Balanço de radiação utilizando métodos de estimativa da radiação solar em cultivo de cana-de-açúcar

Agrometeoros ◽  
2020 ◽  
Vol 27 (1) ◽  
Author(s):  
Joana Madeira Krieger ◽  
Isabella Siqueira Vieira ◽  
Wellerson De Oliveira Alves da Silva ◽  
José Leonaldo Souza ◽  
Guilherme Bastos Lyra ◽  
...  
Keyword(s):  

 Devido à dificuldade de medidas contínuas e de qualidades das componentes do balanço de radiação, existe a necessidade de desenvolver modelos para estimá-las. Este trabalho ajustou os coeficientes dos métodos de Hargreaves-Samani (HaS) e Bristow-Campbell (BrC) para estimava da radiação solar global (Rs), assim como o albedo (α) e os coeficientes de um modelo de balanço de ondas longas (Rnl) em cultivo de cana-de açúcar na região de Rio Largo/AL. Medições das componentes do balanço (Rn) de radiação foram realizadas no período de 03 a 27/06/2006 por um saldo radiômetro. Os coeficientes foram ajustados por meio de técnicas de problemas inversos (Levenberg-Marquardt). Após o ajuste, os modelos de balanço de ondas curtas (Rns), Rnl e Rn obtidos em função de Rs estimado por HaS e BrC foram comparados com observações dessas componentes. O método de BrC ajustado (β0 = 0,478, β1 = 0,016 e β2 = 2,78) teve maior precisão e exatidão que o método de HaS (kt = 0,172). Os métodos de Rs ajustados quando usados na estimava de Rns, Rnl e Rn tiveram estimativas acuradas. Os erros dos modelos quando usados Rs estimados por HaS e BrC foram em sua maioria, repasse dos erros obtidos na estimativa de Rs. Entretanto, os erros dos modelos, principalmente do Rnl, têm baixo impacto no Rn.

2020 ◽  
Vol 71 (6) ◽  
pp. 66-74
Author(s):  
Younis M. Younis ◽  
Salman H. Abbas ◽  
Farqad T. Najim ◽  
Firas Hashim Kamar ◽  
Gheorghe Nechifor

A comparison between artificial neural network (ANN) and multiple linear regression (MLR) models was employed to predict the heat of combustion, and the gross and net heat values, of a diesel fuel engine, based on the chemical composition of the diesel fuel. One hundred and fifty samples of Iraqi diesel provided data from chromatographic analysis. Eight parameters were applied as inputs in order to predict the gross and net heat combustion of the diesel fuel. A trial-and-error method was used to determine the shape of the individual ANN. The results showed that the prediction accuracy of the ANN model was greater than that of the MLR model in predicting the gross heat value. The best neural network for predicting the gross heating value was a back-propagation network (8-8-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.98502 for the test data. In the same way, the best neural network for predicting the net heating value was a back-propagation network (8-5-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.95112 for the test data.


Author(s):  
Karl Kunisch ◽  
Philip Trautmann

AbstractIn this work we discuss the reconstruction of cardiac activation instants based on a viscous Eikonal equation from boundary observations. The problem is formulated as a least squares problem and solved by a projected version of the Levenberg–Marquardt method. Moreover, we analyze the well-posedness of the state equation and derive the gradient of the least squares functional with respect to the activation instants. In the numerical examples we also conduct an experiment in which the location of the activation sites and the activation instants are reconstructed jointly based on an adapted version of the shape gradient method from (J. Math. Biol. 79, 2033–2068, 2019). We are able to reconstruct the activation instants as well as the locations of the activations with high accuracy relative to the noise level.


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