Finite Element Simulation of a Steady-State Stress Distribution in a Four Stroke Compressed Natural Gas-Direct Injection Engine Cylinder Head

The main aim of this work is to predict the design performance based on the stress/strain and thermal stress behaviour of cylinder head under various operating conditions. The effects of engine operating conditions such as combustion gas temperature and maximum internal pressure, components initial temperature and engine speed on the stress and thermal stress behaviour of the cylinder head have been analyzed. The analysis was carried out using a finite element analysis (FEA) software package, MSC.NASTRAN which is use to simulate and predict the von-Mises stress and strain pattern and thermal distribution of the cylinder head structure during the combustion process in the engine and the geometry modelling was carried out using a popular computeraided engineering tool, CATIA V5. The result can be used to determine the quality of the design as well as identify areas which require further improvement. In this investigation, structural analyses of the cylinder head highlight several areas of interest. The maximum stress is found not exceeding the material strength of cylinder head, and thus the basic design criteria, namely no yielding and no structural failure under firing load case, can be satisfied. In addition, the effect of thermal stress/strain provides a good indication on structural integrity and reliability of the cylinder head, which can be improved in the early stages of design. This steadystate finite element method (FEM) stress analysis can play a very effective role in the rapid prototyping of the cylinder head

Catastrophic forgetting: still a problem for DNNs

We investigate the performance of DNNs when trained on class-incremental visual problems consisting of initial training, followed by retraining with added visual classes. Catastrophic forgetting (CF) behavior is measured using a new evaluation procedure that aims at an application-oriented view of incremental learning. In particular, it imposes that model selection must be performed on the initial dataset alone, as well as demanding that retraining control be performed only using the retraining dataset, as initial dataset is usually too large to be kept. Experiments are conducted on class-incremental problems derived from MNIST, using a variety of different DNN models, some of them recently proposed to avoid catastrophic forgetting. When comparing our new evaluation procedure to previous approaches for assessing CF, we find their findings are completely negated, and that none of the tested methods can avoid CF in all experiments. This stresses the importance of a realistic empirical measurement procedure for catastrophic forgetting, and the need for further research in incremental learning for DNNs.Comment: 10 pages, 11 figures, Artificial Neural Networks and Machine Learning - ICANN 201