of both data- and knowledge-based approaches, enhancing the realism of the predicted motion.
Autores: Ausejo, Sergio; Suescun, Ángel María;
ISSN 1384-5640  Vol. 26  Nº 4  2011  págs. 397 - 410
In this paper, we present an optimization method for solving the nonlinear constrained optimization problem arising from a motion reconstruction problem formulated with natural coordinates. A motion reconstruction problem consists in a kinematic analysis of a rigid multibody system whose motion is usually overdetermined by an excess of data. The method has been applied to the analysis of human motion which is a typical case of an overdetermined kinematic problem as a large number of markers are usually placed on a subject to capture its movement. The efficiency of the method has been tested both with computer-simulated and real experimental data using models that include open and closed kinematic loops.