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In this work, a novel distribution-based control strategy of semi-active vehicle suspensions is tested under different conditions. The novelty lies in the use of an appropriate threshold in the operational condition of the control algorithm, with which the operational conditions severity is quantified and the state of the damper is controlled according to the magnitude of the operational conditions and not their sign. The value of the threshold depends on the vibrations induced to the sprung mass by the road profile. In order to be evaluated, the operational conditions of the algorithm are fitted to a t-student distribution. The cumulative distribution function of this distribution is used in order to decrease the fraction of the sample operating with the damper’s stiff state. The strategy is applied to traditional SH control algorithms and is tested using a quarter car model excited by different road excitations. A sensitivity analysis for various threshold values is performed, investigating the impact of adopting the cumulative distribution functioned (CDF) controller to various performance metrics. The results illustrate an increase of up to 13% in the ride comfort of the passengers and increase of 6% in the road holding of the vehicle. Both are achieved by minimizing the switches of the damping ratio up to 80%.

Simultaneous chemical and phase equilibrium (CPE) calculations are essential in chemical engineering, finding numerous applications in industrial processes, such as reactive distillation. CPE calculations can be challenging in systems involving polar and associating components, which also exhibit azeotropes or even reactive azeotropes, with two common examples being methyl tert-butyl ether and isopropyl acetate synthesis systems. To tackle the problem, a robust algorithm for solving CPE is required, coupled with an accurate thermodynamic model for describing system nonideality. In this work, a Gibbs energy minimization algorithm based on the method of Lagrange multipliers is employed for performing CPE calculations in the aforementioned systems. The algorithm is coupled with traditional activity coefficient models UNIQUAC and UNIFAC, as well as the UMR-PRU model. The results indicate that all models can successfully describe the CPE in the studied systems. UMR-PRU yields very satisfactory results, despite being utilized as a purely predictive tool.

A seat that provides good vibration isolation is of prime importance for passenger’s safety and health. The main conflict in seat suspensions implies that the increasing initial deformation of the system (increase in “static discomfort”) leads to better isolation of accelerations (increase in “dynamic comfort”). Many researchers have focused on overcoming or at least suppressing this conflict between load support capacity and vibration isolation by modeling new suspension systems, such as the so-called negative suspension systems. However, apart from the modeling of new suspension systems, optimization is an important part in designing a seat and finding the best compromise between these two objectives. Thus, in this work, four types of seat suspension systems with embedded negative stiffness elements are implemented and optimized in order to be benchmarked. Three of them have already been tested either in passenger or in an off-road vehicle seat. All the vibration isolators are optimized with genetic algorithms in respect to static and dynamic factors of ride comfort by applying constraints oriented to the objectives and the design of the structure. The optimization is implemented for two excitations, which correspond to a vehicle driving over road profiles of Classes A and B, and the common solutions are outlined.

•Two thermodynamic models, PR and UMR-PRU, were extended to mixtures of imidazolium-based ionic liquids with CO2 and H2S.•UMR-PRU predicts accurately the CO2 and H2S solubilities in ionic liquids.•UMR-PRU model predicts accurately the solubilities in ternary CO2+H2S+ionic liquid mixtures.

One of the most important systems in ground vehicles is the suspension system, which is responsible for the ride comfort, the road holding and handling. A crucial part of a passenger car passive suspension system is the control arm or wishbone arm or A-arm. Its main function is to provide a link between the body of the vehicle and the wheels. In this paper, the studied control arm is a part of a MacPherson strut suspension and its dimensions correspond to a front right suspension of a SUV vehicle. Two geometrical models of the control arm were developed in Solidworks® CAD-CAE Software, one with the measured dimensions and one with the outline dimensions of the control arm. A finite element model of the first one was built, using typical loading conditions, in order to determine the deformation and the stress contours. Then a topology optimization analysis was carried out to the second one in order to obtain a new design for the control arm. The FE results of the existing control arm and the optimized one were compared in terms of weight and structural integrity in different loading conditions leading to conclusions regarding the optimization procedure.

This study aims at optimizing the dynamics of a fixed – tank three – axle vehicle, with respect to both its vertical and lateral performance. This is accomplished by developing a hybrid optimization algorithm, which combines the conventional Evolution Strategy with well – known deterministic methods, such as line – search and/or trust – region. By utilizing a Monte Carlo analysis, the stiffness and damping characteristics of vehicle's suspensions are optimized with respect to the pitch and roll angles of the sprung mass, taking under consideration the design limitations, the geometrical constraints of the vehicle, as well as the lateral acceleration at overturn.

In this paper, a half car model of with nonlinear suspension systems is selected in order to study the vertical vibrations and optimize its suspension system with respect to ride comfort and road holding. A road bump was used as road profile. At first, the optimization problem is solved with the use of Genetic Algorithms with respect to 6 optimization targets. Then the k - e optimization method was implemented to locate one optimum solution. Furthermore, an alternative approach is presented in this work: the previous optimization targets are separated in main and supplementary ones, depending on their importance in the analysis. The supplementary targets are not crucial to the optimization but they could enhance the main objectives. Thus, the problem was solved again using Genetic Algorithms with respect to the 3 main targets of the optimization. Having obtained the Pareto set of solutions, the k - e optimality method was implemented for the 3 main targets and the supplementary ones, evaluated by the simulation of the vehicle model. The results of both cases are presented and discussed in terms of convergence of the optimization and computational time. The optimum solutions acquired from both cases are compared based on performance metrics as well.

The aim of this work is to present a comparison of the main semi active suspension systems used in a passenger car, after having optimized the suspension systems of the vehicle model in respect with ride comfort and road holding. Thus, a half car model, equipped with controllable dampers, along with a seat and a driver was implemented. Semi-active suspensions have received a lot of attention since they seem to provide the best compromise between cost (energy consumption, actuators/sensors hardware) and performance in comparison with active and passive suspensions. In this work, the semi active suspension systems studied are comfort oriented and consist of (a) the two version of Skyhook control (two states skyhook and skyhook linear approximation damper), (b) the acceleration driven damper (ADD), (c) the power driven damper (PDD), (d) the combination of Skyhook and ADD (Mixed Skyhook-ADD) and (e) the combination of the two with the use of a sensor. The half car model equipped with the above suspension systems was excited by a road bump, and was optimized using genetic algorithms (GA) in respect with ride comfort and road holding. This study aims to highlight how the optimization of the vehicle model could lead to the best compromise between ride comfort and road holding, overcoming their well-known trade-off. The optimum results were compared with important performance metrics regarding the vehicle's dynamic behaviour in general.