Engineering, phisics and mathematics are the main subjects ETA applies in developing industrial research in the automotive sector. These researches do fined their application in other fields where mechanical competences can be implemented, not just the car industry.
Research experiences that have seen ETA’s involvement
Design of mechanical composite components for use on industrial vehicles;
Composite materials for tooling machineries;
Advanced design and engineering research of mathematical modules in crash proof situations;
Study of neuro/fuzzy modules in a vehicle dynamics;
Assembling of carbon poltruse with the use of aluminium joints;
Creation of mechanical and electrical parts for disables chairs;
Studies on aerodynamic flow visualizations techniques;
Analysis on tyre behaviour modules;
Software development to optimise the fluid-dynamics of industrial furnaces for enamelling;
Designing of PMMA furnace.
E. D’Amato, G. Carfagna, M. Mancinelli - “Numerical experimental analysis of assembled structural joints for motoring use”, AIAS 2005;
F. Tosi, S. Ubertini, S. Succi, H. Chen and I. V. Karlin “A comparison of single-time relaxation lattice Boltzmann schemes with enhanced stability” accepted for publication in International Journal of Modern Physics C.;
F. Tosi, S. Ubertini, S. Succi, H. Chen and I. V. Karlin “Numerical stability of Entropic versus positivity-enforcing Lattice Boltzmann schemes” proceeding of the 14th International Conference on Discrete Simulation of Fluid Dynamics in Complex Systems (DSFD 2005);
F. Tosi, S. Ubertini, S. Succi and I. V. Karlin “Optimization strategies for the entropic lattice Boltzmann method” Accepterd for publication in Journal of Scientific Computing;
F. Tosi “An Application of Lattice Boltzmann Model to Open Systems” Applied and industrial mathematics in Italy. Vol 69 (June 2005).
P.Antonini, S. Longhi, M.L. Corradini, G. Ippoliti, C. Stronati, Race car performance evaluation by a Neuro-Fuzzy Inference System, American Control Conference 2006, Minneapolis;
P.Antonini, M.L.Corradini, S. Longhi, A SENSITIVITY BASED APPROACH FOR RACING CARS DESIGN AND SET-UP, FISITA World Automotive Congress 2006, Yokohama;
Research study of a case
Computational Fluid Dynamic (CFD):
In our era of news coverage and increased use of computational science in engineering applications and carrying outs, a quick and reliable tool to use in simulations becomes essential where extreme applications are everyday necessity (like in car racing).
Is this the reason why we are realising a software to use in engineering analysis and simulation of external fluid-dynamic problems of the vehicle and its separate parts.
Fluid flow analysis mean that we study how fluids like the air, liquids and gases, generally move around a solid object, like the wings of a car, a wing profile and pipes, as well as how the different fluids react to the collision with these shapes.
In fact traditional methods to calculate mathematic formulas behind fluid dynamics make use of the Navier-Stoker equation. We can describe a Navier- Stoker equation as a whole set of equations that looks at basic/fundamental differences when one studies and describe fluid-dynamic fluxes. But because it takes in to account an endless number of variation factors it requires a computer network of some dimension. It also require great ability and to be familiar with its daily use. Finally, it is not a linear system so the method can be applied with results solely to cases that are very simple.
Figura 1: Fig. 1: a graphic representation of space.
Method we use: Our simulator works with a unique approach, different from traditional algorithm and numerical methods for computational comparison of fluid dynamic (CFD).
In fact traditional methods to calculate mathematic formulas behind fluid dynamics make use of the Navier-Stoker equation. We can describe a Navier- Stoker equation as a whole set of equations that looks at basic/fundamental differences when one studies and describe fluid-dynamic fluxes.
But because it takes in to account an endless number of variation factors it requires a computer network of some dimension. It also require great ability and to be familiar with its daily use. Finally, it is not a linear system so the method can be applied with results solely to cases that are very simple.
In building this new simulator our attempt is to express in a mathematical form the movements of fluids in the continuo. It uses Boltzmann fundamental equation that is at the base of molecular density in fluid particles and it is also an easier system to manage, more intuitive and immediate than the one that refers to the Navier-Stokes.
The simplicity of this approach is reflected in the mathematical formulas it express which try to capture the interaction of particles that come into contact with each other in a natural flux the sum of which recreate the actual complexity.
To this is added an high accuracy of the numerical calculus and solution. This system offers reliability with calculus precision and can be applied with good results to tests on laminar material. In simulating complex dynamic solutions, it needs the aid of other methods which enhance the accuracy of the simulation:
01. Conditions check:
Rim condition with input and output outlets;
- Solid walls conditions;
- Open boundary conditions.
03.Models for the reduction of computational and numerical mistakes.
Fig. 2: Networking scheme MIMD type (multiple instruction, multiple data)
The new approach allows local mathematical operations that gives numerous advantages as well as allowing computational savings. This is why the new numerical method is an huge added value as it reduces drastically computational expenditure.
Finally, it is built in a way that makes it easy to associate to mesh creation techniques for simulations, this because dose not require in between stages from the CAD design to the simulator.
By the manual simulations
Uno o più casi studio di lavoro svolto con successo.
Lid driver cavity flow: Simulation of a flux inside a cavity. The movement is dictated by the superior inner side (with a from left to right movement). The number of Reynolds taken into account for the simulation were 7500.
Figura 3: Lid driver cavity flow.
simulazione bi-dimensionale di un flusso all’interno di un canale con altezza variabile (in modo da ottenere un gradino).
Il fenomeno è generato da un flusso entrante dalla parte sinistra del condotto.
Fig. 4: Back step
The Reynolds number used for the simulation is 2000. Wing outline NACA 4412:
Simulation of flux around wing outline.
The image visualise the isolinee of pressure index surrounding the object.
Fig. 5: Streamlines of Cp index pressure surrounding the object.