Performance and Environmental Impact Assessment of Diesel Engine Operating on High Viscous Punnai Oil - Diesel Blends

The increasing demand for energy consumption because of the growing population and environmental concerns has motivated the researchers to ponder about alternative fuel that could replace diesel fuel. A new fuel should be cheaply available, clean, efficient, and environmentally friendly. In this paper, the engine operated with neat punnai oil blends with diesel were investigated at various engine load conditions, keeping neat punnai oil and diesel as base fuels. The performance indicators such as Brake Specific Energy consumption (BSEC), Brake thermal efficiency (BTE) and Exhaust gas temperature (EGT); emission indicators such as Carbon monoxide (CO), Oxides of Nitrogen (NOx), smoke opacity; and combustion parameters like cylinder pressure and heat release rate were examined. The Brake thermal efficiency of diesel is 29.2% whereas, it was lower for neat punnai oil and its blends at peak load conditions. Concerning the environmental aspect, Oxides of Nitrogen emission showed a decreasing trend with higher smoke emissions for Punnai oil blends. Detailed combustion analysis showed that on smaller concentrations of punnai oil in the fuel blend, the duration of combustion has improved significantly. However, for efficiency and emissions, the P20 (20% Punnai oil and 80% Diesel) blend performs similar to that of diesel compared to all other blending combinations. When compared with diesel, the P20 blend shows an improvement in BSEC by 26.37%. It also performs closer in HC emission, a marginal increase in smoke opacity of 4% with reduced NOx and CO2 emission of 7.9% and 4.65% respectively. Power loss was noticed when neat punnai oil and higher blends were used due to the high density and low calorific value of punnai oil blends which leads to injecting more fuel for the same pump stroke.

Study on the test load for the structural test of the flight-load condition of the external fuel tank for fixed-wing aircraft

In this study, for the purpose of conducting the structural tests for the verification of structural soundness of the flight-load conditions of the external fuel tank for the fixed-wing aircraft, the flight load acting on the external fuel tank was converted to test load and the suitability of the converted loads was verified. The loads imposed on the external fuel tank were expressed as the combination of the inertial load (based on the acceleration in the translational direction) and the tangential direction inertial load (based on the angular acceleration of the moment). To calculate the test load, the transfer function table was generated by calculating the shear load and moment based on the unit load. For this purpose, a transfer function table was established by dividing the external fuel tank into a few sections and calculating the shear load and moment generated by the unit shear load and unit moment in each section. In addition, the test load for each section was calculated by computing the established transfer function table and flight-load conditions. However, in actual structural tests, it is often not possible to impose a load in the same position as the point at which the shear load and moment are calculated. For this reason, the actual test-load positions had to be determined and the calculated test loads were redistributed to those positions. Then, the final test load plan was established by applying a whiffle tree to increase the efficiency of the test while also making it easier to apply the actuators. Finally, the suitability of the established test load plan was confirmed by comparison with the flight-load conditions.

Assessment of the Influence of Added Resistance on Ship Pollutant Emissions and Freight Throughput Using High-Fidelity Numerical Tools

The reduction of ship pollutants is a key issue in the international agenda. Emissions estimation is usually based on standard calculations that consider the different scenarios of ships. This work presents research on the influence of added resistance on ship emissions and freight throughput. First, a methodology to assess the added resistance influence is shown. The procedure is applied to a roll on-roll off ship under two load conditions. Analyses are computed to value wind- and wave-added resistances for different seasons. An investigation on ship pollutant emissions for a whole route is performed. Moreover, the influence of added resistance on the ship freight throughput is analyzed. Finally, some relevant information is concluded. For instance, a difference of up to 53% in pollutant emission estimation is observed if added resistance is considered. Additionally, the navigation in added resistance conditions could lead to a freight loss of 18% per operational year.

Misfire Detection Using Crank Speed and Long Short-Term Memory Recurrent Neural Network

In this work, a new approach was developed for the detection of engine misfire based on the long short-term memory recurrent neural network (LSTM RNN) using crank speed signal. The datasets are acquired from a six-cylinder-inline, turbo-charged diesel engine. Previous works investigated misfire detection in a limited range of engine running speed, running load or misfire types. In this work, the misfire patterns consist of normal condition, six types of one-cylinder misfire faults and fifteen types of two-cylinder misfire faults. All the misfire patterns are tested under wide range of running conditions of the tested engine. The traditional misfire detection method is tested on the datasets first, and the result show its limitation on high-speed low-load conditions. The LSTM RNN is a type of artificial neural network which has the ability of considering both the current input in-formation and the previous input information; hence it is helpful in extracting features of crank speed in which the misfire-induced speed fluctuation will last one or a few cycles. In order to select the engine operating conditions for network training properly, five data division strategies are attempted. For the sake of acquiring high performance of designed network, four types of network structure are tested. The results show that, utilizing the datasets in this work, the LSTM RNN based algorithm can overcome the limitation at high-speed low-load conditions of traditional misfire detection method. Moreover, the network which takes fixed segment of raw speed signal as input and takes misfire or fault-free labels as output achieves the best performance with the misfire diagnosis accuracy not less than 99.90%.