This paper describes the evaluation of a single output, online, and time domain modal parameters identification technique based on differential algebra and operational calculus. In addition, an analysis of the frequency response function (FRF) of the system is conducted in a specific set up, emulating its nominal or operational conditions to determine the influence of the non-linearities over the dynamic behavior of the system in those particular magnitudes of deformations; thus, this influence is quantified by a numerical index. This methodology is applied to a wind turbine blade submitted to wind tunnel experiments. The natural frequencies and modal damping ratios of six bending modes associated with the blade are estimated using real-time velocity measurements from one single point of the blade. A comparison with the usual impact hammer modal testing is performed to evaluate and establish the proposed approach’s main contributions. The developed modal parameter identification algorithms are implemented to run into a standard personal computer (PC) where the data acquisition system’s measurements are conditioned and processed. The results show the performance and the fast parametric estimation of the proposed algebraic identification approach.

This paper presents an approach to managing the thermal power plant’s flexible operation based on the steam generation process optimization. A strategy at the process level, as a first step in the operational optimization of the entire power plant, is proposed. The proposed approach focuses on minimizing the drum boiler startup time, since it is considered the most critical element in the steam generation process and in the thermal power plant’s efficient operation. An approach that addresses the problem to find the optimal sequences of control valves that minimize the drum boiler startup time as a dynamic optimization problem is proposed. To solve the optimization problem, a dynamic optimization framework based on a micro genetic algorithm (mGA) coupled with a dynamic simulation model is implemented. The dynamic simulation model is validated against data available in the literature, and the proposed optimization algorithm is characterized by the use of variable length chromosomes and the use of small population sizes. The results show that optimized operating profiles minimize the drum boiler startup time by at least 35 percent and generate control valve operating sequences that must be carried out to achieve the desired profile, while the structural integrity constraints are fulfilled at all times.

Lightweight structural design is a critical objective in automotive engineering, particularly for suspension components that directly influence unsprung mass and vehicle dynamics. Although topology optimization is widely used to achieve high stiffness-to-weight ratios, conventional approaches are often limited by single-objective formulations or by a lack of geometric interpretability in multi-objective solutions. This study proposes a multi-stage topology optimization framework for the conceptual design of an automotive suspension upright. The methodology decouples stiffness-driven and stress-driven optimization processes and introduces a parametric synthesis stage in which key structural features from both solutions are systematically integrated into a geometrically interpretable design. The framework is evaluated on a Formula SAE front upright under representative braking and cornering load conditions. The resulting hybrid configuration achieves a 29.44% reduction in mass (from 616.9 g to 435.3 g) while maintaining structural performance, with a maximum von Mises stress of 220.0 MPa, a safety factor of 2.28, and a maximum deformation of 0.88 mm. The results demonstrate that the proposed approach enables a balanced integration of stiffness and stress criteria through feature-based design synthesis. Beyond numerical performance, the methodology provides a reproducible and interpretable workflow that bridges topology optimization and practical engineering design.