The flows at the intake and the exhaust of an internal-combustion engine are most of the time simplified to a single space dimension, and the hyperbolic partial differential equations that govern the compressible and unsteady air flow are discretized and solved numerically. This method is the basis of today's engine simulation codes. Models for complex parts such as the charge air coolers often need calibration with experimental engine data essentially for the pressure drop coefficients and the corrected lengths. Another technique for understanding wave action inside the pipes of an internal-combustion engine is to use the reciprocating nature of the engine itself and to gain access to the frequency spectrum of the pressure and the mass flow signals. This was achieved in this paper using a dedicated dynamic bench that identifies a transfer matrix which is defined in terms of the pressure and the mass flow rate. This new transfer matrix technique permits the dynamic pressure and the mass flow to be identified under similar conditions to those encountered in an engine. The transfer matrix is measured for two charge air cooler geometries and validated using experimental engine measurements. The results and methodology are explained in the frequency domain and the time domain, and the future objectives and perspectives discussed.

No abstract

No abstract

A new technique for simulating engine pressure waves consisting of linking pressure response and mass flow rate excitation in the frequency domain has been presented. This is achieved on the so-called “dynamic flow bench”. With this new approach, precise, fast and robust results can be obtained while taking into account all the phenomena inherent to compressible unsteady flows. The method exhibited promising results when it was incorporated in a GT-Power/Simulink coupled simulation of a naturally aspirated engine. However, today’s downsized turbocharged engines come with more stringent simulation necessities, where discontinuities such as the charge air cooler (CAC) must be correctly modeled. Simulating such engines with the transfer function methodology is quite difficult because it requires mounting the entire intake line on the bench. Modeling wave action for these engines requires an understanding in the frequency domain of the flow’s characteristics through the different elements that make up the intake line. This leads us to study the acoustic transfer matrices. In order to split the intake line into separate elements, a straight duct of 185mm length is chosen as a first reference. It is mounted on the dynamic flow bench and pressure response is measured after an impulse mass flow excitation. Transfer functions of relative pressure and mass flow rate are then identified at given points upstream and downstream of this reference tube. These functions produce the desired transfer matrix poles. The resulting matrix is validated by inserting the tube in the intake lines of two four-cylinder engines which are modeled in GT-Power. Pressure and mass flow are registered at the measurement points of the tube from the simulation. The time series data upstream of the tube is treated in the frequency domain and the transfer matrix is used to calculate the corresponding downstream values. Measured values from the native simulation and those calculated using the transfer matrix propagation are then compared. Finally, the experimental technique for identifying transfer matrices of more complex elements using two versions of the previous tube is presented.

This study presents two experiments addressing the representation of scientific data, in particular airflows, with a user-centered design approach. Our objective is to provide users feedback to data visualization designers to help them choose an air flow representation that is understandable and attractive for non-experts. The first study focuses on static markers allowing to visualize an airflow, with information characterizing the direction and the intensity. In a second study, carried out in an immersive virtual environment, two information were added, the temporal evolution and the concentration of pollutants in the air. To measure comprehension and attractiveness, participants were asked to answer items on Likert scales (experiment 1) and to answer User Experience Questionnaire (experiment 2). The results revealed that arrows seem to be a very common and understandable form to represent orientation and direction of flow, but that they should be improved to be more attractive by making them brighter and more transparent, as the representation could occlude the scene, especially in virtual reality. To solve this problem, we suggest giving the users the ability to define the specific area where they want to see the air flow, using a cross-sectional view. Vector fields and streamlines could therefore be applied in a virtual reality context.