Nguyen Ba Hung

The Research about Free Piston Linear Engine fueled with Hydrogen using Numerical Analysis

This paper presents a research about free piston linear engine (FPLE) fueled with hydrogen, in which, numerical model was built to simulate the different processes that take place during the full stroke of engine. Dynamic model and thermodynamic model were used to predict piston speed, piston acceleration, in-cylinder pressure, indicated mean effective pressure (IMEP) and indicated efficiency. The compression ratio and bore were changed to provide information for the prediction. In this paper, the thermodynamic model was calculated based on an idealized Otto cycle, including compression, combustion and expansion. Simulation results show that velocity and acceleration increase when compression ratio and bore increase. Beside, by increasing compression ratio, IMEP and indicated efficiency also increase. On the contrary, by increasing bore, IMEP and indicated efficiency will decrease accordingly.

The Study About Deformation of a Peristaltic Pump using Numerical Simulation

The purpose of this study is to investigate the effects of changing dimension of a soft tube in a peristaltic pump on deformation, stress and fluid flow rate of the peristaltic pump. Geometries of the peristaltic pump is created in a Catia drawing software based on specifications of a real peristaltic pump. Afterwards, the geometries of this pump is imported into a commercial Ansys software to calculate deformation, stress, and fluid flow rate of this pump. The simulation results showed that the deformation and stress of the soft tube is increased by increasing soft tube diameter from 2 mm to 4 mm. When the tube diameter is increased to 5 mm and tube thickness is reduced to 0.5 mm, the soft tube is damaged. The highest fluid flow rate could be found at the tube thickness and diameter of 1 mm and 4 mm, respectively.

The Research About Free Piston Linear Engine with Artificial Neural Network

Free piston linear engine (FPLE) is a promising concept being explored in the mid-20th century. On the other hand, Arficial neural networks (ANNs) are non-linear computer algorithms and can model the behavior of complicated non-linear processes. Some researchers already studied this method to predict internal combustion engine characteristics. However, no investigation to predict the performance of a FPLE using ANN approach appears to have been published in the literature to date. In this study, the ability of an artificial neural network model, using a back propagation learning algorithm has been used to predict the in-cylinder pressure, frequency, maximum stroke length of a free piston linear engine. It is advised that, well-trained neural network models can provide fast and consistent results, making it an easy-to-use tool in preliminary studies for such thermal engineering problems.

Simulation of SI-HCCI Transition in a Two-Stroke Free Piston Engine Fuelled with Hydrogen

Abstract >> A free piston linear engine could be operated under HCCI combustion due to its variable compressionratios. To obtain HCCI combustion, the free piston linear engine needs a high compression ratio to achieve auto-ignition of the fuel/air mixture. In this study, an idea for obtaining a high compression ratio using the transitionfrom SI combustion to HCCI combustion was proposed. The fuel used in this study is hydrogen, which is consideredto be an environmentally friendly fuel. Besides, the effects of key parameters such as equivalence ratio (ϕ), load resistance (R L ) and intake temperature (T in ) on the SI-HCCI transition were numerically investigated. The simulationresults show that the SI-HCCI transition is successful without any significant reduction of in-cylinder pressure as the intake temperature is increased from T in =300K (SI mode) to T in =450K (HCCI mode), while the load resistanceand equivalence ratio are retained respectively at R L =120Ω and ϕ=0.6 in both SI mode and HCCI mode.