Nondestructive testing is widely used to detect and to size up discontinuities embedded in a material. Among the several ultrasonic techniques, time of flight diffraction (TOFD) combines high speed inspection, high sizing reliability and low rate of incorrect results. However, the classification of defects through ultrasound signals acquired by the TOFD technique depends heavily on the knowledge and experience of the operator and thus, this classification is still frequently questioned. Besides, this task requires long processing time due to the large amount of data to be analyzed. Nevertheless, computational tools for pattern recognition can be employed to analyze a high amount of data with large efficiency. In the present work, simulation of ultrasound propagation in two-dimensional media containing, each one, different kinds of modeled discontinuities which mimic defects in welded joints were performed. Clustering (k-means) and classification (principal component analysis and k-nearest neighbors) algorithms were employed to associate each simulated ultrasound signal with its corresponding modeled defects. The results for each method were analyzed, discussed and compared. The results are very promising.
KeywordsUltrasound • TOFD • Welding defects • K-NN • Principal component analysis • K-means B Elineudo P. de Moura
The presence of pollutants in our atmosphere has become one of humanity’s greatest challenges. These pollutants, produced primarily by burning fossil fuels, are detrimental to human health, our climate and agriculture. This work proposes the use of a spatiotemporal graph neural network, designed to forecast ozone concentration based on the GraphSAGE paradigm, to aid in our understanding of the dynamic nature of these pollutants’ production and proliferation in urban areas. This model was trained and tested using data from Houston, Texas, the United States, with varying numbers of time-lags, forecast horizons (1, 3, 6 h ahead), input data and nearby stations. The results show that the proposed GNN-SAGE model successfully recognized spatiotemporal patterns underlying these data, bolstering its forecasting performance when compared with a benchmarking persistence model by 33.7%, 48.7% and 57.1% for 1, 3 and 6 h forecast horizons, respectively. The proposed model produces error levels lower than we could find in the existing literature. The conclusions drawn from variable importance SHAP analysis also revealed that when predicting ozone, solar radiation becomes relevant as the forecast time horizon is raised. According to EPA regulation, the model also determined nonattainment conditions for the reference station.
RESUMO: Os movimentos sociais contemporâneos atuam como modificadores das relações socioespaciais, assumindo muitas vezes o protagonismo que deveria ser do Estado, atuando junto às populações menos favorecidas, indicando outros caminhos, para além, da marginalização e exclusão social. Este artigo apresenta reflexões sobre uma ocupação organizada pelo Movimento dos Trabalhadores Sem Teto (MTST), mais precisamente o acampamento Tereza de Benguela, situado no Conjunto Village Campestre II, localizado no bairro Cidade Universitária, na cidade de Maceió, Alagoas. Neste é analisada a forma como os/as acampados/as se relacionam com o espaço, discutindo a importância do Movimento na luta por moradia e pela dignidade dos seus membros. O objetivo aqui é discutir a legitimidade da existência do acampamento Tereza de Benguela, compreender a formação territorial da ocupação, as estratégias usadas pelos acampados/as para se organizarem, as dificuldades que o Movimento enfrenta e a importância do trabalho feminino dentro do MTST. Com abordagem qualitativa, o artigo teve como percurso metodológico a realização de pesquisa bibliográfica; visitas técnicas com o intuito de vivenciar o dia-a-dia dos/as acampados/as que moravam e/ou frequentavam a ocupação; e realização de entrevistas semiestruturadas. A partir das informações coletadas pode-se fazer a sistematização das mesmas e as reflexões pertinentes. A ocupação Tereza de Benguela, é um dos acampamentos do MTST existentes no estado de Alagoas, as ações do Movimento marcam intensamente o espaço geográfico, levando a construção de um território específico e único, cuja permanência dependerá da correlação de forças envolvidas no processo.
PALAVRAS-CHAVE: MTST; Luta pela Moradia; Organização Social.
In this work we present the development, testing and comparison of three different physics-informed deep learning paradigms, namely the ConvLSTM, CNN-LSTM and a novel Fourier Neural Operator (FNO), for solving the partial differential equations of the RANS turbulence model. The 2D lid-driven cavity flow was chosen as our system of interest, and a dataset was generated using OpenFOAM. For this task, the models underwent hyperparameter optimization, prior to testing the effects of embedding physical information on performance. We used the mass conservation of the model solution, embedded as a term in our loss penalty, as our physical information. This approach has been shown to give physical coherence to the model results. Based on the performance, the ConvLSTM and FNO models were assessed in forecasting the flow for various combinations of input and output timestep sizes. The FNO model trained to forecast one timestep from one input timestep performed the best, with an RMSE for the overall x and y velocity components of 0.0060743 m·s−1.
Topics related to the modeling of turbulent flow feature significant relevance in several areas, especially in engineering, since the vast majority of flows present in the design of devices and systems are characterized to be turbulent. A vastly applied tool for the analysis of such flows is the use of numerical simulations based on turbulence models. Thus, this work aims to evaluate the performance of several turbulence models when applied to classic problems of fluid mechanics and heat transfer, already extensively validated by empirical procedures. The OpenFOAM open source software was used, being highly suitable for obtaining numerical solutions to problems of fluid mechanics involving complex geometries. The problems for the evaluation of turbulence models selected were: two-dimensional cavity, Pitz-Daily, air flow over an airfoil, air flow over the Ahmed blunt body and the problem of natural convection between parallel plates. The solution to such problems was achieved by utilizing several Reynolds Averaged Equations (RANS) turbulence models, namely: k-ε, k-ω, Lam-Bremhorst k-ε, k-ω SST, Lien-Leschziner k-ε, Spalart-Allmaras, Launder-Sharma k-ε, renormalization group (RNG) k-ε. The results obtained were compared to those found in the literature which were empirically obtained, thus allowing the assessment of the strengths and weaknesses of the turbulence modeling applied in each problem.
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