Spreadsheet solution of hyperbolic partial differential equations[for EM field calculations]

This paper describes the use of spreadsheet programs for the numerical solution of hyperbolic partial differential equations. A complete 123 macro program is presented and applied to obtain the approximate solution of a model problem in electrical engineering. Through user macros, spreadsheets can now function in the same way as programs written in conventional programming languages. They provide a clear and direct means of entering data and formulas. This important feature will allow the students to easily experiment with their model problem

Calculating Thermal Coefficients Using a Hybrid Method

In this paper we study the problem of determining two thermal parameters of a cylindrical metal sample. This is an inverse problem in heat conduction where boundary conditions are determined on the basis of temperature measurements taken at the selected internal points in the sample. A hybrid method is used to find the parameters based on the experimental data of the temperature of a metalic sample. Both the direct and inverse problems are described and numerical results are given

Spreadsheet solution of hyperbolic partial differential equations[for EM field calculations]

This paper describes the use of spreadsheet programs for the numerical solution of hyperbolic partial differential equations. A complete 123 macro program is presented and applied to obtain the approximate solution of a model problem in electrical engineering. Through user macros, spreadsheets can now function in the same way as programs written in conventional programming languages. They provide a clear and direct means of entering data and formulas. This important feature will allow the students to easily experiment with their model problem

Use of a spreadsheet program in electromagnetics

This paper describes how a spreadsheet program can assist electromagnetics education at the undergraduate level. Spreadsheet software has proven to be an important pedagogical tool for a variety of problems in electrical engineering education. Using the power of the 123 macros, a menu driven spreadsheet program is used to compute the solution of some electrostatic boundary value problems for an introductory-level electromagnetics course. With the on-screen numerical and visual feedback and the ease of entering data, students will be able to take a close look at the effects of varying the input data of the model problem

Calculating three thermal coefficients from one data set

© 2019 Academic Publications. We study the problem of determining three thermal coefficients from one set data of a model problem rising in thermodynamics. This is an inverse problem, that is to coincide the solution of the differential equation with actual experimental results. The used method is based on minimizing the solution of the problem with the experimental data. Both the direct and inverse problems are described and numerical results are given

A nonlinear free boundary value problem

We discuss a mathematical model arising in the filtration of a fluid through a porous medium. The model leads to a free boundary value problem whose governing equation depends on the retention function. A numerical approximation by means of finite elements is used to present an existence and uniqueness theorem along with an error estimate for a linear retention function. Finally, numerical algorithms for finding approximate solutions for linear and quadratic retention functions are suggested and the results of numerical calculations are given

Parametric vis-a-vis non parametric measures describing G x E interactions for salt salinity tolerant barley genotypes in multi-environment trials

GxE interaction to know adaptability of 19 salt salinity tolerant barley genotypes was studied by parametric and non-parametric measures. Genotypes KB1516, RD2907 and RD2794 showed minimum environmental variance over different environments. Superiority index identified genotypes RD2907 and NDB1445 with lowest value accompanied with higher. Wricke’s measure exhibited lower values of DWRB168,  DWRB165 and NDB1445. Higher values of GAI showed consistent performance of RD2907, NDB1445 and RD2552. Non-parametric measures Si(1), Si(3) and Si(6) the considered DWRB165 and DWRB168  as desirable genotypes. Thennarasu’s first measure NPi(1) found DWRB168 and NDB1445 as desirable adaptable and KB1546, RD2907 and NDB1173 were unstable genotypes. Wricke’s parameter was positively correlated with NPi(1), NPi(3) and Kang. GAI had significant positive with Pi and Kang while negative with Si(6), NPi(2) & NPi(4). Worth to mention the negative association of  Pi with Si(6), NPi(2), NPi(4). Non parametric measures Si (3) Si (6) NPi (2) & NPi (4) clubbed together while Kang, Wi 2, s2i ,Si (1),Si (2) ,NPi (1) & NPi (3)  joined in another cluster.  Left over parametric measures were grouped in two separate clusters i.e. (bi, S2xi ,CVi),(Yield, GAI Pi) respectively.  Biplot analysis based on first two principal components showed three groups among the measures

Non parametric measures to estimate GxE interaction of dual purpose barley genotypes for grain yield under multi-location trials

GxE interaction of seventeen dual purpose barley genotypes evaluated at ten major barley locations of the country by non parametric methods. Non parametric measures had been well established and expressed ad-vantages over their counter parts i.e. parametric measures. Simple descriptive measures based on the ranks of gen-otypes i.e. Mean of ranks (MR) pointed towards RD2925 and BH1008 and standard deviation of ranks (SD) for KB1401 and UPB1054 whereas Coefficient of variation (CV) for JB322 and RD2925 as stable genotypes. Nonpara-metric measures based on original values (Si1, Si2, Si3, Si4, Si5, Si6, Si7) indicated the stable performance of NDB1650, JB322 and UPB1054 while UPB1053, RD2715, RD2927 and RD2035 were observed of unstable nature. CSi1, CSi2, CSi3, CSi4, CSi5, CSi6 and CSi7 measures based on the ranks of corrected grain yield identified JB322, RD2552, RD2925 and NDB1650 as stable genotypes. Spearman’s rank correlation established highly significant positive correlation of yield with SD (0.67), Si1(0.65), Si2(0.59), Si5(0.68), Si7(0.67) whereas negative association observed for CMR (Mean of corrected ranks) (-0.62), CMed (Median of corrected ranks) (-0.60). NPi(2) expressed negative correlation with CV(-0.32), Si6 (-0.30), CMR(-0.34) and CMed(-0.48). More over NPi(3) maintained negative correlation with most of the measures though the magnitude was of low magnitude

Use of a spreadsheet program in electromagnetics

This paper describes how a spreadsheet program can assist electromagnetics education at the undergraduate level. Spreadsheet software has proven to be an important pedagogical tool for a variety of problems in electrical engineering education. Using the power of the 123 macros, a menu driven spreadsheet program is used to compute the solution of some electrostatic boundary value problems for an introductory-level electromagnetics course. With the on-screen numerical and visual feedback and the ease of entering data, students will be able to take a close look at the effects of varying the input data of the model problem

G x E evaluation for feed barley genotypes evaluated in country by AMMI analysis

AMMI analysis of feed barley genotypes exhibited highly significant effects of environments, genotypes and interactions for both the years. The major portion of the total variance was described by the environmental effects up to 45.6% and 42.3% in respective years. The genotypes effects contributed marginally as of only 8.6% and 6.9% of total variation. The significant interaction effects were partitioned into IPCA1, IPCA2 , IPCA3 and IPCA4; which explained upto 42.4, 18.3, 9.7 and 8.1% of the first year and 32.2, 20.3, 15.6 and 10.5% for second year. The cumulative effect of first two interaction principal components comes out to 60.7% and 52.3% respectively. Maximum genotype yield during study period varied from 49.8 to 48 whereas the lowest yield ranged from 37 to 36.4 q/ha. AMMI stability index identified genotypes G9(BH 972), G15(JB 274) for former and G23(DWRB 109) & G2(KB 1205) for latter year. AMMI distance marked G15(JB 274) & G7(NDB 1561) for first and genotypes G26(UPB 1034) & G23(DWRB 109) for the second year. Desirable genotypes for selection would be G11(PL 871), G27(PL 872) and G23(DWRB 109), G20(BH 946) for respective years a per the GSI score. Genotypes with IPCA-1 scores close to zero identified G1(PL 751), G9(BH 972) and G27(PL 872 ) for first year and G5(RD 2786), G4(NDB 1554) and G24 (UPB 1036) for second year would have wider adaptation to the tested environments as per AMMI graphical plots