diff --git a/docs/source/pythonapi/data.rst b/docs/source/pythonapi/data.rst index fd29470626..7f75ddaaaf 100644 --- a/docs/source/pythonapi/data.rst +++ b/docs/source/pythonapi/data.rst @@ -132,3 +132,16 @@ Functions openmc.data.endf.get_tab1_record openmc.data.endf.get_tab2_record openmc.data.endf.get_text_record + +NJOY Interface +-------------- + +.. autosummary:: + :toctree: generated + :nosignatures: + :template: myfunction.rst + + openmc.data.njoy.run + openmc.data.njoy.make_pendf + openmc.data.njoy.make_ace + openmc.data.njoy.make_ace_thermal diff --git a/docs/source/usersguide/cross_sections.rst b/docs/source/usersguide/cross_sections.rst index fa61045c6c..48cccfb676 100644 --- a/docs/source/usersguide/cross_sections.rst +++ b/docs/source/usersguide/cross_sections.rst @@ -143,8 +143,8 @@ metastable state of Am242 is 95242 and the ground state is 95642). .. _create_xs_library: -Manually Creating a Library ---------------------------- +Manually Creating a Library from ACE files +------------------------------------------ .. currentmodule:: openmc.data @@ -199,6 +199,34 @@ OpenMC. etc. For a more thorough overview of the capabilities of this class, see the :ref:`notebook_nuclear_data` example notebook. +Manually Creating a Library from ENDF files +------------------------------------------- + +If you need to create a nuclear data library and you do not already have +suitable ACE files or you need to further customize the data (for example, +adding more temperatures), the :meth:`IncidentNeutron.from_njoy` and +:meth:`ThermalScattering.from_njoy` methods can be used to create data instances +by directly running NJOY. Both methods require that you pass the name of ENDF +file(s) that are passed on to NJOY. For example, to generate data for Zr-92:: + + zr92 = openmc.data.IncidentNeutron.from_njoy('n-040_Zr_092.endf') + +By default, data is produced at room temperature, 293.6 K. You can also specify +a list of temperatures that you want data at:: + + zr92 = openmc.data.IncidentNeutron.from_njoy( + 'n-040_Zr_092.endf', temperatures=[300., 600., 1000.]) + +The :meth:`IncidentNeutron.from_njoy` method assumes you have an executable +named ``njoy`` available on your path. If you want to explicitly name the +executable, the ``njoy_exec`` optional argument can be used. Additionally, the +``stdout`` argument can be used to show the progress of the NJOY run. + +Once you have instances of :class:`IncidentNeutron` and +:class:`ThermalScattering`, a library can be created by using the +``export_to_hdf5()`` methods and the :class:`DataLibrary` class as described in +:ref:`create_xs_library`. + Enabling Resonance Scattering Treatments ---------------------------------------- diff --git a/examples/jupyter/nuclear-data.ipynb b/examples/jupyter/nuclear-data.ipynb index def94ff827..4302e5295c 100644 --- a/examples/jupyter/nuclear-data.ipynb +++ b/examples/jupyter/nuclear-data.ipynb @@ -205,7 +205,7 @@ { "data": { "text/plain": [ - "{'294K': }" + "{'294K': }" ] }, "execution_count": 8, @@ -315,7 +315,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 12, @@ -326,7 +326,7 @@ "data": { "image/png": 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pCbGMHZDJRyt2Bry/dGW1V4II534Qnss23gfR+LmWIIyTLEGYVuucYV3YfbCS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\nV0fyenU8VFZaWcOqHcVsKip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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -422,7 +422,7 @@ { "data": { "text/plain": [ - "{'294K': }" + "{'294K': }" ] }, "execution_count": 15, @@ -455,7 +455,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -510,7 +510,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 18, @@ -540,36 +540,36 @@ { "data": { "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]" + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" ] }, "execution_count": 19, @@ -600,7 +600,7 @@ "data": { "image/png": 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Sm8cCTzOf0n9ffxIyE9jQfgMOBg4F7yVu/53o6dORGxlhNXcOmg18CDnygPO7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\nbubUq/7KluI1vHTT1axfttTrsA44lKU71We97J05EsXYGUk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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -660,7 +660,7 @@ { "data": { "text/plain": [ - "{'294K': }" + "{'294K': }" ] }, "execution_count": 22, @@ -691,7 +691,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 23, @@ -702,7 +702,7 @@ "data": { "image/png": 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CT2g1VtGD0ECFSuTgrF8G4IT8Qoy0ZMfdPTZ0GNtCOvBLD9xx/mh+OPtlweOi\nMun/V/bSS5mv/e1oaUX+4q2dkrGRZ0jbuaz/hp1yPGQLO5lGbLl4VcCvL+mry7FaDXhqidiRc+Sh\nNGz3LgDx7fg7ACbGdjqJx2g0YtmyZZg0aRJmzZqFr7/+OuTWwR9//DEKCgpw9NFHM58fNGgQAN9W\nxOkoKEaj7w4xWFD8FfMsQYmlhRK8uLvdbmi1Wsnzej27S1M6CIofFSEYAguGwIJzVOVwUA9+Rht+\noi34iTZj0e4dWLR7B/J1OkzNK8QJ+b6fRHJ0VZbg8ebvuhCcEKdSU3g9qZWOa9IQdPdWzyvpHSaG\nWX2P/qvuAW6x+AkVQxkJYAx8jRmD5TcbSD3HbzgxlGAGDhyIDz/8ENOmTcOpp56KtWvXygqBw+HA\nRx99hJkzZ8r68svKfB1kampqMHny5LDmkmxYgkIpjavLK9gqCc64crlc0Gq1AlFwuVxp7fKSQ0/U\nGI98jCf5AIZj2AkqrGtpxNfNvp8PDvuSPEphxuje2EslcqBPoHuspEwo5EeNYovJ/j2xva63zQ5V\njjI32A2j+woltSrffP/0XR26RIl8Rp0HdsY2xdWjCpjnHbFJtGleUIsXACAeytONEdpCqYRvI6wc\nCOMonQAUxSkSSbgxlGDGjx+PlStX4vTTT8f06dOxatUqlJeXS45btmwZWlpa8Jvf/EZ6kl78gnLg\nwIFwp5F0ggXFbDbD6/XCbrf36/KKpaAEL+5OpxMmk0nWghGTThZKKAYYDLigpAwXlJTBSyl+7urA\nuuYmfLirDl/gENagGhoQVFArduwZgBMLCjHGGn/3WDKwX7+MOW56W1lF/v8dI13m3i9kFx7/a1kx\nc9yjJVC7+r5LKi+gJBH8SLNclO4pP4VS+k2C5pQ0jj/+eKxcuRKzZs3ClClTsGzZMoGF4Xa7cf/9\n92PYsGE47bTTZM+Tm5uL3NzcuOzBEm+CBcXf76y9vb1fl1e0C3KwVSK2UAChKDidzpjXoaSioASj\nIgSjLFaMslhRuXsgnNSD3WgPxF8W7fwZi/Az8nQ6TMnPx9SCAkwpyMdAaomqvdCRiEHnQQ/Dcqmb\nmid4POjzJkFhpFx22JH28SuNoVxHCNlBKW0DgN7teP9BKY28d0mKcuKJJ+Lrr7/GWWedhalTp+KW\nW27B7373O2RlZeHmm2/Gtm3b8N///rffLYUJIaisrEx7QfF3Fm5ra4ury4slIsG/iy2UI01QxOiI\nGmN63V62semzAAAgAElEQVQAMPpXKqxracJXTY34urEJH/l3EtUbcXxuQe9P4uIvWh3gYnwlVCog\nlk0JeuptMDDqXZSgVwEOxlwumN7APP79r4SuMLtF2CLGaHPJ2ivBwXs/mRrEVyoo4/1iAgCU0lZC\nSOh0qAQTaQxFzOjRo7FlyxbcdtttePTRR/Hoo4/6z497770XF1xwQchzVFZWYs2aNVHNIxmwLJTW\n1laBy0ucZZVIQQnXQlFCsgWFEJ9LPhh/hpsSDN0mTDcMxvRBg0FLKQ702LC+vREbbU34rOkw3jvs\n27euVG3GOG0exmvzMEabi2wVu19YtBx3YhZz/OBe6efsdlNoNH3v0+vxpTEr4b1B0nqXSxtugCYv\ntMjMHMzeaO/ZnezvglbjhcsddBOpAxD0tQ+3eDJTXWFKBUVFCMmllLYCACEkL4zXJoxoYihirFYr\nlixZgjvuuAMrVqxAd3c3zjjjDEVpxYBPUF5++WV0dnbCYmFvOJSKsASlra0t4PJKhoUifj7dLRSx\ngGTnSFdQrZ5dHR6qZoQQgnJjFsqNWbjCWA4vBXZ2deC71kZ8crAeX/TUYlWPT2AGq7MwRpuLyboC\nHG3MQ64m8Xsc/rJb2HBDo5UG3wtLlRfJ1M9/MfD7gGd+C+PALLia7dDmR9fvbOoxrYLHu0qFOUk1\nm6VNUwFgyE52p2RAarlkgtWiVBT+AeAbQsjbvY/nAIjd1ocpzLBhw3DTTTeF/brKSl+Xml27dmHi\nxPTJrrbb7cjL87lSgl1e/oLHeMdQWIIiDtrHWlCcTifq6mK8c6AMGg1B4QDx56f8znbQYOWLvk+U\ngAkGCyYUWDC2uhhurRe/0A7spG3Y4W3FZz21WFnrE5ghuixMMObhaJPvJxdsSyPx9Fe/Ls+bJb6t\njQcNkX5mk3++ivkaoxqwx7DIU64JJQtbgw2/Of8/gcfZOQY8/tKc2E0mASjt5fUKIWQjAP/2hBdS\nSrfHb1rpz9ixYwEAP/74Y1oJSmdnJ7KyfAtJsIXiX/QtFkvMBUUuy4tlocTL5XX11VdH9Np0Q0NU\nGE5yMBw5mKUuh5t60TPQgS32Zvxgb8EnnbVY3n4QADDkkBlV1gJUZeejKjsf4uXC0UOhN8Q/6qw1\nsK21eHBVJdtd9uD33YLH2ToI9m0x6L3ocUjjqvYs9g2AqdMhkUjx4w7GZl83XfW2ZDyVhCcct1Ue\nABul9EVCSCEhZKi4ep7TR0VFBbKzs7Fx48a0Wqza29vh79YcHEPxL9bZ2dmSGIo/vgKE5/v3I2eh\n+F1p8bZQXC6X4D0cSWiICuMsuRhnycXlANzUiz09Hfje1owfHa1Y3XQI79b70t9LNCaMM+RhnCEX\nY/S5qF6pZv5fT52eKpaNPN5WO1S5yt1gZg1gC6pleWqGUHhermRX2C9bbISKEVth/YVI7DBG3g9L\nZFhjyULpFsB/BVAFX13Ki/Dt3vgqgBPiN7XwiVVQPhaoVCpMnDgRGzduTPZUwiJYUPR6PYxGI1pb\nW6FSqQTxE41GExCC4AXf6/WG3b8sVNpwNBZKKnUbTgc0RIWRxhyMNObgaqMKHkqxx96BTZ1N2Opq\nxrct9VjdVQMAyIYOFdSKCvh+hsACLUncTl8aDSDeeUCp1dR5/VLmeP5Sdvh14djI4kt1Q9lbaVRs\nqe+tZelDro9YOqH0HVwA4BgAmwGAUlpLCEm5SHMsg/KxYOLEiXj88cfhdDqh08UnoyaWOBwOOJ1O\nBO8nU1RUhPr6epjNZsFujVqtlrmPSCQNMcWC4SdRMRR/NwCOFDUhqDRZUWmyIm/AUfBSit1dndjc\n2ooV2xqxB+3YDF+RoAYqDKUWTNtdiGNz8nCMNRe5ur6FWK2l8LhCLPZhhEtGjZe6p9atsQV+12gB\ntwvweKhkl0s5nE3d0BWYQh+oEJ3OAyejrqW9QDp3a5PQreYlwMVz3xSMpfpWakoFxUkppYQQCgCE\nkMiSv48wjjvuODgcDmzZsgXHHXdcsqcTEv9mY8GCUlpaitraWgwYMEAiKP7Mr2BY3YJDEezmCnY9\nxSLLSwnBu1Jy+kdFCCot2ai0ZKPkJ19tSzt1YA/asRvt2IN2vFS9F88d9PVfGaI3Y0JWHiZk5WHG\neAuGWcwCN9ln7wlrU7Ta2MVkJp3kc719+4VN8tzIY9gWx6YTXmWf67vLQKx9QuNo6oY+SHgMhKKH\nSud+6klsV9imN02gTuHxktRjhjuRqbcpVDypVFCWEkKeAZBDCLkWwDwAyjc9OEI55ZRTAACffPJJ\nWglKsHCUlJRg27ZtMBgMEkFhEYmgBAtGsEj5M8uCzxkPl5fNJl1wOMqxEj0moggT4dvNc/jRBmy3\nteFHWwu2dLXiy7bDWN5cjfsOAHl6LaoKcjCxIAfHFuTAXGSFWcX+LvnxuOOw+6QaYW1i7r3rFcHj\nr5YLrY5fb/s183X/r5M9cesp0ovv+yFf8HjwzmaJVng1DJeil+KKC1+FNceAJ15g79GUKJRmeT1C\nCDkdQAd8cZT/RylNv6q9BFNUVIQJEyZgzZo1uPPOO5M9nZCwLJSSkhJ8/PHHyM7ORn5+3xfebDaj\nuVmaYx/J/uzBghJsofgth+DnHQ6H7DWOVEFJta6/BpUax1rycazF933xUooDPV2otjRifWMbNja2\n4uNDPjcZATBYm4WReitGGXIw1VKACpMFmqBYTP2B/gUnEgrGsD+vXVti6/o0qCl6FP7fqDVeeIKK\nJ6kKUIfRLTkViiWVBuXNAD6jlK4hhFQCqCSEaCml6dWzIgmcfvrpeOyxx9Dd3Q2TKXa+2XggJygd\nHR3YvXs3zj333MB4cXExDh48KDlHJAHu4NcEu5/8C32whWKz2eIiKKn2f+N0UOj0yhaiwWPZuw/W\n7tZDp+s7h1zrE7nMPOm40OGitJWKihAMNVpwcoUBcyt8jVNbHU780NyOVT+0Y0dPO77pbsDqrkP4\nZ5NPkEaZrRidlYPRZitGGK0YYsiCWjRHQiioyM3ECtSHg1rjs4jCpafRDkOhNMJx9hD2TulPt+jR\nLvpTGTK2S/B4r1ra+XjYlgZJRb5Xm7hEiFAoNSTXAjipt4fXKgAbAVwCYG68JpYpzJgxA4888gg+\n/vhjxTtCJouODt9GRMGCMmLECAC+1OHi4r5OrAMHDmSegxVXCUWwoHR1dcFgMKCnpycgKMEWSriC\nooTu7u5Ad4Bk4PVSqFTChXHdJ+y7zUFDdJJj5fj2c+H/RXkFe8cJ38cr/ewMRuFCpTcLP/eigeEl\nmrgcFNpekczV6/CrkkIUHvC5ySilqHN3o0bXiW3dbdje1YZ36w/gtd62AEaVGiNM2RhlzsEosxWj\nTDmYUGqGONY+fmJfyrLXQ6FSExAVQBUazsNkLJdQLWFWTmVvXlux8Vx4s6XfraenS29gLlsljOWx\nAvr1Q6RZY4XVHVB7KajCxIN4olRQCKW0mxAyH8BTlNKHCSE/xHNimcK0adNQWFiIN954I+UFpaWl\nBUBfhTwATJgwIfB7cDt/uXYykQiKP1YC+AQlNzcXdXV1TAulq6tLNk4TjYXir7mJNyVlOjgcwjk1\nHFZu6Le3Jmmv3nAgFGAEqDf/DxALlyWbwOvxtYwp0ZoxSJWF47OKgSzAQykOOrtQre7Ezu527LC1\nYVnjQbxZ3ysyO9QYZcnG2GwrxlqtGJttRRHNDrjLWpt8KpKTJ13m3D2AJowdnVprhQd7vU5Fwj7o\n/uXsJ56+RTIkLpY8Y7rUpfz1MgtcPUKhbypNnYRbxYJCCJkCn0Uyv3cs5TZLT6U6FD9arRZz5szB\niy++mPJ9vRoafJ1Wi4qKAmPBInLssX2bdvo7AYiJhYVSVFSEuro6SQxFrVajq6srpi4vjUbTr9XD\nCZ/sHPZCW1sjHRs6Srg4/rKj7/9LTQiG6i0YbbRihsW3C6qHUhx0dGGnvR0HVe3Y1tGOdw5V45WD\n+wH43GU+S8aKcrUFFYZsWKlRshHZvi/Zy1fuADeUlNI0NQj9YharWrHlCAD2ehuMok7Ji08Vzump\nnyhsovjL8edJtzj++h0LqIsgRG5DQlAqKDcDuBPAe5TSnwghRwH4PH7TioxUq0Pxc/nll+PJJ5/E\n66+/jt/+9rfJno4sDQ0NsFgsAvePSqXCK6+8gvXr16OqqiowfsMNN8DlcuGNN97A9u19XXhiYaEM\nHjwYRqNRYqFYrdaYx1Cys7O5oKQRakIw1GDBUIMFBcWDQIhPZH6xdWFbRzu+b2jHDls7PmisRnev\nu4wAKFabMFRjQbnGgnKtBTpHPgbqjJLYkYvRPsV/kv4aQHS2sy1Ht1MDDcMz+ObgpyRj5x6cB+PA\nPpH5wxBpy5lFB9SSbY2tE6MIGsUYpVlea+GLo/gf7wPwu3hNKtM4/vjjceyxx+Kxxx7DggULUnbT\no4aGBoF14ueKK67AFVdcIRizWCy46667sGrVKsF4tBaKw+GATqeDyWSSxFCsVmvYFkqoNGar1YqW\nlpa0rpZ3OX17kMQb6oXg7j2SNjuhYLXy7+9YwCcyFVkWVGRZcJppMABfZlkT6cbOrg5srG3FPkcn\n9jo78bWt3vei7wGLWosRpmwcZbJgmNH3M85oRr5OJ3lf4rep1QJKWtjt+1HuP0YqAssHvyB4fPne\nS6ERtYe5fYI0djmvtg3tDsCa+GbREtK/1j8NIITglltuwZVXXon3338/ZWMpDQ0NKCwMbyMmcV+v\naC0UANDpdDCbzbIWipxIsMZDWR5FRUX45Zdf0NTELkBLB3ZtYK8khNgFi7PHTaHWKBcAcbKA+O7d\n6fQg2qaN4mw2Vit/Z0/411ARgjKjGWVGM8Y6+r7TNo8Lvzi7UKvuwu7uDuy2d2BFUw1s/tSuHUCu\nVouKLAuGZ1lQkZWFiiwLKq1m5GsNAaGpOkHYr2z9/7oQTgmWIVuFno7+v5vVC6Xxl6M++oNk7Ikz\nU+cGlQtKgrjsssvwwAMP4M9//jNmzZoVdnuSRNDQ0CCImSjBLyjZ2dno6OiI2kIBpIISbKE0NDTI\nikQkguIX0OCamkmTJmH9+vXK30AcUKvBXKDCsQpMZuF3rHo/O4V1+Ch2hltrs/AuOr9Y2XeWlc4L\nAEaj1J30wzrhbX7FqDAi5cy6celYcHqzWa3FWGMuTsjtS8mllKLR1YN99i4ccHRhr70T+7o78UFt\nLTo9ffPL0WswKjcLlblmFLtyUGHOxlGmLBTpDagYyf4MxRuI+bn4Sene9f+58lDIbDRXkw1acduW\nLjuQZQRsdoDdOixhcEFJEBqNBg888ABmz56N559/HgsWLEj2lCQcPnwYkydPDus1/iSDgoICdHR0\nRNTGhGWh5ObmorXVt6mRX3AGDBiAnTt3CkQieIFVIihHH300fvihL0HR7+LzF1See+65eOihhzBq\n1Kiw34dSxJYCSzzkFqiDvziQqFbukZJfxnYfHtgT25sog0W6+na19UWm/e654jLpdT0uobgNgRlD\nrGa4XX0uX0opmlwO7O3uRGtuM3a22LCz1Yb39zWg1XEocJxRpUaJ1oTBejMG6cwo05sDv9fv9DBv\nACY6s6HSCedlMgkfe70+MQzm+zOekZxr3JlBX54HLpM8n0iUFjY+DOB+AHb46lDGA/g9pZTd+IbD\n5MILL8S0adNw++234+yzz0ZpaWmypxTAbrejoaEBQ4YMCet1fotm5MiR2LdvX0AEwoFloRQUFODA\nAV/bdL/glJWVoampSdJMUq/3uXzkmlUGM2XKFOzbty9QcyN28anVahQUSAvKYkVnhwe11cL3Wzkm\n1Vv+xQ9xcSSrJieh8xHUchAM0BgxwGhESaU5UIdCKcXmTWrsd3Zhv70LB+w27GjqwC5bB9Z21MMT\nJPgmaDCAGjEApt4fIwbChMaNQI5JmJal1fniYX4cNpXks6DULRUojQpwewFd8u0DpTM4g1J6OyHk\nAgD7AVwIX5CeC0oYEELw3HPPYfz48Zg3bx5WrFiRMq4v/+Idrstr6NChgdcRQiKKRYgtFL1eD7PZ\njE2bNgHosx5KSkrg8XgE7qmenp6AoCixUFQqlaAPmbhAU6VSCVrMhItWq5XdcEycuhor5Nwq0b5e\n7F5TGpRn3VkD7Mr6ooHCRbWzXWp1mLPkMq+k9S7B74WoKKiXBAochbDbGst1Jm6t0wsC8027nMiC\nGWNhxlgAF+f55uihXtR77Djk7katpxs1LhvqPN3Y527Hd976gNTc+2+gyKjDMKsJQ7KNKLcYUVSe\nhcEmEwabTSgy6FG3T/pZuF0UXq/QQtVPGcz+fJKAUkHxH3c2gLcppe2pmqmU6lRUVGDx4sW47rrr\ncPfdd+O+++5L9pQAAPv37weAsC2UuXPnYuPGjbj55puxdOnSiARFbKHo9XoUFBSgqakJlFI4HA7o\n9XoMGDAAAFBfXx841m63Byr7lQiKWq0WCEpJSYnk+Wi+2+eeey7effdd5nPX58bHjSbel91PlkUl\ncKXJZVD9sptdle9b7Pte4HIIBdHRw95JsaeTndlUUCRdbtpbI095zR0k/f/+elXf+aZf5Pt/3Lle\n+D4AIL+QvfTV1bDddcPHagSfnfizNGX5Pxs1LNCiAr5Gqna7B+7e+wsn9eCwx45Dbhv0I7qwr7Mb\ne9u78b9DLXjL5hDMUK9SYaDWiBKdCSU6E0p1ZhTrjBhdYkaZ0YQsTd932NPtgdqkhsfuZe3JlVCU\nCsqHhJCd8Lm8rieEFAJISCey3pqXPwOwUkqT20ozRixYsADfffcd7r//fowYMUKSkpsMIrVQ8vPz\n8corvk6sBQUFaGxsDPvafsHwer1wuVywWCwoKCiAw+GAzWaDw+GAwWAIuKdqa2sDrw1uJskSFPGY\nWq0WZKYVFBRAp9MFRC3aG6Xg9jRinLR3LgSC9Y16KUgc3DwjRgtdaXt3sf9kxcH3lEP0eflhtUMJ\njke5nYBGB2brFbbVIm+hiK04VjYai7GTEJTFpgFgAWBBW70WJJsAvV5vp9eDLfs7UG2347C7G3Uu\nO+o9dtQ6u/GjrRU2b+//ke/PFDlaLcqMZgwymlD+JwNmlAzAMbl5GHyBomnFDaV1KH/qjaO0U0o9\nhBAbgPMivSgh5AUA5wBooJSODRqfCeBf8FXhP0cp/Xtvzct8Qsg7kV4v1SCE4Mknn8T+/ftx9dVX\nw2w248ILL0zqnPbs2QOdTtfvghiKkpISVFdXh/267u5uGAwGqNVqtLS0ICsrK+B2ampqCri1Bg3y\nVUvv3r078NrgrDKlFkrwmF6vR3FxcUBQVQxfzeDBg5mNMFkMHz5c9jkX8UKjITCLsq+cLkC8Yubl\ns/802fGFxATp5RZhKcp3yRInKFiypQt1Vjb7XLYm6bFDhvXdue/70ff/nF8oPa6hli2i9bVsd6VG\nI0zNFsd6TGYC6mW0m1nrClgowZRXaER7v6gxa6YB8OoB+NoA1ezQQ6vzfR/bXU5U27vx+eZWNKEH\nDV47Grvs2NzZio/re7DucCtePfpk5twTidKg/BwAq3rF5C4Ax8IXpD8c4XVfAvAEgMAmA4QQNYB/\nAzgdQA2ADYSQ5ZTS7cwzpDkGgwHvv/8+zjjjDMyZMwfPPPMMrrnmmqTNZ+vWrRg9enRUMZ0RI0bg\nzTffDLvgraurCyaTKbCYWyyWgCuqpqYmYKH44zU//fRT4LWRCMqsWbPwzDO+bBmdTofS0lKJoJxy\nyin44osvAITXQVku/lKuzcKpWSXM56JFLsVYvLDLubzkugbr9EJx9ffG6nsd+//YZGUXZLQ3SHuD\n7N8ndNeNHGeEVtI9V7lABX/31BoKj5vA5aTQ6pS93r/LoxjxZyeO9Qwfy15Kiwaxa4S+/kSaXj/i\nfAvUur73/sMrbXAKjEotJuuKBIF7AHjauBVtDifqalxgN0RKHEpdXn+hlL5NCDkRwGkAFgF4CkB4\nOaa9UErXEkLKRcOTAOzptUhACHkTPisoIwUF8C2ca9aswezZs3Httddi7969uO+++yTFgolg69at\nOPXUU6M6R2VlJdra2lBfXy/bjZiFzWaD0WgMBLOzsrIC4rF//3709PTAYDDAaDSitLQUe/bsCbzW\n33IfYGd5iQXF6/Vi8eLFePvtt9HS0uJrShgUR/ELymuvvYZly5Zh8eLFuO222xS3zDEYDLjuuuvw\n9NNPC8afKztJ0ev7YC+iLPfNyHHs1vtZecIDxXUpflhWAaCsNX04uF1eaERiIRbDnVulC+2vZrGz\n4FjBf5ezL64zosr3fVj9pvSNDJepdzlOVLAYOK8oTNXa4oY3aN4UFCSMID/rJqD9R7vgRuzUC7SS\n99fTpZa4+Z76yPevOFifDJSuXP63fjaAJZTSjwgh98d4LqUAgv0lNQAmE0LyATwA4BhCyJ2U0gdZ\nLyaELACwAPC5KNKFrKwsfPDBB7jxxhvx97//Hd988w1ef/11SbA4njQ0NKC2thbjxo2L6jzHH388\nAGDt2rW4+OKL+z1W3EF44MCB6Oz0Nb4rLCwMxHJ++eUXQXv5ESNG4NChvhoAf0NL8TnlxtxuN4xG\nI5YtW4Y//vGPGDlypOCz9v9Bl5SU4IYbbsANN9wAwBe3ueeeeyTnN5lMMJlMgWQEg8GAJ554Aldd\ndVXg8wB86cJ+xPucsLKYTNkAy5Ul5wpTgpzLSum+JmIxk9t7RJwN5mfbD1KxOGqEUCx271BeGNvV\nzOqG2DdBf4yFJcLyVjRbyF0ur8ByKq/QCTY10+q86N0hXcChneweYKWD9VKhoR6hFdSihka0JXJ7\no0oyb6fTiy6vG1sbWjG+rg6FhYVJuSkFlAvKod4tgE8H8BAhRA8kJqGAUtoM4DoFxy0BsAQAqqqq\nki/VYaDVarFkyRKceOKJuP766zFmzBgsWrQI8+bNY/r0Y81XX30FADjhhBOiOk9VVRWys7OxevXq\nkIIi7jBsNBpRVlaGAwcOoLS0FAaDAcXFxdi3bx/a29sDmVyTJk3C55/39SUNJSji3Rj9VtBJJ50U\nqIYPrgfS6dgZSnfffTduv/12mM3CKuUlS5bgxRdfxKeffgoAgVjQ5MmTBdX+wYgXOJWKwivxv7MX\nNtZC6HZRycIDSAPJh2XiA1N+xbZwmg4LV+EBg4TXyB/Athw8bnbWGQslvbscPRR6g7LFP9hN11bn\nczfl5krft89tJL2wVs8e3/KFsGB3xsVCV5bDxv477bF7medjZbdVHKMO1kPs2SZVeWuu1NVYPliD\nLfttuM3+LW4rKYFKpUJxcTHKysowaNAglJSUoLCwEAUFBYF//T/5+fkxFR+lZ7oYwEwAj1BK2wgh\nxQBui9ksfBwCUBb0eFDvmGJSsX19OFx55ZWYPHkyFixYgGuvvRb/+c9/sHjxYkHb+Hiwdu1aGAwG\nQTfhSNBoNLjggguwdOlSLF68WLAHvZhgQenp6YHJZMJdd90Fk8kUqNYfN24ctmzZAkppwOoMvutX\nq9Woq6sLPGYJir+A0Q+rRiTYQhk9erTsnFm7OmZlZeG5554LuOiysvpcJiNGjMD3338PQFgwVzRA\neGddUiGdd0+nGqyFiLUQ1taw7+pLBumY5xAjX08iDDyLs6rk61/YYqjTA06R1hSVitrX75G+bt0a\ndnaazxUmfn/B5/PNg1UsKTd3uXiLSg2hi0tkhcmJukZL4XYxXJcMIbUM00IddG3vlw6oRP8vrc1u\nyXv5x6mVuLRyILpcHrhm34C6ujpUV1ejuroaW7duxerVqwPWP4vc3NyYFfMqzfLqJoTsBTCDEDID\nwP8opR/HZAZ9bAAwnBAyFD4huRTAr8M5Qaq2rw+HyspKfP7553jxxRdx++23Y+LEiZgzZw7uu+8+\nVFZWxvx6lFK89957OPXUU2XvzsNh4cKFePnll/Hwww/j/vvlvaLiQLfRaMTUqVMxderUwNjEiROx\naNEiFBYWBtxx06dPB+Bzi+Xm5mLnzp2y5wSEMRYgtKDcdNNN/b09CRqNBuXl5XjhhRcwb948QZbX\njTfeiGuuuQb/KhBafmK/Oiv9Vdb3ztiiVquX+vgB5Z17fSUNrLtoj+g4oRA2HmYnK0wbyQ7Kn3SW\n1EXVKdpDiiU6rDGALYTBbj1f23ga1gZmNQfYlkaJKLhOvURgZbKsCQCYPIM9/v0X0qW3Zavwc2tr\nUdZtssCsw5nlhYBGBev11zOPcTqdaG5uRmNjI5qamtDU1CT5PTh7MlKUZnndDOBaAP59Ll8lhCyh\nlD4eyUUJIW8AOAVAASGkBsBfKaXPE0IWAlgNX9rwC5TSn/o5Deu8aW2h+FGpVJg/fz5mz56Nf/zj\nH3j00Ufx7rvv4rzzzsMtt9yCk046KWZtw9etW4eDBw/2u/iHQ1VVFa644go8/PDDmDVrlmxvMPHi\nn5eXxzyX2+1GXV1doOdWdnY2NmzYAKvVijvvvFPQl6unpydQEOmntbUVKpUKTz75JK677jpm4D7Y\n5RUqy62qqgobN25EXV0dHnzwQZx++ukAgKuvvhpXXHGFwH0wb948TJ8+HdtPEP6RNzcJ55BbKM0E\naq5nd/KtGCP9kzXnSoYAAId2C49tboyu3kRpWxRK2XED5rio2n36BdL3J9cw0WkHxJ9RY33fe8wb\nwIqxRIY2Rw9XW5CqWfRAZ99jXb4Rzmappegx66G2SdVQnaeDp0UkyBYd0Nk3pi80wtEoPKe+wAhH\nk3BMPaYAOqcTLoN8/3p/SUB/ZQFvvfWW7HNKUerymg9gMqXUBgCEkIcAfAMgIkGhlDI7mFFKVwBY\nEck5e1+f9hZKMFarFffeey8WLlyIxYsXY8mSJXjvvfdw9NFH45prrsEll1wStam6aNEi5ObmxrSl\n/uLFi/H111/jvPPOw//+9z9mbYZYUFjptn5rBACOOuqowO9+19yUKVPw7rvv4sCBAxgyZAjsdrtk\nb/jm5mYYjcZ+ra+ysj5Payih/uSTT9Dd3Y2BAwfiX//6l+A5sS+aEIKhQ4fiZ60KHldQQ0tRISPL\nGuNSodIAABe4SURBVJGzUFiZTXIxBqlbR6bdiExbe7E1JE6VlU1DltFkV4/0CWNWbFPJtPkGuJp9\nLjKSbQDt6IG+0ABHo9BtxhoDAF2BEc4mqTBM+0RYdtdhEWaJnaodwJzP7o49zPGRDK1TiVrznKuT\nZkqKjwGAbxv63L4nMq+WOBRvAYy+TC/0/s57rySIoqIiPPjgg/jLX/6C1157DU888QQWLlyIW265\nBTNnzsTFF1+MmTNnhr2XyYcffoj3338f99xzT0y3Js7Pz8eHH36Ik08+GSeccAI++ugjHHfccYJj\nxF2JWYKSk5ODyy+/HG+88YZAXPycc845uPXWW7Fs2TLcfPPN6OnpgdFoxOrVq/HVV1/hvvvuQ01N\nDfR6fUBQxH3DAHZsRA6r1RpIEFDK0OHC1eP7b4XvvalO6tqolWkBYs01S8bWr2R3eDaaWBaJVAEO\nV7NbqAwbJRTh/buE52Pt1e4jCksoywB0iRZ6iwHoZMRRRHf0ADBl89zA76reXcrPptLPUkPYNxga\nlcxdflv/HSA6nB5k66SLfbebwKSRfrZdTiBL1/9Yj8cLg1p499Dl8iJLlHptdxEYtRR2Rqwm0ZBQ\nW6QCACHkDwB+A+C93qHzAbxEKf1nHOcWNkEur2tj4Q9MZX788Ue89tpreO2113Do0CEQQjBp0iTM\nnDkTU6dOxaRJk5CTkyP7+o8++giXXnopjjrqKKxfvz7QYDGW7Nq1C2eccQbq6uqwePFiXH/99QEL\n4KuvvsJJJ/XVZrz00kv4zW9+IzmHy+XC4cOHBVaEH0opJk+ejI6ODmzfvh0XXXQR9u3bhy1btqCu\nri4QGykuLsamTZtQUlKCTz/9lClO/nkp+XsIlz1zLoOntW/R374XcDT2PfYFz4XICcq4YxmC8hU7\n4Go0KcsQHDzOCner9K68eIhwgTzUpIGrpW9hzy4zwtMqFegJ56gAm1TkWtqtoB3C41W5OiBozPrG\nVZLXEZmE0m53u2TMqOm7MfILijsMQel26ZCtly7MjqZGWIL087BRD1OQVXfrN+zvjVbNHrczrDUx\npw2RCvOPLdK57T/cZy29dV7kHTcIIZsopVFl5igNyj9KCPkCfRbV1ZTS76O5cDzINJdXf4wfPx7j\nx4/Hgw8+iM2bN2PFihX46KOPcO+99waClSNHjsTIkSNRXl6OoqIiqFQq1NbW4osvvsCWLVswbtw4\nrFy5Mi5iAviynDZt2oQrr7wSN954I1asWIEnn3wSgwcPRktLi+DYsWPZNb5arZYpJkDfTphz587F\nsmXLYLPZAtZGUVER1Go1PB5PoL1Kf2Jx8sknY+3atbLPR0PF61cKHldqhO6Sb056Eq4m4QKsLzDB\n0SRdlNVWIzztwsVfV2iAU4H7RldggLNJetzUr9hFm3t//Sy8bX3HT1k/X/B8j0dGyLRsN2yOW7q1\ngZcqCzwrpctFkdWbbeX/3eaiMIsysIKPC+Z3n7JTnnNzhQJUahZbdWzrwOUk0OqU3aR4HIA66E+x\n205gMirI0nMCRAcgBXaxDmmh9LZE+YlSOjIxU4qeqqoqunHjxmRPIym0t7djw4YN+Pbbb7F+/Xrs\n3bsX+/fvD7iYTCYTJkyYgMsvvxzz58+Pm5gE4/V68fjjj+P//u//oFKp8Le//Q02mw133nknXnnl\nFfz44494+OGHI0o0cLvdmDBhApxOJ3Q6HYYNG4bly31bpw4aNAiHDh3CiBEj8PPPP/d7ntraWtTW\n1kadOs3EtVL4WCQoHoaLyH93LYZVjd3iZGfXmzRC15zc4m0iUqsHANo9whQsg1roFpUTFIuMoNgU\nCEqPRyW48wcAm4swF/9GezvMoljEvZv7Plt/FxMz47a5WaZUprWV/feQmyt8QanoI9vTpIKGIRzb\nP5MmmwDAUZPbJccfXCU8KWUkQBx9Zju0Ig/tTy/1xQ0/fCry7pAJsVB6+3f9TAgZTClV1iEvSWRK\nllc0WK1WnHbaaTjttNMCY5RS2O12UEphMpliliGmFJVKhZtvvhnnnXcefvvb3+J3v/sdAN+mXNF2\nWtZoNFi0aBHOPvtsAMLizFGjRuHQoUOC2hA5SkpK4tadoMtNkRW0SNpcHpi1qbEPDsD2y0dDp8sD\nC+P9KbEUntwuFddGmeJ5FQlnu2BleFyAWkFymKOHQG/oE4Sd69jCofJ6AYYw7NokTc3LQuiC0D1v\nMN6zypc+l+zW9YDyoHwugJ8IIesBBEp/KaXnxmVWEXIkubzCgRASVuA5XpSXl2PVqlX4+OOPsWbN\nGlx11VUxOe+ZZ56J2bNn45133sGJJ/bluRx99NH45JNP+i2wTASLtrcJHptFQdrrRuVLBKbL5UEW\nY1Hudntg0gjH7W7AyPhLtrm8MAcJRbebSu7+AeC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fltDEFI5orOnmcEvqNansZlIXGzWIbPogjtvUIICuNRVTYnILwiimnwE4bHO8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\nzINwmKtG1lZ2OSYS3ScaQGw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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1336,8 +1336,8 @@ { "data": { "text/plain": [ - "{'0K': ,\n", - " '294K': }" + "{'0K': ,\n", + " '294K': }" ] }, "execution_count": 38, @@ -1348,6 +1348,106 @@ "source": [ "gd157[2].xs" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generating data from NJOY\n", + "\n", + "To run OpenMC in continuous-energy mode, you generally need to have ACE files already available that can be converted to OpenMC's native HDF5 format. If you don't already have suitable ACE files or need to generate new data, both the `IncidentNeutron` and `ThermalScattering` classes include `from_njoy()` methods that will run [NJOY](https://njoy.github.io/NJOY2016/) to generate ACE files and then read those files to create OpenMC class instances. The `from_njoy()` methods take as input the name of an ENDF file on disk. By default, it is assumed that you have an executable named `njoy` available on your path. This can be configured with the optional `njoy_exec` argument. Additionally, if you want to show the progress of NJOY as it is running, you can pass `stdout=True`.\n", + "\n", + "Let's use `IncidentNeutron.from_njoy()` to run NJOY to create data for $^2$H using an ENDF file. We'll specify that we want data specifically at 300, 400, and 500 K." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " njoy 2016.12 02Apr17 05/01/17 12:33:17\n", + " *****************************************************************************\n", + "\n", + " reconr... 0.0s\n", + "\n", + " broadr... 0.0s\n", + " 300.0 deg 0.0s\n", + " 400.0 deg 0.2s\n", + " 500.0 deg 0.3s\n", + "\n", + " heatr... 0.4s\n", + "\n", + " purr... 0.6s\n", + "\n", + " mat = 128 0.6s\n", + "\n", + " ---message from purr---mat 128 has no resonance parameters\n", + " copy as is to nout\n", + "\n", + " acer... 0.6s\n", + "\n", + " acer... 0.7s\n", + "\n", + " acer... 0.8s\n", + " 0.8s\n", + " *****************************************************************************\n" + ] + } + ], + "source": [ + "# Download ENDF file\n", + "url = 'https://t2.lanl.gov/nis/data/data/ENDFB-VII.1-neutron/H/2'\n", + "filename, headers = urllib.request.urlretrieve(url, 'h2.endf')\n", + "\n", + "# Run NJOY to create deuterium data\n", + "h2 = openmc.data.IncidentNeutron.from_njoy('h2.endf', temperatures=[300., 400., 500.], stdout=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can use our `h2` object just as we did before." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'0K': ,\n", + " '300K': ,\n", + " '400K': ,\n", + " '500K': }" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "h2[2].xs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that 0 K elastic scattering data is automatically added when using `from_njoy()` so that resonance elastic scattering treatments can be used." + ] } ], "metadata": { diff --git a/openmc/data/neutron.py b/openmc/data/neutron.py index 3449c7d587..1d3a0176d4 100644 --- a/openmc/data/neutron.py +++ b/openmc/data/neutron.py @@ -1,9 +1,13 @@ from __future__ import division, unicode_literals import sys from collections import OrderedDict, Iterable, Mapping, MutableMapping +from io import StringIO from itertools import chain from math import log10 from numbers import Integral, Real +import os +import shutil +import tempfile from warnings import warn from six import string_types @@ -11,12 +15,13 @@ import numpy as np import h5py from . import HDF5_VERSION, HDF5_VERSION_MAJOR -from .ace import Table, get_table +from .ace import Library, Table, get_table from .data import ATOMIC_SYMBOL, K_BOLTZMANN, EV_PER_MEV -from .endf import Evaluation, SUM_RULES +from .endf import Evaluation, SUM_RULES, get_head_record, get_tab1_record from .fission_energy import FissionEnergyRelease from .function import Tabulated1D, Sum, ResonancesWithBackground from .grid import linearize, thin +from .njoy import make_ace from .product import Product from .reaction import Reaction, _get_photon_products_ace from . import resonance as res @@ -817,3 +822,59 @@ class IncidentNeutron(EqualityMixin): data._evaluation = ev return data + + @classmethod + def from_njoy(cls, filename, temperatures=None, **kwargs): + """Generate incident neutron data by running NJOY. + + Parameters + ---------- + filename : str + Path to ENDF evaluation + temperatures : iterable of float + Temperatures in Kelvin to produce data at. If omitted, data is + produced at room temperature (293.6 K) + **kwargs + Keyword arguments passed to :func:`openmc.data.njoy.make_ace` + + Returns + ------- + data : openmc.data.IncidentNeutron + Incident neutron continuous-energy data + + """ + # Create temporary directory -- it would be preferable to use + # TemporaryDirectory(), but it is only available in Python 3.2 + tmpdir = tempfile.mkdtemp() + try: + # Run NJOY to create an ACE library + ace_file = os.path.join(tmpdir, 'ace') + xsdir_file = os.path.join(tmpdir, 'xsdir') + pendf_file = os.path.join(tmpdir, 'pendf') + make_ace(filename, temperatures, ace_file, xsdir_file, + pendf_file, **kwargs) + + # Create instance from ACE tables within library + lib = Library(ace_file) + data = cls.from_ace(lib.tables[0]) + for table in lib.tables[1:]: + data.add_temperature_from_ace(table) + + # Add fission energy release data + ev = Evaluation(filename) + if (1, 458) in ev.section: + data.fission_energy = FissionEnergyRelease.from_endf(ev, data) + + # Add 0K elastic scattering cross section + pendf = Evaluation(pendf_file) + file_obj = StringIO(pendf.section[3, 2]) + get_head_record(file_obj) + params, xs = get_tab1_record(file_obj) + data.energy['0K'] = xs.x + data[2].xs['0K'] = xs + + finally: + # Get rid of temporary files + shutil.rmtree(tmpdir) + + return data diff --git a/openmc/data/njoy.py b/openmc/data/njoy.py new file mode 100644 index 0000000000..e7d3cd284f --- /dev/null +++ b/openmc/data/njoy.py @@ -0,0 +1,425 @@ +from __future__ import print_function +import argparse +from collections import namedtuple +from io import StringIO +import os +import shutil +from subprocess import Popen, PIPE, STDOUT +import sys +import tempfile + +from . import endf + + +# For a given MAT number, give a name for the ACE table and a list of ZAID +# identifiers +ThermalTuple = namedtuple('ThermalTuple', ['name', 'zaids', 'nmix']) +_THERMAL_DATA = { + 1: ThermalTuple('hh2o', [1001], 1), + 2: ThermalTuple('parah', [1001], 1), + 3: ThermalTuple('orthoh', [1001], 1), + 5: ThermalTuple('hyh2', [1001], 1), + 7: ThermalTuple('hzrh', [1001], 1), + 8: ThermalTuple('hcah2', [1001], 1), + 10: ThermalTuple('hice', [1001], 1), + 11: ThermalTuple('dd2o', [1002], 1), + 12: ThermalTuple('parad', [1002], 1), + 13: ThermalTuple('orthod', [1002], 1), + 26: ThermalTuple('be', [4009], 1), + 27: ThermalTuple('bebeo', [4009], 1), + 31: ThermalTuple('graph', [6000, 6012, 6013], 1), + 33: ThermalTuple('lch4', [1001], 1), + 34: ThermalTuple('sch4', [1001], 1), + 37: ThermalTuple('hch2', [1001], 1), + 39: ThermalTuple('lucite', [1001], 1), + 40: ThermalTuple('benz', [1001, 6000, 6012], 2), + 41: ThermalTuple('od2o', [8016, 8017, 8018], 1), + 43: ThermalTuple('sisic', [14028, 14029, 14030], 1), + 44: ThermalTuple('csic', [6000, 6012, 6013], 1), + 46: ThermalTuple('obeo', [8016, 8017, 8018], 1), + 47: ThermalTuple('sio2-a', [8016, 8017, 8018, 14028, 14029, 14030], 3), + 48: ThermalTuple('uuo2', [92238], 1), + 49: ThermalTuple('sio2-b', [8016, 8017, 8018, 14028, 14029, 14030], 3), + 50: ThermalTuple('oice', [8016, 8017, 8018], 1), + 52: ThermalTuple('mg24', [12024], 1), + 53: ThermalTuple('al27', [13027], 1), + 55: ThermalTuple('yyh2', [39089], 1), + 56: ThermalTuple('fe56', [26056], 1), + 58: ThermalTuple('zrzrh', [40000, 40090, 40091, 40092, 40094, 40096], 1), + 59: ThermalTuple('cacah2', [20040, 20042, 20043, 20044, 20046, 20048], 1), + 75: ThermalTuple('ouo2', [8016, 8017, 8018], 1), +} + + +_PENDF_TEMPLATE = """ +reconr / %%%%%%%%%%%%%%%%%%% Reconstruct XS for neutrons %%%%%%%%%%%%%%%%%%%%%%% +20 22 +'{library} PENDF for {zsymam}'/ +{mat} 2/ +0.001 0.0 0.003/ err tempr errmax +'{library}: {zsymam}'/ +'Processed by NJOY'/ +0/ +stop +""" + +_ACE_TEMPLATE = """ +reconr / %%%%%%%%%%%%%%%%%%% Reconstruct XS for neutrons %%%%%%%%%%%%%%%%%%%%%%% +20 21 +'{library} PENDF for {zsymam}'/ +{mat} 2/ +0.001 0.0 0.003/ err tempr errmax +'{library}: {zsymam}'/ +'Processed by NJOY'/ +0/ +broadr / %%%%%%%%%%%%%%%%%%%%%%% Doppler broaden XS %%%%%%%%%%%%%%%%%%%%%%%%%%%% +20 21 22 +{mat} {num_temp} 0 0 0. / +0.001 1.0e6 0.003 / +{temps} +0/ +heatr / %%%%%%%%%%%%%%%%%%%%%%%%% Add heating kerma %%%%%%%%%%%%%%%%%%%%%%%%%%%% +20 22 23 / +{mat} 3 / +302 318 402 / +purr / %%%%%%%%%%%%%%%%%%%%%%%% Add probability tables %%%%%%%%%%%%%%%%%%%%%%%%% +20 23 24 +{mat} {num_temp} 1 20 64 / +{temps} +1.e10 +0/ +""" + +_ACE_TEMPLATE_ACER = """acer / +20 24 0 {nace} {ndir} +1 0 1 .{ext} / +'{library}: {zsymam} at {temperature}'/ +{mat} {temperature} +1 1/ +/ +""" + +_ACE_THERMAL_TEMPLATE = """ +reconr / %%%%%%%%%%%%%%%%%%% Reconstruct XS for neutrons %%%%%%%%%%%%%%%%%%%%%%% +20 22 +'{library} PENDF for {zsymam}'/ +{mat} 2/ +0.001 0. 0.001/ err tempr errmax +'{library}: PENDF for {zsymam}'/ +'Processed by NJOY'/ +0/ +broadr / %%%%%%%%%%%%%%%%%%%%%%% Doppler broaden XS %%%%%%%%%%%%%%%%%%%%%%%%%%%% +20 22 23 +{mat} {num_temp} 0 0 0./ +0.001 2.0e+6 0.001/ errthn thnmax errmax +{temps} +0/ +thermr / %%%%%%%%%%%%%%%% Add thermal scattering data (free gas) %%%%%%%%%%%%%%% +0 23 62 +0 {mat} 12 {num_temp} 1 0 {iform} 1 221 1/ +{temps} +0.001 4.0 +thermr / %%%%%%%%%%%%%%%% Add thermal scattering data (bound) %%%%%%%%%%%%%%%%%% +60 62 27 +{mat_thermal} {mat} 16 {num_temp} {inelastic} {elastic} {iform} {natom} 222 1/ +{temps} +0.001 4.0 +""" + +_ACE_THERMAL_TEMPLATE_ACER = """acer / +20 27 0 {nace} {ndir} +2 0 1 .{ext}/ +'{library}: {zsymam_thermal} processed by NJOY'/ +{mat} {temperature} '{data.name}' / +{zaids} / +222 64 {mt_elastic} {elastic_type} {data.nmix} {energy_max} 2/ +""" + + +def run(commands, tapein, tapeout, stdout=False, njoy_exec='njoy'): + """Run NJOY with given commands + + Parameters + ---------- + commands : str + Input commands for NJOY + tapein : dict + Dictionary mapping tape numbers to paths for any input files + tapeout : dict + Dictionary mapping tape numbers to paths for any output files + stdout : bool, optional + Whether to display output when running NJOY + njoy_exec : str, optional + Path to NJOY executable + + Returns + ------- + int + Return code of NJOY process + + """ + + # Create temporary directory -- it would be preferable to use + # TemporaryDirectory(), but it is only available in Python 3.2 + tmpdir = tempfile.mkdtemp() + try: + # Copy evaluations to appropriates 'tapes' + for tape_num, filename in tapein.items(): + tmpfilename = os.path.join(tmpdir, 'tape{}'.format(tape_num)) + shutil.copy(filename, tmpfilename) + + # Start up NJOY process + njoy = Popen([njoy_exec], cwd=tmpdir, stdin=PIPE, stdout=PIPE, + stderr=STDOUT, universal_newlines=True) + + njoy.stdin.write(commands) + njoy.stdin.flush() + while True: + # If process is finished, break loop + line = njoy.stdout.readline() + if not line and njoy.poll() is not None: + break + + if stdout: + # If user requested output, print to screen + print(line, end='') + + # Copy output files back to original directory + for tape_num, filename in tapeout.items(): + tmpfilename = os.path.join(tmpdir, 'tape{}'.format(tape_num)) + if os.path.isfile(tmpfilename): + shutil.move(tmpfilename, filename) + finally: + shutil.rmtree(tmpdir) + + return njoy.returncode + + +def make_pendf(filename, pendf='pendf', stdout=False): + """Generate ACE file from an ENDF file + + Parameters + ---------- + filename : str + Path to ENDF file + pendf : str, optional + Path of pointwise ENDF file to write + stdout : bool + Whether to display NJOY standard output + + Returns + ------- + int + Return code of NJOY process + + """ + ev = endf.Evaluation(filename) + mat = ev.material + zsymam = ev.target['zsymam'] + + # Determine name of library + library = '{}-{}.{}'.format(*ev.info['library']) + + commands = _PENDF_TEMPLATE.format(**locals()) + tapein = {20: filename} + tapeout = {22: pendf} + return run(commands, tapein, tapeout, stdout) + + +def make_ace(filename, temperatures=None, ace='ace', xsdir='xsdir', + pendf=None, **kwargs): + """Generate incident neutron ACE file from an ENDF file + + Parameters + ---------- + filename : str + Path to ENDF file + temperatures : iterable of float, optional + Temperatures in Kelvin to produce ACE files at. If omitted, data is + produced at room temperature (293.6 K). + ace : str, optional + Path of ACE file to write + xsdir : str, optional + Path of xsdir file to write + pendf : str, optional + Path of pendf file to write. If omitted, the pendf file is not saved. + **kwargs + Keyword arguments passed to :func:`openmc.data.njoy.run` + + Returns + ------- + int + Return code of NJOY process + + """ + ev = endf.Evaluation(filename) + mat = ev.material + zsymam = ev.target['zsymam'] + + # Determine name of library + library = '{}-{}.{}'.format(*ev.info['library']) + + if temperatures is None: + temperatures = [293.6] + num_temp = len(temperatures) + temps = ' '.join(str(i) for i in temperatures) + + commands = _ACE_TEMPLATE.format(**locals()) + tapein = {20: filename} + tapeout = {} + if pendf is not None: + tapeout[21] = pendf + fname = '{}_{:.1f}' + for i, temperature in enumerate(temperatures): + # Extend input with an ACER run for each temperature + nace = 25 + 2*i + ndir = 25 + 2*i + 1 + ext = '{:02}'.format(i + 1) + commands += _ACE_TEMPLATE_ACER.format(**locals()) + + # Indicate tapes to save for each ACER run + tapeout[nace] = fname.format(ace, temperature) + tapeout[ndir] = fname.format(xsdir, temperature) + commands += 'stop\n' + retcode = run(commands, tapein, tapeout, **kwargs) + + if retcode == 0: + with open(ace, 'w') as ace_file, open(xsdir, 'w') as xsdir_file: + for temperature in temperatures: + # Get contents of ACE file + text = open(fname.format(ace, temperature), 'r').read() + + # If the target is metastable, make sure that ZAID in the ACE file reflects + # this by adding 400 + if ev.target['isomeric_state'] > 0: + mass_first_digit = int(text[3]) + if mass_first_digit <= 2: + text = text[:3] + str(mass_first_digit + 4) + text[4:] + + # Concatenate into destination ACE file + ace_file.write(text) + + # Concatenate into destination xsdir file + text = open(fname.format(xsdir, temperature), 'r').read() + xsdir_file.write(text) + + # Remove ACE/xsdir files for each temperature + for temperature in temperatures: + os.remove(fname.format(ace, temperature)) + os.remove(fname.format(xsdir, temperature)) + + return retcode + + +def make_ace_thermal(filename, filename_thermal, temperatures=None, + ace='ace', xsdir='xsdir', **kwargs): + """Generate thermal scattering ACE file from ENDF files + + Parameters + ---------- + filename : str + Path to ENDF neutron sublibrary file + filename_thermal : str + Path to ENDF thermal scattering sublibrary file + temperatures : iterable of float, optional + Temperatures in Kelvin to produce data at. If omitted, data is produced + at all temperatures given in the ENDF thermal scattering sublibrary. + ace : str, optional + Path of ACE file to write + xsdir : str, optional + Path of xsdir file to write + **kwargs + Keyword arguments passed to :func:`openmc.data.njoy.run` + + Returns + ------- + int + Return code of NJOY process + + """ + ev = endf.Evaluation(filename) + mat = ev.material + zsymam = ev.target['zsymam'] + + ev_thermal = endf.Evaluation(filename_thermal) + mat_thermal = ev_thermal.material + zsymam_thermal = ev_thermal.target['zsymam'] + + data = _THERMAL_DATA[mat_thermal] + zaids = ' '.join(str(zaid) for zaid in data.zaids[:3]) + energy_max = ev_thermal.info['energy_max'] + + # Determine name of library + library = '{}-{}.{}'.format(*ev_thermal.info['library']) + + # Determine if thermal elastic is present + if (7, 2) in ev_thermal.section: + elastic = 1 + mt_elastic = 223 + + # Determine whether elastic is incoherent (0) or coherent (1) + file_obj = StringIO(ev_thermal.section[7, 2]) + elastic_type = endf.get_head_record(file_obj)[2] + else: + elastic = 0 + mt_elastic = 0 + elastic_type = 0 + + # Determine number of principal atoms + file_obj = StringIO(ev_thermal.section[7, 4]) + items = endf.get_head_record(file_obj) + items, values = endf.get_list_record(file_obj) + natom = int(values[5]) + + # Note that the 'iform' parameter is omitted in NJOY 99. We assume that the + # user is using NJOY 2012 or later. + iform = 0 + inelastic = 2 + + # Determine temperatures from MF=7, MT=4 if none were specified + if temperatures is None: + file_obj = StringIO(ev_thermal.section[7, 4]) + endf.get_head_record(file_obj) + endf.get_list_record(file_obj) + endf.get_tab2_record(file_obj) + params = endf.get_tab1_record(file_obj)[0] + temperatures = [params[0]] + for i in range(params[2]): + temperatures.append(endf.get_list_record(file_obj)[0][0]) + + num_temp = len(temperatures) + temps = ' '.join(str(i) for i in temperatures) + + commands = _ACE_THERMAL_TEMPLATE.format(**locals()) + tapein = {20: filename, 60: filename_thermal} + tapeout = {} + fname = '{}_{:.1f}' + for i, temperature in enumerate(temperatures): + # Extend input with an ACER run for each temperature + nace = 28 + 2*i + ndir = 28 + 2*i + 1 + ext = '{:02}'.format(i + 1) + commands += _ACE_THERMAL_TEMPLATE_ACER.format(**locals()) + + # Indicate tapes to save for each ACER run + tapeout[nace] = fname.format(ace, temperature) + tapeout[ndir] = fname.format(xsdir, temperature) + commands += 'stop\n' + retcode = run(commands, tapein, tapeout, **kwargs) + + if retcode == 0: + with open(ace, 'w') as ace_file, open(xsdir, 'w') as xsdir_file: + # Concatenate ACE and xsdir files together + for temperature in temperatures: + text = open(fname.format(ace, temperature), 'r').read() + ace_file.write(text) + + text = open(fname.format(xsdir, temperature), 'r').read() + xsdir_file.write(text) + + # Remove ACE/xsdir files for each temperature + for temperature in temperatures: + os.remove(fname.format(ace, temperature)) + os.remove(fname.format(xsdir, temperature)) + + return retcode diff --git a/openmc/data/thermal.py b/openmc/data/thermal.py index 61eb30ba4c..6e7b0fe02c 100644 --- a/openmc/data/thermal.py +++ b/openmc/data/thermal.py @@ -1,7 +1,10 @@ from collections import Iterable from difflib import get_close_matches from numbers import Real +import os import re +import shutil +import tempfile from warnings import warn import numpy as np @@ -11,10 +14,11 @@ import openmc.checkvalue as cv from openmc.mixin import EqualityMixin from . import HDF5_VERSION, HDF5_VERSION_MAJOR from .data import K_BOLTZMANN, ATOMIC_SYMBOL, EV_PER_MEV, NATURAL_ABUNDANCE -from .ace import Table, get_table +from .ace import Table, get_table, Library from .angle_energy import AngleEnergy from .function import Tabulated1D from .correlated import CorrelatedAngleEnergy +from .njoy import make_ace_thermal from openmc.stats import Discrete, Tabular @@ -582,3 +586,47 @@ class ThermalScattering(EqualityMixin): table.nuclides.append(isotope) return table + + @classmethod + def from_njoy(cls, filename, filename_thermal, temperatures=None, **kwargs): + """Generate incident neutron data by running NJOY. + + Parameters + ---------- + filename : str + Path to ENDF neutron sublibrary file + filename_thermal : str + Path to ENDF thermal scattering sublibrary file + temperatures : iterable of float + Temperatures in Kelvin to produce data at. If omitted, data is + produced at all temperatures in the ENDF thermal scattering + sublibrary. + **kwargs + Keyword arguments passed to :func:`openmc.data.njoy.make_ace_thermal` + + Returns + ------- + data : openmc.data.ThermalScattering + Thermal scattering data + + """ + # Create temporary directory -- it would be preferable to use + # TemporaryDirectory(), but it is only available in Python 3.2 + tmpdir = tempfile.mkdtemp() + try: + # Run NJOY to create an ACE library + ace_file = os.path.join(tmpdir, 'ace') + xsdir_file = os.path.join(tmpdir, 'xsdir') + make_ace_thermal(filename, filename_thermal, temperatures, + ace_file, xsdir_file, **kwargs) + + # Create instance from ACE tables within library + lib = Library(ace_file) + data = cls.from_ace(lib.tables[0]) + for table in lib.tables[1:]: + data.add_temperature_from_ace(table) + finally: + # Get rid of temporary files + shutil.rmtree(tmpdir) + + return data