Unstructured Mesh–Based Neutronics and Thermomechanics Coupled Steady-State Analysis on Advanced Three-Dimensional Fuel Elements with Monte Carlo Code iMC

Author(s):  
HyeonTae Kim ◽  
Yonghee Kim
Author(s):  
Lianjie Wang ◽  
Wenbo Zhao ◽  
Ping Yang ◽  
Yongqiang Ma ◽  
Di Lu

A coupled neutronics/thermal-hydraulics (N/T) three-dimensional code system SNTA is developed for supercritical water-cooled reactor (SCWR) core steady-state analysis by modular coupling the improved neutronics nodal methodological code and SCWR thermal-hydraulic subchannel code. The appropriate outer iteration coupling method and self-adaptive relaxation factor are proposed for enhancing convergence, stability, and efficiency of coupled N/T calculation. The steady-state analysis for the CSR1000 core is applied to verify SNTA. The results calculated by SNTA agreed well with those by CASIR and SRAC. SNTA is more efficient than CASIR and SRAC, where the neutronics modules are based on the finite-difference method. The numeric results show that SNTA can be applied to SCWR core steady-state analysis and core concept design.


2020 ◽  
Vol 6 (3) ◽  
Author(s):  
Lianjie Wang ◽  
Lei Yao ◽  
Ping Yang ◽  
Di Lu ◽  
Wenbo Zhao

Abstract The three-dimensional code system supercritical water-cooled reactor (SCWR) coupled neutronics/thermal-hydraulics analysis (SNTA) code is developed for SCWR core steady-state analysis by coupling neutronics/thermal-hydraulics (N/T). This paper studies the calculation difference between the SNTA code and the standard reactor analysis code (SRAC). By using the impacts exclusive method, it is confirmed that the calculation difference between these two codes is caused by different feedback of the cross section. The cross section data and the energy group structure of the SRAC code differ from the SNTA code, and the density coefficient of reactivity calculated by the SRAC code is higher, which means the feedback of the density and power distribution is bigger and the axial power distribution varies rapidly. The SNTA code with finer energy group structure is suitable for the performance analysis of SCWR core which has strong N/T coupling characteristics.


Evergreen ◽  
2017 ◽  
Vol 4 (1) ◽  
pp. 44-51 ◽  
Author(s):  
Takayuki Oka ◽  
Taro Handa ◽  
Fujio Akagi ◽  
Sumio Yamaguchi ◽  
Toshiyuki Aoki ◽  
...  

Author(s):  
Thomas Y.S. Lee

Models and analytical techniques are developed to evaluate the performance of two variations of single buffers (conventional and buffer relaxation system) multiple queues system. In the conventional system, each queue can have at most one customer at any time and newly arriving customers find the buffer full are lost. In the buffer relaxation system, the queue being served may have two customers, while each of the other queues may have at most one customer. Thomas Y.S. Lee developed a state-dependent non-linear model of uncertainty for analyzing a random polling system with server breakdown/repair, multi-phase service, correlated input processes, and single buffers. The state-dependent non-linear model of uncertainty introduced in this paper allows us to incorporate correlated arrival processes where the customer arrival rate depends on the location of the server and/or the server's mode of operation into the polling model. The author allows the possibility that the server is unreliable. Specifically, when the server visits a queue, Lee assumes that the system is subject to two types of failures: queue-dependent, and general. General failures are observed upon server arrival at a queue. But there are two possibilities that a queue-dependent breakdown (if occurs) can be observed; (i) is observed immediately when it occurs and (ii) is observed only at the end of the current service. In both cases, a repair process is initiated immediately after the queue-dependent breakdown is observed. The author's model allows the possibility of the server breakdowns/repair process to be non-stationary in the number of breakdowns/repairs to reflect that breakdowns/repairs or customer processing may be progressively easier or harder, or that they follow a more general learning curve. Thomas Y.S. Lee will show that his model encompasses a variety of examples. He was able to perform both transient and steady state analysis. The steady state analysis allows us to compute several performance measures including the average customer waiting time, loss probability, throughput and mean cycle time.


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