Advances in Nuclear Fuel Management for Light Water Reactors

3D in-core fuel management optimization for breed-and-burn reactors

“…For an optimization viewpoint, the disadvantage of this approach is that trapping in local minima can occur, thus preventing an optimal SP from being found. [17] An alternative is to define an augmented objective function, in which the main objective function is augmented by the penalty terms reflecting the extent of violation of the active constraints:…”

“…[15] [16] Simulated Annealing (SA), a close relative to GA, is another stochastic approach to largescale combinatorial optimization and has proven to be extremely robust in determining families of near-optimum decision variables in the in-core fuel management problems. [17] [18] [19] Although SA and GA are often viewed as separate, competing paradigms in the field of modern heuristics, the key difference between the two methods is the mechanism of generating a new solution from an existing one. While GA generates a new solution guess by combining several prior solutions, SA creates a new solution by modifying only the most recent solution with a local move.…”

Pattern optimization of PWR reactor using hybrid parallel Artificial Bee Colony

“…LP optimization has been responsible for neutronics characteristics of core such as radial power peaking factor, burn-up, cycle length and moderator temperature coefficient that do not contravene a set of thermal constraints (Turinsky and Parks, 1999;Turinsky et al, 2005).…”

An Overview of AI Methods for in-Core Fuel Management: Tools for the Automatic Design of Nuclear Reactor Core Configurations for Fuel Reload, (Re)arranging New and Partly Spent Fuel

“…12 Within the operating costs of commercial nuclear power stations, the cost of fuel is substantial, 13 hence a major incentive for using nuclear fuel efficiently, for longer, while the potential output of the fuel comes to fruition as fully as possible: whereas it may not be practical to seek a global optimum, very good results are achievable, as technological evolution using artificial intelligence techniques as early as the 1980s for such applications has amply demonstrated. Nuclear reactor plants are important for a power supply grid (failure to supply from a plant may result in a corporation being obliged to buy electricity from a third party so as not to fail the grid), the feasibility of forecasting is limited, and at any rate, careful planning is needed for reactor shutdown periods for maintenance purposes [78], and this includes rearranging the fuel batch in the core of the reactor [72,79,80].…”