Computational Fluid Dynamics (CFD) Analysis of 3D Car Model to Understanding Key Aerodynamic Issues and Their Interaction with Other Motorsport & Automotive Vehicle System

Growing population of vehicles is one the biggest global concern and it led to traffic problems and creates congestion. People are not getting place to park their vehicles. Travel by car for shorter distance also stressful and time consuming because they have to face road traffic and usually cars are big at size so, to travel by car on road need more spacious and traffic free roads. that’s why some manufacturers start designing & manufacturing One seater vehicle which can easily transportable and create less congestion. If a single person wants to ride somewhere then he doesn’t have to take large car for one person, He can use single seater vehicle. In this assignment I have Designed and Tested a single seater electric vehicle which can easily transportable, compact and personal commuter vehicle (PMV). Keywords: CFD analysis, Aerodynamics analysis, automotive vehicle system, 3D modelling, pressure plot, performance optimization, vehicle aerodynamics.

Computational Fluid Dynamic Analysis of Conceptual 3D Car Model

From past decades, people are giving more attention to conservation of the fuels. The increasing number of passenger cars have increased the amount of traffic which directly impacts pollution and traffic congestion. Manufacturers are indulged into making lightweight and performance efficient automobiles. Implementation of different designs and materials has been in practice since ages. We need smaller vehicle designs for personal transport and electric vehicles to tackle the raising problems. In future designs, vehicles will be efficient enough to save more fuel and also the traffic problems may be solved. But for the design optimizations and experiments we need different analyses to be performed, one of which is aerodynamic analysis. In this paper a CFD analysis is done to check the aerodynamic performance of a proposed car design. The car has been designed using Onshape modeling software and analyzed in Simscale software. The car is subjected to different vehicle speeds and the results of drag coefficients and pressure plots are shown. Keywords: Design and analysis of a vehicle, CFD analysis, Aerodynamic analysis, 3D modelling, Drag coefficient, Pressure plot, Concept car, Performance Optimization.

Enhanced Sliding Mode Control for a Nonlinear Active Suspension Full Car Model

This paper delivers findings on optimal robust control studies of nonlinear full car models. A nonlinear active suspension full car model is used, which considers the dynamic of a hydraulic actuator. The investigation on the benefit of using Sliding Mode Control (SMC) structure for the effective trade-off between road handling. The design of SMC in the chassis/internal subsystem is enhanced by modifying a sliding surface based on Proportional-Integral-Derivatives (PID) with the utilization of particle swarm optimization (PSO) algorithm in obtaining the best optimum value of control parameters. The switching control is designed through the Lyapunov function, which includes the boundedness of uncertainties in sprung masses that can guarantee the stability of the control design. The responses of the proposed controller have improved the disturbance rejection up to 60% as compared to the conventional SMC controller design and shown the high robustness to resist the effect of varying the parameter with minimal output deviations. The study proved that the proposed SMC scheme offers an overall effective performance in full car active suspension control to perform a better ride comfort as well as the road handling ability while maintaining a restriction of suspension travel. An intensive computer simulation (MATLAB Simulink) has been carried out to evaluate the effectiveness of the proposed control algorithm under various road surface conditions.

Lane following Learning Based on Semantic Segmentation with Chroma Key and Image Superposition

There are various techniques to approach learning in autonomous driving; however, all of them suffer from some problems. In the case of imitation learning based on artificial neural networks, the system must learn to correctly identify the elements of the environment. In some cases, it takes a lot of effort to tag the images with the proper semantics. This is also relevant given the need to have very varied scenarios to train and to thus obtain an acceptable generalization capacity. In the present work, we propose a technique for automated semantic labeling. It is based on various learning phases using image superposition combining both scenarios with chromas and real indoor scenarios. This allows the generation of augmented datasets that facilitate the learning process. Further improvements by applying noise techniques are also studied. To carry out the validation, a small-scale car model is used that learns to automatically drive on a reduced circuit. A comparison with models that do not rely on semantic segmentation is also performed. The main contribution of our proposal is the possibility of generating datasets for real indoor scenarios with automatic semantic segmentation, without the need for endless human labeling tasks.

Finding More Property Violations in Model Checking via the Restart Policy

Model checking is an efficient formal verification technique that has been applied to a wide spectrum of applications in software engineering. Popular model checking algorithms include Bounded Model Checking (BMC) and Incremental Construction of Inductive Clauses for Indubitable Correctness/Property Directed Reachability(IC3/PDR). The recently proposed Complementary Approximate Reachability (CAR) model checking algorithm has a performance close to BMC in bug-finding, while its depth-first strategy sometimes leads the algorithm to a trap, which will waste lots of computation. In this paper, we enhance the recently proposed Complementary Approximate Reachability (CAR) model checking algorithm by integrating the restart policy, which yields a restartable CAR model (abbreviated as r-CAR). The restart policy can help avoid the trap problem caused by the depth-first strategy and has played an important role in modern SAT-solving algorithms to search for a satisfactory solution. As the bug-finding in model checking is reducible to a similar search problem, the restart policy can be useful to enhance the bug-finding capability. We made an extensive experiment to evaluate the new algorithm. Our results show that out of the 749 industrial instances, r-CAR is able to find 13 instances that the state-of-the-art BMC technique cannot find and can solve more than 11 instances than the original CAR. The new algorithm successfully contributes to the current model-checking portfolio in practice.

Modeling Analysis and Simulation of Wheel Suspension System's Response for Quarter Car Model by Using 20-sim Software for Honda Civic Lx 2019 Sedan

This paper exhibits a study of car passive and active- suspension system to improving drive exhilarate to passengers while also enhancing vehicle stability by decreasing the effect of oscillation on the suspension. Modeling and simulation by using the bond diagram. They much concede a prime arrangement of the machine to the exterior surrounding: street quality, atmospherically circumstances, while guarantying driver as well as passengers, major safeness and more potentially exhilarate. Automotive aid it course manners. The result cleared this action plan at different set during the vehicle mean, but particularly in evolution level. It is also clear the proportion of suspension system's mass to the vehicle's mass. Also graphical representation of suspension system' parameters like vertical passenger displacement, potential energy of mass of suspension system and acceleration. To foretell the comportment of a car, it is necessary to make design, modeling, and simulation. Honda Civic Lx 2019 sedan car has used for modeling, and simulation.

PENGGUNAAN MEDIA FOTO DAN GAMBAR UNTUK MENINGKATKAN HASIL BELAJAR PESERTA DIDIK PADA MATERI PENINGGALAN SEJARAH DAN TOKOH SEJARAH ISLAM DI INDONESIA

Lack of interest in learning about historical heritage materials and Islamic historical figures in Indonesia. This can be seen from the learning achievement of students who are less than the KKM as many as 59.38%. Researchers use photo and image media to improve student learning outcomes, because photo and image media are concrete so students will be able to see clearly what is being discussed. This Classroom Action Research uses John Elliot's CAR Model, covering 2 cycles, each cycle consisting of four stages, namely: planning, implementing, observing, and reflecting. The research location is in SMP Negeri 1 Margahayu Kab. Bandung. The object of research is the students of grade 9 J, totaling 40 people. The results showed that in the pre-cycle of students who scored above the KKM (completed) there were 18 people with an average score of 64.87 or 45% of all students. In the first cycle of students who are above the KKM score as many as 22 people with an average value of all students 72.37 or 62.5%, in the first cycle there is an increase of 17.5% from the completion of pre-cycle activities. Then the average value in the second cycle of students who completed reached 40 people with an average value of 79.5 students who achieved 100% completeness, were in very good qualifications and experienced an increase of 37.5% from cycle I. This shows increasing student learning outcomes on historical heritage materials and Islamic historical figures in Indonesia.

Dynamic Vehicle and Rigid Road Interaction Analysis and Vibration Control of the Quarter Car Model Using Adaptive Neuro Fuzzy Algorithm

In this study 3-DOF quarter car model with the three bumps on the rigid road, the assumption has been modeled with the non-random irregularity. To reduce the excessive vibrations occurred on the vehicle body, an active suspension system with the linear actuator has been considered. Moreover, to control this actuator, an adaptive neuro-fuzzy algorithm is designed. The training and testing data of the ANFIS has been obtained from Proportional Integral Derivative (PID) control algorithm. After that the successful training process, a testing procedure has been applied to ANFIS for the measure of the adaptive neuro-fuzzy system with data that are not considered in the training process. Then, the performance of the ANFIS is compared by the PID algorithm and passive suspension system in terms of vehicle body vertical acceleration, vehicle body vertical displacement, and control force. The road model used in the study has been modeled according to non-random road profile mathematical formulation considering periodical and discrete road profile cases. In this formulation, one can easily determine the height, width, and number of the road defect with the series mathematical formulation. Consequently, with the results obtained from the presented study, it is proven that ANFIS is a very effective controlling algorithm to suppress vibration occurred on the vehicle body due to vehicle road interaction. Furthermore, the performance of the ANFIS has been tested with different parameters, for example, different number membership functions (MF), which used the fuzzification of the input parameters.