A Neural Network Approach for Reconstructing In-Cylinder Pressure from Engine Vibration Data

In this work neural network models are used to reconstruct in-cylinder pressure from a vibration signal measured from the engine surface by a low-cost accelerometer. Using accelerometers to capture engine combustion is a cost-effective approach due to their low price and flexibility. The paper describes a virtual sensor that re-constructs the in-cylinder pressure and some of its key parameters by using the engine vibration data as input. The vibration and cylinder pressure data have been processed before the neural network model training. Additionally, the correlation between the vibration and in-cylinder pressure data is analyzed to show that the vibration signal is a good input to model the cylinder pressure.The approach is validated on a RON95 single cylinder research engine realizing homogeneous charge compression ignition (HCCI). The experimental matrix covers multiple load/rpm steady-state operating points with different start of injection and lambda setpoints. A radial basis function (RBF) neural network model was first trained with a series of two operating points at low loads with data of 1000 consecutive combustion cycles, to build the needed nonlinear mapping. The results show that the developed neural network model is capable of reconstructing in-cylinder pressure at low loads with good accuracy. The error for combustion parameter such as maximum cylinder pressure did not exceed 5%. The approach is further validated with another series of operating points consisting of both low loads and high loads. However, the results in this case deteriorated. Changing the neural network model to generalized regression (GR) improved the in-cylinder pressure reconstruction quality. The performance of the models was also considered in terms of combustion parameters, such as maximum pressure and mass burned fraction. The paper concludes that vibration signal carries sufficient information to estimate combustion parameters independently on the engine platform or combustion concept.Peer reviewe

Mechanisms of Chronic Alcohol Exposure-Induced Aggressiveness in Cellular Model of HCC and Recovery after Alcohol Withdrawal.

International audienceAlcohol-related liver disease is the most prevalent chronic liver disease worldwide, accounting for 30% of hepatocellular carcinoma (HCC) cases and HCC-specific deaths. However, the knowledge on mechanisms by which alcohol consumption leads to cancer progression and its aggressiveness is limited. Better understanding of the clinical features and the mechanisms of alcohol-induced HCC are of critical importance for prevention and the development of novel treatments. Early stage Huh-7 and advanced SNU449 liver cancer cell lines were subjected to chronic alcohol exposure (CAE), at different doses for 6~months followed by 1-month alcohol withdrawal period. ADH activity and ALDH expression were much lower in SNU449 compared with Huh-7 cells and at the 270~mM dose, CAE decreased cell viability by about 50% and 80%, respectively, in Huh-7 and SNU449 cells but induced mortality only in Huh-7 cells. Thus, Huh-7 may be more vulnerable to ethanol toxicity because of the higher levels of acetaldehyde. CAE induced a dose-dependent increase in cell migration and invasion and also in the expression of cancer stem cells markers (CD133, CD44, CD90). CAE in Huh-7 cells selectively activated ERK1/2 and inhibited GSK3β signaling pathways. Most of the changes induced by CAE were reversed after alcohol withdrawal. Interestingly, we confirmed the increase in CD133 mRNA levels in the tumoral tissue of patients with ethanol-related HCC compared to other HCC etiologies. Our results may explain the benefits observed in epidemiological studies showing a significant increase of overall survival in abstinent compared with non-abstinent patients