Neutronic Analysis and Fuel Cycle Simulation of the MIT Reactor Using MCODE-FM and Experimental Validation

2013 ◽  
Author(s):  
Kaichao Sun ◽  
Michael Ames ◽  
Thomas Newton ◽  
Lin-wen Hu
Keyword(s):  
Fuel Cycle ◽  
Search Algorithm ◽  
Reactor Core ◽  
Axial Position ◽  
Monte Carlo Code ◽  
Massachusetts Institute Of Technology ◽  
Fuel Management ◽  
Cycle Simulation ◽  
Start Up ◽  
Institute Of Technology

A neutronic analysis of the Massachusetts Institute of Technology Research Reactor (MITR) is performed using state-of-the-art computational tools: the continuous-energy Monte Carlo code MCNP5 and the point-depletion code ORIGEN2.2. These codes are externally coupled by the in-house code package, MCODE (MCNP-ORIGEN Coupled Depletion Program), more recently, it being extended to MCODE-FM (Fuel Management). The latter features automated input file generation, data manipulation, and post-processing of the output data for the fuel cycle analysis, so that it is used to simulate the fuel management of the MITR. MCODE-FM also has an optional criticality search algorithm to simulate control blade movement. The code validation is carried out by comparing the calculated results to experimental data. Two sets of the comparisons are made in the present paper: 1) the Xe-135 reactivity effect during the reactor start-up and shutdown and 2) the thermal and fast neutron flux in an irradiation capsule in the reactor core. Good agreements have been found. The validated MCODE-FM is therefore useful for neutronic analysis and the fuel cycle simulation of the MITR. The time dependent variation of the key parameters, viz. the control blades’ axial position (maintaining criticality) and the fissile inventory in the fuel, is presented.

MIT as Catalyst for High-Technology Start-up Companies

1999 ◽  
Vol 13 (6) ◽  
pp. 409-411
Author(s):  
Karen Hersey
Keyword(s):  
Research University ◽  
High Technology ◽  
Educational Environment ◽  
Success Factor ◽  
Massachusetts Institute ◽  
Massachusetts Institute Of Technology ◽  
The Past ◽  
Entrepreneurial Spirit ◽  
Start Up ◽  
Institute Of Technology

There are more than 4,000 companies providing services and products worldwide whose roots can be traced to the Massachusetts Institute of Technology (MIT) educational experience. While MIT's unique origins may be partly responsible for the success of its students and faculty in starting new businesses, the main success factor is directly related to an entrepreneurial environment that has been consistently nourished, encouraged, and sustained over the past 100 years. This paper provides the reader with a glimpse of MIT as a breeding ground for entrepreneurs. Its history, its geography, its position as a leading US research university, and its continued strong links with industry are all factors in its unparalleled success as an educational environment that engenders an entrepreneurial spirit among students and faculty that is exceptional.

scholarly journals Optimization of the loading pattern of the PWR core using genetic algorithms and multi-purpose fitness function

Nukleonika ◽  
2021 ◽  
Vol 66 (4) ◽  
pp. 147-151
Author(s):  
Wojciech Kubiński ◽  
Piotr Darnowski ◽  
Kamil Chęć
Keyword(s):  
Genetic Algorithms ◽  
Fuel Cycle ◽  
Fitness Function ◽  
Geometry Optimization ◽  
Total Power ◽  
Massachusetts Institute Of Technology ◽  
Power Flux ◽  
Pressurized Water ◽  
Loading Pattern ◽  
Institute Of Technology

Abstract The study demonstrates an application of genetic algorithms (GAs) in the optimization of the first core loading pattern. The Massachusetts Institute of Technology (MIT) BEAVRS pressurized water reactor (PWR) model was applied with PARCS nodal-diffusion core simulator coupled with GA numerical tool to perform pattern selection. In principle, GAs have been successfully used in many nuclear engineering problems such as core geometry optimization and fuel configuration. In many cases, however, these analyses focused on optimizing only a single parameter, such as the effective neutron multiplication factor (k eff), and often limited to the simplified core model. On the contrary, the GAs developed in this work are equipped with multiple-purpose fitness function (FF) and allow the optimization of more than one parameter at the same time, and these were applied to a realistic full-core problem. The main parameters of interest in this study were the total power peaking factor (PPF) and the length of the fuel cycle. The basic purpose of this study was to improve the economics by finding longer fuel cycle with more uniform power/flux distribution. Proper FFs were developed, tested, and implemented and their results were compared with the reference BEAVRS first fuel cycle. In the two analysed test scenarios, it was possible to extend the first fuel cycle while maintaining lower or similar PPF, in comparison with the BEAVRS core, but for the price of increased initial reactivity.

A Method of Optimized Utilization of Point-Wise Data Format in Monte Carlo Code

2010 ◽  
Author(s):  
Yuxuan Liu ◽  
Ganglin Yu ◽  
Kan Wang
Keyword(s):  
Monte Carlo ◽  
Cross Section ◽  
Cross Sections ◽  
Search Algorithm ◽  
Reactor Core ◽  
Hash Table ◽  
Monte Carlo Code ◽  
Data Format ◽  
Section Data ◽  
Grid Interval

Monte Carlo codes are powerful and accurate tools for reactor core calculation. Most Monte Carlo codes use the point-wise data format, in which the data are given as tables of energy-cross section pairs. When calculating the cross sections at an incident energy value, it should be determined which grid interval the energy falls in. This procedure is repeated so frequently in Monte Carlo codes that its contribution in the overall calculation time can become quite significant. In this paper, the time distribution of Monte Carlo method is analyzed to illustrate the time consuming of cross section calculation. By investigation on searching and calculating cross section data in Monte Carlo code, a new search algorithm called hash table is elaborately designed to substitute the traditional binary search method in locating the energy grid interval. The results indicate that in the criticality calculation, hash table can save 5%∼17% CPU time, depending on the number of nuclides in the material, as well as complexity of geometry for particles tracking.

Extended development of a Monte Carlo code OpenMC for fuel cycle simulation of molten salt reactor

2020 ◽  
Vol 118 ◽  
pp. 103115
Author(s):  
Kun Zhuang ◽  
Ting Li ◽  
Qian Zhang ◽  
Qinghua He ◽  
Tengfei Zhang
Keyword(s):  
Monte Carlo ◽  
Molten Salt ◽  
Fuel Cycle ◽  
Monte Carlo Code ◽  
Molten Salt Reactor ◽  
Cycle Simulation

Applying the Anderson-Darling Test to Suicide Clusters

Crisis ◽  
2013 ◽  
Vol 34 (6) ◽  
pp. 434-437 ◽  
Author(s):  
Donald W. MacKenzie
Keyword(s):  
Poisson Process ◽  
Goodness Of Fit ◽  
Small Samples ◽  
Massachusetts Institute Of Technology ◽  
Homogeneous Poisson Process ◽  
Cornell University ◽  
The Difference ◽  
Suicide Clusters ◽  
Institute Of Technology ◽  
Anderson Darling

Background: Suicide clusters at Cornell University and the Massachusetts Institute of Technology (MIT) prompted popular and expert speculation of suicide contagion. However, some clustering is to be expected in any random process. Aim: This work tested whether suicide clusters at these two universities differed significantly from those expected under a homogeneous Poisson process, in which suicides occur randomly and independently of one another. Method: Suicide dates were collected for MIT and Cornell for 1990–2012. The Anderson-Darling statistic was used to test the goodness-of-fit of the intervals between suicides to distribution expected under the Poisson process. Results: Suicides at MIT were consistent with the homogeneous Poisson process, while those at Cornell showed clustering inconsistent with such a process (p = .05). Conclusions: The Anderson-Darling test provides a statistically powerful means to identify suicide clustering in small samples. Practitioners can use this method to test for clustering in relevant communities. The difference in clustering behavior between the two institutions suggests that more institutions should be studied to determine the prevalence of suicide clustering in universities and its causes.

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