Bir Yüksek Saflıklı Germanyum Dedektörünün Monte Carlo Metodu İle Simülasyonu Ve Verim Kalibrasyonu
Bir Yüksek Saflıklı Germanyum Dedektörünün Monte Carlo Metodu İle Simülasyonu Ve Verim Kalibrasyonu
Dosyalar
Tarih
2018-06-06
Yazarlar
Naci, Kurtar
Süreli Yayın başlığı
Süreli Yayın ISSN
Cilt Başlığı
Yayınevi
Enerji Enstitüsü
Energy Institute
Energy Institute
Özet
Bu tezin amacı bir yüksek saflıklı germanyum dedektörünün Cs-137, Co-60, Eu-152 ve Am-241 gibi kaynaklar için bulduğu spektrum değerlerini MCNP koduyla simüle etmek ve herhangi bir kaynak olmadanda sadece MCNP kodu kullanarak herhangi bir radyoizotopun spektrumunun elde edilebileceğini göstermektir. Bunun için izlenen yöntem dedektör karakteristiklerinin ve kaynağın içinde olduğu geometrinin MCNP programına veri girişinin yapılması ve programın çıkışında da kaynağın enerji ve sayım değerlerini almak olmuştur. Bir diğer amaçta dedektörün çalışma verimini yine farklı kaynaklar için MCNP kodu kullanarak elde edilebileceğini göstermektir. Bunun içinde Cs-137 kaynağı için belli olan deneysel verim değeri MCNP kodu ile hesaplanan verim değerine bölünerek bir katsayı bulundu. Daha sonra bu referans değeriyle diğer kaynaklara ait MCNP koduyla hesaplanan verim değerleri çarpıldı. Burada da amaç herhangi bir mesafe ve kaynak için MCNP koduyla dedektöre ait verimi hesaplamak olmuştur. Yaptığımız deneysel ve simülasyon karşılaştırması için deneysel geometriye mümkün olduğunca yakın bir geometriyi MCNP'ye input olarak girdik. Bu geometride kurşundan imal edilmiş zırh, radyoaktif kaynak ve dedektör bulunmaktadır. Bu geometride oluşan reaksiyonlar, MCNP tarafından sahip olduğu kütüphaneler ile simüle edilmiştir. MNCP kodu nükleer reaksiyonları simüle ederken rastgele sayı üretecini kullanmaktadır. MCNP nükleer reaksiyonların simülasyonunda rastgele sayıları kullanan başarılı bir araçtır. Örnekleme yaptığı her bir parçaçığı, karıştığı reaksiyonlar boyunca izler ve parçacık geçmişini tutar. Bunu yaparken her reaksiyon MCNP tarafından örneklenerek istatistik olarak simüle edilir. Monte Carlo tekniği basitçe, rastgele sayılarla istatiksel prosesleri örnekleme modelidir. Basit görünümüne karşın çok sayıda parçacığın karıştığı olayları, karışık nümerik integrallerde kaybolmadan analiz eder. Tezin sonunda Monte Carlo kodu kullanarak üretilen simülasyon değerleri deneysel değerler ile grafik olarak karşılaştırılmıştır. Bu karşılaştırma iki yöntemle üretilen yani deneysel ve simülasyon değerlerinin uyum içinde olduğunu göstermektedir. Böylece karakteristikleri bilinen bir dedektör için deney yapmadan istenilen bir kaynak için kaynağın spektrumunu ve dedektörün verimini hesaplamanın mümkün olacağı gösterilmiştir.
The aim of this thesis is to simulate a high purity germanium detector through Monte Carlo N Particles code for any given radiation source. In order to simulate the detector we entered the geometry of the source and detector as data into MCNP and the code gave spectrum of the source as output. We used Cs-137, Co-60, Eu-152 and Am-241 as sources of gama resources. After getting the output of MCNP we compared the simulation data with experimental data of detector. Comparison showed that simulation of the detector was successful. In simulating HpGe detector we created the geometry very close to the one in which experimental data was observed. The geometry includes detector, radioactive source inside lead (Pb) shielding. Inside the shielding there is air as first medium for radiation to encounter and then the parts of which detector is made. Geometry of lead shielding, air and the parts of the detector are entered into Monte Carlo code as input data. Monte Carlo code has data library of the interactions of radiation with matter. With this data library Monte Carlo code simulates the reactions happening like Compton scattering, electron-positron annihilations, bremsstrahlung effect, etc. After entering geometry data into Monte Carlo code we evaluated output data according to our need to simulate gama detector. Code listed energy-count data as output in two columns just like a multi-channel gama detector. Second objective of the thesis is to calculate the efficiency calibration of the given detector without using radioactive source. To do it we assumed that intensity values of gama resources were 1 (normalized) hence corresponding probability values calculated by MCNP equaled to efficiency values. First we took Cs-137 as a reference source and calculated its efficiency value with MCNP for a given distance of radiation source from detector. Its difference from the experimental efficiency value was calculated as a coefficient which was 12. After finding this coefficient we were able to calculate efficiency calibration for other sources by multiplying this coefficient with efficiency value calculated by MCNP. The efficiency values found with above mentioned method are inline with experimental efficiency values of the radiation sources as seen in the graphics in the thesis. This compatibility suggest that the energy calibration of any given detector can be calculated by MCNP without using a radition source for any given distance of radiation source from detector. Achieving this two objective provides excellent advantage in measuring gama spectroscopy. This advantage is based on the MCNP code that can measure gama spectroscopy without actual radioactive source. MCNP code is written by Fortran 77 language. To give the brief explanation of how MCNP code runs we must consider it in three parts. 1-Input Data, 2-Code body and 3-Output Data. Every kind of radiation source related geometry can be turned into input data for MCNP code. Source information like energy of source, the distance between source and simulated detector etc. are input datas to MCNP. Code body gets these informations and process through its libraries and outputs in what data it is asked to give. Output data can be anything that from particle flux to gaussian distribution of energy of radiation to probability of counted photons. In this way MCNP code can simulate any kind of radiation count case. Throughout thesis we examined and graphed MCNP output comparing with the experimental results of HpGe detector. In these graphics we showed both MCNP output and experimental data side by side. It was shown that simulation data was in great accord with experimental data. These accord suggests that when radioactive source is none existant we would be able to calculate the spectroscopy of radiation source. Only thing that needs to be known is detector characteristics which are specific to each detector. If detector characteristics are known these datas can be turned into geometry of the detector in question. So this geometry can be input to MCNP and energy count relationship can be calculated by MCNP as we did in this thesis. Above mentioned MCNP code is able to calculate any value about the radiation source within error limits. These limits are also specified in MCNP output. The checking if the results are within these limits is also performed. So the results are reliable datas after the checking is done. The important thing when making calculations is to establish geometry of the given radioactive source case correctly. To do this, it is recommended that the geometry would be as simple as possible at the beginning of simulation. After getting the correct output values more complex geometries can be input to MCNP code. In our case geometry was simple enough so we did not try to get it simpler. In simulating the detectors, geometry is simple enough, one source and one detector and the environment create the geometry. Sometimes the case could be as complex as it is hard to experiment. For case like this MCNP is important tool as it can simulate complex cases as well as simple cases. MCNP simulations is used many areas of nuclear reactions. Especially when there are big number of particles involving. Big particle numbers can be input to MCNP code as the only limit is computing power. So complex cases need more computing power in order to calculate needed values. If computing power is not enough number of particles should be decreased to limit the time it takes MCNP to calculate. Random number generator of MCNP is used to simulate events. MCNP is fairly unique and successful tool in simulating the nuclear reactions by making use of random numbers. It follows every particle throughout their life and holds particle histories for every reaction particle is involved. Each reaction is sampled to simulate them by MCNP. In these samplings Monte Carlo technique is used. Monte Carlo technique is simply a model that sample statistical processes with random numbers. Hence Monte Carlo name comes from the city with its famous casino. As It may seem simple to use, with this method, much complex events involving many particles problem can be solved without getting lost in complex numerical integrals. Therefore, with some simple equations MCNP can help find the solutions. MCNP has developed error checking methods as well as finding solution to the complex problems. Variance reduction is one of them and MCNP does its best to provide physicist with variance reduction tools. It was shown that MCNP code could be used in cases where experimental data was not available or hard to collect for example a reactor and its core is the source of radiation. So another studies can be done using MCNP code simulation wherever experimental difficulties exist.
The aim of this thesis is to simulate a high purity germanium detector through Monte Carlo N Particles code for any given radiation source. In order to simulate the detector we entered the geometry of the source and detector as data into MCNP and the code gave spectrum of the source as output. We used Cs-137, Co-60, Eu-152 and Am-241 as sources of gama resources. After getting the output of MCNP we compared the simulation data with experimental data of detector. Comparison showed that simulation of the detector was successful. In simulating HpGe detector we created the geometry very close to the one in which experimental data was observed. The geometry includes detector, radioactive source inside lead (Pb) shielding. Inside the shielding there is air as first medium for radiation to encounter and then the parts of which detector is made. Geometry of lead shielding, air and the parts of the detector are entered into Monte Carlo code as input data. Monte Carlo code has data library of the interactions of radiation with matter. With this data library Monte Carlo code simulates the reactions happening like Compton scattering, electron-positron annihilations, bremsstrahlung effect, etc. After entering geometry data into Monte Carlo code we evaluated output data according to our need to simulate gama detector. Code listed energy-count data as output in two columns just like a multi-channel gama detector. Second objective of the thesis is to calculate the efficiency calibration of the given detector without using radioactive source. To do it we assumed that intensity values of gama resources were 1 (normalized) hence corresponding probability values calculated by MCNP equaled to efficiency values. First we took Cs-137 as a reference source and calculated its efficiency value with MCNP for a given distance of radiation source from detector. Its difference from the experimental efficiency value was calculated as a coefficient which was 12. After finding this coefficient we were able to calculate efficiency calibration for other sources by multiplying this coefficient with efficiency value calculated by MCNP. The efficiency values found with above mentioned method are inline with experimental efficiency values of the radiation sources as seen in the graphics in the thesis. This compatibility suggest that the energy calibration of any given detector can be calculated by MCNP without using a radition source for any given distance of radiation source from detector. Achieving this two objective provides excellent advantage in measuring gama spectroscopy. This advantage is based on the MCNP code that can measure gama spectroscopy without actual radioactive source. MCNP code is written by Fortran 77 language. To give the brief explanation of how MCNP code runs we must consider it in three parts. 1-Input Data, 2-Code body and 3-Output Data. Every kind of radiation source related geometry can be turned into input data for MCNP code. Source information like energy of source, the distance between source and simulated detector etc. are input datas to MCNP. Code body gets these informations and process through its libraries and outputs in what data it is asked to give. Output data can be anything that from particle flux to gaussian distribution of energy of radiation to probability of counted photons. In this way MCNP code can simulate any kind of radiation count case. Throughout thesis we examined and graphed MCNP output comparing with the experimental results of HpGe detector. In these graphics we showed both MCNP output and experimental data side by side. It was shown that simulation data was in great accord with experimental data. These accord suggests that when radioactive source is none existant we would be able to calculate the spectroscopy of radiation source. Only thing that needs to be known is detector characteristics which are specific to each detector. If detector characteristics are known these datas can be turned into geometry of the detector in question. So this geometry can be input to MCNP and energy count relationship can be calculated by MCNP as we did in this thesis. Above mentioned MCNP code is able to calculate any value about the radiation source within error limits. These limits are also specified in MCNP output. The checking if the results are within these limits is also performed. So the results are reliable datas after the checking is done. The important thing when making calculations is to establish geometry of the given radioactive source case correctly. To do this, it is recommended that the geometry would be as simple as possible at the beginning of simulation. After getting the correct output values more complex geometries can be input to MCNP code. In our case geometry was simple enough so we did not try to get it simpler. In simulating the detectors, geometry is simple enough, one source and one detector and the environment create the geometry. Sometimes the case could be as complex as it is hard to experiment. For case like this MCNP is important tool as it can simulate complex cases as well as simple cases. MCNP simulations is used many areas of nuclear reactions. Especially when there are big number of particles involving. Big particle numbers can be input to MCNP code as the only limit is computing power. So complex cases need more computing power in order to calculate needed values. If computing power is not enough number of particles should be decreased to limit the time it takes MCNP to calculate. Random number generator of MCNP is used to simulate events. MCNP is fairly unique and successful tool in simulating the nuclear reactions by making use of random numbers. It follows every particle throughout their life and holds particle histories for every reaction particle is involved. Each reaction is sampled to simulate them by MCNP. In these samplings Monte Carlo technique is used. Monte Carlo technique is simply a model that sample statistical processes with random numbers. Hence Monte Carlo name comes from the city with its famous casino. As It may seem simple to use, with this method, much complex events involving many particles problem can be solved without getting lost in complex numerical integrals. Therefore, with some simple equations MCNP can help find the solutions. MCNP has developed error checking methods as well as finding solution to the complex problems. Variance reduction is one of them and MCNP does its best to provide physicist with variance reduction tools. It was shown that MCNP code could be used in cases where experimental data was not available or hard to collect for example a reactor and its core is the source of radiation. So another studies can be done using MCNP code simulation wherever experimental difficulties exist.
Açıklama
Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Enerji Enstitüsü, 2018
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Physics, [DATE]
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Physics, [DATE]
Anahtar kelimeler
Germanyum dedektörü,
Monte Carlo metodu,
Germanium detector,
Monte Carlo method