Active special nuclear material (SNM) photoneutron interrogation research with Acoustically Tensioned Metastable Fluid Detector (ATMFD) sensor technology is discussed which provides evidence for enabling real time detection of special nuclear material (SNM) even when deployed under extreme 15,000 R h-1 (9 MeV endpoint) X-ray beams. Experiments to detect 3.2 kg DU are described with use of two designs of the economical E-ATMFD, viz., E-ATMFD.Ver.0 and E-ATMFD.Ver.1, respectively, at standoffs ranging from 0.1 m to 10 m - including with the E-ATMFD directly within the interrogating beam. Under similar conditions and with 100% photon rejection (i.e., 0 cpm with beam on, and w/o SNM), the E-ATMFD.Ver.1 design was shown capable of ~6x (600%) higher gain at ~10x lower drive powers over E-ATMFD.Ver.0 (with beam on and with SNM). The sensitivity gain rises to ~27x (i.e., 2,700%) with the E-ATMFD.Ver.1 operating at 0.99 W and a background count rate of ~1 cpm. The E-ATMFD.Ver.1 demonstrated 100% photon blindness (0 cpm) while operating at ~0.56 W drive power and placed directly within the beam under 15,000 R/h; including the SNM target led to a count rate of up to 50 cpm - revealing the E-ATMFD.Ver.1 as potentially field-capable for detecting U-based SNMs within seconds from photofission neutron signals, even when deployed directly within the interrogating photon beam.
讨论了利用声张力亚稳态流体检测器(ATMFD)传感器技术对特殊核材料(SNM)进行的有源光子中子探测研究，为在极端15000 R h-1 (9 MeV端点)x射线光束下实现特殊核材料(SNM)的实时探测提供了证据。描述了使用两种经济型E-ATMFD设计的3.2 kg DU检测实验，即E-ATMFD。0和E-ATMFD.Ver。1，分别在0.1米至10米的距离内-包括直接在询问光束内的E-ATMFD。在相似的条件下，100%光子抑制(即0 cpm，带光束，无SNM)， E-ATMFD.Ver。与E-ATMFD.Ver.0相比，1设计能够在低驱动功率约10倍的情况下获得约6倍(600%)的增益(有光束和SNM)。使用E-ATMFD.Ver，灵敏度增益提高到~27倍(即2,700%)。工作功率为0.99 W，背景计数速率为~1 cpm。E-ATMFD.Ver。在~0.56 W的驱动功率下，直接置于15000 R/h的光束中，证明了100%的光子盲性(0 cpm);包括SNM目标导致计数率高达50 cpm -揭示了E-ATMFD.Ver。1作为潜在的现场能力，可以在几秒钟内从光裂变中子信号中探测到基于u的SNMs，即使直接部署在询问光子束中。
{"title":"9Mv Linac Photo-Neutron Interrogation Of Uranium With Advanced Acoustically Tensioned Metastable Fluid Detectors","authors":"N. Boyle, S. Ozerov, C. Harabagiu, R. Taleyarkhan","doi":"10.1115/1.4062951","DOIUrl":"https://doi.org/10.1115/1.4062951","url":null,"abstract":"\u0000 Active special nuclear material (SNM) photoneutron interrogation research with Acoustically Tensioned Metastable Fluid Detector (ATMFD) sensor technology is discussed which provides evidence for enabling real time detection of special nuclear material (SNM) even when deployed under extreme 15,000 R h-1 (9 MeV endpoint) X-ray beams. Experiments to detect 3.2 kg DU are described with use of two designs of the economical E-ATMFD, viz., E-ATMFD.Ver.0 and E-ATMFD.Ver.1, respectively, at standoffs ranging from 0.1 m to 10 m - including with the E-ATMFD directly within the interrogating beam. Under similar conditions and with 100% photon rejection (i.e., 0 cpm with beam on, and w/o SNM), the E-ATMFD.Ver.1 design was shown capable of ~6x (600%) higher gain at ~10x lower drive powers over E-ATMFD.Ver.0 (with beam on and with SNM). The sensitivity gain rises to ~27x (i.e., 2,700%) with the E-ATMFD.Ver.1 operating at 0.99 W and a background count rate of ~1 cpm. The E-ATMFD.Ver.1 demonstrated 100% photon blindness (0 cpm) while operating at ~0.56 W drive power and placed directly within the beam under 15,000 R/h; including the SNM target led to a count rate of up to 50 cpm - revealing the E-ATMFD.Ver.1 as potentially field-capable for detecting U-based SNMs within seconds from photofission neutron signals, even when deployed directly within the interrogating photon beam.","PeriodicalId":16756,"journal":{"name":"Journal of Nuclear Engineering and Radiation Science","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88946558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Seungyon Cho, Jeonghun Choi, J. Shin, Seung Jun Lee
Multi-abnormal events, referring to the simultaneous occurrence of multiple single abnormal events in a nuclear power plant, have not been subject to consideration because multi-abnormal events are extremely unlikely to occur and indeed have not yet occurred. Such events, though, would be more challenging to diagnose than general single abnormal events, exacerbating the human error issue. This study introduces an efficient abnormality diagnosis model that covers multi-abnormality diagnosis using a one-vs-rest classifier and compares it with other artificial intelligence models. The multi-abnormality attention diagnosis model deals with multi-label classification problems, for which two methods are proposed. First, a method to effectively cluster single and multi-abnormal events is introduced based on the predicted probability distribution of each abnormal event. Second, a one-vs-rest classifier with high accuracy is employed as an efficient way to obtain knowledge on which particular multi-abnormal events are the most difficult to diagnose and therefore require the most attention to improve the multi-label classification performance in terms of data usage. The developed multi-abnormality attention diagnosis model can reduce human errors of operators due to excessive information and limited time when unexpected multi-abnormal events occur by providing diagnosis results as part of an operator support system.
{"title":"Multi-Abnormality Attention Diagnosis Model Using One-vs-Rest Classifier in a Nuclear Power Plant","authors":"Seungyon Cho, Jeonghun Choi, J. Shin, Seung Jun Lee","doi":"10.3390/jne4030033","DOIUrl":"https://doi.org/10.3390/jne4030033","url":null,"abstract":"Multi-abnormal events, referring to the simultaneous occurrence of multiple single abnormal events in a nuclear power plant, have not been subject to consideration because multi-abnormal events are extremely unlikely to occur and indeed have not yet occurred. Such events, though, would be more challenging to diagnose than general single abnormal events, exacerbating the human error issue. This study introduces an efficient abnormality diagnosis model that covers multi-abnormality diagnosis using a one-vs-rest classifier and compares it with other artificial intelligence models. The multi-abnormality attention diagnosis model deals with multi-label classification problems, for which two methods are proposed. First, a method to effectively cluster single and multi-abnormal events is introduced based on the predicted probability distribution of each abnormal event. Second, a one-vs-rest classifier with high accuracy is employed as an efficient way to obtain knowledge on which particular multi-abnormal events are the most difficult to diagnose and therefore require the most attention to improve the multi-label classification performance in terms of data usage. The developed multi-abnormality attention diagnosis model can reduce human errors of operators due to excessive information and limited time when unexpected multi-abnormal events occur by providing diagnosis results as part of an operator support system.","PeriodicalId":16756,"journal":{"name":"Journal of Nuclear Engineering and Radiation Science","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2023-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90196767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Small Modular Reactors (SMRs) are actively being considered for use in Canada. Some proposed SMRs can make use of solar salt as an intermediate coolant for a heat storage system. The development of thermalhydraulic simulation tools is one of the key capabilities needed to examine the performance of SMRs and license this class of reactors. This article summarizes the implementation of molten solar salt fluid properties into the ARIANT thermalhydraulic code and uses the code to simulate a high temperature gas-cooled SMR with helium and solar salt as its primary and secondary coolants during a pressurized loss of forced circulation (PLOFC) event. This work demonstrates the ability of ARIANT to simulate transient events in a two loop reactor system consisting of helium and solar salt as coolants and helps to establish ARIANT as a tool for SMR analysis.
{"title":"Implementation Of Solar Salt Properties Into Ariant And Simulation Of Pressurized Loss Of Forced Circulation In A High Temperature Gas-Cooled Small Modular Reactor","authors":"Jason Wu, T. Beuthe, Aleksandar Vasić","doi":"10.1115/1.4062917","DOIUrl":"https://doi.org/10.1115/1.4062917","url":null,"abstract":"\u0000 Small Modular Reactors (SMRs) are actively being considered for use in Canada. Some proposed SMRs can make use of solar salt as an intermediate coolant for a heat storage system. The development of thermalhydraulic simulation tools is one of the key capabilities needed to examine the performance of SMRs and license this class of reactors. This article summarizes the implementation of molten solar salt fluid properties into the ARIANT thermalhydraulic code and uses the code to simulate a high temperature gas-cooled SMR with helium and solar salt as its primary and secondary coolants during a pressurized loss of forced circulation (PLOFC) event. This work demonstrates the ability of ARIANT to simulate transient events in a two loop reactor system consisting of helium and solar salt as coolants and helps to establish ARIANT as a tool for SMR analysis.","PeriodicalId":16756,"journal":{"name":"Journal of Nuclear Engineering and Radiation Science","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73772349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jordan R. Stomps, Paul P. H. Wilson, K. Dayman, Michael J. Willis, James M. Ghawaly, Daniel E. Archer
The timely detection of special nuclear material (SNM) transfers between nuclear facilities is an important monitoring objective in nuclear nonproliferation. Persistent monitoring enabled by successful detection and characterization of radiological material movements could greatly enhance the nuclear nonproliferation mission in a range of applications. Supervised machine learning can be used to signal detections when material is present if a model is trained on sufficient volumes of labeled measurements. However, the nuclear monitoring data needed to train robust machine learning models can be costly to label since radiation spectra may require strict scrutiny for characterization. Therefore, this work investigates the application of semi-supervised learning to utilize both labeled and unlabeled data. As a demonstration experiment, radiation measurements from sodium iodide (NaI) detectors are provided by the Multi-Informatics for Nuclear Operating Scenarios (MINOS) venture at Oak Ridge National Laboratory (ORNL) as sample data. Anomalous measurements are identified using a method of statistical hypothesis testing. After background estimation, an energy-dependent spectroscopic analysis is used to characterize an anomaly based on its radiation signatures. In the absence of ground-truth information, a labeling heuristic provides data necessary for training and testing machine learning models. Supervised logistic regression serves as a baseline to compare three semi-supervised machine learning models: co-training, label propagation, and a convolutional neural network (CNN). In each case, the semi-supervised models outperform logistic regression, suggesting that unlabeled data can be valuable when training and demonstrating value in semi-supervised nonproliferation implementations.
{"title":"SNM Radiation Signature Classification Using Different Semi-Supervised Machine Learning Models","authors":"Jordan R. Stomps, Paul P. H. Wilson, K. Dayman, Michael J. Willis, James M. Ghawaly, Daniel E. Archer","doi":"10.3390/jne4030032","DOIUrl":"https://doi.org/10.3390/jne4030032","url":null,"abstract":"The timely detection of special nuclear material (SNM) transfers between nuclear facilities is an important monitoring objective in nuclear nonproliferation. Persistent monitoring enabled by successful detection and characterization of radiological material movements could greatly enhance the nuclear nonproliferation mission in a range of applications. Supervised machine learning can be used to signal detections when material is present if a model is trained on sufficient volumes of labeled measurements. However, the nuclear monitoring data needed to train robust machine learning models can be costly to label since radiation spectra may require strict scrutiny for characterization. Therefore, this work investigates the application of semi-supervised learning to utilize both labeled and unlabeled data. As a demonstration experiment, radiation measurements from sodium iodide (NaI) detectors are provided by the Multi-Informatics for Nuclear Operating Scenarios (MINOS) venture at Oak Ridge National Laboratory (ORNL) as sample data. Anomalous measurements are identified using a method of statistical hypothesis testing. After background estimation, an energy-dependent spectroscopic analysis is used to characterize an anomaly based on its radiation signatures. In the absence of ground-truth information, a labeling heuristic provides data necessary for training and testing machine learning models. Supervised logistic regression serves as a baseline to compare three semi-supervised machine learning models: co-training, label propagation, and a convolutional neural network (CNN). In each case, the semi-supervised models outperform logistic regression, suggesting that unlabeled data can be valuable when training and demonstrating value in semi-supervised nonproliferation implementations.","PeriodicalId":16756,"journal":{"name":"Journal of Nuclear Engineering and Radiation Science","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84410960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Freeze plug is an important passive safety system used in the molten salt reactors (MSRs). It enables automatic drainage of the liquid fuel from the core to the storage tanks in an emergency to stop nuclear fission chain reaction without any operator's action and electric power supply. The opening time, that is the time taken for the freeze plug to open, is therefore of considerable importance to ensure passive safety of the MSRs. In our previous studies, systematic numerical simulations were carried out to understand how the fundamental design parameters such as the tube diameter and wall thickness of the freeze plug affected the opening time. In this work, a simple analytical model was developed for rough estimation of the opening time. It was shown that the opening time calculated by the present simple model was in fairly good agreement with that by the full simulation using the mass, momentum and energy conservation equations for the salt and the heat conduction equation within the wall material. The present simple model was hence shown to be useful particularly for the schematic design of the improved MSR freeze plugs.
{"title":"A Simple Analytical Model to Predict the Freeze Plug Opening Time in Molten Salt Reactors","authors":"M. Ilham, T. Okawa","doi":"10.1115/1.4062879","DOIUrl":"https://doi.org/10.1115/1.4062879","url":null,"abstract":"\u0000 Freeze plug is an important passive safety system used in the molten salt reactors (MSRs). It enables automatic drainage of the liquid fuel from the core to the storage tanks in an emergency to stop nuclear fission chain reaction without any operator's action and electric power supply. The opening time, that is the time taken for the freeze plug to open, is therefore of considerable importance to ensure passive safety of the MSRs. In our previous studies, systematic numerical simulations were carried out to understand how the fundamental design parameters such as the tube diameter and wall thickness of the freeze plug affected the opening time. In this work, a simple analytical model was developed for rough estimation of the opening time. It was shown that the opening time calculated by the present simple model was in fairly good agreement with that by the full simulation using the mass, momentum and energy conservation equations for the salt and the heat conduction equation within the wall material. The present simple model was hence shown to be useful particularly for the schematic design of the improved MSR freeze plugs.","PeriodicalId":16756,"journal":{"name":"Journal of Nuclear Engineering and Radiation Science","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89662526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Tamura, Yuki Hidaka, Haruhiko Ikeda, Norikazu Hamaura
With further applications of AI, IoT, and digital twin technology to plant operation and maintenance, it is becoming increasingly important to ensure data reliability. Data validation and reconciliation (DVR) represents one promising technique to ensure data reliability by minimizing the uncertainty of measurements based on statistics. DVR has been widely applied to nuclear power electrical generation plants in Europe and the United States in recent years. The most important input for DVR analysis is measurement uncertainty. In Japan, performance management of nuclear power plants is often done by measuring condensate flow rate. While the uncertainty of other flowmeters is handled by the JIS standard, the condensate flowmeter is specially calibrated every few cycles. This leads to reduction of effectiveness of DVR analysis due to variations in measurement uncertainty management. To overcome this issue, we propose an estimation method for measurement uncertainty by utilizing process data, an incidence matrix between sensors, and a reference instrument. The conventional method proposed in the previous study only treats the random error. The proposed method quantitatively estimates not only random error but also bias error by considering the uncertainty of the reference instrument. Using several benchmark problems, we found that the proposed method was applicable to various flow conditions, including physically fluctuating flow such as that observed in the feedwater flow in nuclear power plants. We anticipate that the proposed method will promote use of DVR analysis in nuclear power plants in Japan.
{"title":"An Estimation Method For Bias Error Of Measurements By Utilizing Process Data, An Incidence Matrix And A Reference Instrument For Data Validation And Reconciliation","authors":"A. Tamura, Yuki Hidaka, Haruhiko Ikeda, Norikazu Hamaura","doi":"10.1115/1.4062865","DOIUrl":"https://doi.org/10.1115/1.4062865","url":null,"abstract":"\u0000 With further applications of AI, IoT, and digital twin technology to plant operation and maintenance, it is becoming increasingly important to ensure data reliability. Data validation and reconciliation (DVR) represents one promising technique to ensure data reliability by minimizing the uncertainty of measurements based on statistics. DVR has been widely applied to nuclear power electrical generation plants in Europe and the United States in recent years. The most important input for DVR analysis is measurement uncertainty. In Japan, performance management of nuclear power plants is often done by measuring condensate flow rate. While the uncertainty of other flowmeters is handled by the JIS standard, the condensate flowmeter is specially calibrated every few cycles. This leads to reduction of effectiveness of DVR analysis due to variations in measurement uncertainty management. To overcome this issue, we propose an estimation method for measurement uncertainty by utilizing process data, an incidence matrix between sensors, and a reference instrument. The conventional method proposed in the previous study only treats the random error. The proposed method quantitatively estimates not only random error but also bias error by considering the uncertainty of the reference instrument. Using several benchmark problems, we found that the proposed method was applicable to various flow conditions, including physically fluctuating flow such as that observed in the feedwater flow in nuclear power plants. We anticipate that the proposed method will promote use of DVR analysis in nuclear power plants in Japan.","PeriodicalId":16756,"journal":{"name":"Journal of Nuclear Engineering and Radiation Science","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90490442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. Grgić, Paulina Duckic, Vesna Benčik, Siniša Šadek
Passive Containment Filtered Vent (PCFV) was installed in Nuclear Power Plant (NPP) Krsko in 2013 as part of the safety upgrade program. It is intended for severe accident consequences prevention and mitigation by ensuring the containment integrity. In this paper, dose rates around the exhaust line of the PCFV system resulting from radioactivity release in case of a severe accident were determined in a four step methodology. The assumed severe accident scenario is a beyond design basis station blackout in NPP Krsko, which was simulated using the MELCOR code. Its results were input for the RADTRAD radiological calculations to obtain the activities released in the containment. These activities were then transformed into the gamma source intensity and spectrum using the ORIGEN-S libraries. This form of the source term is required for Monte Carlo calculations which were performed using the MCNP6.2. Two Monte Carlo calculations were performed. One for which the radiation source was modeled to emanate from the containment atmosphere and the other from the PCFV duct fluid. The main reason for the calculation was to assess limiting dose rates around PCFV duct (radiation monitor location) during actuation after severe accident. That is why the model is simple and conservative. The other task was to demonstrate that this location is not suitable for longer personnel presence in case of equipment failure during the PCFV actuation. Due to conservative assumptions, predicted dose rates are the highest expected at that location for any severe accident scenario.
{"title":"Dose Rate Assessment Around the PCFV Release Line During Severe Accident Conditions in Nuclear Power Plant Krsko","authors":"D. Grgić, Paulina Duckic, Vesna Benčik, Siniša Šadek","doi":"10.1115/1.4062797","DOIUrl":"https://doi.org/10.1115/1.4062797","url":null,"abstract":"\u0000 Passive Containment Filtered Vent (PCFV) was installed in Nuclear Power Plant (NPP) Krsko in 2013 as part of the safety upgrade program. It is intended for severe accident consequences prevention and mitigation by ensuring the containment integrity. In this paper, dose rates around the exhaust line of the PCFV system resulting from radioactivity release in case of a severe accident were determined in a four step methodology. The assumed severe accident scenario is a beyond design basis station blackout in NPP Krsko, which was simulated using the MELCOR code. Its results were input for the RADTRAD radiological calculations to obtain the activities released in the containment. These activities were then transformed into the gamma source intensity and spectrum using the ORIGEN-S libraries. This form of the source term is required for Monte Carlo calculations which were performed using the MCNP6.2. Two Monte Carlo calculations were performed. One for which the radiation source was modeled to emanate from the containment atmosphere and the other from the PCFV duct fluid. The main reason for the calculation was to assess limiting dose rates around PCFV duct (radiation monitor location) during actuation after severe accident. That is why the model is simple and conservative. The other task was to demonstrate that this location is not suitable for longer personnel presence in case of equipment failure during the PCFV actuation. Due to conservative assumptions, predicted dose rates are the highest expected at that location for any severe accident scenario.","PeriodicalId":16756,"journal":{"name":"Journal of Nuclear Engineering and Radiation Science","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74224902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Safety system design and implementation is critical to the operation of any nuclear plant. For sodium cooled nuclear reactors, hazards external to the reactor core are present in the form of molten sodium that leaks through degraded piping structures. These structures are often clad in high-temperature insulation to preserve the heat needed to keep the sodium molten in the piping. While large sodium leaks are quite noticeable and often result in hazardous fire situations, small leaks of molten sodium are often masked by the shroud of insulation until a large pool of material has collected outside of the failed pipe. This study concentrated on the physical and chemical interactions between molten sodium and standard fiberglass insulation in temperatures ranging from 100 ? to 500 ?. The degradation of the insulation material begins with the volatilization of the organic binder around 250 ?, thereafter the insulation deteriorates at an advanced rate in areas that are in direct contact with the sodium. Chemical profile data was collected for a variety of samples locations that were in contact with the molten sodium, with only a slight increase in the amount of sodium present that can be attributed to the external sodium source. In this way, the molten sodium does not chemically degrade the insulation, but rather accelerates the thermal degradation of the insulation on a local scale, acting as a concentrated heat source to the insulation.
{"title":"Interactions Between Molten Sodium and Standard Pipe Insulation","authors":"D. LaBrier, Jordan Harley, Morgan Robbins","doi":"10.1115/1.4062798","DOIUrl":"https://doi.org/10.1115/1.4062798","url":null,"abstract":"\u0000 Safety system design and implementation is critical to the operation of any nuclear plant. For sodium cooled nuclear reactors, hazards external to the reactor core are present in the form of molten sodium that leaks through degraded piping structures. These structures are often clad in high-temperature insulation to preserve the heat needed to keep the sodium molten in the piping. While large sodium leaks are quite noticeable and often result in hazardous fire situations, small leaks of molten sodium are often masked by the shroud of insulation until a large pool of material has collected outside of the failed pipe. This study concentrated on the physical and chemical interactions between molten sodium and standard fiberglass insulation in temperatures ranging from 100 ? to 500 ?. The degradation of the insulation material begins with the volatilization of the organic binder around 250 ?, thereafter the insulation deteriorates at an advanced rate in areas that are in direct contact with the sodium. Chemical profile data was collected for a variety of samples locations that were in contact with the molten sodium, with only a slight increase in the amount of sodium present that can be attributed to the external sodium source. In this way, the molten sodium does not chemically degrade the insulation, but rather accelerates the thermal degradation of the insulation on a local scale, acting as a concentrated heat source to the insulation.","PeriodicalId":16756,"journal":{"name":"Journal of Nuclear Engineering and Radiation Science","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75057733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Keke Hou, Chao Yan, P. Wang, Changqing Cao, Jun Lin, Yanguang Cui, Junqiang Lu, Libing Zhu
As a candidate material for metallic fuel, U-Mo metal fuel pellets are the most promising. U-Mo and U-Mo-Nb alloy pellets with a certain porosity were successfully prepared by the process of hydrogenation/dehydrogenation - compression molding - argon liquid-phase sintering. In order to study the effect of Nb addition on γ phase uranium alloy fuel pellets, microstructure and thermo-properties of the samples were observed by XRD/SEM etc. Results showed that with the increase of Nb content in the pellets from the non-add to micro-adding, Nb can facilitate the diffusion of Mo into the U matrix, resulting in the formation of a metastable γ-U phase. Meanwhile, during the same liquid phase sintering process of U-Mo fuel pellets, with the increase of Nb content, the number of secondary phases in U-Mo fuel pellets gradually decreased, while the size and number of voids of the secondary phases decreased. And the distribution of voids is more uniform. The specific heat capacity and thermal diffusivity of porous γ phase uranium alloys fuel pellets with different density were measured and thermal conductivity from 373K to 873K were calculated according to the experiment results. It is suggested that the thermal conductivity will increase with the density of pellets.
{"title":"Effect of Nb On Sintering Process of Gamma Phase Uranium Alloys Fuel Pellets","authors":"Keke Hou, Chao Yan, P. Wang, Changqing Cao, Jun Lin, Yanguang Cui, Junqiang Lu, Libing Zhu","doi":"10.1115/1.4062795","DOIUrl":"https://doi.org/10.1115/1.4062795","url":null,"abstract":"\u0000 As a candidate material for metallic fuel, U-Mo metal fuel pellets are the most promising. U-Mo and U-Mo-Nb alloy pellets with a certain porosity were successfully prepared by the process of hydrogenation/dehydrogenation - compression molding - argon liquid-phase sintering. In order to study the effect of Nb addition on γ phase uranium alloy fuel pellets, microstructure and thermo-properties of the samples were observed by XRD/SEM etc. Results showed that with the increase of Nb content in the pellets from the non-add to micro-adding, Nb can facilitate the diffusion of Mo into the U matrix, resulting in the formation of a metastable γ-U phase. Meanwhile, during the same liquid phase sintering process of U-Mo fuel pellets, with the increase of Nb content, the number of secondary phases in U-Mo fuel pellets gradually decreased, while the size and number of voids of the secondary phases decreased. And the distribution of voids is more uniform. The specific heat capacity and thermal diffusivity of porous γ phase uranium alloys fuel pellets with different density were measured and thermal conductivity from 373K to 873K were calculated according to the experiment results. It is suggested that the thermal conductivity will increase with the density of pellets.","PeriodicalId":16756,"journal":{"name":"Journal of Nuclear Engineering and Radiation Science","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90023383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Siniša Šadek, Renato Pavlinac, Karlo Ivanjko, D. Grgić
Uncertainty and sensitivity methods are increasingly used in safety analyzes of nuclear power plants to address the unreliability of input data, numerical models and, in general, the lack of knowledge regarding certain physical phenomena, in determining safety margins and acceptance criteria. The ASYST code, developed as part of an international nuclear technology ASYST Development and Training Program (ADTP) managed by Innovative Systems Software (ISS), is used to perform an uncertainty analysis of the QUENCH-02 experiment conducted at the Karlsruhe Institute of Technology. The code uses a probabilistic methodology based on the propagation of input uncertainties. The QUENCH facility contains electrically heated PWR fuel rod simulators and the aim of the experiment is to examine hydrogen source term and the behavior of the fuel rod cladding during core reflood. For selected input parameters, such as steam/water flow, electrical power and other relevant boundary conditions, it is necessary to define their probability density functions. Input databases are then prepared for individual calculations based on the selected confidence level and confidence interval. The number of performed calculations is 60, large enough to ensure at least 95% coverage of expected output results and uncertainty limits. The results of the calculations are compared with the experimental measurements. The Pearson correlation coefficient is used to obtain correlation between the input uncertain parameters and the output data. Sensitivity analyses cover the influence of variations in the heater electrical power and the steam flow rate on the hydrogen production and the maximum cladding temperature.
不确定性和敏感性方法越来越多地用于核电厂的安全分析，以解决输入数据、数值模型的不可靠性，以及在确定安全裕度和接受标准方面缺乏对某些物理现象的了解。ASYST代码是由Innovative Systems Software (ISS)管理的国际核技术ASYST开发和培训计划(ADTP)的一部分，用于对卡尔斯鲁厄理工学院进行的QUENCH-02实验进行不确定性分析。该代码使用基于输入不确定性传播的概率方法。QUENCH设施包含电加热的压水堆燃料棒模拟器，实验的目的是检查堆芯再灌注过程中氢源项和燃料棒包壳的行为。对于选定的输入参数，如蒸汽/水流、电功率等相关边界条件，需要定义其概率密度函数。然后根据所选的置信水平和置信区间准备输入数据库进行单独的计算。执行的计算次数为60，足以确保至少95%的预期输出结果和不确定性限制的覆盖率。计算结果与实验测量结果进行了比较。使用Pearson相关系数来获得输入不确定参数与输出数据之间的相关性。灵敏度分析包括加热器电功率和蒸汽流量变化对产氢量和最高包层温度的影响。
{"title":"Uncertainty and Sensitivity Evaluation of the QUENCH-02 Experiment Simulation Using the ASYST Code","authors":"Siniša Šadek, Renato Pavlinac, Karlo Ivanjko, D. Grgić","doi":"10.1115/1.4062799","DOIUrl":"https://doi.org/10.1115/1.4062799","url":null,"abstract":"\u0000 Uncertainty and sensitivity methods are increasingly used in safety analyzes of nuclear power plants to address the unreliability of input data, numerical models and, in general, the lack of knowledge regarding certain physical phenomena, in determining safety margins and acceptance criteria. The ASYST code, developed as part of an international nuclear technology ASYST Development and Training Program (ADTP) managed by Innovative Systems Software (ISS), is used to perform an uncertainty analysis of the QUENCH-02 experiment conducted at the Karlsruhe Institute of Technology. The code uses a probabilistic methodology based on the propagation of input uncertainties. The QUENCH facility contains electrically heated PWR fuel rod simulators and the aim of the experiment is to examine hydrogen source term and the behavior of the fuel rod cladding during core reflood.\u0000 For selected input parameters, such as steam/water flow, electrical power and other relevant boundary conditions, it is necessary to define their probability density functions. Input databases are then prepared for individual calculations based on the selected confidence level and confidence interval. The number of performed calculations is 60, large enough to ensure at least 95% coverage of expected output results and uncertainty limits. The results of the calculations are compared with the experimental measurements. The Pearson correlation coefficient is used to obtain correlation between the input uncertain parameters and the output data. Sensitivity analyses cover the influence of variations in the heater electrical power and the steam flow rate on the hydrogen production and the maximum cladding temperature.","PeriodicalId":16756,"journal":{"name":"Journal of Nuclear Engineering and Radiation Science","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87323080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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