Current Issue : October-December Volume : 2022 Issue Number : 4 Articles : 5 Articles
This article is a study of Hydrotreated Vegetable Oil and Butanol Fuel blends, which are mixed in three different proportions (HVOB5, HVOB10 and HVOB20), and the comparison of their combustion (in-cylinder pressure, pressure rise and ROHR), performance (fuel consumption, BSFC and BTE) and emission (CO2, NOX, HC and Smoke) characteristics with those of fossil diesel fuel. In the wake of finding an alternative fuel that requires little to zero modifications to the existing IC engines, it is necessary to account for the necessity of matching the efficiency of conventional fuels as well as greatly reducing its exhaust emissions. As a result of transesterification, HVO is found to have better stability and higher CN compared to other biofuels. It is termed a “renewable diesel” due to its ability to reduce emissions while maintaining efficiency. HVO as a fuel has higher cost efficiency, and for a more stable oxygen content in the fuel, an alcohol substitute is needed. Butanol, which has a considerable advantage over other alcohols due to its higher density, viscosity and CN, is selected. HVOB5 and HVOB10 are found to match diesel fuel in terms of fuel consumption while having a ~1% lesser efficiency. In terms of emissions, all the fuel mixtures including HVO100 are found to have ~4–5% lesser CO2, ~10–15% lesser NOX and a ~25–45% reduction in smoke levels....
The train horn sound is an active audible warning signal used for warning commuters and railway employees of the oncoming train(s), assuring a smooth operation and traffic safety, especially at barrier-free crossings. This work studies deep learning-based approaches to develop a system providing the early detection of train arrival based on the recognition of train horn sounds from the traffic soundscape. A custom dataset of train horn sounds, car horn sounds, and traffic noises is developed to conduct experiments and analysis. We propose a novel two-stream end-to-end CNN model (i.e., THD-RawNet), which combines two approaches of feature extraction from raw audio waveforms, for audio classification in train horn detection (THD). Besides a stream with a sequential one-dimensional CNN (1D-CNN) as in existing sound classification works, we propose to utilize multiple 1D-CNN branches to process raw waves in different temporal resolutions to extract an image-like representation for the 2D-CNN classification part. Our experiment results and comparative analysis have proved the effectiveness of the proposed two-stream network and the method of combining features extracted in multiple temporal resolutions. The THD-RawNet obtained better accuracies and robustness compared to those of baseline models trained on either raw audio or handcrafted features, in which at the input size of one second the network yielded an accuracy of 95.11% for testing data in normal traffic conditions and remained above a 93% accuracy for the considerable noisy condition of-10 dB SNR. The proposed THD system can be integrated into the smart railway crossing systems, private cars, and self-driving cars to improve railway transit safety....
As an auxiliary component with the largest energy consumption in the fuel cell power system, the electric air compressor is of great significance to improve the overall efficiency of the system by reducing its power consumption under the premise of meeting the cathode intake demand. In this paper, the flow state of the gas in the flow field of the fuel cell TSEAC (two-stage electric air compressor) is analyzed by simulation, and the accuracy of the simulation results is verified by experiments. Through the research on the gas compression work of the fuel cell TSEAC, it is found that the higher temperature rise of the gas during the compression process will increase the compression work, thereby reducing the efficiency of the fuel cell TSEAC. Therefore, based on the field synergy theory, this paper designs the heat dissipation structure of the TSEAC elbow. In the common working conditions of fuel cell TSEAC, micro-fin tube is an effective energy-saving structure that takes into account heat dissipation enhancement and flow resistance, and its ratio of micro-fin height to laminar bottom layer thickness ε/δ = 1.6 has the best energy-saving effect. Finally, the energy-saving effect of the micro-fin tube is verified by simulation. The load torque of the optimized fuel cell TSEAC is reduced from 1.540 N·m to 1.509 N·m, and the shaft power is reduced from 14.51 kW to 14.22 kW. Its efficiency increased by 1.9%....
To improve the adaptive control of the neural network under the influence of vehicle suspension control, the neural network control method is proposed. The specific content of the method analyzes the nonlinear properties of vehicle suspensions, proposes neural network-based adaptive control strategies, and develops neural network-based nonlinear algorithms and neural identifiers. Genetic algorithms perform predictive control of rear suspension through a compensation network. The experimental results show that the model structure is order n = m= 2, the AN1 network node is 4-6-1, the AN2 network node is 5-4-1, the AN3 network node is 6-4-1, and the learning correction rate is α = 0:90. In the actual simulation calculation, the number of nodes in the hidden layer of the network is increased, and the minimum number of nodes is chosen to determine the structure of the network, since the control effect obtained is not fundamentally changed. The suspension, which is controlled by the neural network’s adaptive control, has a vibration-reducing effect and is more effective by increasing the control of the rear suspension. The neural network has been shown to be able to effectively control the vehicle’s control arm....
In modern engineering, electromagnetic induction quenching is usually adopted in improving the fatigue performance of steel engine parts such as crankshafts. In order to provide the theoretical basis for the design of the process, correct evaluation of the strengthening effect of this technique is necessary. In this paper, the research aim is the strengthening effect of this technique on a given type of steel crankshaft. First the magnetic-thermal coupling process was simulated by a 3D finite element model to obtain information on the temperature field during the heating and cooling stages. Then the residual stress field after cooling was simulated based on the same model. At last, the fatigue property of this crankshaft was predicted based on the combination of three parameters: the KBM (Kandil–Brown–Miller) multi-axial fatigue model, the residual stress field and the fatigue strength of the material. The experimental results showed that this method can achieve a much more reasonable prediction than the traditional strengthening factor, and thus can be applied in guiding the design of the quenching process....
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