Estimation of distribution algorithms for nuclear reactor fuel management optimisation

Jiang, Shan; Ziver, A.K.; Carter, J.N.; Pain, Christopher C.; Goddard, A.J.H.; Franklin, Simon; Phillips, Heather J.

doi:10.1016/j.anucene.2006.03.012

Cited by 51 publications

(13 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many works in the literature confirm the good performance of EDAs in the solution of problems from diverse fields. Protein Folding [45], Capacitated Vehicle Routing Problems [52], Calibration of Chemical Applications [35], Finding the Optimal Path in 3D Spaces [53], Software Testing [44], Chemotherapy Treatment Optimization for Cancer [6] or Nuclear Reactor Fuel Management Parameter Optimization [21] are some examples of many real-world problems where EDA-based approaches were applied to find optimal solutions.…”

Section: Introductionmentioning

confidence: 99%

A review on estimation of distribution algorithms in permutation-based combinatorial optimization problems

et al. 2012

View full text Add to dashboard Cite

Estimation of Distribution Algorithms (EDAs) are a set of algorithms that belong to the field of Evolutionary Computation. Characterized by the use of probabilistic models to represent the solutions and the dependencies between the variables of the problem, these algorithms have been applied to a wide set of academic and real-world optimization problems, achieving competitive results in most scenarios. Nevertheless, there are some optimization problems, whose solutions can be naturally represented as permutations, for which EDAs have not been extensively developed. Although some work has been carried out in this direction, most of the approaches are adaptations of EDAs designed for problems based on integer or real domains, and only a few algorithms have been specifically designed to deal with permutation-based problems. In order to set the basis for a development of EDAs in permutation-based problems similar to that which occurred in other optimization fields (integer and real-value problems), in this paper we carry out a thorough review of state-of-the-art EDAs applied to permutation-based problems. Furthermore, we provide some ideas on probabilistic modeling over permutation spaces that could inspire the researchers of EDAs to design new approaches for these kinds of problems.

show abstract

Section: Introductionmentioning

confidence: 99%

A review on estimation of distribution algorithms in permutation-based combinatorial optimization problems

et al. 2012

View full text Add to dashboard Cite

show abstract

“…Applications of ACO for (expensive) combinatorial problems, e.g., include the works by Cáceres et al [81] and Teixeira et al [69]. EDAs have been applied to problems in building design [106] and reactor fuel management [107].…”

Section: Other Modelsmentioning

confidence: 99%

Model-based methods for continuous and discrete global optimization

Bartz-Beielstein

Zaefferer

2017

Applied Soft Computing

146

View full text Add to dashboard Cite

show abstract

“…The proposed ANN method can be used in manual reactor core loading pattern design and/or in an automatic optimal LPs searching routine ( [15], [12] and [10]), as a fast reactor core reactivity (k ef f ) estimation tool.…”

Section: Discussionmentioning

confidence: 99%

“…Similarly, a feed forward ANN is used to optimise the LP in [11]. In [12], [15], [10], feed forward ANNs are used to predict key parameters of reactors.…”

Section: The Ann Methodologymentioning

confidence: 99%

Nuclear Reactor Reactivity Prediction Using Feed Forward Artificial Neural Networks

Jiang

Pain

Carter

et al.

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. In this paper, a feed forward artificial neural network (ANN) is used to predict the effective multiplication factor (k ef f ), an indication of the reactivity of a nuclear reactor, given a fuel Loading Pattern (LP). In nuclear engineering, the k ef f is normally calculated by running computer models, e.g. Monte Carlo model and finite element model, which can be very computationally expensive. In case that a large number of reactor simulations is required, e.g. searching for the optimal LP that maximizes the k ef f in a solution space of 10 10 to 10 100 , the computational time may not be practical. A feed forward ANN is then trained to perform fast and accurate k ef f prediction, by using the known LPs and corresponding k ef f s. The experiments results show that the proposed ANN provides accurate, fast and robust k ef f predictions.Keywords: Feed forward neural network, Nuclear reactor, k ef f prediction. Nuclear Reactor Reactivity EstimationNuclear Reactors are loaded with fission fuel elements to generate energy for electricity or neutrons for medical/physics research. A crucial part of nuclear reactor operations is to decided how to load the fuel, in another word, to design the core Loading Pattern (LP), to improve the performance of the reactor, indicated by an estimation of the core reactivity and/or some other key measures, e.g. power and temperature distributions.A reactor core normally contains a number of loadable positions (or fuel channels), from tens to hundred of thousands, to be filled with designated fuel elements. An LP can be regarded as an ordering assignment of n items to m positions. In this case, n is the number of fuel elements, and m is number of fuel channels. When the size of the reactor is large, the number of possible LPs need to be examined before loading the reactor can be very large.Given a designed LP, it is desired that some key parameters of the reactor can be estimated. Such calculations are done by building a computer model and

show abstract

Estimation of distribution algorithms for nuclear reactor fuel management optimisation

Cited by 51 publications

References 40 publications

A review on estimation of distribution algorithms in permutation-based combinatorial optimization problems

A review on estimation of distribution algorithms in permutation-based combinatorial optimization problems

Model-based methods for continuous and discrete global optimization

Nuclear Reactor Reactivity Prediction Using Feed Forward Artificial Neural Networks

Contact Info

Product

Resources

About