An overview has been presented related to the advances in internal combustion engines (IC) for the future solutions of automotive industry. This review discusses as many current research areas as possible. This evaluation will be of great assistance to students, researchers, and enterprises working on the subject of IC engines. Moreover, there are numerous technological options for delivering environmentally friendly vehicles with low carbon emissions. This paper also examines the methods available as well as the use of technology road-mapping to plan for future manufacturer adoption. The low temperature combustion (LTC) technique is among the most sophisticated combustion technologies. Various LTC techniques like HCCI, PCCI, and RCCI have been discussed in detail. The results of the evaluation of LTC against conventional engines were provided in order to demonstrate both the strengths and shortcomings of each. The goal of this review article is to show new combustion approaches and how they may be utilized to increase the engine's thermal efficiency while lowering NOx and particulate matter (PM) emissions.
This paper presents a novel iterative algorithm, based on the (epsilon, delta-definition of continuity, for a linear fractional programming (LFP) problem. Since the objective function is continuous at every point of the feasible region, we construct an iterative constraint by combining our convergence condition and the objective function of the LFP problem. Through this iterative constraint, we construct a new iterative linear programming (LP) problem to obtain the optimal solution of the LFP problem. This iterative LP problem is able to solve all LFP problems that have a bounded feasible region. In order to generate the unbounded solution and the asymptotic solution, we extend our proposed iterative LP problem by adding a new constraint that controls whether the sum of the original decision variables approaches infinity. To demonstrate the efficiency of the proposed method, illustrative numerical examples are provided for all solution cases. Also, we analyse the validity of our algorithm by generating random large-scale production planning test problems.
: Aiming at the non-stationary characteristics of oil pressure vibration signals containing particulate, a method for predicting particulate content in oil was proposed based on vibration characteristic frequency extraction by vibrational mode decomposition (VMD), variable selection using successive projections algorithm (SPA) and T_S fuzzy identification combined. Firstly, the pressure vibration signal was decomposed by VMD and a series of narrow-band characteristic frequency matrices were obtained. Then, variables were selected using SPA to construct the feature vector matrix. Finally, the feature vector matrix was used as the input of T_S fuzzy identification to identify the content of particulate in oil. The results showed that the VMD reconstruction of the original oil sample pressure signals could well characterize the main variation of the original signal; the 19 variables were selected from the characteristic frequency of the vibration signal from the oil pressure using SPA, the 19 pressure vibration characteristic frequency of 11 sample sets SPA selected was taken as the input variable of T_S identification model; for each set of sample, the predicted output of the content of particulate in oil was obtained, model prediction decision coefficient is 0.8637, the root mean square error is 0.1979, a reasonable prediction effect was obtained.
A high-end autonomous vehicle is expected to have at least a hundred different electronic subsystems. Each one of them takes power from the battery through Power Management Unit (PMU). Having an efficient PMU is crucial and is expected to supply the required level of uninterrupted power. PMU consists of several buck converters which translate a higher voltage level to required lower voltage levels. A PMU is more reliable if it consists efficient and well-structured voltage converters. In this paper, a fault-tolerant buck converter is designed which outputs 3.3 volts. A simple yet effective technique is proposed to design a fault-tolerant buck DC-DC converter by bypassing the faulty converter leg. The proposed system utilizes a signal processing-based method for fault detection. The secondary converter is activated only upon the confirmed prognosis of a faulty primary converter. Ripple content in the output Aluminum Electrolytic Capacitor (AEC) voltage is monitored and used as a primary health indicator for the converter. An experimental setup is built and tested in the laboratory. Experimental results indicate a smooth transition from the primary converter to the secondary demonstrating an uninterrupted power supply along with the simplicity and effectiveness of the proposed solution.
This study aims to provide an integrated decision-making approach in maritime transportation problems. The duty of hatch cover is to prevent the ingress of water into the cargo hold and protect the goods from being damped and damaged. Hence, it has the considerable role in productivity of maritime transportation systems. Since each hatch cover has distinguished properties with respect to criteria than the others, the hatch cover selection problem (HCSP) can be considered as a multi-criteria decision-making (MCDM) problem. In this paper, interval type-2 fuzzy sets (IT2FSs) are first used to weight criteria and evaluations of hatch covers with respect to criteria. In addition, an integrated group Shannon entropy- based weighted aggregated sum product assessment (WASPAS) approach is applied to solve the HCSP using the limit distance mean (LDM) in which the interval type-2 fuzzy Shannon entropy approach is used to determine the objective weights and then they are integrated with the subjective weights. On the other hand, in order to demonstrate the effectiveness and practicability of the proposed method, it is fulfilled in a real case study and the ranked results are compared with the others.
