### SOFTWARE APPLICATIONS - MICROCAL ORIGIN FOR POLYNOMIAL INTERPOLATION IN RHYTHM OF ACCUMULATION A DRY MATTER OF VINE PUBLISHED

Marian Nicolae, Adrian Dulugeac, Elena Nicolae, Simona Dulugeac NoneThe dedicated software having in background a powerful matematichal apparatus (specially numerical methods with informathical saucer) was revolution experimental research, having the posibility important anticipation, completing the colection rare data which obtain occasionally with difficulty. Using numerical simulation can find formulas for calculating (based on a collection of experimental data) using the media as a powerful Matlab programming, LabVIEW, Microcal Origin eliminating many experimental calculations difficult to get. If we compare the results between the two modes of working (and experimental data) shows that the interpolation methods used: spline, cubic, linear, polynomial (on different degrees until 10 degree) have very small errors, the degree pof fidelity is almost 100% (95% and 98%). By getting expression of mathematical functions and values with average temperatures during certain periods of time (for example, the average for the past 60 years) we can predict the development of biorithm by predictions of the accumulation of dry matter without further recourse to dense experimental calculations. “Microcal Origin” is a programming language likewise a developing system which integrates the calculation, the visualisation and the programming in an easy way. The problems and their solution are concurred in an available mathematical language. Starting from the experimental data, the accumulation of the dry substance like a function of active temperature (∑°C) and time (t), the software gets a function which brings the increase of vine SU(∑°C, t), through interpolations with a very little step; so, this evolution can be determined empiric. For mathematical thoroughness in the approximation of function - accumulation of dry matter (SU) depending on the temperature have used a variety of functions: exponential, logarithmic, polynomial depending on the type curve nonlinear sometimes fragmenting the diagram on parts. Simultaneous we can choose the function that proximate the best the experimental data by using dedicated software and we can get the values y=f(x) by interpolation y i =f(x i ) , the interpolation step being very small, 10 -6 . We can make such calculations of the value of dry matter (SU) not by experimental way, but by using the applied sciences on computer. Where experimental data collection are a disparate values we can complete, however small it would be intervening Variation Dx , can learn at any time variant DSU.

numerical methods; anticipation; colection data; approximation; interpolation; simulation; process phases

Presentation: oral

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