Mechatronics and optimization development for wind tunnel tests

In the realm of the automotive industry, the development of vehicles entails the fulfillment of numerous requirements such as appealing design, comfort, safety, and efficiency. Notably, in recent years, the significance of efficiency has grown due to increasing environmental concerns regarding internal combustion engine (ICE) vehicles and limitations on the range of battery electric vehicles (BEVs). Of the various engineering aspects, aerodynamics assumes a pivotal role in determining the performance of cars, exerting a substantial influence on vehicle efficiency. To investigate and enhance aerodynamics, automotive companies adopt a combined approach involving both digital and real-world testing. The former is accomplished through the utilization of Computed Fluid Dynamic (CFD) analyses, while the latter entails wind tunnel testing of clay car models. This thesis covers the current approach to the study of aerodynamics, focusing on the issues that affect the existing workflow, including downtime and inaccuracies. In response to these challenges, a novel workflow based on automated mechatronics optimization is introduced and a prototype is tested, thereby showcasing a fresh and more efficient way of working with clay car models tested in wind tunnel facilities. The proposed workflow aims to enhance the aerodynamic optimization of vehicles by implementing a scalable, plug-and-play system that expedites the process and yields advanced, efficient designs. This endeavor has brought to remarkable results, such as the development of an innovative diffuser configuration that enhances efficiency during side-wind conditions, as well as a 73.4\% reduction in time within the current wind tunnel workflow through the application of automated mechatronics

Efficient sizing and optimization of multirotor drones based on scaling laws and similarity models

In contrast to the current overall aircraft design techniques, the design of multirotor vehicles generally consists of skill-based selection procedures or is based on pure empirical approaches. The application of a systemic approach provides better design performance and the possibility to rapidly assess the effect of changes in the requirements. This paper proposes a generic and efficient sizing methodology for electric multirotor vehicles which allows to optimize a configuration for different missions and requirements. Starting from a set of algebraic equations based on scaling laws and similarity models, the optimization problem representing the sizing can be formulated in many manners. The proposed methodology shows a significant reduction in the number of function evaluations in the optimization process due to a thorough suppression of inequality constraints when compared to initial problem formulation. The results are validated by comparison to characteristics of existing multirotors. In addition, performance predictions of these configurations are performed for different flight scenarios and payloads

Active suspension co-design for lateral stability of rail vehicles

Railroad transportation is one of the most cost-effective and energy-efficient modes of land transportation. With an eye toward improving these efficiencies, many efforts have focused on developing high speed railways. Traditionally railways have utilized passive suspension systems, but maintaining dynamic stability at higher speeds demands enhancements to existing rail vehicle suspensions. One strategy to improve dynamic performance is to incorporate active or semi-active elements, such as force actuators or variable dampers, within the suspension system. Modern day road and rail vehicles often utilize such actively-controlled suspensions to improve stability, ride comfort and ride quality at high speeds. The dynamic performance of such mechatronically-controlled suspension systems is related closely to the congruence of the design of passive elements in conjunction with the chosen control system strategy. Historically, design of controlled dynamic systems has followed a sequential process (mechanical design followed by control design). In the field of mechatronics, engineers typically use design rules or heuristics that help account for design coupling, but cannot produce system optimal designs. Passive elements are optimally designed first, followed by the addition of controllers for system performance improvements. New integrated design strategies are required to realize the full potential of such advanced complex dynamic systems and to capitalize on design coupling. This thesis aims to explore and apply a recently developed synergistic approach to design of controlled dynamic systems, called co-design. Theoretical models of existing partitioned, optimization-based design methods are compared to this combined active and passive system design strategy. Parameters for a reduced and a full-scale rail vehicle model are then designed using the developed optimal design formulations. Different control techniques within the co-design framework are tested and compared. Typically feedback controllers are required for actual implementation of control strategies. Early-stage co-design strategies are normally based on open-loop control, therefore, are limited for functional implementation. However, co-design methods provide designers with better knowledge about the true performance limits of dynamic systems, help them make more informed design decisions, and provide a foundation for development of implementable feedback control systems. The results obtained in this thesis show significant improvements achieved by co-design strategies over passive system design and sequential design approaches. The results also demonstrate the potential of this framework in helping systematic selection of optimal plant design variables, controller architecture, and implementable control techniques. Future work includes designing practical feedback controllers built upon results from co-design strategies for rail vehicles using non-linear vehicle models to provide a complete active rail suspension solution

Online system identification development based on recursive weighted least square neural networks of nonlinear hammerstein and wiener models.

The realistic dynamics mathematical model of a system is very important for analyzing a system. The mathematical system model can be derived by applying physical, thermodynamic, and chemistry laws. But this method has some drawbacks, among which is difficult for complex systems, sometimes is untraceable for nonlinear behavior that almost all systems have in the real world, and requires much knowledge. Another method is system identification which is also called experimental modeling. System identification can be made offline, but this method has a disadvantage because the features of a dynamic system may change over time. The parameters may vary as environmental conditions change. It requires big data and consumes a long time. This research introduces a developed method for online system identification based on the Hammerstein and Wiener nonlinear block-oriented structure with the artificial neural networks (NN) advantages and recursive weighted least squares algorithm for optimizing neural network learning in real-time. The proposed method aimed to obtain a maximally informative mathematical model that can describe the actual dynamic behaviors of a system, using the DC motor as a case study. The goodness of fit validation based on the normalized root-mean-square error (NRMSE) and normalized mean square error, and Theil’s inequality coefficient are used to evaluate the performance of models. Based on experimental results, for best Wiener parallel NN model and series-parallel NN model are 93.7% and 89.48%, respectively. Best Hammerstein parallel NN polynomial based model and series-parallel NN polynomial model are 88.75% and 93.9% respectively, for best Hammerstein parallel NN sigmoid based model and series-parallel NN sigmoid based model 78.26% and 95.95% respectively, and for best Hammerstein parallel NN hyperbolic tangent based model and series-parallel NN hyperbolic tangent based model 70.7% and 96.4% respectively. The best model of the developed method outperformed the conventional NARX and NARMAX methods best model by 3.26% in terms of NRMSE goodness of fit