[size=85] [b]Inhalt des Applets[/b]. Dieses Applet ist eine Simulation, die die Interferenz mehrerer Kreiswellen ([i][color=#ff7700]ohne Dämpfung[/color][/i]) zeigt, die sich in verschiedene Richtungen bewegen. Die räumliche Verteilung der Intensität der resultierenden Schwingung (in der Schwingungsebene der Quellen) wird für n- kreisförmigen Wellen der gleichen Wellenlänge und Amplitude berechnet. u -der resultierende Vektor des entsprechenden Vektordiagramms - dessen Modul die resultierende Amplitude am Testpunkt ist: u=Sum(Zip(e[sup](ί * 2π / λ Segment(a, Testpunkt))[/sup], a, l1)), l1-liste der Schwingungsquellen.[br] [b]Eingabe mit Reglern[/b]. Interferenzmuster-Einstellungen für Lichtwellen : [b][color=#0000ff]n[/color][/b]-die Anzahl der Punktquellen emittierende Kreiswellen. Sie befinden sich auf einem Kreis mit dem Radius [color=#0000ff][b]r[/b][/color] und sind gleich weit voneinander entfernt. [color=#0000ff][b]λ[/b][/color] - die Wellenlänge. Sie können sie ändern und die dabei entstehenden Interferenzmuster in Abhängigkeit von der Wellenlänge und den Abständen zwischen den Quellen untersuchen. [br] [b]Visualisierung[/b]. Die [i]Verteilung der relativen Intensität des Interferenzmusters[/i] wird veranschaulicht, indem es im [i][u]grafischen Bereich[/u][/i] des Applets abgetastet und diese [i]Oberfläche[/i] [i]direkt[/i] im [i][u]3D-Bereich[/u][/i] dargestellt wird. Das Scannen ist auf zwei Arten möglich: [br] durch Abbilden von Isolinien mit Intensitätsstufen und [br] durch direktes Färben der Punkte der x-y-Ebene. [br] Die [i]erste[/i] Methode ist unter dem Gesichtspunkt der Lokalisierung der Extrempunkte der Intensitätsoberfläche veranschaulichend. Eine[i] andere[/i] Methode besteht darin, die x-y-Ebene kontinuierlich zu farben. [br] [b]Färbung[/b]. Die Färbung erfolgt durch 3 Arten von Farboptionen: HSV und RGB: farbig Variante und Schwarzweiß.[br] [b]Testpunkt[/b]. Das erzeugte Interferenzbild kann mit einem Testpunkt testen. Sie können sie durch Ziehen mit der Maus im jeden Punkt des Zeichenblatt verschieben. Die entsprechende Größe der relativen Verteilungsintensität kann man oben rechts auf dem Zeichenblatt ablesen. Eine geschätzte Methode zur Bestimmung des einen oder anderen Typs von Extrempunkten wird vorgeschlagen: max, min, Sattelpunkt. Dieser Punkt ist entsprechend in den Farben [color=#ff0000]Rot[/color], [color=#0000ff]Blau[/color], [color=#93c47d]Grün[/color] schattiert. [color=#980000]Вraun[/color]- wenn es kein Extrempunkt gibt. Die beweglichen Hilfspunkte können verwendet werden, um einige Extrempunkte zur Veranschaulichung zu markieren.[br] *Das Applet hat eine lange Ladezeit. Laden Sie es besser herunter und verwenden Sie es offline.[br][b]Berechnungsformeln[/b].[br][/size] 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[/img]
[size=85]a -Konturdiagramm mit unterlegten Farben zur Kennzeichnung der Isolinien [br]b- Scanergebnis durch direktes Färben der Punkte der x-y-Ebene[br] Intensitätsstufen mit 3 Farboptionen HSV, RGB: farbig Variante und Schwarzweiß[/size]
[size=85]*Andere Interferenzmuster finden Sie im [url=https://www.geogebra.org/m/j2kaazvr ]Applet[/url].[/size]