Böcker av P. M. S. Hallika

Aluminum although very ductile, but it is having very poor formability which restricts its use in most of the industrial applications which require forming e.g. automobile engineering. In recent years there is an effort to increase the temperature of the material before and during forming. Generally, in any operation large amount of time is consumed in trial and error method and there are high chances that the tools are to be redesigned whenever the desired products are not obtained. So, this trial and error method involves lot of experience and loss of valuable time. To overcome this problem, process modeling by computer simulation called Finite Element Method (FEM) has been introduced which stimulates the actual process and thus saves time and money. The FEM model is constructed in LS-DYNA and after simulating the process results are compared with the experimental ones.

Die Quantifizierung der Verzerrungen bei der Vorhersage der Oberflächentemperatur des Modells Climate Forecast System Version 2 (CFSv2) wird über Indien im Allgemeinen und über der nordwestlichen Region Indiens im Besonderen während der Wintermonate (d.h. Dezember-Januar-Februar [DJF]) charakterisiert. Statistische Analysen wie Zeitreihen, Temperaturverzerrungen, prozentuale Abweichung, mittlerer quadratischer Fehler usw. werden durchgeführt, um die Verzerrungsmerkmale von CFSv2 und CFSR zu ermitteln.

A quantificação dos enviesamentos na previsão da temperatura à superfície do modelo Climate Forecast System versão 2 (CFSv2) é caracterizada sobre a Índia em geral e sobre a região Noroeste da Índia em particular, durante os meses de inverno (i.e. dezembro-janeiro-fevereiro [DJF]). São efectuadas análises estatísticas, tais como séries temporais, enviesamentos da temperatura, desvio percentual, erro quadrático médio, etc., para conhecer as características dos enviesamentos do CFSv2 e do CFSR.

Kolichestwennaq ocenka smeschenij w prognoze prizemnoj temperatury modeli Climate Forecast System version 2 (CFSv2) harakterizuetsq dlq Indii w celom i dlq Sewero-Zapadnogo regiona Indii w chastnosti w zimnie mesqcy (t.e. dekabr'-qnwar'-fewral' [DJF]). Dlq opredeleniq harakteristik smeschenij CFSv2 i CFSR prowodqtsq statisticheskie analizy, takie kak wremennoj rqd, smescheniq temperatury, procentnoe otklonenie, srednekwadratichnaq oshibka i t.d.

La quantificazione dei bias nella previsione della temperatura superficiale del modello Climate Forecast System versione 2 (CFSv2) è stata caratterizzata sull'India in generale e sulla regione nord-occidentale dell'India in particolare, durante i mesi invernali (cioè dicembre-gennaio-febbraio [DJF]). Le analisi statistiche come le serie temporali, i bias di temperatura, la percentuale di allontanamento, l'errore quadratico medio, ecc. sono state effettuate per conoscere le caratteristiche dei bias di CFSv2 e CFSR.

La quantification des biais dans les prévisions de température de surface du modèle Climate Forecast System version 2 (CFSv2) est caractérisée sur l'Inde en général et sur la région nord-ouest de l'Inde en particulier, pendant les mois d'hiver (c'est-à-dire décembre-janvier-février [DJF]). Des analyses statistiques telles que les séries temporelles, les biais de température, le pourcentage d'écart, l'erreur quadratique moyenne, etc. sont effectuées pour connaître les caractéristiques des biais de CFSv2 et de CFSR.