Automotive Science and Engineering

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In order to reduce both the weight of vehicles and the damage of occupants in a crash event simultaneously, it is necessary to perform a multi-objective optimization of the automotive energy absorbing components. In this paper, axial impact crushing behavior of the aluminum foam-filled thin-walled tubes are studied by the finite element method using commercial software ABAQUS. Comparison of the present simulation results with the results of the experiments reported in the previous works indicated the validity of the numerical analyses. A meta-model based on the feed-forward artificial neural networks are then obtained for modeling of both the absorbed energy (E) and the peak crushing force (Fmax) with respect to design variables using those data obtained from the finite element modeling. Using such obtained neural network models, a modified multi-objective GA is used for the Pareto-based optimization of the aluminum foam-filled thinwalled tubes considering three conflicting objectives such as energy absorption, weight of structure, and peak crushing force.

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In this study the performance and emissions characteristics of a heavy-duty, direct injection, Compression ignition (CI) engine which is specialized in agriculture, have been investigated experimentally. For this aim, the influence of injection timing, load, engine speed on power, brake specific fuel consumption (BSFC), peak pressure (PP), nitrogen oxides (NOx), carbon dioxide (CO2), Carbon monoxide (CO), hydrocarbon (HC) and Soot emissions has been considered. The tests were performed at various injection timings, loads and speeds. It is used artificial neural network (ANN) for predicting and modeling the engine performance and emission. Multi-objective optimization with respect to engine emissions level and engine power was used in order to deter mine the optimum load, speed and injection timing. For this goal, a fast and elitist non-dominated sorting genetic algorithm II (NSGA II) was applied to obtain maximum engine power with minimum total exhaust emissions as a two objective functions.

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Due to the extensive use of cars and progresses in the vehicular industries, it has become necessary
to design vehicles with higher levels of safety standards. Development of the computer aided design and
analysis techniques has enabled employing well-developed commercial finite-element-based crash
simulation computer codes, in recent years. The present study is an attempt to optimize behavior of the
structural components of a passenger car in a full-frontal crash through including three types of energy
absorptions: (i) structural damping of the car body, (ii) viscoelastic characteristics of the constituent
materials of the bumper, and (iii) a proposed wide tapered multi-cell energy absorber. The optimization
technique relies on the design of experimental (DOE) method to enables finding the absolute extremum
solution through the response surface method (RSM) in MINITAB software. First, the car is modeled in
PATRAN and meshed in ANSA software. Then, the full-scale car model is analyzed in ABAQUS/CAE
software. The optimization has been accomplished through a multi-objective function to simultaneously,
maximize the observed energy and minimize the passenger’s deceleration. Results are verified by the
experimental results and effects of using non-equal importance coefficients for the absorbed energy and
passenger’s deceleration in the multi-objective function are also evaluated. Influence of the optimized
parameters on the frontal crash behavior of the vehicle body structure and passenger’s deceleration is
investigated, too.

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In this paper a new type of multi-objective differential evolution employing dynamically tunable mutation factor is used to optimally design non-linear vehicle model. In this way, non-dominated sorting algorithm with crowding distance criterion are combined to fuziified mutation differential evolution to construct multi-objective algorithm to solve the problem. In order to achieve fuzzified mutation factor, two inputs as generation number and population diversity and one output as the mutation factor are used in the fuzzy inference system. The objective functions optimized simultaneously are namely, vertical acceleration of sprung mass, relative displacement between sprung mass and unsprung mass and control force. Optimization processes have been done in two bi- and three objective areas. Comparison of the obtained results with those in the literature has shown the superiority of the proposed method of this work. Further, it has been shown that the results of 3-objective optimization include those of bi-objective one, and therefore it gives more optimum options to the designer

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In this paper, a new version of multi-objective differential evolution with dynamically adaptable mutation factor is used for Pareto optimization of a 5-degree of freedom vehicle vibration model excited by non-stationary random road profile. In this way, non-dominated sorting algorithm and crowding distance criterion have been combined to differential evolution with fuzzified mutation in order to achieve multi-objective meta-heuristic algorithm. To dynamically tune the mutation factor, two parameters, named, number of generation and population diversity are considered as inputs and, one parameter, named, the mutation factor as output of the fuzzy logic inference system. Conflicting objective functions that have been observed to be optimally designed simultaneously are, namely, vertical seat acceleration, vertical forward tire velocity, vertical rear tire velocity, relative displacement between sprung mass and forward tire and relative displacement between sprung mass and rear tire. Furthermore, different pairs of these objective functions have also been chosen for bi-objective optimization processes. The comparison of the obtained results with those in the literature unveils the superiority of the results of this work. It is displayed that the results of 5-objective optimization subsume those of bi-objective optimization and, consequently, this achievement can offer more optimal choices to designers.

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The present paper aims to improve the dynamical balancing of a slider-crank mechanism. This mechanism has been widely used in internal combustion engines, especially vehicle engines; hence, its dynamical balancing is important significantly. To have a full balance mechanism, the shaking forces and shaking moment of foundations should be eliminated completely. However, this elimination is usually impossible. Hence, in the current study, a multi-objective optimization is carried out to maintain the optimal balance of mechanism. The vertical and horizontal components of shaking forces and shaking moment are considered as objective functions. Also, the design variables are included the mass, the moment of inertia and the mass center location of mechanism links. The length of mechanism links is also considered constant for achieving a fixed slider course. The four-objective optimization is applied using a differential evolution algorithm. The optimization results are presented in Pareto diagrams as suitable tools for selecting a mechanism with desired characteristics according to the importance of each objective function. The optimal mechanism is finally introduced by the mapping method. The comparison of optimized mechanisms and the original one indicates a significant reduction of shaking forces and shaking moment as well as the reduction of energy consumption.

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A multi-objective optimization and thermal analysis is performed by both experimental and numerical approaches on a Stirling engine cooler and heater. The power generated is measured experimentally by an electrical engine coupled with the crank case, and the friction is estimated by the difference between the necessary power used for rotating the engine at a specific pressure and speed, versus the actual power measured experimentally. In the experimental approach, different conditions were considered; for example, the charge pressure varied from 5-9 bars, and the engine speed varied from 286-1146 rpm. The maximum power generated was 461.3 W and was reported at 9 bars of charge pressure and 1146 rpm engine speed. Numerical approach was carried to simulate thermal balance for investigations on the effect of friction, engine speed and efficiency on generated engine power. Average values of Nusselt number and coefficient of friction were suggested from simulation results.
The multi-objective optimization was held using DOE method for maximizing engine efficiency and power, and also minimizing pressure drop. The top and bottom boundary values for our optimization were 5-9 bars of pressure and 286-1146 rpm of engine speed; for both helium and carbon dioxide. To do so, all three significance factors (engine speed, efficiency and friction) were given different weights, thus different combinations of weight value was investigated
Amongst different interesting findings, results showed that if the efficiency weight factor changed from 1 to 3, for helium in a specific condition, the optimum engine speed would increase by approximately 30.6 %

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In this study, a multi-objective differential evolution with fuzzy inference-based dynamic adaptable mutation factor with hybrid usage of non-dominated sorting and crowding distance (MODE-FM) is utilized for Pareto optimization of a 5-degree of freedom nonlinear vehicle vibration model considering the five conflicting functions simultaneously, under different road inputs. The significant conflicting objective functions that have been observed here are, namely, vertical seat acceleration, vertical forward tire velocity, vertical rear tire velocity, relative displacement between sprung mass and forward tire and relative displacement between sprung mass and rear tire. Different road inputs are, namely, double-bump, stationary random road and non-stationary random road. It is exhibited that the optimum solutions of 5-objective optimization contain those of 2-objective optimization and, as a result, this important matter creates more options for optimal design of nonlinear vehicle vibration model.

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Connecting point of the longitudinal veins and cross-veins in wing is called Joint.  In some insect wing joints, there is a type of rubber-like protein called Resilin. Due to the low Young's modulus of this protein, its presence in the wing can help to change the shape of the wing during flight. Today, using composite structures in flying vehicles in order to achieve the desired shape of wing is considered. The purpose of this study is the multi-objective optimization of artificial wing by arranging Resilin joints in the artificial wing of Micro air vehicles (MAVs). The amount of torsion and bending of the flapping robot wings is considered as the objective function to improve the flight performance of robots. Two types of artificial wings have been investigated, and considering pareto points, the optimal arrangement of Resilin joints has been achieved.  The result of this study shows that in both wings, with the presence of Resilin in the joints, the amount of torsion has increased to 38.65 degrees.