Mit freundlicher Genehmigung von Michael Rode.[br][br][i][b]Grundgedanken[/b][/i][br][br]Die Einführung eines variablen Potenzials in den EPT mit unendlich hohen Wänden erweist sich in der Umsetzung in die Zeigerdarstellung als sperrig. Vorliegende Versuche müssen daran scheitern, dass es wegen der UBR nicht erlaubt ist, dem Elektron an einem Beobachtungsort eine scharf festgelegte Wellenlänge zuzuweisen.[br]Den sich daraus ergebenden „gordischen Knoten“ durchschlägt man einmal mehr durch sorgfältiges Nachdenken über die Superposition: für jede Denkmöglichkeit einen Zeiger.Das Modell Harmonischer_Osz soll ein erfolgreiches Vorgehen darstellen und verstehen helfen.[br][br][center]Leitfrage: „Worin besteht die[br]Unkenntnis, als deren Folge zu superponieren ist?“[/center]Der hier verfolgte Ansatz behält einerseits die Unkenntnis über die Lage der Quelle (links bzw. rechts) bei, wie bei der Modellierung des Elektrons im EPT. [br][br]Andererseits besteht bei festliegender Gesamtenergie des Elektrons („stationärer Zustand“) aber Unkenntnis über die Verteilung auf potenzielle Energie und kinetische Energie des Elektrons. Dadurch müssen mehr als zwei Denkmöglichkeiten in die Superposition einbezogen werden.[br][br]Die Gesamtenergie wird festgelegt durch das quadratische Potenzial. Diese Gesamtenergie stimmt mit der[br]potenziellen Energie am Rand des Oszillators überein.[br][br]Variable Gesamtenergie zeigt sich dann im Modell durch ebenfalls variablen Radius des Oszillators.[br][br]Abhängig vom Ort ergibt sich die für die Wellenlänge maßgebliche kinetische Energie als Differenz der ortsabhängigen potenziellen Energie und der Gesamtenergie.[br][br]Damit stehen über die deBroglie-Relation die Wellenlängen Lamda1..Lambda5 der zu superponierenden Zeiger fest.[br][br]Man müsste für jeden Ort im Bereich des Oszillators einen Zeiger berechnen. Es hat sich in der Praxis bewährt, mit 5..7 Orten zu arbeiten, wie das Gesamtergebnis zeigen wird.[br][br]Im vorliegenden Modell werden 5 Orte P1..P5 gleichmäßig auf den Durchmesser des Oszillators verteilt.[br][br]Die Definitionen für die verwendeten Elemente können in der linken Spalte des GeoGebra-Modells nachgelesen werden.
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[/img][br][/*][*][img]data:image/png;base64,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[/img][/*][*][img]data:image/png;base64,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[/img];[img]data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAANkAAAASCAIAAABZ8+xWAAAN20lEQVR4nO1aeTyU6xef3YyRfWnsjKWQLaN1dEsqSTctRFotqYgikWxtUroqFVq43dtev3tv2qgk0iVEthQjg2Go7NuMWd7f+45RjLFUt8+9t9v5+ON9zpz3+Z55znm+5zzPQAEAAPsu3+UfIKi/24Hv8l348k3mIqvuXmTQmezqepj12Rv+Bth/BhbnXcapPWEhJ7LIdxuSrCX+ck+4rbmnPB28LkqepOZuVkP+xbOzaHcivVyCkyZea3y0QhY+mjmDcnWfj+f++5NvNiYvlhobxCfmIuttZqyvZyw2Ju8MGT+i8stl6LRceuI0xQ05H03EVz6quzxbbPB7jMJ91i5FIQW/macfzJFEf9Czq8/ON9mrlVQa2e2h59hypCTJkTByxICuorPBYdExv5dxVQxN5ID2hgaW2kKfqJ+8pksjRsQSJkg5C3f3Gcdi66do4D5hGcYsCMnJy6wUvO9o6sn81YkIClp5nsN0mdDnpppiYCK2p280sn3u8ufT3QYiQs2xWnaOpOCIV+YaomOGGHsucluexYccvldV87jw7dIRlV8uw04LF58ZeiNoKj/j4Vhl4yFh7ciKOllpdG6OPEbaPkR1wAdIKSNbp5UEFREca77Tqg598VEjBscbuu3Zmp74O21e4tOrlmKwXuoFB+PVlnQZyl1nJeQIWMNIDzWvDqNjQsCMbvo5wqjJocJUV6oNJWdGRVIyMG+JzpeUCEZ1Tg1CnaQKJR9GdoIpiUMcaQm7q3JqUcTJysJzVZh8Ai9iiQ5R111p0aZ39o+i/HIZdlq0vNncBQtGKHCMyuQnrRqe+uOGlBG4OMnnBIn3uDrq+BgdYb8vKW2FqZmr8ZYUo27ns1jqj8u/5LU7K0mNgCVcehuKqtiqzl+HFsEd3FH+4h1+goGcYFB7Cg857WqLs1ryJbOz3xa96laYQ4RoEYbV3/a/1BHNe+vzKb3K9ppjp8WhucjI9zexTpIy0pJivkzOqFLcmlN+jAQtHgInKw0WzsHWQpVjlk/DGl1YNUlR4QdOVnPwV9xt820OJ/hO4hEBp+npMfelvkmGvzU+sAMnbk9eQljcfp6eal0ft8ne81eRI/mxyKhNYddbVz8tO0oamCoMajYVhrc0VODXXzhGFA3jcoChWNtUik8HhNygYUWZdRQ6A6W3P/2arRTUxuX/HLbn2huESHdVXsZr9AJTRR4tcpqz4wIj7rfA3+UVizonXAubxav7XcWxgi6VhHDOD7HkNmfHBu75gy4qzqKV1il63biyRaU6hwqoLRZJD7PdHn27HEk+kJYSqN+WetR7XWheh5Hfj0umBJ2PILOfRLhs/6NdTpbbSGkjxT2OtxxT7wrRIhykRSybfj/K2y3wulx8TdbSir1OK/c+nPBr4a6KwK3RdypQPFAjHFgCcmtRWhAt9lLi7KYFNjueuhTlqIFqyhgWXTAXgc567sbUQh/97tSN+ndfro4PnvyVdvHnYXVmhzrYxgKdrS0sadOl3sGec5U+Fjy06mKfTWmnfu70vpLip/Wxe0PKkBaaSu0oIGnyijuL/qKcQVhExMPxk1Y56vhdeJkWecnG9+b/CCENKoNLCvt9cQlEi+r84sZuyMl+C9Obryc2GAvVlrmTvDTLOeVerIlYb2mYgeGFCeogJQBtmQHkJU8ck1OizcQhvdFlc4gWuc1pfhaOxZvSb2/R6bw+W8F+190tT5wVwGQUdGn3S0qQxbbSwZaO8OTNM9dRtqQmxU3Cw9tT1jpWYhBAB+VFA7cl+cQzr6jc2t1hBlPjL73eFmlMtjHE78xxPp8cYQR+OUbBzrX734dVZqwlIFof7DghMcZ+gf2upKxTnqwtDkfh5q5ZMD4odeJEabTsbM91ugdS6xLD09wic2qD90zigRoZI+rzK5jKyzRFgY7i3x/id6Vd9TEGGZVRcGh4dMFchEtZhm/GYtqf+m8407PsxuH5soix+foZ8slYiPHO9ymrRHBoOAzorrjsRraaRkkvOWkh/qFGAt3Uwgas9iR5gWMEA9qlmmZ9mdYDNT6aJGgApWU3TsM24idXDTTXMJaDQQi8B9Ki6BxjiBYBJi01cl1Qsc7WB65Qog/A6snbt+YY3K94mwlUwbop2bVoLTOQErpzwp2j2d6FvmbifD1GB6LFruzwtTHA1mI3XSzQ8TzzDUJntYEEH3mwS8T114zmCViKdz7xWp8gFli2GUxE8A2c/potWgQUoziniis/P+BU2HxpBKMAhUCOkxdDQo3bsxokkcTfZexWWksPvbSqAyBISFod3j3WYPFoUa2vWwSfq2Fq66C+FGxRqzgylr5xEQtlB4L2UHNqUEQzGUqsxwGOf6KfMa+0j4w+pEYjcXhkV27w+uPNNheOLVH4CieyL8CCo7B85oSLai8LcAu5dOxk7iELyw8n6V5aPpWr4qIq0KOzGgrLmYo/EvtosfHF6x6CNa/x6anOrUVP3WenDuUuQkRUYDOw3xWXtoEZFWBNDmd3d3GlDW0SCrcvNxBHDMLqzDqRQJ0YtkKTtwOYNc/Ke5WdwU6p48+Yn6t1dzlqi/TrWSrrQVrsyIw5XzsxaBG+7M6x2OjL9HU3U3cb97s8yKWuF+cu0QQtWY9XXH1reGiZBn/DoZUtF4Jb4x0lv1GMvJJ3xAe6qvLpOJ1J4B4CG7dypvJSIv+8hzffETBzRuCMCQWBcadDFmtgx9brcppKS9tlp+hAO4bbVl74XsIAOq4DnZSCRjzZmSwrAFpZUMGUNcxw/+Fo3o+P4z6QxYjoQs4uPYVR6w/XWZ556KD0VTPxS7FQkkoSsI769wwAJtb/lbqpz+vQmkaCB1UGNacWpAZlDH9Qg9Doo8XGQjAtF2rihwkIg5pFhYktS0y/YTVuyIcfsFj12TnNkmaG8rzFBMPzjIbRNlMS6a3PzmuRIpEI6AF6kBZZ9NyCNhHxhvRHFRN/CL29lTAwIoNcEmrJep1f2ilF0he4uWHW5FK5qrZ93QSTllvJUfGABhAtggzV33zA8cYBaVSL+B3uPksM8k6X3nFTH/kqir8SIC3C1Lx4R3RmDdSXroKOcyBoFVfNRk0QFKTFam4jFrUq+IdHgQfv7SfbKyBGRR+Si8yy4xv2Vkw/enONGorbQa3iqBAlv1ZKfiIW8P7+vhvK/h56/dWmrg0mpT8e9zGSvfTCKraSg5pA2wnS4qsewiINKL7c95mXnnSOd+LRYt81hdlw9w59tKgxkyisjf2Ixe1pY8A+/JTKqHoK0t8GkBYBZidzoD6znK3qBtIiwGayYVjNRevXCV6NwgRcEmoJcFgcGEzwp1ugs/I5HadrNJ4X2+7K3HoRHWjQWw8ylKIdnxa5bZWVXHVtuelbEh4pvFW3P5fe5KI+fvQ2jNNcVtIqY6orCdFiO3hcH6enL4uCAa08UGNBUNYbsCNX9jqzx1Er43aYzcFLb+y2gW3NKOgCuciixLuEFJnsL3YjomGMotiI/JWniJKjuioovRXHrcyCgT15D721McNoPgML3pp1jUJ31VMHveY2ZcT+Uq3letlswPU6ozqPhtQwURLILU7n+y4Ol8VktJTeitp/LrMJqWEOlfG+awpLotiwtAh2ixKLjOWF3Xx9xMIoTdYVab0dfbVEZ3bzzUOBZ2mYGVAyYZTM9XEHIL3u7Kbre/1O0zCzJ4NHLYzyzKnSoffOPaCT7Qjw9td3f80Y7+xKkoC8GOyScMuJs2bKhtxOeNQ401YBaCn87UKZiftKlVqQoVSt+bRYm/eGreoKDcAOrRXAy4giABaDg2JmHdjbE3neThYOsLsZMBldNTHE0JDBhgSLUf0M7BA9eDdbzOpckBbtocmZPNAFfXt/ACjUkSOJU9WwcEmL7esVSdGxhR5HzHAdw6LzZNAqc+jXvQKyenHGD3YsT+2gFeV2rXm6tZ+oWLW3jp5IeVn2uBzW3BzqvslYZ5qLn6P4o6HKNfpocNvyZMDkAppPx1qjj1M3BkLmmlxUJ2CZTW+ZGhuSHu8iDUhFVkNxJZNgrS54p4WdsMpj1pnAacq3Fu08FeGccSWlLCEmzSrcvO+aQkXIDTDQVZSw59CZW+2w3uKTgSGUNds3kCQHLtwALLiszdHj9rbbXIxMZnge9p0qG/dnxaWLpXO36C/4KcbBerOLodEMryhfC4XEvJfnfymZ42UwK+pKEN3VSUVKRkvPZL57aNhCCXh/hg9ySVyYJXJG5LUQ2np7JQlpbQPzxd77dmuJAM0gQ2F1+LTY9Sa3ltuUffk2Nchuov0K3QT/KYYpq3bHH7YWkaP7zyXHEUTaaM1yfhcPkQfw7YAACQSL2/qquInbmBx/kbTDWZFSQOc2PoxNMN9pVweBGhMEQP0NeB25NnTvijPy8DaKDtroZ3r9yEjogrmIJDjd63YaGhieoFVsd0Tagg+Jg9RClTBt74x274EKjKDmc7Bg5gfSXh0Y5h0Yb2fWwlWFXPXjDAMeNwf0j6y4zvynOVfa2MKnguMNXSIvgH9jwsLqul8td7/K/2g1cKTfStvlCsXlylC9vNW+B9X7hEwrJuASUpglUnZ2eAo1fJBOZvmj3uX9A2m7B6wPiTQ7powZ0z/QPPig6KCw7zMoQILBQii55gKu/SOnJ9wPgRsG1L+U489/RGv7vuD6joLOk2/pfyPY70pfd8lbEMf6M8i/Beu/It9SLjJreFSlMvZfQP8dWP8V+YZykdPy+mWblLG2xNe7nf9bsP4z8n+wE8OGkyBkegAAAABJRU5ErkJggg==[/img][/*][*][img]data:image/png;base64,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[/img][br][img]data:image/png;base64,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[/img][/*][*]Amplituden 0,25;1;1,75;1;0,25[br][/*][/list][br]Damit stehen die Parameter für die insgesamt zehn Zeiger fest, so dass deren Summe gebildet werden[br]kann.[br][br]Einander wegen der beiden denkbaren „Laufrichtungen“ entsprechende Zeiger sind im Modell-Bildschirm gleichfarbig markiert.[br][br]Die Konstanten wurden (durch in der fertigen Fassung unsichtbar gemachte Schieberegler) am Modell so gewählt, dass sich übersichtliche Bilder ergeben.
Einführung von Gewichtungsfaktoren[br][br]Eine genauere Auseinandersetzung mit der Modellierung bei der Konstruktion des Modells machte deutlich,[br]dass die verwendeten Zeiger nicht alle gleich lang sein dürfen.[br][br]Die Nachweiswahrscheinlichkeit des betrachteten Elektrons wird nicht in allen Intervallen gleich groß[br]sein.[br][br]Deswegen werden Gewichtungsfaktoren eingeführt, denen die Annahme zu Grunde liegt, dass die Nachweiswahrscheinlichkeit quadratisch mit dem Abstand zur Mitte abnehmen wird. Der erste, im Modell rotbraun dargestellte Zeiger erhält willkürlich die Länge 0,25. Die Gewichte der folgenden sind 1; 1; 1,75; 1; 0,25.[br][br][b]Handhabung des Modells [/b][br][br]Man vergrößert den Radius mit Hilfe des Schiebereglers, jeweils bis am rechten Rand ebenfalls ein Knoten[br]vorliegt, die Randbedingung „Nachweiswahrscheinlichkeit = 0“ also erfüllt ist.[br][br]Man notiert die Anzahl [i]n[/i] der dabei auftretenden Peaks und die Gesamtenergie.[br][br]Messbeispiel ([i]Eges[/i] in willkürlichen Einheiten angegeben):[br][br][table][tr][td]n[/td][td]Eges[/td][/tr][tr][td]1[/td][td]156[/td][/tr][tr][td]2[/td][td]310[/td][/tr][tr][td]3[/td][td]460[/td][/tr][tr][td]4[/td][td]602[/td][/tr][tr][td]5[/td][td]749[/td][/tr][tr][td]6[/td][td]895[/td][/tr][tr][td]7[/td][td]1030[/td][/tr][/table][br]
Die Übereinstimmung mit den Erwartungen: „Gesamtenergie proportional zu [i]n[/i]“ wird hervorragend bestätigt, was als Beleg dafür gewertet wird, dass die Darstellung der Superposition im Modell angemessen gewählt wurde.