Marcelo Amorim Savi

High-pressure flanges are essential components for joining pipes, ensuring sealing, and preventing liquid and gas leakage. Gaskets play a critical role when subjected to high stresses, situation where they can cause irreversible deformations that, combined with fluctuations in mechanical and thermal loads, can compromise their integrity. The present work aims to evaluate the use of shape memory alloy (SMA) sealing gaskets, specifically NiTi alloys. This work establishes a comparative performance analysis of NiTi and AISI 316 stainless steel gaskets under the same operating conditions. Finite element method is employed to evaluate the mechanical response of both materials under critical loading conditions, including service overpressure, progressive bolt preload loss, and individual bolt loss scenarios. The analysis combines SMA constitutive nonlinearities and contact interactions to properly capture the sealing behavior. Experimental data are used to calibrate the constitutive model for the NiTi alloy. The analysis framework is based on average and local sealing pressures, allowing a detailed comparison between the two materials in terms of their sealing efficiency and mechanical robustness. Results indicate that SMA gaskets present a better performance, improving the contact area of the sealing surface between the flange and the gasket.

Steel production accounts for approximately 7% of global GHG emissions. Brazil is the largest steel producer in Latin America, and carbon pricing is rapidly moving from a policy debate to an operational reality, making the financial exposure of Brazilian steelmakers to carbon regulation one of the most pressing industrial sustainability questions in an emerging market context. This study evaluates the financial exposure of the Brazilian steel industry to three carbon pricing scenarios: (i) a domestic cap-and-trade mechanism under Brazil’s Greenhouse Gas Emissions Trading System (SBCE); (ii) Carbon Border Adjustment Mechanisms (CBAMs) applied by key trading partners (EU, a hypothetical USA scenario, and a global scenario); and (iii) supply chain (Scope 3) exposure, relevant under net-zero corporate commitments and the expected expansion of EU-CBAM coverage. Using a scenario-based financial impact approach, with both macro-level (industry) and micro-level (company) analyses, results show that domestic carbon pricing could increase production costs by 7–21%, generating USD 1.6–4.9 billion in additional annual costs (equivalent to 4.3–13.3% of annual industry revenue). International CBAM exposure could reduce Brazilian steel export revenues by USD 570 million to USD 1.7 billion in a global scenario (1.5–4.6% of annual industry revenue). Supply chain emissions represent 68% of the industry’s total carbon pricing exposure, equivalent to USD 1.1–3.3 billion in domestic pricing costs and USD 388 million–1.16 billion in CBAM export revenue reduction. A company-level case study confirms the pattern, with lower Scope 3 intensity yielding a comparatively smaller but still material exposure. These findings offer practical decision support for steel companies and policymakers navigating the transition to a low-carbon economy.

Recent advances in artificial intelligence have growing importance, with applications in robotics, control systems, biometric recognition, among others. Intelligent systems for medical support represent another important area, where cardiac diagnosis is a valuable possibility. This work deals with an intelligent system capable of identifying and classifying cardiac rhythms from heart signals extracted from electrocardiograms (ECGs). Artificial neural networks are employed, and different configurations are investigated. A dynamical perspective is adopted, using linear and nonlinear features in both the time and frequency domains. Five cardiac rhythms are selected to test the system: normal sinus rhythm, atrial fibrillation, sinus bradycardia, premature ventricular contraction, and ventricular tachyarrhythmia. The neural network is verified, and clinical cases are subsequently analyzed. Results show satisfactory classification of patients’ heart rhythms, consistent with the diagnoses offered by cardiologists in their studies.

Shape memory alloys present remarkable thermomechanical behaviors due to solid phase transformations. The complexity of the involved phenomena makes the constitutive modeling of this class of smart material a challenging subject. Considering the different approaches presented in the literature, the constitutive models with assumed phase transformation kinetics are popular, achieving reasonable results. This paper deals with one-dimensional constitutive models that employs a novel strategy to describe phase transformation kinetics using polynomial functions. Three different polynomial functions are treated: linear, quadratic, and cubic. Besides, the novel approach defines the phase transformation surfaces in such a way that allows the description of new phenomena including tension-compression behaviors and two-way shape memory effect. Experimental data compiled from the literature guides the investigation treating temperature-induced phase transformation; pseudoelasticity; one-way and two-way shape memory effects; internal subloops due to incomplete phase transformations. Numerical simulations are carried out comparing the novel functions with the classical cosine function. Results indicate that the model with polynomial phase transformation kinetics is able to capture the main features of SMA thermomechanical behavior. The investigation shows that the cubic polynomial is equivalent to the cosine function and the linear polynomial presents good estimations, being related to low computational cost since it avoids iterative approaches for strain-driven cases.

Hysteretic response of smart materials has complex mathematical modeling. Thermodynamic-based constitutive models belong to an important class of models and data-driven models are interesting alternatives that avoid complex algorithms and parameter determinations. The classical Preisach model describes multidisciplinary hysteretic behavior employing mathematical operators in a triangular domain. The Everett function is an alternative build a surface from experimental data, replacing the original integral form to a summation. This paper proposes a novel approach, extending the Preisach triangular domain to a prismatic domain that allows a broader description of distinct phenomena. The idea is to use the Preisach approach for different triangles and then performing a interpolation for a prismatic domain, enabling the representation of distinct phenomena that, otherwise would not be described. Shape memory alloys (SMAs) are employed as a representative example of smart materials. Experimental tests are developed in order to define reference cases to be analyzed. Numerical simulations are carried out and compared with experimental data, evaluating the model capabilities under different loading conditions. Specifically, temperature-dependent and cyclic-dependent behaviors are of concern. The results show the model ability to describe the general thermomechanical behavior of shape memory alloy hysteretic behavior, being in close agreement with experimental data.